CHAPTER 4 RESEARCH METHODOLOGY

Transcription

1 91 CHAPTER 4 RESEARCH METHODOLOGY INTRODUCTION This chapter presents how the study had been designed and orchestrated and provides a clear and complete description of the specific steps that were taken to address the area of research, research questions, research design, identification of population, size of the sample, research instrument, pilot study, selection sample, data collection, reliability analysis of data, statistical tools used and conclusion. 4.1 AREA OF THE RESEARCH The area of the research is customer relationship management (CRM), under the broad areas of service marketing. How far customer relationship management is being practiced or followed as a strategy in consumer durable white goods companies and what actually is the most expected outcome of the study. 4.2 RESEARCH QUESTIONS This study had addressed the following research questions 1. What are the demographic characteristics of the consumer durable white goods customer in Chennai and brand preference? 2. What is consumer durable white goods company and what its competitors are offering?

2 92 3. What is the relationship between customer satisfaction and loyalty, satisfaction and retention of consumer durable white goods? 4. What factors actually drive to customer loyalty? 5. What is the perception of the customer on consumer durable white goods? 4.3 RESEARCH DESIGN Descriptive research design is used to describe accurately the characteristics of consumer durable white goods customers. Descriptive research design describes what exists and to describe the proportion of people who hold various opinions are primarily descriptive in nature. A descriptive research approach is appropriate when the problem is well structured, when the researcher knows what knowledge it aims to collect, and where there is need to look for the cause and effect relationships. The objective with this kind of research is to describe something. It seeks to answer who, what, where and how questions. Consequently, it does not give the answer to why questions, in other words, it doesn t give the explanation of the cause of findings. However, when studying business problems it is often enough with the information obtained from describing a situation. It is not required to know why things are the way they are (Zikmund 2000)[164]. The basic research design of this study is descriptive research on consumer durable white goods. 4.4 IDENTIFICATION OF POPULATION The population for this study is the entire consumer durable white goods customers in the city of South Chennai. Chennai is popularly known as Chennai pattinum of south India, situated in the southern part of the state of Tamilnadu. Covering an extent of 1189 sq km is the district head quarters. Chennai is well known for its information technology, production industries

3 93 and has excellent potential for industrial growth. Chennai population (46.16 million) is the first biggest city of the southern state of Tamilnadu and is identified as one of the fast developing metros of south India. This study has 183 number of consumer durable white goods shops which comprises of 2, 80,310 customer as on 16 April Study of Population A sample of 1050 customers were chosen for the study from the consumer durable white goods shops which comprises of 2, 80,310 customer Sampling Design A sample of 1050 white goods customers is taken through non probability, convenient sampling. In non probability samples, the probability of each case being selected from the total population is not known Data Collection The research is primarily based on primary data. The data is collected from the target sample census of population using the survey method. The survey is administered through questionnaire method. Secondary data available in the form of books, journals, magazines, periodicals and information from different websites were also used for the study Research Instrument Questionnaires were used for the collection of primary data. In order to achieve the objectives of this study, a questionnaire is designed, pre-tested and administered to consumer durable white goods customer in Chennai city. A copy of the questionnaire appears in Appendix 1. The questions are presented with exactly the same wordings and in the same order to the entire customer base. The reason for standardization is to ensure that the entire customer is replying to the same set of questions.

4 94 Thirteen items for analyzing customer satisfaction and the importance of the items to the customer while eleven items used in the five point scale to understand customer loyalty in the questionnaire were taken from the study `redefining customer loyalty the customer s way by Bhatty, Skinkle and Spalding ( 2000)[165]. The questions on customer retention were developed using the studies available on Infosurv and CRM Guru.com. The questions on customer perception were adopted from CRM guru.com and Polaris marketing research. The questionnaire contains open ended and close ended questions that restricted customer to a set of alternatives, using a five point scale. For analyzing the levels of customer satisfaction, loyalty, retention and customer perception, five point rating scales were used. Multiple choice questions, dichotomous questions were included in the questionnaire Pilot Study A pilot study is taken before proceeding with the main study. The pilot study ensures control of non sampling error and also to test the effectiveness as a research instrument. The questionnaire is pre-tested to ensure validity, that is, the questionnaire measured the concepts being investigated. 4.5 RELIABILITY ANALYSIS OF THE STUDY Reliability refers to the consistency of results when the research object has been repeatedly measured. Reliability can be defined as the degree to which measures are free from error and therefore yield consistent results. Reliability is usually measured using Cronbach s alpha methodology, which is based on internal consistency. When testing for internal consistency of the series, Cronbach s alpha analysis can be used. For this study as can be seen from table the composite reliability for the internal consistency shows that values for all constructs are above the suggested threshold of 0.7.

5 95 The overall reliability coefficient for all the items of customer satisfaction, loyalty and retention respectively were found to be very high (above 0.9) exceeding the acceptable benchmark of 0.7. Validity is the extent to which a test measure, what it claims to measure. It is vital for a test to be valid for the results to be accurately applied and interpreted. Table: 4.1 Cronbach s Reliability Analysis Reliability analysis Cronbach's Alpha based on standardized items Number of Items Retention (Question No.14) Retention (Question No.13) Loyalty (Question No.12) Satisfaction (Question No.11) Satisfaction (Question No.10) It establishes a significant reliability. Validity analysis involved testing for construct, convergent and discriminate validity, all of which were acceptable and therefore used to support the measurement of customer satisfaction, loyalty and retention. 4.6 STATISTICAL TOOLS USED Non parametric were used for the analysis. To analyze the data and draw inferences the following statistical methods and tools were used.

6 96 Mean scores are different ways of evaluating performances of a rated attributes. Percentage analysis is applied to find out the distribution of frequencies of and between the variables in the study. Percentage Analysis Percentage refers to a special kind of ratio. Percentages are used in making comparison between two or more series of data to describe the relationships. Number of respondents Percentage = 100 (4.1) Total number of respondents Weighted arithmetic mean is also calculated to find out averages, the distribution of customer on satisfaction, importance to certain criteria etc. Chi-square is a test used for testing the significance of association between two attributes. This is the most widely used statistical tool for use with qualitative variables. If the chi-square is significant at the chosen level then the investigator routinely rejects the null hypotheses of independence and tentatively accepts the alternative hypothesis that the variables are dependent or are related. Chi square test is applied to test, the relationship in the genders, age, income, marital status, period or duration of relationship with white goods customer and nature of products. In chi-square test if the null hypothesis is rejected, then the alternative hypothesis is often accepted regardless of how low the probability associated with the obtained value of chi-square is. The fact is that in such a case nothing can be inferred about the strength or degree of that relationship. A significant chi-square value, at best,

7 97 permits one to say that probably there is some dependence between variables in the population. Hence often chi square used when the scores under analysis result from measurements of ordinal or nominal scales. The value of 2 is given by the formula in the equation (4.2) (f o -f e ) 2 2 =. (4.2) f e Where f o stands for the observed frequency and f e for the corresponding expected frequency. Factor analysis -principal component analysis is used to obtain the two factor solution. There are various rotational strategies that could be used to obtain a clear pattern of loading for some variables and low loadings for others. Varimax rotation strategy is used. The aim is to find a rotation that maximize the variance on the new axes or put in another way; it is to obtain a pattern of loadings on each factor that is as diverse as possible. This is done by looking at the correlations between those variables and the two factors (or new variables.) as they are extracted by default; these correlations are also called factor loadings. A rule of thumb employed by many researchers is to accept items with loading of 0.7 or more. Rotation factor analysis reveals the degree to which the customer scores on the group of items affecting customer s level of satisfaction in the relation durable goods customer and services. Each item measures some part of this common aspect of satisfaction, secondly each item also captures a unique aspect of satisfaction that is not addressed by any other item. Rotation factor analysis aims at extracting factors that account for less and less

8 98 variance. The variance accounted for by successive factors is summarized in a table and the variances extracted by the factors are called the eigen values. To make a decision on the factors to be retained, the Kaiser criterion is used, and this probably is the one method most widely used. According to this criterion all those factors which have an eigen value greater than one can be retained. However, one argument against this criterion is that it retains too many factors. If the model is correct, then it cannot be expected that the factors will extract all variances from the items, rather, only that proportion that is due to the common factors and shared by several items. In the language of factors analysis, this proportion of variance of a particular item, which is due to common factors (shared with other items), is called communalities. Therefore an additional task when applying this model is to estimate the communalities for each variable, that is, the proportion of variance that is unique to each item is then the respective items total variance minus the communality. A common starting point is to use the squared multiple correlation of an item with all other items as an estimate of the communality and the goal is to minimize the variance. Multiple regression analysis helps identify the key indicators of satisfaction. Key indicators of customer satisfaction are items which have been determined, through a multiple regression analysis, to directly affect overall customer satisfaction and retention. Multiple regression analysis can be used to identify the best-fit combination of independent (predictor) variables- the ten specific questions on customer satisfaction on- which are correlated with the dependent variable (overall satisfaction). In this way two or three items that best predict overall satisfaction can be identified. If any of the items highly corrected with overall satisfaction have low satisfaction

9 99 ratings, it means the company should make an informal decision to allocate resources to those areas for improvement analysis of variance (ANOVA) helps in understandings the variance of the findings for regression analysis. The generalized equation (4.3) is Y= X X X n X n. (4.3) Where, 0 = a constant, the value of Y when all X values are zero. i = the slope of the régression surface (the represents the regression assciated with each Xi) = an error, normally distributed about a mean of 0. Gap analysis is applied in the study to measure whether any significant relationship between customer satisfaction and the level of loyalty and retention of customer in durable companies and to verify the various reasons stated by the customer for their preferences in selection of particular consumer durable white goods customer. 1. It is about the study of demographic profile of consumer durable white goods customer: Mean score for evaluating performance of rated attributes: percentage analysis and chi square value 2. Analyses the customer satisfaction of durable white goods customer: mean score, percentage methods, weighted arithmetic mean, rotation factor analysis and regression analysis. 3. Study the customer loyalty among durable white goods customer: mean score, rotation factor analysis.

10 Analyze the factors influencing customer retention: mean score, percentage analysis. 5. Study the SLR (satisfaction, loyalty, retention) model for their pattern and strength of association: gap analysis, correlation 6. Provide specific information that can retain company business to a better understanding of how customer perceive and evaluate consumer durable goods in order to guide the formation for improved service strategies to retain customers: mean score, percentage analysis. The inferences of the following objectives are made with help of above mentioned statistical tools. Objectives of the study Percent age analysis Weighted mean score Chi square test Factor analysis Demographic variables Customer satisfaction Customer loyalty Customer retention Customer perception Relationship between satisfaction, loyalty and retention Multiple regression Correlation

11 101 Conclusion In this chapter detailed study discussed with the research methodology on consumer s durable white goods customer in Chennai city. For their explanation data collections methods, procedure and analysis tools were discussed, followed by the analysis and interpretation of the study.