CHAPTER 4 DATA ANALYSIS

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1 CHAPTER 4 DATA ANALYSIS 4.0 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 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 For analyzing the data both simple and advanced statistical tools have been used. Data collected were analysed 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 Factor Analysis, Analysis of Variance, Correlation and Multiple-Regression were also used. Simple descriptive statistics were used to summarize the respondents characteristics, their frequuency of visiting retail stores, and their ratings of service quality and behavioral intentions. Reliability and validity of the scale was checked. After that, for reducing and summarizing the data, a principle factor analysis with varimax rotation was performed to keep factors as independent of each other as possible. Multiple regressions were applied to assess the relationship between service quality dimensions and overall service quality. Oneway 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).

2 4.1 DEMOGRAPHIC PROFILE OF THE RESPONDENTS Respondents were at least 15 years old since by this age one is definitely shopping for oneself in India. Respondents from the age group accounted for the majority (43 per cent) of the respondents years was taken as a second group because by the age of 25 one is usually more independent of parental influence in India; usually working and/or married; with greater discretionary amount to spend. 18 percent comprises the third age group i.e This group has demanding children and consequently their exposure to a variety of stores is more. Finally, last group comprises of respondents more than 45 years which accounts for 12 percent. Regarding the gender, respondents were almost evenly distributed. The ratio of male to female respondents was about 54:46. Out of the 424 respondents, 56 per cent were unmarried while 44 per cent were married. Occupation-wise only 11 per cent respondents were from business class where as 19 per cent respondents were from government service. 25 per cent people were corporate employees, 35 per cent respondents were students and rest 11 per cent comprised other occupation. 35 per cent of the consumers have income less than Rs 20,000; 44 per cent were in the range of Rs. 20,000-50,000. Another 21 per cent were under the umbrella of monthly income of Rs. 50,000 and more. A clear majority of urban respondents (75 per cent) was observed, rest 25 per cent belong to rural background. Regarding educational qualification, almost 24 per cent of the respondents were undergraduates, 26 per cent were graduates and 50 per cent were post graduates. On the question about the type of outlet visited often, 49 per cent respondent preferred multi brand outlet while 20 per cent and 32 per cent liked to visit large retailer and exclusive outlet respectively. 35 per cent of respondents visit the shopping malls for excursion while 65 per cent were need based shoppers. On the question of frequency of visiting outlets, maximum (41 per cent) are monthly shoppers. Only 9 per cent belongs to the category of half yearly visitors. 29 per cent were weekly shoppers whereas 22 per cent were quarterly shoppers of shopping malls. Since Judgemental sampling was used; so utmost care was taken to have respondents of diversified characteristics, which is a true representation of the population. The demographic characteristics of the respondents are summarized in Table 15.

3 RESPONDENTS PROFILE TABLE 15: RESPONDENTS PROFILE DEMOGRAPHIC CATEGORY/CLASS PERCENT Age More than Gender Male 54.2 Female 45.8 Marital Status Unmarried 55.9 Married 45.8 Occupation Business 10.8 Government Service 18.9 Corporate Employee 24.8 Students 34.9 Others 10.6 Monthly Income Below Rs. 20, Rs. 20,000- Rs. 50, More than Rs. 50, Residential Status Urban 75.0 Rural 25.0 Highest Qualification Under Graduate 24.1 Graduate 25.9 Post Graduate 50.0 Type of outlet do you visit mostly Large Retailer 19.6 Multi Brand Outlet 48.6 Exclusive Outlet 31.8 Reason of visiting outlets Need Based 64.6 Excursion 35.4 Frequency of visiting outlets Weekly 28.5 Monthly 41.0 Quarterly 21.7 Half Yearly 8.7

4 4.2 THE RELIABILITY AND VALIDITY OF THE SCALE Reliability and validity of the scale is 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, r eliability is an indication of how consistent the findings are based on the method of data collection and analysis (Saunders, Lewis & 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 close ly 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 accurately 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 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).

5 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 & Prakash, 2007; Caro & Garcia, 2007; Akbaba, 2006; Jabnoun & Khalifa, 2005; Sureshchandar, Rajendran & Anantharaman, 2002; Dabholkar, Thorpe & 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 16.0, an internal consistency analysis was performed to assess the reliability aspect of the instrument. Internal reliabilities were computed for the overall service quality scale and its dimensions. Internal reliabilities were also computed for the overall behavioural intentions scale and its dimensions. The results of the test indicated that the retail service quality scale proposed by Dabholkar et al. (1996) is a very much reliable instrument, registering an overall Cronbach alpha value of The Cronbach alpha range for the dimensions of service quality was to 0.839, adhering to the minimum value of 0.70 suggested by Nunnally (1978). All the underlying dimensions were reliable except for the Inspiring Confidence (alpha =.678) pertaining to Personal Interaction dimension of service quality (Table 16). Nonetheless, the coefficient for Inspiring Confidence is still considered to be satisfactory as it is over 0.6 (Malhotra, 1993). The overall reliability of the Behavioural Intentions Battery proposed by Zeithaml, Berry, Parasuraman (1996) was satisfactory (Cronbach s coefficient alpha = 0.862). Owing to multidimensionality of customer loyalty construct, coefficient alpha was computed separately for the identified dimension. The reliability coefficients for four factors ranged from to indicating a fair to good internal consistency among the items of each dimension

6 (Table 17). Hence, the internal consistency reliabilities of both the measures were all acceptable. TABLE 16: RSQS AND RELIABILITY RESULTS DIMENSIONS 1. Appearance (4 items) 2. Convenience (3 items) 3. Promises (2 items) 4. Doing-it-right (2 items) 5. Inspiring Confidence (2 items) 6. Helpfulness (6 items) 7. Problem Solving (4 items) 8. Policy (3 items) Overall Scale (26 items) ALPHA RELIABILITY TABLE 17: BIB AND RELIABILITY RESULTS DIMENSIONS 1. Word-of-mouth Communication (2 items) 2. Purchase Intentions (3 items) 3. Price Sensitivity (3 items) 4. Complaining Behaviour (3 items) Overall Scale (13 items) ALPHA RELIABILITY An examination of the item-to-total correlations revealed no items that detract from the scale. Further examination of item statistics identified no items that suppress the alpha

7 level. Based on the statistical analyses, the instrument appears to be a fairly reliable measure of assessing the impact of service quality on customer loyalty in organised retail 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). The validity of the instrument is assessed using three methods: content validity, construct validity and criterion related validity. A. CONTENT VALIDITY Content validity is the extent to which there is a need for the adequate coverage of all the domains of the constructs being examined (Cooper and Schindler, 2001). In other words, it is the degree to which the measure spans the domain of the construct s theoretical definition (Rungtusanatham, 1988). Content validity is an agreement between experts that the scale measures what it is intended to and seems to be a good reflection of the scale (Zikmund & Babin, 2010). Content validity cannot be examined using statistical analysis and thus, a thorough exploration of the literature and an extensive search of measures used in the literature must be applied. For this study, the content validity of the proposed instrument measuring service quality is adequate enough because the instrument has been carefully constructed, validated and refined by Dabholkar et al. (1996). The content validity of the instrument measuring customer loyalty was ensured as the customer loyalty intentions and items were identified from the literature and were thoroughly reviewed by professionals and academicians.

8 B. CONSTRUCT VALIDITY Probably the most important type of validity is construct validity. Construct validity is the assessment of the degree to which an operationalisation correctly measures its targeted variables (O Leary-Kelly and Vokurka, 1998). In fact, Bagozzi, Youjae and Phillips (1991) posit that without assessing construct validity one cannot estimate and correct for confounding influences of random error and method variance, and the results of the theory testing may be ambiguous. Each measurement scale was evaluated by analysing its convergent and discriminant validity, using factor analysis. Nunnally (1978) asserts that factor analysis has a role in testing those aspects of validity. B.1 CONVERGENT VALIDITY Convergent validity is the degree to which multiple methods of measuring a variable provide the same results (O Leary-Kelly and Vokurka, 1998; Anderson and Gerbing, 1991). Garver and Mentzer (1999) advocate that convergent validity is tested by determining whether the items in a scale converge or load together on a single construct in the measurement model. In other words, convergent validity is the degree of convergence seen when two attempts are made to measure the same construct through maximally different methods. If there is no convergence, either the theory used in the study needs to be analyzed, or the purification of measure needs to be implemented by eliminating the items. Different methods are necessary so that common method variance is minimized. To have convergent validity, the scores for the category excellent must be higher than the category very good for each of the dimensions (Aldlaigan & Buttle, 2002 ; Parasuraman, Zeithaml & Berry, 1988). The scores for the category good must also be higher than the scores for the category poor and so on. Convergent validity can be established using the following approaches: Factor Loadings Approach: Convergent validity of the constructs was measured using an exploratory factor analysis. The conducted exploratory factor analysis proposed an 8-factor structure in case of service quality. The factor structure exists with an eigenvalue of greater than one, extracting per cent of the variance. The standardized loading estimate of.45 or above for the model implies that there is a strong evidence of convergent validity. As shown in table 24 that all the constructs exhibit convergent validity as all items of retail service quality loads high on their respective factor.

9 Hence, convergent validity was established by the significant size of the factor loadings, which ranged from to.898. Construct Reliability: Construct reliability equal or greater than.7 indicates adequate convergence or internal consistency. The results of the test indicated that the retail service quality scale and behavioural intention battery proposed by Dabholkar et al. (1996) and Zeithaml et al. (1996) respectively are very much reliable instruments. Table 16 and 17 illustrated the construct reliability results. In the present study, all alpha coefficients ranged from.678 (close to the cut -off value of 0.70) to 0.910, indicating good consistency and thereby convergent validity is ascertained using construct reliability approach. B.2 DISCRIMINANT VALIDITY Discriminant validity shows that the measure is unique in some way. Discriminant validity gauges the extent to which measures of two different constructs are comparatively distinctive from each other, and that their correlation values are neither an absolute value of 0 nor 1 (Campb ell and Fiske, 1959). Discriminant validity assesses the degree to which a concept and its indicators differ from another concept and its indicators. It means that items from one scale should not load or converge too closely with items from a different scale and that different latent variables which correlate too highly may indeed be measuring the same construct rather than different constructs (Garver and Mentzer, 1999). A correlation analysis was run on all the dimensions of retail service quality and customer loyalty. The results were presented in Table 18 and 19 respectively. It was found that all the dimensions are not perfectly correlated as their correlation coefficients fall between 0 and 1, hence establishing the discriminant validity of the RSQS and BIB.

10 TABLE 18: PEARSON CORRELATIONS BETWEEN SERVICE QUALITY DIMENSIONS DIMENS IONS Appearan ce Convenie nce **.553**.458**.393**.344**.370** Appear Conven Promis Doingit-right Confide Helpful Problem Policy ance ience es nce ness Solving 1.447**.323**.448**.339**.121*.412**.343** Promises **.241**.361**.460**.418** Doing-it **.534**.571**.443** right Confidenc e Helpfulne ss Problem Solving **.362**.413** **.315* ** Policy TABLE 19: PEARSON CORRELATIONS BETWEEN CUSTOMER LOYALTY DIMENSIONS DIMENSIONS Purchase Word-of-mouth Price Complaining Intentions Communication Sensitivity Behaviour Purchase Intentions 1.534**.368** -.254** Word-of-mouth ** -.249** communication Price Sensitivity * Complaining Behaviour ** Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the 0.05 level (2-tailed)

11 C. CRITERION-RELATED VALIDITY Criterion-related validity is established when a criterion, external to the measurement instrument is correlated with the factor structure (Nunnally, 1978). Moreover, it could refer to obtain similar results using different measurements. In the present study, criterion validity is established by correlating the customer perceived service quality scales scores with overall service quality, which is considered to be the outcome construct. The correlations were presented in Table 20 which shows that all the dimensions have significant positive correlations with the overall service quality. Thus, criterion related validity is established for all the eight dimensions. TABLE 20: CORRELATIONS AMONG THE EIGHT DIMENSIONS AND EXTERNAL CRITERIA S. No. DIMENSIONS OF SERVICE QUALITY OVERALL PERCEIVED SERVICE QUALITY 1. APPEARANCE.308** 2. CONVENIENCE.607** 3. PROMISES.452** 4. DOING-IT-RIGHT.117** 5. INSPIRING CONFIDENCE.102** 6. HELPFULNESS.314** 7. PROBLEM SOLVING.255** 8. POLICY.438** **Correlation is significant at the 0.01 level (2-tailed)

12 4.3 OBJECTIVE WISE ANALYSIS TO IDENTIFY THE KEY FACTORS INFLUENCING THE CUSTOMER S PERCEPTION OF THE SERVICE QUALITY IN PURVIEW OF ORGANISED RETAIL FORMATS FOR GARMENTS IN INDIA Service quality in retailing is different from any other product/service environment. Service quality measurements of the retail stores, unlike the pure service setups, include the measure of service quality and product quality as retail stores offer a mixture of services and products. In other words, service quality in retailing is a blend of selling goods and services to the customers in purview of customers expectations of knowledgeable and helpful staff to assist them during their shopping experience. That s why the most famous and well discussed service quality dimensions identified by Parasuraman et al. (1985) failed to be fully adopted and validated in a retail setting (Dabholkar et al., 1996) FACTOR ANALYSIS Exploratory factor analysis (EFA) is a technique for data exploration and to determine the structure of factors to be analyzed. The Factor Analysis is a premier data reduction technique and its procedure has several extraction methods for constructing a solution. The sole purpose of running a factor analysis is to minimise the number of variables while the amount of information in the analyses maximised (Steward, 1981). According to Kinnear and Gray (2010) the purpose of exploratory factor analysis is to find the number of factors that explain the correlations. The underlying principle of factor analysis is data parsimony and data interpretation (Zikmund, 2003). In this case, items are reduced to common interrelated and meaningful dimensions with a very small amount of information loss (Hair et al., 2006). One of the major uses of factor analysis is to summarise the data to be more manageable without losing any of the important information therefore making it easier to test theories (Tabachnick and Fidell, 2007). There are three main reasons for using factor analysis: to develop a scale to measure an underlying theme such as service quality, to reduce the variables to a manageable size and to have a better understanding of the variables.

13 According to Cooper and Schindler (2008) factor analysis is a technique used for specific computational techniques. These factors, also called latent variables, aim to measure things that are usually hard to measure directly, such as attitudes and feelings. This is a way to explain the relationships among variables by combining them into smaller factors (Zikmund, 2003; Coakes and Steed, 2001). The scales usually start with many questions, and then by using factor analysis are reduced to a smaller number. These reduced results are then used for other analysis such as multiple regression analysis (Pallant, 2007). Factor analysis is a good way of identifying latent or underlying factors from an array of seemingly important variables. Factor analysis is heavily used for service quality questionnaires and according to Gilbert and Veloutsou (2004) this technique has been adopted by almost one sixth of the authors of journal articles over the past thirty years. That is why the researcher has applied exploratory factor analysis on the responses provided by respondents. Before proceeding for the factor analysis, appropriateness of factor analysis needs to be assessed. Two tests are performed to ensure that the data is suitable for factor analysis: the Kaiser-Meyer-Olkin (KMO) measure for sampling adequacy and the Bartlett s test of sphericity (Pallant, 2007). KMO value greater than 0.6 can be considered as adequate (Kaiser and Rice, 1974). Table 21 provides the SPSS output of data for factor analysis. TABLE 21: KMO AND BARLETT S TEST KMO and Bartlett s Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy.661 Bartlett s Test of Sphericity Approx. Chi-Square Degree of Freedom 325 Sig..000 KMO stands for Keyser-Mayer-Olkin Criteria, where high KMO values signify high correlation among the variables. KMO - the measure of sampling adequacy was used to measure the adequacy of the sample for extraction of factors. MSA is the Measure of Sample Adequacy Criteria, where low values of the variables indicate that they are not sufficiently correlated to other variables in the model. From the Table 21, it can be seen that KMO value

14 was acceptable, as its value found to be which is indicative of a data set considered to be highly desirable for factor analysis (Kim and Mueller, 1978). The Bartlett s test of sphericity was used to test the multivariate normality of the set of distributions. This procedure also tests whether the correlation matrix is an identity matrix (factor analysis would be meaningless with an identity matrix). A significance value of p=0.00 indicates that the data do not produce an identity matrix or differ significantly from identity (George and Mallery, 2000). The analysis focusing on the sphericity of the distribution (Bartlett s sphericity test) allowed rejecting the hypothesis according to which the matrix would be unitary (Approx. Chi -square , df 325, p 0.000). This result implies that the data is thus approximately multivariate normal and acceptable for factor analysis. The most common method of factor analysis is the Principal Component Analysis (Kinnear & Gray, 2010; Cooper & Schindler, 2008) and the most common method of factor rotation is the varimax rotation (Kinnear & Gray, 2010; Zikmund et al., 2010). Principal component technique looks at the correlation of different variables to reveal the relationship between them, and then reduces the variables by empirically summarising them or combining them into a small number of factors under common themes (Tabachnick and Fidell, 2007). Usually, a few components will account for most of the variation, and these components can be used to replace the original variables. The mathematical technique for simplifying the results of the factor analysis results is called factor rotation (Zikmund et al., 2010). Varimax rotation was favoured since it minimized the correlation across factors and maximized within the factors. This helped to yield clear factors (Nunnally, 1978). The perceived retail service quality was tested using principal component factor analysis with varimax rotation. Akbaba (2006) and Gilbert et al. (2004) had also employed principal components and varimax procedure in their respective studies. The communalities for the 26 items were derived. Table 22 showed the communalities values of the 26 item RSQS. Communalities values were all relatively large (greater than 0.5), suggesting that the data set was appropriate (Stewart, 1981) for further analysis.

15 TABLE 22: COMMUNALITIES OF THE 26 RSQS ITEMS 1. The Outlet has modern-looking equipment and fixtures/racks. 2. The Outlet and its physical facilities (trial room and restrooms) are clean, convenient & visually attractive. 3. Materials associated with the Outlet s service (such as shopping bags, loyalty cards) are visually appealing. 4. The store has clean, attractive, and convenient physical facilities (restrooms, fitting rooms) 5. The layout of the Outlet makes it easier for customers to find what they need. 6. The layout of the Outlet makes it easier for customers to move around in the Outlet. 7. When the Outlet promises to do something (such as repairs, alterations) by a certain times, it will do so. 8. The Outlet provides the services at the time it promises to do so. 9. The Outlet performs the service right the first time. 10. The Outlet has merchandise available when the customers want it. 11. The Outlet insists on error-free sales transactions and records. 12. Employees in the Outlet have the knowledge to answer customer s questions. 13. The behaviour of employees in the Outlets instils confidence in customers. 14. Customers feel safe in their transactions with this Outlet. 15. The employees in the Outlet give prompt service to customers. 16. Employees in the Outlet tell customers exactly when services will be performed. 17. Employees in the Outlet are never too busy to respond to customer s requests. 18. The Outlet gives customers individual attention. 19. Employees in the Outlets are consistently courteous with customers. 20. The Outlet willingly handles returns and exchanges. 21. When a customer has a problem, the Outlet shows a sincere interest in solving it. 22. Employees in the Outlet are able to handle customer complaints directly and immediately. 23. The Outlet offers high quality merchandise. 24. The Outlet provides plenty of convenient parking for customers. 25. The Outlet has operating hours convenient for all their customers. 26. The Outlet accepts all major credit cards. Initial Extraction Communalities values greater than 0.5, all items are acceptable for further analysis (Stewart, 1981)

16 With Principal Component analysis eight factors were retained depending on Eigen values and variance explained. Eigen value represents the total variance explained by each factor. The standard practice normally used is that all the factors with an Eigen value of one or more should be extracted. In other words, eight factors compromising twenty-six items, all having Eigen values of unity and above were extracted (a factor must explain at least as much of the variance if not more, than a single original variable) and the results are shown in Table 23. TABLE 23: 8- FACTOR STRUCTURE EXTRACTED FROM EXPLORATORY FACTOR ANALYSIS WITH EIGEN VALUES AND VARIANCE EXPLAINED Component Initial Eigen Values Rotation Sums of Squared Loadings % of variance Rotation Sums of Squared Loadings Cumulative percentage Factor 1: Factor 2: Factor 3: Factor 4: Factor 5: Factor 6: Factor 7: Factor 8: Extraction Method: Principal Component Analysis The eight factor solution accounted for percent of the variance. Total variance explained ( percent) by these eight components exceeds the 60 percent threshold commonly used in social sciences to establish satisfaction with the solution (Hair et al., 1995).

17 The inclusion of items in the factor was determined by their factor loadings. Factor loadings are the correlation of the variable with the factor. It indicates the strength of the relationship between the item and the latent construct and thus, is used to ascertain the convergent and discriminant validity of the scales (Hair et al., 2006). When the loading is clear then the interpretations of the factors become easier (Zikmund et al., 2010). Some variables have a loading or correlation with more than one factor. Rotated Component Matrix shows the loading of each variable on each of the extracted factors. The objective of this matrix is to find variable which have high loading on one factor, but low loading on other factors. This is similar to correlation matrix, with loadings having values between 0 and 1. Values close to 1 represent high loadings and those close to 0, low loadings. High loadings signify that the variable can be assigned to that particular factor. Further, Nunnally (1978) posits that items with loadings higher than 0.50 on one factor are retained for further analysis. Table 24 is the Rotated Component Matrix, which clearly demonstrated that Factor 1 is a linear combination of variable number 14, 15, 16, 17, 18, and 19 (Eigen value = 7.063). Factor 2 is a linear combination of variable number 24, 25, and 26 (Eigen value = 3.542). Factor 3 is a linear combination of variable number 1, 2, 3, and 4 (Eigen value = 1.802). Factor 4 is a linear combination of variable number 12, and 13 (Eigen value = 1.603). Factor 5 is a linear combination of variable number 20, 21, 22 and 23 (Eigen value = 1.547). Factor 6 is a linear combination of variable number 7, and 8 (Eigen value = 1.274). Factor 7 is a linear combination of variable number 10, and 11 (Eigen value =1.206). Factor 8 is a linear combination of variable number 5, 6, and 9 (Eigen value =1.071).

18 FACTOR EXTRACTION RESULTS OF SERVICE QUALITY MEASUREMENT ITEMS TABLE 24: ROTATED COMPONENT MATRIX (Varimax with Kaiser Normalization) DESCRIPTION FACTOR Customers feel safe in their transactions with this Outlet. 15. The employees in the Outlet give prompt service to customers. 16. Employees in the Outlet tell customers exactly when services will be performed. 17. Employees in the Outlet are never too busy to respond to customer s requests. 18. The Outlet gives customers individual attention. 19. Employees in the Outlets are consistently courteous with customers. 24. The Outlet provides plenty of convenient parking for customers. 25. The Outlet has operating hours convenient for all their customers. 26. The Outlet accepts all major credit cards. 1. The Outlet has modern-looking equipment and fixtures/racks. 2. The Outlet and its physical facilities are clean, convenient & visually attractive. 3. Materials associated with the Outlet s service are visually appealing. 4. The store has clean, attractive, and convenient physical facilities. 12. Employees in the Outlet have the knowledge to answer customer s questions. 13. The behaviour of employees in the Outlets instils confidence in customers. 20. The Outlet willingly handles returns and exchanges. 21. When a customer has a problem, the Outlet shows a sincere interest in solving it. 22. Employees in the Outlet are able to handle customer complaints directly and immediately. 23. The Outlet offers high quality merchandise. 7. When the Outlet promises to do something by a certain times, it will do so. 8. The Outlet provides the services at the time it promises to do so * * *.025* * * * * * *.143* * * *.027*.129* *.054*.120* * * *.055*.059* * * *.056* *.072* * * * * * *.309*.216* * * *

19 10. The Outlet has merchandise available when the customers want it. 11. The Outlet insists on error-free sales transactions and records. 5. The layout of the Outlet makes it easier for customers to find what they need. 6. The layout of the Outlet makes it easier for customers to move around in the Outlet. 9. The outlet performs the service right the first time. * indicates negative sign.158* * * * * * * After the number of extracted factors is decided, the next task is to interpret and name the factors. This is done by the process of identifying the factors that are associated with which of the original variables. The rotated component matrix is used for this purpose. NAMING OF FACTORS All the factors have been given appropriate names according to the variables that have been loaded on each factor. The eight factors depicted in Table 24 were discussed below: Factor1: HELPFULNESS The first factor was defined by items mostly from the helpfulness and hence labelled as Helpfulness. The factor was composed of six items and accounted for per cent of the variance. The items in this factor were similar to the original sub-dimension but a new item customers feel safe in their transactions with this outlet was added. Helpfulness of the employees are reflected by the items like employees providing prompt service, tell customers when services will be performed, never too busy to respond, give individual attention, courteous with customers and customers feel safe in their transactions. Factor 2: POLICY Factor 2 comprised three items that related to the policy of the retailer and accounted for further per cent of the variance. Policy included the matter of ease of parking, convenient operating hours as well as acceptance of all major credit cards by the outlet. The items in this factor were similar to the original dimension but one item was loaded to another factor.

20 Factor 3: APPEARANCE The third factor was defined by items 1-4 and is clearly the Appearance factor. This factor constitutes for an additional explained variance of per cent. Modern-looking equipment and fixtures/racks, clean physical facilities, convenient & visually attractive material were highly loaded on this factor and thus named as Appearance. This factor was similar to the original sub-dimension termed Appearance in the Physical Aspects factor. These were all tangibles influences that customers notice before or upon entering a store. These characteristics helped to establish the image of the store and influence customer expectations. Factor 4: INSPIRING CONFIDENCE The fourth factor picked up variance from items and seemed to deal with respondent s risk perception of the store and was termed the Confidence factor. This factor explained the additional per cent of the variance. Employee s knowledge to answer customer s questions and their behaviour instils confidence in customers. The items in this factor were similar to the original sub-dimension except one item which was loaded to helpfulness factor. Factor 5: PROBLEM SOLVING Factor 5 was interpreted as a Problem Solving factor and comprised four items. The extracted factor explained for further per cent of the variance. This factor includes outlet comfortably handling returns and exchanges, showing sincere interest in problem solving, handling customer complaints immediately and offering high quality merchandise. This factor was similar to the original sub-dimension termed Problem Solving in the Personal Interaction factor. Surprisingly, a new item outlet offers high quality merchandise belonged to this factor. It could be explained to a certain extent that the consumers may think that if they got the high quality merchandise, than half of their problems like exchanges, return etc. will sought out in one shot. Factor 6: PROMISES Factor 6 was labelled as Promises that included two items. The factor was analogous to the sub-dimension labelled as Promises in the Reliability factor of the Retail Service Quality Scale. This factor was a combination of outlet providing the services at the

21 time it promises to do so and whenever outlet promises to do repairs/alterations by a certain times, it will do so. In other words, this factor was related to the trustworthiness of the apparel retailers perceived by its customers. It accounted for another per cent of the variance. Factor 7: DOING-IT-RIGHT The seventh factor was relatively easy to interpret as it consisted of two items that were all related to the outlet s Doing-it-Right action. Availability of merchandise when the customers want it and error-free sales transactions/ records is the right way of delivering services constitutes this factor. This factor explained for the additional per cent of the variance. The items in this factor were similar to the original scale but one item was loaded to another factor. Factor 8: CONVENIENCE The last factor picks up variance from the Convenience scale and seems to be picking up aspects of Doing-it-Right. The factor was labelled as Convenience and defined by items 5, 6 and 9. The dimension accounted for an additional per cent of the variance. The factor was composed of items like the outlet makes it easier for customers to find what they need, easy to move around in the outlet and performing the service right the first time. The items in this factor were similar to the original sub-dimension but a new item outlet performs the service right the first time was loaded to this factor RELATIVE IMPORTANCE OF SERVICE QUALITY DIMENSIONS 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). Nadiri and Tumer (2009) assess the relationship between the overall service quality and dimensions of retail service quality in a retail setting of Northern Cyprus. Similarly, in a study by Seth, Momaya and Gupta (2008) multiple regression analysis was used to find the significance of the customer perceived service quality dimensions in explaining the variance in the overall perception of the cellular mobile telephony. Akbaba (2006) used regression analysis to test the relationship between the overall service quality against the service quality dimensions. Another study that used regression analysis measured service quality in Islamic banks by testing the relationship between the overall service quality as the dependent variable

22 and the service quality dimensions as the independent variable (Al -Tamimi & Al-Amiri, 2003). In a study by Mehta, Lalwani & Han (2000) regression analysis was used to find the significance of the RSQS dimensions in explaining the variance in the overall perception of the retail environment. In order to determine the relative importance of eight customers perceived service quality dimensions, they were subjected to multiple regression analysis. For this, based on Parasuraman, Zeithaml and Berry s (1988) approach, multiple regression analysis model was followed in which the respondent s overall judgement of service quality was considered as dependent variable and the eight customer perceived service quality dimensions were made independent variables. Thus, the score (obtained from factor analysis) for each of the dimensions were regressed on the overall service quality score obtained from each respondent surveyed. The standardised beta coefficients provided the relative importance (Table 28). 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. Summary of Regression Analysis treating service quality dimensions as predictors and overall perception of service quality as criterion variable was shown in Table 25, Table 26 and Table 27. 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 2 = 0.74 was statistically significant. It was suggested that the retail service quality dimensions explained 74 per cent of the variance in the customers overall rating. TABLE 25: MODEL SUMMARY Model R R 2 Adjusted R 2 Estimate Std. Error of the The adjusted R 2 square value was which means that retail service quality dimensions account for 74 percent of the variance in Overall Retail Service Quality. It means that 26 percent of Overall Retail Service Quality was explained by something other than the service quality dimensions.

23 TABLE 26: ANOVA Model Sum of Squares Df Mean Square F Sig. 1 Regression Residual Total All dimensions were statistically significant (Sig. T < 0.05). All 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 27, the overall perceived service quality was influenced by all the eight dimensions with Convenience as the most important dimension, beta coefficient =.836. TABLE 27: REGRESSION ANALYSIS RESULTS FOR SERVICE QUALITY DIMENSIONS Model Unstandardized Coefficients Standardized Coefficients B Std. Error Beta 1 (Constant) Helpfulness Policy Appearance Inspiring Confidence T Sig Problem Solving Promises Doing-It-Right Convenience

24 Table 27 depict that the customers tend to make service quality judgements based on these eight dimensions in order of importance as revealed in the regression equation. Convenience had achieved the strongest association with the overall perception of service quality. It could be explained by the fact that the pace of life is very fast in India. They were always running short of time. So, they prefer such an outlet whose physical layout/ fixtures makes it easier to move around and search what they want in the minimum time slot. That s why the physical layout and fixtures are effective in influencing customers general evaluation on the retail service quality. This was consistent with a qualitative study conducted by Dabholkar et al. (1996) in which the physical aspects of the store and its facilities were widely acknowledged as essential determinants of the shopping experience. Promises, Policy, Helpfulness, Appearance, Problem Solving, Doing-it- Right and Inspiring Confidence were decisive factors in making up overall service quality perception. Among all the variables in the regression, Inspiring Confidence appeared to have least association (with beta coefficient =.062) with overall service quality. This shows that the customers perceive Inspiring Confidence i.e. employees in the outlet have the knowledge to answer customer s questions and employees behaviour in the outlets instils confidence in customers as the least important for influencing their service quality perceptions. The results of Table 27 can be summarized as regression equation given below: Overall service quality as perceived by customers = (Convenience) (Promises) (Policy) (Helpfulness) (Appearance) (Problem Solving) (Doing-It-Right) (Inspiring Confidence) Regarding the evaluation of the overall quality of service delivered by apparel retailers, more than half of the respondents ( per cent) were satisfied and highly satisfied with the service quality of apparel retailers while about 15.1 per cent of the consumers were neither satisfied nor dissatisfied with apparel retailers. Only 12 per cent respondents were dissatisfied whereas 3.3 per cent respondents were highly dissatisfied with the service offerings of apparel retailers. The mean of overall perception of service quality is 3.73, which highlights that garment customers are satisfied with the bouquet of services delivered by apparel retailers (Appendix 1: Analysis Tables).

25 TABLE 28: RELATIVE IMPORTANCE OF INDIVIDUAL SERVICE QUALITY DIMENSIONS DIMENSIONS STANDARDIZED COEFFICIENTS BETA RELATIVE IMPORTANCE HELPFULNESS POLICY APPEARANCE INSPIRING CONFIDENCE PROBLEM SOLVING PROMISES DOING-IT-RIGHT CONVENIENCE The above table indicates the standardized beta coefficients value and order of importance for these dimensions from customer s perspectives. The results show that the customers gave importance to all of the retail service quality dimensions in judging the overall retail service quality. Convenience has drawn maximum importance followed by Promises, Policy, Helpfulness, Appearance, Problem Solving, Doing-it-Right and Inspiring Confidence. Thus, H1 (a), H1 (b), H1 (c), H1 (d), H1 (e), H1 (f), H1 (g) and H1 (h) were accepted.

26 OBJECTIVE - II TO STUDY THE EFFECT OF DEMOGRAPHIC VARIABLES ON VARIOUS 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). For detailed analysis, Post hoc analysis had been applied ANOVA between Dependent Variable: Service Quality Dimensions Independent Variable: Age TABLE 29: EFFECT OF AGE ON SERVICE QUALITY DIMENSIONS Sum of Squares df Mean Square F Sig. Helpfulness Policy Appearance Inspiring Confidence Problem Solving Promises Doing-it-right Convenience Analysis of Variance shown in table 29 represents that age had an influence over Problem Solving, rest all factors had no influence of age i.e. people from all age groups perceived the rest of 7 factors as same.

27 TABLE 30: DERCRIPTIVE OF MEAN OF PROBLEM SOLVING More than Total The mean score of Problem Solving for age group was , for age group it was For respondents ranging between age group the mean score was whereas for age group more than 45 it was For further analysis Post hoc analysis was used. Multiple Comparisons using LSD (Least Significant Difference) Method Dépendent Variable: Problem Solving TABLE 31: POST HOC ANALYSIS OF AGE ON PROBLEM SOLVING (I-J) Mean Difference Sig. 95% Confidence Interval (I) Age (J) Age Lower Bound Upper Bound * More than More than * More than More than * The mean difference is significant at the.05 level.

28 Post hoc analysis revealed that respondents of age group differ significantly from the people of age group Positive mean difference marked that these people gave higher importance to Problem Solving factor than respondents of different age group. Respondents of age group were more mature & responsible. They were not as lively and enthusiastic as of age group. They sensed that whenever a problem was encountered it must be sorted out in a short while. On the other side, respondents of age group were free birds who like to spend their time in leisure activities. So they can wait and visit the outlet again for figuring out of their problems. However, no significant difference was observed between age group & and age group & More than 45. EXHIBIT 36: MEAN OF PROBLEM SOLVING v/s AGE Thus, H2 (a) was accepted.

29 ANOVA between Dependent Variable: Service Quality Dimensions Independent Variable: Gender Gender differences appeared in the dimension of Doing-it-Right. In other words, respondents differed significantly on the basis of Doing-it-Right. TABLE 32: EFFECT OF GENDER ON SERVICE QUALITY DIMENSIONS Sum of Squares df Mean Square F Sig. Helpfulness Policy Appearance Inspiring Confidence Problem Solving Promises Doing-it-right Convenience Mean score of males ( ) was significantly lower than the mean score of females (.11130). It indicated that females gave more importance to the handiness of apparels in the store, and an error free environment. TABLE 33: DERCRIPTIVE OF MEAN OF DOING-IT-RIGHT Male Female Total

30 Post hoc analysis in this case couldn t be used as there were only two groups. By comparing the mean scores it was revealed that the female shoppers gave far more importance to Doing-it-Right dimension than their male counterparts. Females are a little bit impatient because of their tight schedule. Due to this they always want that their work should be done in one shot. That is why females give more importance to availability of merchandise in the outlet and error-free transactions. EXHIBIT 37: MEAN OF DOING-IT-RIGHT v/s GENDER The above diagram depicted that females gave more importance to Doing-it-Right than males while patronising. Thus, H2 (b) was accepted.

31 ANOVA between Dependent Variable: Service Quality Dimensions Independent Variable: Marital Status TABLE 34: EFFECT OF MARITAL STATUS ON SERVICE QUALITY DIMENSIONS Sum of Squares df Mean Square F Sig. Helpfulness Policy Appearance Inspiring Confidence Problem Solving Promises Doing-it-right Convenience Significant differences were found in the dimensions of Problem Solving according to the marital status. In other words, married and unmarried shoppers did not perceive this dimension as same. On rest of the dimensions respondents do not differed significantly. TABLE 35: DERCRIPTIVE OF MEAN OF PROBLEM SOLVING Unmarried Married Total The mean score of Problem Solving for unmarried apparel shoppers was , whereas for married shoppers it was Positive mean score indicated that married respondents gave more importance to Problem Solving. Married respondents had to devote more time to their family than bachelors. That s why they preferred an outlet which handled their problems with sincere efforts, and willingly handled returns and exchanges.

32 EXHIBIT 38: MEAN OF PROBLEM SOLVING v/s MARITAL STATUS Post hoc analysis in this case couldn t be used as there were only two groups. The mean scores of unmarried and married respondents revealed that the married shoppers gave far more importance to Problem Solving dimension than the unmarried shoppers. Thus, H2 (c) was accepted.

33 ANOVA between Dependent Variable: Service Quality Dimensions Independent Variable: Occupation TABLE 36: EFFECT OF OCCUPATION ON SERVICE QUALITY DIMENSIONS Sum of Squares Df Mean Square F Sig. Helpfulness Policy Appearance Inspiring Confidence Problem Solving Promises Doing-it-right Convenience Analysis of Variance exhibited mixed results. Two dimensions namely Policy and Problem Solving differed significantly on the basis of occupation whereas other dimensions had no influence of occupation. The mean scores of Policy and Problem Solving was presented in following table. TABLE 37: DERCRIPTIVE OF MEAN OF POLICY Business Government Service Corporate Employee Students Others Total The mean score of Policy for business class was , whereas it was for government employees. For respondents who were corporate employees, the mean score was The mean score of Policy for students was where as it was

34 for respondents belonging to others category. Positive mean score signifies that business class, corporate employees and others gave more weightage to Policy. EXHIBIT 39: MEAN OF POLICY v/s OCCUPATION Post hoc analysis showed that businessmen differed significantly on the basis of Policy from the government employees and students. However, no significant difference was observed between businessmen & corporate employees and businessmen & others. Positive mean difference marked that these people (businessmen) gave higher importance to Policy (retail outlets accepting all major credit cards, operating hours convenient for the customers, providing plenty of convenient parking, and offering high quality merchandise) than rest of the category respondents. Business class represents an affluent society. They preferred customised 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 government employees and students. Further, respondents from Others category turn were also found to be differing significantly from students and government sector employees. However, they were not found to be significantly different from businessmen and corporate employees.

35 TABLE 38: POST HOC ANALYSIS OF OCCUPATION ON POLICY (I-J) Mean Difference Sig. 95% Confidence Interval (I) Occupation (J) Occupation Lower Bound Upper Bound Business Govt. Service * Corporate Employee Student * Others Govt. Service Business * Corporate Employee Student Others * Corporate Business Employee Govt. Service Student Others Student Business * Govt. Service Corporate Employee Others * Others Business Govt. Service * Corporate Employee Student * * The mean difference is significant at the.05 level.

36 TABLE 39: DERCRIPTIVE OF MEAN OF PROBLEM SOLVING Business Government Service Corporate Employee Students Others Total For government and corporate employees the mean score of Problem Solving was positive i.e and respectively. It means that these two groups gave more importance to Problem Solving than the other three groups. For businessmen, students and others the mean score was , and respectively. EXHIBIT 40: MEAN OF PROBLEM SOLVING v/s OCCUPATION The above diagram depicted that government and corporate employees gave more importance to Problem Solving than rest of the group.

37 TABLE 40: POST HOC ANALYSIS OF OCCUPATION ON PROBLEM SOLVING (I-J) Mean Difference Sig. 95% Confidence Interval (I) Occupation (J) Occupation Lower Bound Upper Bound Business Govt. Service * Corporate Employee Student Others Govt. Service Business * Corporate Employee Student * Others Corporate Business Employee Govt. Service Student * Others Student Business Govt. Service * Corporate * Employee Others Others Business Govt. Service Corporate Employee Student * The mean difference is significant at the.05 level.

38 Post hoc analysis showed that government sector employees differed significantly from the other category people viz. respondents possessing their own business and students. Positive mean difference depicts that these people ( government sector employees) gave higher importance to Problem Solving dimension (retail outlet employees shows sincere efforts in solving the problem of customers, willingly handles returns and exchanges of products and quietly listens to the complaints of shoppers) than other category people. However, no significant difference was observed between government sector employees & corporate employees and government sector employees & others. Also, respondents from corporate employees in turn were found to be differing significantly from students. However, corporate employees were not found to be significantly different from businessmen, government employees and others. Thus, H2 (d) was accepted.

39 ANOVA between Dependent Variable: Service Quality Dimensions Independent Variable: Monthly Income TABLE 41: EFFECT OF MONTHLY INCOME ON SERVICE QUALITY DIMENSIONS Sum of Squares Df Mean Square F Sig. Helpfulness Policy Appearance Inspiring Confidence Problem Solving Promises Doing-it-right Convenience Analysis of Variance exhibited that none of the dimensions differed significantly on the basis of monthly income i.e. respondents from all levels of income perceived the retail service quality dimensions as same. Due to the advancement in technology and communication, respondents of different income strata think in a single line regarding service features offered by garment retailers. An income stratum (Below Rs. 20,000; Rs. 20,000-50,000 and More than Rs. 50,000) was not a bar in the similar perception of service quality. Thus, H2 (e) was rejected.

40 ANOVA between Dependent Variable: Service Quality Dimensions Independent Variable: Residential Status TABLE 42: EFFECT OF RESIDENTIAL STATUS ON SERVICE QUALITY DIMENSIONS Sum of Squares df Mean Square F Sig. Helpfulness Policy Appearance Inspiring Confidence Problem Solving Promises Doing-it-right Convenience Analysis of Variance depicted that none of the dimensions differed significantly on the basis of residential status i.e. respondents from urban and rural areas grasped the retail service quality dimensions as same. Due to the widespread educational facilities, the gap between rural and urban people had diminished to a large extent and this is the reason that residential status plays no significant role in perceptual experience of respondents. Thus, H2 (f) was rejected.

41 ANOVA between Dependent Variable: Service Quality Dimensions Independent Variable: Highest Qualification TABLE 43: EFFECT OF HIGHEST QUALIFICATION ON SERVICE QUALITY DIMENSIONS Sum of Squares df Mean Square F Sig. Helpfulness Policy Appearance Inspiring Confidence Problem Solving Promises Doing-it-right Convenience Respondents were found to be differing significantly on the basis of Problem Solving. Mean score of Post graduate ( ) was significantly higher than the mean score of Graduate ( ) and Under graduate ( ). It indicated that post graduate respondents preferred those outlets which handles customer complaints immediately and willingly handles return and exchanges. The mean score of Problem Solving for various categories of educational qualification was shown in following table: TABLE 44: DERCRIPTIVE OF MEAN OF PROBLEM SOLVING Under graduate Graduate Post graduate Total

42 Post Hoc Tests Multiple Comparisons using LSD (Least Significant Difference) Method Dependent Variable: Problem Solving TABLE 45: POST HOC ANALYSIS OF HIGHEST QUALIFICATION ON PROBLEM SOLVING (I) Highest Qualification Under Graduate Graduate Post Graduate (I-J) Mean Difference Sig. 95% Confidence Interval (J) Highest Lower Bound Upper Bound Qualification Graduate Post * Graduate Under Graduate Post Graduate * Under * Graduate Graduate * * The mean difference is significant at the.05 level. Postgraduates were found to be differing significantly from the other category respondents i.e. graduates and under graduates. Positive mean difference indicated that Problem Solving was more crucial dimension for postgraduate respondents than graduates and under graduates. Being more educated post graduates know that what the customer meant for a retailer. If the complaints of customers were not sorted out quickly than they will switch over to another competitor and complaint to external agency.

43 Due to more awareness of consumer s rights, post graduated respondents preferred that whenever they came across a problem the retailer should show sincere interest in solving it, employees willingly handle exchanges and are capable enough to sort out their complaints. EXHIBIT 41: MEAN OF PROBLEM SOLVING v/s HIGHEST QUALIFICATION The above diagram depicted that Post graduates gave more importance to Problem Solving than rest of the group. Thus, H2 (g) was accepted.

44 ANOVA between Dependent Variable: Service Quality Dimensions Independent Variable: Type of outlet visited mostly Analysis of Variance exhibited that none of the dimension differed significantly on the basis of type of outlet. For the garments shoppers Multiple brand outlet, Large retailer and Exclusive outlet were offering the similar retail services. Competition in the garment retail is very fierce. Due to which all retail formats (Multiple brand outlet, Large retailer and Exclusive outlet) are providing high quality service to their footfalls. All the three retail outlets were putting efforts to minimize knowledge gap, standard gap, delivery gap and finally communication gap. TABLE 46: EFFECT OF TYPE OF OUTLET ON SERVICE QUALITY DIMENSIONS Sum of Squares df Mean Square F Sig. Helpfulness Policy Appearance Inspiring Confidence Problem Solving Promises Doing-it-right Convenience Thus, H2 (h) was rejected.

45 ANOVA between Dependent Variable: Service Quality Dimensions Independent Variable: Reason of visiting outlets TABLE 47: EFFECT OF REASON OF VISITING OUTLETS ON SERVICE QUALITY DIMENSIONS Sum of Squares Df Mean Square F Sig. Helpfulness Policy Appearance Inspiring Confidence Problem Solving Promises Doing-it-right Convenience Analysis of Variance bring forth that none of the dimension differed significantly on the basis of reason of visiting outlet i.e. need based shopping and excursion based shopping makes no difference in dissimilar sensing of service quality dimensions. Whether the respondents visit the outlet for need based patronising or for enjoyment they want similar sort of service quality attributes. Thus, H2 (i) was rejected.

46 ANOVA between Dependent Variable: Service Quality Dimensions Independent Variable: Frequency of visiting outlets TABLE 48: EFFECT OF FREQUENCY OF VISITING OUTLETS ON SERVICE QUALITY DIMENSIONS Sum of Squares Df Mean Square F Sig. Helpfulness Policy Appearance Inspiring Confidence Problem Solving Promises Doing-it-right Convenience Respondents were found to be differing significantly on the basis of Promises. Rest of the dimensions had no influence of frequency of visiting outlet i.e. weekly shoppers, monthly shoppers, quarterly shoppers and half yearly shoppers perceived these factors as same. Mean score of monthly respondents ( ) and quarterly respondents ( ) was significantly higher than the mean score of weekly respondents ( ) and half yearly respondents ( ). The mean score of Promises for various categories of frequency of visiting outlets was shown in following table: TABLE 49: DERCRIPTIVE OF MEAN OF PROMISES Weekly Monthly Quarterly Half Yearly Total

47 Post Hoc Tests Multiple Comparisons using LSD (Least Significant Difference) Method Dependent Variable: Promises TABLE 50: POST HOC ANALYSIS OF FREQUENCY OF VISITING OUTLETS ON PROMISES (I-J)Mean Difference Sig. 95% Confidence (I) Frequencyvisiting (J) Frequency- Lower Bound Upper Bound outlet visiting outlet Weekly Monthly * Quarterly * Half Yearly Monthly Weekly * Quarterly Half Yearly Quarterly Weekly * Monthly Half Yearly Half Yearly Weekly Monthly Quarterly * The mean difference is significant at the.05 level. Respondents who visited outlets on monthly basis were found to be differing significantly from those who visited outlets frequently i.e. weekly. Positive mean difference indicated that monthly visitors gave more importance to Promises than those who often (weekly) visit the outlet. However, no differences were observed between other categories. Also, quarterly visiting respondents were differed significantly from weekly visiting respondents. However, no significant difference was observed between quarterly visiting respondents & monthly visiting respondents and quarterly visiting respondents & half yearly visiting respondents.

48 Weekly visitors were the regular footfalls at apparel outlets in comparison to monthly and quarterly visitors. On a weekly basis they (weekly visitors) visit the outle t and whenever an over-promise is committed they can wait for its fulfilment. For example, outlet employees promises the customers (weekly visitors) that white colour shirt will be available in 1 st week of January, but due to unforeseen circumstances the desired shirt was not delivered on promised date but will be delivered on 3 rd 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 shirt in their next visit but the same does not hold good for monthly and quarterly visitors. They prefer that whenever the outlet promises to do repairs, and alterations by a certain times, it will do so and the outlet provides the services at the time it promises to do so. EXHIBIT 42: MEAN OF PROMISES v/s FREQUENCY OF VISISTING OUTLETS The above diagram depicted that monthly and quarterly visitors gave more importance to Problem Solving than other two groups. Thus, H2 (j) was accepted.