CHAPTER - 4 RESEARCH METHODOLOGY

Transcription

1 CHAPTER - 4 RESEARCH METHODOLOGY 4.1 Introduction Consumer attitude Purchase decision Decision machining process 4.2 Statement of Research Problem 4.3 Rationale of the Study 4.4 Objectives of the Research Study 4.5 Universe of the Study 4.6 Sample Design Sampling Units Sampling Method Sample Size 4.7 Sources of Data Questionnaire Development 4.8 Research Hypothesis 4.9 Data Analysis and Statistical Tools 4.10 Supportive Technology 4.11 Limitations of the Study 68

2 Research is a scientific and systematic search for pertinent information on a specific topic. The formidable problem that follows the task of the defining the research problem is the preparation of the design of the research project, popularly known as the research design. Decision regarding what, where, when, how much, by what means concerning an inquiry or a research study constitute a research design. 4.1 Introduction The interaction between technology and society has been studied in the context of a technological revolution in industry: automated factories, massive business computers, and so forth. Households eventually enter a similar technological race. The technological revolution affects daily life within a household in time allocation patterns, in the choice of social functions, in the transmittal of cultural values, and in overall human behavior. When a given technology begins to affect the life of a household, it is a safe conclusion that the technology is being integrated into the social system and is accepted as a basis for future social behavior. Other technologies popularized in the past two or three decades have introduced structural changes and new ideologies within the household: washing machines and refrigerators, entertainment oriented products such as radio, television, and stereo equipment, architectural changes in the design of kitchens, bathrooms, and other units of physical space, all give new meaning to child rearing, women's roles, family interactions, shopping behavior, and value systems Consumer Attitude It is the attitude of the consumer as to why, when, how and where the consumer intends to buy the product. It blends elements of psychology, sociology, social psychology, anthropology and economics. It attempts to understand the buyers decision process. It studies characteristics of individual consumer such as demographic and behavioral variables in an attempt to understand people s want. It also tries to assess the influence on the consumer from groups such as family, friends, reference groups and society in general. Customer behavior study is based on 69

3 consumer buying behavior, with the consumer playing three distinct roles of user, payer, and buyer Purchase Decision In many purchase situations the consumer is confronted with a complex set of alternatives. He has to choose among a variety of alternative products from a variety of products. From a variety of products he makes selection, based on size, color, models and brands. The consumer can make decisions about when and where to buy the products. Some purchase decisions are routine and may not require much attention. Some other purchase decisions include more cash outlays. The economic concept of consumers sovereignty points out that the consumer is the king of the market. According to this concept all the productive resources are deployed so as to fulfill the needs of the consumer. Hence, it is important to understand in depth the term purchase decision of the consumer Decision Making Process There are five stages in the purchase decision process 7. They are: a) Need/Want/Desire is recognized b) Search for information c) Evaluate options. d) Purchase. e) After-Purchase Evaluation. Deciding what to buy is one of the consumer s most basic tasks. No purchase takes place unless this fundamental decision is made. The consumer has to make decisions on brands, price and product features. The study broadly aims at examining perceptions of the consumers mainly in terms of the information gathered and used for the purpose of the ultimate purchase decision. This study attempts to determine the sources and factors that influence the purchase of Car. 4.2 Statement of the Research Problem This research work focuses on the factors influencing the households to adapt personal travel facility at the home and to assess the perceptions of the households, 70

4 with the societal implications of traveling facility in his hand. Two fundamental assumptions are focuses in this research. First, the household is assumed as a social system with differentiated actors, behaviors and patterns of socialization. Second, the act of acquiring a car is in itself significant as evidence of adoption by the household. Households adopt car, and acquire the facility of traveling in his hand with a view to realizing some goals. Over time, households manifest certain types of behavior in relation to the automobile technology, such behaviors having been determined by the needs and characteristics of the household and the nature of the available technology. If these behaviors persist over time they are in turn likely to have an impact on the future behavior of the household. If the impact affects a larger segment of the population they can have broader societal implications. To acquire and possess car the consumer faces problems relating to acquiring authentic information about car, regarding sources of information, genuineness of car, the economic price to be paid etc. After acquiring one, the consumer faces the problem of adopting it at the household and its social implication on the household. Through this research the researcher has addressed some of these issues. 4.3 Rationale of the Study In the study area researchers need to understand the extent to which policies aimed at increasing access to purchase the car. Household surveys not only measure the impact of policies and interventions affecting the purchase behavior, but can also provide valuable information on the relative importance of different types of outlets in providing computers from a consumer standpoint. Repeated Household Surveys ultimately allow the impact of all consumer behavior. Household Surveys can also facilitate an understanding of the determinants of appropriate purchasing behavior. 4.4 Objectives of the Research Study The overall objective of the present study is to analyze the consumer attitudes and purchase decisions with reference to the consumers of car in Saurashtra Region. The study is undertaken with the following objectives: 1. To study the conceptual background with focus on consumer behaviour 2. To find out the sources of information for purchase of car. 3. To assess the perceptions of the households regarding the economic and social/psychological benefits. 71

5 4. To investigate and explore the characteristics of personal traveling facility and its implications. 5. To offer suggestions for effective marketing of Car. 4.5 Universe of the Study Universe of the study is finite. This primary research is conducted in the the entire district of Saurashtra region i.e., Rajkot, Junagadh, Amreli, Jamnagar, Surendranagar, and Porbandar. So, Saurashtra Region is the size of the population. 4.6 Sample Design Research design constitutes the blueprint for the data collection, measurement and analysis of data. This is a descriptive research study. Research study describes the buying patterns of consumers. Descriptive research includes surveys & fact-finding enquiries of sampled respondents. The main characteristic of this method is that the researcher has no control over the variables; he can only report what has happened or what is happening Sampling Units Sampling design will be imperative in every scientific study. Hence, the researcher has planned to adopt non-probability sampling method. The sampling units are Rajkot, Junagadh, Amreli, Jamnagar, Surendranagar, and Porbandar Sampling Method All the samples are selected haphazardly from the sub-geographical urban areas of the Gujarat State. So, area sampling method was adopted to find the list of respondents for the research study Sample Size The Total sample size of the study will be 600 samples on data will be collected through 100 samples in each district of Saurashtra region. 4.7 Sources of Data Primary data will be collected from the respondents through questionnaire. The variables will be measure using three points, five points scale with closed, openended and multiple-choice questions. To test the hypothesis of the present study primary as well as secondary data will be collected for the purpose. Researcher will 72

6 visit various libraries of the colleges and car distributors. The secondary data will be obtain from research publications, books, articles, journals, seminar papers and magazines, other reports available in different libraries and from websites Questionnaire Development A well-structured questionnaire was developed after an extensive review of Internet commerce literatures. Close ended questions have been asked to each respondent from the questionnaire. The respondents were requested to assess dichotomous questions, multi-dichotomous questions, ranking scale, constant sum scale questions, numerical scale questions and some of the scale items on a Likert point scale used for each statement where 1 = strongly disagree (not important at each and every one) and 5 = strongly agree (extremely important). Questionnaires were administered in English to consumers near office premises, shopping mall, colleges and Internet centers. A pilot study and survey was conducted with a small one number of 25 respondents to arrive at the twelve factors that the consumer feels are significant and also to understand the degree to which respondents understand the questions. 4.8 Research Hypothesis Research hypothesis provides the base to derive the research conclusions. It was preferred to test hypothesis at significance level (α) of 5% and at confidence level (1- α) of 95%. This allowed to fix the acceptance region is equal to 95% & the rejection region is equal to 5% to accept or reject the null hypothesis H 0 or alternative hypothesis H a. Following is the list of hypothesis used to verify in this research study. Basing on the Planned Behavior Theory the following hypothesis is formed. H 0 : Income does not affect to purchase either old or new car. H 0 : Income does not affect to the purchasing price of a car. H 0 : Occupation does not affect to the purchasing price of a car. H 0 : Income does not affect to purchase payment mode of a car. H 0 : Occupation does not affect the consideration of fuel to purchase a car. H 0 : Gender is independent of owning a car. H 0 : Income is independent of owning a car. H 0 : Occupation is independent of owning a car. H 0 : Employment of family member is independent of owning a car. 73

7 H 0 : Occupation is independent of purchasing a car. H 0 : Gender is independent of considering a brand of a car. H 0 : Education is independent of considering a brand of a car. 4.9 Data Analysis and Statistical Tools To make this research more scientific and systematic, the researcher will use Master Sheet, formation of One Way Tables, Cross Tables, Chi-square Test, Correlations, ANOVA and Factor Analysis Test will be use to find out the factors contributing to the preferences for a particular brand of Car. The output of the analysis of data will be present in tables, figures and charts for the better understanding and presentation of findings. Data Analysis will be as done with the help of SPSS package in computer. Variables and their relationship were analyzed through Cross Tables Supportive Technology There could be the support of information technology and computer to speed up calculations all the way with acceptable accuracy of research study. Researcher used MS-Excel application software of MS-OFFICE package for sorting all the collected data with the numerical codes. Researcher used Statistical Package of Social Science (SPSS) software with version 19 to process the collected data and give appropriate conclusions according the selected hypothesis of this research study Limitations of the Study The main purpose of our research was to investigate Consumer Attitude and Purchase Decision of car, consumer perceptions about the use of car and the attributes they associate with use of car of districts of Saurashtra region. The sample will be dividing into six clusters based on the sampling plan. As almost no research has so far been implemented in Saurashtra Region with regard to attitudes, purchase decisions, perceptions and use of car, substantial room exists for further research. 74

8 CHAPTER 5 DATA ANALYSIS AND INTERPRETATION 5.1 Frequency Analysis: Gender 5.2 Frequency Analysis: Age 5.3 Frequency Analysis: Marital Status 5.4 Frequency Analysis: Income 5.5 Frequency Analysis: Education Level 5.6 Frequency Analysis: Occupation 5.7 Frequency Analysis: Owning a Car 5.8 Frequency Analysis: Purpose of a Car 5.9 Frequency Analysis: New/Old Car Purchase 5.10 Frequency Analysis: Brand of a Car 5.11 Frequency Analysis: Price of a Car 5.12 Frequency Analysis: Payment Mode 5.13 Frequency Analysis: Fuel Based Car 5.14 Frequency Analysis: Time to Purchase a Car 5.15 Frequency Analysis: Importance of Decision 5.16 Frequency Analysis: Discussion to Purchase a Car 5.17 Frequency Analysis: Dealer Visit 5.18 Frequency Analysis: Role in Purchasing a Car 5.19 Frequency Analysis: Main Car User 5.20 Frequency Analysis: Overall Satisfaction 5.21 Linear Regression: Income & Old/New Car 5.22 Linear Regression: Income & Price 75

9 5.23 Linear Regression: Occupation & Price 5.24 Linear Regression: Income & Payment Mode 5.25 Linear Regression: Occupation & Fuel 5.26 Cross Tabulation and Chi-square Analysis: Gender & Owning a Car 5.27 Cross Tabulation and Chi-square Analysis: Income & Owning a Car 5.28 Cross Tabulation and Chi-square Analysis: Occupation & Owning a Car 5.29 Cross Tabulation and Chi-square Analysis: Employed Family Members & Purpose of a Car 5.30 Cross Tabulation and Chi-square Analysis: Occupation & Purpose of a Car 5.31 Cross Tabulation and Chi-square Analysis: Gender & Brand of a Car 5.32 Cross Tabulation and Chi-square Analysis: Education & Brand of a Car 5.33 Factor Analysis 5.34 Factor Analysis: Information Gathering and Consumer Purchase Initiation (IGCP) 5.35 Factor Analysis: Preference Based on Personal Needs (PPP) 5.36 Factor Analysis: Personal Preference Based on Convenience Factors (PPC) 5.37 Factor Analysis: Personal Preference Based on Comfort Factors (PPCF) 5.38 Factor Analysis: Influence Factor Based on Car Dealer (IFD) 5.39 Factor Analysis: Influence Factor Based on Car Model (IFM) 5.40 Factor Analysis: External Influence (EI) 5.41 Factor Analysis: Satisfaction Level (SL) 76

10 This chapter describes the analysis of collected statistics and facts and their interpretation. Frequency distribution and cross tabulation is the way to examine the relationship between two variables. Frequency distribution is one of the most primary tools to bifurcate the data according the selected variables in a tabular format. Cross tabulation is one of the vital tools to understand and to measure an association between independent and dependent variable. From the developed questionnaire, researcher have picked up several independent demographic variables like age, gender, residential area, income, etc to be analyzed with one of the dependent variable that s the e-buying preference. researcher used SPSS 19 version to analyze all these analysis. Along with the cross tabulation analysis, researcher calculated their association in terms of numerical magnitudes by using the Chi-square (Test of Independence), cross tabulation, factor analysis. In the following section, initially researcher discussed frequency distribution, cross tabulation and statistical analysis of the data. 5.1 Frequency Analysis: Gender In the following Table 5.1 and Chart 5.1, it is comprehensible that 88.4% of the respondents uses internet and 11.6% of the respondents do not use internet. It means that 428 respondents are male and 56 respondents are female. 77

11 Table 5.1 Gender Frequency Percent Valid Percent Cumulative Percent Valid Male Female Total Chart 5.1 Gender 5.2 Frequency Analysis: Age In the following Table 5.2 and Chart 5.2, it is comprehensible that 9.3 %, 8.5%, 30.0%, 36.6% and 15.7% respondents belongs to below 20, 21-30, 31-40, 41-50, and above 50 age groups respectively. 78

12 Table 5.2 Age Frequency Percent Valid Percent Cumulative Percent Valid Below Above Total Chart 5.2 Age 5.3 Frequency Analysis: Marital Status In the following Table 5.3 and Chart 5.3, it is comprehensible that 84.3 % respondents are married and 15.7% respondents are unmarried. 79

13 Table 5.3 Marital Status Frequency Percent Valid Percent Cumulative Percent Valid Married Unmarried Total Chart 5.3 Marital Status 5.4 Frequency Analysis: Income In the following Table 5.4 and Chart 5.4, it is comprehensible that 4.1 %, 15.5%, 24.2%, 32.2%, 8.3%, 5.2%, 6.2% and 4.3% respondents belongs to below 20,000, 20,001 to 50,000, 50,001 to 1,00,000, 10,0001 to 2,00,000, 2,00,0001 to 2,50,000, 2,50,001 to 3,00,000 and above 3,00,000 income respectively. 80

14 Table 5.4 Monthly Incomes Frequency Percent Valid Percent Cumulative Percent Valid Below Above Total Chart 5.4 Monthly Incomes 81

15 5.5 Frequency Analysis: Education Level In the following Table 5.5 and Chart 5.5, it is comprehensible that 5.2%, 21.9%, 48.1%, and 24.8% respondents belongs to up to 12, graduate, post graduate, and professional respectively. Table 5.5 Educational Level Frequency Percent Valid Percent Cumulative Percent Valid Up to Graduate Post Graduate Professional Total

16 Chart 5.5 Educational Level 5.6 Frequency Analysis: Occupation In the following Table 5.6 and Chart 5.6, it is comprises that 40.9%, 19.8%, 3.1%, 15.5%, 7.2%, 8.3%. 3.1% and 2.1% percentages respondents belongs to government service, business, unemployment, private service, foreign company service, house wife, student and agriculture occupation respectively. 83

17 Table 5.6 Occupation Frequency Percent Valid Percent Cumulative Percent Valid Government Service Business Unemployment Private Service Foreign Company Service House Wife Student Agriculture Total Chart 5.6 Occupation 84

18 5.7 Frequency Analysis: Owning a Car In the following Table 5.7 and Chart 5.7, it is comprises that 95% respondents do have a car and 5% respondents do not have a car. Table 5.7 Owning a Car Frequency Percent Valid Percent Cumulative Percent Valid Yes No Total Chart 5.7 Owning a Car 85

19 5.8 Frequency Analysis: Purpose of a Car In the following Table 5.8 and Chart 5.8, it is comprises that 7.4%, 42.4%, and 50.2% respondents have their car for business purpose, personal/family purpose and both purchases respectively. Table 5.8 Purpose of a Car Frequency Percent Valid Percent Cumulative Percent Valid Business Purpose Personal/Family Purpose Both Purpose Total Chart 5.8 Purpose of a Car 86

20 5.9 Frequency Analysis: New/Old Car Purchase In the following Table 5.9 and Chart 5.9, it is comprises that 91.7% respondents have brand new car and 8.3% respondents have second hand car. Table 5.9 New/Old Car Purchase Frequency Percent Valid Percent Cumulative Percent Valid Brand New Second Hand Total Chart 5.9 New/Old Car Purchase 5.10 Frequency Analysis: Brand of a Car In the following Table 5.10 and Chart 5.10,, it is comprises that 17.8%, 11.6%, 9.3%, 16.5%, 4.1%, 8.5%, 10.3%,7.2%,6.4%, 5.2%, and 3.1% percentage respondents have Maruti, Hyundai, Tata, Honda, Toyota, Chevrolet, Ford, Nissan, Volkswagon, Renault and Skoda brand respectively. 87

21 Table 5.10 Brand of a Car Frequency Percent Valid Percent Cumulative Percent Valid Maruti Hyundai Tata Honda Toyota Chevrolet Ford Nissan Volkswagon Renault Skoda Total

22 Chart 5.10 Brand of a Car 5.11 Frequency Analysis: Price of a Car In the following Table 5.11 and Chart 5.11, it is comprises 8.3%, 50.2%, 33.3% and 8.3% respondents who prefers prices below 3,00,000, 3,00,001 to 5,00,000, 5,00,001 to 10,00,000 and 10,00,001 to 15,00,000 respectively. 89

23 Table 5.11 Price of a Car Frequency Percent Valid Percent Cumulative Percent Valid Below Total Chart 5.11 Price of a Car 90

24 5.12 Frequency Analysis: Payment Mode In the following Table 5.12 and Chart 5.12, it comprises 35.5% respondents who prefers cash payment and 64.5% respondents who prefers EMI payment mode. Table 5.12 Payment Mode Frequency Percent Valid Percent Cumulative Percent Valid Cash EMI Total Chart 5.12 Payment Mode 91

25 5.13 Frequency Analysis: Fuel Based Car In the following Table 5.13 and Chart 5.13, it comprises 37.2% respondents prefer diesel, 29.3% respondents prefer petrol and 33.5% respondents prefer petrol as well as gas based cars. Table 5.13 Fuel based Car Frequency Percent Valid Percent Cumulative Percent Valid Diesel Petrol Petrol & Gas Total Chart 5.13 Fuel based Car 92

26 5.14 Frequency Analysis: Time to Purchase a Car In the following Table 5.14 and Chart 5.14, it comprises 50.0% respondents require 2 weeks to 1 month time to purchase a car, 33.3% respondents require 1 month to 3 months time to purchase a car and 16.7% respondents require 3 months to 6 months time to purchase a car. Table 5.14 Decision Time to Purchase a Car Frequency Percent Valid Percent Cumulative Percent Valid 2 Week-1 Month Month - 3 Month Month - 6 Month Total Chart 5.14 Decision Time to Purchase a Car 93

27 5.15 Frequency Analysis: Importance of Decision In the following Table 5.15 and Chart 5.15, it comprises 3.1%, 2.1%, 36.65, 25.2%, and 33.1% respondents believes that decision regarding to purchase a car is very unimportant, fairly important, neutral, fairly important and very important respectively. Table 5.15 Importance of the Decision of Purchasing a Car Frequency Percent Valid Percent Cumulative Percent Valid Very unimportant Fairly important Neutral Fairly important Very important Total Chart 5.15 Importance of the Decision of Purchasing a Car 94

28 5.16 Frequency Analysis: Discussion to Purchase a Car In the following Table 5.16 and Chart 5.16, it comprises 16.7% respondents who discuss car purchasing decision with their family members and friends, and 83.3% respondents who did not discuss car purchasing decision with their family members. Table 5.16 Discussion for Purchasing a Car with Family and Friends Frequency Percent Valid Percent Cumulative Percent Valid Yes No Total Chart 5.16 Discussions for Purchasing a Car with Family and Friends 95

29 5.17 Frequency Analysis: Dealer Visit In the following Table 5.17 and Chart 5.17, it comprises 77.1%, 16.7%, 4.1% and 2.1% respondents who have contacted dealers under 3 times, 3 to 5 times, 5 to 7 times and more than 7 times respectively. Table 5.17 Contacted/Visited the Dealers Frequency Percent Valid Percent Cumulative Percent Valid Under 3 times to 5 times to 7 times More than 7 times Total Chart 5.17 Contacted/Visited the Dealers 96

30 5.18 Frequency Analysis: Role in Purchasing a Car In the following Table 5.18 and Chart 5.18, it comprises 14.7%, 78.1% 6.2% and 1.0% respondents who is only decision maker, is one of the decision makers and play the decisive role, one of the decision makers but not play the decisive role and totally decided by others respectively. Table 5.18 Role in Purchasing a Car Frequency Percent Valid Percent Cumulative Percent Valid I am the only decision maker I am one of the decision makers, and play the decisive role I am one of the decision makers, but not play the decisive role Totally decided by others Total

31 Chart 5.18 Roles in Purchasing a Car 5.19 Frequency Analysis: Main Car User In the following Table 5.19 and Chart 5.19, it comprises 61.6% respondents who are self main car users, 25.0% respondents are spouses who are main car users, 5.2% respondents are parents who are main car users and 8.3% who are other family members who are main car users. 98

32 Table 5.19 Main Car Users Frequency Percent Valid Percent Cumulative Percent Valid My self My husband/wife My parents Other family members Total Chart 5.19 Main Car Users 5.20 Frequency Analysis: Overall Satisfaction In the following Table 5.20 and Chart 5.20, it comprises 3.1%, 6.2%, 7.2%, 66.7%, and 16.7% respondents who are very dissatisfied, dissatisfied, and neutral, satisfied and very satisfied respectively. 99

33 Table 5.20 Overall Satisfaction of a Car Frequency Percent Valid Percent Cumulative Percent Valid Very dissatisfied Dissatisfied Neutral Satisfied Very Satisfied Total Chart 5.20 Overall Satisfaction of a Car 5.21 Linear Regression: Income & Old/New Car Linear regression indicates that whether independent factor is having effect on dependent variable or not. Here value of R Square indicates the measurement about these phenomena. Histogram shows the illustrative relationship among these variables. Table 5.21 provides the model summary. In this table, adjusted R square value is means income does affect in deciding to purchase either old or new car. Chart 5.22 is the histogram for the same. 100

34 Table 5.21 Model Summary b Std. Error Change Statistics Adjusted of the R Square F Sig. F Mode R R Square R Square Estimate Change Change df1 df2 Change a a. Predictors: (Constant), Monthly Income b. Dependent Variable: Is this car bought brand-new or second-hand? Chart 5.21 Histogram 5.22 Linear Regression: Income & Price Linear regression indicates that whether independent factor is having effect on dependent variable or not. Here value of R Square indicates the measurement about these phenomena. Histogram shows the illustrative relationship among these variables. Table 5.22 provides the model summary. In this table, adjusted R square value is means income does affect price based consideration to purchase a car. Chart 5.22 is the histogram for the same. 101

35 Table 5.22 Model Summary b Std. Error Change Statistics Model R R Square Adjusted R Square of the Estimate R Square Change F Change df1 df2 Sig. F Change a a. Predictors: (Constant), Monthly Income b. Dependent Variable: What is the purchase price of car? Chart 5.22 Histogram 5.23 Linear Regression: Occupation & Price Linear regression indicates that whether independent factor is having effect on dependent variable or not. Here value of R Square indicates the measurement about these phenomena. Histogram shows the illustrative relationship among these variables. Table 5.23 provides the model summary. In this table, adjusted R square value is means occupation does affect price based consideration to purchase a car. Chart 5.23 is the histogram for the same. 102

36 Table 5.23 Model Summary b Std. Error Change Statistics Model R R Square Adjusted R Square of the Estimate R Square Change F Change df1 df2 Sig. F Change a a. Predictors: (Constant), What is your current occupation? b. Dependent Variable: What is the purchase price of car? Chart 5.23 Histogram 5.24 Linear Regression: Income & Payment Mode Linear regression indicates that whether independent factor is having effect on dependent variable or not. Here value of R Square indicates the measurement about these phenomena. Histogram shows the illustrative relationship among these variables. Table 5.24 provides the model summary. In this table, adjusted R square value is means income does affect payment mode to purchase a car. Chart 5.24 is the histogram for the same. 103

37 Table 5.24 Model Summary b Std. Error Change Statistics R Adjusted of the R Square F Sig. F Model R Square R Square Estimate Change Change df1 df2 Change a a. Predictors: (Constant), Monthly Income b. Dependent Variable: What was your mode of payment of car? Chart 5.24 Histogram 5.25 Linear Regression: Occupation & Fuel Linear regression indicates that whether independent factor is having effect on dependent variable or not. Here value of R Square indicates the measurement about these phenomena. Histogram shows the illustrative relationship among these variables. Table 5.25 provides the model summary. In this table, adjusted R square value is means occupation does affect fuel consideration to purchase a car. Chart 5.25 is the histogram for the same. 104

38 Table 5.25 Model Summary b Adjuste Change Statistics Model R R Square d R Square Std. Error of the Estimate R Square Change F Change df1 df2 Sig. F Change a a. Predictors: (Constant), What is your current occupation? b. Dependent Variable: Which fuel based car you have? Chart 5.25 Histogram 5.26 Cross Tabulation and Chi-square Analysis: Gender & Owning a Car Case processing summary table 5.26 furnish the information regarding the size of population which is 484 means 100%. Cross tabulation table 5.27 establishes the relationship between two selected variables. Researcher has applied contingency chi square test which is also known as test of independence. Here in the table 5.28, the calculated chi-square value is which is less than tabulated This mean gender doesn t have any significant impact on owning a car factor. 105

39 Table 5.26 Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent Gender * Do you/your family own a car? (SA) % 0.0% % Table 5.27 Crosstabulation Do you/your family own a car? (SA) Yes No Total Gender Male Female Total Table 5.28 Chi-Square Tests Value df Asymp. Sig. (2-sided) Exact Sig. (2-sided) Exact Sig. (1-sided) Pearson Chi-Square Continuity Correction Likelihood Ratio Fisher's Exact Test Linear-by-Linear Association N of Valid Cases 484 a. 1 cells (25.0%) have expected count less than 5. The minimum expected count is b. Computed only for a 2x2 table 106

40 5.27 Cross Tabulation and Chi-square Analysis: Income & Owning a Car Case processing summary table 5.29 furnish the information regarding the size of population which is 484 means 100%. Cross tabulation table 5.30 establishes the relationship between two selected variables. Researcher has applied contingency chi square test which is also known as test of independence. Here in the table 5.31 the calculated chi-square value is which is more than tabulated This mean income does have any significant impact on owning a car factor. Table 5.29 Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent Monthly Income * Do you/your family own a car? (SA) % 0.0% % Table 5.30 Crosstabulation Do you/your family own a car? (SA) Yes No Total Monthly Income Below Above Total

41 Table 5.31 Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square a Likelihood Ratio Linear-by-Linear Association N of Valid Cases 484 a. 6 cells (37.5%) have expected count less than 5. The minimum expected count is Cross Tabulation and Chi-square Analysis: Occupation & Owning a Car Case processing summary table 5.32 furnish the information regarding the size of population which is 484 means 100%. Cross tabulation table 5.33 establishes the relationship between two selected variables. Researcher has applied contingency chi square test which is also known as test of independence. Here in the table 5.34 the calculated chi-square value is which is more than tabulated This mean occupation does have any significant impact on owning a car factor. Table 5.32 Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent What is your current occupation? * Do you/your family own a car? (SA) % 0.0% % 108

42 Table 5.33 Cross tabulation Do you/your family own a car? (SA) What is your current occupation? Yes No Total Government Service Business Unemployment Private Service Foreign Company Service House Wife Student Agriculture Total Table 5.34 Chi square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square a Likelihood Ratio Linear-by-Linear Association N of Valid Cases 484 a. 7 cells (43.8%) have expected count less than 5. b. The minimum expected count is

43 5.29 Cross Tabulation and Chi-square Analysis: Employed Family Members & Purpose of a Car Case processing summary table 5.35 furnish the information regarding the size of population which is 484 means 100%. Cross tabulation table 5.36 establishes the relationship between two selected variables. Researcher has applied contingency chi square test which is also known as test of independence. Here in the table 5.37 the calculated chi-square value is which is more than tabulated This mean employed family member does have any significant impact on purpose of a car. Table 5.35 Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent How many employed people are in your family? * Which purpose you use your car? % 0.0% % Table 5.36 Cross tabulation Which purpose you use your car? Personal/Family Business Purpose Purpose Both Purpose Total How many employed people are in your family? Total

44 Table 5.37 Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square a Likelihood Ratio Linear-by-Linear Association N of Valid Cases 484 a. 0 cells (.0%) have expected count less than 5. b. The minimum expected count is Cross Tabulation and Chi-square Analysis: Occupation & Purpose of a Car Case processing summary table 5.38 furnish the information regarding the size of population which is 484 means 100%. Cross tabulation table 5.39 establishes the relationship between two selected variables. Researcher has applied contingency chi square test which is also known as test of independence. Here in the table 5.40 the calculated chi-square value is which is more than tabulated This mean occupation does have any significant impact on purpose of a car. Table 5.38 Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent What is your current occupation? * Which purpose you use your car? % 0.0% % 111

45 Table 5.39 Cross tabulation Which purpose you use your car? Personal Business /Family Both Purpose Purpose Purpose Total What is your current occupation? Government Service Business Unemployment Private Service Foreign Service Company House Wife Student Agriculture Total Table 5.40 Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square a Likelihood Ratio Linear-by-Linear Association N of Valid Cases 484 a. 6 cells (25.0%) have expected count less than 5. b. The minimum expected count is

46 5.31 Cross Tabulation and Chi-square Analysis: Gender & Brand of a Car Case processing summary table 5.41 furnish the information regarding the size of population which is 484 means 100%. Cross tabulation table 5.42 establishes the relationship between two selected variables. Researcher has applied contingency chi square test which is also known as test of independence. Here in the table 5.43 the calculated chi-square value is which is more than tabulated This mean gender does have any significant impact on brand of a car. Table 5.41 Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent Gender * What is the brand of your car? % 0.0% % Table 5.42 Cross tabulation What is the brand of your car? Maruti Hyundai Tata Honda Toyota Chevrolet Ford Nissan Volkswagon Renault Skoda Total Gender Male Female Total

47 Table 5.43 Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square a Likelihood Ratio Linear-by-Linear Association N of Valid Cases 484 a. 6 cells (27.3%) have expected count less than 5. b. The minimum expected count is Cross Tabulation and Chi-square Analysis: Education & Brand of a Car Case processing summary table 5.44 furnish the information regarding the size of population which is 484 means 100%. Cross tabulation table 5.45 establishes the relationship between two selected variables. Researcher has applied contingency chi square test which is also known as test of independence. Here in the table 5.46 the calculated chi-square value is which are more than tabulated This mean education does have any significant impact on brand of a car. Table 5.44 Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent What is your current educational level? * What is the brand of your car? % 0.0% % 114

48 Table 5.45 Cross tabulation What is the brand of your car? Maruti Hyundai Tata Honda Toyota Chevrolet Ford Nissan Volkswagon Renault Skoda Total What is Up to your Graduate current Post educational Graduate level? Professional Total Table 5.46 Chi square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square a Likelihood Ratio Linear-by-Linear Association N of Valid Cases 484 a. 15 cells (34.1%) have expected count less than 5. The minimum expected count is Factor Analysis Factor analysis attempts to identify underlying variables, or factors, that explain the pattern of correlations within a set of observed variables. Factor analysis is often used in data reduction to identify a small number of factors that explain most of the variance that is observed in a much larger number of manifest variables. Factor analysis can also be used to generate hypotheses regarding causal 115

49 mechanisms or to screen variables for subsequent analysis (for example, to identify collinearity prior to performing a linear regression analysis) Factor Analysis: Information Gathering and Consumer Purchase Initiation (IGCP) With a view to studying about information gathering and consumer purchase initiation, the responses of respondents have been examined with the help of factor analytical approach using principal component method with varimax rotation. Initially, test to check the adequacy of data for the application of factor analysis (Stewert, 1981) were conducted. Table 5.47 KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy..687 Bartlett's Test of Approx. Chi-Square Sphericity df 28 Sig..000 The value of the Kaiser-Meyer-Okin (KMO) measure of sampling adequacy statistics found to be 0.687, which is adequately large. Moreover, the correlation matrix reveals that there is enough correlation for the application of factor analysis. Besides, Bartlett s test of sphericity value was found to be , which is also significant (p < 0.001). Communalities for each factor are presented in table 5.48 and total variable explain presented in table Result of component matrix is presented in table 5.50 (a). Eventually, the decision for arriving at the number of factors to be retained was made on the basis of latent root criterion, i.e., variables having eigenvalues greater than 1 and also on the basis of scree plot which reveals that there are seven underlying factors. Moreover, factors having loading greater than or equal to 0.40 (ignoring signs) have been retained (Dixon, 1997) which yielding three interpretable factors. Varimax rotated factor analytic results for all respondents are presented in Table 5.50 (b). 116

50 Table 5.48 Communalities Initial Extraction Search in Internet websites of the manufacturer (IGCP1) Information received from friends (IGCP2) Information received from office colleagues (IGCP3) Opinion from family members (IGCP4) Advertisement in newspapers / magazine (IGCP5) TV commercials on car models and brands (IGCP6) Visit to dealers / distributors (IGCP7) Dealer Sales Staff assurance (IGCP8) Extraction Method: Principal Component Analysis. Table 5.49 Total Variance Explained Component Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative % Extraction Method: Principal Component Analysis. Table 5.49 depicts three rotated factors which together explain % of the total variance. The last column in the table shows the communalities which represent the portion of variance that a variable shares with other variables. Eigenvalues for factors F1 to F3 are 3.045, 1.728, and respectively. Further, appropriate names have been assigned to all the three dimensions extracted based on the various variables representing each case. The names factors with constituting and their respective factor 117

51 loadings are summarized in Table. The respective factor loadings represent the relationship between original variable and factor. Moreover, on each factor, like signs of factor loadings reflect positive correlation between factor loadings and the factor and opposite signs of factor loadings reveal negative correlation between factor loadings and factor. But the sign of factor loading relates to only that factor on which they appear, not to other factors as they are orthogonally rotated (Hair et al., 2006). Table 5.50 (a) Component Matrix Component Search in Internet websites of the manufacturer (IGCP1) Information received from friends (IGCP2) Information received from office colleagues (IGCP3) Opinion from family members (IGCP4) Advertisement in newspapers / magazine (IGCP5) TV commercials on car models and brands (IGCP6) Visit to dealers / distributors (IGCP7) Dealer Sales Staff assurance (IGCP8) Extraction Method: Principal Component Analysis. a. 3 components extracted. 118

52 Table 5.50 (b) Rotated Component Matrix a Component Search in Internet websites of the manufacturer (IGCP1) Information received from friends (IGCP2) Information received from office colleagues (IGCP3) Opinion from family members (IGCP4) Advertisement in newspapers / magazine (IGCP5) TV commercials on car models and brands (IGCP6) Visit to dealers / distributors (IGCP7) Dealer Sales Staff assurance (IGCP8) Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 4 iterations. A scree plot is a plot is of the eigen values against the number of factors in order of extraction. The point of interest is where the curve starts to flatten. It can be seen that the curve begins to flatten between TV Commercial facto 6 & Dealer visit factor 7. Chart 5.26 Scree Plot 119

53 Information Gathering and Consumer Purchase Initiation (IGCP) The broad dimensions Information Gathering and Consumer Purchase Initiation of respondents have been detailed below. Factor 1 Commercial Sources contained four attributes explained % of variance in the data, with the eigenvalue of The attributes associated with this factor dealt with information received from friends, office colleagues, news paper advertisements and TV commercials. Factor 2: Personal Sources accounted for % variance in the data with eigenvalue This factor loaded with opinion from family members, visit to dealers/distributors and dealer sales staff assurance. Factor 3 Web Sources loaded with two attributes. This factor accounted for % of the variance, with an eigenvalue of These attribute was search in internet websites of the manufacturer Factor Analysis: Preference Based on Personal Needs (PPP) With a view to studying about information gathering and consumer purchase initiation, the responses of respondents have been examined with the help of factor analytical approach using principal component method with varimax rotation. Initially, test to check the adequacy of data for the application of factor analysis (Stewert, 1981) were conducted. Table 5.51 KMO Value Kaiser-Meyer-Olkin Measure of Sampling Adequacy..650 Bartlett's Test of Approx. Chi-Square Sphericity df 36 Sig..000 The value of the Kaiser-Meyer-Okin (KMO) measure of sampling adequacy statistics found to be 0.650, which is adequately large. Moreover, the correlation matrix reveals that there is enough correlation for the application of factor analysis. 120

54 Besides, Bartlett s test of sphericity value was found to be , which is also significant (p < 0.001). Communalities for each factor are presented in table 5.52 and total variable explain presented in table Result of component matrix is presented in table 5.54 (a). Eventually, the decision for arriving at the number of factors to be retained was made on the basis of latent root criterion, i.e., variables having eigenvalues greater than 1 and also on the basis of scree plot which reveals that there are seven underlying factors. Moreover, factors having loading greater than or equal to 0.40 (ignoring signs) have been retained (Dixon, 1997) which yielding three interpretable factors. Varimax rotated factor analytic results for all respondents are presented in Table 5.54 (b). Table 5.52 Communalities Initial Extraction Need to upgrade from two-wheeler to four-wheeler(ppp1) Need of your business firm(ppp2) Peer pressure from family members owning a car (PPP3) Need of Fuel Efficiency (PPP4) Upgraded the model to suit personal ambition (PPP5) Family wanted a car for functions, social gathering (PPP6) Need to travel long distance on Trips (PPP7) Need to suit social standings (PPP8) Social pressure from friends / neighbors (PPP9) Extraction Method: Principal Component Analysis. 121

55 Total 5.53 Variance Explained Component Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative % Extraction Method: Principal Component Analysis. Table 5.53 depicts three rotated factors which together explain % of the total variance. The last column in the table shows the communalities which represent the portion of variance that a variable shares with other variables. Eigenvalues for factors F1 to F3 are 2.804, 1.654, and respectively. Further, appropriate names have been assigned to all the three dimensions extracted based on the various variables representing each case. The names factors with constituting and their respective factor loadings are summarized in Table The respective factor loadings represent the relationship between original variable and factor. Moreover, on each factor, like signs of factor loadings reflect positive correlation between factor loadings and the factor and opposite signs of factor loadings reveal negative correlation between factor loadings and factor. But the sign of factor loading relates to only that factor on which they appear, not to other factors as they are orthogonally rotated (Hair et al., 2006). 122