Consumer behaviour towards buying cars


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Consumer behaviour towards buying cars

  1. 1. Submitted To :Prof. Sarika Tandon
  2. 2. Group Members Taqweem Iqbal Ahmed Kuldeep Singh Vivek Morjaria Nishant Singh Dristhi Sharma Arpit Maan
  3. 3. INTRODUCTIONProblem Statement: New Car Buyer Behaviour - Quantifying Key Stages & Activities in the Consumer Buying Process.Research Objectives:• Managing demand.• Understanding influences on timingof purchase decisions.• Validate current positions on consumerbehaviour.
  4. 4. Questionnaire Design To design the buying behaviour of consumer. The respondents were asked to give the preference about the brand they want.
  5. 5. Sample Characteristics “Sample” consisted of the customers of five CAR companies in India viz. VW,Maruti, Hyundai, Tata, Mahindra These cars were selected, as they are representative of the major segments in the car industry from full fare to low priced cars. Targeted sample size was 40 per car, and achieved sizes were as follows. Table 1 – Car (Brand) wise Composition of Sample NO Company Obtained number of samples 1 VW 39 2 Maruti 40 3 Hyundai 35 4 Tata 38 5 Mahindra 36
  6. 6. DATA ANALYSIS & RESULTS• The statistical analyses used were ANOVA, Regression analysis, Factor analysis.• Analysis of research data used the level of significance, a = 0.05.• The objective of this study was to examine customer perception of service quality. ANOVA was performed and the result showed a significant difference among the five car companies in India viz. VW, Maruti, Hyundai, Tata, Mahindra
  7. 7. Testing for Significance: F Test The F test is used to determine whether a significant relationship exists between the dependent variable and the set of all the independent variables. The F test is referred to as the test for overall significance.
  8. 8. Testing for Significance: F Test  Hypotheses H0: 1 = 2 = . . . = p = 0 Ha: One or more of the parameters is not equal to zero.  Rejection Rule Reject H0 if F > F where F is based on an F distribution with p d.f. in the numerator and n - p - 1 d.f. in the denominator.
  9. 9.  As adjusted square is 0.004, it implies that 0.4% of variance of the dependent variable is explained by independent variable. As R= 0.182, it explains a very weak correlation. H0: 1 = 2 = . . . = p = 0 Ha: One or more of the parameters is not equal to zero. p = 0.285 p  = .05 Since p > p  we accept the null hypothesis and our model is not good.
  10. 10. Testing for Significance: t Test Hypotheses H0: i = 0 Ha: i = 0 Rejection Rule Reject H0 if t < tor t > t where t is based on a t distribution with n - p - 1 degrees of freedom.
  11. 11. H0 :  i = 0 Ha: i = 0 p = 0.000 p < .05Since p < 0.05, we reject the null hypothesis.
  12. 12. K.M.O Test If two variables share a common factor with other variables, their partial correlation (aij) will be small, indicating the unique variance they share. Used to measure sampling adequacy. This index is used to measure the appropriateness of the test . High values (.5 – 1) means factor analysis is adequate.
  13. 13. Interpretation of the KMO as characterized byKaiser, Meyer, and Olkin … KMO Value Degree of Common Variance 0.90 to 1.00 Marvelous 0.80 to 0.89 Meritorious 0.70 to 0.79 Middling 0.60 to 0.69 Mediocre 0.50 to 0.59 Miserable 0.00 to 0.49 Dont Factor
  14. 14. KMO and Bartlett’s TestKaiser-Meyer-Olkin Measure of Sampling adequacy 0.524Barlett’s Test of Sphericity Approx. Chi Square 79.957 df 28 Significance 0.0000Since the value of KMO is 0.524, therefore it implies thatthe degree of variance is very bad, in fact the variables donot factor with the other variables.
  15. 15. Limitations The findings of this study are limited to the behaviour of the consumer towards car in India. This study has not considered industry measures to measure service quality. We have measured only the customer perception of service quality.
  16. 16. Conclusion Timing of orders & delivery bias towards weekends– Fridays for collection– Saturdays for order– supports dealer research Differences between men & women– females less willing to wait– reference growth in female motorists & change in relative influence & role• Information Sources– Dealer still critical• Friend, Brochure, Magazine– 4 different sources of information – growth of internet – now nearly 20%• Research suggests that the consumer demand for a Car would be strong