A THESIS. Submitted by. V.P.SRIRAM (Reg. No ) in partial fulfillment for the award of the degree of DOCTOR OF PHILOSOPHY

Transcription

1 1 MEASUREMENT OF SERVICE QUALITY, CUSTOMER SATISFACTION, BEHAVIOURAL INTENTION AMONG THE ORGANIZED RETAIL STORES WITH REFERENCE TO SELECTED CITIES OF TAMIL NADU A THESIS Submitted by V.P.SRIRAM (Reg. No ) in partial fulfillment for the award of the degree of DOCTOR OF PHILOSOPHY DEPARTMENT OF BUSINESS ADMINISTRATION KALASALINGAM UNIVERSITY (KALASALINGAM ACADEMY OF RESEARCH AND EDUCATION) ANAND NAGAR, KRISHNANKOIL JULY 2014

2 2 CERTIFICATE This is to certify that all the corrections and suggestions pointed by the Indian/Foreign Examiner(s) are incorporated in the Thesis titled MEASUREMENT OF SERVICE QUALITY, CUSTOMER SATISFACTION, BEHAVIOURAL INTENTIONS AMONG THE ORGANIZED RETAIL STORES WITH REFERENCE TO SELECTED CITIES OF TAMIL NADU submitted by Mr.V.P.SRIRAM. Place: Anand Nagar Date: SUPERVISOR Dr.S.Rajaram, Associate Professor, Department of Business Administration, Kalasalingam University. Tamil Nadu.India.

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4 i KALASALINGAM UNIVERSITY KRISHNANKOIL BONAFIDE CERTIFICATE Certified that the thesis titled Measurement Of Service Quality, Customer Satisfaction and Behavioural Intentions among the Organized Retail Stores with reference to selected cities of Tamil Nadu,India is the bonafide work of Mr.V.P.SRIRAM, who carried out the research under my supervision. Certified further, that to the best of my knowledge the work reported herein does not form part of any other thesis or dissertation on the basis of which a degree or award was conferred on an earlier occasion on this or any other scholar. Signature of the Supervisor Dr.S.RAJARAM, Associate Professor, Department of Business Administration, Kalasalingam University.

5 ii ABSTRACT This research aims at measuring the service quality among the organized retail stores in Chennai, Madurai and Coimbatore cities of Tamilnadu State and identifying its relationship to customer satisfaction and behavioral intention. The study was conducted among 900 organized retail stores customer by using Retail Service Quality Scale (RSQS) instrument with 27 items. Behavioral intention of the customers was measured by using the behavioral intention battery. The researcher has used a seven point likert scaling to measure the expected and perceived service quality (performance) and the behavioral intention of the customers. The RSQS instrument is selected as the most reliable device to measure the difference-score conceptualization. It is used to evaluate service gaps between expectation and perception of service quality. Modifications are made on the RSQS instrument to make it specific to the Retail Sector. The literature review was gathered enough from various sources and reflecting both Indian and foreign context. A number of hypotheses were proposed in the thesis and examined using Structural Equation Modeling. The hypotheses were tested with the software AMOS 21 and SPSS 21 to fulfill the research objectives. The data were examined using confirmatory factor analysis to confirm RSQS instrument reliability and validity of the retail industry performance and service quality dimensions. The resultant CFA model value shows good psychometric properties. This research is designed to address the literature gaps. Path analysis, Multiple regression, correlation, Paired t test, Chi-square test, Oneway ANOVA and descriptive statistics were applied to interpret the data. Structural Equation Model (SEM) is applied to study the relationship and impact between service quality, customer satisfaction and behavioural intention.

6 iii The findings of the study revealed that the customer s perception (performance) is lower than expectation of the service quality rendered by the organized retail stores. Responsiveness and Assurance SQ dimensions were the most important dimensions in service quality scored less SQ gap. The study concluded that the individual service quality dimensions have a positive impact on Overall Satisfaction and to the behavioural intention of the customers towards the service provider. This research also identified the association difference between demographic factors and satisfaction of the customers towards the service provider. Both customer satisfaction and service quality have a significant effect on influencing the behavioural intention. Few recommendations for further research were also suggested. Key Words: Retail Service Quality, Behavioral Intention, Customer satisfaction, Customer s Expectation and Perceived Service Quality.

7 iv ACKNOWLEDGEMENTS My sincere thanks to our Chairman Kalvivallal Mr.T.KALASALINGAM, Illayavallal Mr.K.SRIDHARAN Chancellor, Dr.S.SARAVANA SHANKAR, Vice-Chancellor Kalasalingam University, for providing opportunity to carry out the research work in our University. Dr. S. RAJARAM, my mentor and supervisor have extended his valuable guidance and motivation throughout this research, he is a big inspiration to me. His approach and kindness has motivated me to execute this research lively. It was possible to maintain quality throughout the research only because of his freedom and trust. I extend my thanks to Dr.M.JEYAKUMARAN, Professor, Department of Business Administration, Kalasalingam University, who gave all moral support behind the screen. I dedicate all my work to my parents, wife and daughter. I express my gratitude to the scarification, effort and pain they have taken in this regard. I express my pleasure in thanking the students, friends, and my colleagues for the support and valuable suggestions for the improvement of this research. There are many others, who have helped me directly and indirectly to complete this research. I thank them whole-heartedly. V.P.SRIRAM

8 v Abstract Acknowledgement Table of contents List of Tables List of Figures List of Abbreviations TABLE OF CONTENTS ii iv v xiii xix xxi Chapter I DESIGN OF THE STUDY Page. No 1.1 Introduction Background of The Study Problem Statement Research Questions Objectives of The Study Justification of The Research Study Research Gap Purpose of The Study Research Methodology Sampling Techniques Determination of Sample Size Research Questionnaire Data Analysis Structural Content of The Thesis Conclusion 22 Chapter II OVERVIEW OF INDIAN RETAIL SECTOR 2.1 Introduction Indian Unorganized Retail Market Indian Organized Retail Market 27

9 vi 2.4 Various Formats In Indian Organized Retail Sector Conclusion 34 Chapter III REVIEW OF LITERATURE 3.1 Introduction Overview of Service Quality Service Quality- An Introduction Perspectives on Service Quality Determinants of Service Quality How Service Quality Is Perceived Expected Quality Vs. Experienced Quality Total Perceived Quality Managerial Process For Service Quality Service Quality Measurement SERVQUAL Scale SERVPERF Scale Retail Service Quality Service Quality And Demographics Importance of Retail Service Quality The Gaps Model For Improving Retail Service Quality Measuring Retail Service Quality Hierarchical Structure of Retail Service Quality Customer Loyalty Assessing The Relationship Between Service Quality And Customer Loyalty The Impact of Service Quality on Customer Loyalty In Organized Retail Environment Dimensions of Customer Loyalty on a Services Environment Behavioural-Intentions Battery Loyal Customers - New Goal For The Retailers 147

10 vii 3.6 Frame of Reference Development of Hypothesis Hypothesis of The Study Conclusion 153 Chapter IV CONCEPTUAL MODEL FOR MEASURING SERVICE QUALITY 4.1 Introduction Conceptual Model For Measuring Service Quality Justification for Proposed model Proposed Research Model Conclusion 159 Chapter V RESEARCH METHODOLOGY 5.1 Introduction Research Design Area of Study Sample Design Sample Unit Sampling Technique Sample Size Sources of Data Collection Scale And Measurement Demographic Profile of The Respondents The Reliability And Validity of The Scale Reliability of The Scale Validity of The Scale Tools Used For Data Analysis Structural Equation Modeling (SEM) Determination of Sample Size For Model Testing Univariate And Multivariate Statistical Analysis Analysis of Variance (ANOVA) Multiple Regression Analysis 210

11 viii Path Analysis Paired Sample T-Test Conclusion 211 Chapter VI DATA ANALYSIS AND INTERPRETATIONS 6.1 Introduction Service Quality Gap Analysis Relative Importance of Service Quality Dimensions on Overall Retail Service Quality Effects of Demographic Variables On Different Service Quality Dimensions Relative Importance of Service Quality Dimensions on Customer Loyalty Dimensions Relative Importance of Service Quality Dimensions on Word of Mouth Factors Relative Importance of Service Quality Dimensions on Switch To Competitor Factors Relative Importance of Service Quality Dimensions on Willing To Pay More Factors Relative Importance of Service Quality Dimensions on Shoppers Response Factors Effects of Demographic Variables on Different Customer Loyalty Dimensions Relationship Between Perceived Service Quality Dimensions Relationship Between Customer Loyalty Dimensions Relative Importance of Service Quality Dimensions on Customer Satisfaction Relative Importance of Customer Satisfaction Factors on Customer Loyalty. 285

12 ix 6.11 Effects of Demographic Variables on Customer Satisfaction Factors Relative Importance of Factor Affects Purchase on Customer Satisfaction Relationship Between Purchasing Factors, Customer Satisfaction, Expect & Perceived Service 310 Quality And Customer Loyalty 6.14 Effects of Most Liked And Disliked Factors on Purchase Attribute Factors Effects of Most Liked And Disliked Factors on Factors Affecting Retail Purchase Difference Between Perception And Expectation Service Quality Dimensions Association Between Demographic Difference of The Respondents And Most Liked And Disliked 322 Factors About Retail Shops 6.18 Level Of Service Quality, Customer Satisfaction And Customer Loyalty Association Between Perceived Level of Service Quality, Customer Satisfaction And Customer Loyalty Towards The Retail Stores And 328 Demographic Profiles 6.20 Structural Equation Modeling Effects of Service Quality Dimensions on Overall Customer Satisfaction Effect of Service Quality Dimensions And Overall Customer Satisfaction on Customer Loyalty Effect of Customer Loyalty Dimensions On Overall Customer Satisfaction Customer Evaluation Model For Retail Stores Conclusion 351

13 x Chapter VII DISCUSSION AND CONCLUSION 7.1 Introduction Relative Importance of Service Quality Dimensions Effects of Demographic Variables on Different Service Quality Dimensions Relative Importance of Service Quality Dimensions on Customer Loyalty Dimensions Effects of Demographic Variables on Different Customer Loyalty Dimensions Relationship Between Perceived Service Quality Dimensions, Customer Loyalty Dimensions Relative Importance of Service Quality Dimensions on Customer Satisfaction And 358 Customer Satisfaction on Customer Loyalty 7.8 Effects of Demographic Variables on Customer Satisfaction Factors Relative Importance of Factor Affects Purchase on Customer Satisfaction Relationship Between Purchasing Factors, Customer Satisfaction, Expect & Perceived Service 359 Quality And Customer Loyalty 7.11 Effects of Most Liked And Disliked Factors on Purchase Attribute Factors Effects of Most Liked And Disliked Factors on Factors Affecting Retail Purchase Difference Between Perception And Expectation Service Quality Dimensions Association Between And Most Liked And Disliked Factors About Retail Shops 360

14 xi 7.15 Level of Service Quality, Customer Satisfaction And Customer Loyalty Association Between Perceived Level of Service Quality, Customer Satisfaction And Customer 361 Loyalty And Demographics 7.17 Effects of Service Quality Dimensions on Overall Customer Satisfaction Effect of Service Quality Dimensions And Overall Customer Satisfaction On Customer Loyalty Effect of Customer Loyalty Dimensions on Overall Customer Satisfaction Customer Evaluation Model For Retail Stores Implications of the Study Managerial Implication Practical Implications of the Research Contribution of the Study Theoretical Contributions of the Research Measurement of Retail Service Quality Filling The Gap In The Knowledge Utilization Of SEM For Key Constructs Relationship Testing Contribution To The Services Marketing Theory Contribution To Management Practices Recommendations Limitations of the study Direction for the future study Conclusions 382 References 383

15 xii Appendices Appendix I Appendix II Determination of sample size by custom insight sample size calculator Questionnaire for Measuring the Service Quality, Customer Satisfaction and Behaviour Intention LIST OF PUBLICATIONS 424 CURRICULUM VITAE 425

16 xiii LIST OF TABLES Table No. Particulars Page No. 3.1 Five Major Factors Determining Service Quality Dimensions of Service Quality Classification of Retail Service Quality Behavioural Intentions Battery Type of the Retail Stores Location of the Retail Stores Gender Wise Classification among the respondents Age Wise Classification among the respondents Marital Status of the respondents Educational Qualifications of the respondents Occupational Wise Classification among the respondents Family Monthly income Wise Classification among the 175 respondents 5.9 Family Size Classification among the respondents Family Type Classification among the respondents Preferred Purchasing Mode among the respondents Shopping Frequency among the respondents Amount spent in a month for shopping among the respondents 5.14 Factor Influencing to Purchase the Product in this Retail Store 5.15 Preferred Mode of Payment among the respondents Most Liked Factor of the Retail store among the respondents 5.17 Most Disliked Factor of the Retail store among the respondents 5.18 Estimation of Cronbach s coefficient alpha and CFA loadings for purchase intentions of retail stores 5.19 Reliability item statistics for purchase intention scales

17 xiv 5.20 Estimation of Cronbach s coefficient alpha and CFA 194 loadings for perception service quality dimensions of retail stores 5.21 Reliability item statistics for perception service quality 195 dimension 5.22 Estimation of Cronbach s coefficient alpha and CFA 197 loadings for expected service quality dimensions of retail stores 5.23 Reliability item statistics for expected service quality 199 dimension 5.24 Estimation of Cronbach s coefficient alpha and CFA 201 loadings for customer loyalty dimensions of retail stores 5.25 Reliability item statistics for customer loyalty dimensions Model fit statistics Service Quality Gap Analysis Calculation of Un-Weighted Score Rating of Preference on the Service Quality Dimensions Calculation of Weighted Score Relative Importance of Service Quality Dimensions on 219 Overall Retail Service Quality 6.4 Effects of Demographic Variables on Different Service 223 Quality Dimensions Effects of store type on service quality dimensions Effects of location of the store on service quality 226 dimensions Effects of gender on service quality dimensions Effects of age on service quality dimensions Effects of marital status on service quality dimensions Effects of educational qualification on service quality 232 dimensions Effects of occupation on service quality dimensions 234

18 xv Effects of family income on service quality dimensions Effects of family size on service quality dimensions Effects of family type on service quality dimensions Effects of most preferred purchase mode on service 239 quality dimensions Effects of frequency of shopping on service quality 240 dimensions Effects of amounts spent in a month for shopping on 242 service quality dimensions Effects of influencing factor on service quality 244 dimensions Effects of preferred payment modes on service quality 245 dimensions Relative Importance of Service Quality Dimensions on 248 Word of Mouth Factors Relative Importance of Service Quality Dimensions on 251 Switch to Competitor Factors Relative Importance of Service Quality Dimensions on 254 Willing to Pay More Factors Relative Importance of Service Quality Dimensions on 257 Shoppers Response Factors 6.6 Effects of Demographic Variables on Different Customer 260 Loyalty Dimensions Effects of store type on customer loyalty dimensions Effects of location of the store on customer loyalty 263 dimensions Effects of gender on customer loyalty dimensions Effects of age on customer loyalty dimensions Effects of marital status on customer loyalty dimensions Effects of Educational Qualification on customer loyalty dimensions 267

19 xvi Effects of occupation on customer loyalty dimensions Effects of family income on customer loyalty dimensions Effects of family size on customer loyalty dimensions Effects of family type on customer loyalty dimensions Effects of preferred purchase mode on customer loyalty 272 dimensions Effects of frequency of shopping on customer loyalty 273 dimensions Effects of amount spent in a month for shopping on 274 customer loyalty dimensions Effects of influencing factors on customer loyalty 275 dimensions Effects of preferred payment mode on customer loyalty 276 dimensions 6.7 Relationship Between Perceived Service Quality 277 Dimensions 6.8 Relationship Between Customer Loyalty Dimensions Relative Importance of Service Quality Dimensions on 281 Customer Satisfaction 6.10 Relative Importance of Customer Satisfaction Factors on Customer Loyalty Effects of Demographic Variables on Customer 289 Satisfaction Factors Effects of store type on customer satisfaction factors Effects of location of the store on customer satisfaction 292 factors Effects of gender difference on customer satisfaction 293 factors Effects of age difference on customer satisfaction factors Effects of marital status difference on customer satisfaction factors 295

20 xvii Effects of Qualification difference on customer 296 satisfaction factors Effects of occupational difference on customer 297 satisfaction factors Effects of family income difference on customer 298 satisfaction factors Effects of family size difference on customer satisfaction 299 factors Effects of family type difference on customer satisfaction 300 factors Effects of preferred purchase mode on customer 301 satisfaction factors Effects of frequency of purchase on customer satisfaction 302 factors Effects of amounts spent in a month for shopping on 303 customer satisfaction factors Effects of influencing factor on customer satisfaction 304 factors Effects of preferred mode of payment on customer 305 satisfaction factors 6.12 Relative Importance of Factor Affects Purchase on 306 Customer Satisfaction 6.13 Relationship Between Purchasing Factors, Customer 311 Satisfaction, Expect & Perceived Service Quality And Customer Loyalty Effects of most liked factors on purchase attribute factors Effects of most disliked factors on purchase attribute 313 factors Effects of most liked factors on factors affecting retail purchase 316

21 xviii Effects of most disliked factors on factors affecting retail 318 purchase 6.16 Difference Between Perception And Expectation Service 320 Quality Dimensions Association between demographic difference of the 323 respondents and most liked factors about retail shops Association between demographic difference of the 325 respondents and most disliked factors about retail shops Level of Perceived service quality, Customer satisfaction 327 and customer loyalty towards the retail shopping Association between perceived level of service quality 329 towards the retail stores and demographic profiles Association between customer satisfaction towards the 331 retail stores and demographic profiles Association between customer loyalty towards the retail 333 stores and demographic profiles Effects of Service Quality Dimensions on Overall 336 Customer Satisfaction 6.21 Effect of Service Quality Dimensions And Overall 341 Customer Satisfaction on Customer Loyalty 6.22 Effect of Customer Loyalty Dimensions on Overall 346 Customer Satisfaction 6.23 Customer Evaluation Model For Retail Stores 351

22 xix LIST OF FIGURES Figure. Title Page No. No. 3.1 Hierarchical Structure of Retail Service Quality The Relationship between Service Quality Dimensions, 130 Satisfaction 3.3 The Relationship between Service Quality Dimension, 135 Perceived Service Quality, Emotional Satisfaction and Behavioural Intentions 4.1 Conceptual Model for Measuring Service Quality Proposed Customer Evaluation Model for Retail Stores Measurement model of factors affecting purchase intention Measurement model for perceived service quality dimensions Measurement model for expected service quality dimensions Measurement model for customer loyalty dimensions Effects of service quality dimensions on overall customer 336 satisfaction 6.4 Effect of service quality dimensions and overall customer 342 satisfaction on customer loyalty 6.7 Effect of customer loyalty dimensions on overall customer 347 satisfaction Customer Evaluation Model for Retail Stores 351

23 xx ABBREVIATIONS 1. SQ Service Quality 2. Overall SAT Overall Satisfaction 3. BI Behavioral Intention 4. CFA Confirmatory Factor Analysis 5. SEM Structural Equation Model 6. SPSS Statistical Packages for Social Sciences 7. AMOS Analysis of Moment Structure 8. RATER Reliability, Assurance, Tangible, Empathy and Responsiveness 9. WOM Word of Mouth 10. P mean Perception Mean 11. E mean Expectation Mean 12. ASCI American Customer Satisfaction Index 13. GFI Goodness of Fit Index 14. AGFI Adjusted Goodness of Fit Index 15. KMO Kaiser Meyer Olkin 16. C.R Critical Ratio 17. S.E Standard Error 18. C.S Convergence Statistics

24 1 CHAPTER I DESIGN OF THE STUDY The first section of this chapter mainly focuses on the Service quality and its importance in the Indian Organized Retail Sector. Further, in this chapter also discusses about the research problem, followed by its objectives, then discusses about the research questions, methodologies, purpose of the study with proper justification followed by it and Finally, a disposition of the thesis can be found in order to give a clear view towards the organization of the thesis. 1.1 INTRODUCTION Every Industrial Era has had its own philosophies about how to find a competitive edge over competitors. Essentially, this race has been about creating value in ways that competitors have yet to utilize. In the 18 th and 19 th centuries, value was added to raw materials through manufacturing and by the beginning of the 20 th century, we saw the emergence of industrial revolution and mass-production (Vargo and Lusch 2004). Marketing, then, was a field concerned mostly with the distribution. Only in the 1950s, marketing started to become known as it is today, understanding and fulfilling the needs of consumers (Wilkie and Moore 2003). What has changed radically since the 1950s, however, is how we understand the needs of a consumer and the nature of the products. Until the 1970s, the predominant business thinking was that people make their purchase decisions purely based on the tangible product (Kotler ). This was largely in line with the prevailing view of consumers in economics, homo

25 2 economicus, stating that consumers act rationally and maximize their utility in relation to their income (Campus, Antonietta 1987). This assumption has been key to modeling the world with mathematical methods. Psychologists and marketers, however, have long seen that this assumption doesn t always hold true in real decision-making situations, because homo economicus neglects the existence of human emotions in decision making (Anttila, Mai 1990). In 1973, Kotler was the first to suggest that buyers, in fact, respond to the total product rather than just the tangible product. Hence, services, warranties, packaging, advertising, financing, pleasantries, images and other features that accompany the product also affect consumer decision-making. This was also the first scientific article stating that store atmosphere has an effect on consumer behavior (Kotler ). Kotler s view was fundamentally different to what we had been taught, since the born of economics as a discipline: exchange is about trading things to other things (Smith, Adam 1776). This view, by large, neglects the possibility that services or experiences could be something people are willing to pay for (Lusch et al. 2007). The increased understanding of consumer value has slowly also raised questions about the validity of the rational economic theory. At the same time as behavioral economics has slowly picked up in popularity in economics, Service-Dominant logic has changed the way what we considered a product in marketing. While research and understanding about customer value perceptions and retail environments has increased, standing out from the crowd with traditional tools of marketing, such as pricing, marketing communications and distribution has become more and more difficult (Solomon et al. 2002). As a result, retail design and the service level of the staff have become increasingly important sources of competitive advantage. The importance of these two factors is especially critical in service settings, where the actual product cannot

26 3 be assessed based on physical qualities. Therefore, the store where the service is sold at can be said to be the packaging of the product (Zeithaml et al. 2006). In Multi-branded retail settings, store environment's impact the perceived quality and value of products sold in the store (Baker et al. 2002). Even more, consumers beliefs about the physical attractiveness of a store has a higher correlation with patronage intentions than does merchandise quality, general price level, selection, and six other store or product beliefs (Darden et al. 1983). Thus, if retail design is one of the key competitive factors in the modern era, as (Solomon et al. 2002) suggest, it is necessary to understand where customer value in store environments stems from. This study will provide an overview of the theoretical discussion around the topic and finally examine the phenomenon empirically. 1.2 BACKGROUND OF THE STUDY This research is mainly designed to investigate the relationship between service quality, demographic characteristics, and customer loyalty and repurchase intentions in organized retail. This research complements and adds value to previous research by expanding the study of service quality on customer behavioral intentions in various retail store formats, a sector that has been under-researched in India. The present work will unearth the superior performance of the most important retail service quality dimension, its important relationship with customer satisfaction and behavioral intention towards loyalty, which will add value to previous research carried out in this research area in India. Service quality is A Global Judgment, or Attitude, relating to the superiority of the service (Parasuraman, Zeithaml and Berry, 1988). Service quality is receiving a great deal of attention to practitioners, managers and

27 4 researchers over the past few decades, due to its strong impact on business performance, lower costs, customer satisfaction, customer loyalty and profitability. All mentioned subjects were studied by Cronin and Taylor in 1992; Chang and Chen in 1998; Lasser et al. in There has been a continued research on the definition, modeling, measurement, data collection procedure, data analysis etc., Issues of service quality, leading to the development of sound base for the researchers (Seth and Deshmukh, 2005). All these researches have brought service quality topic to another level by exploring more possibilities to measure, analyze and conclude about service quality as a significant issue to study. The important issues of service quality performance were discovered in order to investigate the methods for the service quality improvement. The retail environment is changing very rapidly in our days. It is characterized by intensifying competition from both domestic and foreign companies, a spate of mergers and acquisitions, and more sophisticated and demanding customers who have greater expectations related to their consumption experiences (Dabholkar, 2001). According to Berry, a basic retailing strategy for creating competitive advantage is the delivery of high service quality (Reichhel 2003). Thus, Service Quality is perceived as a tool to increase value for the consumer; as a means of positioning in a competitive environment (Mehta, Lalwani and Han, 2000) and to ensure consumer satisfaction (Sivadas and Baker-Prewitt, 2000), retention and patronage (Yavas, Bilgin and Shemwell, 1997). Vargo and Lusch (2004) Define service as the application of specialized competences (knowledge and skills), through deeds, processes, and performances for the benefit of another entity or the entity itself. There are some differences between service quality, and the store service quality for the definition of the retail service quality. A retail store experience involves more

28 5 than a non-retail service experience in terms of customers, negotiating with several store personnel along the way, finding the products they want to buy, and returning the products, which all influence the service quality based on customers' evaluations. Thus, although measures of service quality for pure service environments and for retail environments are likely to share some common dimensions, measures of retail service quality must capture additional dimensions (Dabholkar and Thorpe Joseph, 2001). Furthermore, as a means of positioning in a competitive environment to ensure consumer satisfaction, retention and patronage, the service quality is being increasingly perceived as an important element to increase value for the consumer. Delivering superior service and ensuring higher customer satisfaction have become strategic necessities for companies to survive in a competitive business environment (Reichheld, 2003). By satisfying customers through high quality service, business firms not only retain their current customers, but also increase their market share (Finn and Lamb, 1991). Thus, the practice of excellent service quality has been proven to lead to increased customer satisfaction (Sivadas and Baker-Prewitt) and significantly indicate the effectiveness of the retailers performance. As service quality can be the cornerstone of retail success, retailers need to constantly evaluate their service quality through the use of a reliable measuring instrument. By referring to Sivadas and Backer-Prewitt (2000) the excellent service quality can be reach by high employee s performance in the store because the service-oriented employees are able to deliver high retail service quality to the customers and let them occurrence favorable service experiences. Owing to the service-oriented employees in the store are able to deliver the high retail service quality to the customers and let them feel favorable service experiences, excellent service quality can be reached by high employee s

29 6 performance. So service-oriented employees contribute a lot to the overall service quality of the store in terms of having a much clearer understanding of retail service quality and knowing how to add value to the customers. In this way, retail employees should understand the customers service experience, deliver a clear and consistent message to customers about quality and implement them at ground level. The personal interaction between employees and customers is strongly emphasized concerning the increased awareness of the significance of trying to satisfy customers and their needs (Bettencourt and Gwinner 2001) and thereby achieving high service quality. Additionally, retail employees should take part in the evaluating and measuring the different retail service quality s dimensions which influence on the excellent customers experience, and how the experience is linked to customers value perceptions of the company s service offerings (Sandstrom, Edvardson et al., 2008). Hence it is really important to deal with the service-oriented employees in order to have the best implementation of service quality. Providing an excellent service quality in the store service-orientated employees create a significant value for the customers and thus service quality directly leads to higher satisfaction amongst the customers. Many companies fail to meet customer expectations with respect to creating customer value (van Riel and Lievens, 2004), probably because managers are not completely sure of what brings value to the customer, or how it is created. Retail employees should perform processes and exchange skills and services in which value is co-created with the customer (Prahalad and Ramaswamy, 2004). At this time companies are searching for new and better ways to create value and differentiate their service offerings in order to attract and keep customers, as well as make a profit. That s why researchers and managers thrive in learning details about components of service quality (Seth

30 7 and Deshmukh, 2004) and prove the importance of service quality and its dimensions in determining overall service quality, as perceived by customers. By addressing this issue, firms can gain an understanding of the areas they should concentrate on when seeking to improve their overall service quality provisions (Oliva, Oliver and MacMillan, 1992). Thus, service quality issue is a relevant topic to study. According to Seth and Deshmukh (2005) conceptual models in service quality enable management to identify quality problems and thus help in planning for the launch of a quality improvement program thereby improving the efficiency, profitability and overall performance of the store. In conclusion, the integral involvement of the consumer within the service process suggests that we need to increase customer perceived value by developing close and trusting relationships with customers, and such relationships are logically fostered by a market orientation. The consumer plays an active role in the service experience and they interact with personnel, the service script and supporting tangibles. The consequent transparency of the service encounter enables an impression to be formed of the firm s commitment to deliver excellent service and in this way increasing value for the customers. In the same time the interaction which appears with service personnel enables improved market sensing by a company, a capability of a market oriented company. This is especially the case in the context of the services industries where a high degree of intangibility may confound the relationship (Sin and Tse, 2002), and intermediate variables such as service quality are also likely to significantly impact firm performance (Chang and Chen, 1998). It is observed that the service quality outcome and measurement can also be changed with respect to factors like time, new technology, type of service, competitive environment, etc. (Seth and Deshmukh, 2005). Thus, the

31 8 measurement tools are also should be adjusted in evaluating the perception of the service quality by customers. These demands for a continued effort to learn, validate and modify the existing concepts of service quality were interested to trace the development of the models in the literature. In the previous researches different measurement tools were explained and applied to measure the service quality, however the most modern and appropriate Retail Service Quality Scale (RSQS) was not experimented much at the Indian organized retail stores. Considering the importance of the evaluation of the different dimensions of retail service quality and measurement of those dimensions in order to deliver high value to the consumer, it was deemed necessary to conduct further research to gain an understanding the overall perception of retail service quality of customers. Moreover, it was concluded from a review of the previous studies that there was lack of studies on retail service quality according to different dimensions at retail stores by applying the measurements of the Retail Service Quality Scale (RSQS) conducted and executed. 1.3 PROBLEM STATEMENT Service quality is a critical component of customer perceptions about the service. Customers perceive services in terms of its quality and how satisfied they are overall with their experiences (Zeithaml et al., 2000). Thus, satisfying customer needs through excellent service quality provided by customer-oriented salespeople will increase the likelihood of customers returning to shop and eventually recommending the stores to others, thus allowing the retailer to compete effectively in the marketplace (Yesmine et al., 2003).

32 9 Additionally, a number of correlated factors, including the scope of services and contraceptives available to clients, the way in which individuals are treated by providers, the promotion of individual choice, the quality of the information provided to the clients and quality of the counselling skills, the accessibility and continuity of services, and the technical competence of providers are also a form of service quality. Parasuraman et al. (1985) also found that the customer s perception of service quality depends upon the size and direction of the gap between the service the customer expects to receive and what he or she perceive to have been received. According to gap model service quality is defined as the gap between customers expectation of service and their perception of the service experience. A service quality gap exists when there is a shortfall in which the service provider would like to close (Lewis et al., 1994). That s why the challenge which the retail service providers face is to build and retain a good relationship with the customers by providing to them as excellent service is possible and make customers satisfied and sequentially, step by step, obtains customer loyalty, through great retail service delivering. The established logic is that a market orientation provides the basis for devising a strategy that creates value for customers, and that such a strategy provides the foundation for a sustainable competitive advantage that contributes to financial performance (For example, see the hypotheses related to business performance developed by Jaworski and Kohli, 1993). However, this line of reasoning does not in itself explain why a firm can realize value for its shareholders by pursuing a strategy of creating customer value. Nor is an explanation readily apparent in the market orientation literature. Kohli and Jaworski (1990, 1993), for example, found

33 10 that an emphasis on profitability was conspicuously absent as a component of a customer value-based business strategy. Chang and Chen (1998) make an important contribution to identifying the steps that fall between a market-oriented business culture and performance outcomes. These authors developed a conceptual model that postulates both a direct effect for market orientation on business performance, and an indirect effect through helping to improve service quality (Chang and Chen, 1998). The model is tested with a sample of retail stockbrokers in Taiwan. The results support the hypothesis that a market orientation can assist firms to achieve a higher quality level, and that quality has a positive relationship to profitability. Quality is found to explain more of the variation in profitability than market orientation. The model including service quality (and a number of covariates) explains 38 percent of the variation in profitability between firms, and the addition of market orientation to the model only increases this to 45 percent. Chang and Chen (1998) conclude that there are other potential intermediate variables unaccounted for, the pure direct effect of market orientation on profitability may be even smaller. This illustrates the importance of the identification of intermediate variables. That s why it has crucial for managers of the retail business constantly improved, service quality within the stores. All necessary dimensions of retail environment should be evaluated and analyzed in order to become aware of the overall performance of the retail service quality in a store and conclude some issues for its improvement. Building upon and synthesizing previous work, Brady and Cronin (2001) advanced the hierarchical conceptualization of service quality. Here, service quality is a multilevel construct consisting of sub-dimensions. The interaction quality dimension, in turn, is comprised of attitude, behavior, and expertise of the service provider. The physical environment quality dimension

34 11 is comprised of ambient conditions, design, and social factors of the service facilities. The outcome quality dimension is comprised of waiting time, tangibles, and valence. There are numerous instruments measuring sub - dimensions of service quality. However, some of them are not applicable to the measurement exactly the retail environment and attributes. One of the most popular measure instruments of the retail store coming from the SERVQUAL model developed by Parasuraman et al. in Despite the fact that SERVQUAL has been empirically tested in various studies involving pure service settings, it has not been proven to be successfully applied in a retail setting (Dabholkar at al., 1996; Mehta et al., 2000) and also, more specifically, in apparel specialty stores. The reason why it is not so effective with retail stores is because retail stores are also dependent on suppliers for goods that they sell and an essential part of customer satisfaction comes from the quality of goods sold by the stores. So, later on the need for a measurement instrument that can accurately assess service quality in a retail environment was answered by Dabholkar et al. (1996) who developed and empirically validated a scale to measure retail service quality distinctively. Dabholkar s research about the retail service quality resulted in the development of the Retail Service Quality Model (RSQS). According to the scale, reveals how well the respondents ratings of each of the five RSQS factors - physical aspects, reliability, personal interaction, problem solving and policy - explain customers judgments of overall service quality. There are also evidences shows that consumers in some cases tend to not be satisfied with the service quality because of some particular reasons that could be because of the quality of goods or after sales service or ease of transaction. The measurement tool such as RSQS allows retailers to determine service areas that are not causing to customer satisfaction and as a result not

35 12 building consumer loyalty. And one way to do this is to test the RSQS model in practice. Therefore, by applying the Retail Service Quality Scale the dark points of the service can be investigated for the managers what means that this particular model also identifies the causes that are beyond process management and a diagnostic tool that allow retailers to determine service to identify and find solutions and areas that are weak and in need of attention for the future improvements (Dabholkar and Thorpe Joseph, 2005). Therefore, well awareness and analysis of the consumer s perceptions about service quality lead to delivering great service to the customers in the stores and therefore it is a significant topic to study. Based on the literature review and to meet the objectives of this study the key research questions which should be answered are: Which are the key factors influencing the customer s perception of the retail service quality in organized formats? Is there any association between service quality dimensions and demographic profile of the respondents in organized retail stores? Is there an association between service quality dimensions and customer loyalty in organized retail stores? Which is the decisive service quality factors influencing the customer s repeated patronage? The relationship between service quality dimensions, demographic information, and customer loyalty and repurchase intentions is in line with previous research as evident in the literature review chapter.

36 RESEARCH QUESTIONS This research work tries to fulfill its core purpose and mainly identifies service quality dimensions importance in the Indian Organized Retail Sector. The antecedents of behavioral intention of customers, commencing with the service sector's performance, which includes factors that influence loyalty both directly and indirectly, have been investigated. In particular, the impact of service quality and customers satisfaction are investigated to identify their effect on future behavioral intentions. This research includes the identification of the aspects of the experience of the customers to find the importance in the Indian Organized Retail Sector in order to evaluate their performance. This research addresses the following questions: How to identify the impact of demographic variables on the service quality measurement of RSQS dimensions in the Indian Organized Retail Sector? How the impacts of preconception on the service quality dimensions towards the overall evaluation of the service in the Indian Organized Retail Sector are determined? How the service gaps are determined among the perception and expectation level of customers in the Indian Organized Retail Sector? What is the relationship in the customer evaluation of overall service quality and discrepancies in customer service quality perception? Which service quality dimension is the best predictor of overall service quality in the Indian Organized Retail Sector? How to identify the relative importance of service quality dimensions in the Indian Organized Retail Sector? How the impact of behavioral intention towards the service quality dimensions and customer satisfaction (SAT) in the Indian Organized Retail Sector are determined?

37 14 How a structural model is designed to link preconception, service quality, overall satisfaction and behavioral intention of the Indian Organized Retail Sector under the study? The impacts of preconception on service quality and customers satisfaction are also incorporated to identify their effect on future behavioral intention among the customers of the Indian Organized Retail sector. When all the proposed research questions are explained, the study will conclude by answering the purpose. The research questions are systematically answered throughout the study and finally summed up to answer the purpose. The specific hypotheses designed for each of these research questions are set in Chapter IV and empirically tested in the Chapter V and VI. 1.5 OBJECTIVES OF THE STUDY In the business world, it is possible to understand an organization s success or failure through an analysis of the economics of its offering compared to those of its competitors. For an organization in which services are incorporated, understanding the nature of the service encounter may yield equally actionable insights. With this in mind, this research is designed to investigate the relationship between service quality, demographic characteristics and customer loyalty in organized retail. The objectives of the study are: To analyze the demographic profile of the customer among the Organized Retail Stores in Tamil Nadu. To Identify and determine the service quality gap between the Excepted Service and Perceived Service among the Organized Retail Stores

38 15 To determine the relative importance of Service Quality Dimensions (SQD) over Customer Satisfaction from the customer point of view. To identify the impact of SQD on overall Satisfaction and behavioral intention of customers among the organized retail stores. To find out the relationship between the service quality, customer satisfaction and customer retention among the organized retail stores. 1.6 JUSTIFICATION OF THE RESEARCH STUDY This study is justified by considering its significance, the extent to which it is a researchable topic, and the feasibility of the study. This study has the potential to contribute to the body of knowledge about service quality that leads to understand the need for customer retention. All the five dimensions of service quality like tangibility, reliability, responsiveness, assurance and empathy are important for service providers. In contrast, as far as preconception is concerned, the interest lies in less objective beliefs. These beliefs are related not so much to the preconception of a person as to prejudices and stereotypes that a person associates with the service category. The sooner the service providers are able to improve their service quality, the more they are able to help customers to receive what they want (Cook, Macaulay & Coldicott, 2004). It is rational to focus on examining the relationship between preconception about the service quality and service quality dimensions as a potentially important dimension of customer retention. Customers retention is the most common marketing strategy that companies attempt to implement in their businesses. It is less expensive to retain existing customers than to obtain new customers (Reichheld & Saccer, 1990). Researchers suggest that customer retention is a focus on the behavioral

39 16 intention to repeat purchase behavior (Hennig Thurau & Klee, 1997). It improves service quality and customer relationship (Hanson et al., 1996). No research explicitly explored the relationship among preconception about the service, service quality and customer retention. Exploring the relationship among preconception, service quality and customer retention adds to the knowledge base. 1.7 RESEARCH GAP Numerous studies have discussed clearly the relationship between service quality, customer satisfaction and loyalty, for customer retention (Johnson & Gustafsson, 2000). But no study is found to have examined the relationship between each service quality dimension and the Behavioral Intention (BI). The linkages of each dimension within the two instruments have not been explored yet. In order to provide superior service quality, the service providers need to investigate the level of customers perception and expectations towards their service quality. One of the characteristics of service is their intangibility. (Zeithamal, Parasuraman & Berry, 1985). The products are not seen by the customer before purchase. Pre-decisional evaluation therefore depends on other factors, such as the prestige of the company, recalled attributes of the product, or prior overall judgment (Lynch, Marmorstein&Weigold1998). The customer, faced by a dearth of processing resources, is likely to base a decision on an more effective process than on cognitions (Shiv & Fedorikhin, 1999). This study also focuses on the effects of customer preconception about the service; customer perception and expectations to service quality and the overall evaluation of the service. This research work endeavors to fill the research gap in the service quality literature. It explores the dimensions of customer perceived service quality in the context of the Indian retail industry.

40 17 This study is feasible because it can be implemented in a reasonable amount of time. The concepts in the theoretical framework are measured very accurately. 1.8 PURPOSE OF THE STUDY In the present day of retailing, service quality has become the basic tool for retailers to create competitive advantage and to enhance the shopping experience. The quality of services significantly affects customer satisfaction, company revenues, cross selling and also repeat purchase behavior (Berry, 1986; Hummel and Savit, 1988; Reichheld and Sasser, 1990). The fast pace of the Indian retail industry presents many companies with a host of daily challenges. In today s competitive environment and with the growing importance of services, delivering high quality services has become the basic retailing strategy. The studies present the impact of service quality on retail customer satisfaction that leads to customer loyalty intentions and also identify the critical factors of service quality from the customer s perspective. 1.9 RESEARCH METHODOLOGY The research starts with an extensive review of literature. On the basis of the knowledge acquired, it sets hypotheses about the constructs and relationships under examination. Finally, through fieldwork, it seeks an evidence to confirm or disconfirm the hypotheses. Being quantitative, this research; (a) generates hypotheses (b) develops instruments and methods for measurement, (c) evaluates the results. To collect appropriate empirical data, it employs a survey strategy. Structured questionnaires are designed and used to collect the data. Further, this research examines the applicability of the RSQS (Retail Service Quality Scale), a revised SERVQUAL instrument for the Retail sector. It explores the relationships among preconception about the service, service quality, customer satisfaction and behavioral intention.

41 SAMPLING TECHNIQUES Sampling techniques are methods used to select a sample from the population by reducing it to a more manageable size (Saunders, Lewis & Thornhill, 2007). According to de Leeuw, Hox and Dillman (2008) these sampling techniques are used when inferences are made about the target population. In the present study, Simple Random Sampling was used for the selection of hypermarkets, super markets and departmental stores whereas Judgmental Sampling was used for the selection of respondents from hypermarkets, super markets and departmental stores. Utmost care has been taken to take respondents from various demographic characteristics DETERMINATION OF SAMPLE SIZE As per the revised customer insight online sample size calculator, sample size is calculated for the research. Sample size distribution tolerance error generally lies between 3 to 6%. In this research work, the sample size distribution tolerance error of 4% is considered with 95% confidence interval. Based on the response rate calculation, sample size of 900 is determined for the organized retail sector. Totally 950 questionnaires are distributed for collecting the primary data from the customers of various Hypermarkets, Supermarkets and Departmental Stores RESEARCH QUESTIONNAIRE In this research study, a structured questionnaire was focused on measuring service quality, demographic characteristics, customer satisfaction, purchase intentions and behavioral intentions. The questionnaire used in the present study consisted of five sections A, B, C, D and E. Section A dealt with background information of the participants. Section B and C consisted of factors affecting purchasing in retail stores and customer satisfaction factors.

42 19 Section D consists of 27 items that used to measuring service quality and Section E includes 13 items used to measuring the customer loyalty respectively. The researcher used a 7 point scale for the study, instead of a 5 point Likert scale because 7 point scale increases the rate of accuracy and quality of the responses (Prayag, 2007; Buttle, 1996). Thus, all statements employed a seven-point scale because it would give a better normal spread of observations. To measure customer loyalty, the instrument must consider behavioral, attitudinal and cognitive aspects of behavioral intentions. That s why Zeithaml et al. (1996) behavioral intention battery was used. Each of the 13 items was accompanied by a seven-point scale ranging from 1 (not at all likely) to 7 (extremely likely). The wording of the BIB items was adapted to the retail service setting. Validated Service Quality Scale developed by Dabholkar et al. (1996) was employed to measure perceived service quality. The items of RSQS were evaluated on a seven-point scale ranging from 1 (strongly disagree) to 7 (strongly agree) (Dabholkar, Thorpe, and Rentz, 1996). The diagrammatic rating scale used in the questionnaire is as follows: Small adaptations to the RSQS instrument were made. Review of literature (empirical research Kaul, 2007) along with opinion of store managers (SIS) and independent experts (consultants, Indian Retail) highlighted that two items of RSQS were not relevant in Indian retail appertained to store s own credit cards seems premature in the Indian retail environment where credit cards have only recently started getting widespread acceptance and very few retail stores have their own credit cards. That is why out of the 27 statements of RSQS one was not included in the questionnaire because of its inapplicability in the Indian organized retail (Kaul, 2007).

43 ANALYSIS OF DATA The analysis is undertaken with a view to give a clear cut idea of the customers in the organized retail sector. Various tables, diagrams are incorporated to make it more useful and easy to understand. The Software Analysis of Moment Structure (AMOS 21) and Statistical Package for Social Sciences (SPSS 21) are used for analysis and interpretation of primary data. The statistical tools like a reliability test for the content and construct validity of the questionnaire, correlation, multiple regressions, one way ANOVA, Chisquare and paired t-test is used to analyze the primary data. SEM is constructed to identify the relative importance among the preconception about the service, RSQS dimensions, customer satisfaction (SAT) and behavioral intention (BI) STRUCTURAL CONTENT OF THE THESIS This Thesis consists of seven chapters. The following describes briefly about each chapter. Chapter One: Presents the researcher s main focus on service quality, delivery over the retail stores among the organized retail sector, which in turn leads to customer satisfaction and customer loyalty. Further the chapter commences with background knowledge, and also illustrates the major objectives of the study, problem statement, the need for the study, as well as the significance of the study. This study is unique in many ways because it is conducted in the retail sector, which is considered as the potential goldmine of Indian economy. The main aim of this research is to analyze the impact of retail service quality on customer loyalty and behavioral intentions.

44 21 Chapter Two: Presents the introduction about the Indian retail sector, its present status, and its future prospects, Status of Organized and Unorganized Retail Market in India. Further, the discussion was followed by SWOT Analysis of Indian retail sector, finally it discusses on the role of FDI in Indian Retail Sector. Chapter Three: Presents the literature review of service quality, retail service quality, customer loyalty was also presented. The next part of the literature review presents the association between service quality and demographic characteristics. It was followed by the detailed discussion on the assessing the relationship between service quality and customer loyalty. After reviewing the customer loyalty, the relationships between service quality dimensions and customer loyalty in different sectors with special emphasis on retail environment was discussed. Chapter Four: Presents the conceptual and theoretical framework of the research study, where it focuses on introductory part the service quality conceptualization and measurement of service quality. This discussion is followed by the review of retail service quality and the need to differentiate it from pure services. Then RSQS is the most dominant scale for measuring retail service quality; that s why most of the researchers employed it. However, it was found that the scale is more appropriate if it is modified for different countries; therefore, the need for modifying the scale. Then it discusses about the proposed model of the research study. Finally, the main and sub-hypothesis was developed based on the main objectives of the research study. Chapter Five: Discusses the underpinning methodology of this study, beginning with a presentation of the research design, followed by area of

45 22 study, sample design, sources of data and scale and measurement. The main method for data collection was using the structured questionnaire. The questionnaire was comprised of three parts: Demographic information, Customer Shopping Experience, Retail Service Quality, Scale and Customer Loyalty Intentions questionnaires were included in the research study. Further, this chapter discusses about the reliability and validity of the scale used for the research study. Meanwhile, this chapter illustrates the demographic profile of the respondents with respect to the retail settings. Chapter Six: Discusses the findings of the research study. This data analysis is followed by relative importance of service quality dimensions. Further, the association between service quality and demographics was then investigated. Thereafter, multiple regression analysis was used whereupon the relationships between service quality dimensions and customer loyalty intentions were demonstrated. Chapter Seven: Discusses in detail about the conclusions drawn from the findings of the research study. Furthermore, the suggestions and contributions of the study are presented, followed by limitations and future research directions CHAPTER CONCLUSION This chapter provides a broad overview of the structure and direction of the thesis. The thesis addresses gaps that exist within the literature of the expectations and consumption of a quality product in the organized retail sectors. The research will benefit the service industry by proving the importance of customers attachment to the aspects of service industries performance and gaps. This research provides evidence and knowledge that contribute towards closing important literature gaps. It also explores the

46 23 decision making process of retail managers in their effort to survive intensifying competition by achieving higher customer satisfaction. The purpose of the study is related to the organized retail stores (Hypermarket, Supermarket and Departmental Stores) situated in major cities in Tamil Nadu State. The study is justified because it is significant, researchable, and feasible. The rationale for the present research and the research methodology are summarized.

47 24 CHAPTER II OVERVIEW OF INDIAN RETAIL SECTOR This Chapter gives a complete overview and a clear introduction about the Indian retail sector, its present status, and its future prospects, Status of Organized and Unorganized Retail Market in India. Further, the discussion was followed by SWOT Analysis of Indian retail sector, finally it discusses on the role of Foreign Direct Investment in the emerging competitive Indian Retail Sector. 2.1 INTRODUCTION Retailing is the largest private sector industry in the world economy with the global industry size exceeding $6.6 trillion and a latest survey has projected India as the top destination for retail investors. India is currently the twelfth largest consumer market in the world. A McKinsey report, The rise of Indian Consumer Market, estimates that the Indian consumer market is likely to grow four times by A good talent pool, unlimited opportunities, huge markets and availability of quality raw materials at cheaper costs is expected to make India overtake the world s best retail economies by 2042, according to industry players. There are exciting times for Indian Retail. Markets in Asian giants like China are getting saturated, the AT Kearney s 2007 Global Retail Development Index (GRDI), for the third consecutive year placed India the top retail investment destination among the 30 emerging markets across the world. Commercial real estate services company, CB Richard Ellis findings state that India s retail market has moved up to the 39th most preferred retail destination in the world in 2009, up from 44 last year. The recent growth spurt was

48 25 achieved primarily through a surge in productivity and is sustainable. Similarly, the study undertaken by ICRIER estimates that the total retail business in India will grow at 13 per cent annually from US$ 322 billion in to US$ 590 billion in The Indian retail industry is the fifth largest in the world. With continued economic expansion and retail growth, India is set to become a US$ 450 billion retail market by 2015, comparable in size to Italy (US$ 462 billion) and much larger than Brazil (US$ 258 billion) today. The present value of the Indian retail market is estimated by the India Retail Report to be around Rs. 12, 00,000 crores ($270 billion) and the annual growth rate is 5.7 percent. Furthermore around 15 million retail outlets help India win the crown of having the highest retail outlet density in the world. The retail sector is the largest source of employment after agriculture, and has deep penetration into rural India. It is also believed that 21 million people are employed in the retail sector, which is 7 per cent of the total national workforce, whereas the global average is around per cent. It is estimated that an additional eight million jobs will be generated through direct and indirect employment related to the retail sector. The retail sector is a sunrise industry in India and the prospect for growth is simply huge. The India Retail Industry is gradually inching its way towards becoming the next boom industry. India has the highest number of retail outlets in the world at over 15 million retail outlets, and the average size of one store is square feet. It also has the highest number of outlets (11,903) per million inhabitants. The per capita retail space in India is among the lowest in the world, though the per capita retail store is the highest. The majority of these stores are located in rural areas. The BMI India Retail Report

49 26 for the third-quarter of 2010, forecasts that the total retail sales will grow to US$ billion in 2014 from US$ 353 billion in Mass grocery retail (MGR) sales in India are forecast to undergo enormous growth over the forecast period. BMI further predicts that sales through MGR outlets will increase by 154 per cent to reach US$ billion by This is a consequence of India s dramatic, rapid shift from small independent retailers to large, modern outlets. 2.2 INDIAN UNORGANISED RETAIL MARKET According to the National Accounts Statistics of India the unorganized sector includes units whose activity is not regulated by any statute or legal provision and/or those, which do not maintain regular accounts. Thus, unorganized retailing refers to the traditional formats of low-cost retailing, for example, the local kiranashops, owner managed general stores, paan/beedi shops, convenience stores, hand cart and pavement vendors, etc. Unorganized retailing is characterized as unstructured and high degree of fragmentation with street markets constitutes form peddlers, vegetable vendors, neighbourhood stores and consumer durable stores to manufacturer owned retail outlets. Unorganized retail sector covers all those forms of trade, which sell an assortment of products and services ranging from fruits and vegetables to shoe repair. These products and services may be sold or offered out of a fixed or a mobile location and the number of people employed could range between people. Thus the neighbourhood baniya, the paanwala, the cobbler, the vegetable, fruit vendor, etc. Would be termed as the unorganized sector. Traditionally, three factors have plagued the Indian retail industry:

50 27 Unorganized: India is known as a nation of shopkeepers where the vast majority of the retail stores is small father and son outlets. Traditionally it is a family s livelihood, with their shop in the front and the house at the back, while they run the retail business. Fragmented: India has some 15 million retail outlets, however a disturbing point is that 96 per cent of them are smaller than 500 square feet in area. This means that India per capita retailing space is about 2 square feet (compared to 16 square feet in the United States). India s per capita retailing space is thus the lowest in the world. Rural bias: Nearly two thirds of the stores are located in rural areas. The rural retail industry has typically two forms: Haats and Melas. Haats are the weekly markets: serve groups of villages and sell day-to-day necessities. Melas are larger in size and more sophisticated in terms of the goods sold (like TVs). The unorganized retail sector is expected to grow at approximately 10 per cent per annum with sales rising from US$ 309 billion in to US$ 496 billion in It is a low cost structure, mostly owner-operated, has a negligible real estate and labour costs and little or no taxes to pay. According to a survey by AT Kearney, an overwhelming proportion of the Rs. 4, 00,000 crore retail markets are UNORGANISED. Consumer familiarity that runs from generation to generation is one big advantage of the traditional retailing sector. 2.3 INDIAN ORGANISED RETAIL MARKET Organized retailing refers to trading activities undertaken by licensed retailers, that is, those who register for sales tax, income tax, etc. These

51 28 include the corporate-backed hypermarkets and retail chains, and also the privately owned large retail businesses. In other words, it is a network of similarly branded stores with an element of self-service. Organized retail in India today holds only a fraction of the market share potential in India. In 2001, organized retail trade in India was worth Rs 11,228.7 billion. It has risen from 0 to 6 percent in a very short period, mainly on volumes and not a value-driven growth. The organized retail sector is catching up very fast and by the year 2013, it is expected to grow at a CAGR of 40 per cent. Associated Chambers Of Commerce and Industry (ASSOCHAM) reported that the organized retail sector is recording phenomenal growth and will completely revolutionize retailing over next 3-4 years. As per estimates made by ASSOCHAM, the organized retail in urban market is expected to grow at the rate of 50 percent to reach a value of 30 percent of the total retail market in India. According to McKinsey and Company report titled The Great Indian Bazaar: Organized Retail Comes of Age in India, organized retail in India is expected to increase from 5 per cent of the total market in 2008 to per cent of the total retail market and reach US$ 450 billion by Furthermore, according to a report titled India Organized Retail Market 2010, published by Knight Frank India in May 2010, around 55 million square feet (sq ft) of retail space will be ready in Mumbai, national capital region (NCR), Bengaluru, Kolkata, Chennai, Hyderabad and Pune. Besides, between 2010 and 2012, the organized retail real estate, the stock will grow from the existing 41 million sq ft to 95 million sq ft.

52 29 The share of organized retail in developed countries is much higher than developing countries like India. Among the BRIC countries only in India the share of organized retail is low. The share of the other BRIC countries is Brazil (36 per cent), Russia (33 per cent), and China (20 per cent). In 2008, the share of organized retail in the US was around 85 per cent, in Japan it was 66 per cent, and in the UK it was 80 per cent, while in developing countries like China and Russia it was 20 per cent and 33 per cent respectively. It is seen that the organized sector in India is still has a long way to go because the unorganized retail still continues to dominate the retail market. The growth in retail sector is assured and inevitable. In this sense the retail industry does indeed spread its benefit to all. India is perhaps the last virgin BRIC (Brazil, Russia, India, China) market for organizing retailers. The game here has just begun. By 2015, around 65 million households will patronize organized retail, amounting to over 300 million shoppers, almost equivalent to the population of the US today. The Organized Retail Penetration (ORP) is the highest in footwear with 22 per cent, followed by clothing with 12 per cent. Though food and grocery account for the largest share of retail spend by the consumer at about 76 per cent, only 1 per cent of this market is in the organized sector. However, it has been estimated that this segment would multiply five times taking the share of the organized market to 30 percent in the coming years. The food and grocery constitute the highest retail volume and this share has shown a tremendous growth over the years. According to NSSO 60th round, 54 per cent of the rural and 42 per cent of urban expenditure was on food. The second largest share is commanded by the apparels. Clothing and textile are the largest organized market and is dominated by retailers like Pantaloon, Westside, Globus,

53 30 Koutons. This owes to the increasing disposable incomes and changing lifestyles. Industry trends for retail sector indicate that organized retailing has major impact on controlling inflation because large organized retailers are able to buy directly from producers at most competitive prices. World Bank attributes the opening of the retail sector to FDI to be beneficial for India in terms of price and availability of products as it would give a boost to food products, textiles and garments, leather products, etc., To benefit from largescale procurement by international chains; in turn, creating job opportunities at various levels. 2.4 VARIOUS FORMATS IN INDIAN ORGANISED RETAIL SECTOR Formats new to the Indian marketplace have emerged rapidly over the past five years. The impact of the alterations in the format of the retail sector changed the lifestyle of the Indian consumers drastically. The evident increase in consumerist activity is colossal which has already chipped out a money making recess for the retail sector of Indian economy. These modern retail formats are encouraging development of well-established and efficient supply chains in each segment ensuring efficient movement of goods from farms to the kitchens, which will result in huge savings for the farmers as well as for the nation. The Indian retail industry is categorized into different retail formats on the basis of the retail operation. The formats are basically defined on the basis of the size of the outlet, the pricing strategy followed, the type of merchandise sold, and also the location.

54 31 Shopping Malls: the biggest form of retail in India, malls offers customers a mix of all types of products and services including entertainment and food under a single roof. Malls are located mainly in metro cities, in proximity to urban outskirts and ranges from 60,000 sq ft to 7, 00,000 sq ft and above. They lend an ideal shopping experience with an amalgamation of product, service and entertainment, all under a common roof. Examples include Ambience Mall, Ansal Plaza, and Shipra Mall etc. Convenience Stores: are located in residential areas with slightly higher price goods due to the convenience offered. The stores are basically small in size (500-3,000 square feet), which allows quick shopping and fast checkouts. They stock a limited range of high-turnover convenience products and are usually open for extended periods during the day, seven days a week. Convenience stores offer easy purchase, experience through easily accessible store locations. Subhiksha and Reliance Fresh are some major players in this format. E-Trailers: are retailers providing online buying and selling of products and services. E- Tailing is slowly making its presence felt in India. Discount Stores: as the name suggests, discount stores or factory outlets, offer discounts on the MRP through selling in bulk reaching economies of scale or excess stock left over at the season. The focus of these stores is to offer merchandise at a price that is lower than the market price, and to gain profit from volumes. These stores keep merchandise, mainly on the basis of its saleability. Usually these are no-frill stores with simple surroundings and less service. The product category can range from a variety of perishable/ non perishable goods.

55 32 Vending: it is a relatively new entry, in the retail sector. Here beverages, snacks and other small items can be bought via vending machine. Specialty Stores: are retail chains dealing with specific categories and provide a deep assortment. These stores usually specialize in one line/category of merchandise. As these stores are concerned with only one type of merchandise, they are able to offer a wider range of products at a lower price. Chains such as the Bangalore based Kids Kemp, the Mumbai books retailer Crossword, RPG s, Music World and the Times Group s music chain Planet M, are focusing on specific market segments and have established themselves strongly in their sectors. Department Stores: are general retail Merchandisers offering quality products and services. Departmental Stores are expected to take over the apparel business from exclusive brand showrooms. These stores are typically lifestyle stores where most of the merchandise constitutes apparels and products other than food and grocery. These stores offer high quality service to consumers. These stores stock lesser merchandise than other formats since the merchandise is stored in a presentable manner. Among these, the biggest success is K Raheja s Shoppers Stop, which started in Mumbai and now has more than seven larger stores (over 30,000 sq. ft) across India and even has its own in store brand for clothes called Stop! Hypermarkets: big-box formats with an average size that ranges between 60, ,000 square feet, and they stock multiple lines of products such as food and grocery, general merchandise, sports goods, and apparels. These are located in or near residential high streets. Hypermarkets are mammoth outlets that are fewer in number, but cater to a larger area (3-5 kilometers). Hypercity,

56 33 Big Bazaar, RPG Spencer s and Shoprite Hyper are some major players in this format. Supermarkets: The average size of supermarkets range from 10,000-30,000 square feet. They are a smaller version of hypermarkets that holds multiple lines of merchandise, but is limited in number when compared with supermarkets. Supermarkets are spread across the city, are greater in number, but cater to a smaller area (1-2 kilometer). Food world, Food Bazaar and Spinach are some major players in this format. MBO s (Multi Brand Outlets): offer several brands across a single product category. These usually do well in busy market places and Metros. They are also known as category killers as they focus on specific categories, such as electronics and sporting goods. Ezone, which specializes in electronics, and Staples, which specializes in office stationery, are examples of category killers. Cash-And-Carry Outlets: cash-and-carry outlet is strictly not a retail format, but considering the business dynamics it follows it can qualify for a retail format. In a retail business usually a consumer has to purchase one or more products, but under this format, the consumers have to buy a minimum volume of products or value specified by the cash-and-carry retailer. In this format the buyers are basically small retailers or catering service providers who purchase in bulk quantities. This stores size ranges from 1, 00,000 square feet to 3, 00,000 square feet. At present, Metro is a major player that falls under this format. Wal-Mart s alliance with Bharti and Tesco s with Trent will also come under the cash-and-carry format.

57 CONCLUSION This Chapter concludes with a brief overview of the Indian retail industry, where the retail industry plays a major role in the development of Indian Economy. During the last few years, the Indian retail market has seen considerable growth in the organized segment. Major domestic players have entered the retail arena and have ambitious plans to expand in the future years across verticals, formats, and cities which have been clearly depicted in this chapter earlier. High Competition in the Indian retail sector was a major threat among the other retailers, because they need to provide excellent product and service delivery in order gain a competitive advantage with higher levels of customer satisfaction, then only they could be able to withstand in this Indian Retail Sector with a Unique Competitive Advantage. Hence, this chapter gave a predominant view of the Indian Retail Sector and its emerging challenges and opportunities in a strategic way.

58 35 CHAPTER III REVIEW OF LITERATURE This chapter discusses the literature on service quality (the dimensions and the approach in measuring service quality), service quality dimensions in a retail environment, customer loyalty, assessing the relationship between service quality and customer loyalty, the impact of service quality on customer loyalty in organized retail environment and hypothesis development. This chapter begins with an exploration of the concepts related to service quality. 3.1 INTRODUCTION According to Parasuraman, Zeithaml and Berry (1988) service quality means the customer s overall judgment of the excellence of the service or the difference between one s expectation and the actual service performed. This is followed by a discussion on the retail service quality. Berry (1986) opined that providing service quality in retail was a basic strategy that leads to differential advantage over competitors. This is followed by a brief exploration of demographics and its relationship with service quality dimensions. A discussion on the customer loyalty concept and the definition of customer loyalty is presented. Every organization is running behind the loyal customers because of the enormous benefits offered by them. This special category of customers allows for a continuous stream of profit, reduces marketing and operating costs, increase referral, and was immune to competitors promotion efforts (Reicheld and Sasser, 1990). A more in depth discussion is presented by the association between service quality and customer loyalty which is the focus of this research. This is followed by a discussion on the impact of retail service

59 36 quality on customer loyalty in retail environment. It is vital to review all the relevant literatures in order to understand the whole concept of service quality and customer loyalty in various sectors. It founded a strong basis for the development of the research framework and instrument. Based on the review of literature, four hypotheses were developed. The first hypothesis were regarding an association between service quality dimensions and overall service quality. The second hypothesis tests the link between identified service quality dimensions and demographic characteristics of the customers. The third hypothesis identified the relationship between service quality and customer loyalty intentions. In the last, an affiliation between retail service quality dimensions and repurchase intentions is hypothesized. 3.2 OVERVIEW OF SERVICE QUALITY SERVICE QUALITY- AN INTRODUCTION With the liberalization and internationalization in service sector, service quality has become an important means of differentiation and the path to achieve business success. Such differences based on service quality can be a key source of competitiveness for many service providers and hence have implications for leadership in such organizations. Service Quality is a blend of two words: service and quality. Services are behavioral rather than physical entities, and have been described as deeds, performances or effort, acts or performances, activities or processes. In other words, the service is an activity or series of activities of more or less intangible nature that normally, not necessarily take place in interactions between the customer and service employees and /or physical resources or goods and/or

60 37 systems of the service providers, which are provided as solutions to customer problems (Gronroos 1984). Services are intangible because they cannot be grasped mentally. The abstract nature of services causes problems for both providers and consumers. It is difficult for service providers to differentiate their offerings from those of competitors, while it is equally difficult for consumers to evaluate a service before it is acquired and consumed. Quality has been defined differently by different authors. Quality is in the eye of the customers. It can be seen and can be measured. The quality s gurus, experts and researchers have given various definitions on quality in particular areas i.e. manufacturing of products and services. Some prominent definitions include conformance to requirements (Crosby, 1979), fitness for use (Juran and Gryna, 1988), conformance to specification (Gilmore, 1974), meeting and/or exceeding customer s expectation (Parasuraman et al., 1985), one that satisfies the customer, performance over expectation (Besterfield, 1999), zero defect (Crosby, 1979) or products or services ability to perform to its intended function without harmful effect. Quality may be viewed as a property of products or services, or processes producing these products or services. As per the Japanese production philosophy, quality implies zero defects in the firm s offerings. Quality is a dynamic state associated with, products, services, people, processes, and environments that meets or exceeds customer expectation (Geotsch and Davis, 2003). In other words, the quality of a product or service is a customer s perception of the degree to which the product or service meets his or her expectations. Lehtinen and Lehtinen (1982) defined service quality in terms of physical quality, interactive quality and corporate (image) quality. They also

61 38 suggested that when compared with the other two quality dimensions, corporate quality tended to be more stable over time.thereafter, in 1983 Grönroos elaborated service quality as both technical and functional, the first signifies what the customer gets and the latter how the customer receives the service. When a customer assessed service quality, the company s profile or image acts a filter. If a company had a positive image, it is easier to overlook smaller mistakes in its service delivery; to regard them as temporary disturbances. Lehtinen (1983) explained service quality in terms of process quality and output quality. Process quality is judged by a customer during a service, whereas output quality is judged by a customer after a service has been performed. Berry, Parasuraman and Zeithaml (1985) were among the best-known researchers on service quality. They have studied customer-perceived quality in four service categories: banks, stockbrokers, credit card companies, and companies selling household machinery. They described service quality by means of ten factors: dependability, willingness, competence, availability, courtesy, communication, trustworthiness, assurance, empathy and tangibility. In a later study, the authors reduced the ten factors to five claiming that these were valid in general terms (Parasuraman et al., 1985): Tangibility; Dependability; Willingness, Readiness; Assurance; Empathy, Insight.

62 39 According to the developers of SERVQUAL, service quality is derived from a comparison between customer expectations and customer perceptions of actual service performance. The difference between perceptions and expectations results in the service quality gap (Q = P-E), also known as GAP 5 (Parasuraman et al., 1985; 1988). A wide gap would represent the poor service quality and shows that the service provider needs to improve on the service offered to its customers. Parasuraman et al. (1988) defined service quality as a consumer attitude reflecting the perceived overall superiority and excellence in the process and outcome of a service provider. They identified a set of 22 variables/items tapping five different dimensions of service quality construct. Since they operational Zed service quality as being a gap between the customer s expectations and perceptions of performance on these variables, their service quality measurement scale is comprised of a total of 44 items (22 for expectations and 22 for perceptions). The SERVQUAL scale was designed to uncover broad areas of good or bad service quality and can be used to show service quality trends over time, especially when used with other service quality techniques. The SERVQUAL scale is based on a difference score between customer expectations of service and their perceptions after receiving the service. Bakakus and Boller (1992) elicited that although SERVQUAL had been applied in the study of different types of service industries, there were certain limitations and criticisms. Some of the widespread concerns were the 5 dimensional configuration of the scale, the appropriateness of operationalizing service quality as the expectations-performances gap score, and the scale s applicability to a retail setting.

63 40 With an argument that Parasuraman et al. (1985) gap theory of service quality was supported by little empirical or theoretical evidence, Cronin and Taylor (1992) developed a performance-based service quality measurement scale called SERVPERF. The major difference between these two scales is that SERVQUAL operational service quality by comparing the perceptions of the service received with expectations, while SERVPERF maintained only the perceptions of service quality. The SERVPERF scale consists of 22 perception items excluding any consideration of expectations. According to Cronin and Taylor (1992), their unweighted performance-based SERVPERF scale was a better method of measuring service quality. This scale s reliability ranged between.884 and.964, depending on industry type, and exhibited both convergent and discriminant validity. York (1993) investigated the effects of consumer evaluation of quality, satisfaction, and value on service patronage. His findings did not confirm the five dimensions of service quality/performance hypothesized; however, three dimensional structures were in conformance with the following dimensions: tangible operations, tangible communication and reliability/empathy. The dimensions of service quality were found to have a positive impact on overall quality, satisfaction and value with the exception of tangible communications which did not display a significant effect. Overall quality, satisfaction and value were in turn found to influence service patronage. Zeithaml et al. (1993) proposed that three levels of expectations can be defined against which quality was assessed: the desired level of service - reflecting what the customer wants; the adequate service level - defined as the standard that customers were willing to accept; and the predicted service levelwhich they believe is most likely to actually occur.

64 41 On the basis of ten year study of service quality in America Berry et al. (1994) concluded that service quality has many facets. The ten lessons learned from their study were as follows: Listening - Businesses must listen to their customers. Reliability - Businesses must perform the promised service dependably and accurately. Basic Service - Customers want the basics, the fundamentals, and performance; not promises. They do not expect fanciness, and they are not unreasonable in their expectations. Service Design - Customers expect a system or systems that provide good, reliable customer service. Recovery - Businesses must handle problems quickly, efficiently, and fairly. Surprising Customers - Businesses should surprise customers with uncommon swiftness, grace, courtesy, competence, commitment, and understanding. Fair Play - Customers expect companies to treat them fairly and become resentful and mistrustful when they perceive otherwise. Teamwork - Various systems within a company must work as an overall team in providing quality service to customers. Employee Research - Businesses should gather information from employees concerning the level of service quality provided to customers, things that hinder the provision of quality service, and potential problems in providing quality service. Servant Leadership - Top management should lead by serving those who provide direct service to customers and by providing what is needed for quality service.

65 42 Deyong (1994) developed a methodology to identify conceptual linkages between customer satisfaction dimensions and process performance metrics. Their methodologies indicated a link between the customer satisfaction dimensions and process performance metrics. It also identified a relationship between the dimensions of quality proposed by Garvin and service quality dimensions proposed by Parasuraman, Zeithaml and Berry. Giblon (1994) examined the relationship between service quality and supplier marketplace performance and between a market orientation and supplier marketplace performance. Results showed that improving customer satisfaction improves supplier marketplace performance. Increasing customer satisfaction increases customer commitment to the supplier. There was a positive relationship between supplier quality and customer satisfaction with the supplier: raising supplier quality raises customer satisfaction with the supplier. Finally, there was a positive relationship found between customer satisfaction with the distributor and with the supplier: customer satisfaction with the distributor was found to be increasing customer satisfaction with the suppliers. A more recent conceptualization of the service quality dimensions was proposed by Rust and Oliver (1994). They proposed a three-component model in which the overall perception of service quality is based on a customer s evaluation of three dimensions of the service encounter: The customer-employee interaction (i.e. Functional or process quality), The service environment, and The outcome (i.e. Technical quality).

66 43 Burch et al. (1995) examined the applicability of the service quality measurement scale (SERVPERF) to the rental industry. The SERVPERF scale was found to explain a great deal of the variation in service quality. While satisfaction seems to have a significant positive effect on purchase intention, service quality does not seem to have a similar effect. Indeed, satisfaction seems to be more closely tied to purchase intentions than service quality. The research findings of Edvardsson (1996) concluded that unlike manufacturing, the service experience involves the customer as co-producer. He explained that how customers access service quality and the factors they perceive as contributing to quality. He puts forward 13 propositions on service quality as guides to new service development. Johnston (1997) forwarded a framework which will assess the impact of any service quality initiative. He categorized quality factors in terms of their relative importance and their effect on satisfaction and dissatisfaction. He suggested that certain actions, such as increasing the speed of processing information and customers, we re likely to have an important effect in terms of delighting customers; however, other activities, such as improving the reliability of equipment, will lessen dissatisfaction rather than delight customers. He further suggested that it was more important to ensure that dissatisfies were dealt with before the satisfiers. Moreover, there were two areas where banks could achieve a distinct advantage: genuine commitment and attentiveness of front-line staff. Oh (1997) compared the three models: Expectancy disconfirmation model (EDM), SERVQUAL (Parasuraman, Zeithaml and Berry, 1988) and SERVPERF (Cronin and Taylor 1992) to identify the most reliable customer satisfaction (CS) and service quality (SQ) measurement model for the lodging

67 44 industry. EDM appeared to be the most appropriate basis for measuring CS and SQ in the lodging industry. The supplementary data also revealed that the subjective disconfirmation method of EDM is more valid than the objective disconfirmation method of SERVQUAL. Rushchano (1997) investigated the relationship between US and THAI corporate consumers perceptions of telephone service quality and satisfaction with telephone service. Results of the study indicated that perceived telephone service quality in terms of reliability of service, responsiveness of service provider, competence of service provider, accessibility of service provider, courtesy of service employee and technological aspects were differently related to US and THAI corporate consumers satisfaction in terms of availability of service, punctuality of service installation, problem elimination, punctuality of repair service, accessibility of public telephone and price and value. Chaoprasert (1998) demonstrated a model of service quality improvement, from personnel counter services to electronic services. He highlighted that banking practitioners should focus on a differentiated strategy, known as service quality improvement, to strengthen their core competitive edge, and urgently make a decision to focus on either the area of personnel counter services or electronic services or both, to be able to allocate limited resources to serve that decision. This strategy to focus on either personnel counters services or electronic services or both, will be successful if there is commitment from bank management and involvement from all employees to develop an exact understanding of the customers needs. O Neill et al. (1998) argued that an individual s perception may not be stable over time and that suppliers should be particularly interested in

68 45 consumers perceptions at the time that the next repurchase decision was made. A model of the time elapsed effects of service quality perception was also presented. Rhoades et al. (1998) examined the service quality of 26 US airlines for the period using data from the Department of Transportation s Air Travel Consumer Report. The results indicated that there had been improvement in the service quality of the industry overall. Further, there were significant differences between the service quality of major and regional airlines. Regional airlines performed much worse on all measures of service quality. Ruyter et al. (1998) identified three dimensions of service loyalty: preference loyalty, price indifference loyalty and dissatisfaction response. Moreover, a positive relationship was found between perceived service quality and preference loyalty and price indifference loyalty. No significant relationship between perceived service quality and dissatisfaction response was found. They also investigated the influence of industry type on the perceived service quality - service loyalty relationship. It was found that the influence of service quality on preference loyalty generally varies per industry and that findings from one industry cannot be generalized to other industries. Burke (1999) examined relationships between levels of job-related managerial feedback, developmental climate, cultural values, job satisfaction and service quality and products provided to clients. LISREL analysis indicated that levels of both partner and manager feedback had direct effects on perceived quality of services and products provided by the firm through both developmental climate and cultural values. The Presence of a developmental climate had direct effects on cultural values, job satisfaction

69 46 and quality of products and services. Grossman (1999) examined the impact of feedback and follow-up on the consumer s perception of service quality. The relationship for the satisfaction level of the account representative and the company overall were also explored. Results indicated that the key drivers of satisfaction for the account representative and the company closely reflect the dimension defined by SERVQUAL model. There was a strong positive relationship between the satisfaction ratings for the account representative and for the company overall. Groth and Dye (1999) focused their research on the perceived value of a service by a customer, the perceived quality and value of a service and the role of expectations, shortfalls, and bonuses in the valuation process. The proposed model yields a value vector that summarizes the perceived value of a service and service quality to a customer. Mei et al. (1999) examined the dimensions of service quality in the hospitality industry by extending the SERVQUAL scale to include eight new items that specifically pertain to the hospitality industry, subsequently referred to as HOLSERV. They found that service quality is represented by three dimensions in the hospitality industry, relating employees (behavior and appearance), tangibles and reliability. However, the best predictor of overall service quality is the dimension referred to as employees. Peters (1999) discussed service quality and total quality management as a business strategy designed to add value to customers. He discussed the roots of quality assurance and total quality management, business process reengineering and supply chain management, and argued for a moments of truth analysis approach to deliver service quality.

70 47 Watson (1999) determined the relationship between product quality, service quality, image of the firm and customer satisfaction in a commodity industry. Product quality and certain items of service quality and image of the firm were found to be significant predictors of customer s satisfaction. In the area of service quality it was found that people intensive areas were significant, while process related service areas were not significant. More items in the area of image, or reputation of the firm were found to be significant predictors than in the area of service quality. Bahia and Nantel (2000) developed a reliable and valid scale for the measurement of the perceived service quality of bank services. The proposed scale is called Banking Service Quality (BSQ) and comprises 31 items which span dimensions: effectiveness and assurance; access; price; tangibles; services portfolio and reliability. Hays (2000) proposed a theoretical framework that related service guarantees to perceived service quality, customer satisfaction, customer loyalty and business performance via the intervening constructs of employee motivation/vision, learning through service failure, defensive marketing / service recovery and offensive marketing. The results of the study showed that both employee motivation/vision and learning through service failure positively influenced perceived service quality. However, employee motivation/vision mediated the relationship between learning through service failure positively influenced perceived service quality. Further, service guarantees positively impacted both employee motivation/vision and perceived service quality but no effect was found on learning through service failure.

71 48 Kerlin (2000) used the SERVQUAL survey instrument to assess student satisfaction with service quality. Student expectations and perceptions of service quality in registration, financial aid, counseling, career center and library services were probed. It was found that students placed less emphasis on the tangible aspects of service quality, such as appearance of facilities and brochures and more emphasis on aspects that provide them with reliable services and demonstrate attention to their personal needs. Kim (2000) worked on four dimensions of Grove s scale (customer focus, prior customer relationship, organizational support and service under pressure) and five dimensions of DINESERV (tangibles, reliability, responsiveness, assurance and empathy). He concluded that service orientation had a direct effect on service quality but impacted customer satisfaction indirectly via service quality. Tsikriktsis (2000) empirically investigated dimensions of service quality, customer satisfaction and technical quality, in 10 major airlines of US domestic airline industry. The data included objective measures of technical quality (late arrivals, lost baggage and denial boarding), customer dissatisfaction (complains), efficiency (capacity utilization of seats and fleet and cost/unit) and profitability (operating margin). He found that efficiency explained more variation in profitability than does service quality, while the relationship between service quality and profitability depends on a company s operating model. Choi (2001) investigated the influence of overall service quality on customer satisfaction and member s repurchase intentions at fitness clubs in Seoul, South Korea. He also examined the influence of customer satisfaction on the level of their repurchase intentions. The questionnaire consisted of four

72 49 sections: service quality scale, customer satisfaction scale, customer repurchase intentions scale and demographic information. He found that the perceived service quality factor was the most influential predictor of Customer Satisfaction and their repurchase intention. The variance of the level of overall Customer Satisfaction was explained by the following predictors in order of higher to lower: Perceived Service Quality, Contact with physical environment, Interpersonal Interaction and Program. Also the variance of the level of repurchase intentions was predicted by the following factors in order of higher to lower: Perceived Service Quality and Interpersonal Interaction. Jasfar (2001) determined whether customer trust, consumer commitment and customer satisfaction mediate service quality antecedents to consumer behavioral intentions in auto service centers. Finding indicated that customer trust, consumer commitment and customer satisfaction were the key mediating variables of the relationship between service quality antecedents and consumer behavioral intention expressly focused from consumer perspective on business to consumer relationship. Siu and Cheung (2001) expressed their concern over the length of the SERVQUAL questionnaire. Respondents may end up either bored or confused having to answer a 22 expectations item and 22 perceptions item scale, and this can certainly affect the quality of data obtained. Caruana (2002) examined the concept of service loyalty and proceeds to distinguish between service quality and customer satisfaction. A model that links service quality to service loyalty via customer satisfaction was proposed. Results indicated that customer satisfaction does play a mediating role in the effect of service quality on service loyalty.

73 50 Gans (2002) developed a model of customer choice in response to random variation in quality. The choice model yields closed-form expressions which reflect the effect of competing suppliers service quality on the long-run fraction of purchases a customer makes at the various competitors. He then used the expressions as the basis of simple normative models for suppliers seeking to maximize their long-run average profits. Martinez (2002) examined the hypothesized relationship between Organizational Citizenship Behavior (performance that supports the social and psychological environment in which job-specific tasks function) at the group level and two important organizational outcomes: service quality and customer satisfaction. Results showed that at group level Organizational Citizenship Behavior leads to better perceptions of service quality. However, Organizational Citizenship Behavior relationship with customers satisfaction results was generally not significant. Wong and Sohal (2002) elaborated the relationship between service quality and overall relationship quality at two retail levels i.e. company and employee level. They concluded that empathy was the most significant contributor to relationship quality at both the retail levels. Kang and James (2004) empirically examined the European perspective (i.e. Gronroos model) that service quality consists of three dimensions: technical, functional and image, and that image functions as a filter in service quality perception. The results from a cell phone service sample revealed that Gronroos model was a more appropriate representation of service quality than the American perspective with its limited concentration on the dimension of functional quality.

74 51 Long and McMellon (2004) developed a multidimensional measure of perceived online service quality, which was based on consumers comments about their experiences with online retailers. These comments were organized and compared to the SERVQUAL scale. While reflective of the SERVQUAL dimensions, the new measure became less reliant on interpersonal interactions and more technologically relevant. A new dimension was also emerged that reflects consumers concerns for the geographic distance and facelessness of their experience. Svensson (2004 a) presented a customized construct of sequential service quality and highlighted the importance of time, context, and performance threshold in service-encounter chains. Furthermore, he presented a generic five-phase performance process, and a customized six-dimensional construct of sequential service quality. Svensson (2004 b) examined the construct of interactive service quality in service encounters. Interactive service quality requires the simultaneous consideration of the service provider s perspective and the service receiver s perspective. The study was conducted in the Swedish automotive industry and focused on the issues of interactive service quality between a vehicle manufacturer and a selection of its most important suppliers. The major contributions of the research provide an on-the-spot account of interactive service quality. Chow and Luk (2005) developed a technique that considers competition using the Analytic Hierarchy Process (AHP) framework to measure service quality. The AHP-SQ approach assists management to devise and maintain a relevant, competitive plan for ongoing improvements in service quality. Specifically, such analysis enables the following questions to be addressed:

75 52 How does the firm perform in terms of service quality in relation to its competitors? ; Given the firm s resources, which service initiatives will enhance its service competitiveness? ; Which service areas require immediate improvement? ; How should the firm s service improvement be prioritized?, and What opportunities exist for service improvement in relation to the competition? Edvardsson (2005) highlighted that service quality was perceived and determined by the customer on the basis of co-production, delivery and consumption experiences. He opined that favorable and unfavorable customer experiences seem to be more and more important in forming service quality perceptions. Further, he described that there were two categories of service quality clues: clues of experience related to functionality and clues of experience related to emotions. Positive and negative emotions seem to be more and more important in forming service quality perceptions, and negative emotions had a stronger effect on perceived service quality than positive emotions. Gupta et al. (2005) developed a conceptual model that was used in understanding the relationships between sustaining structures that support the total quality service (TQS) philosophy and customer satisfaction. They develop three constructs: leadership, organizational culture and employee commitment, which were very important in achieving total quality service objectives. The proposed model links these three constructs with business processes and total quality service. Jabnoun and Khalifa (2005) proposed to develop a measure of service quality in the UAE and then tested this measure in UAE conventional and Islamic banks. Four dimensions of service quality were identified: personal

76 53 skills, reliability, values, and image and all four dimensions were significant in determining service quality in conventional banks. Values and image were however the most important of these dimensions. On the other hand, only personal skill and values were significant in determining service quality in Islamic banks. Kang (2006) empirically examined the conceptualization of service quality (both technical and functional). A two-component model yields better fit than a model concentrating on functional quality alone (such as SERVQUAL). Miguel et al. (2006) measured internal service quality by applying a service quality measuring instrument usually used for assessing external service quality. They found that the assessment was feasible and effective to capture the characteristics of internal customer service by using a set of well known quality dimensions that varied across the studied manufacturing cells. Voon (2006) showed that the service-driven market orientation (SERVMO) that consists of six components (customer orientation, competitor orientation, inter-functional orientation, performance orientation, long-term orientation, and employee orientation) had a significantly strong and positive relationship with service quality. Chowdhary and Prakash (2007) investigated whether any generalization in importance of service quality dimensions was possible or not. They found that generalization of quality dimensions was not possible among all types of services taken together. Enquist et al. (2007) presented a model for values-based sustainable service business grounded in the concept of values-based service quality. They distinguished four dimensions of values-based service quality and these

77 54 dimensions were technical, functional, experiential, and HRM and corporate climate. Lee (2007) compared two leading measurement instruments of service quality (i.e., SERVQUAL and SERVPERF) in a cross-cultural setting. Psychometric properties of each scale were compared in three countries of distinctive characteristics: developed, industrialized, and developing. They concluded that the SERVPERF scale has slightly better reliability while the SERVQUAL scale has an edge in validity, implying the necessity of including cultural diversities of expectations in the measurement of service quality for cross-cultural studies. In 2007, Solvang discovered that the effect of service quality on satisfaction was more profound in the furniture branch than in the grocery branch of the four retail stores selected. On the other hand, customer loyalty seems to be more important in affecting repurchase decisions in the grocery branch. Vanniarajan and Anbazhagan (2007) highlighted that financial services were inherently intangible and high on experience and credence qualities. In order to promote them effectively, a service provider must first identify the dimensions used by consumer to evaluate the service quality of banks prior to becoming a customer. They identified four dimensions reliability, responsiveness, assurance and tangibles which form the domain of customer s evaluation of service quality in the financial services industry. The results indicated that the customer s perception on the service quality factors in private sector banks was higher than the public sector and co-operative banks.

78 55 Ladhari (2008) identified the key conceptual and empirical issues that should be considered in the development of alternative industry-specific measurement scales of service quality (other than SERVQUAL). They found deficiencies in some of the alternative service-quality measures; however, the identified deficiencies do not invalidate the essential usefulness of the scales. Pollack (2008) revealed that satisfiers exhibit initially no relationship with satisfaction, but after the acceptable level of service quality (i.e. inflection point) had been reached, become positively related. Dissatisfiers followed initially a positive relationship path with satisfaction but after the inflection point exhibit no relationship, or at best a significantly weakened one, with satisfaction. The relationship patterns were found to be service attribute as well as service type dependent. Seth et al. (2008) measured customer perceived service quality incorporating both service delivery as well as technical quality aspects. The validated instrument comprised of dimensions including reliability, responsiveness, assurance, empathy, tangibles, convenience, and customer perceived network quality. The study indicated that among the various dimensions, responsiveness was the best predictor, followed by reliability, customer perceived network quality, assurance, convenience, empathy, and tangibles. Aykac et al. (2009) employed six dimensions of service quality scale that was developed by Carman (2000) and Kara et al. (2005) to better understand the factors underlying healthcare customers perceptions of service quality. The dimensions investigated were: tangibility, reliability, responsiveness, assurance, courtesy and empathy. Through a 5 point Likert-type scale, they compared healthcare customers expectations of a perfect service provider

79 56 with the practices of Marmara University Hospital to determine if there were any gaps. Further they analyzed the quality of the Marmara University Hospital s healthcare services and its impact on customer satisfaction and customer loyalty through a regression analysis. Hossain and Leo (2009) exhibited that customers perceptions vary according to the nature of service. In the banking industry they found that customers perception was highest in the tangibles area such as infrastructure facilities of the bank, followed by the empathy area such as timing of the bank and returns on deposit. On the other hand, the lowest perception was in the competence area, such as the method of imposing service charges followed by reliability, such as customers guidance. Because of the wide variation of responses, the banks need to consider the weak areas in order to meet customer requirement. Hoang et al. (2010) proposed a conceptual framework of the influence of service culture on customer service quality via the mediation of employee attitudes. They also conceptualized the role of potential moderators such as cultural differences, personal relationships towards service employee attitudes and customer service quality. Jain et al. (2010) concluded that service quality in higher education comprises of twelve factors such as visual appeal, outcome, campus, reputation, input quality (students), industry interaction, support facilities, input quality (faculty), inter personal relationships, curriculum, academic facilities and processes. Korda and Snoj (2010) attempted to validate the perceived retail banking service scale in the case of a small transitional economy of Europe. Their analysis showed that the perceived value variable had a potential to be mediating variable between perceived quality and customer satisfaction

80 57 relationship in retail banking settings. Malik and Danish (2010) analyzed the impact of different quality services on student satisfaction in higher educational institutes of a big division of Punjab province of Pakistan. They found that students are overall satisfied with services of tangibility, assurance, reliability and empathy but not much satisfied with parking facilities, computer labs, cafeteria services, complaint handling system. Ravichandran, et al. (2010) examined the influence of perceived service quality on customer satisfaction in private retail banking services. They concluded that increase in service quality of the banks increases customer satisfaction which ultimately retains valued customers. Shahin and Samea (2010) has critically reviewed and developed the model of service quality gaps in order to make it more comprehensive. The developed model has been verified using a survey on 16 experts. Compared to the traditional models, the proposed model involves five additional components and eight additional gaps. The five new components included in the developed model were ideal standards; translation of strategy and policy into service quality specifications; service quality strategy and policy; employee perceptions of customer perceptions; and management perceptions of customer perceptions. Also, the eight new additional service quality gaps were as follows: Gap 2: Management perception versus service quality strategy and policy; Gap 3: Service quality strategy and policy versus service specifications; Gap 4: service specifications versus ideal standards; Gap 5: service specifications external communication; Gap 11: Customers perceptions versus management perceptions;

81 58 Gap 12: The discrepancy between management perceptions and service quality strategy; Gap 13: Customers perceptions versus employee perceptions; and Gap 14: The discrepancy between employee s perceptions and management perceptions of customer Singh and Khanduja (2010) applied SERVQUAL methodology to identify the gap between customer expectations and perceptions of the actual service received taking higher education as a service industry. They outlined the major gaps of expectations and perceptions of the faculty of higher education and therefore presented a framework for prioritizing critical factors to close the gap. Tan et al. (2010) used SERVQUAL model to evaluate the link between service quality dimensions and knowledge sharing. They found that assurance and the reliability dimensions of service quality were the two most important dimensions and had significant positive relationship with knowledge sharing PERSPECTIVES ON SERVICE QUALITY The word quality means different things to people. David Garvin identifies five perspectives on quality. The Transcendent View: Transcendent means something that is intuitively understood but nearly impossible to communicate such as beauty or love. It is synonymous with innate excellence a mark of uncompromising standards and high achievement. It argues that people learn to recognize quality only through the experience gained from repeated exposure.

82 59 The Product-Based Approach: Product-based means the quality in the components and attributes of a product. It sees quality as a precise and measurable variable. It argues that differences in quality reflect the differences in the amount of an ingredient or attribute possessed by the product. User Based Definition: User-based means the customer satisfaction of the product. The Manufacturing Based Approach: Manufacturing-based means the product conformations to design specification or conformance to requirement. It is supply based and is concerned primarily with engineering and manufacturing practices. Value Based Definition: It defines quality in terms of value and price. In other words, value-based means better value for the price of the product. Consumers perceive the quality of a service by experiencing the consumption process and by comparing the experience with their expectations. Thus, service quality is the extent to which a service meets customers needs or expectations and it involves a comparison of customer expectations with customer perceptions of actual service performance. The will-o - the-wisp nature of service quality is nicely captured by Tan (1986), who describes it as being like beauty in the eye of the beholder; in other words, it has different meaning for different people. Gronroos (1984) has defined service quality as the outcome of an evaluation process, where the customers compare their expectations with the service they have received. Service quality is an abstract concept, difficult to define and measure (Parasuraman, Zeithaml and Berry, 1985, 1988). Parasuraman et al. (1985) define service quality as consumers comparisons

83 60 between service expectations and service performance. In other words, service quality is the degree and direction of the discrepancy between consumer s perceptions and expectations in terms of different but relatively important dimensions of the service quality, which can affect their future purchasing behavior. Arnauld et al. (2002) defined service quality with reference to a product or service as the consumer s evaluative judgments about an entity s overall excellence or superiority in providing desired benefits. Service quality is perceived as a tool for increasing value for the consumer; as a means of positioning in a competitive environment (Mehta, Lalwani and Han, 2000) and for ensuring consumer satisfaction (Sivadas and Baker-Prewitt, 2000), retention, and patronage (Yavas, Bilgin and Shemwell, 1997). As a gap or difference between customer expectations and perceptions, service quality is viewed as lying along a continuum ranging from ideal quality to totally unacceptable quality, with some points along the continuum representing satisfactory quality. Parasuraman, Zeithaml and Berry (1988) held that when perceived or experienced service is less than expected service, it implies less than satisfactory service quality. But, when perceived service is less than expected service, the obvious inference is that service quality is more than satisfactory. Service quality is one of the most influencing factors in a consumer s purchase decision process. According to Buzell and Gale (1987) empirical research clearly shows the positive relationship between service quality and organizational performance. Using a large database with thousands of strategic business units, research shows that the most critical factor affecting a business unit s performance is the service quality of its products and services as perceived by the market relative to the perception about its competitors.

84 61 From the previous definitions of service quality it is apparent that they include the perception and expectations of services. Perception of service is the customer s opinion of the service or product (Foster, 2004) or the general judgment of a service which is affected by many factors such as the education level, background and others (O Neill and Palmer, 2003). The perception could be a one time or single perception of the company, such as an experience buying a product at a specific time, or an overall perception of the company based on many experiences with the organization. The perception of service quality is not constant which means that it changes for many reasons such as time, culture, consumer taste and promotional activities of the company (Zeithaml, Bitner and Gremler, 2009). According to Zeithaml, Bitner and Gremler (2009) customer expectations are beliefs about service delivery that serve as standards or reference points against which performance is judged. Another definition of expectations is based on consumption-norms, values, wishes and needs which depend on the individual (Kasper, Helsdingen and Gabbott, 2006). Therefore, expectations are different from one consumer to another since it depends on the individual which causes satisfaction levels to change from person to person (Kasper, Helsdingen and Gabbott, 2006). Moreover, expectations are usually formed from previous experiences and it is that level of service the customer expects from the service provider. It is important for organizations to understand the different levels of customer expectations because serving customers at a level that exceeds these expectations will lead to customer satisfaction (Dutta and Dutta, 2009).

85 62 According to Zeithaml, Bitner and Gremler (2009) there are two different levels of expectations: Desired Service Adequate Service Desired service is the level of service the customer anticipates to receive from the service provider. At the adequate service level, the customer anticipates a minimal standard of service from the service provider; this is the minimal level of the acceptable performance the customer will tolerate. The difference between the desired level and the adequate level is called the tolerance zone or the zone of acceptability as stated by Kasper, Helsdingen and Gabbott (2006). The zone of tolerance changes over time, from service to service and from customer to customer (Kasper, Helsdingen and Gabbott, 2006). According to Zeithaml, Bitner and Gremler (2009) the zone of tolerance is the extent to which customers recognize and are willing to accept variations in services. If the service level goes below the adequate service level, this usually causes customers to become irritated and dissatisfied. However, if the service level is above the desired level, it usually makes customers happy and satisfied with the service (Wilson et al., 2008) DETERMINANTS OF SERVICE QUALITY The determinants of service quality are not universal but are service specific. Alfrecht and Zemke (1985) identified four factors that influence the perceived service quality. They are:

86 63 Care and Concern: How devoted employees and operational systems of a service are in solving the problems of customers. Spontaneity: How employees demonstrate willingness and readiness to serve. Problem Solving: The expertise and skill of contract employees in performing services. Recovery: The special efforts of the service provider in handling a situation when something goes wrong or something unexpected happens. A Comprehensive Study was carried out by Parasuraman, Zeithaml and Berry (1985) identifies the determinants of perceived service quality. They have identified the following ten determinants of service quality: Reliability involves consistency of performance and dependability. The important measures of reliability are: 1. Performance of the initial service 2. Accuracy in billing 3. Keeping records correctly 4. Performing the service punctually Responsiveness concerns the willingness or readiness of employees to provide service. The measures include: 1. Timeliness of service 2. Mailing transaction slips immediately 3. Efficient customer support 4. Giving prompt service

87 64 Competence means possession of the required skills and knowledge. The measures include: 1. Knowledge and skills of the contact employees 2. Knowledge and skills of the operational support personnel 3. Research capability of the organization Access involves approachability and ease of contact. The accessibility of a service is determined mostly by the following: 1. Easily accessible by telephone 2. Waiting time for service is not long 3. Convenient hours of operation 4. Convenient location of service facility Courtesy involves politeness, respect, consideration and friendliness of contact personnel. The organization should provide: 1. Consideration for the consumer s property 2. Clean and neat appearance of public contact personnel Communication means keeping customers informed in a language they can understand and listening to them. The important criteria are: 1. Explaining the service itself 2. Explaining how much the service will cost 3. Explaining the trade-offs between service and cost 4. Assuring the consumers that the problem will be handled

88 65 Credibility involves trustworthiness, believability, honesty and having the customer s best interest at heart. The indicators are: 1. Company name 2. Company reputation 3. Personal characteristics of the contact personnel 4. The degree of hard sell involved in interactions Security is freedom from danger, risk or doubt. It includes: 1. Physical safety 2. Financial security 3. Confidentiality Understanding/knowing the customer involves making the effort to understand the customer s needs. It includes: 1. Learning the customer s specific requirements 2. Providing individual attention 3. Recognizing the regular customers Tangibles include physical evidence of the service. They are: 1. Physical facilities 2. Appearance of the personnel 3. Tools or equipment used to provide the service 4. Physical representation of the service 5. Other customers in the service facility

89 66 The researchers later in 1998 condensed the list of ten determinants to give in order to avoid repetitiveness and provide universal applicability. The five new quality determinant factors are: Dimension Tangibility Reliability Responsiveness Assurance Empathy Description The appearance of physical facilities, equipment, appearance of personnel and communication materials. The ability to perform the promised service dependably and accurately. The willingness to help customers and provide prompt service. The knowledge and courtesy of employees and their ability to inspire trust and confidence. The caring, individualized attention the firm provides to its customers. Table.No.3.1 Five Major Factors Determining Service Quality Gronroos (1990) developed six criteria of good perceived service quality based upon a solid body of empirical and conceptual research. The six criteria are: Professionalism and skills: Customers realize that the service provider, its employees, operational systems, and physical resources have the knowledge and skills required to solve their problems in a professional way (outcome-related criteria). Attitude and behavior: Customers feel that the service employees (contact persons) are concerned about them and are interested in

90 67 solving their problems, in a friendly and spontaneous way (processrelated criteria). Accessibility and flexibility: Customers feel that the service provider, its locations, operating hours, employees and operational systems are designed and operated in such a way that it is easy to access the service and that the service providers are prepared to adjust to the demands and wishes of the customer in a flexible way (process-related criteria). Reliability and trustworthiness: Customers know that whatever takes place or has been agreed upon, they can rely on the service provider, its employees and systems, to keep promises and perform with the best interest of the customers at heart (process-related criteria). Recovery: Customers realize that whenever something goes wrong or something unpredictable happens, the service provider will immediately and actively take action to control the situation and find a new, acceptable solution (process-related criteria). Reputation and credibility: Customers believe that the operations of the service provider can be trusted and give adequate value for money, and that they stand for good performance and values that can be shared by customers and the service provider (image-related criteria) HOW SERVICE QUALITY IS PERCEIVED When service organizations understand how services are evaluated by consumers in terms of quality, it is possible to design strategies to manage these evaluations and influence them in a desired direction. In a service encounter, buyer-seller interactions take place in large number. Each interaction will obviously have a critical impact on customers quality perceptions. Thus, a methodology is necessary to understand how customers

91 68 perceive quality. Gronroos has identified two dimensions of service quality in relation to quality perception by customers. These are technical quality and functional quality. Technical Quality: What is offered to the customer from the organization and what customers receive in their interactions with the service firm is called the technical product. The quality in designing the basic service package is reflected in the technical quality of a service. In other words, it speaks of the technical quality of blueprinting and its execution. Technical quality moulds the first impression of customers. Functional Quality: Technical quality contributed only to a part of the total quality experienced by the customer. Customers are also influenced by how they receive the service and how they experience the service process in which they also played a part. Research studies indicate that customers will be influenced mostly by the way technical quality is transferred to them. The way service processes are handled in a service encounter is called functional quality. Image: Most of the consumers will evaluate a firm by taking into consideration its resources, history and ways of operating service activities. Therefore, a firm s image in the corporate as well as at the local level is of utmost importance in quality perception. If an organization enjoys a favorable image, customers probably might forgive the occasional minor mistake of the organization. However, if the mistakes are repetitive, there is a danger of spoiling the market image. Customers use a firm s image as a filter or a net while perceiving quality.

92 EXPECTED QUALITY vs. EXPERIENCED QUALITY Every consumer makes an assessment of quality based upon the expectations that he or she has developed a service offering. Generally, consumers get influenced by four important factors while forming expectations. They are as follows: Market communication: Service firms communicate, through direct and indirect channels, to the target market relating to the features and specialties of the BSP. This is the promise the service providers makes with the customer. Market communication is the authentic source of information with an identified sponsor and, therefore, plays a vital sold in the form of consumer expectation. Image: The image of a service firm at the corporate level as well as the local level influences the expectations of the customers. It pervades various dimensions. An image of proven skills, consistency, innovativeness, case and concern, empathy, handling problems, aptly, performance and so on are some of the identities companies develop over a period of time. These identities mould consumer expectations. Word-of-mouth communication: This is an informal communication channel. Word-of- mouth communication is considered to be the more powerful, particularly in the case of services. As services are intangible and variable, comparison of alternatives and trials are not possible. Consumers often feel less confident about taking a purchase decision, they look for advice and information support from the service provider. They also look for advice and information support from others, whom they consider as having more knowledge and experience in that particular service, and who will give a frank opinion about the service.

93 70 Customer needs: Besides the three factors mentioned above, the need intensity of consumers influences the expectations. A relaxed consumer may expect quality of a high level compared to a customer who is hard pressed for time TOTAL PERCEIVED QUALITY The Total Perceived Quality (TPQ) of customers can be calculated by comparing expected quality with experienced quality. If the two are the same, the consumer feels satisfied with the service. If the expected quality is more than the experienced quality, the consumer is dissatisfied. If the experienced quality is more than the expected quality, the consumer is highly satisfied. The degree of dissatisfaction can be assessed by ascertaining the extent of deviation (negative) between the expected quality and the experienced quality. Similarly, the degree of satisfaction can be ascertained by measuring the deviation (positive) between the two factors. To know the level of satisfaction of the customer, it is necessary for service companies to study and measure expected quality and experienced quality MANAGERIAL PROCESS FOR SERVICE QUALITY Service firms should develop quality focussed managerial processes to ensure continuous quality performance as desired by the customers. In order to achieve the quality objectives, an integrated and coordinated work of three participant groups is necessary. The groups that influence the service quality are: The management of the service organization The employees The customers

94 71 The traditional demand analysis and quality control, measurement are the first step in the quality management process. A study of demand patterns of the service at various levels of quality and market responses are studied in detail. Such study develops a basic understanding of desired quality of the customers. The second major source is the employees perception of desired quality and performance. Employees involved in the process will be in a better position to collect and interpret feedback from the customers. This group, with its knowledge of the company s vision, policies, procedures, competencies and limitations and comparative knowledge of competitive offers, is an invaluable resource to the company in developing quality specifications. Management, therefore, has to conduct an internal analysis of employee perceptions of desired quality and performance. The information input from the two processes will help the management to develop their own perceptions relating to desired service quality by the market. After a thorough analysis of various factors, the management can finally arrive at decisions on service quality specifications. The quality specifications, thus decided by the management will be communicated to employees through internal marketing and to consumers through external marketing. The internal marketing efforts result in employee perceptions of desired service quality. The decisions of the top management also influence the willingness and the ability of the employees to perform the service. External marketing which aims at communicating with the target market develops quality expectations in them. Customers with quality expectations will interact with employees of the service firm. The employees have to understand the specific problems of customers on the spot and decide the service package to be executed in consultation with customers. Besides, an employee has to ascertain his or her ability and willingness and the customer s

95 72 ability and willingness to participate in the service production and consumption process. By taking all these considerations, the employee concerned decides the service to be offered and executes it. Customers who experience service quality will evaluate quality by comparing experienced quality with expected quality and come to a conclusion on the perceived service quality SERVICE QUALITY MEASUREMENT Service organizations understandably are under the constant pressure of outperforming their competitors in determining the antecedents, determinants, and consequences of service quality. Such practical importance of service quality makes the measurement of service quality and its subsequent management utmost important. But it is difficult to measure service quality as compared to good s quality because of the unique characteristics of service: intangibility, heterogeneity, inseparability and perishability. In other words, the difficulty to measure service quality is due to fewer tangible cues available when consumers purchase services (Parasuraman s et al., 1985). It also requires higher consumer involvement in the consumption process (Gronroos, 1984). A researcher operationalises the service quality construct either as a gap between expectation of service and perceived performance of service, or just perceived performance alone. On the other hand, service quality dimensions are seen as the criteria to assess service quality (Parasuraman s et al., 1985). They also posit that consumer-perceived service quality is usually seen as multi- dimensional construct. The most widely used service quality measurements tools include SERVQUAL (Parasuraman, Zeithaml and Berry, 1988) and SERVPERF (Cronin and Taylor, 1992). According to a popular

96 73 Web search engine (i.e., Google Scholar), SERVQUAL has been cited by more than 3,000 papers and SERVPERF more than 400, as of May SERVQUAL SCALE The SERVQUAL scale constitutes an important landmark in the service quality literature and has been extensively applied in different service settings. The foundation for the SERVQUAL scale is the gap model proposed by Parasuraman, Zeithaml and Berry (1988, 1985). With roots in disconfirmation paradigm, the gap model maintains that satisfaction is related to the size and direction of disconfirmation of a person s experience vis- à-vis his/her initial expectations (Parasuraman, Zeithaml and Berry, 1985; Churchill and Surprenant, 1982). Parasuraman, Zeithaml and Berry (1988, 1985) posited and operationalized service quality as a difference between consumer expectations of what they want and their perceptions of what they get (Cui, Lewis and Park, 2003). Based on this conceptualization and operationalization, they proposed a service quality measurement scale called SERVQUAL. The SERVQUAL instrument developed by Parasuraman et al. (1988) originally consisted of 97 items and ten dimensions, it was then refined and reduced to 22 items that measure five dimensions (Akbaba, 2006). Thus, SERVQUAL scale measures service quality based on the difference between expectations and performance perceptions of customers using twenty-two items and five-dimensional structures. According to the developers of SERVQUAL, the difference between perceptions and expectations results in the service quality gap (Q = P-E), also known as GAP 5 (Parasuraman et al., 1988; 1985). Since they operationalized service quality as being a gap between customer s expectations and perceptions of performance on these variables, their service quality measurement scale is comprised of a total of 44 items (22 for expectations and 22 for perceptions). Customers responses to their

97 74 expectations and perceptions are obtained on a 7-point Likert scale and are compared to arrive at (P-E) gap scores. A wide gap would represent poor service quality and shows that the service provider needs to improve on the service offered to its customers. Parasuraman, Zeithaml and Berry (1988) posited that while a negative discrepancy between perceptions and expectations - a performance-gap as they call it - causes dissatisfaction, a positive discrepancy leads to consumer delight. According to the SERVQUAL conceptualization, service quality can be assessed by five dimensions: tangibles, reliability, responsiveness, assurance, and empathy (Parasuraman et al., 1988). The tangibles dimension in SERVQUAL, measured by 4 items, deals with the appearance of physical facilities, equipment, personnel and communication materials. Reliability, measured by 5 items, refers to the ability to perform the promised service dependably and accurately. Responsiveness, measured by 4 items, measures the willingness to help customers and to provide prompt service. Assurance, measured by 4 items, deals with the knowledge, courtesy of employees and their ability to inspire trust and confidence. Finally, the empathy dimension of SERVQUAL, containing 5 items, refers to the provision of caring, individualized attention to customers. Parasuraman et al. (1988) contended that the final 22-item scale and its five dimensions have sound and stable psychometric properties. The importance of Parasuraman, Zeithaml and Berry s (1988) scale is evident by its application in a number of empirical studies across varied service settings (Witkowski and Wolfinbarger, 2002; Kassim and Bojei, 2002; Carman, 1990). Despite its extensive application, the SERVQUAL scale has been criticized on various conceptual and operational grounds. Some major objections against the scale relate to use of (P-E) gap scores, length of the questionnaire, predictive power of the instrument, and

98 75 validity of the five-dimension structure (Dabholkar, Shepherd and Thorpe, 2000; Babakus and Boller, 1992; Cronin and Taylor, 1992). Some of the widespread concerns are the 5 dimension configuration of the scale, the appropriateness of operationalizing service quality as the expectationsperformances gap score, and the scale s applicability to a retail setting (Bakakus and Boller, 1992). DIMENSIONS ITEM DESCRIPTION Modern equipment Visually appealing facilities TANGIBLES Employees who have a neat, professional appearance Visually appealing materials associated with the service Providing services as promised Dependability on handling customers service problems RELIABILITY Performing services correctly the first time Providing services at the promised time Maintaining error-free records Informing Customers about when services will be performed RESPONSIVENESS Prompt service to customers Willingness to help customers Readiness to respond to customers requests Employees who instill confidence in customers Making customers feel safe in their transactions ASSURANCE Employees who are consistently courteous Employees have the knowledge to answer customer questions Giving customers individual attention Employees who deal with customers in a caring fashion EMPATHY Having the customer s best interest at heart Employee understands customer needs and serves accordingly. Table.No.3.2 Dimensions of Service Quality

99 76 As discussed earlier, SERVQUAL is used to measure service quality as a multi-dimensional construct across five dimensions: tangibility, reliability, responsiveness, assurance and empathy. Above shows the items under each dimension in the SERVQUAL scale SERVPERF SCALE Cronin and Taylor (1992) questioned the conceptual basis of the SERVQUAL scale and found it confusing with service satisfaction. Basing the scale on an earlier work by Bolton and Drew (1991), they noted that a customer s perception of service quality is based on his preconceived attitude about the service. Indeed, Bolton and Drew (1991) noted that a consumer s current attitude is based on their residual attitude from a previous period about the service quality and their satisfaction or dissatisfaction with the service. As consumers experience a service, their attitudes about the service quality may be revised, thereby causing a change in future attitudes. Cronin and Taylor (1992) noted that a customers perception of service quality can be best measured by his/her perceived attitude about the service being rendered. They, therefore, opined that expectation (E) component of SERVQUAL be discarded and instead performance (P) component alone be used. They proposed what is referred to as the SERVPERF scale. In the SERVPERF scale, service quality is operationalized through performance only scores based on the same twenty-two items and five-dimensional structure of SERVQUAL. Besides theoretical arguments, Cronin and Taylor (1992) provided empirical evidence across four industries (namely banks, pest control, dry cleaning, and fast food) to corroborate the superiority of their performance-only instrument over disconfirmation- based SERVQUAL scale.

100 77 Being a variant of the SERVQUAL scale and containing perceived performance component alone, performance only scale is comprised of only 22 items. A higher perceived performance implies higher service quality. According to Cronin and Taylor (1992), their unweighted performance-based SERVPERF scale was a better method of measuring service quality. This scale's reliability ranged between.884 and.964, depending on industry type, and exhibited both convergent and discriminant validity. Methodologically, the SERVPERF scale represents marked improvement over the SERVQUAL scale. Not only is the scale more efficient in reducing the number of items to be measured by 50 per cent, it has also been empirically found superior to the SERVQUAL scale for being able to explain greater variance in the overall service quality measured through the use of single-item scale. The SERVPERF scale has been applied in many empirical studies on service quality. Published research adopting SERVPERF are found across many consumer, business, and non-profit service industries including retail, bank, airline, higher education, neighborhood shopping center, dental office, air cargo, hotels, public transportations, business to business repair, tourism. The investigations on SERVPERF applications have also been intense but not as much as SERVQUAL. SERVPERF scale is also not free from criticisms. Parasuraman et al. (1994) argue that Cronin and Taylor's (1992) analysis does not take into account, the possible intercorrelations among the five latent constructs. Further, they assert that Cronin and Taylor (1992) cite studies that focus on the formation of attitudes and not the attitude level, (which is what SERVQUAL attempt to measure).numerous studies have been undertaken to assess the

101 78 superiority of two scales, but consensus continues to elude as to which one is a better scale. However, the continued use of and reference to SERVQUAL in marketing literature suggest that consensus has not yet been reached relative to the superiority of performance-only measures of service quality (Brady et al., 2002) RETAIL SERVICE QUALITY In retail setting, especially retail stores where there is a mix of product and service, retailers are likely to have impact on service quality more than on product quality. As retailers can create such effects, service quality plays a significant strategic role in creating quality perceptions. Retail offerings are a mix of merchandise and service, and the experience of customers in retail stores thus involves such activities as negotiating their way through the store, finding the merchandise, interacting with a variety of store personnel, and returning unsatisfactory merchandise-all of which have a direct influence on the customers evaluations of service quality. There s little doubt that the retail industry is evolving into an exceedingly competitive scene, with retail players fighting for a share in the customers minds and hearts. In light of this, service quality has long been accepted as the most basic marketing tool for retailers to differentiate their retail offers, create competitive advantage and to enhance the customers shopping experience (Christo and Terblanche, 1997). In the era of globalization, small retailers cannot compete with the retail giants only on price, the winning strategy will be superior service quality. Nevertheless, maintaining excellent service quality within the stores is no simple task as it requires continual measurement from time to time to monitor and identify areas of activity that may be responsible for the standards of service quality.

102 79 Although measures of service quality in pure service environments and retail environments are likely to share some common dimensions, it has been argued that measures of retail service quality must take additional dimensions into consideration. Retailers today must differentiate themselves by meeting the needs of their customers better than their competitors. There is general agreement that a basic retailing strategy for creating competitive advantage is the delivery of high service quality (Reichheld and Sasser, 1990; Berry, 1986). In the retail context, perceptions of service encounters accumulate over time and a customer s relationship with an organization are a continuation of exchanges or interactions both past and present. When customers evaluate retail service, they compare their perceptions of the service they receive with their expectations. Customers are satisfied when the perceived service meets or exceeds their expectations. They re dissatisfied when they feel the retail service falls below their expectations (Levy and Weitz, 2005) and there is strong evidence that many department stores fall to offer desired services (Dotson and Patton, 1992). Service quality in retail has been considered as a critical aspect to achieve differential advantage. Studies elicit that service is an important criterion for store patronage in specialty stores. In 1984, Lumpkin and McConkey highlighted that for the shoppers of specialty store personnel was more important determinant of patronage than the shoppers of departmental store or discount store.even though service quality leads to competitive advantage but surveys had confirmed that retail stores provides an inadequate level of customer service. A 1987 Washington Post survey found that nearly half of all shoppers in the Washington area thought that store service was mediocre and declining. Shoppers, who were

103 80 surveyed in a national poll, had similar thoughts about the causes of poor service. Among these were long waits for service, impolite sales clerks, unavailability of advertised goods, and sales clerks who had little or no product knowledge (Mayer and Morin, 1987). In apparel specialty store very little research had taken place regarding service quality expectations. Keeping this fact in mind Finn and Lamb (1991) undertake a study in apparel where they categorized the stores into four generalized groups including those similar to: Kmart; J.C. Penny; Dillards; and Saks. The purpose of their study was not to differentiate among these store types but to evaluate the SERVQUAL scale in a retail setting. Austin (1992) opined that retailers traditionally think of customer service in terms of store hours, gift wrapping, and credit options, but consumers typically viewed customer service in relative terms based on their expectations and experiences. Customer service satisfaction depends on how well the received services match their expectations.moreover, the study undertaken by Berman and Evans (1992) highlighted that not only service quality, there were also other factors which influenced the store patronage namely: merchandise, price, location, and advertising. Gagliano and Hathcote (1994) highlighted that service quality in pure service settings and retail settings differ in the sense that quality was seen from the perspective of not only services but goods as well. Measuring service quality, therefore, can be rather complicated and difficult especially in apparel specialty retailing where it combines the selling of goods and services to the customers as well as the customers expectations of knowledgeable, helpful staff to assist them during their shopping experience.

104 81 Dabholkar et al. (1996) highlighted that current measures of service quality do not adequately capture customers perceptions of service quality for retail stores. In retail setting, especially retail stores where there is a mix of product and service, retailers are likely to have impact on service quality more than on product quality. Using both quantitative and qualitative research methods, Dabholkar et al. (1996) developed the retail service quality scale (RSQS), a multi-item scale measuring five dimensions of retail service quality and detecting changes required in the services provided. RSQS consist of five dimensions-physical aspects, reliability, personal interaction, problem solving and policy. Boshoff and Terblanche (1997), in a replication of the Dabholkar, Thorpe, and Rentz (1996) study, reported highly encouraging results for the RSQS applicability in the context of department stores, specialty stores, and hypermarkets in South Africa. Christo and Terblanche (1997) elicited that service quality was the most basic marketing tool for retailers to differentiate their retail offers, create competitive advantage and to enhance the customers shopping experience. Marshall et al. (1999) investigated differences in retail shopping experiences of African- American and White residents of a middle-size city in the southern United States. With the SERVPERF items, they reported racial differences in retail clothing shopping experiences of African- American and White residents. In 2000, Fogarty et al. measured service quality with SERVPERF in four small retail businesses within provincial cities in South East Queensland. They employed four different datasets, of a shortened 15-item version of the

105 82 SERVPERF scale to be called SERVPERF-M. Exploratory and confirmatory factor analytic techniques were used to explore the dimensionality of the scale. They suggested that the five factors can be treated as five different stages of service quality, rather than as five qualitatively different dimensions. Mehta et al. (2000) explored the usefulness of SERVPERF and a retail service quality scale in measuring the service quality of different productservice retail environments. Specifically, they investigated the relative performance of two scales measuring the service quality of retailers where goods purchase was the primary focus, against another where both goods and services were equally important. Results showed that the scale was superior within the context of a more goods and less services environment, i.e. a supermarket, while SERVPERF was better for a retailing context where the service element becomes more important, i.e. an electronic goods retailer. With a validated Retail Service Quality Scale Siu and Cheung (2001) studied the service quality delivery of a department store chain and its impact on consumption behavior. The findings showed that the impact of physical appearance and policy were salient on the overall perceived service quality and the future shopping behavior respectively. Among the six service dimensions, the physical appearance and policy had the greatest impact on the overall service quality and on future consumption respectively. Vazquez et al. (2001) attempted to extend the conceptualization and measurement of service quality in the retail environment. The review of the retail and service quality literatures and the findings from a qualitative study conducted by the authors revealed that service quality in retail companies adopting the commercial format of supermarkets had a four factor structure (physical aspects, reliability, personal interaction and policies).

106 83 Kim and Jin (2002) found that U.S and Korean consumers perceived service quality of discount stores differently. They concluded that in a discount store customers appeared to view the store s problem solving ability as an indicator of its ability to give customers personal attention, and confidence about the products they were purchasing. Abu (2004) advocated that there was a need to look into service quality dimensions for each country, as each country is believed to have its own unique set of quality dimensions. He identified the service quality dimensions critical to urban grocery shoppers for small, medium, and large-sized grocery stores. He identified that the inter-personal relationship and problem solving dimensions contribute significantly to the overall service quality measure of a small-sized grocery store, the physical aspects and inter-personal relationship dimensions contribute significantly to the overall service quality measure of a medium-sized grocery store, and the physical aspects, reliability, and policy dimensions contribute significantly to the overall service quality measure of a large-sized grocery retailer. Choi et al. (2004) highlighted that how web retail service quality has different effects on perceived product quality, value, and willingness to buy according to product categories. They presented a research model on the basis of service quality, product categories on the web, and marketing theories for consumers purchase behavior. They concluded that functional web retail service quality had a direct effect on willingness to buy and technical web retail service quality influences consumer perceptions of product quality and value. Jain and Gupta (2004) assessed the diagnostic power of the two service quality scales: SERVQUAL and SERVPERF. In fast food restaurants of Delhi,

107 84 they found the SERVPERF scale more appropriate in explaining convergent and discriminant validity of service quality construct. However, the scale was found deficient in its diagnostic power. It was the SERVQUAL scale which outperforms the SERVPERF scale by virtue of possessing higher diagnostic power to pinpoint areas for managerial interventions in the event of service quality shortfalls. Leen et al. (2004) aimed at validating the retail service quality (RSQS) instrument developed by Dabholkar et al. (1996) in the Malaysian business setting, specifically in the context of apparel specialty stores. Findings obtained from the confirmatory factor analysis and reliability tests indicated that all the five dimensions of physical aspects, reliability, personal interaction, problem-solving and policy were highly suited for measuring retail service quality in clothing stores, also proving that the instrument was applicable in the Malaysian culture. Through the correlation analysis, it was shown that retail service quality is furthermore associated with future consumption behaviour in terms of the customers intention to visit, purchase and recommend the stores to others. Raven and Welsh (2004) examined retail service in Kuwait and Lebanon regions with long histories of trade. Further, they examined customer and salespeople perceptions of service encounters in these countries in light of their culture, religion, and nationalities. They found indeed differences between expectations of quality of service encounters and national cultures. Kuwaiti national customers were more similar in their expectations of service quality to Lebanese customers. Customers from India/Sri Lanka/Bangladesh had higher expectations than Kuwaiti customers for tangibility, reliability, and assurance, but no differences were found in responsiveness or empathy.

108 85 Munoz et al. (2005) examined small/medium enterprises (SMEs) management and employee perceptions on a number of service quality dimensions. They suggested that managers and employees in the Philippines behaved in similar ways to those in Western countries, but there were differences, probably related to cultural characteristics. Further, they highlighted that shopping experience influences service quality expectations and perceptions in the Philippines. Shoppers with more experience tend to expect higher service quality and perceived than they got it. He advocated that training can influence the customer orientation of sales people in improving responsiveness, reliability, and empathy, among other service dimensions when coupled with good management techniques. McKenzie (2006) examined how the Estonian consumers interpret and perceive retail service quality. They found retail service quality was a relevant construct for examination in Estonia. There is an expectation by consumers to exert their own sense of shopping capabilities. There is an expectation that selling staff need to be authoritative, and to show consideration to the consumer through acts of politeness and courtesy. Also there is an expectation that policies exist to make things right when a problem occurs. In Vietnam, Nguyen (2006) found that retail service quality composed of five dimensions: goods assortment, personnel, appearance, physical aspects and safety. He recommended that SERVQUAL and RSQS could be applied in Vietnam provided that they were adjusted to the specific context of study. Parikh (2006) empirically assessed the gap between the customer s expectations and their perceptions about the service quality of retail stores in India. Statistical analysis showed that although the instrument was found to be quite reliable, the gap scores did not merge into five dimensions of service

109 86 quality as proposed by the scale developers; rather, the gap scores roughly merged into nine dimensions. RSQS may not be applicable to the retail sector in India without further restructuring. Kaul (2007) opined that service quality measures developed internationally were often accepted as adequate in India. She evaluated the Retail Service Quality Scale (RSQS) developed in the U.S. and considered valid across a variety of formats and cultural contexts. She concluded that the RSQS can be used to assess overall service quality levels and for tracking overall improvements over a period of time. However, the different dimensions of service quality were not clearly identifiable. Confirmatory factor analysis of the component structures using AMOS 4.0 indicated that the RSQS dimensions was not valid in India. Nhat and Hau (2007) measured the retail service quality in Vietnamese supermarkets and considered the impact of retail service quality on customer s overall evaluation of retail service quality. Service Personnel was the key factor impacting customer s perception of service quality in supermarkets. By improving the performance of employees, supermarkets can increase customer s satisfaction. Chavadi and Kokatnur (2008) evaluated the most influencing factors and identified the service gap between the actual perception and expectation of fast food services. They found that product was the most important factor influencing customer perception followed by service, convenience and value pricing. Regarding SERVQUAL they explored that the highest gap exists for assurance and empathy.

110 87 Das et al. (2008) studied whether RSQS model was fit for measuring retail service quality in Kazakhstan or not. They collected the data from the departmental stores, discount stores and supermarkets. Confirmatory factor analysis indicated a good fit of the RSQS dimensions and the items in Kazakhstan. Ravichandran et al. (2008) identified the critical quality dimension of Chennai City food and grocery shoppers based on the Retail Service Quality Scale proposed by Dabholkar et al. (1996). As far as validation of the instrument was concerned, they found that four factors were enough to evaluate the service quality under 27 items of the food retailers in Chennai. Torlak et al. (2010) observed that to meet the requirement of consumers in a retail context, retailers must emphasize the importance of product quality and service quality. They employed RSQS in grocery store of Turkey. They employed exploratory factor analysis and found four dimensions namely personal interaction, reliability, physical aspects and policy. Hayworth, C., Hobson, R., and Mia, Z. (2012) study reveals that two constructs, namely Physical Aspects and Personal Interaction, had a direct relationship with Customer Satisfaction. Customer satisfaction was also confirmed to be positively linked to store loyalty SERVICE QUALITY AND DEMOGRAPHICS Demographic information such as age, gender and education level need to be examined when measuring service quality in retailing; in order to discover the relationship between demographic information and dimensions (Siu and Cheung, 2001).

111 88 Demographic information allows researchers to obtain characteristics of their sample therefore making the classification of the data more meaningful (Elanain, 2003). Research suggests that demographics do have an effect on some service quality dimensions such as the reliability dimension (Paulins, 2005). According to Ganesan-Lim, Russell-Bennett and Dagger (2008) it is important to understand the relationship between the customer s perception of service quality and demographic information such as age, gender, education level and income level. This information is useful for ensuring there are suitable products available for the target market. They hypothesized that service quality dimensions were different depending on the age, gender and income level of customers; however only age was found to have a relationship with service quality dimensions. The Demographic Information is discussed below: Homburg and Giering (2001) found a relationship between age and service quality dimensions. There were also age differences in some of the retail dimension in study in Hong Kong (Siu and Cheung, 2001). Other studies found that there were differences in shopping behavior when it involves age (Nadiri and Tumer, 2009; Foucault and Scheufele, 2002). Age was a crucial factor for retail organizations. This was even more evident when the product range is associated with the age of the consumer. Furthermore, one of the problems facing retailers these days is coping with an aging population which means retailers must adapt to the changes in the age of their consumers (Varley and Rafiq, 2004). Usually people in the same age group display similar shopping behavior and this information is important to retailers to understand more about their market (Ogden and Ogden, 2005). Retailers must

112 89 consider age differences when studying consumption behavior because there were differences in product choices depending on the age of the consumer (Rocha, Hammond and Hawkins, 2005). In a retailing study by Ganesan-Lim, Russell-Bennett and Dagger (2008) age had a big influence on the perception of service quality. There were gender differences in the physical appearance and personal interaction dimension in a retail study in Hong Kong (Siu and Cheung, 2001). Other studies confirmed that there were differences in shopping behavior when it involves gender (Foucault and Scheufele, 2002). There is little attention given to gender based research concerned with perceived service quality (Snipes, Thomson and Oswald, 2006). On the other side, Yaghi (2010) and Ganesan-Lim, Russell-Bennet and Dagger (2008) found no differences in the perception of service quality based on gender. According to Ogden and Ogden (2005) the most important demographic information is marital status because it shows if customers were buying for themselves, for a spouse, or a family with children. Nadiri and Tumer (2009) found significant differences between marital status and the dimensions namely: Physical Aspects (0.017) and Reliability (0.001). According to Kaushik (2009) occupation/profession had a big influence on the satisfaction via service quality. Three factors namely: tangibility, customization and comfort differed significantly on the basis of profession whereas empathy, in-flight services and reliability had no influence of profession in aviation sector. Income has a relationship with purchasing decisions, thus high income customers gather information prior to buying a product and this may have an

113 90 influence on satisfaction (Homburg and Giering, 2001). Also, there were income level differences in the physical appearance and promises dimension in a retail study in Hong Kong (Siu and Cheung, 2001). Knowing customers income is another important factor that needs to be considered because customers that have different levels of income might prefer to buy different products or services (Ogden and Ogden, 2005). According to Meng et al. (2009) consumers shop at different stores based on their income level which indicates that there might be differences in the perception of service quality based on income level. Customers with high income might favour retailers with high levels of service quality while customers with low incomes might be more tolerant to lower levels of service quality (Sum and Hui, 2009). There were residential status differences in the customization and reliability dimension in Indian domestic aviation sector (Kaushik, 2009). Respondents from rural background gave more importance to customization whereas respondents from urban background gave more importance to reliability. Education level is important demographic information because as customers become more educated they demand different products and different levels of service (Kent and Omar, 2003). Kotler and Armstrong (2010) suggest there has been an increase in educated people in the United States and this leads to an increase in the demand for quality products. On the other side of the coin, Yaghi (2010) noticed no significant association regarding education across service quality dimensions. According to Meng et al. (2009) customers with different demographic characteristics shop at different outlets/stores which further indicate that they have different perceptions of service quality. Frequency of visits could be a

114 91 predictor of desires, intentions and behaviors (Perugini and Bagozzi, 2001). According to Shankar, Amy and Rangaswamy (2003) there are opinions that suggest a positive or negative relationship between frequency of visits and satisfaction. The argument that suggests a positive relationship between frequency of visits and satisfaction is that the more the customer visits the service provider the more they are satisfied with the service. Whereas the argument that suggests a negative relationship between frequency of visits and satisfaction is that the more the customer visits the service provider the higher the customer s expectations become. According to Raajpoot (2004) the more frequent the visits of the customers to the service provider the more the customer places importance on the service quality dimensions. From the above discussion it is clear that age, gender, marital status, occupation, income, residential status, education level, type of outlet, frequency of visiting could have an effect on service quality dimensions. Various studies found that there are differences in shopping behavior when it involves age and gender; however education level and income are more important indicators of shopping behaviour (Foucault and Scheufele, 2002). A study on Islamic banks in the UAE found that there are differences in the perception of service quality based on age, education level and the number of years the customer has been dealing with the bank (Al-Tamimi and Al-Amiri, 2003) THE IMPORTANCE OF RETAIL SERVICE QUALITY Researchers have found that one of the most important factors that affect the consumer s choice of store is service quality (Swoboda et al., 2007) for this reason it is important to understand retail service quality. According to Zeithaml, Bitner and Gremler (2009) In cases in which customer service or

115 92 services are offered in combination with a physical product, service quality may also be very critical in determining customer satisfaction. Therefore, it is important to maintain high standards when interacting with customers and delivering the service-product to customers (Varley and Rafiq, 2004). Some researchers see retail service quality as being the same as service quality and others make a distinction between the two (Gaur and Agrawal, 2006) as retail shops offer both a service and a product (Siu and Cheung, 2001). The best way to describe a retail business is to think of it as a continuum with products or tangible goods at one end and services or intangible goods at the other end (Varley and Rafiq, 2004). Service quality is important in every aspect of the business and it helps in creating a positive image for the retailer s brand (Swoboda et al., 2007). Therefore, customer service must be the focus of a successful retail business that has loyal customers (Paulins, 2005). The GAP model of service quality which was developed by Parasuraman, Zeithaml and Berry helps in identifying whether the customer is satisfied or dissatisfied with the retailers service THE GAPS MODEL FOR IMPROVING RETAIL SERVICE QUALITY The GAPS Model indicates what retailers need to do to provide highquality customer service. When customers expectations are greater than their perceptions of the delivered service, they are dissatisfied and feel the quality of the retailer s service is poor. Thus, for improving customers satisfaction with their services, retailers needs to reduce the service gap.

116 93 There are four factors which affect the service gap: Knowledge Gap: The difference between customers expectations and the retailer s perception of customer expectations. Most organizations would happily believe that they know what the consumers want, but they may be mistaken. For example, for customers at a supermarket price may be the most important factor, others may prefer quality, while yet another segment may prefer speedy checkouts. Standards Gap: The difference between the retailers perceptions of customers expectations and the customer service standards it sets. Service standards need to be based on the customers expectations. The management of the organizations needs to be committed towards providing high standards of service. For example, Dominos Pizza has set a standard of delivering pizzas in 30 minutes. In case the pizza delivery is late, a part of the amount is refunded to the customer. Delivery Gap: The difference between the retailer s service standards and the actual services provided to customers. The level of service provided would vary from employee and by the same employee, over the period of time. Clarity of the role of the employee and adequate training would enable an employee to serve the customer in an efficient manner. Communication Gap: The difference between the actual service provided to customers and the service promised in the retailer s promotional program. If advertising or sales promotions promise one kind of service and the customer receives a different kind of service, the communication gap increases.

117 94 Just 16 percent of traditional, retail store shoppers are extremely satisfied with their most recent customer service experience, whereas online shoppers are nearly three times as likely to be extremely satisfied with their online customer service experience. Thus, the retailer s objective is to reduce the service gap by reducing each of the four gaps. And the key to improve service quality is to (1) understand the level of service customers expect, (2) set standards for providing customer service, (3) implement programs for delivering service that meets the standards, and (4) undertake communication programs to inform customers accurately about the service offered by the retailer MEASURING RETAIL SERVICE QUALITY One of the early classifications of retailing is provided by Gagliano and Hathcote (1994) who classified retailing into two categories: Store service Sales service Store service includes returns and exchange, availability and variety of the merchandise, quality and reliability of service and after sales services. Sales service includes attitude and knowledge of salesmen, timely service and attention to customers. This classification makes it easier for the managers to focus on the area of service that necessitates improvement (Gagliano and Hathcote, 1994). Another classification that has been more popular is the classification of Dabholkar, Thorpe and Rentz (1996) who argue that retail service quality has five dimensions: physical aspects, reliability, personal interaction, problem

118 95 solving and policy. There have been other classifications of retail service quality which are summarized in below table: AUTHOR Teas (1994) RETAIL COMPONENTS 1. Upscale: high/low quality of merchandise, prestigious store, appealing physical facilities, high/low price 2. Merchandise: selection and sales and promotions 3. Transaction Effectiveness: checkout lines, prompt service and personal attention 4. Responsiveness: employees willing to help, easy to get questions answered and problem solving Chowdhury, Reardon and Srivastava (1998) 1. Employee Services: pleased with the services and friendly employees 2. Product Quality: high quality products and branded products 3. Atmosphere: appearance of the store 4. Convenience: ease of getting into the store 5. Prices: the prices at the store are fair Gomez, McLaughlin and Wittink (2004) Morschett, Swoboda and 1. Quality such as friendliness of cashiers, speed of checkout and cleanliness of parking lot 2. Customer service such as variety and quality of products and availability of products 3. Value such as value for money and prices. 1. Quality of performance such as quality and store design

119 96 Foscht (2005) 2. Scope of offers such as variety of products and one stop shopping 3. Price level such as prices of products Table.No.3.3Classification of Retail Service Quality The Above table shows the various classifications of retail service quality and the next section discusses the most popular method of measuring retail service quality. The review of various service quality model revealed that the service quality outcome and measurement is dependent on type of service setting, situation, time, need etc factors (Seth et al., 2005). Undoubtedly, service quality in retailing is different from any other product/service environment. Measuring service quality, therefore, can be rather complicated and difficult in retailing where it combines the selling of goods and services to the customers as well as the customers expectations of knowledgeable, helpful staff to assist them during their shopping experience. That s why the most famous and well discussed service quality model in the 1990 s- SERVQUAL - by Parasuraman s et al. (1985) failed to be fully adopted and validated in a retail setting (Dabholkar et al., 1996). Due to the failure of SERVQUAL to be fully adapted and validated in a retail store setting that offers a mixture of services and merchandise, Dabholkar et al. (1996) developed the Retail Service Quality Scale (RSQS) taking into account retailing-related dimensions. They proposed that retail service quality has a hierarchical factor structure. While consumers think of retail service quality at three levels- a dimensional level, an overall level, and a sub dimensional level, Dabholkar, Thorpe and Rentz (1996) proposed five dimensions- physical aspects, reliability, personal interaction, problem

120 97 solving, and policy. They also gave sub- dimensions of each dimension to combine related attributes into sub-groups Dabholkar et al. (1996) used only performance-based measures instead of the gap between perceptions and expectations because evidence exists that perception measures have a stronger predictive power than the gap score (Cronin and Taylor, 1992). To formulate the scale Dabholkar et al. (1996) conducted qualitative research using three different methodologies - phenomenological interviews, exploratory depth interviews, and tracking the customer through the store. And finally, DTR s proposed a measure of retail service quality which is a 28-item scale, consisting of 17 items from SERVQUAL and 11 items developed from their literature review and qualitative research. Five items from SERQVUAL were deemed inappropriate and dropped. The scale has high construct reliability and validity in measuring service quality of retail stores HIERARCHICAL STRUCTURE OF RETAIL SERVICE QUALITY The Below model shows the five dimensions of retail service quality and the six sub-dimensions which are: Physical Aspects with two sub-dimensions Appearance and Convenience, Reliability with two sub-dimensions, Promises and Doing-it-Right; Personal Interaction with two sub-dimensions Inspiring Confidence and Courteousness/Helpfulness, Problem Solving, and Policy. The RSQS scale used a 5 point rating scale by using 1 = strongly disagree and 5 = to strongly agree. The RSQS is a five dimensional structure of which three dimensions comprise of two sub dimensions each. Below is an explanation of the

121 98 dimensions followed by Table 9 that illustrates the dimensions and subdimensions of the RSQS. FIG.NO 3.1 HIERARCHICAL STRUCTURE OF RETAIL SERVICE QUALITY DIMENSION -1: PHYSICAL ASPECTS: Service is said to be distinguished from goods due to its Intangibility. The tangibility aspects of a service have a significant effect on perceived service quality. The tangibility importance varies according to types of service. For a retail store, the tangibility aspect will be critical as the retailers offer a mix of merchandise and service quality. Physical aspects of retailer include equipment and fixtures, physical facilities, convenience of physical facilities and layouts. The importance of physical environment in a service setting is due to its ability to influence consumer attitudes, behaviour intention and behaviour. As customers are involved in the production and consumption

122 99 process of a service conducted within a physical environment, the physical environment will have a deep impact on customers perception of service experiences. Bitner (1992) also noted that physical environment is often used as cues of a firm s competences and quality by consumers before a purchase. Specifically, proper layout in a store will reduce shopper s search time, colour combine with lighting were suggested to affect consumers cognitive representation and affective reaction, and a light and pleasing scent affects shoppers perceptions of a shopping environment in which the latter will have a significant effect on shoppers mood. Researchers have given several names with different interpretations to the physical elements of service quality measure. Dabholkar et al. (1996) used the term physical aspects to refer to the physical appearance of store and layout convenience. Parasuraman et al. (1988) called it as tangibles adding appearances of staff besides physical facilities and equipment. The appearance of staff was also acknowledged as part of tangibles by several researchers. This dimension has broader meaning than does the SERVQUAL s tangible dimension. In addition to the appearance of the facilities, it also takes into account the convenience offered the customer by the layout of physical facilities. The higher customers appreciate on the physical aspects, the higher the overall evaluation of retail service quality is. Therefore the sub-dimensions of this dimension are appearance and convenience. DIMENSION -2: RELIABILITY: The second dimension is reliability. Customers view reliability as a combination of keeping promises. Dabholkar, Thorpe and Rentz (1996) pointed out that keeping promises and doing it right, were important sub

123 100 dimensions which were identified during their interviews. The reliability dimension comprise of promises and doing it right sub-dimensions (Dabholkar et al., 1996). Besides fulfilling promise and performing the right service as part of relithe doing it right sub-dimension. The higher customers appreciate on reliability, the higher the overall evaluation of retail service quality is. According to a survey by PricewaterhouseCoopers, consumers in Asia demand superb quality, especially the availability of merchandise in stores, much more than the Western customers. This dimension is similar to the reliability dimension of the SERVQUAL the difference being that problem solving is part of reliability in the SERVQUAL scale and in the RSQS it is a separate dimension. Another difference between the reliability dimension in the SERVQUAL and the RSQS is that the availability of products is part of the reliability dimension in the RSQS but not in the SERVQUAL ability, the researchers added the availability of merchandise as part of the doing it right sub-dimension. The higher customers appreciate on reliability, the higher the overall evaluation of retail service quality is. According to a survey by PricewaterhouseCoopers, consumers in Asia demand superb quality, especially the availability of merchandise in stores, much more than the Western customers. This dimension is similar to the reliability dimension of the SERVQUAL the difference being that problem solving is part of reliability in the SERVQUAL scale and in the RSQS it is a separate dimension. Another difference between the reliability dimension in the SERVQUAL and the RSQS is that the availability of products is part of the reliability dimension in the RSQS but not in the SERVQUAL.

124 101 DIMENSION -3: INTER-PERSONAL RELATIONSHIP: The interaction among store personnel and store customers are important as customers are more loyal to a store if the store is seen as warm, friendly, and impulsive. Odekerken-Schröder et al. (2001) in their research emphasized the importance of inter-personal relationship which refers to the opportunity for customers to affiliate with other individuals during the retail encounter. They elaborated the interaction as both the customer-to-customer and customer-to-service provider social interaction. This dimension is a combination of the responsiveness and assurance dimension of SERVQUAL and includes the employee s helpfulness and the ability to instil trust (Kim and Jin, 2002). The higher customers appreciate personal interaction, the higher the overall evaluation of retail service quality is. This dimension was suggested as a separate dimension because interviews revealed the importance of feeling confident, feeling comfortable when shopping at the store and the help customers receive from employees of the store (Dabholkar, Thorpe and Rentz, 1996). Dabholkar et al. (1996) put forward that the personal interaction has two sub-dimensions namely inspiring confidence of customers by store personnel and courteousness/helpfulness of store personnel. These subdimensions are very closely related and capture how the customer is treated by the employee. Inspiring confidence of customers includes error-free sales transactions and record, the ability to answer customers questions, the behaviour of employees in this store instils confidence in customers, and customers feel safe in their transactions with this store. Incorporated in the courteousness/helpfulness factor are employees provide prompt service to customers, employees tell customers exactly when

125 102 services will be performed, customers are given individual attention, employees are consistently courteous with customers, and employees treat customers courteously on the telephone. Darian et al. (2001) also pointed on the importance of sales personnel s knowledge who is aware of new products, technical developments, prices, and other variations of store offerings, who is responsive but provides only information required, and who is not talking down to a customer. DIMENSION -4: PROBLEM SOLVING: Dabholkar et al. (1996) proposed a new dimension problem solving which was not addressed in SERVQUAL. The fourth dimension addresses the issues of handling of goods returned, exchanges as well as complaints. The problem solving dimension of retailers includes: willingness of retailers to handle returns and exchanges, sincere interest in solving the problem and handling customer complaints directly and immediately. Service recovery is recognized as a critical part of good services. Dabholkar et al. (1996) highlighted the need to have problem solving as a dimension by itself because of the importance of service recovery in providing good service. Customers were quite sensitive to how service providers attend to problems and complaints. The ease of returning and exchanging merchandise is very important to retail customers. The higher customers appreciate problem solving, the higher overall evaluation of retail service quality is. This dimension does not have any sub-dimension. DIMENSION -5: POLICY: The fifth proposed dimension- policy- captures aspects of services quality that are directly influenced by store policy. For example, when

126 103 customers evaluate a store on the basis of convenient hours, it is viewed as whether the store s policy is responsive to customers needs. The higher customers appreciate policy, the higher the overall evaluation of retail service quality is. Dabholkar et al. (1996) elaborated store policy to include high quality merchandise, parking facilities, convenient operating hours, acceptance of major credit cards, and store s own credit card. An important criterion on which customers evaluate stores is the credit and charge account policies of the store. Customers also appear to value parking availability for retail shopping. Mehta et al. (2000) seemed in agreement with Dabholkar et al. (1996) that the service quality measurement of the retail stores should include the measure of service quality and product quality as retail stores offer a mix of services and products. This is also a new dimension, which is not similar to any of the SERVQUAL dimensions; it has no sub-dimensions. This dimension was added based on literature reviews and the interviews (Dabholkar, Thorpe and Rentz 1996).The customers evaluate the retail services based on the abovementioned service quality dimensions. The service quality of the retail sector depends upon the differences between the expectations of the customer and the perception on receiving the service. The RSQS dimensions have similarities with retail components discussed earlier and presented in Table.No.4.2, which exhibits the retail components suggested by Teas (1994), Chowdhury, Reardon and Srivastava (1998), Gomez, McLaughlin and Wittink (2004) and Morschett, Swoboda and Foscht (2005). Below are the similarities between the RSQS dimensions and the retail components:

127 104 The retail components in Teas (1994) study are similar to the RSQS dimensions. An example of this is the quality of merchandise; this is similar to a question on the RSQS scale which asks if this store offers high quality merchandise. The second component is selection and sales promotion; this is similar to a question on the RSQS which asks if materials associated with the store are appealing. The third component which is the prompt services, and the last component which is responsiveness, are also similar to questions under the personal interaction dimension on the RSQS scale. Chowdhury, Reardon and Srivastava (1998) conducted a study consisting of five components. A close look at these components shows that all of the components, with the exception of price, are part of the RSQS scale. The study by Gomez, McLaughlin and Wittink (2004) consists of three components. The first two components are similar to questions on the RSQS. The third component which is value for money is not part of the RSQS scale. The study by Morschett, Swoboda and Foscht (2005) consists of three components of which the first two are similar to questions on the RSQS scale. The third component is price and is not part of the RSQS scale. Price has been mentioned in three out of the four studies mentioned above; however price is not measured in the RSQS, which is consistent with other studies that did not consider price as part of service quality (Wong and Sohal, 2002). 3.4 CUSTOMER LOYALTY Customer loyalty is defined as repeated purchasing and referring a company to other customers (Heskett et al., 1997), generating positive and

128 105 measurable financial results (Duffy, 2003). Pearson (1996) has defined customer loyalty as the mindset of the customers who hold favorable attitudes toward a company, commit to repurchase the company s product/service, and recommend the product/service to others. In other words, customer loyalty is the degree to which a customer exhibit repeat purchasing behaviour from a service provider possesses a positive attitudinal disposition toward the provider, and considers using only this provider when a need for this service exists. Loyalty is developed over a period of time from a consistent record of meeting, and sometimes even exceeding customer expectations (Teich, 1997). Improvements in retention and increase in the share of the company are the obvious economic benefits of customer loyalty. Customer loyalty is an indispensable performance measurement tool for profit as well as non-profit organizations to sustain competitive advantage (Kotler, 1998) and to enhance business/service performance measures. Therefore, loyalty is essential for the organization because it is cheaper to retain its old customers than to find new customers; in addition to this customer retention is linked to the company s profit. Customer loyalty (or the absence of it) is exhibited both through customer behaviour and also through attitude. Singh and Sirdeshmukh (2000) suggested the customer loyalty as the market place currency of the twenty-first century. Customer loyalty is concerned with the likelihood of customer returning, making business referrals, providing strong word-of-mouth references and publicity (Bowen and Shoemaker, 1998). Loyal customers are less likely to switch to a competitor due to price inducement, and these customers make more purchases compared to less loyal customers (Baldinger and Rubinson, 1996). However, customers who are retained may not always be satisfied and

129 106 satisfied customers may not always be retained. Customers may be loyal due to high switching barriers or the lack of real alternatives, customers may also be loyal because they are satisfied, thus wanting to continue with the relationship. People become loyal customers in stages, according to Griffin (1995) and Vavra (1995). In the first stage, the prospective customer becomes a suspect, who may be anyone that might buy the product or service. In the second stage, a prospect must have a need for the product or service. In the third stage, the customer is a disqualified prospect as the company has discovered that the customer does not need the product or does not have the ability to buy the product. First-time customers are those who have bought once and repeat customers have bought twice or more. A client purchases regularly and retailer has ongoing relationship with this customer. The customer as advocate is the last stage. An advocate purchases regularly as a client, but additionally encourages others to buy from the company. An inactive customer has bought from the company, but has not purchased from the company for a period that is longer than the normal purchase cycle (Griffin, 1995). Jacoby and Chestnut (1978) have explored the psychological meaning of loyalty in an effort to distinguish it from behavioral (i.e., repeat purchase) definitions. Their analysis concludes that consistent purchasing as an indicator of loyalty could be invalid because of happenstance buying or a preference for convenience and that inconsistent purchasing could mask loyalty if consumers were multi-brand loyal. More specifically, all three decision- making phases must point to a focal brand preference if true brand loyalty exists. Thus, (1) the brand attribute ratings (beliefs) must be preferable to competitive offerings, (2) this information must coincide with an affective preference (attitude) for the

130 107 brand, and (3) the consumer must have a higher intention (conation) to buy the brand compared with that for alternatives. In marketing literature, the word loyalty is used in at least three different senses: As Transactional Retention: Customers or employees are retained to act repeatedly in favor of the company s interests in exchange for something attractive. This, for example, is what happens when newspapers include collectible items so that buyers will not cease to purchase the same newspaper, or when businesses or commercial chains offer certain advantages to customers who make repeat purchases in the same establishment or chain. Managers and employees are also retained by the firm by means of economic compensation or other personal or family perks. As Sentimental Attraction: This is present when someone more or less habitually chooses a certain product or brand because he likes it, or because it inspires a feeling of confidence: the man, for instance, who always buys the same newspaper because he enjoys it, has grown accustomed to it, or because its editorial line appeals to him. This kind of loyalty is also generated when one works for a company that he finds comfortable, or trustworthy. This feeling may arise in very different ways. One of these is falling in love with a business. When one notices how it takes a genuine interest in intelligently meeting a customer s or employee s needs. This leads a person to believe that he can place his trust in it. As Willingness To Commit Oneself: This is the case when a person understands that he ought to dedicate his activity perseveringly to a person, cause, or institution that he considers valuable and to which he has made

131 108 some sort of commitment. In contrast to transactional retention, which corresponds to an instrumental, calculating, self-interested rationality, loyalty understood as willingness to commit is based on a deliberately created bond that obliges the person to maintain this commitment. Such loyalty is considered intrinsically valuable. This understanding of loyalty is also distinct from a mere sentimental attraction or reflexive habit, which occurs without reflection or responsible decision. Jacoby and Kyner (1973) defined customer loyalty as a biased (i.e. nonrandom), behavioural response (i.e. purchase), expressed over time, by some decision making unit, with respect to one or more alternative brands out of a set of such brands, and was a function of psychological processes. Nordstrom and Swan (1976) discovered that the change in ownership resulted in altered patterns of customer loyalty. Alteration of any market variable is likely to upset the probability of continued loyalty. Dealer ownership was shown to be a significant influence on customer loyalty. In particular, a change in ownership had an impact on brand and source selections. A change in the marketing structure variable, ownership had a significant effect on behavioural patterns of customers. This effect was reflected in a shifting of loyalty patterns among members of the experimental group. In 1978, Jacoby and Chestnut explored the psychological meaning of loyalty in an effort to distinguish it from behavioral (i.e., repeat purchase) definitions. Their analysis concluded that consistent purchasing as an indicator of loyalty could be invalid because of happenstance buying or a preference for convenience and that inconsistent purchasing could mask loyalty if consumers were multi-brand loyal. Therefore, the authors summarized that it would be

132 109 unwise to infer loyalty or disloyalty solely from repetitive purchase patterns without further analysis. Dick and Basu (1994) discovered that loyal customers were less motivated to search for alternatives, were more resistant to counter-persuasion from other brands, and were more likely to pass along positive word-of-mouth communication about the service to other consumers. Further, they demonstrated that loyalty was more prevalent among service customers than among customers of tangible products. In the services context, intangible attributes such as reliability and confidence played a major role in building or maintaining loyalty. Ellis (1995) examined customer s motivation for long term relationships, the nature of these relationships and their outcomes like customer satisfaction, customer loyalty, word of mouth and purchases. He made this study on the relationship marketing in a retail clothing/accessories setting. Results indicated that the personal needs variables were useful in classifying customers and in determining the types of relationships certain customers likely to have, in comparison to other types of customers. Reichheld (1996) opined that some customers were inherently more loyal than others. He introduced a loyalty coefficient which helps in understanding customers predispositions to being loyal. He also revealed that one of the salient benefits of customer loyalty, especially for service organizations, is word of mouth (WOM) communication. Loyal customers often generate new business via WOM recommendations to prospective and other existing customers of the firm.

133 110 Carbone (1997) wonders whether loyalty was a subversive doctrine and, after various reflections, responded affirmatively. In his own words: loyalty is a vicious hoax we perpetrate on ourselves, revealed when the other guy inevitably trades loyalty for self-interest. Loyalty in the business world was generally understood in three ways: i) transactional retention, ii) sentimental attraction, and iii) willingness to commit oneself. In the third type, the commitment to adhere to a person, cause, or institution may contribute to human flourishing and therefore generate the human virtue of loyalty. Kandampully (1997) suggested that there was no disagreement that customer loyalty is the goal, but claims that this presupposes the establishment of trust and a long-term relationship, and that the only way to gain this trust and long-term relationship is by first offering it. Organizations will thus need to commit themselves to their customers a commitment to offer loyalty of service. Andreassen and Lindestad (1998) revealed that in the package tour industry which is a complex service industry, corporate image rather than customer satisfaction was the main predictor of customer loyalty. This finding challenges the disconfirmation of expectations paradigm, which predicts customer satisfaction as the primary route to customer loyalty. Bowden (1998) described that how Nortel Wireless Networks initially recognized the need to enhance the existing emphasis on purely customer satisfaction to that of an evolving focuses on customer loyalty. He also highlighted the rationale behind the evolution from a customer satisfaction strategy to that of a customer value management methodology leading towards increased customer loyalty.

134 111 Gabbott and Hogg s (1998) review of loyalty suggested that bonding arrangements between the parties in the relationship can act as a form of glue and they proposed six forms of bonding which act in concert: goal compatibility, trust, satisfaction, investment, social and structural ties. In 1998 Mittal and Lassar concluded that in service industry the relationship between satisfaction and loyalty was asymmetrical: while dissatisfaction nearly guarantees switching, satisfaction does not ensure customer loyalty. Even more importantly, the drivers of loyalty beyond satisfaction were different from what drives dissatisfaction versus satisfaction. The potency of technical quality ( the quality of the work performed ) and functional quality ( the quality of the service ) in delivering satisfaction and loyalty differed. And it varied between a low contact and a high contact service. For a low contact service (e.g. car repair), technical quality was needed to first obtain satisfaction, and then functional quality was needed to drive loyalty beyond satisfaction. The converse was the case for a high contact (e.g. health care) service. Soderlund (1998) supported a positive association between customer satisfaction and customer loyalty, but he also noted that increasing satisfaction does not produce an equal increase in loyalty for all customers. Andreassen (1999) proposed and tested a theoretical model focusing negative effect, satisfaction with complaint resolution, and corporate image as antecedents to customer loyalty. He found that satisfaction with complaint resolution had a positive impact on customer loyalty. Complaint resolution is thus an important element of the company s customer retention strategy. Second, negative affect caused by the initial service failure had a negative impact on satisfaction with complaint resolution and customer loyalty. Finally,

135 112 corporate image had a positive impact on customer loyalty. Ehrenberg and Scriven (1999) exhibited that few consumers were monogamous i.e. 100 percent loyal or promiscuous i.e. no loyalty to any brand. Rather most people were polygamous i.e. loyal to a portfolio of brands in a particular category. From this perspective, loyalty was defined as an ongoing propensity to buy the brand, usually as one of several. Grayson and Ambler (1999) ascertained that loyal customers were more likely to expand their relationship within the product range and so the rewards from this group were long term and cumulative. Gremler and Brown (1999) concluded that the influence of loyal customers can reach far beyond their proximate impact on the company. This impact was analogous to the ripple caused by a pebble tossed into a still pond. They introduced the loyalty ripple effect construct and defined it as the influence; both direct and indirect, customers had on a firm through (1) generating interest in the firm by encouraging new customer patronage or (2) other actions or behaviours that create value for the organization. That is, in addition to their revenue stream, they suggested that loyal customers may engage in several behaviors, including word-of-mouth communication that add value to or reduce costs for the firm. Oliver (1999) revealed that satisfaction was a necessary step in loyalty formation but becomes less significant as loyalty begins to set through other mechanisms. Further, customer loyalty cannot be achieved or pursued as a reasonable goal by many providers because of the nature of the product category or consumer disinterest. For some firms, satisfaction was the only feasible goal for which they should strive; thus, satisfaction remains a worthy

136 113 pursuit among the consumer marketing community. Rowley and Dawes (1999) reviewed the theoretical work on customer loyalty and identified that loyalty has both attitudinal and behavioral elements. Categories of loyal proposed by Dick and Basu were defined by the relationship between attitudinal and behavioral aspects of loyalty. Antecedents to loyalty include cognitive, affective and cognitive factors. The outcomes of any program to manage loyalty needs to be evaluated and measured. The measurement of loyalty poses some interesting challenges in terms of the definitions of the attitudes and behaviors that it might be appropriate to measure. According to Shoemaker and Lewis (1999) loyal customers enact as information channels, informally linking networks of friends, relatives and other potential customers to the organization. Alonso (2000) tested a model of customer loyalty which compromised of two facets: the firm and the consumer. The firm was the entity that starts the process with the production of a consumer value package that includes a product or service and a strategy to deliver it into the consumer s hand. The results suggested that consumer trust and commitment had a key-mediating role in the process of building loyalty. The buying process with trust and commitment will be able to generate loyalty involving repeated purchases in a long term relationship between the firm and its consumers. Stevens (2000) found that the relationship between competition and customer loyalty becomes more intense as the level of competition rises, especially in the services sector where there is a wide range of choices and rapidly emerging innovative products and services.

137 114 Young (2000) pointed that while customer satisfaction was clearly an important part of a company s financial performance, satisfaction was not the sole determinant of profitability. Most companies had learned that it was far more profitable to keep customers than attracting new customers and more specifically the right type of customers to maintain. He further elaborated a customer loyalty index which composed of four factors: use again, recommend to others, exceeding expectations and satisfaction. Boenitz (2001) postulated that most of the company s financial systems do not capture the value of a customer loyalty, focusing instead on current period costs and revenues while ignoring expected customer cash flows. By understanding the economies of defections, managers often came to realize that product and service quality improvements was not costs, but investments in customer retention. Improving customer satisfaction yields to customer retention, market share and profitability. Mattila (2001) examined the impact of relationship type on customers behavioral intentions in a context of service failures. Overall, the results from the two scenario-based experiments indicated that building a true service relationship with the customer might be a critical factor in ensuring customer loyalty with a failed recovery attempt. Moreover, he suggested that bonding the customer to the company might reduce customer resistance to premium prices. Straughan and Albers-Millers (2001) explored that cultural individualism was negatively correlated with customer loyalty to domestic retailers, uncertainty avoidance was positively related to customer loyalty to domestic retailers, the ratio of foreign imports to GDP was negatively correlated to customer loyalty to domestic retailers, and men exhibited greater

138 115 loyalty to domestic retailers than women do. Widzer (2001) measured the aspects of customer loyalty and customer satisfaction that were most likely to impact retention rates of business travelers among their choice of traveler providers. He discussed the importance of an organizational culture that promotes loyalty and satisfaction, the effect of customer loyalty and satisfaction on the internal dynamics of an organization, the difference and similarities between customer loyalty and satisfaction. He found airfare as the most important attribute impacting retention rates. Chiou et al. (2002) proposed a cognitive-affective-conative baseline model. Perceived service quality (both tangible company-related and employee-related factors) was modeled antecedent to satisfaction and trust, which in turn were antecedents to customer loyalty responses (word of mouth and traditional loyalty). These relationships were then hypothesized to be moderated by high versus low knowledge, a moderation based on central versus peripheral processing. The results showed that employee service quality had a greater impact than company service quality on trust and satisfaction in both knowledge groups. Froehle (2002) studied the impact of computer mediated communication technologies on customer s perception which in turn could improve customer satisfaction and engender customer loyalty. They developed a model that posited how several service process design parameters associated with computer mediated communication influenced the customer s beliefs and attitudes towards: (a) the contact episode specifically, and (b) the service provider generally. The findings indicated that personalization and synchronicity significantly impact customer intentions regarding both loyalty and the propensity to use the communication medium for future contacts with

139 116 the firm. Technologies that allowed for increased levels of personalization in the customer service process were found to have the greatest impact on customer satisfaction. Heskett (2002) concluded that an important antecedent of customer loyalty was customer satisfaction. Research suggested that while customer satisfaction and loyalty provide a foundation for high levels of customer lifetime value, he supported a range of customer behaviors with widely varying values, characterized by mere loyalty, commitment, apostle-like behavior, and ownership. Fullerton (2003) investigated the roles played by different forms of commitment in the relationship between customers and their service provider. It was found that when customer commitment was based on shared values and identification, it had a uniformly positive impact on customer loyalty. When customer commitment was based on switching costs and dependence, it had mixed effects on customer loyalty. In addition, it was found that there were significant interactions between these two forms of commitment on customer loyalty. Uncles et al. (2003) revealed that loyalty in competitive repeatpurchase markets was shaped more by the passive acceptance of brands than by strongly held attitudes about them. They reviewed three perspectives on loyalty and relate these to a framework for understanding customer loyalty that encompasses customer brand commitment, customer brand acceptance and customer brand buying. Beerli et al. (2004) proposed that satisfaction together with personal switching costs were antecedents leading directly to customer loyalty, with the

140 117 former exerting the greatest influence and perceived quality were a consequence of satisfaction. They also showed that the degree of elaboration in the bank selection process does not have a moderating influence on the causal relationship between satisfaction/switching costs and customer loyalty. Morais et al. (2004) tested a conceptual framework of the development of loyalty that was grounded in resource theory, reciprocity, and customer equity. The results indicated that if customers perceived that a provider was making an investment in them, they in turn made a similar investment in the provider, and those investments led to loyalty. The findings revealed that investments of love, status, and information were more closely associated with customer loyalty than investments of money. These findings supported the proposed theoretical model and helped in explaining how well-designed loyalty programs may lead to increased psychological attachment. Carpenter and Fairhurst (2005) supported for significant, positive relationships between utilitarian and hedonic shopping benefits, customer satisfaction, customer loyalty and word of mouth communication in retail apparel branded context. Rowley (2005) found that all loyal customers were not at the same ladder, they were standing at different ladder of customer loyalty and to retain those differentiated strategies had to be adopted. Rundle-Thiele (2005) highlighted that there were different ways in which customers can be loyal and attitudinal loyalty was the most important dimension for marketers to monitor. Further, the dimensions of loyalty may include propensity to be loyal, behavioral intentions, complaining behavior, resistance to competing offers, attitudinal loyalty and behavioral loyalty.

141 118 Ball et al. (2006) showed that the effect of service personalization on customer loyalty exists, but the effect was not all direct. Personalization works through improving service satisfaction and trust. Personalization and improved communication act together in such a way that they account for the variance in customer loyalty that would be otherwise explained by corporate image. Leverin and Liljander (2006) investigated the relationship marketing (RM) strategy of a retail bank. They found no significant differences between the segments on customers evaluations of the service relationship or their loyalty toward the bank. Furthermore, regression analysis revealed that in the profitable segment, relationship satisfaction was a weak determinant of customer loyalty. Turner and Wilson (2006) identified the impact of the Tesco Club card on customer loyalty. A positive moderate relationship was found between the owning of a Club card and loyalty to store. They also found that there was a positive moderate relationship between the Club card returns and customer loyalty. Zineldin (2006) examined and developed a better understanding of triangular relationship between quality, customer relationship management (CRM) and customer loyalty (CL) which might lead to companies competitiveness (CC). Changes in quality over time within various segments or related to specific products or categories of products/services can be used as an indicator of the level of customer loyalty. By linking infrastructure, interaction and atmosphere indicators to the quality of object and processes, researchers and managers can document which changes in CRM strategy improve the overall satisfaction and loyalty, hence the ultimate outcomes.

142 119 Jones and Farquhar (2007) examined minor service failures in UK banking and considered the impact that satisfaction with service recovery had on customer intentions to continue their custom and make recommendations. A few customers who complained about minor service failures reported that they were very satisfied with the service recovery. Weak service recovery influenced customer intentions about continued custom and recommendation. Minor failures in account management and bank charges were shown to have a marked effect on intended loyalty behaviors. Keiningham et al. (2007) examined different customer satisfaction and loyalty metrics (satisfaction, expectations, value, etc.) and tested their relationships to customer retention, recommendation as well as share of wallet. The data were collected from US customers of three industries: mass merchant retail, retail banking and ISPs. The results indicated that recommendation intention alone was not an indicator of performed better in prediction of customer recommendations and retention. Kaushik (2007) studied the influence of various factors on customer loyalty. The main hypothesis of his study insisted that the list of most important factors affecting loyalty was dependent on the level of loyalty of costumers. LOGIT method was used for testing the hypotheses on the sample of survey data about 1000 private customers of the biggest telecommunication company in Estonia. The results revealed that four analyzed factors affecting customer loyalty: satisfaction, trustworthiness, image and importance of relationship were playing different role on the different levels of customer loyalty. Brunner et al. (2008) discovered that for new customers satisfaction was crucial whereas image plays a much smaller role in terms of customer

143 120 loyalty. For experienced customers, however, the importance of satisfaction decreases whereas the impact of image increases. Ganguli and Kumar (2008) explored the drivers of customer satisfaction and loyalty among retail store customers. They found that neither customer satisfaction nor loyalty was affected by parking. Satisfaction and loyalty was mostly influenced by pricing features, which established the fact that India is still a highly price conscious market. The next best driver of satisfaction and loyalty was store ambience emphasizing the fact that in case of a supermarket, retail shopping customers preferred to shop in an environment which is cool and calm, and they can spend their time in a leisurely manner choosing assorted products in an easy manner. Han and Back (2008) investigated the relationship between image congruence and consumption emotions and the possible influence of this relationship on customer loyalty in the lodging industry. The results showed that their model, which linked image congruence, consumption emotions, and customer loyalty, was generally supported, whereas the linkage between social image congruence and consumption emotions was not significant. McMullan and Gilmore (2008) highlighted the importance of identifying, understanding and managing mediating effects, in the context of loyalty development. They emphasized the importance of a differentiated approach to develop and manage customer loyalty by appropriately rewarding customers at different levels. Raimondo et al. (2008) investigated the influences of relational equity on attitudinal loyalty and behavioral loyalty. Moreover, they tested the hypothesis that relationship age moderates the impact of relational equity on

144 121 loyalty, adopting a cross-sectional design and data from a sample of Italian customers of mobile phone services (N = 461). Relational equity was recognized as a significant determinant of customer loyalty over and above satisfaction and its influence increases along with relationship age. Xiaofei et al. (2008) defined the relationship among Guanxi investment, affective commitment and customer loyalty. They proposed a customer win-back model and found that Guanxi investment strategy had significant effects on customer s loyalty. According to Davis-Sramek et al. (2009) affective commitment (affective commitment is the relationship with the service provider based on the customer positive experience of the service provider) was directly related to loyalty whereas continuance commitment (continuance commitment is a relationship with the service provider because the customer might not have other alternatives or be at an economic disadvantage if he or she switches from the current service provider) was not. Han and Ryu (2009) examined the relationships among three components of the physical environment (i.e., décor and artifacts, spatial layout, and ambient conditions), price perception, customer satisfaction, and customer loyalty in the restaurant industry. The three factors of the physical environment strongly influenced how customers perceived price, and this price perception, in turn, enhanced customer satisfaction level and directly/indirectly influenced customer loyalty. Decor and artifacts were the most significant predictors of price perception among the three components of the physical environment. Furthermore, both price perception and customer satisfaction played significant partial/complete mediating roles in the proposed model.

145 122 Anuwichanont and Rajabhat (2010) examined the impact of the multidimensional conceptualization of commitment (informational complexity, position involvement and volitional choice) on loyalty in the airline context. The results supported the three dimensions of commitment as the determinants of loyalty. But no support was found for the hypothesized relationships between informational complexity and attitudinal loyalty and between volitional choice and attitudinal loyalty. Yeng, L. C., Kamariah, N., and Mat, N. (2013) study determine the antecedents of customer loyalty from both attitudinal perspective (cognitive loyalty, affective loyalty, and cognitive loyalty) and behavioral perspective (action loyalty). The findings disclose that the antecedents of cognitive loyalty are the components of store image, namely, service quality, product quality, store atmosphere, and promotion activity. The antecedents for affective loyalty are customer satisfaction, loyalty program, and retailer brand equity, while the antecedent for cognitive loyalty is customer commitment. Likewise, the antecedents for action loyalty are cognitive loyalty, customer commitment, and customer satisfaction. The study identifies three strategic tools that dominate attitudinal aspects, namely, store image, loyalty program, and retailer brand equity. Thus, these strategic tools provide retailers with a direction in strategy formulation, which allows them to capitalize customer loyalty as a means of gaining competitive advantage ASSESSING THE RELATIONSHIP BETWEEN SERVICE QUALITY AND CUSTOMER LOYALTY Various researchers had empirically examined the relationship between overall service quality and individual service loyalty dimensions. In regards to customer loyalty, Parasuraman et al. (1988) argued that reliability was the

146 123 most important dimension whereas the tangibles dimension was considered as the least critical service quality aspect by service customers. DuWors and Haines (1990) found that the level of loyalty can change over time. Or, quite simply, many companies can overcome the limiting characteristics of their service industry and build loyalty through superior quality and outstanding customer service. Moreover, a positive relationship was observed between service quality and the intention to remain loyal even in case of price increment (Zeithaml et al., 1990). Cronin and Taylor (1992) noticed that service quality did not appear to have a significant (positive) effect on intentions to purchase again. In 1993, Boulding et al. found a positive relationship between service quality and repurchase intentions and willingness to recommend. Ghobadian et al. (1993) ascertained that a high level of service quality was anticipated to lead to customer satisfaction and eventually to better customer loyalty and higher profits. Ostrowski et al. (1993) examined issues related to service quality and customer loyalty in the commercial airline industry. The results of an empirical study, using data collected on two air carriers, indicated that current levels of perceived service quality were below potential; hence customer loyalty to airlines was low. A significant relationship was found to exist between service quality (carrier image) and retained preference, a measure of customer loyalty. Storbacka et al. (1994) posited that service quality leads to relationship strengths, which in turn leads to customer loyalty in the form of repeat purchase behavior. Iacobucci and Grayson (1995) highlighted that service

147 124 quality and customer satisfaction was important means of creating competitive advantages and customer loyalty. Service quality is one of the ways to achieve customer loyalty. Johnson et al. (1995) highlighted that there were several reasons for using measures of service quality to understand customer loyalty. The first reason for using and measuring quality to explain loyalty is that quality ratings tell us the state of the service provider s resources and actions. Measuring quality tells us what aspects of service was below par and need improvement. Secondly, satisfaction is a rating of customer s experience with the service outcome, whereas quality is a judgment made about a firm s resources and skills. Unsatisfactory personal outcomes may be due to factors related to the customer s specific characteristics, and the customer may still rate the firm high or low on quality. Rapp (1995) opined that customer satisfaction was achieved by a company s overall performance. The performance dimensions could be distinct as product quality, service quality, reputation quality, relationship quality and price elements. He studied the development of a customer satisfaction model (PROSAT) taking German car market in the customer segment of executives. Results of the study indicated that the performance dimensions contribute to different levels of customer satisfaction. Service quality leads to customer satisfaction and the result was a high correlation with customer loyalty. This loyalty leads to repurchase intention and the positive word-of-mouth communication. Sasser and colleagues (1995) reported a strong relationship between the level of quality offered by a supplier and the resulting loyalty displayed by customers. Leading service organizations strived to maintain a superior quality

148 125 of service in an effort to gain customer loyalty (Zeithaml et al., 1996) therefore, a service organization s long-term survival in a market was essentially determined by its ability to expound and retain a large and loyal customer base. Andreassen and Lindestad (1997) discussed and tested corporate image and customer satisfaction as two routes to customer loyalty. Based on data from 600 individual customers of package tour industry, they proposed a conceptual model using structural equation modeling. They found that perceived quality had positive effects on value and customer satisfaction for the industry consolidated. Value had only significant impact on customer satisfaction for customers with a low degree of service expertise. Corporate image was positively correlated with perceived quality, customer satisfaction, and customer loyalty. Furthermore, they discovered that for complex services, corporate image and customer satisfaction was not two separate routes to customer loyalty. Corporate image impacts customer loyalty directly whereas customer satisfaction does not. Maloles (1997) found that both service quality and customer satisfaction influenced customer loyalty intentions directly. Service quality also affected customer loyalty intentions through overall satisfaction. The results of the research also showed that both, firm/service characteristics and equity, influence perceptions of service quality and overall satisfaction. Bloemer et al. (1998) investigated how image, perceived service quality and satisfaction determined customer loyalty in a retail bank setting at the global construct level, as well as the level of construct dimensions. At the global level the results of a large-scale empirical study revealed that image was indirectly related to bank loyalty via perceived quality. In turn, service

149 126 quality was both directly and indirectly related to bank loyalty via satisfaction. The latter had a direct effect on bank loyalty. At the level of the dimensions underlying aforementioned constructs, it becomes clear that reliability (a quality dimension) and position in the market (an image dimension) were relatively important drivers of retail bank customer loyalty. Bloemer et al. (1999) focused on the refinement of a scale for measuring service loyalty dimensions and the relationships between dimensions of service quality and service loyalty dimensions. The results of an empirical study of a large sample of customers from four different service industries suggested that four dimensions of service loyalty can be identified: purchase intentions, word-of-mouth communication; price sensitivity; and complaining behavior. Further analysis yields an intricate pattern of service quality-service loyalty relationships at the level of the individual dimensions with notable differences across industries. Yang (2001) assessed the customer loyalty by embracing e-service quality dimensions along with perceived product/service value, as the determinants of customer loyalty and found five e-service quality dimensions: care/help, reliability, ease of use, security and product/service portfolio. While perceived product/service had no significant influence on overall service quality assessment, it does considerably impact on the assessment of customer satisfaction and customer loyalty. Zins (2001) investigated the antecedents of future customer loyalty in the commercial airline industry by applying structural models under four prototypical past loyalty conditions. He found that corporate image of the service provider along with service quality and customer satisfaction, were powerful and illustrative components for explaining future customer loyalty.

150 127 Dean (2002) found that both service quality and perceived customer orientation of call centers affect customer loyalty to the providing organization, and perceptions of quality partially mediated the customer orientation to loyalty relationships.lee-kelley et al. (2002) explored the concept of employing service quality in a non-service industry to raise switching barriers and to create customer longevity. A survey of the UK steel industry revealed that the higher the level of perceived service quality, the higher the expressed intended customer loyalty. Kandampully and Suhartanto (2003) revealed that the quality of service was more significant than price in segregating a service firm from its competitors and in fostering customer loyalty. Lee et al. (2003) found that traditional brick-and-mortar companies were embracing the use of modern technologies to enhance the service quality they offered and to gain customer loyalty. They found that technology affects the ability of hotels to support employees, enhance the quality of service, improve efficiencies, and gain competitive advantage which leads to customer loyalty. Wong and Sohal (2003 a) measured the impact of service quality dimensions on customer loyalty, on two levels of retail relationships: personto-person (salesperson level) and person-to-firm (store level). They revealed that service quality was positively associated with customer loyalty, and that the relationship between the two was stronger at the company level, rather than at the interpersonal level. Specifically, among the dimensions of service quality, the most significant predictor of customer loyalty at a company level was tangibles, while the most significant predictor of customer loyalty at an interpersonal level was empathy.

151 128 Ribbink et al. (2004) empirically investigated the roles of service quality, satisfaction and trust in an e-commerce context. In the study, e-trust was found to directly affect loyalty. The e-service quality dimension of assurance, i.e. trusting the merchant, influences loyalty via e-trust and e- satisfaction. Other e-quality dimensions, such as ease of use, responsiveness, and customization influence e-loyalty mainly indirectly, via satisfaction. Bell et al. (2005) investigated the effects of customer investment expertise and perceived switching costs on the relationships between technical and functional service quality and customer loyalty. Technical service quality was hypothesized to be a more important determinant of customer loyalty than functional service quality as expertise increases. Both technical and functional service quality was hypothesized to have a reduced relationship with customer loyalty as perceived switching costs increase. Three-way interactions between the main effects of service quality, customer expertise, and perceived switching costs yields additional insight into the change in relative importance of technical and functional service quality in customers decision to be loyal. Arasli, Mehtap-Smadi and Katircioglu (2005) found a positive relationship between four service quality dimensions in Greek banks and overall satisfaction and subsequently, a link between satisfaction and loyalty. Fig. No. 3.1 above summarizes the relationship between service quality dimensions, overall satisfaction and loyalty. Lei and Mac (2005) investigated the relationship between service quality and customer loyalty in the context of Macau, a small city in South China. Based on an empirical study of 387 valid responses, they concluded that tangibles, assurance, empathy and responsiveness were important determinants of customer loyalty in the transport service sector. Besides improving service

152 129 quality, the public bus service providers should also consider offering differentiated service as they found that frequency of usage of bus services does moderate the relationship between service quality and customer loyalty. According to Akbaba (2006) service quality was an antecedent of customer loyalty which leads to new customers, increases the company s performance which reduces costs and raises the organization s positive image. Leung (2006) investigated the service quality of the six network operators in Hong Kong and explored the determinants of service quality in the industry. He identified six dimensions of service quality of the mobile network services industry in Hong Kong, which were Empathy, Responsiveness, Network Quality, Reliability, Assurance, and Tangibles. All service quality dimensions identified were positively related to the overall perceived service quality. Further, both service quality and customer satisfaction were found to be linked to behavioral intentions such as continue to use the service, increase usage rate, refer the services to friends, and pay premium price for the services. Olorunniwo et al. (2006) investigated that whether the typology to which a service belongs explain the nature of the service quality (SQ) construct and its relationship to customer satisfaction (SAT) and behavioural intentions (BI) in service factory or not. They found that tangibles, recovery, responsiveness, and knowledge were the dominant dimensions of SQ construct in the service factory. Further they highlighted that, although the direct effect of SQ on BI was significant, the indirect effect (with SAT playing a mediating role) was a stronger driver for BI in the context of the service factory

153 130 TANGIBILITY RELIABILITY ASSURANCE OVERALL CUSTOMER SATISFACTION RECOMMANDING BANK TO OTHERS EMPATHY Source: Arasli, Mehtap-Smadi and Katircioglu (2005, p.51) Fig.No.3.2: The Relationship between Service Quality Dimensions, Satisfaction Chen et al. (2007) explored the relationship between hot springs hotels service quality, customer satisfaction, customer loyalty and lifestyle. The outcome of their research indicated that hot springs hotel operators need to enhance customer satisfaction in order to improve customer loyalty directly or enhance service quality in order to improve customer loyalty indirectly to enhance profitability and sustain operations. Because customers of different lifestyles differ in every dimension, hot springs hotel operators can segment the market via lifestyle variables and undertake different strategies in response to the service quality and customer satisfaction factors valued by the target market segment in order to attain the goal of enhancing customer loyalty and corporate profitability. Huang et al. (2007) highlighted that both psychological and economical factors influence customers loyalty to ASPs (application service providers), yet affective commitment was more important than continuous commitment.

154 131 Paying more attention to psychological considerations may be good in B2B relationships. Value-added service and service quality deepen affective commitment which in turn promotes loyalty. Increasing investment size and reducing attractiveness of alternatives enhance continuous commitment. Kandampully and Hu (2007) revealed that corporate image was influenced both by service quality and customer satisfaction, which in turn influences customer loyalty. Thus, the key to customer loyalty appeared to be the fostering of a favorable image of the hotel firm created by improving service quality and satisfying customers. Rauyruen et al. (2007) presented a picture of how relationship quality influenced customer loyalty or loyalty in the business-to-business context. Building on prior research, they proposed relationship quality as a higher construct comprising trust, commitment, satisfaction and service quality. These dimensions of relationship quality can reasonably explain the influence of relationship quality on customer loyalty. They concluded that to maintain customer loyalty to the supplier, a supplier need to enhance all four aspects of relationship quality which were trust, commitment, satisfaction and service quality. Satisfaction appears to be a crucial factor in maintaining purchase intentions whereas service quality strongly enhances both purchase intentions and attitudinal loyalty. Su et al. (2007) explored issue about convenience stores which offered delivery on demand service. With survey and statistic techniques, they found three dimensions of service quality of E-commerce: conveniently, information quality, and instant; and two dimensions of service quality of delivery on demand service: just-in-time delivery and quality of pick-up contribute significantly. They also concluded a positive relationship between total

155 132 satisfaction on E-commerce and delivery on demand service. With the factor of satisfaction as a mediator, the influence of service quality on loyalty was also positive. Sousa (2007) exhibited the relationship between quality and loyalty in multi-channel e-service settings. In this connection, he empirically tested the quality-loyalty relationship in a multi-channel e-service; and examined whether a customer s degree of focus on the internet (DFI) channel moderates this relationship or not. He employed structural equation modeling, and found a strong and significant link between e-service (web site) quality and loyalty intentions. Bastos and Gallego (2008) developed a model to demonstrate that loyalty was a consequence of service quality and customer satisfaction. A specific scale had been developed and applied to a survey at a two level of Portuguese pharmacies: rural (with no competition) and urban pharmacies (with some competition). Using a structural equation modeling methodology they demonstrated that the more competition (urban pharmacies) less loyalty, the more dependent with the service (high consume in product pharmacies) the more loyal. Akbar and Parvez (2009) proposed a conceptual framework to investigate the effects of customers perceived service quality, trust, and customer satisfaction on customer loyalty. They exhibited that trust and customer satisfaction was significantly and positively related to customer loyalty. Customer satisfaction was found to be an important mediator between perceived service quality and customer loyalty.

156 133 Clemes et al. (2009) developed a hierarchical model to examine the interrelationships between behavioral intentions, service quality, customer satisfaction, perceived value and image in the Taiwan hotel sector. They identified the dimensions of service quality through the literature review and focus group discussions. Statistical analyses showed that perceived value had the most influential moderating effect on the relationship between service quality and customer satisfaction. Further, they demonstrated that service quality had a direct impact on customer perceptions of value and customer satisfaction. Customer satisfaction and image directly influenced behavioral intentions. Hazra and Srivastava (2009) examined the relationship of service quality with customer loyalty, commitment and trust from the customer s perspective in the Indian banking sector. The results showed that dimensions of service quality such as assurance-empathy, reliability and tangibles significantly predict customer commitment and trust. They revealed that service quality was positively associated with customer loyalty. Ladhari (2009) found a direct relationship between perceived service quality and behavioral intention and a direct relationship between satisfaction and behavioral intention. Furthermore, he suggested that behavioral intention could be viewed as a multidimensional construct which consists of recommendation, loyalty and willingness to pay more. According to Ladhari (2009) recommendation means customers talking positive about the service provider, loyalty means repeat purchase and willingness to pay more means that the customer is willing to pay more money for service provided by the service provider. The relationship between service quality, satisfaction and behavioral intentions are illustrated in Fig.No.3.2 below.

157 134 Lenka et al. (2009) examined whether service quality of Indian commercial banks increases customer satisfaction that fosters customer loyalty. Analysis showed that better human, technical and tangible aspects of service quality of the bank branches increase customer satisfaction. Human aspects of service quality were found to influence customer satisfaction more than the technical and tangible aspects. Increase in service quality of the banks can satisfy and retain customers. In the Indian banking sector, human aspects were more important than technical and tangible aspects of service quality that influences customer satisfaction and enhances customer loyalty. Qin and Prybutok (2009) explored the relationship among service quality, food quality, perceived value, customer satisfaction and behavioral intentions in Fast-Food Restaurants (FFRs). Structural equation modeling was employed to estimate the relationship among service quality, customer satisfaction, and behavioral intentions. They highlighted that the dimensions namely tangibles, reliability/responsiveness, recovery, assurance, and empathy were significant for the customers. Service quality and food quality were two main determinants of customer satisfaction. The insignificance of perceived value was potentially due to the homogeneous nature of the construct within the FFR group rather than the importance of the perceived value construct within food service. Saha and Theingi (2009) demonstrated the order of importance of the dimensions of service quality namely flight schedules; flight attendants; tangibles; and ground staff. Passenger satisfaction with these service quality dimensions was found to be very important in explaining behavioural intentions. Satisfied passengers were mostly influenced by the schedule. Such customers engaged in positive word-of- mouth communication and had high repurchase intentions. Dissatisfied passengers preferred to change airlines,

158 135 rather than providing feedback to them. Tangibles Reliability Emotional Intelligence Recommendation Responsivenes s Perceived Service Quality Behavioural Intention Loyalty Confidence Willingness to Pay Communicatio n Source: Ladhari,(2009b, p.321) Fig.No.3.3: The Relationship between Service Quality Dimension, Perceived Service Quality, Emotional Satisfaction and Behavioural Intentions Serenko and Stach (2009) investigated the impact of expectation disconfirmation on user loyalty and recommendation behavior with respect to online travel and tourism services. For this, the Expectation Disconfirmation Theory was employed as a lens of analysis, and the critical incident technique was applied to survey 94 Expedia users. They observed a lack of clear link between the type (i.e., positive or negative) of expectation disconfirmation and loyalty / service recommendation. Consistent with prior research, it was

159 136 concluded that the relationship between customer experience, satisfaction, loyalty and word-of-mouth was very complicated. Bilal (2010) attempted to find the factors of customer loyalty and their relationships in banking industry of Pakistan. He reported that perceived quality, satisfaction, trust, switching cost and commitment were the factors influencing the loyalty of the customers and also, these factors influence each other. Kheng et al. (2010) employed SERVQUAL (Parasuraman et al., 1988) with five dimensions to evaluate the impact of service quality on customer loyalty among bank customers in Penang, Malaysia with customer satisfaction mediating these variables. They found that improvement in service quality can enhance customer loyalty. Service quality dimensions playing a significant role in formation of customer loyalty were reliability, empathy, and assurance. Maiyaki and Mokhtar (2010) investigated the influence of perceived service quality, perceived value, corporate image and switching cost on the consumer behavioral intention (customer loyalty) in the context of commercial banks in Nigeria. They concluded that service quality had a significant influence on the consumers behavioral intention in the Nigerian commercial banks. Further, they exhibited that corporate image played a significant role in the formation of customer behavioral intention of Nigerian commercial banks. Siddiqi (2010) focussed on the interrelationships between service quality attributes, customer satisfaction and customer loyalty in the retail banking sector in Bangladesh. He ascertained that all the service quality attributes were positively related to customer satisfaction and customer satisfaction was positively related to customer loyalty in the retail banking

160 137 settings in Bangladesh. Empathy demonstrated the highest positive correlation with customer satisfaction and tangibility showed the least positive correlation with customer satisfaction. Abu Ali and Howaidee (2012) conducted a study to investigate the causal relationships among the components of tourism product and overall tourist satisfaction in Jerash, Jordan. The study supported that destination facilities and accessibility and attraction directly influenced tourist satisfaction, it was also confirmed that there is a significant impact of the service quality on tourist satisfaction Jerash as one of the major tourism destinations in Jordan. While Abu Alroub et al. (2012) investigated the impact of service quality on customer satisfaction in the tourist restaurants in Amman, Jordan. Their study clarified that there is a significant relationship between service quality and customer satisfaction in tourist restaurants. Muchtar Rizka and Astuti Widji (2013) result show that customer relationship marketing does play a mediating role in the effect of service quality on customer loyalty. The findings provide usable model for assurance item to enhance service quality that contribute to high customer relationship marketing and loyalty. Poku, K., Zakari, M., and Soali, A. (2013) reveals that customer satisfaction is not based solely on the rankings/classification of the hotels but on service quality that gives value for money which in turn produces customer loyalty. Miklin Hotel produced most satisfied and loyal customers, followed by Golden Tulip Hotel and then Lizzie s Hotel contrary to the classification order. In addition to responsiveness service quality variable for Miklin, empathy and assurance variables made significant impact on customer loyalty for guests from Miklin and Golden Tulip hotels, while reliability

161 138 accounts for the loyalty of guests from Lizzie s Hotel. This confirms the direct relationship between customer satisfaction and loyalty. Tangibility does not play any significant role in developing customer loyalty for all the hotels because the guests were least satisfied with it and are likely to take it for granted in their quest for change. The study recommends that hotel classification should not be based mainly on the tangible factors alone but rather on comprehensive service that provide value for money and impact on customer loyalty. Al-ababneh, Mukhles (2013) findings confirmed that service quality directly impacted tourist satisfaction throughout destination facilities, destination accessibility and destination attraction. As a result, this study argued that there is a significant impact of the service quality on tourist satisfaction, and therefore service quality plays an important role in tourism by increasing the level of tourist satisfaction. Osman and Sentosa (2013) studied the impact of service quality on customer satisfaction in Malaysian rural tourism; they found that service quality has significant impact and positive relationship with customer satisfaction and loyalty THE IMPACT OF SERVICE QUALITY ON CUSTOMER LOYALTY IN ORGANISED RETAIL ENVIRONMENT Nowadays, in a severe competitive environment, the most central key to sustainable competitive advantage is to provide the best possible service quality which will result in improved customer satisfaction (Sureshchandar et al., 2002). This will result in advanced customer loyalty and retention which will lead to more successful and more profitable organization (Johnson and Gustafsson, 2000). Jacoby and Chestnus (1978) said that The success of a brand in the long term is not based on the number of consumers that buy it, but

162 139 on the number of consumers who become regular buyers of the brand. This statement can link to the importance of developing consumer loyalty to retail store. As Samli (1989) stated that consumer loyalty can serve as a distinctive advantage for organizations in a highly competitive industry such as retailing. Maintaining service quality allows retailers to obtain more new customers while building and cultivating their existing customer relationships. It can be said that by differentiating through quality customer service, retailers are able to meet the needs of their customers better than the competitors. Little empirical research has focused explicitly on the relationship between service quality perceptions and customer loyalty (behavioural intentions). And empirical research supports that service quality is a significant predictor of behavioural intentions, e.g. repeat purchase, likelihood of recommending, switching and/or complaining (Dabholkar et al., 1996; Bitner, 1990; Woodside et al., 1989). Parasuraman, Berry, and Zeithaml (1991, 1988) found a positive and significant relationship between customers perceptions of service quality and their willingness to recommend the company. Boulding et al. (1993) found a positive correlation between service quality and repurchase intentions and willingness to recommend. Taylor and Baker (1994) using a three-item purchase scale, obtain significant effects for service quality, satisfaction and an interaction term on purchase intention. On the other side, Cronin and Taylor (1992) didn t find a significant effect of service quality in purchase intentions. With regards to the response to a negative service experience, majority of the customers simply remain inactive and do not undertake any action (Day, 1984).

163 140 Wong and Sohal (2003b) examined the relationship between the dimensions of service quality and customer loyalty in a retail chain departmental store setting in Victoria. The results showed that service quality was positively associated with customer loyalty and the most significant predictor of customer loyalty in the retail district was empathy, while the most predictor of customer loyalty in the country retail district was tangibility. Wong and Sohal (2006) investigated consumer perceptions of their shopping experience in a retail environment. They found that service quality related factors such as being consistently courteous to customers, instilling confidence in customers, knowledge to answer customers enquires, and ability to handle customer complaints assist in the establishment of higher levels of trust. They empirically tested the effect of service quality, trust, and commitment on relationship strength. Further, they proposed a model of relationship strength which explored the impact of relationship strength on attitudinal outcomes such as perceived relationship quality and behavioral outcomes such as customer loyalty. Suwannapirom and Lertputtarak (2008) investigated the relationship between service quality, customer satisfaction, customer loyalty and word of mouth communication in a convenience store. They found that service quality had a positive relationship with customer satisfaction, customer loyalty and word of mouth communication. They also demonstrated that each element of service quality has a different relationship with customer satisfaction, customer loyalty and word of mouth communication. Therefore, good service quality ensures satisfaction and attempt to establish loyalty tendencies in customers which results in the development of customer advocacy and finally leads to positive word of mouth communication.

164 141 Molina et al. (2009) investigated the effects of consumer service on loyalty in retail establishments. They empirically examined the relationship between waiting time, product quality, store atmosphere and loyalty. With empirical support, they suggested that consumer service through three dimensions influences loyalty. Further, they suggested that consumer service in retail establishments can be viewed as a threshold factor in order to maintain satisfied and loyal customers. Nadiri and Tumer (2009) applied the RSQS among 648 customers of a large chain of retail stores in Northern Cyprus. They confirmed the applicability of the original five dimensions of the RSQS in the setting of Northern Cyprus. Retail service quality was shown to be positively related to behavioral intentions (intention to repurchase and intention to recommend). Multi-regression analyses revealed that the dimensions of physical aspects, problem solving and reliability had the greatest impact on customers behavioral intentions. Naik et al. (2010) ascertained whether the typology to which service belongs explain the relationship between Service Quality (SQ), Behavioral Intentions (BI) and Customer Satisfaction (SAT) in a retail store. They found that the dominant dimensions of service quality were tangibility, recovery, responsiveness, and knowledge. The results established the direct influence of SQ on Behavioral intentions, and the mediating role of SAT on influencing Behavioral Intentions. They ascertained that SAT was a strong driver of Behavioral Intentions in the context of retail sector in India. Danesh, S. N., Nasab, S. A., and Ling, K. C. (2012), performed a descriptive research study with the objective to evaluate relationship of customer satisfaction over customer retention, customer trust over customer

165 142 retention and switching barriers over customer retention as well as the correlation between satisfaction and trust. This research study was carried out among the Malaysian hypermarkets especially in Kuala Lumpur City. The Researcher used structured questionnaire as the research instrument and collected over 150 samples across various customers of the Malaysian hypermarkets. Finally the outcome and end results of the research confirmed that there is a positive relationship among switching barrier, trust, customer satisfaction with overall retention of the customers. A.R. Azhar, M. Z. Mohd Salehuddin, Mohd Faeez B. Saiful Bakhtiar and Mohd Syaquif Yasin B. Kamaruddin (2012), performed a quantitative research on customers' satisfaction with fresh food attributes in hypermarkets. The Researcher used a self reported questionnaire and collected over 387 samples by adopting convenience sampling method. The Results reveals that the majority respondents are highly satisfied with the quality of the products offered overall and very few of the respondents are satisfied with the availability and price of fresh foods in the hypermarket. Finally the researcher comes to a conclusion that it is very difficult to understand the importance of fresh food attributes and also its impacts with overall customer's satisfaction. R. P. C. S. Rajaram and V. P. Sriram (2014), findings of the research study reveal a significant positive effect on service quality of the hyper markets to customer behavioral intention. Service quality highly influences customer behavioral intention and purchase preference. When a customer chooses a hyper market that provides highest service quality that meets customer's expectations, then they used to choose a same hyper market again and again. Customer satisfaction is the strong predictors of the behavioral intention. Satisfaction of the customers is more important for all hyper markets, because it is usually believed to be significant predictor of increased

166 143 sales, willing to pay more and loyalty. It is found that effect on customer satisfaction to behavioral intention is positive and significant. The present study findings are evidenced that customer satisfaction is more likely to reach a high level of significance as perceived service quality of the retail stores and satisfaction have a significant and positive effect on behavioral intentions of the customers towards the hyper markets DIMENSIONS OF CUSTOMER LOYALTY IN A SERVICES ENVIRONMENT In a service environment, customer loyalty is defined as an observed behaviour (Liljander and Strandvik, 1995). Ultimately it is actual behaviour that drives a service organization s performance. Therefore, operationalization of customer loyalty in service settings is a composite index of behavioural, attitudinal and cognitive aspects. With regards to behavioural intentions in a services setting, Zeithaml et al. (1996) proposed a comprehensive, multidimensional framework of customer behavioural intentions in services. This framework was initially comprised of the following four main dimensions: Word-of-mouth Communication Purchase Intention Price Sensitivity and Complaining Behaviour. Arndt (1967) defined word of mouth as the informal conversation by which opinions on products and brands are developed, expressed, and spread. Word-of-mouth is a flow of information about products, services, or companies from one customer to another. As such, word-of-mouth represents a trusted external source of information by which customers can evaluate a

167 144 product or service. Loyal customer are those who not only gladly use the services but they are so pleased with them that they tell other people about them (Gould, 1995). Research has shown that word of mouth communication is a part of that which shapes consumer attitudes and behavioural tendencies (Mangold, Miller, and Brockway, 1999). Besides the meaning of external recommendation, the term of word-of-mouth also includes the meaning of internal communications with service staff. So it is believed that loyal customers are likely to give positive feedback to the service company (Soderlund, 1998). Service quality affects an organization s ability to influence word of mouth. Research indicates that twice as many people hear about a bad experience, as about a good experience. Also, people tend to pay more attention to bad word of mouth. Negative word of mouth has twice the negative impact as positive word of mouth has positive impact. One out of 50 customers hearing negative word of mouth will not buy, while one out of 100 customers hearing positive word of mouth will buy (Evalue, 2003). Customer loyalty is basically the extent of repeat purchase intention from the same service provider with affective commitment (Shemwell et al., 1998; Soderlund, 1998). Loyalty can be only attained when the customer expressed high repeat patronage as well as strong positive preference on an entity (Dick and Basu, 1994). In fact, consistent repeat purchase is one kind of loyalty-prone behaviour (Cunningham, 1956) by showing continuance commitment (Shemwell et al., 1998) on an entity. Undoubtedly, loyal customers willing to pay the premium even the price is increased because the perceived risk is very high, so they instead to pay the higher price for avoiding the risk of any change (Yoon and Kim, 2000; de Ruyter et al., 1999). Generally, the developed long term relationship of customer loyalty makes loyal customers more price tolerant, since loyalty

168 145 discourages customers to have price comparison with others and to shopping around (de Ruyter et al., 1999). By delighting customers on the key determinants of price sensitiveness, service providers can even charge a higher price than other competitors, because loyal customers value maintaining the relationship. In addition, the initial costs of attracting and establishing these customers have already been absorbed and, due to experience curve effects, they often can be served more efficiently (Reichheld and Sasser, 1990).When a problem occurs, service providers can retain the loyalty of customers only if they tell service providers about the problem. Unfortunately, many customers never contact the organization when they need assistance or have a problem, thereby depriving it of a chance to retain their loyalty. On average across all industries, around 50% of all consumers and 25% of all business customers with problems never complain to anyone (Evalue, 2003). 3.5 BEHAVIOURAL-INTENTIONS BATTERY Zeithaml, Berry and Parasuraman (1996) proposed a comprehensive, multi-dimensional framework of customer behavioural and attitudinal intentions for use within a service industry. The framework (presented in below table) incorporates 13-items across five-dimensions: loyalty to company (loyalty) propensity to switch (switch), willingness to pay more (pay more), external responses to a problem (external responses), and internal responses to a problem (internal responses). Bloemer, de Ruyter and Wetzels (1999) raised a number of conceptual and empirical criticisms of the Behavioural-Intentions Battery. Their conceptual criticism focused on inter dimensional overlap (i.e., various expressions of customer complaining behaviour or response to a dissatisfactory service encounter are distributed over two factors, external

169 146 response to a problem and internal response to a problem ; pricing related loyalty intentions are placed on two factors, propensity to switch and willingness to pay more. Empirically, they claimed that the use of singleitem measure, Internal response to a problem should be avoided. Further they argued that the five factor solution did not appear to provide an unambiguous and consistent factor pattern and that this impacts the reliability of the measure. DIMENSION LOYALTY SWITCHING BARRIERS WILLING TO PAY MORE EXTERNAL RESPONSES INTERNAL RESPONSE ITEM DESCRIPTION Say positive things about XYZ to other people Recommend XYZ to someone who seeks your advice. Encourage friends and relatives to do business with XYZ. Consider XYZ your first choice to buy services. Do more business with XYZ in the next few years. Do less business with XYZ in the next few years. Take some of your business to a competitor that offers better prices. Continue to do business with XYZ if its prices increase somewhat. Pay a higher price than competitors charge for the benefits you Currently receive from XYZ. Switch to a competitor if you experience a problem with XYZ s service Complain to other customers if you experience a problem with XYZ s service. Complain to external agencies, such as consumer organizations, if you experience a problem with XYZ s service. Complain to XYZ s employees if you experience a problem with XYZ s service. Table.No.3.4 Behavioural Intentions Battery

170 147 On the basis of analysis across four service industries: Entertainment, Fast Food, Supermarkets And Health Care, they concluded that service loyalty is a multi-dimensional construct consisting of the following four dimensions: Word-of-mouth, Purchase intentions, Price sensitivity, and Complaining behaviour (refer to Figure, for allocation of variables, presented in below table to factors identified by Bloemer, de Ruyter and Wetzels, 1999) LOYAL CUSTOMERS - NEW GOAL FOR THE RETAILERS Loyalty is a commitment that a customer is prepared to make to strengthen a relationship with a retailer and a brand. Levy and Weitz (2009) agree that customer loyalty means dedication from the customer to buy products or services from the same retailer. Customer retail loyalty is the attitudinal and behavioural preference for the retailer when compared with available competitive alternatives. Attitudinal loyalty is the bond the customer has with the retailer or brand and the preference for that certain brand while behavioural loyalty is purchase and repeated behaviour (Terblanche and Boshoff, 2010). Terblanche and Boshoff (2006) suggest that customers are unlikely to have absolute loyalty to one retailer and the best that retailers can do is to increase the level of customer loyalty. There has been more research conducted into brand loyalty than there has into retail loyalty. Brand loyalty is the preference of a particular brand over another brand whereas retail loyalty is loyalty to a particular store regardless of the brands they carry (Wallace, Giese and Johnson, 2004). Loyal customers are a key to any retail business. At the same, they are the least understood people. Sales figures tell nothing about them. As a result,

171 148 the potential is lost which could have been provided by them as a result of better targeting. It is argued that the total store environment and offerings should be built around those customers. An environment needs to be maintained and the store staff should be trained to spot the loyal customers and get aligned to their needs. The real problems facing stores is to increase their sales and market share in view of ever growing competition. Loyal customers are the insurance which a store always needs. It is extremely important for them to know, recognize and reward those who shop their often. In this context it should not be forgotten that any customer has a potential to be a loyal customer, so new visitors should be treated with full energy, but the fact remains that store should be loyal customer centric. A good way to measure loyal customers and their contribution is to measure the following: Number of buying trips per year and the total amount spend. It would be useful to segment the customers based on this and the movement every year to gauge the effectiveness of campaign. Basically the following questions should shape a retailers offer: Which customers matter most, what do they buy, how do they buy and why do they buy. In short customer orientation is now become increasing difficult criterion for the success of a store. Instead of asking how much we sold last week, the question should be who bought what we were selling last week. Similarly all promotion campaigns should engage the best customers in the short term and long term. Pricing decisions should be based on the price sensitive customers and the items on which they are most prices sensitive. Competition should be defined not by gaining new customers but by gaining a greater share of the existing customers. Of course, among the loyal customers

172 149 too, it is the best customers- customers who buy the most to whom most of the organizational resources should be devoted. According to a study, in Home Depot, 2 per cent of the shoppers drive around 30 per cent of the sales. It is the best customers which matter the most and for which the retailers should orient themselves. 3.6 FRAME OF REFERENCE This research topic explains the key factors, variables and relationships among theories or models and provides a theoretical overview. The conceptualization helps the researcher to answer the study s research questions. In the earlier stages of this chapter various definitions for service quality, customer satisfaction and behavioral intention of the customers were discussed. Moreover the linkages between the dimensions of the service quality and customer satisfaction were also discussed. There were only few researches studying the relationship between the service quality dimensions, the service quality dimension leading to customer satisfaction and behavioral intention are also studied. 3.7 DEVELOPMENT OF HYPOTHESES With a structured questionnaire the association between the service quality dimensions and overall service quality, demographic information and customer loyalty in Indian organized retail was investigated. The affinity between the service quality dimensions and customer s repurchase intentions was investigated too. The association between service quality dimensions and overall service quality, demographic information and customer loyalty intentions has been previously researched in various studies. Also, service

173 150 quality dimensions were found to be positively correlated with customer s repurchase intention HYPOTHESIS OF THE STUDY H 01 : There is no significant impact of service quality dimensions on overall retail service quality. H 02: There is no significant relationship between demographic profile of the respondents and retail service quality dimensions. H 03 : There is no significant impact of retail service quality dimensions on customer loyalty dimensions. H 04 : There is no significant relationship between demographic profile of the respondents and customer loyalty dimensions. H 05 : There is no significant relationship exist among perceived service quality dimensions. H 06 : There is no significant relationship exist among customer loyalty dimensions. H 07 : There is no significant impact of retail service quality dimensions on customer satisfaction. H 08 : There is no significant impact of customer satisfaction factors on customer loyalty. H 09 : There is no significant relationship between demographic profile of the respondents and customer satisfaction factors.

174 151 H 10 : There is no significant impact of factors affecting purchase on customer satisfaction. H 11 : There is no significant relationship between purchasing factors, customer satisfaction, expects and perceived service quality and customer loyalty H 12 : There is no significant relationship between most liked and disliked factors and purchase attribute factors. H 13 : There is no significant relationship between most liked and disliked factors and factors affecting retail purchase. H 14 : There is no significant difference between perception and expectation service quality dimensions. H 15 : There is no association between demographics and most liked and disliked factors about retail shops. H 16 : There is no association between perceived level of service quality, customer satisfaction and customer loyalty towards the retail stores and demographic profiles. H 17 : There is no impact among the Overall service quality of the retail stores on overall satisfaction of the customers. H 18 : Individual service quality dimension have no impact on overall customer satisfaction of the retail customers that leads to overall behavioural loyalty. H 18a : Physical aspects dimension have no impact on overall customer satisfaction of the retail customers that leads to overall behavioural loyalty.

175 152 H 18b : Reliability dimension have no impact on overall customer satisfaction of the retail customers that leads to overall behavioural loyalty. H 18c : Personal interaction dimension have no impact on overall customer satisfaction of the retail customers that leads to overall behavioural loyalty. H 18d : Problem solving dimension have no impact on overall customer satisfaction of the retail customers that leads to overall behavioural loyalty.. H 18e : Policy dimension have no impact on overall customer satisfaction of the retail customers that leads to overall behavioural loyalty. H 19 : There is no effect on overall customer satisfaction of the customers towards the individual dimensions of behavioural loyalty. H 19a : There is no effect on overall customer satisfaction of the customers towards the word of mouth dimension of behavioural loyalty. H 19b : There is no effect on overall customer satisfaction of the customers towards the switch to competitor dimension of behavioural loyalty. H 19c : There is no effect on overall customer satisfaction of the customers towards the willingness to pay more dimension of behavioural loyalty. H 19d : There is no effect on overall customer satisfaction of the customers towards the internal and external response dimension of behavioural loyalty. H 20 : There is no impact of purchase intention on perception of retail service quality leads to positive or negative effect on loyalty which act as mediating variable of customer satisfaction.

176 CONCLUSION This Chapter concludes with an in-depth analysis on the previous researches done in the retail sector area, especially on customer satisfaction, customer relationship management, assessment of service quality and assessing their behaviour intention across various contexts. Based on the literature reviews collected, the researcher identified the research gap that there are very few researches been carried out on the relationship between service quality and customer satisfaction and their customer loyalty behavioural intentions in retail sector. Especially in India, there were only limited researches been done. Hence, this chapter is highly useful for the researcher in order to identify those various research gaps and resolve it in the present study.

177 154 CHAPTER IV CONCEPTUAL MODEL OF SERVICE QUALITY This Chapter presents the conceptual and theoretical framework of the research study, where it focuses on introductory part the service quality conceptualization and measurement of service quality. This discussion is followed by the review of retail service quality and the need to differentiate it from pure services. Then RSQS is the most dominant scale for measuring retail service quality; that s why most of the researchers employed it. However, it was found that the scale is more appropriate if it is modified for different countries; therefore, the need for modifying the scale. Then it discusses about the proposed model of the research study. Finally, the main and sub-hypothesis was developed based on the main objectives of the research study. 4.1 INTRODUCTION The Theoretical and Conceptual Framework is based on the findings presented in the literature reviewed. The model provides the framework for the research design and data analysis. The following theories and theoretical frameworks serve as a foundation for the proposed model and the discussion highlights the relationship and the influence of these theoretical concepts in relation to the development of the model. Some researchers, such as Johnson and Gustafsson (2000), avoid addressing the difference between service quality and satisfaction and use both terms interchangeably in practice and theory. By contrast, other researchers, such as Berry, Parasuraman and Zeithaml (1988), Parasuraman, Zeithaml and Berry (1986, 1994), Rust, Zahorik and Keiningham (1995),

178 155 argue that, while service quality and customer satisfaction are related, they are two distinct constructs. Service quality is a global judgment or attitude relating to the superiority or excellence of the service, whereas satisfaction is related to a specific transaction. This implies that satisfaction is less enduring and more situation oriented (Bolton and Drew 1991; Parasuraman et al. 1986) suggest that service quality is a consumer's judgment about the service itself (in other words, it is descriptive and based on fact), whereas satisfaction is more of a judgment of how the service affects the consumer emotionally (in other words, it is more evaluative and it is based on emotion). Both service quality and customer satisfaction are usually measured by means of the gap approach, that is, the difference between perceptions and expectations (Rust et al. 1995). The difference between service quality and customer satisfaction arises mainly because of different definitions of expectations. In the service quality literature, expectations are regarded as the desires or wants of consumers, in other words, what customers feel a service provider should offer them, rather than what a service provider would offer (Parasuraman et al. 1986). By contrast, customer satisfaction is believed to result from a comparison between what did happen in a service experience on the one hand and what customers believed (predicted) would happen on the other (Bitner 1990; Gilbert, Churchill & Surprenant 1982; Parasuraman et al. 1986; Schneider and White 2004). Since a consumer's expectation in a satisfaction context represents a prediction, it is expressed by a mean expectation value, with a degree of uncertainty surrounding the mean, because the consumer is unsure about what to expect. By contrast, since a consumer's expectation in a service quality context represents what he or she desires, that expectation can be regarded as a distinct value with little or no uncertainty

179 156 surrounding it (Parasuraman et al. 1986). It was originally believed that the two constructs were related, in that incidents of satisfaction decay over time into an overall consumer attitude or judgment of perceptions of service quality (Biter 1990; Parasuraman et al. 1986). Further research altered the original beliefs about customer satisfaction. It was found that it might be more correct to regard service quality as an antecedent of customer satisfaction (Dabholkar et al. 2000; Olivier 1993; Parasuraman et al. 1994; Spreng and Mackoy 1996). Spreng and Mackoy (1996) modified a model originally developed by Olivier (1993) because they found empirical evidence that illustrates that service quality is an antecedent of customer satisfaction. Behavioural intentions in the marketing literature relate predominantly to purchase intentions, particularly to customer loyalty and the intention to repurchase in relation to optimizing sales, as well as the net profit of the organisation. 4.2 CONCEPTUAL MODEL FOR MEASURING SERVICE QUALITY Source: Parasuraman, Berry and Zeithaml, (1985, p.44) Fig.No.4.1 Conceptual Model for Measuring Service Quality

180 157 Consumer Expectation-Management Perception Gap (Gap 1): There is a difference between the customer expectation and the way the managers perceive this expectation. Management does not always have a clear understanding of the customer s expectation of service and they are unaware of the important service aspects customers look for when being served. Therefore, there are many reasons for the management s lack of understanding of customer s expectations such as the lack of communication between them or the unwillingness of the management to tackle the customer s problems. Management Perception-Service Quality Specification (Gap 2): This gap is the difference between customer s expectations and the standards that are established by the organisation which might not be enough to meet the customer s expectations of the service. Gap 2 is the second milestone the companies should cross with excellence in performance. Service design and performance standards are prerequisites for that. Translation of the service quality specifications is really a complex job the service providers have to handle. Service Quality Specifications-Service Delivery Gap (Gap 3): This gap is the difference between the service quality standards and the delivery itself, which can result in services being inadequate because they are dependent on employees who may be poorly trained. To ensure that the delivery matches or exceeds these standards, employees must be properly trained and the systems in place need to accommodate the delivery of the standards. Service Delivery-External Communications Gap (Gap 4): This gap is the difference between what the service provider promises and the actual service received by the customer. External factors such as advertising may increase the customer s expectation of the service

181 158 delivery, therefore it is important not to promise what cannot be delivered as this will lead to customer frustration. It is important for the service provider to manage the expectations of the customers by clearly communicating the services that the company provides without any ambiguity. Expected Service-Perceived Service Gap (Gap 5): The difference between the services the customers expect and the service they perceive. Customers have expectations that are based on past experiences and these expectations are what customers think the service should be. On the other hand, the customer perception is the subjective evaluation of the actual service at the time of the service. Customer satisfaction is imperative for the competitiveness of the organisation, therefore it is important to understand the customers in order to deliver a quality service. 4.3 JUSTIFICATION FOR PROPOSED MODEL The path diagram of research model is designed for impact of purchase intentions and expected rating of service quality dimensions and perception ratings of service quality dimensions on Overall customer satisfaction leads to Behavioural Intention which further leads to Customer retention in a retail stores. The research reported makes a number of contributions to advance knowledge from both an industry perspective and theoretical perspective. The research integrates the service quality of Retail stores context and a number of five individual service quality dimensions relationships between overall service quality, customer satisfaction and behavioural intention, as well as providing a new research in the Indian context. The research contained in this chapter

182 159 provides valuable new insights into the relationship between service quality, customer satisfaction and behavioural intention for retail stores sector. 4.4 PROPOSED RESEARCH MODELS Customer Evaluation Model for Retail Sector Purchase Intention (Belief) Perceived Service Quality -RSQ Customer Satisfaction Behavioural Intention Expected Service Quality RSQ Fig.No.4.2 Proposed Customer Evaluation Model for Retail Stores 4.5 CONCLUSION This Chapter explains research topics key factors, variables and relationships among the theories or models and provides a theoretical or conceptual overview. The conceptualization helps the researcher to focus and formulate the research questions accordingly. In the earlier stages of this chapter various definitions for service quality, customer satisfaction and behavioral intention of the customers were discussed. Moreover the linkages between the dimensions of the service quality and customer satisfaction were also discussed. There were only few researches studying the relationship between the service quality dimensions, the service quality dimension leading to customer satisfaction and behavioral intention are also studied.

183 160 CHAPTER V RESEARCH METHODOLOGY This Chapter discusses the underpinning methodology of this study, beginning with a presentation of the research design, followed by area of study, sample design, sources of data and scale and measurement. The primary method for data collection was using the structured questionnaire. The questionnaire was made up of three parts: Demographic data, Customer Shopping Experience, Retail Service Quality, Scale and Customer Loyalty Intentions questionnaires were included in the research field. Further, this chapter discusses about the reliability and validity of the scale used for the research study. Meanwhile, this chapter illustrates the demographic profile of the respondents with respect to the retail settings. 5.1 INTRODUCTION The Research methodology is the systematic method/process dealing with identifying the problem, collecting facts or data, analyzing these data and reaching at a certain conclusion either in the form of solutions towards the problem concerned or certain generalization for some theoretical formulation. Moreover, research methodology describes the methods employed to gather the data and analyzed it by accompanying the research design, sampling technique, measurement and instrumentation, data collection, conceptual framework and information analysis. It also comprised of a number of alternative approaches and interrelated and frequently overlapping procedures and practices. Since there are many aspects of research methodology, the line of action has to be chosen from a variety of alternatives. The choice of suitable

184 161 method can be arrived at through assessment of the objectives and comparison of various alternatives. In this chapter the philosophical stance of the researcher is explored which will clarify the reasons for the choice of methodology used in this research. Therefore, the main purpose of this chapter is to present the research methodology and methods used in this study in order to answer the research questions and to achieve the research objectives. The chapter begins with the elements of the research process which include research design, area of study and sample design. Subsequently, the chapter explains the sources of data and the research instrument. The steps involved were elaborated in detail and had been carried out systematically in order to achieve a high degree of reliability and validity. 5.2 RESEARCH DESIGN Research Design is a master plan specifying the methods and procedures guiding the researcher to collect their data and analysis of their research. The most common research designs that the researchers always uses is exploratory, descriptive and causal. In the present study, exploratory and descriptive study is used as a purpose of the study to obtain and analyze the data. Exploratory Study is important for obtaining a good grasp of the phenomena of interest and for advancing knowledge through good theory building and hypothesis testing. In this study, the exploratory research includes literature reviews in order to gain more detailed information about the research problems and issues related to the shoppers perception of service quality in organized retail. Descriptive research is typically more formal and structured

185 162 than Exploratory research (Malhotra, 2005). It is based on large, representative samples and the data obtained are subjected to quantitative analysis. The findings from this research are used as input into managerial decision making. In this study, descriptive study is undertaken in order to ascertain and describe the characteristics of the variables of the customers perceptions about the service quality provided by organized retailers, leading to customer loyalty. Thus, the present study is Exploratory-cum-Descriptive in nature as it endeavors to assess the relationship between service quality and customer loyalty in the formation of customers repurchases intention. 5.3 AREA OF STUDY The sample was selected from three different metropolitan cities like Chennai, Madurai and Coimbatore in Tamilnadu. 5.4 SAMPLE DESIGN The population comprised retail shoppers as defined in similar studies (Kaul, 2007; Boshoff and Terblanche, 1997; Dabholkar, Thorpe, and Rentz, 1996). But, in most of the research studies, it became almost impossible to examine the entire universe; the only alternative thus is to resort to sampling. The present study is also of the same nature. A sample is taken from the target population being researched. A sample is a part of the population, which is studied in order to make inferences about the whole population. If the sample is adequate it will have the same characteristics of the population (Zikmund, 2003) and the findings are usually used to make conclusions about the population. Thus a good sample is a miniature version of the population and good sample design involves the following:

186 163 Sample Unit Sample Techniques Sampling Size Sample Unit Since the objective of the present study is to analyze the factors of service quality that lead to customer satisfaction and customer loyalty in purview of Indian organized retail stores; hypermarkets, super markets and departmental stores are taken as the sample unit Sampling Technique Sampling Techniques are methods used to select a sample from the population by reducing it to a more manageable size (Saunders, Lewis and Thornhill, 2007). According to de Leeuw, Hox and Dillman (2008) these sampling techniques are used when inferences are made about the target population. In the present study, Simple Random Sampling was used for the selection of hypermarkets, super markets and departmental stores whereas Judgmental Sampling was used for the selection of respondents from hypermarkets, super markets and departmental stores. Utmost care has been taken to take respondents from various demographic characteristics Sample Size To ensure required sample size and to allow for the possibility of spoiled questionnaires, trained research assistants targeted 950 retail shoppers (hyper markets, super markets and departmental stores). On the basis of Simple Random Sampling 90 retail shops (Hypermarkets, Supermarkets and Departmental Stores) were selected and out of each shop-in-shop respondents

187 164 were approached on the basis of Judgmental Sampling. Thus the total number of respondents came out in 950. Out of the 950 respondents, 900 questionnaires were received at a response rate of per cent. On further filtering, 900 responses were found to be completely filled, which is more than response rate is higher than the response rate of the acceptable limits to ensure the validity of the data (Miller, 1991). 5.5 SOURCES OF DATA COLLECTION Data sources are classified as being either primary sources or secondary sources. A source is primary if the data collector is the one using the data for analysis. A source is secondary if one organization or individual has compiled the data to be used by another organization or individual. Both primary and secondary data have been collected in this research. Secondary data have been collected from published thesis works, unpublished thesis works, websites and research articles from journals. On the other side the primary data were collected by means of a structured, comprehensive questionnaire that was developed by the researcher based on the literature review on the relevant topics. The questionnaires were distributed to retailers of hypermarkets, super markets and departmental stores in selected retail stores during December 2012-October The research assistants explained the voluntary nature of the survey to the shoppers, assured them of the anonymity of their responses, and told them to feel free to opt out at any time. They provided each respondent with a copy of the questionnaire, explained how the questionnaire was to be filled out and collected the completed questionnaires. The questionnaire was administered prior, during and after the respondents shopping at the shopping stores. Like shoppers were given a questionnaire

188 165 while they waited in a queue to pay for their goods. Some of them filled the questionnaire while they were having refreshments after they had finished their shopping. This allowed for sufficient time to fill up the questionnaire without interfering with their shopping. Data collection is most meaningful when the responses are taken from customers in the store after the shopping is completed (Boshoff and Terblanche, 1997; Dabholkar, Thorpe and Rentz, 1996). Asking shoppers to complete a questionnaire in the shop provides them with a chance to pay attention to the dimensions while answering the questionnaire; this also eliminates problems with customers trying to recall the shopping experience (Burns and Bush, 2010). In addition, attempts were made to collect data at different days and at different times of the day, i.e. Morning, Afternoon and Evening. 5.6 SCALE AND MEASUREMENT The Questionnaire is a collection of written queries, which is arranged putting all the essential variables for the research and can be completed by the respondents in presence, in absence, directly or indirectly. The questions in a questionnaire are the key to the survey research. Therefore, they must be developed with caution and be vital to the survey. Also, the questionnaire has to keep short or otherwise it would frighten the respondents. Hague et al. (2004) give a number of guidelines regarding a good questionnaire: Ensure questions are without bias Make the questions as simple as possible Make the questions very specific Avoid jargon or shorthand Steer clear of sophisticated or uncommon words Avoid questions with a negative with them

189 166 Use response band Ensure that the fixed responses do not overlap Allow for others in fixed response questions It is bad to use open-ended questions in self-completion surveys because the answers would be inadequate and be very typical (Hague et al., 2004). Usually, close-ended questions are using numbers, yes/no, or multiple choices (Brace, 2004). One main advantage of using close-ended questions in a questionnaire is that they are pre-coded. This kind of questions suits selfcompletion questionnaires because they save the respondent s time writing in the answers (Hague et al., 2004). Also, as there is a set of answers known beforehand, the researcher can save a lot of time in data entry and analysis at the later stage (Brace, 2004). Therefore, all the questions in the questionnaire of this study are close-ended questions, in which the respondents are asked to choose between a numbers of alternative answers. In this study, the structured questionnaire was focused on measuring service quality, demographic characteristics, customer satisfaction, purchase intentions and behavioral intentions. The questionnaire used in the present study consisted of five sections A, B, C, D and E. Section A dealt with background information of the participants. Section B and C consisted of factors affecting purchasing in retail stores and customer satisfaction factors. Section D consists of 27 items that used to measuring service quality and Section E includes 13 items used to measuring the customer loyalty respectively. The researcher used a 7 point scale for the study, instead of a 5 point Likert scale because 7 point scale increases the rate of accuracy and quality of the responses (Prayag, 2007; Buttle, 1996). Thus, all statements employed a

190 167 seven-point scale because it would give a better normal spread of observations. To measure customer loyalty, the instrument must consider behavioral, attitudinal and cognitive aspects of behavioral intentions. That s why Zeithaml et al. (1996) behavioral intention battery was used. Each of the 13 items was accompanied by a seven-point scale ranging from 1 (= not at all likely) to 7 (= extremely likely). The wording of the BIB items was adapted to the retail service setting. Similar constructs to measure behavioral intentions was also found in some previous literature (Lei and Mac, 2005; Bloemer et al., 1996). The validated Retail Service Quality Scale developed by Dabholkar et al. (1996) was employed to measure perceived service quality. The items of RSQS were evaluated on a five-point scale ranging from 1 (= strongly disagree) to 5 (= strongly agree) (Dabholkar, Thorpe, and Rentz, 1996). The diagrammatic rating scale used in the questionnaire is as follows: Small adaptations to the RSQS instrument were made. Review of literature (empirical research Kaul, 2007) along with opinion of store managers (SIS) and independent experts (consultants, Indian Retail) highlighted that two items of RSQS were not relevant in Indian retail appertained to store s own credit cards seems premature in the Indian retail environment where credit cards have only recently started getting widespread acceptance and very few retail stores have their own credit cards. That is why out of the 27 statements of RSQS one was not included in the questionnaire because of its inapplicability in the Indian organized retail (Kaul, 2007). Finally, the questionnaire included 27 statements on the retail service quality and one statement on the overall perception of service quality. The overall perception of service quality was assessed using a single item, Overall the quality of the service in the retail outlet is excellent measured on a seven-point scale.

191 DEMOGRAPHIC PROFILE OF THE RESPONDENTS Type of the Retail Stores Type of Retail store No.of.Respondents Percentage (%) Hyper Market Super Market Departmental store Total Table 5.1 Type of the Retail Stores Inference From the above table 5.1 related to the Type of the Retail Stores, it is inferred that 33.3% of the samples for the research study was taken from the various Hypermarkets, and then 33.3% of the samples for the research study was taken from the various supermarkets and the remaining 33.3% of the samples for the research study was taken from the various Departmental Stores.

192 Location of the Retail Stores Location Of The Retail Store No.of.Respondents Percentage (%) Chennai Coimbatore Madurai Total Table 5.2 Location of the Retail Stores Inference From the above table 5.2 related to the Location of the Retail Stores, it is inferred that 33.3% of the samples for the research study was taken from Chennai City, and then 33.3% of the samples for the research study was taken from Coimbatore City and the remaining 33.3% of the samples for the research study was taken from Madurai City.

193 Gender Wise Classification among the respondents Gender No.of.Respondents Percentage (%) Male Female Total Table 5.3 Gender Wise Classification among the respondents Inference From the above table 5.3 related to the Gender Wise Classification among the respondents, Majority 54.67% of the respondents belongs to the Female Gender classification and remaining 45.33% of the respondents belongs to the Male Gender classification.

194 Age Wise Classification among the respondents Age Classifications No.of.Respondents Percentage (%) Less than 20 years Between 21 to 30 years Between 31 to 40 years Between 41 to 50 years Greater than 50 years Total Table 5.4 Age Wise Classification among the respondents Inference From the above table 5.4 related to the Age Wise Classification among the respondents, Majority 41.78% of the respondents belongs to the Age Category of Between 31yrs to 40yrs, 31.67% of the respondents belongs to the Age Category of Between 21yrs to 30yrs, 15.33% of the respondents belongs to the Age Category of Between 41yrs to 50yrs, 6.78% of the respondents belongs to the Age Category of Greater than 50yrs and the remaining 4.44% of the respondents belongs to the Age Category of less than 20yrs.

195 Marital Status of the respondents Marital Status No.of.Respondents Percentage (%) Married Unmarried Separated Divorced Widow Total Table 5.5 Marital Status of the respondents Inference From the above table 5.5 related to the Marital Status of the respondents, Majority 74.11% of the respondents marital status are found to be married, 24.67% of the respondents marital status are found to be unmarried, 0.56% of the respondents marital status are found to be Widows, 0.33% of the respondents marital status are found to be separated and remaining 0.33% of the respondents marital status are found to be divorced.

196 Educational Qualifications of the respondents Educational Qualifications No.of.Respondents Percentage (%) SSLC/HSC Undergraduate Postgraduate Diploma Others Total Table 5.6 Educational Qualifications of the respondents Inference From the above table 5.6 related to the Educational Qualifications of the respondents, Majority 41.11% of the respondents Educational Qualification are Undergraduates, 34.67% of the respondents Educational Qualification are Postgraduates, 16.78% of the respondents Educational Qualification are SSLC or HSC, 6.78% of the respondents Educational Qualification are Diploma Holders and remaining 0.67% of the respondents belongs to other Educational Qualification Category.

197 Occupational Wise Classification among the respondents Occupation No.of.Respondents Percentage (%) Business Professionals Students Housewife Clerk Police/Army Retired Unemployed Total Table 5.7 Occupational Wise Classification among the respondents Inference From the above table 5.7 related to the Educational Qualifications of the respondents, Majority 33.11% of the respondents are Housewife, 21.44% of the respondents are Professionals, 21.11% of the respondents are businessman, 15.56% of the respondents are students and 4.33% of the respondents are Clerk, 2.11% of the respondents are Police and Army, 1.56% of the respondents are retired persons, 0.78% of the respondents are unemployed.

198 Family Monthly income Wise Classification among the respondents Family Income No.of.Respondents Percentage (%) Less than Rs Between Rs to Rs Between Rs to Rs Between Rs to Rs Greater than Rs Total Table 5.8 Family Monthly income Wise Classification among the respondents Inference From the above table 5.8 related to the Family Income Wise Classification among the respondents, Majority 53.11% of the respondents belongs to the Family Income Category ranges Between Rs to Rs.30000, 19.56% of the respondents belongs to the Family Income Category ranges Between Rs to Rs.45000, 16.33% of the respondents belongs to the Family Income Category ranges Between Rs to Rs.60000, 10.22% of the respondents belongs to the Family Income Category of Less than Rs.15000, 0.78% of the respondents belongs to the Family Income Category of greater than Rs

199 Family Size Classification among the respondents Family Size No.of.Respondents Percentage (%) Less than 3 members Between 4-6 members Between 7-9 members Between 9-11 members Greater than 11 members Total Table 5.9 Family Size Classification among the respondents Inference From the above table 5.9 related to the Family Size Wise Classification among the respondents, Majority 57.78% of the respondents Family size ranges Between 4 to 6 Members, 34.44% of the respondents Family size of less than 3 Members, 5.11% of the of the respondents Family size ranges Between 7 to 9 Members, 2.22% of the respondents Family size ranges Between 9 to 11 Members, 0.44% of the respondents Family size of greater than 11members.

200 Family Type Classification among the respondents Family Type No.of.Respondents Percentage (%) Nuclear Family Joint Family Single Total Table 5.10 Family Type Classification among the respondents Inference From the above table 5.10 related to the Family Type Wise Classification among the respondents, Majority 80.33% of the respondents belongs to the Nuclear Family Category, 11.22% of the respondents belongs to the Joint Family Category and the remaining 8.44% of the respondents are living alone as Single.

201 Preferred Purchasing Mode among the respondents Preferred Purchasing Mode No.of.Respondents Percentage (%) Online Purchase Personal visit Telephonic order Sending Representatives Others Total Table 5.11 Preferred Purchasing Mode among the respondents Inference From the above table 5.11 related to the Preferred Purchasing Mode among the respondents, Majority 98.78% of the respondents prefers to personally visit the retail store for purchasing products, 0.44% of the respondents prefers to purchase the products through telephonic order, 0.33% of the respondents prefers to purchase the products through online, 0.33% of the respondents prefers to purchase the products by sending representatives to the retail stores and remaining 0.11% of the respondents prefers other mode of shopping.

202 Shopping Frequency among the respondents Frequency of Shopping No.of.Respondents Percentage (%) Occasionally Once in a day Once in 3 days Once in a week At least once in a month Total Table 5.12 Shopping Frequency among the respondents Inference From the above table 5.12 related to the Shopping Frequency among the respondents, Majority 39.22% of the respondents have shopped once in a week in the retail store, 30.78% of the respondents have shopped at least once in a month in the retail store, 22.44% of the respondents have shopped once in 3 days in the retail store, 5.44% of the respondents have shopped once in a day, 2.11% of the respondents have shopped occasionally in the retail store.

203 Amount spent in a month for shopping among the respondents Amount spent in a month for shopping No.of.Respondents Percentage (%) Less than Rs Between Rs.5001 to Rs Between Rs to Rs Between Rs to Rs More than Rs Total Table 5.13 Amount spent in a month for shopping among the respondents Inference From the above table 5.13 related to the Amount spent in a month for shopping among the respondents, Majority 49.33% of the respondents spent less than Rs.5000 for shopping in a month, 45.00% of the respondents spent amount ranges between Rs to Rs for shopping in a month, 4% of the respondents spent amount ranges between Rs to Rs for shopping in a month, 0.89% of the respondents spent amount ranges between Rs to Rs for shopping in a month, 0.78% of the respondents spent amount more than Rs for shopping in a month

204 Factor Influencing to Purchase the Product in this Retail Store Influencing factor No.of.Respondents Percentage (%) Advertisements Friends and Relatives Family Members Colleagues Other sources Total Table 5.14 Factor Influencing to Purchase the Product in this Retail Store Inference From the above table 5.14 related to the Factor Influencing to Purchase the Product in this Retail Store, Majority 40.44% of the respondents are influenced by Friends and relatives for purchasing products in their retail store, 34.33% of the respondents are influenced by Advertisements for purchasing products in their retail store, 22.33% of the respondents are influenced by Family Members for purchasing products in their retail store, 2.44% of the respondents are influenced by colleagues for purchasing products in their retail store and remaining 0.44% of the respondents are influenced by other sources for purchasing products in their retail store.

205 182 Table Preferred Mode of Payment among the respondents Preferred Mode of Payment No.of.Respondents Percentage (%) On Cash Debit Card Credit card Total Table 5.15 Preferred Mode of Payment among the respondents Inference From the above table 5.15 related to the Preferred Mode of Payment among the respondents, Majority 80.44% of the respondents preferred to pay by cash for the products purchased from their retail store, 13.56% of the respondents preferred to pay through their Debit Cards for their Product Purchases and remaining 6.11% of the Preferred to pay through their Credit Cards for their Product Purchase.

206 Most Liked Factor of the Retail store among the respondents Most liked Percentage No.of.Respondents Factor of the Retail store (%) Pricing of the product Availability of variety of brands On-time service delivery Visually appealing Environment Brand image Sales promotions/discounts Customer service convenient parking space Quality of the product and service Total Table 5.16 Most Liked Factor of the Retail store among the respondents Inference From the above table 5.16 related to the Most liked Factor of the Retail store among the respondents,43.22% of the respondents most liked factor of the retail are the availability of the various branded products, 22% of the respondents most liked factor of the retail are the Pricing of the Product, 13.11% of the respondents most liked factor of the retail are the Quality of the Product and services offered, 6.67% of the respondents most liked factor of the retail are the brand image, Then 6.0% of the respondents most liked factor of the retail are the Visually Appealing Environment, 3.89% of the respondents most liked factor of the retail are the customer service, 3.0% of the respondents most liked factor of the retail are the on-time service Delivery, 1.33% of the respondents most liked factor of the retail are the sales promotion and discounts, 0.78% of

207 184 the respondents most liked factor of the retail are the availability of the convenient parking spaces.

208 Most Disliked Factor of the Retail store among the respondents Most disliked Percentage No.of.Respondents Factor of the Retail store (%) Pricing of the product Non Availability of branded Products Delivery of Product and Services Non Attractive Environment Sales promotions/discounts Customer service Continent parking space Quality of the product and service Others None of the Above Total Table 5.17 Most Disliked Factor of the Retail store among the respondents Inference From the above table 5.17 related to the Most Disliked Factor of the Retail store among the respondents, Majority 31.11% of the respondents most disliked factor of the retail are the Continent parking space, 22.67% of the respondents most disliked factor of the retail are the Sales promotions and Discounts, 17.44% of the respondents most disliked factor of the retail are the customer service, 14.00% of the respondents most disliked factor of the retail are found to be none, 6.67% of the respondents most disliked factor of the retail are the delivery of product and services, 4.89% of the respondents most disliked factor of the retail are the non attractive environment, 1.22% of the

209 186 respondents most disliked factor of the retail are the pricing of the product, 1.00% of the respondents most disliked factor of the retail are the Quality of the product and service, 0.89% of the respondents most disliked factor of the retail are the Non Availability of branded Products, 0.11% of the respondents most disliked factor of the retail are the various other factors.

210 THE RELIABILITY AND VALIDITY OF THE SCALE Reliability and validity of the scale are important for obtaining meaningful results. Validity and reliability are the tools used to evaluate the characteristics of a good measurement and these tools involved a measurement of accuracy and applicability (Malhotra, 2004; Cooper and Schindler, 2001). The main concern for performing validity and reliability is to develop a measurement that reflects a true score of the variables being measured (Churchill and Iacobucci, 2002) Reliability of the Scale A test must be reliable, that is, it must have the ability to consistently yield the same results when repeated measurements are taken of the same individuals under the same conditions (Hair, 2006). In other words, reliability is an indication of how consistent the findings are based on the method of data collection and analysis (Saunders, Lewis and Thornhill, 2007). Furthermore, reliability is more important when the questionnaire is a Likert-type because there are many variables testing the concept. In the words of Freeman (1965) The term reliability has two closely related but somewhat different connotations in psychological testing. First, it refers to the extent to which a test is internally consistent, that is, consistency of results obtained throughout the test when administered once. In other words, how accurate is the test measuring a particular item? Second, reliability refers to the extent to which a measuring device yields consistent results upon testing and retesting. That is, how dependable is it for predictive purposes? Usually, the Cronbach s alpha is used to measure the reliability of the instrument (Pallant, 2007; Green et al., 2000; Hair et al., 1998). Cronbach s

211 188 alpha estimate tells us how highly the items in the questionnaire are interrelated. Unlike the split-half reliability method, however, this estimate does not have to be corrected for length. Cronbach s Coefficient Alpha which is derived from the assumption that if all the items are drawn from the domain of a single construct, responses to the items composing the measurement model should be highly correlated (Hatcher, 1994). Calculation of Cronbach s estimate is usually done with the help of a statistical package designed to calculate this reliability estimate. Cronbach s (1951) estimate of reliability is calculated using the variance of individual items and co-variances between the items. This estimate, however, can also be calculated using the correlations between the items. Given those items within a questionnaire use the same scale, both approaches give similar estimates, but the latter approach is easier to understand. The Cronbach alpha coefficient ranges from 0 to 1 with a minimum of 0.6 while other studies suggest that anything above 0.7 suggest high levels of internal reliability (Hair et al., 1998). Nunnally (1978) suggested that an alpha value of 0.7 is acceptable. Many studies have used reliability to test their modified service quality scale that ranged from 0.6 to 0.96 (Chowdhary and Prakash, 2007; Caro and Garcia, 2007; Akbaba, 2006; Jabnoun and Khalifa, 2005; Sureshchandar, Rajendran and Anantharaman, 2002; Dabholkar, Thorpe and Rentz, 1996; Malhotra, 1993). For the purpose of this research the researcher had used Cronbach alpha coefficient (Cronbach, 1951), the most common method for testing reliability, and 0.6 will be used as the minimal accepted level. Using SPSS version 21.0, an internal consistency analysis was performed to assess the reliability aspect of the instrument.

212 Validity of the scale The test, as a data collection tool, must produce information that is not only relevant, but free from systematic errors; that is, it must produce valid information. In general a test is valid if it measures what it claims to measure. A test, however, does not possess universal and eternal validity. It may be valid for use in one situation, but invalid if used in another. Cronbach (1964) states that a test which helps in making one decision in a particular research situation may have no value at all for another. According to Zikmund and Babin (2010) validity is the accuracy of a measure or the extent to which a score truthfully represents a concept. In other words, Validity is concerned with the test being capable of testing what it was designed for, which is not as simple as it seems (Hair, 2006). Figure 5.1: Measurement model of factors affecting purchase intention

213 Estimation of Cronbach s coefficient alpha and CFA loadings for purchase intentions of retail stores Items Cronbach's CFA Alpha if Loadings Item Deleted Pricing of various branded Products Availability of Various branded Products Quality of Various branded Products Overall Sales Promotional Activities Overall After Sales Services Arrangement of the Products Waiting time for billing Cronbach's Alpha Value Purchase Intentions Table 5.18 Estimation of Cronbach s coefficient alpha and CFA loadings for purchase intentions of retail stores Inference Above table illustrates the Cronbach Alpha-value if an item is to be deleted. It presents the mean for the 7 items of purchase intention of instrument, consisting of the seven point scale. As can be seen in the reliability item statistics (Cronbach Alpha = 0.902) all the 7 items seems to be reasonably well to the scale s reliability. A deletion of any item doesn t reflect much on the Cronbach s alpha value (reliability). It ranges from to

214 The CFA loadings are indicated in the above table and also suggest that all the items taken for scale construction qualify to develop the scale. This is due to the fact the CFA loadings are greater than 0.50 for all the items. Table 5.8.2: Reliability item statistics for purchase intention scales Purchase intention scales Mean Item Means Item Variances Inter-Item Covariance Inter-Item Correlations Table 5.19: Reliability item statistics for purchase intention scales As per the Hair et al (1998) suggests, the Inter- item correlation should exceed 0.30 for the data to be reliable. The item statistics present the current study statistics, where Inter-item correlation is for purchase intention scale.

215 Figure 5.2: Measurement model for perceived service quality dimensions 192

216 Estimation of Cronbach s coefficient alpha and CFA loadings for perception service quality dimensions of retail stores Items CFA Loadings Cronbach's Alpha if Item Deleted Cronbach's Alpha Value PPA PPA PPA PPA PPA Physical aspects PPA PRE PRE PRE PRE Reliability PRE PPI PPI PPI PPI PPI PPI Personal interactions PPI PPI PPI PPS Problem solving PPS

217 194 PPS PP PP Policy PP PP Table 5.20 Estimation of Cronbach s coefficient alpha and CFA loadings for perception service quality dimensions of retail stores Above table illustrates the Cronbach Alpha-value if an item is to be deleted. It presents the mean for the 27 items of perception service quality dimension of instrument, consisting of the seven point scale. As can be seen in the reliability item statistics (Cronbach Alpha = 0.982) all the 27 items seems to be reasonably well to the scale s reliability. A deletion of any item doesn t reflect much on the Cronbach s alpha value (reliability). It ranges from to This establishes the reliability of all the items included under perception service quality dimension. Furthermore, the estimate value of cronbach s alpha in respects of all variables exceeds the alpha if item deleted value and hence, no item needs to be dropped from the study. The CFA loadings are indicated in the above table and also suggest that all the items taken for scale construction qualify to develop the scale. This is due to the fact the CFA loadings are greater than 0.50 for all the items.

218 Reliability item statistics for perception service quality dimension Perception service quality dimension Mean Item Means Item Variances Inter-Item Covariance Inter-Item Correlations Table 5.21: Reliability item statistics for perception service quality dimension Inter- item correlation should exceed 0.30 for the data to be reliable. The item statistics present the current study statistic, where Inter-item correlation is for perceived service quality dimensions.

219 Figure 5.3: Measurement model for expected service quality dimensions 196

220 Estimation of Cronbach s coefficient alpha and CFA loadings for expected service quality dimensions of retail stores Items CFA Loadings Cronbach's Alpha if Item Deleted Cronbach's Alpha Value EPA EPA EPA EPA EPA Physical appearance EPA ERE ERE ERE ERE Reliability ERE EPI EPI EPI EPI EPI EPI EPI Personal interactions EPI EPI EPS Problem solving EPS

221 198 EPS EP EP Policy EP EP Table 5.22 Estimation of Cronbach s coefficient alpha and CFA loadings for expected service quality dimensions of retail stores Above table illustrates the Cronbach Alpha-value if an item is to be deleted. It presents the mean for the 27 items of the expected service quality dimension of instrument, consisting of the seven point scale. As can be seen in the reliability item statistics (Cronbach Alpha = 0.953) all the 27 items seems to be reasonably well to the scale s reliability. A deletion of any item doesn t reflect much on the Cronbach s alpha value (reliability). It ranges from to This establishes the reliability of all the items included under expected service quality dimension. Furthermore, the estimate value of cronbach s alpha in respect of all variables exceeds the alpha if item deleted value and hence, no item needs to be dropped from the study. The CFA loadings are indicated in the above table and also suggest that all the items taken for scale construction qualify to develop the scale. This is due to the fact the CFA loadings are greater than 0.50 for all the items.

222 Reliability item statistics for expected service quality dimension Expected service quality dimension Mean Item Means Item Variances Inter-Item Covariance Inter-Item Correlations Table 5.23: Reliability item statistics for expected service quality dimension Inter- item correlation should exceed 0.30 for the data to be reliable. The item statistics present the current study statistic, where Inter-item correlation is for expected service quality dimensions.

223 Figure 5.4: Measurement model for customer loyalty dimensions 200

224 Estimation of Cronbach s coefficient alpha and CFA loadings for customer loyalty dimensions of retail stores Items Cronbach's Alpha Cronbach's Alpha CFA Loadings if Item Deleted Value WOM WOM Word of mouth WOM WOM WOM SC Switch to competitor SC and Willingness to pay WPM more WPM RES Internal and External RES response RES RES Table 5.24 Estimation of Cronbach s coefficient alpha and CFA loadings for customer loyalty dimensions of retail stores Above table illustrates the Cronbach Alpha-value if an item is to be deleted. It presents the mean for the 13 items of the customer loyalty dimension of instrument, consisting of the seven point scale. As can be seen in the reliability item statistics (Cronbach Alpha = 0.894) all the 13 items seems to be reasonably well to the scale s reliability. A deletion of any item doesn t reflect much on the Cronbach s alpha value (reliability). It ranges from to

225 This establishes the reliability of all the items included under customer loyalty dimension. Furthermore, the estimate value of cronbach s alpha in respect of all variables exceeds the alpha if item deleted value and hence, no item needs to be dropped from the study. The CFA loadings are indicated in the above table and also suggest that all the items taken for scale construction qualify to develop the scale. This is due to the fact the CFA loadings are greater than 0.50 for all the items.

226 Reliability item statistics for customer loyalty dimensions Customer loyalty dimensions Mean Item Means Item Variances Inter-Item Covariance Inter-Item Correlations Table 5.25: Reliability item statistics for customer loyalty dimensions Inter- item correlation should exceed 0.30 for the data to be reliable. The item statistics present the current study statistic, where Inter-item correlation is for customer loyalty dimensions.

227 Model fit statistics Goodness of Fit Statistics Purchase Perceived Expected Customer Fit intention SQ SQ loyalty values Chi Square Value (CMIN) Degree of Freedom (Df) Chi Square / Df (CMIN/Df) to 5 Goodness of Fit Index (GFI) > 0.9 Root Mean Square Error of Approximation (RMSER) < 0.08 Adjusted Good of Fit Index (AGFI) > 0.9 Comparative Fit Index (CFI) > 0.9 Normed Fit Index (NFI) > 0.9 Table 5.26: Model fit statistics To analysis the validity CFA approach (AMOS 21) has been used. The SEM approach allows concurrent estimations of multiple regression analysis in one single framework. Browne and Cudeck (1993) study indicate the model fit can be checked by RMSEA which is less than 0.08 has a good fit and less than 0.05 has a closer fit. Chin and Todd (1995) study proposed that for goodness of model fit GFI (Goodness of Fit Index) and NFI (Normed Fit Index) should be above 0.9 and AGFI (Adjusted goodness-of-fit Index) should be above 0.8. Bentler (1990) study suggests for good model fit CFI (Comparative Fit Index) should be greater than 0.9. The goodness of final model fit has been shown in the above table.

228 TOOLS USED FOR DATA ANALYSIS In this study, the main objective is to assess relationships among certain variables and to test specific hypotheses regarding the nature of the relationships. The analysis is undertaken with a view to give a clear cut idea from the primary data collection. Various tables, diagrams and charts are incorporated to make it more useful and easy to understand. The software packages employed for the study are AMOS 21.0 and SPSS 21.0 for Windows. AMOS is a structural equation modeling software package and is used to undertake confirmatory factor analysis. It is also used to test the structural equation model that links loyalty to its antecedents. The packages have been used to perform a number of statistical techniques to analyze the data collected in the phase of the study. The same packages are utilized for appropriately screening the data before the application of each technique. The results of various analyses are depicted in figures and tables as evidenced in subsequent chapters. The techniques that are used in the phases of the study are explained in the consequent paragraphs Structural Equation Modeling (SEM) SEM, also known as latent variable analysis (Baumgartner and Homburg 1996; Hair et al., 1998), is a developer of multiple regression analysis to combine a series of multiple regression equations within one structural model (Hair et al., 1998). The approach simultaneously runs several multiple regression equations, and is used in this research to combine the relationships investigated into one broad model. That model integrates the relationships in the pathway from service performance to behavioral loyalty.

229 206 SEM is a confirmatory approach and is used to test theory rather than to develop a theory (Byrne, 2001; Tabachnick and Fidell, 2001). SEM has a number of benefits over multiple regressions. It possesses interdependence and allows a dependent variable in one multiple regression to become an independent variable in a subsequent equation (Hair et al., 1998). It also allows independent variables to act simultaneously on more than one dependent variable. It identifies both direct and indirect effects on a dependent variable (Hair et al., 1998). In addition the approach enables the inclusion of latent variables within the model. Latent variables are hypothesized, but unobserved variables (Byrne, 2001; Hair et al. 1998; Tabachnick and Fidell, 2001). An additional strength of SEM is the treatment of error variance. In most data, there are elements of errors incorporated into the data. SEM includes estimation of error variance in contrast to other multivariate approaches that ignore errors (Byrne 2001, Hair et al., 1998). Although SEM provides a number of advantages over other statistical approaches, there are limitations associated with its use. While using SEM it is important, to ensure that the model is correctly specified as SEM is vulnerable to specification error. A predictor variable is omitted from the model thus distorting results for the included variables (Hair et al., 1998). A number of indicators are used to assess the validity of a hypothesized model. It is the fit between the sample and the estimated population covariance matrices (Hair et al., 1998; Tabachnick and Fidell, 2001). Although the chi-square (X 2 ) is accepted as the conventional overall test of fit, a number of alternative fit indices have been developed. It overcomes the concerns with the chi-square statistics, which mainly associated with issues of sample size (Hu and Bentler, 1995). Whilst the model fit is important, the issue of over fitting the model is also of consequence. It is

230 207 necessary to balance the model fit with parsimony (Hair et al., 1998). The fit indicators are grouped into categories of absolute fit indices; incremental or comparative fit indices and parsimonious fit indices (Byrne, 2001; Hair et al., 1998). The absolute fit indices include chi-square (X 2 ); goodness of fit index (GFI); adjusted goodness of fit index (AGFI); root mean square residual (RMR) and the root mean square error of approximation (RMSEA). The incremental fit measures include the Normed fit index (NFI) and the comparative fit index (CFI). The chi-square (X 2 ) statistics is recognized as the conventional overall test of fit (Hu and Bentler, 1995). However, research has shown that the chi-square statistic is not entirely reliable as an indicator of good model fit. It rejects an acceptable hypothesized model (Byrne, 2001; Hu and Bentler, 1995). In particular the chi-square statistic is sensitive to sample size. Large samples often result in high values of chi-square indicating a poor fit, whereas alternative measures suggest an acceptable fit. In this research the chi-square statistic is reported as accepted as a fundamental measure of fit (Hu and Bentler, 1995). Absolute fit indices, such as the goodness of fit index (GFI) effectively compare the hypothesized model with the null model. They measure the relative level of variance and covariance (Byrne, 2001). Hu and Bentler (1995) say that the GFI performs better than the other absolute fit indices. Although theoretically a negative result is possible, the hypothesized model is a worse fit than the null model. Results for the GFI are normally in the range from zero to one with higher values indicating a better fit (Byrne, 2001). A GFI of above 0.90 is generally accepted as indicative of a good fit (Hair et al., 1998; Hu and Bentler, 1995).

231 208 The adjusted goodness of fit index (AGFI) is similar to the GFI but addresses the issue of parsimony by adjusting the degrees of freedom. The result with GFI normally ranges between zero and one with higher values indicating a better fit (Byrne, 2001). The results with GFI values above 0.90 are acceptable (Hair et al., 1998). The root mean square error of approximation (RMSEA) is recognized as one of the most informative criteria in covariance structure modeling (Byrne, 2001 Pg 84). The RMSEA reports the discrepancy or misfit in the fit of the model to the population covariance matrix. It is adjusted for the degrees of freedom (Byrne, 2001). RMSEA is affected by sample size. There is a tendency to reject acceptable models when the sample size is small (Byrne, 2001). Values range from zero to one and lower values indicate better fits. Values between 0.05 and 0.08 are seen as representing well fitted models. Values between 0.08 and 0.10 represent mediocre fits and above 0.10 is a poor fit (Byrne, 2001; Hair et al., 1998). Although a lower value indicates a better fit, a result of zero will indicate a perfect fit. This is optimistic (Byrne, 2001). In contrast to the absolute fit indices the incremental or comparative indices of fit compare the hypothesized model with a baseline model, normally the null model (Byrne, 2001; Hair et al., 1998). The comparative fit index (CFI) is developed by Bentler (1990) to reflect the criticisms associated with the NFI. As with the NFI the result for the CFI range from zero to one with larger results indicating better fit. A result of above 0.90 is indicative of a good fit (Byrne, 2001). The Normed chi-square statistic is proposed by Joreskog (Hair et al., 1998) to overcome some of the concerns over the chi-square statistic. The statistic provides a range of acceptable results. It indicates models whether it is above

232 209 or under fit. An over fitted model is typically represented by a value less than one, whilst a model that is not reflective of the data is represented by a value above 3 (Hair et al., 1998). A more liberal limit of five is suggested as appropriate. In this research, SEM applications are used as a confirmatory technique to validate the proposed research model for the service industry Determination of Sample Size for Model Testing Bollen (1989) recommends 3 to 5 participants per estimated parameter, whereas Bentler (1990) recommend 5 to 10 participants per estimated parameter. It is observed that there is a limited consensus in determining the sample size for adequate power. The research indicates that some goodness-offit indices perform adequately with sample sizes as small as 100 participants. In general, statistical indices perform adequately and yield meaningful and interpretable values. These values are attained when the sample size is 200 or more participants. Sample size is an important consideration in SEM analyses, including (a) low power to detect significant path coefficients and variances, and (b) instability in the covariance matrix, leading to attenuation of fit indices. Totally 900 Retail Store customers are selected as samples for conducting the research Univariate and Multivariate Statistical Analyses In order to test hypotheses through data analysis, several Univariate and multivariate statistical techniques are employed. These techniques include Analysis of Variance (ANOVA), Reliability and Validity Analysis, Multiple regression analysis, Path analysis, Independent Paired t-test and Paired Sample t-test. They are elaborated in the following paragraphs.

233 Analysis of Variance Analysis of variance is a technique often used to test statistical significance and differences by means for groups or variables. The null hypothesis means that no difference exists between the means while the alternative hypothesis is that the means are different from each other Multiple Regression Analysis Multiple regression analysis is a statistical technique that allows the researcher to assess the relationship between one dependent variable and several independent variables (Tabachnick and Fidel, 2001). It provides information about the model as a whole, and the relative contribution of each of the independent variable that makes up the model. In this study, multiple regression analysis is used to examine how well the dimensions of service quality can predict customers satisfaction Path Analysis Path analysis is employed for studying the relationships between the dimensions of quality, overall satisfaction and the dimensions of loyalty. Path analysis is a statistical technique, within the family of Structural Equation Modeling techniques. In models an explanatory relationship exists between the variables. It is usually employed when the models under examination do not contain latent variables. The advantage of path analysis over regression is that, it concurrently performs multiple regression analyses. By the same time, it produces an overall assessment of the fit of the model that is usually based on a single chi square statistic (Singh and Wilkes, 1996). In addition, several goodness of fit indexes is available to judge the fit better.

234 Paired Sample t-test Paired-sample t-test is used when data from the same sample are collected either under two different conditions or using the measurement of two different variables (Pallant, 2001). Paired sample t-test is employed to examine the relative importance of service quality dimensions by establishing differences in the expectation and perception scores CONCLUSION This Research methodology chapter provides a description of the procedures employed to assess the constructs using the collected primary data. The sectors selected for the work are the organized retail stores of the major cities like Chennai, Madurai and Coimbatore of Tamilnadu in India. The revised SERVQUAL instrument is chosen as the most reliable device to quantify the difference-score conceptualization. It evaluates gaps between expectation and perception in service quality. Adjustments are made on the SERVQUAL instrument to make it specific to Retail sectors and such tool has been renamed as Retail Service Quality Scale (RSQS). The research questions are contained in separate divisions to hold additional information such as preconception about the service; behavioral intention of the customer; satisfaction level of the customers etc., which the researcher has specified to hold valuable data. The research sample consists of 900 customers from various organized retail stores and those samples are chosen on the basis of their status and customers randomly selected. Structured questionnaires are developed and are tested in focus group sessions, and the appropriate modifications are produced. The questionnaire asks the respondents to supply answers to Preconception

235 212 about services, twenty-seven questions regarding their expectation of service quality along with twenty-seven questions on their perception of the service providers. Additionally, there are questions calling for assignment of values related to the five pertinent factors, along with questions related to gender, age, income, occupation. Hence this chapter concludes that it has a description of the proposed research design, the sampling plan and setting, instrumentation, data collection processes and methods of information analysis.

236 213 CHAPTER VI DATA ANALYSIS AND INTERPRETATIONS This Chapter discusses about the findings of the research study. This data analysis is followed by finding the relative importance of service quality dimensions. Further, the association between service quality and demographics was then investigated. Thereafter, multiple regression analysis was used whereupon to find the relationships between service quality dimensions and customer loyalty intentions were also clearly demonstrated with proper illustrations. 6.1 INTRODUCTION After the data were collected, the researcher proceeded to the data processing and data analysis stage. Data processing concerns activities and technologies which prepare the collected data for analysis data checking, entry, coding, and editing (Gromme, 1998). Data analysis comes after the data have been collected to make sense of the study and reach certain findings (Yaghi, 2010). Data analysis concerns activities and technologies which provide statistical insight in the collected data weighting, tabulations, and response analysis (Gromme, 1998). Data were analyzed in order to draw the conclusion from the collected data. This chapter examines the results of the study and presents the different techniques used for data analysis by the researcher in this study. In the present study, responses from respondents were collected, coded and tabulated in SPSS 21. Data collected were analyzed through a series of validated tools and procedures. In some cases simple statistics like average, percentage, weighted average and mean score were calculated. Advanced tools like Analysis of

237 214 Variance, Correlation and Multiple-Regression were also used. Simple descriptive statistics were used to summarize the respondents characteristics, their frequency of visiting retail stores, and their ratings of service quality and behavioral intentions. Reliability and validity of the scale were checked. Multiple regressions were applied to assess the relationship between service quality dimensions and overall service quality. One way ANOVA tests (testing the difference between the mean of two or more independent variables) were employed to examine if the service quality dimensions means varied among respondents with different demographic characteristics. Last but not least, multiple regressions were applied to demonstrate the relationship of the service quality dimensions with customer loyalty. Throughout the analysis process, significance tests were used to decide whether to accept or reject the hypotheses concerning the sample data that have been collected (Harris, 1998). The confidence level was taken as 95% (or 5% level of significance). 6.2 SERVICE QUALITY GAP ANALYSIS Service quality Expected Mean Perceived dimensions (P) Mean (E) Gap (P-E) Physical Aspects Physical Aspects Physical Aspects Physical Aspects Physical Aspects Physical Aspects Average Gap Score Reliability Reliability Reliability

238 215 Reliability Reliability Average Gap Score Personal Interaction Personal Interaction Personal Interaction Personal Interaction Personal Interaction Personal Interaction Personal Interaction Personal Interaction Personal Interaction Average Gap Score Problem solving Problem solving Problem solving Average Gap Score Policy Policy Policy Policy Average Gap Score Table 6.2.1: Service Quality Gap Analysis The Above table shows the mean average gap score for five service quality dimensions.

239 216 Categories Gap Scores Average Gap Score for Physical aspects -0.4 Average Gap Score for Reliability Average Gap Score for Personal interactions Average Gap Score for Problem Solving Average Gap Score for Policy Un-weighted Score Table 6.2.2: Calculation of Un-Weighted Score Assigning Weights This step is the extension of the SERVQUAL score and is only required if weighted score is calculated. Putting weights against each of the dimensions is critical and tactful because the amount of weight represents the relative importance of the dimensions of the customer. The questionnaire has a separate page asking each respondent to put relative weight against each dimension. The points against each of the dimensions are totaled and averaged to normalize it. Total 100 points have been allocated for; five dimensions as stated below preferences are calculated considering respondents viewpoint on it:

240 217 Dimensions Points Retail store appearance and store layout (Physical aspects) 27 Retailers keep their promises and do the right things (Reliability) 22 Retail store personnel are courteous, helpful, and inspire confidence in customers (Personal interaction) 20 Retail store personnel are capable to handle returns and exchanges, customers Problems and complaints (Problem 17 Solving) Retail store s policy on merchandise quality, parking, operation hours, and credit cards (Policy) 14 Total 100 Table 6.2.3: Rating of Preference on the Service Quality Dimensions Finally, the score should be analyzed to find out the weak areas where more attention is required. The gap score indicates the extent of gap in service quality. The larger the gap score is, the more is the dissatisfaction. Dimensions Un-Weighted Score Weights Weighted Score Physical aspects Reliability Personal interactions Problem Solving Policy Table 6.2.4: Calculation of Weighted Score

241 218 Inference: The above table shows that the Service Gap Score Analysis for the retail shops in Chennai, Madurai and Coimbatore. It shows that lowest service gap has occurred in policy and problem solving dimension and little high service gap has occurred in the personal interaction and reliability dimension. Physical aspect dimension is has occurred very high service gap compared to other dimensions. The lower order dimensions should be paid more concentration and the retails should see that the customer s expectations are met for all dimensions.

242 RELATIVE IMPORTANCE OF SERVICE QUALITY DIMENSIONS ON OVERALL RETAIL SERVICE QUALITY Testing of Hypothesis 1 H 01 : There is no positive impact of service quality dimensions on overall retail service quality. H A : There is positive impact of service quality dimensions on overall retail service quality. Regression analysis is used for service quality studies that involve the attitudes and perceptions of consumers or the decisions that consumers make about products (Hair et al., 1995). The dimension with the largest coefficient represents the most important dimension in terms of its influence on overall quality perceptions. The next largest coefficient represents the second most influential dimension and so forth. R R Square Adjusted R Square Std. Error of the Estimate Table 6.3.1: Summary Table The adjusted R square value was which means that retail service quality dimensions account for 68 percent of the variance in Overall Retail Service Quality. It means that 32 percent of Overall Retail Service Quality was explained by something other than the service quality dimensions.

243 220 Model Sum of Squares df Mean Square F P value Regression Residual Total ** ** Significant at 5 level Table 6.3.2: Model fit table (ANOVA) Summary of Regression Analysis treating service quality dimensions as predictors and overall perception of service quality as criterion variable was shown below table. It was observed that the overall regression model was significant (F= , p<0.00). It is parallel to the findings of Leung (2006) that the relationship between service quality and overall service quality was significant and strong. In terms of the relationship between individual dimensions and overall service quality rating, the adjusted R square = was statistically significant. It was suggested that the retail service quality dimensions explained 68 per cent of the variance in the customers overall rating.

244 221 Unstandardized Standardized Coefficients Coefficients Model t value p value Std. B Beta Error (Constant) ** Physical Aspects ** Reliability ** Personal Interaction ** Problem Solving Policy ** ** Significant at 5 per cent level Table regression analysis results for service quality dimensions All dimensions physical aspects, Reliability, personal interaction and policy was statistically significant (p < 0.01). Physical aspects, Reliability, personal interaction and policy dimensions remained in the equation explaining overall service quality. The higher the beta co-efficient, more is the contribution of factors in explaining overall service quality. As shown in the Table 6.3.3, the overall perceived service quality was influenced by all the eight dimensions with Policy and Reliability dimensions as the most important dimension, beta coefficient = and Above table depict that the customers tend to make service quality judgments based on these four dimensions in order of importance as revealed in the regression equation. Policy and reliability had achieved the strongest

245 222 association with the overall perception of service quality. Among all the variables in the regression, Personal interaction appeared to have least association (with beta coefficient = 0.011) with overall service quality. This shows that the customers perceive personal interaction i.e. employees in the outlet have the knowledge to answer customer s questions and employees behavior in the outlets instills confidence in customers as the least important for influencing their service quality perceptions. The results of above table can be summarized as regression equation given below: Overall service quality as perceived by customers= (Physical aspects) (Reliability) (Personal interaction) (Policy) Hence null hypothesis is rejected, it concludes that there is positive impact of service quality dimensions on overall retail service quality.

246 EFFECTS OF DEMOGRAPHIC VARIABLES ON DIFFERENT SERVICE QUALITY DIMENSIONS Testing of Hypothesis 2 H 02 : There is no significant relationship between demographic difference of the respondents and retail service quality dimensions. H A : There is significant relationship between demographic difference of the respondents and retail service quality dimensions. In non-professional service like retailing, there is a need to examine the demographic characteristics of customers when evaluating service quality (Webster 1989). Therefore, the demographic data were adopted to examine their association with various retail service quality dimensions. In this study, Analysis of Variance had been used to determine whether these factors were influenced by the demographics. Significance value less than 0.05 indicate existence of some relationship between the independent variable (demographic characteristic) and dependent variables (dimensions of service quality). Sub Hypothesis H 2a: There is no significant relationship between type of retail store of the respondents and retail service quality dimensions. H 2b: There is no significant relationship between location of the retail store of the respondents and retail service quality dimensions. H 2c: There is no significant relationship between gender of the respondents and retail service quality dimensions.

247 224 H 2d: There is no significant relationship between age of the respondents and retail service quality dimensions. H 2e: There is no significant relationship between marital status of the respondents and retail service quality dimensions. H 2f: There is no significant relationship between educational qualifications of the respondents and retail service quality dimensions. H 2g: There is no significant relationship between occupation of the respondents and retail service quality dimensions. H 2h: There is no significant relationship between family income of the respondents and retail service quality dimensions.

248 : Effects of store type on service quality dimensions H 2a: There is no significant relationship between type of retail store of the respondents and retail service quality dimensions. Service quality dimension F P value Physical Aspects ** Reliability ** Personal Interaction ** Problem Solving ** Policy ** ** Significant at 5 per cent level Table 6.4.1: Effects of store type on service quality dimensions Inference: Above table shows that p value is less than 0.05 for all service quality dimensions, hence null hypothesis is rejected. It concludes that store type had significantly influence over all service quality dimensions like physical aspects, reliability, personal interaction, and problem solving and policy dimensions. Post hoc analysis shows that hyper markets differed significantly on the basis of all service quality dimensions aspects from super market and departmental stores, super market differed significantly on departmental stores.

249 Effects of location of the store on service quality dimensions H 2b : There is no significant relationship between location of the store and retail service quality dimensions. Service quality dimension F P value Physical Aspects ** Reliability ** Personal Interaction ** Problem Solving ** Policy ** ** Significant at 5 per cent level Table Effects of location of the store on service quality dimensions Inference: Above table shows that p value is less than 0.05 for all service quality dimensions, hence null hypothesis is rejected. It concludes that location of the store had significantly influence over all service quality dimensions like physical aspects, reliability, personal interaction, and problem solving and policy dimensions. Post hoc analysis shows that location of retail stores in Chennai differed significantly on the basis of all service quality dimensions from location of retail stores in Madurai and Coimbatore.

250 Effects of gender on service quality dimensions H 2c : There is no significant relationship between Gender and retail service quality dimensions. Service quality dimension F P value Physical Aspects Reliability ** Personal Interaction ** Problem Solving ** Policy ** ** Significant at 5 per cent level Table 6.4.3: Effects of gender on service quality dimensions Inference: Above table shows that p value is less than 0.05 for service quality dimensions like reliability, personal interaction, and problem solving and policy dimensions, hence null hypothesis is rejected. It concludes that gender had significantly influence over service quality dimensions like reliability, personal interaction, and problem solving and policy dimensions. It indicates that physical aspects do not differ significantly based on gender difference. Based on the mean value, females differed significantly on the basis of reliability, personal interaction, and problem solving and policy service quality dimensions from males.

251 Effects of age on service quality dimensions H 2d : There is no significant relationship between age and retail service quality dimensions. Service quality dimension F P value Physical Aspects ** Reliability ** Personal Interaction ** Problem Solving ** Policy ** ** Significant at 5 per cent level Table Effects of age on service quality dimensions Inference: Above table shows that p value is less than 0.05 for all service quality dimensions like physical aspects, reliability, personal interaction, and problem solving and policy dimensions, hence null hypothesis is rejected. It concludes that age had significantly influence over all service quality dimensions like physical aspects, reliability, personal interaction, and problem solving and policy dimensions. Post hoc analysis shows that age group between years old differed significantly on the basis of all physical aspects dimensions from age group between years, years and above 50 years. Age group between years old differed significantly on the basis of all physical aspects dimensions from age group between less than 20 years, years.age group between years old differed significantly on the basis of all reliability dimensions from age group between years and years. Age group

252 229 between years old differed significantly on the basis of all personal interaction dimensions from all other age groups. Age group between years old differed significantly on the basis of all problem solving dimensions from age group between years, years and greater than 50 years and age group between years old differed significantly on age group less than 20 years and between years.

253 Effects of marital status on service quality dimensions H 2e : There is no significant relationship between marital status and retail service quality dimensions. Service quality dimension F P value Physical Aspects ** Reliability ** Personal Interaction Problem Solving Policy ** ** Significant at 5 per cent level Table Effects of marital status on service quality dimensions Inference: Above table shows that p value is less than 0.05 for service quality dimensions like physical aspects, reliability and policy dimensions, hence null hypothesis is rejected. It concludes that marital status had significantly influence over service quality dimensions like physical aspects, reliability and policy dimensions.post hoc analysis shows that widow group differed significantly on the basis of all physical aspects dimensions from married and unmarried group. Widow group differed significantly on the basis of all reliability dimensions from married, unmarried and divorced group. Widow group differed significantly on the basis of all personal interaction dimensions from married, unmarried and separated group. Widow group differed significantly on the basis of all problem solving dimensions from married and unmarried group. Widow group differed significantly on the basis of all policy dimensions from married, unmarried and separated group.

254 Effects of educational qualification on service quality dimensions H 2f : There is no significant relationship between educational qualification and retail service quality dimensions. Service quality dimension F P value Physical Aspects Reliability ** Personal Interaction ** Problem Solving Policy ** ** Significant at 5 per cent level Table Effects of educational qualification on service quality dimensions Inference: Above table shows that p value is less than 0.05 for service quality dimensions like reliability, personal interaction and policy dimensions, hence null hypothesis is rejected. It concludes that educational qualification had significantly influence over service quality dimensions like reliability, personal interaction and policy dimensions. Respondents were found to be differing significantly on the basis of reliability, personal interaction and policy dimensions. Post hoc analysis shows that diploma group differed significantly on the basis of all reliability dimensions from SSLC/HSC, UG and PG group. UG group differed significantly on the basis of all personal interaction dimensions from PG and diploma group and PG group differed from diploma group, Diploma group differed from UG group on the basis of personal interaction dimensions.

255 232 HSC/SSLC group differed significantly on the basis of all policy dimensions from UG and Diploma group differed significantly on UG and PG educational group.

256 Effects of occupation on service quality dimensions H 2g : There is no significant relationship between occupations and retail service quality dimensions. Service quality dimension F P value Physical Aspects Reliability Personal Interaction ** Problem Solving ** Policy ** ** Significant at 5 per cent level Table Effects of occupation on service quality dimensions Inference: Above table shows that p value is less than 0.05 for service quality dimensions like personal interaction, problem solving and policy dimensions whereas other service quality dimensions had no influence of occupation, hence null hypothesis is rejected. It concludes that occupations had significantly influence over service quality dimensions like personal interaction, problem solving and policy dimensions. For example, business class represents an affluent society. They preferred customized services like abundant parking space, outlet accepting all credit cards etc. where importance was given to their individual needs. That s why they differed from service quality perception of other categories of occupations. Post hoc analysis shows that professional group differed significantly on the basis of all reliability dimensions from business, students and clerk; clerk occupational group differed from police occupation. Business occupation

257 234 group differed significantly on the basis of all personal interaction dimensions from professionals and police/army group and clerk group differed from students group, clerk group differed from students, house wives and police/army group. Business group differed from clerk and police/army group on the basis of personal interaction dimensions, clerk group differed from professional, house wives and police/army group. Business group differed from housewives, clerk and professional group on the basis of problem solving dimensions; clerk group differed from students, house wives and retired, business, professional group. Clerk group differed significantly on the basis of all policy dimensions from business, professional, student, police/army, house wives and retired occupation group.

258 Effects of family income on service quality dimensions H 2h : There is no significant relationship between family income and retail service quality dimensions. Service quality dimension F P value Physical Aspects ** Reliability ** Personal Interaction ** Problem Solving ** Policy ** ** Significant at 5 per cent level Table Effects of family income on service quality dimensions Inference: Above table shows that p value is less than 0.05 for all service quality dimensions like personal interaction, problem solving and policy dimensions, it exhibited that all the dimensions differed significantly on the basis of family income i.e. respondents from all levels of income perceived the retail service quality dimensions are different. Hence null hypothesis is rejected. It concludes that family income had significantly influence over all service quality dimensions. Post hoc analysis shows that between Rs Rs income group differed significantly on the basis of all service quality dimensions from Between Rs , Between Rs , greater than Rs.60000; Greater than Rs income group differed from all other income groups.

259 Effects of family size on service quality dimensions H 2i : There is no significant relationship between family size and retail service quality dimensions. Service quality dimension F P value Physical Aspects Reliability Personal Interaction Problem Solving Policy Table Effects of family size on service quality dimensions Inference: Above table shows that p value is greater than 0.05 for all service quality dimensions it exhibited that none of the dimensions differed significantly on the basis of family size i.e. respondents from all different size of family perceived the retail service quality dimensions as same. Hence null hypothesis is accepted. It concludes that family size don t have any significantly influence over all service quality dimensions.

260 Effects of family type on service quality dimensions H 2j : There is no significant relationship between family type and retail service quality dimensions. Service quality dimension F P value Physical Aspects Reliability Personal Interaction Problem Solving Policy Table Effects of family type on service quality dimensions Inference: Above table shows that p value is greater than 0.05 for all service quality dimensions it exhibited that none of the dimensions differed significantly on the basis of family type i.e. respondents from all different type of family (joint and nuclear) perceived the retail service quality dimensions as same. Hence null hypothesis is accepted. It concludes that family type don t have any significantly influence over all service quality dimensions.

261 Effects of most preferred purchase mode on service quality dimensions H 2k : There is no significant relationship between most preferred purchase mode and retail service quality dimensions. Service quality dimension F P value Physical Aspects Reliability Personal Interaction Problem Solving Policy Table Effects of most preferred purchase mode on service quality dimensions Inference: Above table shows that p value is greater than 0.05 for all service quality dimensions it exhibited that none of the dimensions differed significantly on the basis of most preferred purchase mode i.e. respondents from most preferred purchase modes perceived the retail service quality dimensions as same. Hence null hypothesis is accepted. It concludes that most preferred purchase mode don t have any significantly influence over all service quality dimensions.

262 : Effects of frequency of shopping on service quality dimensions H 2l : There is no significant relationship between frequency of shopping and retail service quality dimensions. Service quality dimension F P value Physical Aspects ** Reliability ** Personal Interaction ** Problem Solving ** Policy ** ** Significant at 5 per cent level Table : Effects of frequency of shopping on service quality dimensions Inference: Above table shows that p value is less than 0.05 for all service quality dimensions like Physical aspects, reliability, personal interaction, problem solving and policy dimensions, hence null hypothesis is rejected. It concludes that frequency of shopping had significantly influence over service quality all dimensions like personal interaction, problem solving and policy dimensions. For example, Weekly visitors were the regular footfalls at hyper market in comparison to monthly and quarterly visitors. On a weekly basis they (weekly visitors) visit the hyper market and whenever an over- promise is committed they can wait for its fulfillment. For example, hyper market employees promises the customers (weekly visitors) that particular product will be available in 1st week of January, but due to unforeseen circumstances the desired product was not delivered on promised date but will be delivered on

263 240 3rd week of January. This over-promise will not impact the perception of weekly visitors as it will impact the monthly and quarterly visitor s perception. It is because weekly visitors often visit the outlet, they can collect the desire product in their next visit but the same does not hold good for monthly and quarterly visitors. They prefer that whenever the hyper market promises to do repairs, and alterations by a certain times, it will do so and the hyper market provides the services at the time it promises to do so. Post hoc analysis shows that respondents who made purchase at least once in a month group differed significantly on the basis of all physical aspects and personal interaction dimensions from once in three days and once in a week purchase group. Respondents who made purchase at least once in a month group differed significantly on the basis of all reliability dimensions from once in three days purchase group. Respondents who made purchase at least once in a three days group differed significantly on the basis of all policy dimensions from occasionally, once in a week and at least once in a month purchase group.

264 Effects of amounts spent in a month for shopping on service quality dimensions H 2m : There is no significant relationship between amounts spent in a month for shopping and retail service quality dimensions. Service quality dimension F P value Physical Aspects Reliability Personal Interaction ** Problem Solving Policy ** ** Significant at 5 per cent level Table Effects of amounts spent in a month for shopping on service quality dimensions Inference: Above table shows that p value is less than 0.05 for service quality dimensions like personal interaction and policy dimensions, it exhibited that all other dimensions are not significantly differ on the basis of amounts spent in a month for shopping i.e. respondents from different buying power perceived the retail service quality dimensions are different. Hence null hypothesis is rejected. It concludes that amounts spent in a month for shopping had significantly influence over service quality dimensions like personal interaction and policy dimensions. Post hoc analysis shows that respondents who spent amount for shopping between Rs group differed significantly on the basis of all

265 242 physical aspects and reliability dimensions from between Rs group. Respondents who spent amount purchase between Rs group differed significantly on the basis of all personal interaction and policy dimensions from less than Rs.5000 and between Rs group. Respondents who made purchase at least once in a three days group differed significantly on the basis of all policy dimensions from occasionally, once in a week and at least once in a month purchase group.

266 Effects of influencing factor on service quality dimensions H 2n : There is no significant relationship between influencing factor and retail service quality dimensions. Service quality dimension F P value Physical Aspects ** Reliability ** Personal Interaction ** Problem Solving ** Policy ** ** Significant at 5 per cent level Table Effects of influencing factor on service quality dimensions Inference: Above table shows that p value is less than 0.05 for all service quality dimensions, it exhibited that all other dimensions are significantly differ on the basis of influencing factors i.e. respondents from different influencing factors like family, friends, advertisements and other sources are perceived difference in the retail service quality dimensions. Hence null hypothesis is rejected. It concludes that influencing factors had significantly influence over all service quality dimensions. Post hoc analysis shows that other source of influencing factors differed significantly on the basis of all service quality dimensions from influencing factors like advertisement, friends & family, family members and colleagues.

267 Effects of preferred payment modes on service quality dimensions H 2o : There is no significant relationship between preferred payment modes and retail service quality dimensions. Service quality dimension F P value Physical Aspects ** Reliability ** Personal Interaction ** Problem Solving ** Policy ** ** Significant at 5 per cent level Table Effects of preferred payment modes on service quality dimensions Inference: Above table shows that p value is less than 0.05 for all service quality dimensions, it exhibited that all other dimensions are significantly differ on the basis of preferred payment modes i.e. respondents from different payment modes like credit, debit cards, on cash and other retail card usage are perceived difference in the retail service quality dimensions. Hence null hypothesis is rejected. It concludes that influencing factors had significantly influence over all service quality dimensions. Post hoc analysis shows that credit card payment mode differed significantly on the basis of all service quality dimensions from payment modes like on cash and debit card.

268 RELATIVE IMPORTANCE OF SERVICE QUALITY DIMENSIONS ON CUSTOMER LOYALTY DIMENSIONS Testing of hypothesis 3 H 03 : There is no significant relation between retail service quality dimensions and customer loyalty dimensions. H A : There is significant relation between retail service quality dimensions and customer loyalty dimensions. Baumann et al. (2007) demonstrated that customer loyalty is measured by the willingness to recommend, short term and long term intentions to stay with the same company. According to Reichheld (2003) the question: How likely is it that you would recommend company X to a friend or colleague is the most effective question in measuring loyalty, and is based on a survey involving 4,000 consumers across a variety of industries. Similarly, Koskela (2002) puts forward that customers who heard of word-of mouth recommendations need less selling time, have greater loyalty potential, and are ready to buy. According to a study for the US Office of Consumer Affairs, a satisfied customer tells five people about their experience and an unsatisfied person tells eleven people about his experience (Heskett et al., 1997). Sub Hypothesis H 3a : There is no significant relation between retail service quality dimensions and word of mouth. H 3b : There is no significant relation between retail service quality dimensions and switch to competitor.

269 246 H 3c : There is no significant relation between retail service quality dimensions and willing to pay more. H 3d : There is no significant relation between retail service quality dimensions and response of the respondents.

270 RELATIVE IMPORTANCE OF SERVICE QUALITY DIMENSIONS ON WORD OF MOUTH FACTORS H 3a : There is no significant relation between retail service quality dimensions and word of mouth. R R Square Adjusted R Square Std. Error of the Estimate Table Summary Table In terms of the relationship between individual service quality dimensions and word of mouth, the adjusted R square = was statistically significant. It was observed that the retail service quality dimensions explained 4.2 per cent of the variance for the criterion measure. It entailed that 95.8 percent of word of mouth was explained by something other than service quality. Sum of Mean Model 1 df F P value Squares Square Regression Residual ** Total ** Significant at 5 level Table Model fit table (ANOVA) Summary of Regression Analysis treating service quality dimensions as predictors and word of mouth factors as criterion variable was shown below table. It was observed that the overall regression model was significant (F=8.898, p<0.00).

271 248 Unstandardized Standardized Coefficients Coefficients Model1 t value p value Std. B Beta Error (Constant) Physical Aspects Reliability ** Personal Interaction Problem Solving Policy ** Significant at 5 level Table regression analysis results for service quality dimensions and customer loyalty factors Word of mouth factors was primarily determined in a positive manner by reliability (beta=0.229) service quality dimension. Reliability dimension was a key factor in determining customer preference and recommendations to others. The findings showed that reliability dimension was statistically significant in explaining the variance of intention to recommend the retailer to others. Remaining four dimensions namely physical aspects, personal interaction, problem solving and policy did not contribute significantly towards explaining the dependant variable.

272 249 These dimensions were not important constituents in development of intention to recommend the retails to other prospective shoppers. Thus, these dimensions were not retained in the regression. Word of mouth = (Reliability) Hence null hypothesis is rejected; it concludes that there is significant relation between the service quality dimensions and word of mouth factors.

273 RELATIVE IMPORTANCE OF SERVICE QUALITY DIMENSIONS ON SWITCH TO COMPETITOR FACTORS. H 3b : There is no significant relation between retail service quality dimensions and switch to competitor. The customer s every purchase has an effect on the customer relationship and customer loyalty (Koskela, 2002). Customer loyalty is basically the extent of repeat purchase intention from the same service provider with affective commitment (Shemwell et al., 1998; Soderlund, 1998). Additionally, Howat, Crilley & McGrath (2008) put forward that repeat purchase or frequency of visits is an act of behavioural loyalty. Dick and Basu (1994) argue that loyalty is determined by the strength of the relationship between relative attitude and repeat patronage. R R Square Adjusted R Square Std. Error of the Estimate Table Summary Table In terms of the relationship between individual service quality dimensions and switching to competitor, the adjusted R square = was statistically significant. It was observed that the retail service quality dimensions explained 3 per cent of the variance for the criterion measure. It entailed that 97 percent of switching to competitor was explained by something other than service quality.

274 251 Model 1 Sum of Squares df Mean Square F P value Regression Residual ** Total ** Significant at 5 level Table Model fit table (ANOVA) Summary of Regression Analysis treating service quality dimensions as predictors and switching to competitor factors as criterion variable was shown below table. It was observed that the overall regression model was significant (F=6.741, p<0.00). Model1 Unstandardized Coefficients Standardized Coefficients t value p value B Std. Error Beta (Constant) Physical Aspects Reliability ** Personal Interaction Problem Solving ** Policy ** ** Significant at 5 level Table regression analysis results for service quality dimensions and switching to competitor factors Customer loyalty factors was primarily determined in a positive manner by reliability (beta=0.165) and problem solving (beta=0.130) service quality dimension. Policy (beta=-0.233) service quality dimension was a key factor in

275 252 determining customer preference and recommendations to others. The findings showed that reliability, problem solving and policy dimension was statistically significant in explaining the variance of switching to competitors. Remaining dimensions namely physical aspects and personal interaction did not contribute significantly towards explaining the dependant variable. These dimensions were not important constituents in development of switching to competitors the retails to other prospective shoppers. Thus, these dimensions were not retained in the regression. Switching to competitors = (Reliability) (Problem solving) (Policy) Hence null hypothesis is rejected; it concludes that there is significant relation between the service quality dimensions and switching to competitor factors.

276 RELATIVE IMPORTANCE OF SERVICE QUALITY DIMENSIONS ON WILLING TO PAY MORE FACTORS. H 3c : There is no significant relation between retail service quality dimensions and willing to pay more. Loyal customers are willing to pay higher costs for a set of products or services (Gee et al., 2008). In other words, the developed long term relationship of customer loyalty makes loyal customers more prices tolerant, since loyalty discourages customers to have price comparison with others and to shopping around (de Ruyter et al., 1999). Loyal customers are less likely to switch to a competitor due to price inducement, and these customers make more purchases compared to less loyal customers (Baldinger and Rubinson, 1996). R R Square Adjusted R Square Std. Error of the Estimate Table Summary Table The adjusted R square value was which means that 47 percent of the variance in willingness to pay more can be explained by four mentioned antecedents. It conveyed that 53 percent of willingness to pay more was explained by something other than the service quality dimensions.

277 254 Model 1 Sum of Squares df Mean Square F P value Regression Residual ** Total ** Significant at 5 level Table Model fit table (ANOVA) The association between individual dimensions of retail service quality and price sensitivity was statistically significant (F= , p<0.00). The resultant output had an adjusted R square of 0.465, ensuring that the willingness to pay more model was significant. Model1 Unstandardized Coefficients Standardized Coefficients t value p value B Std. Error Beta (Constant) Physical Aspects ** Reliability ** Personal Interaction ** Problem Solving Policy ** ** Significant at 5 level Table regression analysis results for service quality dimensions and willingness to pay more factors According to above table, Physical aspects (beta = 0.105), Reliability (beta =0.168) Policy (beta =0.282) and Personal interaction (beta = 0.233) significantly contribute (p < 0.05) to explain the shoppers willingness to pay more. Policy and personal interaction dimensions are contributing more on explaining the shopper s willingness to pay more. Out of five service quality

278 255 dimensions above mentioned four dimensions were retained in the regression analysis. This implicated that Problem solving dimension did not contribute significantly towards the explanation of the variance. Respondents were not considered them as important determinants while forming their willingness to pay more. Willingness to pay more = (Physical aspects) (Reliability) (Personal interaction) (Policy) Hence null hypothesis is rejected; it concludes that there is significant relation between the service quality dimensions and willingness to pay more factors.

279 RELATIVE IMPORTANCE OF SERVICE QUALITY DIMENSIONS ON SHOPPERS RESPONSE FACTORS. H 3d : There is no significant relation between retail service quality dimensions and response of the respondents. According to Singh (1988), dissatisfaction leads to consumer-complaining behavior that is manifested in voice responses (such as seeking redress from the seller), private responses (negative word-of-mouth communication), or third-party responses (taking legal action). Andreassen (1999) advocated that the negative affect caused by negative disconfirmation of expectations from the initial service encounter may have a negative impact on the customer loyalty. R R Square Adjusted R Square Std. Error of the Estimate Table Summary Table The identified combination of variables were explaining maximum amount of variance in the criterion, as measured by the adjusted coefficient of determination (R square). The adjusted R square = which meant that the five dimensions of service quality explained only 44 per cent of the variation in response (Internal and external response). In other words, service quality was not the only weapon in determining response of customers. Response of the shoppers requires additional dimensions like the subjective probability that complaining will be successful, the attitude towards the act of complaining, the perceived cost of complaining (Day, 1984; Nantel, 1985) and incidental nature of service problems which

280 257 may require incident-based measurement such as the Critical Incident Technique (Bloemer et. al., 1999). Model 1 Sum of Squares df Mean Square F P value Regression Residual ** Total ** Significant at 5 level Table Model fit table (ANOVA) The association between individual dimensions of retail service quality and price sensitivity was statistically significant (F= , p<0.00). The resultant output had an adjusted R square of 0.435, ensuring that the response of the respondents model was significant. Unstandardized Standardized Coefficients Coefficients Model1 t value p value Std. B Beta Error (Constant) Physical Aspects ** Reliability ** Personal Interaction ** Problem Solving Policy ** ** Significant at 5 level Table regression analysis results for service quality dimensions and response of the respondents factors

281 258 According to above table, Physical aspects (beta = 0.119), Reliability (beta =0.159) Policy (beta =0.272) and Personal interaction (beta = 0.201) significantly contribute (p < 0.05) to explain the shoppers response. Policy and personal interaction dimensions are contributing more on explaining the shopper s response. These aspects can really increase perceived service quality and diminish the response of shoppers. It can be elaborated as follows: Retail is the industry with has relatively high service encounter density. It can be characterized as relationship-intensive (Keaveney, 1995). Therefore, most of the shoppers are relatively dependent on this type of service and it seems important that retail outlets deliver on promises regarding its core services, i.e. promises about delivery. If the Policy and personal interaction are kept then only external and internal response to problem will diminish. Out of five services quality dimensions above mentioned four dimensions were retained in the regression analysis. This implicated that Problem solving dimension did not contribute significantly towards the explanation of the variance. Shoppers response = (Physical aspects) (Reliability) (Personal interaction) (Policy) Hence null hypothesis is rejected; it concludes that there is significant relation between the service quality dimensions and response of the respondents.

282 EFFECTS OF DEMOGRAPHIC VARIABLES ON DIFFERENT CUSTOMER LOYALTY DIMENSIONS Testing of hypothesis 4 H 04 : There is no significant relationship between demographic difference of the respondents and customer loyalty dimensions. H A : There is significant relationship between demographic difference of the respondents and customer loyalty dimensions. The demographic data were adopted to examine their association with various retail customer loyalty dimensions. In this study, Analysis of Variance had been used to determine whether these factors were influenced by the demographics. Significance value less than 0.05 indicate existence of some relationship between the independent variable (demographic characteristic) and dependent variables (dimensions of customer loyalty). Sub Hypothesis H 4a: There is no significant relationship between type of retail store of the respondents and customer loyalty dimensions. H 4b: There is no significant relationship between location of the retail store of the respondents and customer loyalty dimensions. H 4c: There is no significant relationship between gender of the respondents and customer loyalty dimensions. H 4d: There is no significant relationship between age of the respondents and customer loyalty dimensions.

283 260 H 4e: There is no significant relationship between marital status of the respondents and customer loyalty dimensions. H 4f: There is no significant relationship between educational qualifications of the respondents and customer loyalty dimensions. H 4g: There is no significant relationship between occupation of the respondents and customer loyalty dimensions. H 4h: There is no significant relationship between family income of the respondents and customer loyalty dimensions. H 4i: There is no significant relationship between family size of the respondents and customer loyalty dimensions. H 4j: There is no significant relationship between family type of the respondents and customer loyalty dimensions. H 4k: There is no significant relationship between preferred purchasing mode and customer loyalty dimensions. H 4l: There is no significant relationship between frequency of shopping and customer loyalty dimensions. H 4m: There is no significant relationship between amount spent in a month for shopping and customer loyalty dimensions. H 4n: There is no significant relationship between influencing factor of the respondents and customer loyalty dimensions. H 4o: There is no significant relationship between preferred mode of payment for shopping and customer loyalty dimensions.

284 Effects of store type on customer loyalty dimensions H 4a : There is no significant relationship between store type and customer loyalty dimensions. Customer loyalty dimension F P value Word of mouth Switching to competitors ** Willingness to pay more ** Response ** ** Significant at 5 per cent level Table Effects of store type on customer loyalty dimensions Inference: Above table shows that p value is less than 0.05 for customer loyalty dimensions like switching to competitors, willingness to pay more and response of the a respondent, hence null hypothesis is rejected. It concludes that store types had significantly influence over customer loyalty dimensions like switching to competitors, willingness to pay more and response of the respondents dimensions. Respondents were found to be differing significantly on the basis of word of mouth dimensions.

285 Effects of location of the store on customer loyalty dimensions H 4b : There is no significant relationship between location of the store and customer loyalty dimensions. Customer loyalty dimension F P value Word of mouth ** Switching to competitors ** Willingness to pay more ** Response ** ** Significant at 5 per cent level Table 6.6.2: Effects of location of the store on customer loyalty dimensions Inference: Above table shows that p value is less than 0.05 for all customer loyalty dimensions like word of mouth, switching to competitors, willingness to pay more and response of the respondents, hence null hypothesis is rejected. It concludes that location of the store had significantly influence over all customer loyalty dimensions like word of mouth, switching to competitors, willingness to pay more and response of the respondents dimensions.

286 Effects of gender on customer loyalty dimensions H 4c : There is no significant relationship between gender and customer loyalty dimensions. Customer loyalty dimension F P value Word of mouth ** Switching to competitors Willingness to pay more Response ** Significant at 5 per cent level Table Effects of gender on customer loyalty dimensions Inference: Above table shows that p value is less than 0.05 for customer loyalty dimensions like word of mouth, hence null hypothesis is rejected. It concludes that gender difference of the respondents had significantly influence over customer loyalty dimension like word of mouth dimensions. Respondents were found to be differing significantly on the basis of switching to competitors, willingness to pay more and response of the respondents. By comparing the mean scores it was revealed that the female shoppers gave far more importance to word of mouth dimension than their male counterparts. Females are a little bit impatient because of their nature. That is why females give more importance to word of mouth in the retails.

287 Effects of age on customer loyalty dimensions H 4d : There is no significant relationship between age and customer loyalty dimensions. Customer loyalty dimension F P value Word of mouth Switching to competitors ** Willingness to pay more ** Response ** ** Significant at 5 per cent level Table Effects of age on customer loyalty dimensions Inference: Above table shows that p value is less than 0.05 for customer loyalty dimensions like switching to competitors, willingness to pay more and response of the respondents, hence null hypothesis is rejected. It concludes that age difference of the respondents had significantly influence over customer loyalty dimensions like switching to competitors, willingness to pay more and response of the respondents dimensions. Respondents were found to be differing significantly on the basis of word of mouth dimensions.

288 265 Table 6.6.5: Effects of marital status on customer loyalty dimensions H 4e : There is no significant relationship between marital status and customer loyalty dimensions. Customer loyalty dimension F P value Word of mouth ** Switching to competitors ** Willingness to pay more Response ** Significant at 5 per cent level Table Effects of marital status on customer loyalty dimensions Inference: Above table shows that p value is less than 0.05 for customer loyalty dimensions like word of mouth and switching to competitors, hence null hypothesis is rejected. It concludes that marital difference of the respondents had significantly influence over customer loyalty dimensions like word of mouth and switching to competitors dimensions. Respondents were found to be differing significantly on the basis of word of willingness to pay more and response of the respondents.

289 Effects of educational qualification on customer loyalty dimensions H 4f : There is no significant relationship between educational qualification and customer loyalty dimensions. Customer loyalty dimension F P value Word of mouth ** Switching to competitors ** Willingness to pay more ** Response ** ** Significant at 5 per cent level Table Effects of educational qualification on customer loyalty dimensions Inference: Above table shows that p value is less than 0.05 for all customer loyalty dimensions like word of mouth, switching to competitors, willingness to pay more and response of the respondents, hence null hypothesis is rejected. It concludes that educational qualification of the respondents had significantly influence over all customer loyalty dimensions like word of mouth, switching to competitors, willingness to pay more and response dimensions.

290 Effects of occupation on customer loyalty dimensions H 4g : There is no significant relationship between occupation and customer loyalty dimensions. Customer loyalty dimension F P value Word of mouth ** Switching to competitors ** Willingness to pay more ** Response ** ** Significant at 5 per cent level Table Effects of occupation on customer loyalty dimensions Inference: Above table shows that p value is less than 0.05 for all customer loyalty dimensions like word of mouth, switching to competitors, willingness to pay more and response of the respondents, hence null hypothesis is rejected. It concludes that occupation of the respondents had significantly influence over all customer loyalty dimensions like word of mouth, switching to competitors, willingness to pay more and response dimensions.

291 Effects of family income on customer loyalty dimensions H 4h : There is no significant relationship between family income and customer loyalty dimensions. Customer loyalty dimension F P value Word of mouth ** Switching to competitors Willingness to pay more ** Response ** ** Significant at 5 per cent level Table Effects of family income on customer loyalty dimensions Inference: Above table shows that p value is less than 0.05 for customer loyalty dimensions like word of mouth, switching to competitors and response of the respondents, hence null hypothesis is rejected. It concludes that family income of the respondents had significantly influence over customer loyalty dimensions like word of mouth, switching to competitors and response dimensions. Respondents were found to be differing significantly on the basis of willingness to pay more dimensions.

292 Effects of family size on customer loyalty dimensions H 4i : There is no significant relationship between family size and customer loyalty dimensions. Customer loyalty dimension F P value Word of mouth ** Switching to competitors ** Willingness to pay more Response ** Significant at 5 per cent level Table 6.6.9: Effects of family size on customer loyalty dimensions Inference: Above table shows that p value is less than 0.05 for customer loyalty dimensions like word of mouth and switching to competitors, hence null hypothesis is rejected. It concludes that family size of the respondents had significantly influence over customer loyalty dimensions like word of mouth and switching to competitors. Respondents were found to be differing significantly on the basis of willingness to pay more and response dimensions.

293 Effects of family type on customer loyalty dimensions H 4j : There is no significant relationship between family type and customer loyalty dimensions. Customer loyalty dimension F P value Word of mouth Switching to competitors ** Willingness to pay more Response ** Significant at 5 per cent level Table Effects of family type on customer loyalty dimensions Inference: Above table shows that p value is less than 0.05 for customer loyalty dimensions like switching to competitors, hence null hypothesis is rejected. It concludes that family size of the respondents had significantly influence over customer loyalty dimensions like switching to competitors. Respondents were found to be differing significantly on the basis of word of mouth, willingness to pay more and response dimensions.

294 Effects of preferred purchase mode on customer loyalty dimensions H 4k : There is no significant relationship between preferred purchase mode and customer loyalty dimensions. Customer loyalty dimension F P value Word of mouth ** Switching to competitors Willingness to pay more Response ** Significant at 5 per cent level Table Effects of preferred purchase mode on customer loyalty dimensions Inference: Above table shows that p value is less than 0.05 for customer loyalty dimensions like word of mouth, hence null hypothesis is rejected. It concludes that preferred purchase mode of the respondents had significantly influence over customer loyalty dimensions like word of mouth. Respondents were found to be differing significantly on the basis of switching to competitor, willingness to pay more and response dimensions.

295 Effects of frequency of shopping on customer loyalty dimensions H 4l : There is no significant relationship between frequency of shopping and customer loyalty dimensions. Customer loyalty dimension F P value Word of mouth ** Switching to competitors ** Willingness to pay more ** Response ** ** Significant at 5 per cent level Table Effects of frequency of shopping on customer loyalty dimensions Inference: Above table shows that p value is less than 0.05 for customer loyalty dimensions like word of mouth, switching to competitor, willingness to pay more and response dimensions; hence null hypothesis is rejected. It concludes that frequency of purchase pattern of the respondents had significantly influence over all customer loyalty dimensions like word of mouth, switching to competitor, willingness to pay more and response dimensions.

296 Effects of amount spent in a month for shopping on customer loyalty dimensions H 4m : There is no significant relationship between amount spent in a month for shopping and customer loyalty dimensions. Customer loyalty dimension F P value Word of mouth Switching to competitors Willingness to pay more ** Response ** ** Significant at 5 per cent level Table Effects of amount spent in a month for shopping on customer loyalty dimensions Inference: Above table shows that p value is less than 0.05 for customer loyalty dimensions like willingness to pay more and response dimensions; hence null hypothesis is rejected. It concludes that amount spent in a month for shopping of the respondents had significantly influence over all customer loyalty dimensions like word of mouth, switching to competitor, willingness to pay more and response dimensions. Respondents were found to be differing significantly on the basis of word of mouth, switching to competitor dimensions.

297 Effects of influencing factors on customer loyalty dimensions H 4n : There is no significant relationship between influencing factors and customer loyalty dimensions. Customer loyalty dimension F P value Word of mouth Switching to competitors Willingness to pay more Response Table : Effects of influencing factors on customer loyalty dimensions Inference: Above table shows that p value is greater than 0.05 for all customer loyalty dimensions; hence null hypothesis is accepted. It concludes that influencing factors don t had significantly influence over all customer loyalty dimensions like word of mouth, switching to competitor, willingness to pay more and response dimensions. Respondents were found to be differing significantly on the basis of word of mouth, switching to competitor dimensions, willingness to pay more and response dimensions.

298 Effects of preferred payment mode on customer loyalty dimensions H 4o : There is no significant relationship between preferred payment mode and customer loyalty dimensions. Customer loyalty dimension F P value Word of mouth ** Switching to competitors ** Willingness to pay more ** Response ** ** Significant at 5 per cent level Table Effects of preferred payment mode on customer loyalty dimensions Inference: Above table shows that p value is less than 0.05 for all customer loyalty dimensions like word of mouth, switching to competitor dimensions, willingness to pay more and response dimensions; hence null hypothesis is rejected. It concludes that preferred payment mode had significantly influence over all customer loyalty dimensions like word of mouth, switching to competitor, willingness to pay more and response dimensions.

299 RELATIONSHIP BETWEEN PERCEIVED SERVICE QUALITY DIMENSIONS Testing of Hypothesis 5 H 05 : There is no significant relationship among perceived service quality dimensions. H A : There is significant relationship among perceived service quality dimensions Inter relationship among perceived service quality dimensions H 05 : There is no significant relationship among perceived service quality dimensions. Perceived service quality dimensions Physical Aspects ** ** ** ** Reliability ** ** ** Personal Interaction ** ** Problem Solving ** Policy 1 ** Significant at 1 per cent level Table Inter relationship among perceived service quality dimensions Above table shows the inter correlation between the perceived service quality dimensions. Correlation coefficient between physical aspects and reliability dimension is 0.828, it means that physical aspects having 83 per cent positive effect on reliability dimension. Correlation coefficient between physical aspects and personal interaction dimension is 0.842, it means that physical

300 277 aspects having 84 per cent positive effect on personal interaction dimension. Correlation coefficient between physical aspects and problem solving dimension is 0.779, it means that physical aspects having 78 per cent positive effect on problem solving dimension. Correlation coefficient between physical aspects and reliability dimension is 0.580, it means that physical aspects having 58 per cent positive effect on policy dimension. Correlation coefficient between reliability and personal interaction dimension is 0.850; it means that reliability having 85 per cent positive effect on personal interaction dimension. Correlation coefficient between reliability and problem solving dimension is 0.742; it means that reliability having 74 per cent positive effect on problem solving dimension. Correlation coefficient between reliability and policy dimension is 0.591; it means that reliability having 59 per cent positive effect on policy dimension. Correlation coefficient between personal interaction and problem solving dimension is 0.797; it means that personal interaction having 80 per cent positive effect on problem solving dimension. Correlation coefficient between personal interaction and policy dimension is 0.619; it means that personal interaction having 62 per cent positive effect on policy dimension. Correlation coefficient between problem solving and policy dimension is 0.667; it means that problem solving having 68 per cent positive effect on policy dimension.

301 RELATIONSHIP BETWEEN CUSTOMER LOYALTY DIMENSIONS Testing of Hypothesis 6 H 06 : There is no significant relationship among customer loyalty dimensions. H A : There is significant relationship among customer loyalty dimensions Interrelationship among customer loyalty dimensions H 06 : There is no significant relationship among customer loyalty dimensions. Customer loyalty dimensions Word of mouth **.277 ** Switch to competitor Willing to Pay more ** Responses 1 ** Significant at 1 per cent level Table 6.8.1: Interrelationship among customer loyalty dimensions Inference: Above table shows the inter correlation between the customer loyalty dimensions. Correlation coefficient between word of mouth and switching to competitor dimension is , it means that word of mouth and switching to competitor dimensions don t have any relationship. Correlation coefficient between word of mouth and willingness to pay more dimensions is 0.322, it means that word of mouth having 32 per cent positive effect on willingness to pay more and correlation coefficient between word of mouth and response of

302 279 the customers dimension 0.277, and it means that word of mouth having 28 per cent effect on response dimensions. Correlation coefficient between switching to competitor and willingness to pay more and response dimension is 0.024, 0.035, it means that switching to competitor don t have relation with willingness to pay more and response dimension. Correlation coefficient between willingness to pay more and response dimension is 0.972; it shows that willingness to pay more having 97 per cent effect on response dimensions.

303 RELATIVE IMPORTANCE OF SERVICE QUALITY DIMENSIONS ON CUSTOMER SATISFACTION Testing of Hypothesis 7 H 07 : There is no significant relation between retail service quality dimensions and customer satisfaction. H A : There is significant relation between retail service quality dimensions and customer satisfaction. Satisfying customers is one of the main objectives of every business. Businesses recognize that keeping current customers is more profitable than having to win new ones to replace those lost. Management and marketing theorists underscore the importance of customer satisfaction for a business s success (McColl-Kennedy & Schneider, 2000; Reichheld & Sasser, 1990). Accordingly, the prestigious Malcolm Baldrige National Quality Award recognizes the role of customer satisfaction as the central component of the award process (Dutka, 1993). Services and products are the two major orientations of business. Products also referred to as goods, are the physical output of a business. These are tangible objects that exist in time and space. These are first created, then inventoried and sold. It is after purchase that these are actually consumed (Sureshchander, Rajendran, & Kamalanabhan, 2001; Berry, 1980).The concept of customer satisfaction is composed of several components from distinct sources (McColl-Kennedy & Schneider, 2006). Customer satisfaction begins with clear, operational definitions from both the customer and the organization. Understanding the motivations, expectations, and desires of both

304 281 gives a foundation in how to best serve the customer. It may even provide information on making improvements in the nature of business. This is the heart of research into customer satisfaction (Naylor & Greco, 2002). The importance of clearly defining the key concepts and elements of satisfaction provide a template by which information can be gathered about what is, and what is not, working. This includes both the hard measures those that are more tangible and observable (i.e., number of complaints, average waits time, product returns, etc) and the soft measures those less tangible aspects (i.e., friendliness, helpfulness, politeness, etc) (Hayes, 1998). These definitions often start with the most vague and general, and become more to the highly specified and precise examples. The bottom line is that in order to know about customer satisfaction, one needs to know what to look for (Mitchell, 1999). The organization needs to seek this information from both within and without. R R Square Adjusted R Square Std. Error of the Estimate Table Summary Table In terms of the relationship between individual service quality dimensions and customer satisfaction, the adjusted R square = was statistically significant. It was observed that the retail service quality dimensions explained 67 per cent of the variance for the criterion measure. It entailed that 33 percent of customer satisfaction was explained by something other than service quality.

305 282 Model 1 Sum of Mean df Squares Square F P value Regression Residual ** Total ** Significant at 5 level Table 6.9.2: Model fit table (ANOVA) Summary of Regression Analysis treating service quality dimensions as predictors and customer satisfaction factors as criterion variable was shown below table. It was observed that the overall regression model was significant (F= , p<0.00). Unstandardized Standardized Coefficients Coefficients Model1 t value p value Std. B Beta Error (Constant) Physical Aspects ** Reliability Personal Interaction ** Problem Solving Policy ** Significant at 5 level Table regression analysis results for service quality dimensions and customer satisfaction factors Customer satisfaction factors was primarily determined in a positive manner by physical aspects (beta=0.423) and personal interaction (beta=0.397) service

306 283 quality dimension. The findings showed that physical aspects and personal interaction dimensions were statistically significant in explaining the variance of retail customer satisfaction. Remaining four dimensions namely reliability, problem solving and policy did not contribute significantly towards explaining the dependant variable. These dimensions were not important constituents in development of customer satisfaction the retails to other prospective shoppers. Thus, these dimensions were not retained in the regression. Customer satisfaction = (Physical aspects) (Personal interaction) Hence null hypothesis is rejected; it concludes that there is significant relation between the service quality dimensions and customer satisfaction factors.

307 RELATIVE IMPORTANCE OF CUSTOMER SATISFACTION FACTORS ON CUSTOMER LOYALTY. Testing of Hypothesis 8 H 08 : There is no significant relation between customer satisfaction factors and customer loyalty. H A : There is significant relation between customer satisfaction factors and customer loyalty. Retail customer loyalty provides the foundation of a company s sustained competitive edge, and is a crucial component of a company s growth and performance (Lee and Cunningham, 2001; Reichheld, 1996). Researchers suggest that Retail customer loyalty is a key variable in explaining customer retention (Pritchard and Howard, 1997) and is determined by a combination of repeat purchase level and a general level of attachment (Bodet, 2008 & Dick and Basu, 1994). The latter relates to an individual customer s attitude towards a product, service or organization (Hallowell, 1996). Other researchers suggest that Retail customer loyalty is a behavioral construct. This includes customer retention, repeat purchases and positive word of mouth (Hallowell, 1996; Liu and Wu, 2007). As these differences are rather slight, Retail customer loyalty and retention will be considered synonymous in the context of this study. Whilst there is considerable evidence that customer satisfaction is correlated with loyalty, most scholars are of the view that not all satisfied customers will be loyal; neither will all customers abandon a retail chain due to dissatisfaction with a particular store (Vazquez-Carrasco and Foxall, 2006; Ellram et al, 1999). Shankar et al (2003) suggest that if customers have a negative experience and become dissatisfied with a service provider, they might gain a

308 285 higher level of satisfaction by switching to a new provider. However, in this instance, the customer risks incurring losses in the form of loyalty benefits, such as emotional investment and the benefits lost from a rewards program. In addition, he/she may face a potentially unfamiliar service encounter with the new company. These factors therefore act as a deterrent. Yang and Zhu (2006) expand on the relationship between satisfaction and loyalty. The authors argue that the relationship is influenced by the competitive environment of the market. In markets where the competition is not intense, customers are likely to remain loyal despite their levels of satisfaction. On the contrary, in markets where competition is intense, a slight drop in satisfaction will cause a considerable drop in loyalty. This ultimately results in the customer switching Retail customers or even retail chains (Gomez et al, 2004)

309 286 R R Square Adjusted R Square Std. Error of the Estimate Table Summary Table In terms of the relationship between individual customer satisfaction and customer loyalty, the adjusted R square = was statistically significant. It was observed that the retail customer satisfaction explained 22 per cent of the variance for the criterion measure. It entailed that 78 percent of customer loyalty in retail shop was explained by something other than customer satisfaction. Model 1 Sum of Squares Df Mean Square F P value Regression Residual ** Total ** Significant at 5 level Table : Model fit table (ANOVA) Summary of Regression Analysis treating customer satisfaction as predictors and customer loyalty factors as criterion variable was shown below table. It was observed that the overall regression model was significant (F= , p<0.00).

310 287 Model1 Unstandardized Coefficients Standardized Coefficients t value p value B Std. Error Beta (Constant) Product ** Service ** ** Significant at 5 level Table regression analysis results for service quality dimensions and customer satisfaction factors Customer loyalty factors was primarily determined in a positive manner by service satisfaction (beta=0.325) and product satisfaction (beta=0.212) factors. The findings showed that service and product satisfaction were statistically significant in explaining the variance of retail customer loyalty. Customer satisfaction = (Product satisfaction) (Service satisfaction) Hence null hypothesis is rejected; it concludes that there is significant relation between the customer satisfaction and customer loyalty.

311 EFFECTS OF DEMOGRAPHIC VARIABLES ON CUSTOMER SATISFACTION FACTORS Testing of Hypothesis 9 H 09 : There is no significant relationship between demographic difference of the respondents and customer satisfaction factors H A : There is significant relationship between demographic difference of the respondents and customer satisfaction factors The demographic data were adopted to examine their association with various retail customer satisfaction factors. In this study, Analysis of Variance had been used to determine whether these factors were influenced by the demographics. Significance value less than 0.05 indicate existence of some relationship between the independent variable (demographic characteristic) and dependent variables (dimensions of customer satisfaction). Sub Hypothesis H 9a: There is no significant relationship between type of retail store of the respondents and customer satisfaction factors. H 9b: There is no significant relationship between location of the retail store of the respondents and customer satisfaction factors. H 9c: There is no significant relationship between gender of the respondents and customer satisfaction factors. H 9d: There is no significant relationship between age of the respondents and customer satisfaction factors.

312 289 H 9e: There is no significant relationship between marital status of the respondents and customer satisfaction factors. H 9f: There is no significant relationship between educational qualifications of the respondents and customer satisfaction factors. H 9g: There is no significant relationship between occupation of the respondents and customer satisfaction factors. H 9h: There is no significant relationship between family income of the respondents and customer satisfaction factors. H 9i: There is no significant relationship between family size of the respondents and customer satisfaction factors. H 9j: There is no significant relationship between family type of the respondents and customer satisfaction factors. H 9k: There is no significant relationship between preferred purchasing mode and customer satisfaction factors. H 9l: There is no significant relationship between frequency of shopping and customer satisfaction factors. H 9m: There is no significant relationship between amount spent in a month for shopping and customer satisfaction factors. H 9n: There is no significant relationship between influencing factor of the respondents and customer satisfaction factors.

313 Effects of store type on customer satisfaction factors H 9a : There is no significant relationship between store type and customer satisfaction factors. Customer satisfaction factors F P value Product satisfaction ** Service satisfaction ** ** Significant at 5 per cent level Table Effects of store type on customer satisfaction factors Inference: Above table shows that p value is less than 0.05 for all customer satisfaction factors like product satisfaction and service satisfaction; hence null hypothesis is rejected. It concludes that store type had significantly influence over all customer satisfaction factors like product satisfaction and service satisfaction. Post hoc analysis shows that level of customer satisfaction may differ according to the each types of retail outlet.

314 : Effects of location of the store on customer satisfaction factors H 9b : There is no significant relationship between location of the store and customer satisfaction factors. Customer satisfaction factors F P value Product satisfaction ** Service satisfaction ** ** Significant at 5 per cent level Table Effects of location of the store on customer satisfaction factors Inference: Above table shows that p value is less than 0.05 for all customer satisfaction factors like product satisfaction and service satisfaction; hence null hypothesis is rejected. It concludes that store location had significantly influence over all customer satisfaction factors like product satisfaction and service satisfaction. For the present study three different locations like Chennai, Coimbatore and Madurai are taken. Post hoc test result shows that each location has different product qualities and service. So level of customer satisfaction may differ according to the location of retail outlet.

315 Effects of gender difference on customer satisfaction factors H 9c : There is no significant relationship between gender difference and customer satisfaction factors. Customer satisfaction factors F P value Product satisfaction Service satisfaction Table Effects of gender difference on customer satisfaction factors Inference: Above table shows that p value is greater than 0.05 for all customer satisfaction factors like product satisfaction and service satisfaction; hence null hypothesis is accepted. It concludes that gender difference had no significantly influence over all customer satisfaction factors like product satisfaction and service satisfaction. Both male and female customer expects same level of customer satisfaction.

316 Effects of age difference on customer satisfaction factors H 4d : There is no significant relationship between age difference and customer satisfaction factors. Customer satisfaction factors F P value Product satisfaction ** Service satisfaction ** ** Significant at 5 per cent level Table Effects of age difference on customer satisfaction factors Inference: Above table shows that p value is less than 0.05 for all customer satisfaction factors like product satisfaction and service satisfaction; hence null hypothesis is rejected. It concludes that age difference had significantly influence over all customer satisfaction factors like product satisfaction and service satisfaction. Post hoc analysis shows that age group between years old differs with between years and years age group with respect to customer satisfaction.

317 Effects of marital status difference on customer satisfaction factors H 9e : There is no significant relationship between marital status difference and customer satisfaction factors. Customer satisfaction factors F P value Product satisfaction ** Service satisfaction ** ** Significant at 5 per cent level Table Effects of marital status difference on customer satisfaction factors Inference: Above table shows that p value is less than 0.05 for all customer satisfaction factors like product satisfaction and service satisfaction; hence null hypothesis is rejected. It concludes that marital status difference had significantly influence over all customer satisfaction factors like product satisfaction and service satisfaction. Post hoc analysis shows that married and unmarried group differs with between widow and divorced group with respect to customer satisfaction.

318 Effects of Qualification difference on customer satisfaction factors H 9f : There is no significant relationship between Qualification difference and customer satisfaction factors. Customer satisfaction factors F P value Product satisfaction Service satisfaction Table Effects of Qualification difference on customer satisfaction factors Inference: Above table shows that p value is greater than 0.05 for all customer satisfaction factors like product satisfaction and service satisfaction; hence null hypothesis is accepted. It concludes that qualification difference had no significantly influence over all customer satisfaction factors like product satisfaction and service satisfaction. Different education qualification group respondents also expect same level of customer satisfaction from the retail shops.

319 Effects of occupational difference on customer satisfaction factors H 9g : There is no significant relationship between occupational difference and customer satisfaction factors. Customer satisfaction factors F P value Product satisfaction Service satisfaction ** ** Significant at 5 per cent level Table Effects of occupational difference on customer satisfaction factors Inference: Above table shows that p value is less than 0.05 for customer satisfaction factor like service satisfaction; hence null hypothesis is rejected. It concludes that occupation difference had significantly influence over customer satisfaction factor like service satisfaction. Post hoc test shows that there is difference exists between unemployed respondents and occupation groups like business, professionals, students, housewife and retired personals on service customer satisfaction.

320 Effects of family income difference on customer satisfaction factors H 9h : There is no significant relationship between family income difference and customer satisfaction factors. Customer satisfaction factors F P value Product satisfaction ** Service satisfaction ** ** Significant at 5 per cent level Table Effects of family income difference on customer satisfaction factors Inference: Above table shows that p value is less than 0.05 for all customer satisfaction factors like product satisfaction and service satisfaction; hence null hypothesis is rejected. It concludes that family income difference had significantly influence over all customer satisfaction factors like product satisfaction and service satisfaction. Post hoc analysis shows that greater than Rs income groups differ significantly with other income group respect to customer satisfaction.

321 Effects of family size difference on customer satisfaction factors H 9i : There is no significant relationship between family size difference and customer satisfaction factors. Customer satisfaction factors F P value Product satisfaction Service satisfaction Table Effects of family size difference on customer satisfaction factors Inference: Above table shows that p value is greater than 0.05 for all customer satisfaction factors like product satisfaction and service satisfaction; hence null hypothesis is accepted. It concludes that family size difference had no significantly influence over all customer satisfaction factors like product satisfaction and service satisfaction. Different family sizes of the respondents also expect same level of customer satisfaction from the retail shops. There is no difference on their customer satisfaction level.

322 Effects of family type difference on customer satisfaction factors H 9j : There is no significant relationship between family type difference and customer satisfaction factors. Customer satisfaction factors F P value Product satisfaction Service satisfaction Table Effects of family type difference on customer satisfaction factors Inference: Above table shows that p value is greater than 0.05 for all customer satisfaction factors like product satisfaction and service satisfaction; hence null hypothesis is accepted. It concludes that family type difference had no significantly influence over all customer satisfaction factors like product satisfaction and service satisfaction. Different family types of the respondents also expect same level of customer satisfaction from the retail shops. There is no difference on their customer satisfaction level.

323 Effects of preferred purchase mode on customer satisfaction factors H 9k : There is no significant relationship between preferred purchase mode and customer satisfaction factors. Customer satisfaction factors F P value Product satisfaction Service satisfaction Table Effects of preferred purchase mode on customer satisfaction factors Inference: Above table shows that p value is greater than 0.05 for all customer satisfaction factors like product satisfaction and service satisfaction; hence null hypothesis is accepted. It concludes that preferred purchase mode had no significantly influence over all customer satisfaction factors like product satisfaction and service satisfaction. Preferred purchase modes of the respondents also expect same level of customer satisfaction from the retail shops.

324 Effects of frequency of purchase on customer satisfaction factors H 9l : There is no significant relationship between frequency of purchase and customer satisfaction factors. Customer satisfaction factors F P value Product satisfaction Service satisfaction Table Effects of frequency of purchase on customer satisfaction factors Inference: Above table shows that p value is greater than 0.05 for all customer satisfaction factors like product satisfaction and service satisfaction; hence null hypothesis is accepted. It concludes that frequency of purchase had no significantly influence over all customer satisfaction factors like product satisfaction and service satisfaction. Frequency of purchase of the respondents also expects same level of customer satisfaction from the retail shops.

325 Effects of amounts spent in a month for shopping on customer satisfaction factors H 9m : There is no significant relationship between amounts spent in a month for shopping and customer satisfaction factors. Customer satisfaction factors F P value Product satisfaction ** Service satisfaction ** Significant at 5 per cent level Table Effects of amounts spent in a month for shopping on customer satisfaction factors Inference: Above table shows that p value is less than 0.05 for customer satisfaction factor like product satisfaction; hence null hypothesis is rejected. It concludes that amounts spent in a month for shopping had significantly influence over customer satisfaction factor like product satisfaction. Post hoc test shows that between Rs amounts spent in a month for shopping group differ significantly with less than Rs.5000 and between Rs amounts spent in a month for shopping groups on customer satisfaction.

326 Effects of influencing factor on customer satisfaction factors H 9n : There is no significant relationship between influencing factor and customer satisfaction factors. Customer satisfaction factors F P value Product satisfaction Service satisfaction ** ** Significant at 5 per cent level Table Effects of influencing factor on customer satisfaction factors Inference: Above table shows that p value is less than 0.05 for customer satisfaction factor like service satisfaction; hence null hypothesis is rejected. It concludes that amounts spent in a month for shopping had significantly influence over customer satisfaction factor like service satisfaction. Post hoc test shows that other influencing factors like store sales person differ significantly on remaining influencing factors like advertisement, family, friends, colleagues and relatives on service satisfaction.

327 Effects of preferred mode of payment on customer satisfaction factors H 9o : There is no significant relationship between preferred mode of payment and customer satisfaction factors. Customer satisfaction factors F P value Product satisfaction ** Service satisfaction ** ** Significant at 5 per cent level Table Effects of preferred mode of payment on customer satisfaction factors Inference: Above table shows that p value is less than 0.05 for all customer satisfaction factors like product satisfaction and service satisfaction; hence null hypothesis is rejected. It concludes that preferred mode of payment had significantly influence over all customer satisfaction factors like product satisfaction and service satisfaction. Post hoc analysis shows that credit card payment mode differ significantly on cash and debit card payment modes with respect to customer satisfaction.

328 RELATIVE IMPORTANCE OF FACTOR AFFECTS PURCHASE ON CUSTOMER SATISFACTION Testing of Hypothesis 10 H 10 : There is no significant relation between factors affecting purchase and customer satisfaction. H A : There is significant relation between factors affecting purchase and customer satisfaction. Purchase or behavioral intention is used to demonstrate intention of buyers to buy goods or services (J. S. Armstrong, V. G. Morwitz, and V. Kumar, 2000). Consumer s decision is based on a complex set of factors such as quality, value, and satisfaction, which can directly influence behavioral intention (J. J. Joseph Cronin, M. K. Brandy, and G. T. M. Hult, 2000). Intentions have normally been accepted as the cognitive component of an attitude and it is usually assumed that this cognitive component is associated with the attitude s affective component (M. Fishbein and I. Ajzen, 1975). Purchase intention is more suitable for short time measurement than for long time measurement (V. G. Morwitz, J. H. Steckel, and A. Gupta, 2007). Purchase intention indicates the customer s intention to repurchase, intention of cross buying-purchase another product from the same company, and Intention can be used to describe customer s satisfaction (H. J. R. Juhl, K. Kristensen, and P. Stergaard, 2002).

329 306 R R Square Adjusted R Square Std. Error of the Estimate Table Summary Table In terms of the relationship between individual factor affects purchase and customer satisfaction, the adjusted R square = was statistically significant. It was observed that the factor affects purchase explained 19 per cent of the variance for the criterion measure. It entailed that 81 percent of customer satisfaction was explained by something other than factor affects purchase. Mean Model 1 Sum of Squares df F P value Square Regression Residual ** Total ** Significant at 5 level Table Model fit table (ANOVA) Summary of Regression Analysis treating factor affects purchase as predictors and customer satisfaction factors as criterion variable was shown below table. It was observed that the overall regression model was significant (F=31.429, p<0.00).

330 307 Model1 Unstandardized Coefficients Standardized Coefficients t value p value B Std. Error Beta (Constant) Pricing of various branded Products ** Availability of Various branded ** Products Quality of Various branded Products Overall Sales Promotional Activities Overall After Sales Services ** Arrangement of the Products ** Waiting time for billing ** ** Significant at 5 level Table regression analysis results for factor affects purchase and customer satisfaction factors Customer satisfaction factors was primarily determined in a positive manner by pricing of various branded products (beta=0.279) factor affects purchase. The findings showed that overall after sales services, arrangement of the products, waiting time for billing and availability of various branded products

331 308 were also statistically significant in explaining the variance of retail customer satisfaction. Remaining three factors namely overall sales promotional activities and quality of various branded products did not contribute significantly towards explaining the dependant variable. These dimensions were not important constituents in development of customer satisfaction the retails to other prospective shoppers. Thus, these dimensions were not retained in the regression. Customer satisfaction = (Pricing of various branded Products) (availability of various branded products) (Overall After Sales Services) (Arrangement of the Products) (Waiting time for billing) Hence null hypothesis is rejected; it concludes that there is significant relation between the factors affect purchase and customer satisfaction factors.

332 RELATIONSHIP BETWEEN PURCHASING FACTORS, CUSTOMER SATISFACTION, EXPECT & PERCEIVED SERVICE QUALITY AND CUSTOMER LOYALTY Testing of Hypothesis 11 H 11 : There is no significant relationship between purchasing factors, customer satisfaction, expects & perceived service quality and customer loyalty H A : There is significant relationship between purchasing factors, customer satisfaction, expects & perceived service quality and customer loyalty Correlation Purchase intension factors ** ** ** ** Expected service quality ** ** ** Perceived service quality ** ** Customer satisfaction ** Customer loyalty 1 ** Significant at 1 level Table Inter correlation between purchasing factors, customer satisfaction, expect & perceived service quality and customer loyalty. Above table indicates that purchase intension factors have significant and positive relation with expected service quality (Correlation coefficient r=0.942, p<0.00), perceived service quality (Correlation coefficient r=0.953, p<0.00), customer satisfaction (Correlation coefficient r=0.779, p<0.00) and customer loyalty factors (Correlation coefficient r=0.560, p<0.00). Expected service quality have significant and positive relation with perceived service quality (Correlation coefficient r=0.999, p<0.00), customer satisfaction

333 310 (Correlation coefficient r=0.784, p<0.00) and customer loyalty factors (Correlation coefficient r=0.598, p<0.00). Perceived service quality have significant and positive relation with customer satisfaction (Correlation coefficient r=0.788, p<0.00) and customer loyalty factors (Correlation coefficient r=0.597, p<0.00). Customer satisfaction have significant and positive relation with customer loyalty factors (Correlation coefficient r=0.471, p<0.00).

334 EFFECTS OF MOST LIKED AND DISLIKED FACTORS ON PURCHASE ATTRIBUTE FACTORS Testing of Hypothesis 12 H 12 : There is no significant relationship between most liked and disliked factors and purchase attribute factors H A : There is significant relationship between most liked and disliked factors and purchase attribute factors Most liked and disliked factors associated with retail purchase were adopted to examine their association with various retail purchase attribute factors. In this study, Analysis of Variance had been used to determine whether these factors were influenced by the liked and disliked factors. Significance value less than 0.05 indicate existence of some relationship between the independent variable (liked and disliked factors) and dependent variables (purchase attribute factors). Sub Hypothesis H 12a : There is no significant relationship between most liked factors and customer purchase attribute factors. H 12b : There is no significant relationship between most disliked factors and customer purchase attribute factors.

335 Effects of most liked factors on purchase attribute factors H 12a : There is no significant relationship between most liked factors and customer purchase attribute factors. Purchase attribute factors F P value Good place for Entertainment ** Family Shopping ** Value for Money Emergency and Safety Measures ** ** Significant at 5 per cent level Table Effects of most liked factors on purchase attribute factors Above table shows that p value is less than 0.05 for purchase attribute factors like good place of entertainment, family shopping and emergency and safety measures; hence null hypothesis is rejected. It concludes that most liked factors had significantly influence over purchase attribute factors like good place of entertainment, family shopping and emergency and safety measures.

336 Effects of most disliked factors on purchase attribute factors H 12b : There is no significant relationship between most disliked factors and customer purchase attribute factors. Purchase attribute factors F P value Good place for Entertainment Family Shopping ** Value for Money Emergency and Safety Measures ** Significant at 5 per cent level Table Effects of most disliked factors on purchase attribute factors Inference: Above table shows that p value is less than 0.05 for purchase attribute factors like family shopping; hence null hypothesis is rejected. It concludes that most disliked factors had significantly influence over purchase attribute factors like family shopping.

337 EFFECTS OF MOST LIKED AND DISLIKED FACTORS ON FACTORS AFFECTING RETAIL PURCHASE Testing of Hypothesis 13 H 13 : There is no significant relationship between most liked and disliked factors and factors affecting retail purchase H A : There is significant relationship between most liked and disliked factors and factors affecting retail purchase Most liked and disliked factors associated with retail purchase were adopted to examine their association with various retail purchase attribute factors. In this study, Analysis of Variance had been used to determine whether these factors were influenced by the liked and disliked factors. Significance value less than 0.05 indicate existence of some relationship between the independent variable (liked and disliked factors) and dependent variables (factors affecting retail purchase). Sub Hypothesis H 13a : There is no significant relationship between most liked factors and factors affecting retail purchase. H 13b : There is no significant relationship between most disliked factors and factors affecting retail purchase.

338 Effects of most liked factors on factors affecting retail purchase H 13a : There is no significant relationship between most liked factors and factors affecting retail purchase. Factors affecting retail purchase. F P value Pricing of various branded Products ** Availability of Various branded Products ** Quality of Various branded Products ** Overall Sales Promotional Activities ** Overall After Sales Services ** Arrangement of the Products ** Waiting time for billing ** ** Significant at 5 per cent level Table Effects of most liked factors on factors affecting retail purchase Inference: Above table shows that p value is less than 0.05 for factors affecting retail purchase like pricing of various branded products, availability of various branded products, quality of various branded products, overall sales promotional activities, overall after sales services, arrangement of the products and waiting time for billing; hence null hypothesis is rejected. It concludes that most liked factors had significantly influence over factors affecting retail purchase like pricing of various branded products, availability of various branded products, quality of various branded products, overall sales promotional activities, overall after sales services, arrangement of the products and waiting time for billing. Post hoc analysis shows that sales promotion and

339 316 discount liking factors differ significantly with other liked factors with reference to factors affecting retail purchase.

340 : Effects of most disliked factors on factors affecting retail purchase H 13b : There is no significant relationship between most disliked factors and factors affecting retail purchase. Factors affecting retail purchase. F P value Pricing of various branded Products ** Availability of Various branded Products ** Quality of Various branded Products ** Overall Sales Promotional Activities ** Overall After Sales Services ** Arrangement of the Products ** Waiting time for billing ** ** Significant at 5 per cent level Table Effects of most disliked factors on factors affecting retail purchase Inference: Above table shows that p value is less than 0.05 for factors affecting retail purchase like pricing of various branded products, availability of various branded products, quality of various branded products, overall sales promotional activities, overall after sales services, arrangement of the products and waiting time for billing; hence null hypothesis is rejected. It concludes that most disliked factors had significantly influence over factors affecting retail purchase like pricing of various branded products, availability of various branded products, quality of various branded products, overall sales

341 318 promotional activities, overall after sales services, arrangement of the products and waiting time for billing.

342 DIFFERENCE BETWEEN PERCEPTION AND EXPECTATION SERVICE QUALITY DIMENSIONS Testing of hypothesis 14 H 14 : There is no significant difference between perception and expectation service quality dimensions. H A : There is significant difference between perception and expectation service quality dimensions. Pair statements Pair 1 Pair 2 Pair 3 Pair 4 Pair 5 Perception & Expectation rating for physical aspects dimensions Perception & Expectation rating for reliability dimensions Perception & Expectation rating for personal interaction dimensions Perception & Expectation rating for problem solving dimensions Perception & Expectation rating for policy dimensions Paired Differences Mean SD t value p value ** ** ** ** ** ** Significant at 5 per cent level Table Difference between perception and expectation service quality dimensions

343 320 Inference: Above table shows the gap between the perception and expectation service ratings. Expectation ratings are significantly lesser than perception ratings of service quality for all pairs. Since p value is less than 0.05 for all service quality pair, hence null hypothesis is rejected. It can be conclude that there is a significant difference between all perception and expectation service quality dimensions.

344 ASSOCIATION BETWEEN DEMOGRAPHIC DIFFERENCE OF THE RESPONDENTS AND MOST LIKED AND DISLIKED FACTORS ABOUT RETAIL SHOPS Testing of Hypothesis 15 H 15 : There is no association between demographics and most liked and disliked factors about retail shops. H A : There is association between demographics and most liked and disliked factors about retail shops. This part of analysis is used to find the associations between demographic details of the respondents and most liked and disliked factors of retail stores. Chi square analysis used to find the associations between the variables. Significance value less than 0.05 indicate existence of some association between both variables. Sub Hypothesis H 15a : There is no association between demographics and most liked factors about retail shops. H 15b : There is no association between demographics and most disliked factors about retail shops.

345 Association between demographic difference of the respondents and most liked factors about retail shops. H 15a : There is no association between demographics and most liked factors about retail shops. Demographic profile Chi square Value p value Result Type of Retail store ** Association Location of the retail store ** Association Gender No Association Age ** Association Marital Status ** Association Educational Qualifications ** Association Occupation ** Association Family Income ** Association Family Size ** Association Family Type ** Association Preferred Purchasing Mode No Association Frequency of Shopping ** Association Amount spent in a month for shopping ** Association Influencing factor ** Association Preferred Mode of Payment ** Association ** Significant at 5 per cent level Table Association between demographic difference of the respondents and most liked factors about retail shops.

346 323 Inference: Above table indicates that significance of chi square value is less than 0.05 for the demographic variables like type of retail store, location of the retail store, age, marital status, educational qualifications, occupation, family income, family size, family type, frequency of shopping, amount spent in a month for shopping, influencing factor and preferred mode of payment. Hence null hypothesis is rejected. It concludes that demographic variables like type of retail store, location of the retail store, age, marital status, educational qualifications, occupation, family income, family size, family type, frequency of shopping, amount spent in a month for shopping, influencing factor and preferred mode of payment have association between most liked factors about the retail shops. Since significance of chi square value is greater than 0.05 for the demographic variables like gender and preferred purchasing mode. So there is no association between gender and preferred purchasing mode with most liked factors about the retail shops.

347 Association between demographic difference of the respondents and most disliked factors about retail shops. H 15b : There is no association between demographics and most disliked factors about retail shops. Demographic profile Chi square Value p value Result Type of Retail store ** Association Location of the retail store ** Association Gender ** Association Age ** Association Marital Status No Association Educational Qualifications ** Association Occupation ** Association Family Income ** Association Family Size ** Association Family Type No Association Preferred Purchasing Mode ** Association Frequency of Shopping ** Association Amount spent in a month for shopping ** Association Influencing factor ** Association Preferred Mode of Payment ** Association ** Significant at 5 per cent level Table Association between demographic difference of the respondents and most disliked factors about retail shops.

348 325 Inference: Above table indicates that significance of chi square value is less than 0.05 for the demographic variables like type of retail store, location of the retail store, age, gender, educational qualifications, occupation, family income, family size, preferred purchase mode, frequency of shopping, amount spent in a month for shopping, influencing factor and preferred mode of payment. Hence null hypothesis is rejected. It concludes that demographic variables like type of retail store, location of the retail store, age, gender, educational qualifications, occupation, family income, family size, preferred purchase mode, frequency of shopping, amount spent in a month for shopping, influencing factor and preferred mode of payment have association between most disliked factors about the retail shops. Since significance of chi square value is greater than 0.05 for the demographic variables like marital status and family type. So there is no association between marital status and family type with most disliked factors about the retail shops.

349 LEVEL OF SERVICE QUALITY, CUSTOMER SATISFACTION AND CUSTOMER LOYALTY Level of Perceived service quality, Customer satisfaction and customer loyalty towards the retail shopping Levels Service quality Customer satisfaction Customer loyalty Frequency Percent Frequency Percent Frequency Percent Low Medium High Total Table Level of Perceived service quality, Customer satisfaction and customer loyalty towards the retail shopping Above table indicate the different levels of service quality, customer satisfaction and customer loyalty per cent of the respondent s opinion about service quality in retail stores is low, 47.8 per cent of the respondent s opinion about service quality in retail stores is medium and 26.6 per cent of the respondent s opinion about service quality in retail stores is high. Out of 900 respondents, 32.4 per cent respondents have low level satisfaction with retail stores product and service, 32.8 per cent respondents have medium level satisfaction with retail stores product and service and 34.8 per cent respondents have high level satisfaction with retail stores product and service per cent respondent s customer loyalty toward retail stores is low, 47.3 per cent respondent s customer loyalty toward retail stores is medium and 26.4 per cent respondent s customer loyalty toward retail stores is high.

350 ASSOCIATION BETWEEN PERCEIVED LEVEL OF SERVICE QUALITY, CUSTOMER SATISFACTION AND CUSTOMER LOYALTY TOWARDS THE RETAIL STORES AND DEMOGRAPHIC PROFILES Testing of Hypothesis 16 H 16 : There is no association between perceived level of service quality, customer satisfaction and customer loyalty towards the retail stores and demographic profiles H A : There is association between perceived level of service quality, customer satisfaction and customer loyalty towards the retail stores and demographic profiles This part of analysis is used to find the associations between demographic details of the respondents and perceived level of service quality, customer satisfaction and customer loyalty towards the retail stores. Chi square analysis used to find the associations between the variables. Significance value less than 0.05 indicate existence of some association between both variables. Sub Hypothesis H 16a : There is no association between perceived level of service quality towards the retail stores and demographic profiles H 16b : There is no association between customer satisfaction towards the retail stores and demographic profiles H 16c : There is no association between customer loyalty towards the retail stores and demographic profiles

351 Association between perceived level of service quality towards the retail stores and demographic profiles H 16a : There is no association between perceived level of service quality towards the retail stores and demographic profiles Demographic profiles Chi square Value p value Result Type of Retail store ** Association Location of the retail store ** Association Gender ** Association Age ** Association Marital Status No Association Educational Qualifications No Association Occupation ** Association Family Income ** Association Family Size ** Association Family Type No Association Preferred Purchasing Mode No Association Frequency of Shopping ** Association Amount spent in a month for shopping ** Association Influencing factor ** Association Preferred Mode of Payment ** Association ** Significant at 5 per cent level Table Association between perceived level of service quality towards the retail stores and demographic profiles

352 329 Inference: Above table indicates that significance of chi square value is less than 0.05 for the demographic variables like type of retail store, location of the retail store, age, gender, occupation, family income, family size, frequency of shopping, amount spent in a month for shopping, influencing factor and preferred mode of payment. Hence null hypothesis is rejected. It concludes that demographic variables like type of retail store, location of the retail store, age, gender, occupation, family income, family size, frequency of shopping, amount spent in a month for shopping, influencing factor and preferred mode of payment have association between levels of perceived service quality towards the retail shops. Since significance of chi square value is greater than 0.05 for the demographic variables like marital status, educational qualifications, preferred purchasing mode and family type. So there is no association between marital status, educational qualifications, preferred purchasing mode and family type with level of perceived service quality towards the retail shops.

353 Association between customer satisfaction towards the retail stores and demographic profiles H 16b : There is no association between customer satisfaction towards the retail stores and demographic profiles Demographic profiles Chi square Value p value Result Type of Retail store ** Association Location of the retail store ** Association Gender No Association Age ** Association Marital Status No Association Educational Qualifications No Association Occupation ** Association Family Income ** Association Family Size ** Association Family Type ** Association Preferred Purchasing Mode No Association Frequency of Shopping ** Association Amount spent in a month for shopping ** Association Influencing factor No Association Preferred Mode of Payment ** Association ** Significant at 5 per cent level Table Association between customer satisfaction towards the retail stores and demographic profiles

354 331 Inference: Above table indicates that significance of chi square value is less than 0.05 for the demographic variables like type of retail store, location of the retail store, age, occupation, family income, family size, family type, frequency of shopping, amount spent in a month for shopping and preferred mode of payment. Hence null hypothesis is rejected. It concludes that demographic variables like type of retail store, location of the retail store, age, occupation, family income, and family size, and family type, frequency of shopping, amount spent in a month for shopping and preferred mode of payment have association between levels of customer satisfaction towards the retail shops. Since significance of chi square value is greater than 0.05 for the demographic variables like gender, marital status, educational qualifications, preferred purchasing mode and influencing factor. So there is no association between gender, marital status, educational qualifications, preferred purchasing mode and influencing factor with level of customer satisfaction towards the retail shops.

355 Association between customer loyalty towards the retail stores and demographic profiles H 16c : There is no association between customer loyalty towards the retail stores and demographic profiles Demographic profiles Chi square Value p value Result Type of Retail store ** Association Location of the retail store ** Association Gender No Association Age ** Association Marital Status No Association Educational Qualifications ** Association Occupation No Association Family Income ** Association Family Size ** Association Family Type No Association Preferred Purchasing Mode No Association Frequency of Shopping ** Association Amount spent in a month for shopping ** Association Influencing factor No Association Preferred Mode of Payment ** Significant at 5 per cent level ** Association Table : Association between customer loyalty towards the retail stores and demographic profiles

356 333 Inference: Above table indicates that significance of chi square value is less than 0.05 for the demographic variables like type of retail store, location of the retail store, age, educational qualification, family income, family size, frequency of shopping, amount spent in a month for shopping and preferred mode of payment. Hence null hypothesis is rejected. It concludes that demographic variables like type of retail store, location of the retail store, age, educational qualification, and family income, and family size, frequency of shopping, amount spent in a month for shopping and preferred mode of payment have association between levels of customer loyalty towards the retail shops. Since significance of chi square value is greater than 0.05 for the demographic variables like gender, marital status, occupation, family type, preferred purchasing mode and influencing factor. So there is no association between gender, marital status, occupation, family type, preferred purchasing mode and influencing factor with level of customer loyalty towards the retail shops.

357 STRUCTURAL EQUATION MODELING Structural equation modeling (SEM) is a tool for analyzing multivariate data that has been long known in marketing to be especially appropriate for theory testing (e.g., Bagozzi, 1980). Structural equation models go beyond ordinary regression models to incorporate multiple independent and dependent variables as well as hypothetical latent constructs that clusters of observed variables might represent. They also provide a way to test the specified set of relationships among observed and latent variables as a whole, and allow theory testing even when experiments are not possible. As a result, these methods have become ubiquitous in all the social and behavioral sciences (e.g., MacCallum & Austin, 2000).

358 EFFECTS OF SERVICE QUALITY DIMENSIONS ON OVERALL CUSTOMER SATISFACTION H 17 : There is no impact among the Overall service quality of the retail stores on overall satisfaction of the customers. Figure No. 6.1 Effects of service quality dimensions on overall customer satisfaction To analysis the relationship between these factors SEM approach (AMOS 21) has been used. SEM approach allows concurrent estimations of multiple regression analysis in one single frame work. Browne & Cudeck (1993) study indicates the model fit can be checked by RMSEA which is less than 0.08 has a good fit and less than 0.05 has a closer fit. Chin and Todd (1995) study proposed that for goodness of model fit GFI (Goodness of Fit Index) and NFI (Normed Fit Index) should be above 0.9 and AGFI (Adjusted good-of-fit Index) should be above 0.8. Bentler (1990) study suggest for good model fit CFI (Comparative Fit Index) should be greater than 0.9. The goodness of final

359 336 model fit has been shown in table 1. As per the various model fit statistics indicates that model was good fit. Goodness of Fit Statistics Value Good fit value Chi Square Value (CMIN) Degree of Freedom (Df) 6 0 Chi Square / Df (CMIN/Df) to 5 Goodness of Fit Index (GFI) > 0.9 Root Mean Square Error of Approximation (RMSER) < 0.08 Adjusted Good of Fit Index (AGFI) > 0.9 Comparative Fit Index (CFI) > 0.9 Normed Fit Index (NFI) > 0.9 Physical Aspects Reliability Personal Interaction Problem Solving Policy Overall customer satisfaction Structural Path Table Model fit statistics <-- Overall service quality <-- Overall service quality <-- Overall service quality <-- Overall service quality <-- Overall service quality <-- Overall service quality β Estimate ** denotes Significance at 1 per cent level. S.E. C.R. P value H 0 Result ** Rejected ** Rejected ** Rejected ** Rejected ** Rejected ** Table Effects of service quality dimensions on overall customer satisfaction

360 337 The significance test is the critical ratio (CR), which represents the parameter estimate divided by its standard error. The parameter estimate is significant at p 0.01 and value of C.R is > Six significant structural paths among the exogenous and endogenous latent variables are found to be significant. The probability of getting a critical ratio as large as and are having an absolute value which is less than In other words, the regression weight for problem solving and physical aspects dimension is having high regression weight than other service quality dimensions. The policy and personal interactions dimension are also a significant variable which having an impact on Overall service quality leads to positive effect on Overall satisfaction, which is significantly different from zero at the level (two-tailed). The critical ratio index can be used as a guide for eliminating the existing paths. In this model all the structural path are accepted because CR values are greater than This regression weight represents the degree of association between the constructs and the manifesting variables. For example, if Overall service quality increased by 1 standard deviation, the standard deviation of individual service quality dimensions like physical aspects, reliability, personal interactions, problem solving and policy would have increased by 0.94,0.88,0.96,0.83 and 0.65 (Standardized estimates) respectively. These results are salient in identifying the customer satisfaction elements that can improve the customer satisfaction retail shoppers.

361 338 Bayesian Analysis for effects of service quality dimensions on overall customer satisfaction For access Convergence, with a large dataset, the posterior mean will tend to be close to the maximum likelihood estimate. AMOS provides several diagnostics that help to check convergence. Notice the value on the toolbar of the Bayesian SEM window. Each time the screen refreshes, AMOS updates the C.S. for each parameter in the summary table. The C.S. value on the toolbar is the largest of the individual C.S. values. AMOS displays an unhappy face (Figure No. 6.2), when the overall C.S. is not small enough. Figure No.6. 2: Before Bayesian Analysis

362 339 Reflecting the satisfactory convergence, AMOS now displays a happy face (YELLOW) given in the Figure No Gelman et al. (2004) suggest that for many analyses, values of or smaller are sufficient and it is conservative. Judging that the MCMC chain has converged by this criterion does not mean that the summary table will stop changing. As the overall convergence statistic (C.S.), C.S. value on the toolbar approaches However, there is more precision to be gained by taking additional samples, it might stop as well. The Posterior dialog box now displays a frequency polygon of the distribution of the samples. Figure No. 6.3: After Bayesian Analysis

363 EFFECT OF SERVICE QUALITY DIMENSIONS AND OVERALL CUSTOMER SATISFACTION ON CUSTOMER LOYALTY H 18 : Individual service quality dimension have no impact on overall customer satisfaction of the retail customers that leads to overall behavioural loyalty. H 18a : Physical aspects dimension have no impact on overall customer satisfaction of the retail customers that leads to overall behavioural loyalty. H 18b : Reliability dimension have no impact on overall customer satisfaction of the retail customers that leads to overall behavioural loyalty. H 18c : Personal interaction dimension have no impact on overall customer satisfaction of the retail customers that leads to overall behavioural loyalty. H 18d : Problem solving dimension have no impact on overall customer satisfaction of the retail customers that leads to overall behavioural loyalty.. H 18e : Policy dimension have no impact on overall customer satisfaction of the retail customers that leads to overall behavioural loyalty.

364 341 Figure No.6.4: Effect of service quality dimensions and overall customer Goodness of Fit Statistics satisfaction on customer loyalty Value Chi Square Value (CMIN) Degree of Freedom (Df) 2 0 Values for good fit Chi Square / Df (CMIN/Df) to 5 Goodness of Fit Index (GFI) > 0.9 Root Mean Square Error of Approximation (RMSER) < 0.08 Adjusted Good of Fit Index (AGFI) > 0.9 Comparative Fit Index (CFI) > 0.9 Normed Fit Index (NFI) > 0.9 Table Model fit statistics As per the various model fit statistics indicates that model was good fit.

365 342 Structural Path β Estimate S.E. C.R. P H 0 Results Overall customer <--- Reliability ** Rejected satisfaction Overall customer <--- Problem Accepted satisfaction solving Overall customer <--- Personal ** Rejected satisfaction interactions Overall customer <--- Policy Accepted satisfaction Overall customer <--- Physical ** Rejected satisfaction appearance Overall customer <--- Overall ** Rejected loyalty customer satisfaction ** denotes Significance at 1 per cent level. Table Effect of service quality dimensions and overall customer satisfaction on customer loyalty The regression weight for physical appearance dimension is having high impact on the overall SAT of retail customers. It is highly significant which is different from zero at the level (two-tailed). The overall satisfaction has positive effect leads to customer loyalty of customers in the retail stores. This regression weight represents the degree of association between the constructs and the manifesting variables. For example, if individual service quality dimensions like physical aspects, reliability, personal interactions, problem solving and policy increased by 1 standard deviation, the Overall satisfaction would have increased by 0.42, 0.10, 0.35, and 0.01 respectively. These results are salient in identifying the service quality and customer satisfaction elements that can improve the retail customer loyalty.

366 343 Bayesian Analysis for effect of service quality dimensions and overall customer satisfaction on customer loyalty With a large dataset, the posterior mean will tend to be close to the maximum likelihood estimate. AMOS provides several diagnostics that help to check convergence. The value obtained is is obtained on the toolbar of the Bayesian SEM window and AMOS displays an unhappy face Figure No. 6.5, when the overall C.S. is not small enough. Reflecting the satisfactory convergence, AMOS now displays a happy face (YELLOW) which is displayed in the Figure No As the overall C.S. value on the toolbar approaches 1.000, however, there is more to be gained by taking additional samples, so it might stop as well. The posterior dialog box now displays a frequency polygon of the distribution of the service quality dimensions and customer satisfaction leads to Overall customer loyalty across the samples in retail stores.

367 344 Figure No. 6.5: Before Bayesian Analysis Figure No. 6.6: After Bayesian Analysis

368 EFFECT OF CUSTOMER LOYALTY DIMENSIONS ON OVERALL CUSTOMER SATISFACTION H 19 : There is no effect on overall customer satisfaction of the customers towards the individual dimensions of behavioural loyalty. H 19a : There is no effect on overall customer satisfaction of the customers towards the word of mouth dimension of behavioural loyalty. H 19b : There is no effect on overall customer satisfaction of the customers towards the switch to competitor dimension of behavioural loyalty. H 19c : There is no effect on overall customer satisfaction of the customers towards the willingness to pay more dimension of behavioural loyalty. H 19d : There is no effect on overall customer satisfaction of the customers towards the internal and external response dimension of behavioural loyalty.

369 346 Figure No. 6.7 Effect of customer loyalty dimensions on overall customer satisfaction Goodness of Fit Statistics Value Values for good fit Chi Square Value (CMIN) Degree of Freedom (Df) 3 0 Chi Square / Df (CMIN/Df) to 5 Goodness of Fit Index (GFI) > 0.9 Root Mean Square Error of Approximation (RMSER) < 0.08 Adjusted Good of Fit Index (AGFI) > 0.9 Comparative Fit Index (CFI) > 0.9 Normed Fit Index (NFI) > 0.9 Table Model fit statistics As per the various model fit statistics indicates that model was good fit.

370 347 Structural Path β Estimate S.E. C.R. P H 0 Result Word of mouth <--- Overall Customer satisfaction ** Rejected Switch to <--- Overall competitor Customer satisfaction Accepted Willingness to <--- Overall pay more Customer satisfaction ** Rejected Response <--- Overall Customer satisfaction ** Rejected ** denotes Significance at 1 per cent level. Table Effect of customer loyalty dimensions on overall customer satisfaction The regression weight for willingness to pay more dimensions is having high regression weight than other customer loyalty Dimension. The Response and word of mouth are also a significant variable which having an impact on Overall satisfaction, which is significantly different from zero at the level (two-tailed). The critical ratio index can be used as a guide for eliminating the existing paths. In this model three the structural paths are accepted because CR values are greater than This regression weight represents the degree of association between the constructs and the manifesting variables. For example, if overall satisfaction increased by 1 standard deviation, the standard deviation of individual customer loyalty Dimensions like word of mouth, Switching to Competitors, Willingness to pay more and response would have increased by 0.175, , and respectively. These results are salient in identifying the

371 348 customer satisfaction elements that can improve the bank customer satisfaction. Bayesian Analysis for effect of customer loyalty dimensions on overall customer satisfaction For accessing convergence, with a large dataset, the posterior mean will tend to be close to the maximum likelihood estimate. AMOS provides several diagnostics that help to check convergence. Notice the value (Figure No. 1) on the toolbar of the Bayesian SEM window. Each time the screen refreshes, AMOS updates the C.S. for each parameter in the summary table. The C.S. value on the toolbar is the largest of the individual C.S. values. AMOS displays an unhappy face (Figure No. 6.8), when the overall C.S. is not small enough Figure No. 6.8: Before Bayesian Analysis

372 349 Reflecting the satisfactory convergence, AMOS now displays a happy face (YELLOW) given in the Figure No.6.9. Gelman et al. (2004) suggest that for many analyses, values of or smaller are sufficient and it is conservative. Judging that the MCMC chain has converged by this criterion does not mean that the summary table will stop changing. As the overall convergence statistic (C.S.), C.S. value on the toolbar approaches However, there is more precision to be gained by taking additional samples, it might stop as well. The Posterior dialog box now displays a frequency polygon of the distribution of the samples. Figure No.6. 9: After Bayesian Analysis

373 CUSTOMER EVALUATION MODEL FOR RETAIL STORES H 20 : There is no impact of purchase intention on perception of retail service quality leads to positive or negative effect on loyalty which act as mediating variable of customer satisfaction. Figure No.6. 10: Customer evaluation model for retail stores Goodness of Fit Statistics Value Values for good fit Chi Square Value (CMIN) Degree of Freedom (Df) 2 0 Chi Square / Df (CMIN/Df) to 5 Goodness of Fit Index (GFI) > 0.9 Root Mean Square Error of Approximation (RMSER) < 0.08 Adjusted Good of Fit Index (AGFI) > 0.9 Comparative Fit Index (CFI) > 0.9 Normed Fit Index (NFI) > 0.9 Table Model fit statistics As per the various model fit statistics indicates that model was good fit.

374 351 Structural Path Perception <-- Purchase intention β Estimate S.E. C.R. P H 0 result Perception <-- Expectation Customer satisfaction Customer satisfaction Loyalty <-- <-- Perception <-- Expectation Customer satisfaction ** denotes Significance at 1 per cent level * * 0.000* * 0.000* * 0.000* * 0.000* * Rejected Rejected Rejected Rejected Rejected Table Customer evaluation model for retail stores Five significant structural paths among the exogenous and endogenous latent variables are found to be significant. Perception rating on overall satisfaction has positive effect leads to increase customer loyalty in the retail stores. Expectation ratings on overall satisfaction have negative effects leads to reduce the level of loyalty in the retail stores. The purchase intention, perception and expectation ratings of Service Quality constructs are having high impact on overall service evaluation. Bayesian Analysis for estimation Customer evaluation model for retail stores With a large dataset, the posterior mean will tend to be close to the maximum likelihood estimate. AMOS provides several diagnostics that help to check convergence. The value obtained is on the toolbar of the Bayesian SEM window and AMOS displays an unhappy face, when the overall C.S. is not small enough.

375 352 Figure No. 6.11: Before Bayesian Analysis Reflecting the satisfactory convergence, AMOS now displays a happy face (YELLOW) which is displayed in the Figure No As the overall C.S. value on the toolbar approaches , however, there is more to be gained by taking additional samples, so it might stop as well. The posterior dialog box now displays a frequency polygon of the distribution of the Impact of Perception and expectation ratings of service quality on Overall satisfaction leads to Customer loyalty across the samples in retail stores.

376 353 Figure No. 6.12: After Bayesian Analysis 6.24 CONCLUSION This chapter concludes with the various findings related to the hypothesis framed for the research study. For proving the hypothesis the researcher has used various statistical tools such as path analysis using AMOS 21, multiple regression, correlation, ANOVA, independent sample T test, paired T test and chi square test. In addition, a thorough explanation and interpretation of the study s hypothesis results and how they relate to previous research findings in the Service quality literature are also given.

377 354 CHAPTER VII DISCUSSION AND CONCLUSION This chapter provides an overview of the general findings and conclusions of the study in relation to its objectives, hypotheses and research problems. Proposed recommendations are considered by various retail stores in order to improve the quality of their services by knowing the weak factors. 7.0 INTRODUCTION The chapter concludes by highlighting theoretical and methodological contributions and practical implications based on the developed SEM, thus closing the service quality gap on Customer Expectation and Perception. Further, it discusses the limitations of the study and provides suggestions for further researches that contribute to the literature on Service Quality in Retail Sectors. 7.1 SERVICE QUALITY GAP ANALYSIS Service Gap Score Analysis for the retail shops, shows that, the lowest service gap has occurred in policy and problem solving dimension and the little high service gap has occurred in the personal interaction and reliability dimension. Physical aspect dimension is has occurred very high service gap compared to other dimensions. The lower order dimensions should be paid more concentration and the retails should see that the customer s expectations are met for all dimensions.

378 RELATIVE IMPORTANCE OF SERVICE QUALITY DIMENSIONS Retail service quality dimensions account for 68 percent of the variance in Overall Retail Service Quality and Policy and reliability had achieved the strongest association with the overall perception of service quality. 7.3 EFFECTS OF DEMOGRAPHIC VARIABLES ON DIFFERENT SERVICE QUALITY DIMENSIONS Type of retail store, location of the retail store, gender, age, marital status, educational qualifications, occupation, family income, and frequency of shopping, the amount spent in a month for shopping, influencing factors and preferred mode of payment have significant difference with service quality dimensions. 7.4 RELATIVE IMPORTANCE OF SERVICE QUALITY DIMENSIONS ON CUSTOMER LOYALTY DIMENSIONS It was observed that the retail service quality dimensions explained 4.2 per cent of the variance for the criterion measure and reliability dimension was statistically significant in explaining the variance of intention to recommend the retailer to others. The retail service quality dimensions explained 3 per cent of the variance in the criterion measure and service quality dimensions like reliability problem solving and policy dimension was statistically significant in explaining the variance of switching to competitors.

379 356 The retail service quality dimensions explained 47 percent of the variance in willingness to pay more and policy and personal interaction dimensions are contributing more on explaining the shoppers willingness to pay more. Five dimensions of service quality explained only 44 percent of the variation in response (Internal and external response) and the Policy and personal interaction dimensions are contributing more on explaining the shopper s response. 7.5 EFFECTS OF DEMOGRAPHIC VARIABLES ON DIFFERENT CUSTOMER LOYALTY DIMENSIONS Type of retail store, location of the retail store, gender, age, marital status, educational qualifications, occupation, family income, family size, family type, preferred purchasing mode, frequency of shopping, the amount spent in a month for shopping and preferred mode of payment factors have a significant difference in customer loyalty dimensions. 7.6 RELATIONSHIP BETWEEN PERCEIVED SERVICE QUALITY DIMENSIONS, CUSTOMER LOYALTY DIMENSIONS Opinion about perceived service quality dimensions like physical aspects, reliability, personal interaction, problem solving and policy have a positive and high level significant relationship with other dimensions. Opinion about customer loyalty dimensions like word of mouth, switching to a competitor, willingness to pay more and internal, external response of the respondents also have a positive and high level significant relationships with other customer loyalty dimensions.

380 RELATIVE IMPORTANCE OF SERVICE QUALITY DIMENSIONS ON CUSTOMER SATISFACTION AND CUSTOMER SATISFACTION ON CUSTOMER LOYALTY The retail service quality dimensions explained 67 per cent of the variance for the criterion measure. It entailed that 33 percent of customer satisfaction was explained by something other than service quality and physical aspects and personal interaction dimensions were statistically significant in explaining the variance of retail customer satisfaction. Customer satisfaction explained 22 per cent of the variance for the criterion measure. It entailed that 78 percent of customer loyalty in retail shop was explained by something other than customer satisfaction and service satisfaction, product satisfaction were statistically significant in explaining the variance of retailer customer loyalty. 7.8 EFFECTS OF DEMOGRAPHIC VARIABLES ON CUSTOMER SATISFACTION FACTORS Type of retail store, location of the retail store, age, marital status, occupation, family income, and amount spent in a month for shopping, influencing factors and preferred mode of payment factors have a significant difference in product and service satisfaction offered by the retail stores. 7.9 RELATIVE IMPORTANCE OF FACTOR AFFECTS PURCHASE ON CUSTOMER SATISFACTION Factor affects purchase explained 19 per cent of the variance for the criterion measure. It entailed that 81 percent of customer satisfaction was explained by something other than factor affects purchase and overall after

381 358 sales services, arrangement of the products, waiting time for billing and availability of various branded products were also statistically significant in explaining the variance of retail customer satisfaction RELATIONSHIP BETWEEN PURCHASING FACTORS, CUSTOMER SATISFACTION, EXPECT & PERCEIVED SERVICE QUALITY AND CUSTOMER LOYALTY Correlation analysis between purchasing factors, customer satisfaction, expects & perceived service quality and customer loyalty shows that higher levels of positive and significant relation exist between above mentioned factors EFFECTS OF MOST LIKED AND DISLIKED FACTORS ON PURCHASE ATTRIBUTE FACTORS Most liked factors had significantly influence over purchase attribute factors like a good place of entertainment, family shopping and emergency and safety measures and most disliked factors had significantly influence over purchase attribute factors like family shopping EFFECTS OF MOST LIKED AND DISLIKED FACTORS ON FACTORS AFFECTING RETAIL PURCHASE Most liked factors had significantly influence over factors affecting retail purchase like pricing of various branded products, availability of various branded products, the quality of various branded products, overall sales promotional activities, overall after sales services, arrangement of the products

382 359 and waiting time for billing. Post hoc analysis shows that sales promotion and discount liking factors differ significantly with other liked factors with reference to factors affecting retail purchase and most disliked factors had significantly influence over factors affecting retail purchase like pricing of various branded products, availability of various branded products, quality of various branded products, overall sales promotional activities, overall after sales services, arrangement of the products and waiting time for billing DIFFERENCE BETWEEN PERCEPTION AND EXPECTATION SERVICE QUALITY DIMENSIONS Paired T test between opinion about perceived and expected service quality dimensions shows that there is a significant difference between all perception and expectation service quality dimensions ASSOCIATION BETWEEN AND MOST LIKED AND DISLIKED FACTORS ABOUT RETAIL SHOPS Demographic variables like type of retail store, location of the retail store, age, marital status, educational qualifications, occupation, family income, family size, family type, frequency of shopping, amount spent in a month for shopping, influencing factor and preferred mode of payment have association between most liked factors about the retail shops and demographic variables like type of retail store, location of the retail store, age, gender, educational qualifications, occupation, family income, family size, preferred purchase mode, frequency of shopping, amount spent in a month for shopping, influencing factor and preferred mode of payment have association between most disliked factors about the retail shops.

383 LEVEL OF SERVICE QUALITY, CUSTOMER SATISFACTION AND CUSTOMER LOYALTY 25.7 per cent of the respondent s opinion about service quality in retail stores is low, 47.8 percent of the respondent s opinion about service quality in retail stores is medium and 26.6 percent of the respondent s opinion about service quality in retail stores is high per cent respondents have low level satisfaction with retail stores product and service, 32.8 per cent respondents have medium level satisfaction with retail stores product and service and 34.8 per cent respondents have high level satisfaction with retail stores product and service percent respondent s customer loyalty toward retail stores is low, 47.3 percent respondent s customer loyalty toward retail stores is medium and 26.4 percent respondent s customer loyalty toward retail stores is high ASSOCIATION BETWEEN PERCEIVED LEVEL OF SERVICE QUALITY, CUSTOMER SATISFACTION AND CUSTOMER LOYALTY AND DEMOGRAPHICS Demographic variables like type of retail store, location of the retail store, age, gender, occupation, family income, family size, frequency of shopping, the amount spent in a month for shopping, influencing factors and preferred mode of payment have an association between levels of perceived service quality towards the retail shops. Demographic variables like type of retail store, location of the retail store, age, occupation, family income, and family size, and family type, frequency of shopping, the amount spent in a month for shopping and preferred mode of payment have an association between levels of customer satisfaction towards the retail shops.

384 361 Demographic variables like type of retail store, location of the retail store, age, educational qualification, family income, and family size, frequency of shopping, the amount spent in a month for shopping and preferred mode of payment have an association between levels of customer loyalty towards the retail shops EFFECTS OF SERVICE QUALITY DIMENSIONS ON OVERALL CUSTOMER SATISFACTION Regression weight for problem solving and physical aspects dimension is having high regression weight than other service quality dimensions. The policy and personal interactions dimension are also a significant variable which having an impact on Overall service quality leads to positive effect on Overall satisfaction, which is significantly different from zero at the level This regression weight represents the degree of association between the constructs and the manifesting variables. For example, if Overall service quality, increased by 1 standard deviation, the standard deviation of individual service quality dimensions like physical aspects, reliability, personal interactions, problem solving and policy would have increased by 0.94,0.88,0.96,0.83 and 0.65 (Standardized estimates) respectively. These results are salient in identifying the customer satisfaction elements that can improve the customer satisfaction retail shoppers EFFECT OF SERVICE QUALITY DIMENSIONS AND OVERALL CUSTOMER SATISFACTION ON CUSTOMER LOYALTY The regression weight for physical appearance dimension is having a high impact on the overall SAT of retail customers. It is highly significant

385 362 which is different from zero at the level (two-tailed). The overall satisfaction has positive effect leads to customer loyalty of customers in the retail stores. This regression weight represents the degree of association between the constructs and the manifesting variables. For example, if individual service quality dimensions like physical aspects, reliability, personal interactions, problem solving and policy increased by 1 standard deviation, the Overall satisfaction would have increased by 0.42, 0.10, 0.35, and 0.01 respectively. These results are salient in identifying the service quality and customer satisfaction elements that can improve the retail customer loyalty EFFECT OF CUSTOMER LOYALTY DIMENSIONS ON OVERALL CUSTOMER SATISFACTION The regression weight for willingness to pay more dimensions is having high regression weight than other customer loyalty Dimension. The Response and word of mouth are also a significant variable which having an impact on Overall satisfaction, which is significantly different from zero at the level. This regression weight represents the degree of association between the constructs and the manifesting variables. For example, if overall satisfaction increased by 1 standard deviation, the standard deviation of individual customer loyalty Dimensions like word of mouth, Switching to Competitors, Willingness to pay more and response would have increased by 0.175, , and respectively. These results are salient in identifying the customer satisfaction elements that can improve the retail customer satisfaction.

386 CUSTOMER EVALUATION MODEL FOR RETAIL STORES Perception rating for overall satisfaction has positive effect leads to increase customer loyalty in the retail stores. Expectation ratings of overall satisfaction have negative effects leads to reduce the level of loyalty in the retail stores. The purchase intention, perception and expectation ratings of Service Quality constructs are having high impact on overall service evaluation IMPLICATIONS OF THE STUDY MANAGERIAL IMPLICATION According to the results obtained from the Structural Equation Modeling, Service Quality influences the satisfaction level of the retail store customers. This indicates that the ability to provide high quality service is the key to achieving better customer satisfaction. Furthermore, satisfaction and behavioral intentions are also strongly influenced by service quality. This implies that Service quality is important to predict customer satisfaction and behavioral intentions. Despite the existence of significant direct relationships, this thesis also finds the important role of service quality and customer satisfaction as mediating variables. From the managerial perspective, the findings indicate that the efficacy of the quality policies of service industries will vary considerably as a function of customer s needs which affect perceptions about different facets of the quality of the service. It would be essential for companies to keep the expectation of the customers in mind as a criterion to segment markets. It is understood that the satisfied customers will behave in a positive manner. This study proves if the customer s expectations of service quality are offered they

387 364 will be satisfied and they will exhibit their positive behavior. It is apparent from the present study that managers and decision makers in the retail stores can seek and improve the elements of service quality that make the most significant contributions to customer satisfaction. In making such an assessment, managers should examine customers' responses to the five dimensions of service quality used in this study. From a managerial point of view, it can be concluded that service quality is indeed an important antecedent to customer satisfaction. It should be noted that the high relative weights of the different service dimensions do not mean that customers are satisfied on the contrary, it could be argued that there is still room for improvements in that area to further improve the perceived service quality. The importance of the findings of managerial decision-making processes is evident. Managers seeking to improve their customers satisfaction levels, in their effort to increase loyalty, retention rates and to attract new customers, may benefit from information about the effect of individual dimensions of service quality on customer satisfaction. From these conclusions, Managers should focus on high quality services, and improve service quality effectiveness PRACTICAL IMPLICATIONS OF THE RESEARCH This research builds on existing literature that provides an understanding of service quality measurement. The present study takes this work forward by considering how the service providers perceive their customers expectations of service delivery. While most of the studies deal with various aspects of the relationship between customer expectations and customer perceptions of service quality, the unit delivering the service quality has been uniquely ignored to this point.

388 365 Unlike other studies there are useful means provided for the evaluation of how the services industry entity is delivering all-important services which perceives the expectations of the consumer. The value of determining the importance of a perceptions and customer expectations is readily apparent when the existing literature points to many examples of customers leaving organizations. Because they do not deliver the expected level of service quality. Certainly, there are some difficulties in influencing customer behavior as well as understanding what the customer absolutely expects. While not implying that service providers should be mind readers. This present study provides a method to evaluate the benefits that might be obtained by them that seek to understand their customers expectations. It is evident that service quality determined in this study is the most important factor in the selection of the retail store. This should provide an impetus for the service industry to evaluate seriously the need to make an effort to provide the highest level of service quality to obtain and retain customers CONTRIBUTION OF THE STUDY The study contributes to the effective managerial decisions to be made by the Indian retailers, when they have to decide on the service quality factors which can delight the customers and subsequently affecting their loyalty. For the study, retailers can use the perception items to pinpoint which service areas are very important for customer s and need special attention. This study is also unique because it resulted in dimensions that are specific to the retail industry in India. The findings showed that the original five dimensions of RSQS do not factor out and RSQS in India is not in harmony with the previous researchers Boshoff and Terblanche, 1997 and Dabholkar, Thorpe and Rentz, The results of the study found the overall service quality to be positively

389 366 associated with service quality dimensions. The study also highlighted the relative importance of service quality attributes and revealed that not all the dimensions contribute equally to the customers perceptions of service quality in Indian retail context. It is the Convenience at patronizing, which makes a difference to customers. This was followed by a difference between the RSQS dimensions and demographic information. This study is one of the few studies that investigated service quality at the dimension level and its association with demographic characteristics such as age, gender, marital status, occupation, monthly income, residential status, highest qualification, type of store visited, reason of visiting stores and frequency of visiting stores. There were no differences regarding the demographic information, namely, Monthly Income, Residential Status, Type of store and Reason of Visiting Store in this study. In terms of the association between customer loyalty and service quality, it was found that the dimension of physical aspects and Policy were relatively influential in affecting customers positive recommendation and future consumption behavior respectively. Moreover, superior performance on the service quality dimension i.e. Convenience and Promises will be helpful in making customers loyal under increased or decreased pricing. Consumer behaviors signaling complaints to employees and other agencies can be controlled by fulfilling the Promises and inculcating Confidence in the customers. Retailers can use these relationships as significant reference points while developing their strategies to retain customers. This study contributes to the body of knowledge regarding customer s repurchase intentions. Analysis of data found that customer s behavioural

390 367 intentions was positively associated with service quality dimensions. A retailer can use these service results to develop improvement strategies or to make necessary adjustments to existing strategy to increase customer s repatronage. Using information, retailers can formulate customized policies and redesign its physical store in order to increase footfalls. Retailers can help their employees to improve their performance in service situation. Retailers can guide salespersons on an individual basis and also use information from the scale to modify their employee training methods so that sales personnel offer better customer service THEORETICAL CONTRIBUTIONS OF THE RESEARCH Guided by the research objectives, an examination of the three four key constructs of interest has contributed to the theory as follows: MEASUREMENT OF RETAIL SERVICE QUALITY The main objective of this research study is to measure the service quality offered among the organized retail stores (Hypermarket, Supermarket, Departmental Stores) in the major cities of Tamilnadu. This research attempts to delete two items in the existing RSQS model as we discussed earlier in the research methodology. Normally, the customers have greater expectations and are satisfied if they provide all those expected facilities. This study has proven evidence that this Retail Service Quality Scale is highly reliable and suitable to those of Indian Conditions with respect to the organized retail formats selected. There is a growing body of research pointing to the fact that service quality has a greater impact on satisfaction among the customers and these both (service quality and customer satisfaction) have an impact on positive or negative behavioral intentions.

391 FILLING THE GAP IN THE KNOWLEDGE The relationships between service quality and customer satisfaction have been widely discussed in the literature. However, there were very few empirical researches as a lack of studies that incorporated Service, quality, customer satisfaction and behavioral intention in the retail sector that too in south India. Previous studies have mostly investigated the direct (bivariate) relationships or indirect relationships involving three constructs. The importance of this study is to find the linkage between Service Quality Customer Satisfaction Behavioral Intention. The simultaneous investigation of the relationships among all the constructs would provide a more accurate and comprehensive picture of the nature of the relationships. In addition to measuring service quality this thesis contributes to the theory in a way that service quality is measured using multidimensional measurement and the impact of service quality on customer satisfaction. Moreover, this study gives room for studying the impact on behavioral intention too. The utilization of the multidimensional construct helps the researchers to explain the complex nature of many marketing constructs. By involving multidimensional conceptualizations of service quality and customer satisfaction, this thesis provides an extension of the earlier studies targeting to study the measurement of service quality and customer satisfaction. In short, the proposed model provides a comprehensive picture of the relationships among the key constructs (Service, quality, Customer satisfaction and Behavioral Intention) UTILIZATION OF SEM FOR KEY CONSTRUCTS RELATIONSHIP TESTING This research utilized the Structural Equation Modeling (SEM) Method and AMOS software in data analysis. This study contributes by expanding the

392 369 use of SEM in analyzing empirical data in the service quality discipline, that is to say, not in the use of SEM per se, but rather in the rigorous testing of relationships between key constructs. This study is one of the few recent studies that have attempted to explain the relationships between Service, quality, Customer Satisfaction and Behavioral Intention to retain the customers CONTRIBUTION TO THE SERVICES MARKETING THEORY The findings of the current research with providing additional support for the use of service industry research model to explain the process of the customer s evaluation of the offering in a service setting. There are many contributions to the knowledge base within the services marketing context. These include the demonstration that service quality is a higher order construct and the examination of the relationships among service quality, customer satisfaction and behavioral intention with a retail stores service context. This section continues by addressing each of the contributions. As a whole the research contributes to marketing theory to extend the existing conceptualizations of service quality. The findings of the current research should be seen in the light of contributions towards the development of a comprehensive model. It explains the development of richer and more complete conceptualizations of the constructs of service quality CONTRIBUTION TO MANAGEMENT PRACTICES The research highlights the role of customers perceptions in shaping the overall evaluation of the service offered. Another major contribution of the research model is that, it can be used as a guideline to identify the factors

393 370 which are significant in shaping the customers perceptions of the service offering. The factors Service, quality, Satisfaction and Behavioral intentions can be then profitably used to develop different strategies. The research model provides assistance to the practitioners in monitoring their service delivery systems through customer feedbacks, to evaluate the value provided to their patrons in terms of the benefits. The research also contributes in developing, testing and validating measures of the constructs like Service, quality, Satisfaction and Behavioral intention. This can be used for the purpose of collecting feedbacks from the customers to monitor and improve the service to enhance the retail stores performance RECOMMENDATIONS Due to the exponential development of the sector, it becomes extremely vital to understand the consumer perceptions and delivery of services in the areas of physical appearance, Reliability, Personal Interaction, Problem solving and Policy. As these are prime service dimensions for any retail store, they generate interest in knowing the perception of consumers after the delivery of service. The present study will facilitate the stores in understanding the service quality factors, ultimately the way to customer loyalty. Sureshchander et al. (2001) aroused the question of whether service quality scales such as the SERVQUAL and SERVPERF address the critical aspects of customer perceived service quality in India (Kaul, 2005). The present study lends further credence to their argument as a different component structure of RSQS emerges in the Indian retail environment. The haziness of dimensions will disappoint Indian retailers seeking greater visibility in identifying areas for service improvement. Due to this vagueness of dimensions, retailers and researchers applying internationally developed

394 371 multi-dimensional RSQS to the Indian environment are advised to pay special attention to scale adaptation to ensure that the scale has reliable diagnostic ability. If consumers perceptions of store service quality do not differ from one culture to another, there would be no need for global retailers to modify their service-related strategies when crossing national boundaries. But cultural differences do exists, thus global retailers need to be responsive while developing customer service. The scales that are developed for a specific country or context might not be suitable for another country or context because of the unique and different economic and socio-cultures (Kumar, Kee & Nanshor, 2009). According to Tsoukatos & Rand (2007) culture has an influence on the service quality dimensions and the way customers view these service quality dimensions. An instrument that is applied in the West will not fit Asian markets without modifications (Cui, Lewis & Park, 2003). In this regard, a great need is felt especially for global retailers targeting India to carefully re-think before exercising their existing perspectives on service quality gained in other countries to Indian consumers. A thoughtful customer research should be conducted to advance an understanding of local customers behaviour and their shopping experience. Retailers who carry out periodic inspections can use the validated and reliable Retail Service Quality Scale to benchmark their current levels of retail service quality. By specifying the weight for each of the eight factors of service quality, existing organized retailers and new/ potential entrants can propose appropriate action plans. Retailers are suggested to analyze data at different levels (i.e. Overall level and dimension level) to identify priority areas of service improvement.

395 372 Thus, the retailers will be able to channelize its resources in the direction of strengthening the most important dimension (Seth et al., 2008).Further, the employees should be so trained that they perform the right service in the first encounter, leaving the customer fully satisfied. By making shopping a convenient exercise, retailers would be able to transform shopping into shoppertainment i.e. shopping + entertainment. Retailers wishing to enhance their perceived service quality are needed to assure that: Physical facilities are clean, tidy, modern-looking and attractive. Store layout is architecture in a convenient manner that enables the customers to find what they need and to move around with ease. Services are delivered reliably by fulfilling all promises made to customers. Doing things right the first time without mistakes and having the merchandise available when the customers want it. Store employees instill confidence in the customers at all times during their personal interaction with the customers. Store salespeople are courteous, helpful, and knowledgeable with the ability to give prompt service to customers (Leen et al., 2004). Prompt and professional problem-solving methods, including a proper system of returns and exchanges (Christo & Terblanche, 1997). Their store policy is responsive to customer needs such as trading high quality merchandise, having convenient operating hours, ample parking spaces, and credit payment options (Leen et al., 2004). Paulins (2005) opined that demographics do have an effect on some service quality dimensions such as the reliability dimension. As one size does

396 373 not fit all, similarly one strategy will not work with different demographics. Thus, retailers are recommended to priorities on different service quality attributes to capture customers with different demographics. A suggestion to management is to take into consideration the problems/complaints of years old customers with priority. For fulfilling this, retailers can train store employees to solve problems immediately and to show customers that they are trying their level best to solve problems. Females are hard core shoppers. Retailers should look at increasing the product range in the store. Moreover, retailers can develop its store employees to insist on error-free sales transactions and records, especially when the customer is a female. Retailers should consider the fact that married people have to maintain a professional - personal life balance. So, whenever married consumers encounter a problem, store employees should sincerely put efforts to sort it out. Looking at this individual dimension, it is suggested that the contact employees should resolve the customer s complaints timely and that the customer s queries are taken seriously. To achieve this, retailers can incorporate a training program for the employees. Such measures could include the way employees solve problems, the way employees interact with customers and pay attention to customers needs. As businessmen belong to the elite group, retailers can formulate customized policies to delight this group. During weekends and peak hour shop-in-shop runs out of parking space, this inconveniences the customers and forces them to buy goods from elsewhere. So retailers can redevelop its store

397 374 policies where a different parking space can be allocated to a business group. Retailers can also modify their operating hours according to their demand. Analysis of data showed that government sector employees gave higher importance to Problem Solving dimension than other category people. Retailers targeting government employees are advised to exhibit earliness in sorting out the complaints/problems of this group. Retailers must ensure that high quality goods, private labels and designer outfits will be delivered to government employees. Retailers should invest in empowering the contact employees and providing them with adequate resources so that they can take prompt actions to customer queries. For this, they need to ensure that the employees are able to make important decisions regarding customer s requirements at their level, thereby providing adequate solution to the problems. Postgraduates seem to rank the Problem Solving dimension higher than the graduates and undergraduates. A suggestion to management is to take into consideration the suggestion of the postgraduate customer when they try to enhance the problem solving capabilities of store employees. Further, retailers must look at the job description of the customer service and merchandising clerk to make sure that the employees are aware that problem solving is part of their job description. One of the items of the job descriptions of the customer service and merchandising clerk is to greet the customers. The management must look at this item to see if this was covered during orientation and if there are systems in place that reward or penalize the employee for doing a good or bad job in this area. Another item on the list for the job description for the customer service and merchandising clerk is finding the correct answers for customers. It needs to be clear to the employees that

398 375 there are steps that need to be followed when looking for the answer (Yaghi, 2010). Monthly and quarterly visiting customers gave a good deal of business to retailers. As these customers are the regular footfalls, retailers should not delay in delivering products at the promised time. They must not over-promise or report unrealistic times for delivery of promised products. This can be performed by setting realistic goals, having proper systems for ordering in place and giving ample time for product delivery. The identification of customer loyalty as a multi-dimensional construct may help the retailer in an accurate assessment of customer loyalty (Bloemer et al., 1999) because changes in level of customer loyalty signal changes in the value of customer assets. To attain customer loyalty, first of all retailers should satisfy the customers with their service quality attributes. Making an investment in retail service quality to satisfy customers is worth. Based on this improvement, retailers efforts should be concentrated on customers that rank lowest in satisfaction surveys (Heskett et al., 1997 and Jones and Sasser, 1995). Retailers should concentrate on their strengths rather than weaknesses, which is the most effective way of developing customer loyalty. Retailers should emphasize the differences that are; considered important by customers, distinct from competitors and superior in terms of delivering the overall benefit (Armstrong & Kotler, 2000) - in this case in terms of service quality. Successful companies have initiated the transition to customer-loyalty by revamping their core process, changing the measurement and reward system, and exercising selectivity with customers. In order to establish one-on-one rapport with customers and assure customers that there is someone who knows

399 376 and cares about them; retailers can establish a single point of contact in which an account specialist handles all the concerns of a particular customer (Wiersema, 1998). When customers experienced high levels of service quality then only they will engage in favorable customer loyalty intentions like intentions to revisit and repurchase from the same store, spreading positive word-of-mouth communication, and willing to pay more. Thus, retailers need to delight the shoppers with a high level of service quality. With the tailor-made offerings to fit customer demands, retailers can be rest assured of influencing customers to positive word-of-mouth communication and thereby impacting Customer Loyalty. Fulfilling customer demands can be expensive, but the retailer should look at the lifetime value of the customer. In order to raise positive word of mouth communication, the retailer should (1) provide appealing, eye-catchy, and smashing physical layout; (2) modern-looking equipment and fixtures/racks; (3) consistently courteous with customers; (4) provide high quality at convenient hours; (5) provide readymade packed products available when customers need; (6) employees should be neat and well-dressed in good looking uniform; (7) increase willingness to handle returns and exchanges goods; (8) customer service and merchandising clerk should quietly listens to the complaints of shoppers and show sincere efforts in figuring out their problems; (9) retain successful employees and minimize staff turnover; (10) employees paying proper attention to customers needs and (11) trained employees to inculcate confidence in the customers. According to the study, the researcher recommend retailers to improve their service performance in order to enhance customer s repatronage

400 377 intentions by (1) customized policies like abundant parking, convenience operating hours and transacting all major credit cards (2) increase ability of employees to handle customers complaints and problems; (3) employees should be neat and well-dress in good looking uniform; (4) offer branded, private labels and designer outfits; (5) charismatic shop layout, with nifty physical facilities; (6) customer service and merchandising clerk who must ensure that the display of the merchandise in the shop is attractive; (7) insisting on error-free transactions; (8) delegate authority to empower staff members to handle customers problems and complaints promptly (9) train its store employees to give individualized attention to each customer and not treat them by the dozen, despite the fact that the service is subject to high degrees of standardization; (10) attractive display of the merchandise in the shop; (11) ensuring that the employees are aware that problem solving is part of their job description and (12) handiness of goods when customers want them. In order to grasp premium price from the loyal customer, the researcher recommends retailers to improve their retail service quality by (1) designing the shop layout in a manner where it is easy for customers to move around and find what they need; (2) accomplishes the right service in the first time; (3) fascinating and commodious physical facilities; (4) not to over-promise or report unrealistic times for delivery of goods; (5) improve knowledge and ability of employees; (6) empowering employees to handle customer complaints directly and immediately; (7) volitionally handling returns and exchange goods; (8) enhancing ability of employees to solve problems; (9) reacting favorably to customer s requests; (10) personalized policies like convenient operating hours and ample parking; (11) individual attention to every individual (12) consistently courteous with customers.

401 378 In order to minimize redressed complaining behaviour of customer s, the researcher proposed that the retailers should ameliorate their service quality by (1) keeping its commitment; (2) engrain confidence in customers; (3) enhancing and developing employees knowledge to answer customer s query; (4) extending operating hours with plentiful parking facility; (5) volitionally accepting returns and exchanges; (6) assuring safety in their transactions; (7) visually attractive and appealing tangibles; (8) availability of branded, private labels and designer outfits; (9) courtesy and prompt service to customers and (10) arranging ongoing training programs for staff members regarding customer relations. Retailers can enhance the sales services by providing continuous personal empowerment programs on interpersonal communication skills and product knowledge. Staff empowerment will enable staff in the retail business to be more responsive to the needs of their customers (Siu and Cheung, 2001). Retailers can also use surveys eliciting behavioural intentions as an early warning system to identify customers in danger of defection and to take timely corrective action (Zeithaml et al., 1996). It has long been speculated that many service customers exhibit spurious loyalty - absence of alternatives make them remain loyal to companies even though they are dissatisfied with the company. In a number of small cities, RMG customers exhibited spurious loyalty due to non-availability of branded products and organized players. The complaints and negative experiences of the shoppers should be sought out on a priority basis. The more effective the complain handling, the more loyal/attached the customer will be. So, from time to time retailer must check the Contact Handling Effectiveness. Below mentioned are the key points through which the Contact Handling Effectiveness of the store

402 379 employees can be measured by the retailer: This information can be used to set customer-driven service standards for contact handling. In many industries, a complaining customer whose problem is solved becomes more loyal than a customer with no problem. This is because until the customer has a problem, service and quality are merely advertising slogans. Once the customer encounters a problem and the organization acts to resolve it, the customer thinks, Wow! They really do have great service (Evalue, 2003). The retail service quality measurement needs to be conducted regularly to measure the extent of service enhancement in order to establish customer loyalty intentions. Retailers should learn that service quality is a necessary condition rather than a sufficient condition for a successful long-term relationship, especially when the Indian retail is getting highly competitive and organized. Although service quality is an effective antecedent to customer loyalty, retailers cannot make differentiation and keep competitive only by providing good service. Retailers should look for other determinants for a successful long-term relationship like focusing on their core competencies and strengths LIMITATIONS OF THE STUDY The sample for the present study comprised of 900 shoppers of the organized Retail Stores. This sample is only a very small proportion of the entire population of retail shoppers in the country. Therefore, research studies with much larger sample size would be required to ensure appropriate generalization of the findings of the study. The study was limited to individual shopping behavior. India being a collectivistic country, most of the shopping happens in a family set up. Consideration of family shopping behavior might have revealed interesting findings too.

403 380 The construct of shopping orientations was measured through an instrument developed by a researcher in other country. Though the instrument shows scientific reliability and validity, yet this is the first study of its kind with this combination, which it has been adapted in India and more studies are required before it is established as an acceptable tool for exploring Shopping orientations. The present study has relied largely on quantitative methodology of data collection and is therefore restrictive. Therefore, more of qualitative methodology of data collection should be undertaken in future to provide wider perspective to the present study. For instance, the research design can employ case study methodology or content analysis to provide a holistic picture to the given subject DIRECTIONS FOR FUTURE RESEARCH The proposed model developed in this study is limited to include perceived service quality and repurchase intentions as the latent constructs. In order to focus on the interrelationships among them, the effects of other important marketing variables or situational factors are omitted. Therefore, inclusion of other situational factors or marketing variables into the proposed model may provide further insights of the relationships among perceived service quality and repurchase intentions. For example, further study can focus on the new perspectives of service loyalty in answering the proposed questions like 1) how can the level of repurchase intentions be affected by situational factors? Or 2) how can the level of repurchase intentions be affected by the quality of relationship with the contacted service staff?

404 CONCLUSION This chapter has presented a review of the stages of the research; conclusions on the overall model findings; the contributions for marketing theory and implications for practice and limitations. It offers suggestions for future research. This thesis investigates on identifying the dimensions of service quality in retailing and its impact on customer loyalty. Using a wellstructured questionnaire data were collected from 900 shoppers of shop-inshop. Questionnaire consisted of 27 statements (RSQS) measures the service offerings of retail stores, 13 statements (BIB) were used to analyze customer behavioral intentions, 7 purchase intentions and 2 customer satisfaction used. The customers in the Chennai, Madurai and Coimbatore cities have been focused throughout this study. Madurai and Coimbatore cities are the most important cities next to Chennai. People in the Chennai, Madurai and Coimbatore cities hold the better conditions in terms of demographic factors as income, social status, and life pattern etc. Before we come to the general conclusion in the Indian context, studies among retail service quality should be taken in the various service industries, cities and districts. Further, the concept like retail service quality should be connected with customer loyalty, customer retention and brand equity get, the more insights into the marketing practices of the retail supermarkets. Additionally, we have suggested to the future researchers or scholars to carry the research on the factor analysis in order to find out the applicability of RSQS across various retail formats and industries in the Indian perspective. This research builds on the previous findings on the relationships of service quality with customer satisfaction and behavioral intention of customers. It has examined these relationships under a new perspective that

405 382 associates the importance of service quality dimensions. Further, this study associates the importance of service quality dimensions with the strength of their relationships with customer satisfaction. A wide variety of industries will be benefited by replications of this study. The conclusions from this study are valuable on a number of accounts.

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440 417 APPENDIX -I SURVEY RANDOM SAMPLE CALCULATER - SCREENSHOT