CHAPTER 4 DATA ANALYSIS AND INTERPRETATION …shodhganga.inflibnet.ac.in/bitstream/10603/5123/12/12_chapter 4.pdf · DATA ANALYSIS AND INTERPRETATION INTRODUCTION This chapter the

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CHAPTER 4

DATA ANALYSIS AND INTERPRETATION

INTRODUCTION

This chapter the results of the statistical analysis done for testing

hypothesis are presented and interpreted. The analysis was done with

SPSS 15.0 & AMOS 7.0 software packages.

The first section presents the results of various relationships between the

demographic and purchase motivating factors and testing of inter-

correlation matrix.

The second section presents the construction and validation of Structural

Equation Model of SEM-CPD mediated model with the dimensions

Affordability, Attributes, Sales Support, the mediating parameter

external factors and the outcome of the purchase decision. And also the

SEM-CPD mediated model is tested with Bayesian testing and

estimation.

4.1 ANALYSIS OF VARIOUS RELATIONSHIP BETWEEN

DEMOGRAPHIC, CONSUMER PREFERENCES,

PRIORITIES AND THE STUDY VARIABLE

In order to understand the relationship between the demographic

variables and the study variables i.e., the dimensions of purchase

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decisions, empirically tested. The summary of the analyses and the

interpretation thereon is set out in the following sections.

4.1.1 ANALYSIS OF VARIOUS RELATIONSHIPS AMONG

DEMOGRAPHIC VARIABLES

Table 4.1: Cross Tabulated Relationship among Age & Family

Size

Personal Information

FAMILY SIZE

2-3 4-5 6-7 8-9 <9

AGE

21-25

Count 66 151 26 5 2

% 9.8% 22.5% 3.9% 0.7% 0.3%

26-35

Count 71 95 25 4 4

% 10.6% 14.1% 3.7% 0.6% 0.6%

36-45

Count 39 31 11 3 4

% 5.8% 4.6% 1.6% 0.4% 0.6%

45-50 Count 14 28 7 6 3

% 2.1% 4.2% 1.0% 0.9% 0.4%

Above

50

Count 14 35 13 9 6

% 2.1% 5.2% 1.9% 1.3% .9%

SPSS15.0 OUTPUT Source: primary data

The table 4.1 reveals that the age group of 21-25 having a family size of

four to five members, 2-3 and 6-7 members has greatest inclination of

purchasing a motorbike in that order. Consumers in the age group of 26-

35 closely follow the similar trend but with a variation of high relative

frequency in the family size of 6-7 members. The trend observed from

the table 4.1 is that younger, the consumers and medium their family

size, their inclination towards possession of motorbike is greater.

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Targeting such an age group and delivering product to meet their needs

will be helpful in gaining reasonable market share for manufacturers.

Table 4.2: Cross Tabulated Relationship among Age & Income


INCOME

>50000 50001-100000

100000-150000

150000-200000 >200000

AGE

21-25

Count 38 62 52 27 71

% 5.7% 9.2% 7.7% 4.0% 10.6%

26-35

Count 14 26 46 36 77

% 2.1% 3.9% 6.8% 5.4% 11.5%

36-45

Count 9 17 20 9 33

% 1.3% 2.5% 3.0% 1.3% 4.9%

45-50

Count 8 13 16 8 13

% 1.2% 1.9% 2.4% 1.2% 1.9%

Above 50

Count 7 9 15 11 35

% 1.0% 1.3% 2.2% 1.6% 5.2%


The table 4.2 reveals that the age group of 21-25 having an annual

income of more than 200 thousands have greatest inclination for

possessing a two wheeler followed by those in the income group

between 50 thousands to 150 thousands. Another significant age group

following the similar trend is the age group of 26-35 with a little

variation of having higher relative frequency in income group of 100 –

150 thousands. The trend observed from the table 4.2 is that younger the

age of consumers and higher their annual income, their inclination

towards possession of motorbike is greater. The inclination for

possession of two-wheeler decreases with the increase in age and/or

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decrease in income. Targeting such an age group and delivering product

to meet their needs will be helpful in better product positioning.

Table 4.3: Cross Tabulated Relationship among Age &

Employment


EMPLOYMENT

GOVT SECTOR

PVT SECTOR PROFESSIONALS

SELF/ BUSINESSMEN OTHERS

AGE

21-25

Count 12 57 96 21 64

% 1.8% 8.5% 14.3% 3.1% 9.5%

26-35

Count 8 60 105 17 9

% 1.2% 8.9% 15.6% 2.5% 1.3%

36-45

Count 7 38 24 7 12

% 1.0% 5.7% 3.6% 1.0% 1.8%

45-50

Count 9 12 11 17 9

% 1.3% 1.8% 1.6% 2.5% 1.3%

Above 50

Count 17 16 13 15 16

% 2.5% 2.4% 1.9% 2.2% 2.4%


The table 4.3 reveals that the professionals in the age groups of 21-25,

26-35 are more interested in making motorbikes purchase decision.

Significantly, those employed in the private sector in above age groups

are inclined in possessing the motorbikes. Another group, that is making

interesting decision of possessing is others, which include self

employed, students and others. Students make up the major portion of

those in the group others. Those in business and government sector do

not respond positively to the purchase decision-making. Consumerism

of motorcycle is quite high in respect of those employed privately and in

independent profession.

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Table 4.4: Cross Tabulated Relationship among Age &

Qualification

Personal Information QUALIFICATION

SCHOOLING GRADUATE

- UG GRADUATE

- PG PROFESSIONALS

AGE

21-25

Count 3 68 125 54

% .4% 10.1% 18.6% 8.0%

26-35

Count 3 46 87 63

% .4% 6.8% 12.9% 9.4%

36-45

Count 6 22 32 28

% .9% 3.3% 4.8% 4.2%

45-50

Count 11 17 17 13

% 1.6% 2.5% 2.5% 1.9%

Above 50

Count 12 23 28 14

% 1.8% 3.4% 4.2% 2.1%


Interestingly, the table 4.4 reveals that those possessing educational

qualification of masters and professionally qualified form the significant

portion of the consumers of the motorcycle. Another interesting

revelation is that those having college education are inclined in the

possession of the product under study. As the level of education

increased, notable rise in the consumer’s inclination towards purchasing

an automobile increases. In the group of those less educated the increase

in age has positive influence in the purchase decision. Targeting those

with reasonable educational qualification will boost the sales of the

companies.

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Table 4.5: Cross Tabulated Relationship among Employment &

Income


INCOME

>50000 50001-100000

100000-150000

150000-200000 <200000

EMPLOYMENT

GOVT

Count 7 9 16 9 12

% 1.0% 1.3% 2.4% 1.3% 1.8%

PVT

Count 19 37 46 17 64

% 2.8% 5.5% 6.8% 2.5% 9.5%

PROFESSIONALS

Count 21 31 43 45 109

% 3.1% 4.6% 6.4% 6.7% 16.2%

SELF

Count 7 17 23 11 19

% 1.0% 2.5% 3.4% 1.6% 2.8%

OTHERS

Count 22 33 21 9 25

% 3.3% 4.9% 3.1% 1.3% 3.7%


The table 4.5 reveals an interesting angle. It reveals that the respondents

employed in government sector are drawing salary between Rupees

100-150 thousands, above 200 thousands. These sections of government

employees have inclination of purchase of motorbikes than those in

other compensation package. Interestingly, the professionals and private

sector employees receiving an annual compensation above Rupees 200

thousands have notable inclination of purchase of motorbikes. Self-

employed in the annual income between 150-200 thousands are likely to

purchase or already purchased motorbikes. The table is quite interesting

that the consumers who receive an annual compensation more than

Rupees 200 thousands think that the motorbikes are affordable to them.

Hence, the motorbikes can be positioned for this segment of consumers.

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Family Size


FAMILYSIZE

2-3 4-5 6-7 8-9 <9

EMPLOYMENT

GOVT

Count 18 25 8 2 0

% 2.7% 3.7% 1.2% .3% .0%

PVT

Count 59 96 18 7 3

% 8.8% 14.3% 2.7% 1.0% .4%

PROFESSIONALS

Count 86 116 32 7 8

%

12.8% 17.3% 4.8% 1.0% 1.2%

SELF

Count 14 41 14 7 1

% 2.1% 6.1% 2.1% 1.0% .1%

OTHERS Count 27 62 10 4 7

% 4.0% 9.2% 1.5% .6% 1.0%


The table 4.6 reveals that the professionals in the age groups of 21-25,

26-35 is more interested in making the motorbike purchase decision.

Significantly, those employed in the private sector in above age groups

are inclined in the possessing the motorbikes. Another group, that is

making interesting decision of possessing is others, which include self

employed, students and others. Students make up the major portion of

those in the group others. Those in business and government sector do

not respond positively to the purchase decision-making. Consumerism

of motorcycle is quite high in respect of those employed privately and in

independent profession.

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Qualification

Personal Information QUALIFICATION

SCHOOLING GRADUATE -

UG GRADUATE

- PG PROFESSIO

NALS

EMP LOY MEN T

GOVT

Count 6 15 26 6

% .9% 2.2% 3.9% .9%

PVT

Count 8 63 85 27

% 1.2% 9.4% 12.6% 4.0%

PROFESSIONALS

Count 5 43 90 111

%

.7% 6.4% 13.4% 16.5%

SELF

Count 10 27 26 14

% 1.5% 4.0% 3.9% 2.1%

OTHERS

Count 6 28 62 14

% of Total .9% 4.2% 9.2% 2.1%


Like the table 4.4, the table 4.7 reveals that many of the employed

consumers are either undergraduate or post-graduate. But the need for

higher education diminishes as the consumers become employed in

government sector. Obviously, those in the private sector are more

qualified educationally than those in other sectors. Self employed

display professionalism in their skills. The advertising campaign shall

be directed keeping these dimensions in mind.

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Table 4.8: Cross Tabulated Relationship among Income &

Family Size


FAMILY SIZE

2-3 4-5 6-7 8-9 <9

INCOME

>50K

Count 22 34 17 2 1

% 3.3% 5.1% 2.5% .3% .1%

50K – 100K

Count 39 63 13 8 4

% 5.8% 9.4% 1.9% 1.2% .6%

100K -150K

Count 30 81 27 7 4

% 4.5% 12.1% 4.0% 1.0% .6%

150K -200K

Count 24 49 11 5 2

% 3.6% 7.3% 1.6% .7% .3%

<200K

Count 89 113 14 5 8

% 13.2% 16.8% 2.1% .7% 1.2%


The table 4.8 reveals that the level of annual compensation received

determines the size of the family. Higher the level of annual income,

higher the size of the family going well with the popular notion that the

wealthy will have moderate sized family i.e., between 2-5 membership.

The uniqueness observed in Kancheepuram district is that the family

system is joint family system. But the increase in annual compensation

is not a matter as far as having a family size above 5 members. The

increasing awareness of family planning and birth control besides the

economic well-being is the reason for this trend. The manufacturers,

therefore, has to be target the consumers having family size of 4-5

members. Because the increasing need of ease of transportation compels

the low sized family, they may also be targeted.

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Table 4.9: Cross Tabulated Relationship among Income &

Qualifications


QUALIFICATIONS

SCHOOLING GRADUATE

- UG GRADUATE

- PG PROFESSIONA

LS

INCO ME

>50K

Count 8 18 37 13

% 1.2% 2.7% 5.5% 1.9%

50K – 100K

Count 8 40 60 19

% 1.2% 6.0% 8.9% 2.8%

100K -150K

Count 11 41 67 30

% 1.6% 6.1% 10.0% 4.5%

150K -200K

Count 3 26 35 27

% .4% 3.9% 5.2% 4.0%

<200K

Count 5 51 90 83

% .7% 7.6% 13.4% 12.4%


The table 4.9 signifies that the higher qualification is high rewarding

ones. Those having more than Rupees 200K are mostly either master

degree holders or professionally qualified. Those who are qualified less

than under-graduation receive lower annual income. The consumers

who can afford motorbikes are in group of graduate or higher with an

annual income of more than Rupees 50 K and less than 150 K.

Therefore, the potential consumers lie in this group, which is the middle

class consumers of India and form major consumer base for the

consumer durables and automobiles.

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Table 4.10: ONE WAY ANALYSIS OF VARIANCE OF AGE

AMONG PURCHASE DECISION DIMENSIONS

Sum of

Squares df Mean

Square F Sig.

Statistical Inference

Affordability

Between Groups

835.499 4 208.875 1.421 0.225 Insignificant

Within Groups

98014.714 667 146.949

Total 98850.213 671

Attributes

Between Groups

719.704 4 179.926 .831 0.506 -do=

Within Groups

144375.295 667 216.455

Total 145094.999 671

Sales Support

Between Groups

258.478 4 64.619 1.565 0.182 -do-

Within Groups

27543.141 667 41.294

Total 27801.619 671

External Factors

Between Groups

164.133 4 41.033 .684 .603 -do-

Within Groups

40023.080 667 60.005

Total 40187.213 671

Purchase Decision

Between Groups

45.968 4 11.492 .295 .881 -do-

Within Groups

25980.245 667 38.951

Total 26026.213 671

SPSS 15.0 OUTPUT Source: primary data

Table 4.10 reveals the significant difference on the various purchase

decisions dimensions with respect to age of the respondents. From the

above table, it is evident that there is no significant difference of age of

the respondents with regard to Affordability, Attributes, Sales Support,

External Factors and overall purchase decision. Purchase is an outcome

of various dimensions of consumer behavior in India, but the researcher

found out that there were quite significant differences among the

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respondents with respect to age. The researcher identified the following

reasons as the cause; the influence of the peers, family and friends and

also the availability of credit, consumers’ sensitivity to consumption of

fuel, information on spare price across all ages. The above stated factors

are influencing various dimensions of purchase decision- making under

study.

Table 4.11: ONE WAY ANALYSIS OF VARIANCE OF

OCCUPATION AMONG PURCHASE DECISION

DIMENSIONS

Sum of

Squares df Mean

Square F Sig.


Affordability

Between Groups

6371.324 4 1592.831 11.488 .000 Significant

Within Groups

92478.889 667 138.649

Total 98850.213 671

Attributes

Between Groups

1596.275 4 399.069 1.855 .117 Insignificant

Within Groups

143498.724 667 215.141

Total 145094.999 671

Sales Support

Between Groups

283.916 4 70.979 1.720 .144 Insignificant

Within Groups

27517.703 667 41.256

Total 27801.619 671

External Factors

Between Groups

607.708 4 151.927 2.560 .038 Significant

Within Groups

39579.505 667 59.340

Total 40187.213 671

Purchase Decision

Between Groups

269.292 4 67.323 1.743 .139 Insignificant

Within Groups

25756.921 667 38.616

Total 26026.213 671


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decisions dimensions with respect to employment or occupation of the

respondents. From the above table, it is evident that there is no

significant difference of employment status of the respondents with

regard to Affordability, External Factors. From the above table, there is

no significant difference among employment status with regard to

purchase decisions as it is dependent upon individual dimensions of

purchase behavior of consumers in India, but the researcher found out

that there were quite significant differences among the respondents with

respect to occupation. The research identified the following reasons as

the causes: the cost of fuel, geographical diversity, cultural bias, rural-

urban divergence, standard of education acquired which spread across

almost all types of employment. The above stated factors are

influencing various dimensions of purchase decision- making under

study.

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INCOME AMONG PURCHASE DECISION DIMENSIONS

Sum of

Squares df Mean

Square F Sig.


Affordability

Between Groups

1593.689 4 398.422 2.732 0.028 Significant

Within Groups

97256.524 667 145.812

Total 98850.213 671

Attributes

Between Groups

2997.751 4 749.438 3.518 0.007 Significant

Within Groups

142097.247 667 213.039

Total 145094.999 671

Sales Support

Between Groups

480.044 4 120.011 2.930 0.020 Significant

Within Groups

27321.575 667 40.962

Total 27801.619 671

External Factors

Between Groups

196.192 4 49.048 .818 0.514 Insignificant

Within Groups

39991.021 667 59.957

Total 40187.213 671

Purchase Decision

Between Groups

1665.317 4 416.329 11.399 0.000 Significant

Within Groups

24360.896 667 36.523

Total 26026.213 671



decisions dimensions with respect to level of annual income of the

respondents. From the above table, it is evident that there is significant

difference of level of annual income of the respondents with regard to

Affordability, Attributes, Sales Support and Purchase decision. From

the above table, there is no significant difference of level of annual

income with regard to external factors such as advertising, word of

mouth in India, but the researcher found out that there were quite

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significant differences among the respondents with respect to annual

income. The research identified the following reasons as the causes: the

needs such as neighbor’s possession and maintenance requirements. The

above stated factors are influencing various dimensions of purchase

decision- making under study.


FAMILY SIZE AMONG PURCHASE DECISION

DIMENSIONS

Sum of

Squares df Mean

Square F Sig.


Affordability Between Groups

2429.358 4 607.339 4.201 .002 Significant

Within Groups

96420.855 667 144.559

Total 98850.213 671

Attributes Between Groups

4085.872 4 1021.468 4.832 .001 -do-

Within Groups

141009.126 667 211.408

Total 145094.999 671

Sales Support

Between Groups

317.470 4 79.368 1.926 .104 Insignificant

Within Groups

27484.149 667 41.206

Total 27801.619 671

External Factors

Between Groups

1196.566 4 299.141 5.117 .000 Significant

Within Groups

38990.647 667 58.457

Total 40187.213 671

Purchase Decision

Between Groups

1512.613 4 378.153 10.289 .000 -do-

Within Groups

24513.600 667 36.752

Total 26026.213 671


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decisions dimensions with respect to size of the family of the


difference of size of the family of the respondents with regard to

Affordability, Attributes, External Factors and Purchase decision. From

the above table, there is no significant difference of size of the family

with regard to Sales Support such as dealer network, number of service

stations in India, but the researcher found out that there were quite

significant differences among the respondents with respect to size of the

family. The research identified the following reasons as the causes:

Location, Trained Service Personnel and neighborhood help. The above

stated factors are influencing various dimensions of purchase decision-

making under study.

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EDUCATIONAL QUALIFICATIONS AMONG PURCHASE

DECISION DIMENSIONS

Sum of

Squares df Mean

Square F Sig.


Affordability Between Groups

41217.376 3 13739.125 159.245 .000 Significant

Within Groups

57632.836 668 86.277

Total 98850.213 671

Attributes Between Groups

1383.919 3 461.306 2.144 .093 Insignificant

Within Groups

143711.079 668 215.136

Total 145094.999 671

Sales Support

Between Groups

351.898 3 117.299 2.855 .036 Significant

Within Groups

27449.721 668 41.092

Total 27801.619 671

External Factors

Between Groups

458.335 3 152.778 2.569 .050 Significant

Within Groups

39728.878 668 59.474

Total 40187.213 671

Purchase Decision

Between Groups

647.644 3 215.881 5.682 .001 Significant

Within Groups

25378.569 668 37.992

Total 26026.213 671



decisions dimensions with respect to level of education received of the


difference of size of the family of the respondents with regard to

Affordability, Sales Support, External Factors, Overall Possession needs

and Purchase decision. From the above table, there is no significant

difference of level of education received with regard to Attributes of the

product such as product features, style, price and road conditions in

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India, but the researcher found out that there were quite significant

differences among the respondents with respect to level of education

received. The research identified the following reasons as the causes:

Word of mouth of friends, neighbors, highest reach of advertising,

proximity of the study area with largest metropolitan city in the state

and need for self esteem. The above stated factors are influencing

various dimensions of purchase decision- making under study.

4.2 REGRESSION MODEL OF THE SQM-HEI MEDIATED

STRUCTURAL EQUATION MODEL

In hierarchical regression, the predictor variables are entered in sets of

variables according to a pre-determined order that may infer some

causal or potentially mediating relationships between the predictors and

the dependent variable (Francis, 2003). Such situations are frequently of

interest in the social sciences. The logic involved in hypothesizing

mediating relationships is that “the independent variable influences the

mediator which, in turn, influences the outcome” (Holmbeck, 1997).

However, an important pre-condition for examining mediated

relationships is that the independent variable is significantly associated

with the dependent variable prior to testing any model for mediating

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variables (Holmbeck, 1997). Of interest is the extent to which the

introduction of the hypothesized mediating variable reduces the

magnitude of any direct influence of the independent variable on the

dependent variable.

Hence the researcher empirically tested the hierarchical regression for

the model conceptualized in the figure 4.1, with in the AMOS graphics

environment. The path diagram for the hypothesized mediated model is

given in the path diagram (see Figure 4.1).

Figure 4.1: Hypothesized mediated model specification for AMOS

graphics input

M1, V1

Attributes

M2, V2

Affordability

M3, V3 Sales

Support

I1

External Factors

I2 Purchase

Decision

W1

W2

W3

W4

W5

W6

W7

C1

C2

C3

e1

e2

STANDARDIZED PARAMETER ESTIMATES FOR

MEDIATED SEM – CPD Overall MODEL

125

The analyses are conducted; the parameter estimates are then viewed

within AMOS graphics. Figure 4.2 displays the standardized parameter

estimates.

FIGURE 4.2: STANDARDIZED PARAMETER ESTIMATES FOR MEDIATED SEM-CPD Overall MODEL

The regression analysis revealed that the student’s perception on the

various dimensions of purchase decision, attributes explained 0.10 of

the purchase decision, followed by affordability which explains -0.01 of

the purchase decision. The R2

value of 0.42 is displayed above the box

101.00, 215.92

Attributes

56.95, 168.34

Affordability

32.98, 41.37

Sales Support

4.17

External Factors

.42

Purchase Decision

.23

.06

.17

.11

-.01

.08

.10

64.74

20.46

61.46

e1

e2

126

purchase decision in the AMOS graphics output. The visual

representation of results suggest that the relationships between the

dimensions of purchase motives and the mediated factor. The Attributes

results a significant impact on the mediated factor, External Factors.

The Affordability results very limited negative influence on the

purchase motives. It shows that the consumer attitude towards the

affordability issues like credit facility, influence of the environment

towards the outcome of affordability is insignificant; where as the

impact of the same is very high on the mediating variable. The

covariance is quite significantly high between affordability and sales

support, which shows that consumer, requires high level of after sales

service and support from the dealer to buy the motorbikes and for

advice on maintenance. The covariance between affordability and

attributes is moderate, which means that the affordability and attributes

play a modest yet key role in the decision-making process towards

purchase. According to Hoyle, (1995) a model is a statistical statement

about the relation among variables, in the present study reveals the

relationship among the various dimensions of purchase motives & the

outcome of the purchase decision.

127

4.2.1 BAYESIAN ESTIMATION AND TESTING FOR

REGRESSION MODEL OF SEM-CPD MEDIATED

STRUCTURAL EQUATION MODEL

The research model is a SEM, while many management scientist are

most familiar with the estimation of these models using software that

analyses covariance matrix of the observed data ( e.g. LISREL,

AMOS, EQS), the researcher adopt a Bayesian approach for

estimation and inference in AMOS 7.0 environment((Arbuckle &

Wothke, 2006). Since it offers numerous methodological and

substantive advantages over alternative approaches.

TABLE 4.15: BAYESIAN CONVERGENCE DISTRIBUTION FOR

SEM-CPD REGRESSION MODEL

Mean S.E. S.D. C.S. Median

95%

Lower

bound

95%

Upper

bound

Skewness Kurtosis Min Max Name

Regression

weights

External

Factors<--

Attributes

0.235 0.000 0.023 1.000 0.235 0.190 0.280 -0.014 -0.018 0.140 0.322 W1

External

Factors<--

Affordability

0.062 0.000 0.020 1.000 0.062 0.024 0.101 0.019 0.026 -0.016 0.140 W2

External

Factors<--

Sales Support

0.167 0.000 0.051 1.000 0.166 0.066 0.267 0.010 -0.039 -0.036 0.363 W3

Purchase

Decision<--

External

Factors

0.113 0.000 0.017 1.000 0.113 0.079 0.147 0.008 0.008 0.044 0.189 W4

Purchase

Decision<--

Affordability

-0.009 0.000 0.009 1.000 -0.008 -0.026 0.009 -0.004 -0.016 -0.047 0.027 W5

Purchase

Decision<--

Sales Support

0.080 0.000 0.023 1.001 0.080 0.035 0.126 0.012 -0.012 -0.021 0.176 W6

128

Purchase

Decision<--

Attributes

0.103 0.000 0.011 1.000 0.103 0.082 0.125 0.002 0.011 0.056 0.149 W7

Means

Attributes 101.000 0.004 0.574 1.000 100.999 99.873 102.118 0.002 -0.017 98.825 103.217 M1

Affordability 56.938 0.004 0.506 1.000 56.939 55.940 57.921 -0.027 -0.009 54.966 58.877 M2

Sales Support 32.977 0.002 0.252 1.000 32.978 32.482 33.468 -0.013 -0.024 32.011 33.935 M3

Intercepts

External

Factors 4.177 0.019 1.782 1.000 4.179 0.696 7.652 -0.009 0.026 -3.795 11.255 I1

Purchase

Decision 0.417 0.008 0.795 1.000 0.416 -1.154 1.980 -0.008 -0.008 -3.028 3.612 I2

Covariances

Attributes<-

>Sales Support 65.582 0.037 4.532 1.000 65.427 57.275 75.041 0.241 0.089 49.959 85.090 C1

Affordability<-

>Sales Support 20.697 0.035 3.371 1.000 20.652 14.318 27.518 0.093 0.040 8.215 35.579 C2

Attributes<-

>Affordability 62.202 0.071 7.928 1.000 61.960 47.355 78.499 0.172 0.086 31.443 99.828 C3

Variances

Attributes 218.843 0.089 12.059 1.000 218.369 196.601 243.732 0.236 0.047 179.028 272.712 V1

Affordability 170.638 0.123 9.424 1.000 170.301 153.170 190.083 0.224 0.120 137.190 211.322 V2

Sales Support 41.901 0.018 2.312 1.000 41.805 37.634 46.676 0.243 0.108 33.632 52.675 V3

e1 39.353 0.022 2.161 1.000 39.281 35.334 43.791 0.203 0.064 31.502 49.552 V4

e2 7.789 0.004 0.425 1.000 7.774 6.998 8.666 0.206 0.042 6.347 9.568 V5

The table 4.8 shows the Bayesian convergence distribution of the SEM-

CPD mediated regression model. In this research the researcher has

adopted for the procedure of assessing convergence of MCMC (Markov

Chain Monte Carlo) algorithm of maximum likelihood. To estimate the

MCMC convergence the researcher has adopted two methods namely,

convergence in distribution, convergence of posterior summaries. The

values of posterior mean accurately estimate the SEM-CPD mediated

SEM model. From the above table the highest value of Convergence

129

Statistics (C.S) is 1.001 which is less than the 1.002 conservative

measures (Gelman et al. 2004).

4.2.2 POSTERIOR DIAGNOSTIC PLOTS OF SQM-HEI

MEDIATED REGRESSION MODEL

To check the convergence of the Bayesian MCMC method the posterior

diagnostic plots are analyzed. The following figures (figure 4.3 to 4.9)

shows the posterior frequency polygon of the distribution of the

parameters across the 60,000 samples. The Bayesian MCMC diagnostic

plots reveals that for all the figures the normality is achieved, so the

structural equation model fit is accurately estimated.

Figure 4.3: Posterior frequency polygon distribution of the External

Factors and Attributes, regression weight (W1)

130


Factors and Affordability, regression weight (W2)


Factors and Sales Support, regression weight (W3)

131

Figure 4.6: Posterior frequency polygon distribution of the Purchase

Decision and External Factors, regression weight (W4)


Decision and Affordability, regression weight (W5)

Group number 1

Purchase Decision<--External Factors (W4) 0.1 0.2 0.3

F r e q u e n c y

132


Decision and Sales Support, regression weight (W6)


Decision and Attributes, regression weight (W7)

133

To ensure that Amos has converged to the posterior distribution is a

simultaneous display of two estimates of the distribution, one obtained

from the first third of the accumulated samples and another obtained

from the last third. The following figures (Figure 4.10 to Figure 4.12)

show the simultaneous display of two estimates of the distribution for

the mediated factor External Factors with the other dimensions across

50,000 samples. From the three figures, it is observed that the

distributions of the first and last thirds of the analysis samples are

almost identical, which suggests that Amos has successfully identified

the important features of the posterior distribution of the relationship

between the mediated factor External Factors and other purchase

decision dimensions.

134

Figure 4.10 Posterior frequency polygon distributions of the first and

last third of the samples of the SEM-CPD regression model for the

mediated factor External Factors and Attributes



mediated factor External Factors and Affordability

135



mediated factor External Factors and Sales Support

\

The trace plot also called as time-series plot shows the sampled values

of a parameter over time. This plot helps to judge how quickly the

MCMC procedure converges in distribution. The following figures

(figure 4.13 – figure 4.15) shows the trace plot of the SEM-CPD model

for the mediated factor External Factors with other dimensions across

60,000 samples. All the three figures exhibit rapid up-and-down

variation with no long-term trends or drifts. If we mentally break up this

plot into a few horizontal sections, the trace within any section would

not look much different from the trace in any other section. This

136

indicates that the convergence in distribution takes place rapidly. Hence

the SEM-CPD MCMC procedure very quickly forgets its starting

values.

Figure 4.13 Posterior trace plot of the SEM-CPD regression model

for the mediated factor External Factors and Attributes

137

Figure 4.14Posterior trace plot of the SEM-CPD regression model for

the mediated factor External Factors and Affordability

Figure 4.15Posterior trace plot of the SEM-CPD regression model for

the mediated factor External Factors and Sales Support

138

To determine how long it takes for the correlations among the samples

to die down, autocorrelation plot which is the estimated correlation

between the sampled value at any iteration and the sampled value k

iterations later for k = 1, 2, 3,…. is analyzed for the SEM-CPD

regression model. The figures (figure 4.16 to figure 4.18) shows the

correlation plot of the SEM-CPD model for the mediated factor External

Factors with other dimensions across 60,000 samples. The three figures

exhibits that at lag 90 and beyond, the correlation is effectively 0. This

indicates that by 90 iterations, the MCMC procedure has essentially

forgotten its starting position. Forgetting the starting position is

equivalent to convergence in distribution. Hence, it is ensured that

convergence in distribution was attained, and that the analysis samples

are indeed samples from the true posterior distribution.

139

Figure 4.16 Posterior correlation plot of the SEM-CPD regression

model for the mediated factor External Factors and Attributes


model for the mediated factor External Factors and Affordability

.

140


model for the mediated factor External Factors and Sales Support

Even though marginal posterior distributions are very important, they do

not reveal relationships that may exist among the two parameters. The

summary table given in table 4.8 and the frequency polygons given in

the figure 4.3 to figure 4.9 describe only the marginal posterior

distributions of the parameters. Hence to visualize the relationships

among pairs of Parameters in three dimensional the following figures

(figure 4.19 to figure 4.21) provide bivariate marginal posterior plots of

the SEM-CPD model for the mediated factor External Factors with

other dimensions across 60000 samples. From the three figures it

reveals that the three dimensional surface plots also signifies the

141

interrelationship between the mediating variable External Factors with

the other dimensions Attributes, Affordability and Sales Support

Figure 4.19: Three-dimensional surface plot of the marginal posterior

distributions of the mediating factor External Factors with the

Attributes & Sales Support

142


distributions of the mediating factor External Factors with the

Attributes & Affordability


distributions of Attributes with Purchase Decision & Affordability

143

The following figures (figure 4.22 to figure 4.24) display the two-

dimensional plot of the bivariate posterior density across 50000

samples. The plot shows range of darkness from dark to light. The three

shades of gray represent 50%, 90%, and 95% credible regions

respectively. From the three figures, it reveals that the sample

respondent’s responses are normally distributed.

Figure 4.22: Two-dimensional plot of the bivariate posterior density

for the regression weights External Factors to Attributes and

Attributes to Sales Support

144


for the regression weights External Factors to Attributes and

Attributes to Affordability

Figure 4.24 Two-dimensional plot of the bivariate posterior density

for the regression weights Purchase Decision to Attributes and

Attributes to Affordability

145

The reliability of the data has been carried out in the SPSS 15.0 and the

worthwhileness of the data is proved. The Cronbach’s Alpha value for

the sample data is 0.896, which is very close to 0.90. The value below

1.0 is accepted. That proves the sample data is reliable for analysis and

the results based on those analysis are statistically accepted. The

Cronbach’s Alpha of 0.896 signifies the sample, size of which is small,

validity of the data is most significant than similar yet larger sample.

4.3 STRUCTURAL EQUATION MODELING OF MEDIATED

SEM-CPD MODEL

Since the purchase decision is a theoretical construct, researcher has

defined its dimension based on the setting used to explore the construct.

If Mediated SEM-CPD Model is to be applicable in the Indian context,

the dimensions and the sub dimensions on purchase decision/motives

have to be reliable and valid in measuring consumer purchase behavior.

The model examines the relative importance of dimensions of

possession to overall purchase behavior of motorbikes and/or any

automobiles in India especially urbanized geographical area with

abutting rural pockets.

146

The SEM-CPD Model examines the relative importance of External

Factors as a mediating factor for motorbike purchase behavior in India.

The SEM-CPD model includes the measurement of sub dimensions of

purchase behavior as follows Needs, Features, After Sales Service and

Support (Support), Advertising Media (AdMedia), Price, Availability of

Credit Facility (CreditFaci), Infrastrcture, Maintenance (ServiceInfra),

Referral and finally the measure of Purchase Decision. Needs dimension

include lifestyle, cost of transportation, journey time, job compulsions

and influence of family, friends and others. Features dimension include

product color, style, fuel consumption, torque, transmission, comfort

and stability (at high speed & bends), Sales Service and Support

dimension include service quality, spare availability, spare price, trained

service personnel, timeliness of delivery and instructions. Advertising

Media dimension include newspaper, television, internet, magazine,

showroom and radio. Price dimension include economicality,

exorbitancy, normal and high quotes. Availability of Credit Facility

include flexible of credit (installment), rate of interest, initial

commitment and ease of documentation. Infrastructure and maintenance

dimension include road condition, ease of riding, mobility and space

requirements. The individual variables of sub dimensions are

147

considered not necessary for inclusion in the model as the sub

dimension to which they belong take care of it. The measure of purchase

decision includes convenience, handling, and economics of price,

parking ease and maintenance ease are grouped as single observed

variable. After identifying a potential model that best explains the data

in terms of theory and model fit, a confirmatory factor analysis (CFA)

using structural equation modeling (SEM) was used to test the

invariance of the factorial model. All tests of model invariance begin

with a global test of the equality of covariance structures across groups

(Joreskog, 1971). The data for all groups were analyzed simultaneously

to obtain efficient estimates (Bentler, 1995). The constraints used

include, from weaker to stronger: (1) model structure, (2) model

structure and factor loadings, and (3) model structure, factor loadings,

and unique variance.

Evaluation of Model Fit

Several well-known goodness-of-fit indices were used to evaluate

model fit: the chi-square χ2

, the comparative fit index (CFI), the

unadjusted goodness-of-fit indices (GFI), the normal fit index (NFI), the

Tucker-Lewis Index (TLI), the root mean square error of approximation

(RMSEA) and the standardized root mean square error residual

148

(SRMR).

Goodness-of-fit (GOF) indices provide “rules of thumb” for the

recommended cutoff values to evaluate data-model fit. Hu and Bentler

(1999) recommend using combinations of GOF indices to obtain a

robust evaluation of model fit. The criterion values they list for a

model with good fit are CFI > 0.95, TLI > 0.95, RMSEA < 0.06, and

SRMR < 0.08 for assessing fit in structural equation modeling. Hu and

Bentler offer cautions about the use of GOF indices, and current

practice seems to have incorporated their new guidelines without

sufficient attention to the limitations noted by Hu and Bentler.

Moreover, some researchers (Beauducel & Wittmann, 2005; Fan &

Sivo, 2005; Marsh, Hau, & Wen, 2004; Yuan, 2005) believe that these

cutoff values are too rigorous and the results by Hu and Bentler may

have limited generalizability to the levels of misspecification

experienced in typical practice. In general practice, a “good enough”

or “rough guideline” approach is that for absolute fit indices and

incremental fit indices (such as CFI, GFI, NFI, and TLI), cutoff values

should be above 0.90 (0.90 benchmark) and for fit indices based on

residuals matrix (such as RMSEA and SRMR), values below 0.10 or

0.05 are usually considered adequate. All analyses were conducted

149

using AMOS 7.0 (Arbuckle & Wothke, 2006).

Figure 4.25 shows Amos’s path diagram output for the final SEM-CPD

Structural Equation model. One can see that the Affordability had needs

(eleven sub dimensional variables), credit facility (four sub dimensional

variables), attributes had price (four sub dimensional variables), features

(ten sub dimensional variables) and infrastructure (four sub dimensional

variables), Sales Support (six sub dimensional variables), External

factors had Advertising Media (six sub dimensional variables), mediated

factor referral (one sub dimensional variable) and overall possession

(Purchase Decision) had five sub dimensional variables. For the latent

variable purchase behavior, “e1” is the residual for the latent variable.

The RMSEA fit statistics for the model was 0.050, which is considered

as a most appropriate fit model (Brown and Cudeck, 1993;

Diamantopoulos and Siguaw, 2000). The path diagram shows the

referral is the mediating factor for purchase decision. The regression co-

efficient 0.66 signifies the impact of mediating factor referral on the

other dimensions towards the purchase decision-making.

150

STANDARDIZED PARAMETER ESTIMATES FOR

MEDIATED SEM – CPD (WOM) MODEL

Figure 4.25: Hypothesized mediated model specification for

AMOS graphics input

M1, V1

Needs

M2, V2

Features

M3, V3

Support

M4, V4

AdMedia

M5, V5

CreditFaci

M6, V6

ServiceInfra

M7, V7

Price

I1

Referral

I2

Purchase Decision

W1

W2

W3

W4

W5

W6

W7

W8

W9

W10

W11

W12

W13

W14

W15

0, V1

e1 1

0, V2

e2

1

C1

C2

C3

C4

C5

C6

C1

C1

C1

C1

C1

C1

C1

C1

C1

C1

C1

C1

C1

C1

C1

151

Fig 4.26: STANDARDIZED PARAMETER ESTIMATES FOR

MEDIATED SEM– CPD (WOM) MODEL

Among the dimensions of purchase decision of consumer in the SEM-

CPD (WOM) Model above, the needs sub dimension has a regression

weight of -0.02. It signifies that need for life style change, savings on

45.97, 87.76

Needs

60.66, 41.55

Features

32.98, 31.77

Support

31.57, 39.10

AdMedia

21.78, 32.36

CreditFaci

21.36, 26.29

ServiceInfra

24.32, 24.70

Price

.76

Referral

.83

Purchase Decision

.01

-.02

.01

.13

.13

.12

.39

.09

.03

.03

.03

.01

.01

.66

0, 87.76

e1 1

0, 41.55

e2

1

10.24

16.61

10.03

8.63

15.05

8.53

10.24

10.24

10.24

10.24

10.24

10.24

10.24

10.24

10.24

10.24

10.24

10.24

10.24

10.24

10.24

.08

152

transport, influence of family and others is quite negligible towards

purchase behavior of motorcycle consumer in study geographical area.

While the credit facility sub dimension has a regression weight of 0.12,

which reveals that the rate of interest, flexibility in repayment and ease

of documentation is the key for making purchase decision. As the study

area is a rural-urban conglomeration, the availability of credit at a

reasonable rate with the flexible terms influences purchase decision

when the affordability is broken down into sub dimensions. Individual

variables loses its significance in view of the major sub-dimensions are

themselves self explanatory.

Features dimension of the motorcycles have a less significant regression

weight of 0.01. Though the features dimension have positive impact on

purchase decision outcome, it does not have significant impact to

change the consumers’ behavior. Whereas the Infrastructure (available

for servicing) and maintenance dimension have very significant

regression weight of 0.39. It is this requirement that keep on lingering in

the minds of the consumer while purchase decision. The consumers’

attach importance good condition of roads, effortless riding, finally, the

price, the third sub dimension of attributes dimension, has a regression

weight of 0.08. The price of the product is not given importance by the

153

consumers towards purchase inclination. This attitude stems out of the

higher needs such as flexible credit, serviceability, family compulsions.

The serviceability is given more importance than other two sub

dimensions such as price and features fuel consumption in the sample

study area.

Advertising media has a regression weight of 0.13, which is less

significant yet significant considering the other dimensions such as

features, needs and price. Advertising, as empirically proved a priori,

has considerable impact on making purchase decisions. The

newspapers, radio and television has a wide reach among the

consumers. Internet advertising is yet to gain its place in the study area.

After sales support has a regression weight of 0.13, which is again

significant considering other dimensions mentioned above. Service

quality is the first and foremost requirement of the consumers as

empirically proven by Parasuraman et al. As often, the motorcycles

need maintenance, spare availability and its price influence the purchase

behavior of the consumers in the study area.

While the needs has a regression weight of 0.01, features has 0.03, Sales

Support has 0.09, advertising media has 0.03, credit facility has 0.03

and finally the infrastructure, price have regression weights of 0.01

154

respectively. It is evident from the SEM-CPD (WOM) model, that the

individual sub-dimensions except infrastructure have lesser or no

significant impact on the consumer purchase behavior. While the

individual sub-dimensions when mediated through the mediating factor

referral has a regression weight of 0.66, which is considered as strong

and sustainable impact.

Cross validations of dimensions in the SQM-HEI mediated model

Needs and Features had correlated 0.19 (Table 4.18). It reveals that

needs and features are having a significant role in changing the

consumers’ perceptions. Similarly, Features and Sales Support had

correlated 0.59, which is quite significant. Features and Support Service

have together a crucial role in molding purchase attitudes. Another

interesting correlation is between AdMedia and Price, which is 0.59.

Together, they have significant influence on the purchase decisions.

Infrastructure and maintenance needs of the consumers are highly

correlated with sales support with a positive correlation of 0.56. All the

sub-dimensions have significant correlation or degree of association

with purchase decision except the needs expressed by the consumers.

The above correlation results show that the Support Service,

Infrastructure availability along with higher level of advertising reach is

155

considered essential for a successful selling of motorcycles. Availability

of credit decides the purchase by almost 50%, it is the advertising and

price that have higher correlation among them and purchase decisions.

Therefore, the infrastructure and maintenance requirements are the most

crucial factors to be considered before launching a motorcycle for sale.

Evaluation of SQM-HEI Mediated Model

The following table, table 4.9 gives the summary of the various

measures of goodness of fit statistics and other values corresponding to

the SQM-HEI mediated structural equation model. Also the last column

in the table provides the acceptable level for the various measures of

goodness of fit statistics and other values.

TABLE 4.16: SUMMARY OF THE VARIOUS GOODNESS OF

FIT STATISTICS AND OTHER VALUES CORRESPONDING

TO THE SEM-CPD (WOM) MEDIATED STRUCTURAL

EQUATION MODEL

S.NO Measures of fit Output of

SQM-HEI

model

Acceptable

level for good

fit

1. Chi-square ( 2) at p 0.01 2.679 Highly

Significant

2. Degree of freedom (df) 1 -

3. Goodness-of-fit index (GFI) 0.934 >0.90

4. Adjusted Goodness of Fit

Index (AGFI) 0.921 >0.90

156

5. Parsimony Goodness of Fit

Index ( PGFI) 0.923 >0.90

6. Comparative fit index (CFI) 0.999 >0.90

7.

Bentler –Bonett Index or

Normed Fit Index ( NFI)

0.999 >0.90

8.

Tucker Lewis Index (TLI) or

Non-Normed Fit Index (

NNFI)

0.968 >0.90

9. Comparative fit Index ( CFI)

0.999 >0.90

10. Root mean squared error of

approximation (RMSEA)

0.050

<0.05

reasonable fit

=0.050 perfect

fit

11. Standardized Root Mean

Square Error Residual

( SRMR)

0.057 Smaller the

better

12. Non Centrality Parameter

(NCP) 1.679

Lower the

better <2

13. Non Centrality Parameter,

Lower boundary(NCPLO) 1.000 -

14. Non Centrality Parameter,

Upper boundary (NCPHI) 10.769 -

15. Parsimony adjusted NFI

(PNFI) 0.028 -

16. Parsimony adjusted CFI

(PCFI) 0.028 -

17. Minimum value of

Discrepancy ( FMIN) 0.004

Lower the

closeness of

157

Fit

18. Lower Limit of FMIN (LO90) 0.000 -

19. Upper limit of FMIN (HI90) 0.016 -

20. Browne-Cudeck Criterion

(BCC) 110.283 -

21. Akaike Information Criterion

(AIC) 108.679 -

22. ECVI 0.162

Lower the

closeness of

Fit

23. LO90 0.159 -

24. HI90 0.176 -

25. MECVI 0.164 -

26. HOELTER .05 963 <= 75 poor fit

27. HOELTER .01 1662 At least 200

Source: AMOS 15.0 output

From the above table it is revealed that all the criterions of goodness of

fit statistics and other measures of statistics are acceptable for the SEM-

CPD mediated structural equation model.

158

TABLE 4.17: Correlation Matrix of Dimensions of SEM-CPD

(WOM) Model

Needs Features Support Ad

Media Price Credit Faci

Service Infra Referral

Pur Decision

Needs 1 0.19** 0.16** 0.20** 0.21** 0.18** 0.14** 0.14** 0.14**

Features .0 1 0.59** 0.45** 0.38** 0.38** 0.43** 0.31** 0.48**

Support 0 0 1 0.45** 0.44** 0.43** 0.56** 0.26** 0.53**

AdMedia 0 0 0 1 0.59** 0.34** 0.39** 0.29** 0.48**

Price 0 0 0 0 1 0.30** 0.38** 0.24** 0.43**

CreditFaci 0 0 0 0 0 1 0.55** 0.26** 0.49**

ServiceInfra 0 0 0 0 0 0 1 0.25** 0.62**

Referral 0 0 0 0 0 0 0 1 0.40**

PurDecision 0 0 0 0 0 0 0 0 1

** Correlation is significant at the 0.01 level (2-tailed).

4.3.1 BAYESIAN ESTIMATION AND TESTING OF SQM-HEI

MEDIATED STRUCTURAL EQUATION MODEL

The table 4.10 shows the Bayesian convergence distribution of the

SQM-HEI mediated structural equation model. In this research the

researcher has adopted for the procedure of assessing convergence of

MCMC (Markov Chain Monte Carlo) algorithm of maximum

likelihood.

159

TABLE 4.18: BAYESIAN CONVERGENCE DISTRIBUTION FOR

SEM-CPD (WOM) MODEL

Mean S.E. S.D. C.S. Median

95%

Lower

bound

95%

Upper

bound

Skewn

ess

Kurtos

is Min Max Name

Regression

weights

Referral<--

ServiceInfra 0.011 0.000 0.015 1.000 0.010 -0.019 0.040 0.005 -0.015 -0.052 0.070 W1

Referral<--

CreditFaci 0.031 0.000 0.013 1.000 0.031 0.005 0.058 0.003 0.041 -0.023 0.090 W2

Referral<--

Price 0.012 0.000 0.015 1.000 0.012 -0.019 0.042 0.006 -0.004 -0.047 0.076 W3

Referral<--

AdMedia 0.034 0.000 0.011 1.000 0.034 0.013 0.055 0.037 0.059 -0.009 0.084 W4

Referral<--

Support 0.009 0.000 0.012 1.000 0.009 -0.015 0.033 0.015 -0.034 -0.046 0.063 W5

Referral<--

Features 0.026 0.000 0.009 1.000 0.026 0.009 0.043 -0.033 0.014 -0.011 0.060 W6

Referral<--

Needs 0.004 0.000 0.006 1.000 0.004 -0.007 0.016 -0.004 -0.023 -0.020 0.029 W7

Purchase

Decision<--

Referral

0.668 0.001 0.106 1.000 0.669 0.456 0.874 -0.054 0.012 0.218 1.122 W8

Purchase

Decision<--

ServiceInfra

0.384 0.000 0.042 1.000 0.384 0.301 0.465 -0.043 0.022 0.210 0.549 W9

Purchase

Decision<--

CreditFaci

0.127 0.000 0.036 1.000 0.127 0.057 0.199 0.023 -0.045 -0.006 0.292 W10

Purchase

Decision<--

Price

0.082 0.001 0.042 1.000 0.082 -0.001 0.163 -0.006 0.054 -0.093 0.265 W11

Purchase

Decision<--

AdMedia

0.135 0.000 0.029 1.000 0.135 0.077 0.192 -0.015 0.003 0.023 0.264 W12

Purchase

Decision<--

Support

0.130 0.000 0.033 1.000 0.130 0.065 0.196 0.001 -0.002 -0.005 0.269 W13

Purchase

Decisions<--

Features

0.011 0.000 0.024 1.000 0.011 -0.035 0.057 0.014 0.039 -0.077 0.120 W14

Means

160

Needs 42.011 0.007 0.443 1.000 42.012 41.142 42.875 -0.012 -0.003 40.220 43.819 M1

Features 58.394 0.006 0.379 1.000 58.394 57.641 59.136 -0.025 0.033 56.515 60.017 M2

Support 39.104 0.004 0.286 1.000 39.106 38.541 39.658 -0.027 0.025 38.042 40.395 M3

AdMedia 31.559 0.004 0.274 1.000 31.558 31.022 32.103 0.010 0.015 30.504 32.609 M4

Price 18.974 0.003 0.178 1.000 18.975 18.623 19.320 -0.016 0.002 18.172 19.677 M5

CreditFaci 21.771 0.003 0.225 1.000 21.770 21.331 22.215 0.023 0.010 20.898 22.689 M6

ServiceInfra 21.361 0.003 0.214 1.000 21.361 20.944 21.779 0.018 0.008 20.577 22.264 M7

Intercepts

Referral 1.012 0.005 0.438 1.000 1.016 0.154 1.862 -0.022 0.046 -0.869 2.707 I1

BuyingAtt 1.568 0.018 1.156 1.000 1.575 -0.712 3.837 -0.001 -0.027 -3.429 5.988 I2

Covariances

CreditFaci<-

>ServiceInfra 17.572 0.020 1.424 1.000 17.537 14.874 20.493 0.177 0.103 12.383 24.643 C1

CreditFaci<-

>Price 6.749 0.016 1.088 1.000 6.727 4.668 8.961 0.130 0.076 2.429 11.852 C2

Price<-

>AdMedia 13.547 0.021 1.394 1.000 13.510 10.921 16.361 0.141 0.022 8.686 19.719 C3

AdMedia<-

>Support 24.472 0.028 2.271 1.000 24.389 20.237 29.115 0.169 0.037 16.003 34.197 C4

Support<-

>Features 46.362 0.043 3.406 1.000 46.208 40.031 53.413 0.222 0.080 33.722 62.334 C5

Features<-

>Needs 32.636 0.075 4.550 1.000 32.563 23.996 41.793 0.119 0.027 15.779 52.701 C6

ServiceInfra<-

>Needs 8.141 0.038 2.468 1.000 8.108 3.362 13.044 0.059 0.116 -3.255 18.807 C7

ServiceInfra<-

>Features 23.477 0.032 2.306 1.000 23.421 19.160 28.199 0.183 0.097 15.137 33.595 C8

ServiceInfra<-

>Support 22.961 0.020 1.842 1.000 22.918 19.499 26.724 0.172 0.055 15.604 31.457 C9

ServiceInfra<-

>AdMedia 15.334 0.021 1.639 1.000 15.304 12.190 18.679 0.128 0.079 9.026 23.078 C10

ServiceInfra<-

>Price 8.279 0.015 1.054 1.000 8.255 6.273 10.439 0.147 0.047 4.842 12.831 C11

CreditFaci<-

>Needs 10.816 0.039 2.600 1.000 10.801 5.797 15.968 0.054 0.078 -0.084 21.184 C12

CreditFaci<-

>Features

23.056

0.034

2.397

1.000

22.991

18.537

27.880

0.179

0.140

12.978

33.878

C13

CreditFaci<-

>Support 18.791 0.029 1.840 1.000 18.759 15.283 22.563 0.138 0.068 12.439 26.723 C14

CreditFaci<-

>AdMedia 13.981 0.026 1.713 1.000 13.934 10.746 17.440 0.137 0.012 6.503 22.372 C15

Price<-

>Needs 10.405 0.024 2.103 1.000 10.379 6.335 14.589 0.077 0.090 1.652 21.312 C16

161

Price<-

>Features 13.780 0.023 1.873 1.000 13.725 10.242 17.606 0.152 0.061 5.742 22.360 C17

Price<-

>Support 12.987 0.022 1.430 1.000 12.941 10.298 15.880 0.152 0.057 7.435 19.431 C18

AdMedia<-

>Needs 16.940 0.039 3.247 1.000 16.886 10.762 23.487 0.118 0.079 4.652 30.629 C19

AdMedia<-

>Features 32.058 0.039 3.035 1.000 31.950 26.425 38.325 0.211 0.080 20.246 46.086 C20

Support<-

>Needs 13.443 0.049 3.326 1.000 13.386 7.140 20.081 0.099 0.065 -1.107 26.974 C21

Variances

Needs 130.186 0.097 7.213 1.000 129.929 116.854 145.046 0.213 0.107 104.29

2 162.154 V1

Features 97.399 0.081 5.417 1.000 97.215 87.424 108.660 0.236 0.032 79.729 120.272 V2

Support 55.365 0.037 3.067 1.000 55.268 49.702 61.694 0.232 0.072 44.434 68.878 V3

AdMedia 51.062 0.040 2.772 1.000 50.964 45.906 56.783 0.225 0.078 41.049 64.192 V4

Price 21.572 0.014 1.184 1.000 21.517 19.397 24.043 0.250 0.141 17.420 26.770 V5

CreditFaci 33.707 0.022 1.851 1.000 33.654 30.270 37.540 0.205 0.111 26.472 43.130 V6

ServiceInfra 30.145 0.017 1.682 1.000 30.083 27.015 33.548 0.210 0.104 24.100 38.971 V7

e1 2.576 0.002 0.141 1.000 2.569 2.317 2.870 0.261 0.143 2.031 3.359 V8

e2 19.071 0.015 1.058 1.000 19.043 17.094 21.251 0.186 0.040 15.217 23.836 V9

To estimate the MCMC convergence the researcher has adopted two

methods namely, convergence in distribution, convergence of posterior

summaries. The values of posterior mean accurately estimate the SQM-

HEI mediated SEM model. From the above table the highest value of

Convergence Statistics (C.S) is 1.001 which is less than the 1.002

conservative measure (Gelman et al. 2004).

162

4.3.1.1 POSTERIOR DIAGNOSTIC PLOTS OF SEM-CPD

(WOM) MEDIATED MODEL

To check the convergence of the Bayesian MCMC method the posterior

diagnostic plots are analyzed. The following figures (figure 4.27 to

4.34) shows the posterior frequency polygon of the distribution of the

parameters across the 60000 samples. The Bayesian MCMC diagnostic

plots reveals that for all the figures the normality is achieved, so the

structural equation model fit is accurately estimated.

Figure 4.27: Posterior frequency polygon distribution of the

mediating factor Referral and Service Infrastructure (W1)

163


mediating factor Referral and Credit Facility (W2)


mediating factor Referral and Price (W3)

164


mediating factor Referral and AdMedia (W4)


mediating factor Referral and Support Service (W5)

165


mediating factor Referral and Features (W6)


mediating factor Referral and Needs (W7)

166


mediating factor Referral and Purchase Decision (BuyingAtt) (W8)

To ensure that Amos has converged to the posterior distribution is a

simultaneous display of two estimates of the distribution, one obtained

from the first third of the accumulated samples and another obtained

from the last third. The following figures ( Figure 4.35 to Figure 4.37)

shows the simultaneous display of two estimates of the distribution for

the mediated factor referral with the other dimensions across 70 000

samples. From the three figures it is observed that the distributions of

the first and last thirds of the analysis samples are almost identical,

which suggests that Amos has successfully identified the important

features of the posterior distribution of the relationship between the

mediated factor referral and other quality dimensions.

167



mediating factor Referral and Service Infrastructure

168



mediating factor Referral and Credit Facility



mediating factor Referral and Price

169



mediating factor Referral and AdMedia



mediating factor Referral and Support Service

170



mediating factor Referral and Features



mediating factor Referral and Needs

171



mediating factor Referral and Purchase Decision

The trace plot also called as time-series plot shows the sampled values

of a parameter over time. This plot helps to judge how quickly the

MCMC procedure converges in distribution. The following figures

(figure 4.54 – figure 4.61) show the trace plot of the SEM-CPD (WOM)

model for the mediated factor referral with other dimensions across

60000 samples. All the three figures exhibit rapid up-and-down

variation with no long-term trends or drifts. If we mentally break up this

plot into a few horizontal sections, the trace within any section would

not look much different from the trace in any other section. This

indicates that the convergence in distribution takes place rapidly. Hence

172

the SEM-CPD (WOM) MCMC procedure very quickly forget its

starting values.

Figure 4.43 Posterior trace plot of the SEM-CPD regression model

for the mediated factor Referral and Service Infrastructure

173



mediating factor Referral and Credit Facility



mediating factor Referral and Price

174



mediating factor Referral and AdMedia



mediating factor Referral and Support Service

175



mediating factor Referral and Features



mediating factor Referral and Needs

176



mediating factor Referral and Purchase Decision (BuyingAtt)

To determine how long it takes for the correlations among the samples

to die down, autocorrelation plot which is the estimated correlation

between the sampled value at any iteration and the sampled value k

iterations later for k = 1, 2, 3,…. is analyzed for the SEM-CPD

regression model. The figures (figure 4.16 to figure 4.18) shows the

correlation plot of the SEM-CPD model for the mediated factor Referral

with other dimensions across 60,000 samples. The three figures

exhibits that at lag 90 and beyond, the correlation is effectively 0. This

indicates that by 90 iterations, the MCMC procedure has essentially

177

forgotten its starting position. Forgetting the starting position is

equivalent to convergence in distribution. Hence, it is ensured that

convergence in distribution was attained, and that the analysis samples

are indeed samples from the true posterior distribution.


model for the mediated factor Referral and Service Infrastructure

178


model for the mediated factor Referral and Credit Facility


model for the mediated factor Referral and Price

179


model for the mediated factor Referral and AdMedia


model for the mediated factor Referral and Support Service

180


model for the mediated factor Referral and Features


model for the mediated factor Referral and Needs

181

Even though marginal posterior distributions are very important, they do

not reveal relationships that may exist among the two parameters. The

summary table given in table 4.8 and the frequency polygons given in

the figure 4.3 to figure 4.9 describe only the marginal posterior

distributions of the parameters. Hence to visualize the relationships

among pairs of Parameters in three dimensional the following figures

(figure 4.70 to figure 4.75) provide bivariate marginal posterior plots of

the SEM-CPD model for the mediated factor Referral with other

dimensions across 60000 samples. From the three figures it reveals that

the three dimensional surface plots also signifies the interrelationship

between the mediating variable Referral with the other dimensions

Needs, Features, Support, Price, Infrastructure, AdMedia, Credit

Facility.

182


distributions of the mediating factor Referral with the Service

Infrastructure & Credit Facility


distributions of the mediating factor Referral with the Price & Credit

Facility

183


distributions of the mediating factor Referral with the Price &

AdMedia


distributions of the mediating factor Referral with the AdMedia &

Support Service

184


distributions of the mediating factor Referral with the Support

Service Infrastructure & Features


distributions of the mediating factor Referral with the Feature &

Needs

185

Figure 4.64 : Three-dimensional surface plot of the marginal

posterior distributions of the mediating factor Referral with the Needs

& Purchase Decision (Buying Attitude)

The following figures (figure 4.77 to figure 4.83) displays the two-

dimensional plot of the bivariate posterior density across 60000

samples. Ranging from dark to light, the three shades of gray represent

50%, 90%, and 95% credible regions, respectively. From the three

figures, it reveals that the sample respondent’s responses are normally

distributed.

186


for the regression weights Referral to Service Infrastructure and

Credit Facility


for the regression weights Referral to Price and Credit Facility

187


for the regression weights Referral to Price and AdMedia


for the regression weights Referral to Support Service and AdMedia

188


for the regression weights Referral to Support Service and Features


for the regression weights Referral to Features and Needs

189


for the regression weights Referral to Needs and Purchase Decision

(Buying Attitude)

4.4 CONCLUSION

The researcher has empirically analyzed the objectives with the help of

hypotheses and statistical tools for the study. The study reveals that the

conceptual research models are empirically proved. These findings are

interpreted in the final chapter for future research and suggestions for

gauging consumer purchase behavior to the benefit of both the

consumers and companies.

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