The document summarizes research on consumer buying behavior for new cars. It describes the research objectives, questionnaire design, sample characteristics of customers from 5 major car companies in India, and data analysis using ANOVA, regression, and factor analysis. The analysis found significant differences between the 5 car companies and that the regression model was not a good fit. Factor analysis yielded a KMO value indicating poor common variance. The conclusion discusses findings on timing of orders and influences on men and women buyers.

1. Submitted To :
Prof. Sarika Tandon

2. Group Members
 Taqweem Iqbal Ahmed
 Kuldeep Singh
 Vivek Morjaria
 Nishant Singh
 Dristhi Sharma
 Arpit Maan

3. INTRODUCTION
Problem Statement:
 New Car Buyer Behaviour - Quantifying Key Stages & Activities in the
Consumer Buying Process.
Research Objectives:
• Managing demand.
• Understanding influences on timing
of purchase decisions.
• Validate current positions on consumer
behaviour.

4. Questionnaire Design
 To design the buying behaviour of consumer.
 The respondents were asked to give the preference
about the brand they want.

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

7. 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

8. 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.

9. 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.

10.  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.

11. 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.

12. H0 :  i = 0
Ha: i = 0
p = 0.000
p < .05
Since p < 0.05, we reject the null hypothesis.

13. 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.

14. Interpretation of the KMO as characterized by
Kaiser, 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 Don't Factor

15. KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling adequacy 0.524
Barlett’s Test of Sphericity Approx. Chi Square 79.957
df 28
Significance 0.0000
Since the value of KMO is 0.524, therefore it implies that
the degree of variance is very bad, in fact the variables do
not factor with the other variables.

16. 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.

17. 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