CHAPTER 5 DATA ANALYSIS - Shodhgangashodhganga.inflibnet.ac.in/bitstream/10603/31875/14/14_chapter 5.pdfSPSS version 16.0 software is used for conducting exploratory factor analysis

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CHAPTER 5

DATA ANALYSIS

5.1 INTRODUCTION

Two stage factor analysis; exploratory and then confirmatory has been used to analyse the

data collected from the sample consisting of marketing personnel of automobile companies.

First stage purification of scale was done using exploratory factor analysis while confirmatory

factor analysis was used to carry out second stage purification.

Figure 5.1: Factor Analysis procedure

Factor Analysis is a general name denoting a class of procedures primarily used for data

reduction and summarization [118]. Putting more simply factor analysis is used for

condensing the large information into small manageable factors. Specifically following are

the functions served by factor analysis:

To help researcher find out number of latent variables underlie a set of items.

Scale Development

•Marketing Flexibility scale development

EFA

•Exploratory Factor Analysis (EFA)

CFA

•Confirmatory Factor Analysis (CFA)

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To condense the information from larger variables into small group of newly

variables.

To provide means of explaining variation among large variables using newly created

small variables [118]

SPSS version 16.0 software is used for conducting exploratory factor analysis while AMOS

20.0 is used for confirmatory factor analysis. For the initial reliability assessment and

exploratory factor analysis, sample 1 having 356 complete responses is used.

5.2 RELIABILITY ASSESSSMENT

Reliability coefficient; Cronbach Alpha is one of the most important indicators in scale

development process. It describes the reliability of items with higher value of alpha indicating

the high internal consistency. This means that all the items used in scale development is

measuring the construct of interest. Alpha is an indication of the proportion of variance in the

scale scores that is attributable to the true score [119]. Internal consistency of items is tested

with Cronbach alpha coefficient. Using SPSS 16.0 version, scale reliability is calculated by

estimation of Cronbach’s alpha coefficient. Value of alpha came out as 0.88 which is well

above the suggested minimum value of 0.7

Table 5.1: Reliability coefficient

5.3 ITEM-TO-TOTAL CORRELATION

Item to total correlation (ITTC) is another measure of checking internal consistency or

homogeneity of items. ITTC is correlation of item to summated scale score [120]. Correlation

coefficient (r) is used to calculate ITTC between individual items to total score. Low value of

correlation coefficient; r indicates that particular item lacks consistency and hence must be

Reliability Statistics

Cronbach's Alpha N of Items

.880 50

90

questioned for its existence. On other hand high correlation coefficient of items indicates

higher internal consistency and homogeneity of items used for scale development. In this

study ITTC was calculated using correlation coefficient function in SPSS software.

Total sum of all the items were created as additional column in data list and then each items’

individual correlation with total item score was found. There are different opinions about the

minimum value of correlation coefficient for retaining items though Nunally [121] suggested

that r value of retained items must not be less than 0.3. Items having r value greater than 0.45

significant at p <0.01 were retained. Table 5.2 given below show the items that were rejected

as these were having r value less than aforementioned threshold. Total items rejected in this

test were 17 with their respective numbers as 6,9,12,13,14,15,24,25,26,27,31,32,37,38,41,45

and 46.

Table 5.2: List of rejected items in ITTC

Items Dimension Origin of Item

We have quick and effective customer complaint-redress mechanism

Customer Orientation Self-Observation

We offer customized products and services Product Literature Review

We don't indulge in political activities to counteract trade regulations

Environment Expert Interview

We don't care at all about our competitors Environment Self-Observation

We focus on developing inimitable competencies Environment Literature Review

We have joint ventures with some of our competitors Environment Self-Observation

We are competent to dismantle current strategies to match the evolved business conditions

Environment Expert Interview

We don't change the price at all Price Self-Observation

Our product development cycle is long Product Expert Interview

We believe in First-time-right-decision philosophy Product Literature Review

We are responsive towards social and environmental changes Environment Literature Review

Our prices are based upon value for money notion Price Self-Observation

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We quickly add or subtract a region/place according to need of the situation

Place Expert Interview

We have quick mechanism to add dealership/franchise to capitalize on the opportunities

Place Self-Observation

Our dealerships are regularly audited for customer satisfaction Place Literature Review

Marketing people are fully involved in promotional activities Promotion Literature Review

We don't involve our partners in promotional campaigns Promotion Expert Interview

Out of these 17 rejected items, 6 items belongs to self-observation category while 6

correspond to literature review. Further 5 items are related to the expert interviews. Further

analysing these items, we find that even literature is not very supportive of the items that were

found low on correlation. For example involvement of marketing personnel in promotion and

first time right decision policy has found only indirect reference in theory. Further the items

related to environment dimension have not been rated significantly by the respondents. In

order to make sure about the weeded out items, panel of experts were once again consulted

and after their positive nod exploratory factor analysis was conducted on remaining 33 items.

5.4 EXPLORATORY FACTOR ANALYSIS

Third step of first stage purification involves exploratory factor analysis that is aimed to

identify the factor structure of remaining 33 items. Items were subjected to principal

component analysis with varimax rotation and criterion for retaining items were having factor

loading more than 0.4 with significant loading on only one factor without any cross loading.

Six-component structure emerged with F1 relating to theme of Price (PE), F2 with customer

orientation (CO), F3 with product (PR), F4 with place (PL), F5 with promotion (PM) and F6

with Structural Hierarchy (SH). The step-wise detail of results of exploratory factor analysis

is given as below:

Kaiser-Meyer-Olkin (KMO) and Bartlett’s Test of Sphericityisconductedbefore

proceeding with factor analysis, there is need to check whether there exists underlying

structure between testing variables or not. KMO and Bartlett’s test is performed to support the

viability of applying factor reduction to data. Both KMO measure of sampling adequacy and

Bartlett’s test of sphericity identify whether application of factor analysis is appropriate or

not.

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Bartlett’s Test is a test that provides statistical significance that correlation matrix has

significant correlations among at least some of variables. Similarly KMO measure of

sampling adequacy is another measure to quantify the degree of inter-correlation among

variables and its value must exceed 0.5 [122].

In our study Bartlette’s test comes out 0.000 which is significant at 0.01 level indicating that

factor analysis can be performed. Similarly value of KMO measure of sampling adequacy

came out as 0.870; well above the minimum acceptable score of 0.5. Refer table 3 below for

test results of KMO and Bartlett’s test of spehricity.

Table 5.3: KMO and Bartlett’s test of sphericity

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .870

Bartlett's Test of Sphericity Approx. Chi-Square 2054.217

df 325

Sig. .000

Principal component analysis with varimax rotation was performed on remaining 33 items.

Table 5.4 – 5.7 below shows the step-wise detail of exploratory factor analysis. A total of six

factors having Eigen value more than 1 is 64.192%. This means that these six components

account for 64.192% of variation. Factor 1 alone accounted for 36.39% of variation while

factor 2 explains 7.69% variation. While factor 3 explains 5.75% of variance factor 4

narratives accounts for 5.55% of variation. Factor 5 and Factor 6 accounted 4.58% and 4.20%

of variance respectively.

Table 5.4: Table of Communalities

Initial Extraction

Q1 1.000 .702

Q2 1.000 .687

Q3 1.000 .723

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Q5 1.000 .591

Q7 1.000 .585

Q8 1.000 .605

Q17 1.000 .573

Q18 1.000 .708

Q19 1.000 .573

Q20 1.000 .715

Q21 1.000 .764

Q22 1.000 .646

Q23 1.000 .718

Q28 1.000 .502

Q30 1.000 .574

Q33 1.000 .524

Q35 1.000 .614

Q36 1.000 .653

Q39 1.000 .655

Q42 1.000 .582

Q43 1.000 .598

Q44 1.000 .588

Q47 1.000 .783

Q48 1.000 .666

Q49 1.000 .689

Q50 1.000 .670

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Table 5.5: Scree Plot

Table 5.6: Total variance explained

Component

Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

1 9.463 36.397 36.397 9.463 36.397 36.397

2 2.001 7.694 44.091 2.001 7.694 44.091

3 1.496 5.754 49.845 1.496 5.754 49.845

4 1.445 5.558 55.403 1.445 5.558 55.403

5 1.191 4.582 59.985 1.191 4.582 59.985

6 1.094 4.207 64.192 1.094 4.207 64.192

Extraction Method: Principal Component Analysis.

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Rotated Component Matrix is one of the most important steps in interpreting factors. Factor

extraction done by principal component analysis as mentioned above is according to the

variances extracted by factors. First factor accounts for maximum variance as each of its

variable loading significantly as this factor accounts for highest amount of variation. Now this

unrotated factor matrix is of little use as the information it has is not in most interpretable

way.

Factor rotation is done to redistribute the earlier factor variance to later ones in order to get

more meaningful and interpretable factor structure. Basically there are two broad ways of

rotating factors; orthogonal and oblique. While former technique involves rotating the factor

at 90 degree latter relies on non-perpendicular; slanting rotation. There are different

motivations behind choosing one or another method of rotation. Orthogonal method of

rotation is chosen when the motive is data reduction while oblique is applied when one is

interested in finding many several constructs.

Under orthogonal method, varimax rotation method is used in study as focus is to reduce the

large number of variables. The varimax method has emphasis on column simplification. In an

ideal situation rotation with varimax will result only in two results’- either 1s or 0. Now with

varimax method only high loadings like +1 or -1 are expected along with 0.

These high loadings of ± 1 and 0 is very simple from interpretation point of view as +1 or -1

denotes perfectly positive and negative relation of particular variable with a factor. 0 on the

other hand simply means there is no association between variable and factor. Items with

primary factor loading of more than 0.4 without any cross loading were retained. Items not

meeting this criterion were deleted one by one and factor analysis was repeated until all

remaining items met the aforementioned value of factor loading. In sum 7 items get deleted in

this process. Following table shows the remaining 26 items that get grouped under 6 factors.

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Table 5.7: Rotated Component Matrixa

Component

1 2 3 4 5 6

F1:We change prices to accelerate demand .784

F1:We have multiple price points in each category of products

.711

F1:Marketing people are consulted before price finalization

.702

F1:We benchmark our prices with competitors .621

F1:We readily adjust our prices according to industry changes

.543

F1:We are fully capable of renewing our pricing strategy

according to environment

.474

F1:We follow flexible pricing policy for our entire range of

products

.467

F2: We give utmost importance to customer satisfaction

.822

F2: Our products adds value to life of customers .763

F2: Our product innovations are customer driven

.688

F2: We do take care of changing customers’ needs

.608

F2: Our main focus on customer relation rather than sales

only

.425

F3: Our new product launches has input from marketing

people

.622

F3: We prefer product quality over shelf life .587

F3: We are market leader in product innovations

.586

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F3: Product development is done in-line with flexible design

philosophy

.578

F3: New product development is done in close coordination

with suppliers and channel partners

.495

F4: We are fully competent to reconfigure our dealership

format

.811

F4: We have system of rewarding best performing dealership

.579

F4: We provide specialized training to our channel partners to

improve their performance

.555

F5: We respond quickly to promotional activity launched by

competitor

.823

F5: Marketing people are fully involved in promotional

activities

.557

F5: Impact assessment of the promotional campaign is done

by external agency

.506

F6: Marketing people works in teams having cross-

department participation

.835

F6: There is enough decision making power delegated to

marketing people

.500

F6: We have low level of formal regulations for marketing

employees

.457

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 15 Iterations

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5.5 RELIABILITY ANALYSIS

Reliability Analysis of 6 factors emerged after EFA that consist total of 26 items under them.

Factors and items numbers after this initial purification stage is as follows: Factor 1 – Price (7

items), Factor 2 – Customer Orientation (5 items), Factor 3 – Product (5 items), Factor 4 –

Place (3 items), Factor 5 – Promotion (3 items) and Factor 6 – Structural Hierarchy (3 items).

These individual factors were then checked for their individual reliabilities and then overall

reliability of the scale emerged after first stage purification was calculates. Refer tables 5.8-

5.14 for details of individual reliability test of factors along with scale’s overall reliability.

Reliability of Factor 1 (Price): Factor 1 consisting of 7 items grouped under the theme of

Price has Cronbach’s value of 0.851.

Table 5.8: Reliability value of factor 1


Cronbach's Alpha Cronbach's Alpha Based on Standardized

Items

N of Items

.851 .851 7

All the values under head of Cronbach’s alpha is item deleted ranges between 0.809 to 0.844

that is below the overall reliability value 0.851 of factor 1; implying that removal of any item

will not increase the overall reliability of factor.

Reliability of Factor 2 (Customer Orientation): Factor 2 that consists of 5 items grouped

under the theme of Customer Orientation has Alpha value of 0.833.




Items

N of Items

.828 .833 5

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Reliability of Factor 3 (Product): Alpha value for Factor 3; Price consisting of 5 items

comes out as 0.772.




Items

N of Items

.772 .772 5




Reliability of Factor 4 (Place): Alpha value for Factor 4; Place consisting of 3 items comes

out as 0.706

Table 5.11: Reliability detail of factor 4



Items

N of Items

.706 .717 3




Reliability of Factor 5 (Promotion): Factor 5 in which 3 items are grouped under the theme

of Promotion has alpha value of 0.716

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Items

N of Items

.716 .718 3




Reliability of Factor 6 (Structural Hierarchy): Factor 6 in which 3 items are grouped under

the theme of Structural hierarchy has alpha value of 0.689




Items

N of Items

.689 .688 3




Overall Reliability: Overall reliability of scale emerged after first stage purification of EFA

consisting of 26 items came out as 0.927

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Table 5.14: Reliability value of overall scale items



Items

N of Items

.927 .927 26

The findings of the reliabilities of different factors and overall scale reliability are

summarized in table 5.15 given below:

Table 5.15: Summary of Reliability values

Factor No of Items Cronbach’s Alpha

Factor F1 (Price) 7 0.851

Factor F2 (Customer) 5 0.828

Factor F3 (Product) 5 0.772

Factor F4 (Place) 3 0.706

Factor F5 (Promotion) 3 0.716

Factor F6 (Structural Hierarchy) 3 0.689

Scale Reliability 26 0.92

5.6 CONFIRMATORY FACTOR ANALYSIS

Confirmatory Factor Analysis (CFA), as name suggests, is a measurement model and used

as a confirmatory tool for testing measurement theory. CFA is used as a method to confirm

the results of EFA analysis and is used for determining how well our sample date fits the

theoretical model [120]. In a sense, CFA statistics tells us how well our sample data fits

theoretical specifications. Therefore CFA represents structural modelling method that helps

the researcher to find overall fit between hypothesized model and sample data. Analysis of

moment structure (AMOS) software is used to conduct CFA.

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Second stage purification of the scale was done with data set 2 which was collected on

modified version of pilot instrument. Out of 257 total numbers of respondents, 183

questionnaires were found useful for second stage analysis involving confirmatory factor

analysis method. The input related to 183 respondents was fed into AMOS and system

returned the following output:

Notes for Model (Default model):

Computation of degrees of freedom (Default model)

Number of distinct sample moments: 351

Number of distinct parameters to be estimated: 67

Degrees of freedom (351 - 67): 284

Further the model structure emerged after feeding the inputs of 183 respondents is shown in

following figure 5.2.

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Figure 5.2: Confirmatory factor analysis model

Further the values of standardized regression weights and correlations of default model are

given in tables below:

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Table 5.16: Standardized Regression Weights: (Group number 1 - Default model)

Table 5.17: Correlations: (Group number 1 - Default model)

Estimate

PRICE <--> Customer .093

PRICE <--> Product .240

PRICE <--> Place .138

PRICE <--> Promo .087

PRICE <--> SH -.125

Customer <--> Product .150

Customer <--> Place .218

Customer <--> Promo .026

Customer <--> SH .261

Product <--> Place .251

Product <--> Promo .201

Product <--> SH -.239

Place <--> Promo .283

Place <--> SH -.041

Promo <--> SH .096

Estimate

PE7 <--- PRICE .813

PE6 <--- PRICE .842

PE5 <--- PRICE .708

PE4 <--- PRICE .813

PE3 <--- PRICE .882

PE2 <--- PRICE .885

PE1 <--- PRICE .620

CO5 <--- Customer Orientation .748





PR5 <--- Product .803





PL3 <--- Place .748

PL2 <--- Place .790

PL1 <--- Place .700

PM3 <--- Promotion .775



SH3 <--- Structural Hierarchy .750



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5.6.1 Measurement Model Validity

In order to examine the theoretical model against the sample data, we examined both the

overall model fit as well as construct validity as recommended in the literature. Detail of both

the procedures follows as given below:

Overall model fit is used to assess the level of fit between theoretical model and sample data.

Result of confirmatory factor analysis gives a range of model fit indices as output which can

be used to assess overall model fit of sample data against hypothesized theory. Model fit can

be defined as best model on which sample data fits well and best represents the underlying

theoretical model. There are two types of fit indices: absolute and incremental which are used

to assess the overall model fit. These indices provide a fair idea whether the sample data fits

hypothetical model well or not.

Absolute Fit indices determine how well a priori model fits the sample data [123]. The

prominent absolute fit indices that are used to assess the fitness of model are: Chi-square (χ2),

Root mean square error of approximation (RMSEA), Goodness-of-fit (GFI) and Adjusted

goodness of fit (AGFI).

Chi-square (χ2) value is the traditional measure for evaluating overall model fit and,

‘assesses the magnitude of discrepancy between the sample and fitted covariance matrices’

[124]. In this test value of p should come insignificant as significant result points towards the

difference in variance of sample and fitted covariance matrices. Chi-square value must be

used in conjunction with other indices as relying only upon this value is not recommended. As

the value of Chi-square is significantly get affected by sample size, its credibility as a fit index

is not very high. Alternatively the value of CMIN/DF is more credible index value that

demonstrates the fit of sample and fitted matrix. There is no unanimity on value of CMIN/DF

though opinions ranges from 5.0 [125] to 2.0 [126].

Following are values of Chi-Square statistic and CMIN/DF result of CFA analysis:

Chi-square (CMIN): 503.196

Degree of Freedom (DF): 284

CMIN/DF: 1.772

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Root mean square error of approximation (RMSEA) is one of the most informative and

important fit indices that are used in evaluation of model fit. The RMSEA tells us how well

the model, with unknown but optimally chosen parameter estimates would fit the population

covariance matrix [127]. The value of RMSEA of a good fitting model should be lower than

0.08 with less than 0.05 is considered as excellent [128].

RMSEA value required: less than 0.08

RMSEA value obtained: 0.06

Goodness-of-fit (GFI) and adjusted goodness-of-fit (AGFI) values range from 0 to 1 and

even though many authors have noted [129] [130] that these statistics are sensitive to sample

size, there are still quoted in order to determine model fit.

GFI value required: more than 0.9

GFI value obtained: 0.924

AGFI value required: more than 0.9

AGFI value obtained: 0.882

Incremental Fit indices are also called comparative or relative fit indices as these uses the

comparison of models unlike absolute fit indices where raw form of chi-square is used.

Normative fit index (NFI), Comparative fit index (CFI) and Tucker-Lewis Index (TLI):

All three indices mentioned above comes under classification of incremental fit indices and

ranges from 0 to 1. NFI value more than 0.90 indicates a good fit [131] and same is criterion

set for CFI index [132]. In the same vein TLI should also exceed the threshold mark of 0.9

[133].

NFI value required: more than 0.9

NFI value obtained: 0.902

CFI value required: more than 0.9

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CFI value obtained: 0.924

TLI value required: more than 0.9

TLI value obtained: 0.906

5.6.2 CONSTRUCT VALIDITY

The biggest merit of using Confirmatory factor analysis lies in its ability to test the construct

validity of proposed measurement theory. Construct validity is extent to which a set of

measured items actually reflect the theoretical latent construct those items are designed to

measure [122]. Construct validity is type of validity that subsumes all other categories of

validity. Construct validity refers to the extent to which any measuring instrument measures

what it is intended to measure [134] [135]. Construct validity is made up of two types of

validity: convergent validity and discriminant validity.

Convergent Validity means that the items that are indicator of specific construct should

converge or share a high proportion of variance in common, known as convergent validity

[122]. There are mainly 3 methods to estimate convergent validity.

Factor loadings point towards the high convergence on some common point. Statistically

significant loadings with minimum estimates of 0.5 is recommended while loadings greater

than 0.7 is considered ideal. Table below gives the factor loading estimates for model. All the

loadings expect for PE1, PR1 and SH1 have loading estimates values more than ideal value of

0.7. Further these three have values more than lower acceptable value of 0.4 as suggested by

Nunnaly [121] in case of new scale development.

Table 5.18: Factor loading table

Estimate

PE7 <--- PRICE .813

PE6 <--- PRICE .842

PE5 <--- PRICE .708

PE4 <--- PRICE .813

PE3 <--- PRICE .882

PE2 <--- PRICE .885

PE1 <--- PRICE .620

CO5 <--- Customer .748



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Average Variance extracted (AVE) The Square of factor loadings represents the variation in

item caused by construct. The rationale behind considering standardized factor loading of 0.7

as ideal can be explained in terms of this variance extracted term [122]. As square of 0.71

amounts to 0.5 that in terms of percentage equals to 50%. Now this 50% or 0.5 means that the

half of variation in that particular item is explained by that factor while other half is error

variance. So if this value falls below 50% then it will be difficult to justify as more variation

will account for error variance and less for factor part.

Variance extracted is another indicator of convergent validity and it can be calculated with the

help of standardized loadings. AVE is calculated as total of all squared standardized factors

loadings (squared multiple correlation) divided by number of items.

Average variance extracted (AVE) = Ʃƛ²/ n

Value more than 0.5 is considered as good convergence property. Using the above stated

formula we calculated AVE for each of factor.

Table 5.19: Average variance extracted (AVE)

Average variance extracted (AVE)

Factor AVE








PL3 <--- Place .748

PL2 <--- Place .790

PL1 <--- Place .700

PM3 <--- Promo .775

PM2 <--- Promo .812

PM1 <--- Promo .761

SH3 <--- SH .750

SH2 <--- SH .749

SH1 <--- SH .554

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Factor 1 : F1(PE) (.62²+.885²+.882²+.813²+.708²+.842²+.813²/7) = (4.41/7) = 0.63

Factor 2 : F2(CO) (.724²+.855²+.864²+.755²+.748²/5) = (3.11/5) = 0.622

Factor 3: F3(PR) (.657²+.756²+.758²+.808²+.803²/5) = (2.86/5) = 0.572

Factor 4 :F4(PL) (.70²+.79²+.748²/3) = (1.66/3) = 0.553

Factor 5: F5(PM) (.761²+.812²+.775²/3) = (1.82/3) = 0.60

Factor 6: F6(SH) (.554²+.749²+.75²/3) = (1.42/3) = 0.476

All the factors have shown higher value than 0.5 (except F6 that is almost 0.5) hinting

towards the sufficient convergent validity.

Construct reliability (CR) is another measure to determine convergent validity. It is

calculated with the help of squared factor loadings and error variance as:

CR = (Ʃƛ) ² / (Ʃƛ) ² + (Ʃe)

CR value of 0.7 represents good reliability while anything between 0.6 and 0.7 is acceptable

[122].

Factor 1: F1 (PE) The value of squared sum of factor loadings in numerator is calculated as:

(Ʃƛ) ² = Ʃ (.62+.885+.882+.813+.708+.842+.813) = (Ʃ (5.563)) ² = 30.94

Error variance is calculated by subtracting the squared loadings from 1. For example error

variance for PE1 can be calculated as = 1 – (0.62)² = 1- 0.38 = 0.62

Error variance PE2 = 1 – (0.885)² = 1 – 0.78 = 0.21

Error variance PE3 = 1 – (0.882)² = 1 – 0.77= 0.22




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Total value of error variance for factor 1 = Ʃ e = Ʃ 0.62 + 0.21 + 0.22 + 0.33 + 0.49 + 0.29 +

0.33 = 2.49

Factor 1: F1 (PE) = CR = (Ʃ (5.563)) ² / (Ʃ (5.563)) ² + 2.49 = 30.94/33.43 = 0.92

For Factor 2: F2 (CO): (Ʃƛ) ² = Ʃ (.724+.855+.864+.755+.748) = (Ʃ (3.946)) ² = 15.57

Error variance CO1 = 1 – (0.724)² = 1 – 0.524 = 0.47





Total value of Error variance for factor 2: F2 (CO) = Ʃ e = Ʃ 0.47 + 0.26 + 0.25 + 0.42 + 0.44

= 1.84

Factor 2: F2 (CO) = CR = (Ʃ (3.946)) ² / (Ʃ (3.946)) ² + 1.84 = 15.57/17.41 = 0.89

Factor 3: F3 (PR) (Ʃƛ) ² = Ʃ (.657+.756+.758+.808+.803) = (Ʃ (3.782)) ² = 14.30

Error variance PR1 = 1 – (0.657)² = 1 – 0.431 = 0.56





Total value of Error variance for factor 3: F3 (PR) = Ʃ e = Ʃ 0.56 + 0.42 + 0.42 + 0.34 + 0.35

= 2.09

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Factor 3: F3 (PR) = CR = (Ʃ (3.782)) ² / (Ʃ (3.782)) ² + 2.09 = 14.30/16.39 = 0.87

Factor 4: F4 (PL):(Ʃƛ) ² = Ʃ (.70+.79+.748) = (Ʃ (2.238)) ² = 5

Error variance PL1 = 1 – (0.70)² = 1 – 0.49 = 0.51



Total value of Error variance for factor 4: F4 (PL) = Ʃ e = Ʃ 0.51 + 0.37 + 0.44 = 1.32

Factor 4: F4 (PL) = CR = (Ʃ (2.238)) ² / (Ʃ (2.238)) ² + 1.32 = 5/6.32 = 0.79

Factor 5: F5 (PM): (Ʃƛ) ² = Ʃ (.761+.812+.775) = (Ʃ (2.348)) ² = 5.51

Error variance PM1 = 1 – (0.761)² = 1 – 0.579 = 0.42



Total value of Error variance for factor 5: F5 (PM) = Ʃ e = Ʃ 0.42 + 0.34 + 0.39 = 1.15

Factor 5: F5 (PM) = CR = (Ʃ (2.348)) ² / (Ʃ (2.348)) ² + 1.15 = 5.51/6.66 = 0.82

Factor 6: F6 (SH): (Ʃƛ) ² = Ʃ (.554+.749+.75) = (Ʃ (2.053)) ² = 4.21

Error variance SH1 = 1 – (0.554)² = 1 – 0.306 = 0.69



Total value of Error variance for factor 6: F6 (SH) = Ʃ e = Ʃ 0.69 + 0.43 + 0.43 = 1.55

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Factor 6: F6 (SH) = CR = (Ʃ (2.053)) ² / (Ʃ (2.053)) ² + 1.55 = 4.21/5.76 = 0.73

All these values of CR for factors are summarized in table 25 as given below:

Table 5.20: Construct Reliability (CR)

Factor CR

Factor 1 : F1 (Ʃ (5.563)) ² / (Ʃ (5.563)) ² + 2.49 = 30.94/33.43 = 0.92

Factor 2 : F2 (Ʃ (3.946)) ² / (Ʃ (3.946)) ² + 1.84 = 15.57/17.41 = 0.89

Factor 3: F3 (Ʃ (3.782)) ² / (Ʃ (3.782)) ² + 2.09 = 14.30/16.39 = 0.87

Factor 4 :F4 (Ʃ (2.238)) ² / (Ʃ (2.238)) ² + 1.32 = 5/6.32 = 0.79

Factor 5: F5 (Ʃ (2.348)) ² / (Ʃ (2.348)) ² + 1.15 = 5.51/6.66 = 0.82

Factor 6: F6 (Ʃ (2.053)) ² / (Ʃ (2.053)) ² + 1.55 = 4.21/5.76 = 0.73

Discriminant validity is extent to which a construct is truly discriminant from other

constructs and thus high value of discriminant validity provides the evidence that it captures

some phenomenon that other measures do not [122]. In order to prove discriminant validity

value of squared correlation between any two constructs must be less than average variance

extracted for each factor. This AVE must be greater than the squared correlation between any

two constructs implying that that factor explains variance in its items that it shares with other

constructs [122]. Table 5.21-5.23 represent the value of correlations, inter-construct

correlation-variances and discriminant validity respectively.

Table 5.21: Inter-factor correlation values

Estimate

PRICE <--> Customer .093

PRICE <--> Product .240

PRICE <--> Place .138

PRICE <--> Promo .087

PRICE <--> SH -.125

Customer <--> Product .150

Customer <--> Place .218

Customer <--> Promo .026

Customer <--> SH .261

Product <--> Place .251

Product <--> Promo .201

Product <--> SH -.239

Place <--> Promo .283

Place <--> SH -.041

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Estimate

Promo <--> SH .096

Table 5.22: Inter-construct correlations (values below diagonal elements) and squared correlation values (shaded

values)

PE CO PR PL PM SH

PE 1.00 0.008 0.05 0.01 0.006 0.01

CO 0.09 1.00 0.02 0.04 0.0004 0.06

PR 0.24 0.15 1.00 0.06 0.04 0.05

PL 0.13 0.21 0.25 1.00 0.07 0.16

PM 0.08 0.02 0.20 0.28 1.00 0.008

SH -0.12 0.26 -0.23 -0.41 0.09 1.00

Table 5.23 represents the comparison of squared correlations values (values below diagonal

elements) with values of average variance extracted (diagonal values).

Table 5.23: Comparison of AVE values with inter-construct variance

PE CO PR PL PM SH

PE 0.63

CO 0.008 0.62

PR 0.05 0.02 0.57

PL 0.01 0.04 0.06 0.55

PM 0.006 0.0004 0.04 0.07 0.60

OS 0.01 0.06 0.05 0.16 0.008 0.47

Table 5.23 above reveals that all values of AVE (diagonal values) is greater than

corresponding inter-construct variance (below diagonal values); thereby proving discriminant

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validity. Also our congeneric model doesn’t reveal any cross-loading among items or error

terms; thereby giving another proof of discriminant validity.

Thus final scale, with fully checked reliability and validity, consisting of 26 items classified 6

dimensions of price, customer orientation, product, place, promotion and structural hierarchy

emerges as given in table 5.24. We have christened it as AUTOFLEX.

Table 5.24: AUTOFLEX: Marketing flexibility measurement scale

Items

We change the prices to accelerate the demand

We have multiple price points in each category of products

Marketing people of our organization are consulted before finalizing the price of product

We benchmark our prices with competitors

We readily adjust our model prices according to industry changes

We are fully capable of renewing our pricing strategy according to environment alteration

We follow flexible pricing policy for our entire range of products

We give utmost importance to customer satisfaction

Our products add value to the life of customers

Our new product innovations are customer driven

We do take care of changing customers' needs

Our main focus is on making relation with customers rather than sales only

Our new product launches has inputs from marketing people

We prefer product quality over its shelf life

We are market leader in product innovations

Product development is done in-line with the flexible design philosophy

New product development is done with the close coordination of suppliers and channel partners

We are fully competent to reconfigure our dealership format

We have the system of rewarding the best performing dealer

We provide specialized training to our channel partners to improve their performance

We respond quickly to promotional activity launched by competitor

Marketing people are fully involved in promotional activities

Impact assessment of the promotional campaign is done by external agency

Marketing people works in teams having cross-department participation

There is enough decision making power delegated to marketing people

We have low level of formal regulations for marketing employees

5.7 AUTOFLEX SCALE STANDARDS

On the basis of respondents’ scores, AUTOFLEX scale has been categorized into four

categories ranging from Excellent marketing flexibility to Poor marketing flexibility. These

four categories are made with the help of binning procedure featured in SPSS tool. The detail

of these categories is as below:

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Low Marketing Flexibility

Average Marketing Flexibility

Good Marketing Flexibility

Excellent Marketing Flexibility

Table 5.25 given below has the details of the AUTOFLEX scale standards that has been made

from the respondents score.

Table 5.25: AUTOFLEX Standards

AUTOFLEX Standards

S.No. Total Score Level of Marketing Flexibility (MF)

1. 63-88 Low MF

2. 89-99 Average MF

3. 100-109 Good MF

4. 110-123 Excellent MF

5.8 POST DEVELOPMENT VALIDATION OF AUTOFLEX SCALE

5.8.1 QUALITATIVE VALIDATION

Qualitative validationof AUTOFLEX scale has been done by domain and industry experts.

The final scale items and their detailed analysis have been presented to experts’ panel in order

to analyse the final outcome. AUTOFLEX; the scale to measure marketing flexibility of an

automobile organization that emerges after two stage analysis consists of 26 items grouped

under six dimensions of Price, Customer Orientation, Product, Place, Promotion and

Structural Hierarchy.

Analysis of AUTOFLEX reveals that Price, with mean score of 4.26, is most important

factor in AUTOFLEX scale. Furthermore respondents ranked the practice of benchmarking

prices as most important item under dimension of pricing as well as scale in general. This

result is quite consistent with the line towed by industry experts who are unanimously agreed

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on importance of benchmarking practice. In addition to nod of auto-industry experts, literature

too that throws its considerable weight behind this pricing practice.

After pricing, the second most important factor that emerged in AUTOFLEX is Customer

Orientation with mean score of 4.06. A number of authors have emphasized the importance

of customer orientation in literature and in line with the theory; the factor of customer

orientation is well rated by the target respondents. Customer driven innovations and

importance of making customer relations are two items that have been rated as most important

under this dimension.

Another important dimension Product follow the customer orientation closely with overall

mean of 3.91. There is well described notion about importance of Product in literature and this

dimension has got the unequivocal support of both domains as well as industry experts. Input

from marketing personnel in products and market leadership in the product innovations

are rated as most important items in that order.

Structural Hierarchy in the marketing department represents the fourth most important

dimension in AUTOFLEX scale with mean of 3.73. The delegation of decision making

power to marketing personnel is most important rated item under this dimension. This is

followed by low formalization and cross-department participation items that further shows

the high level of marketing flexibility. Industry experts have given their strong support to

hierarchical aspect and final scale echoes the same point of view.

With mean of 3.54, Promotion trails the Structural Hierarchy. Impact assessment is rated as

most important item under this dimension with mean of 3.81. This impact assessment by

external agency is one significant characteristic of marketing flexibility as it gives a fair and

unbiased view on limitations inherited by company that would have not retrospect view had

the assessment been done internally. Further the involvement of marketing people in

promotion and responsiveness are other items that come significant in this dimension.

Place with a mean score of 3.43 stacks at the bottom of dimensions list. While consulting

with industry experts it has been found that this result shows that companies still not attach a

great importance to their partners and this is reason why most of the customers have

complaint about the poor and uncourteous behaviour that they often face in their after-sales

experience. This also indicates that automobile organizations have still some way to go before

they catch up with the importance of this important dimension.

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For mean and standard deviation of individual items of AUTOFLEX scale, refer

appendix G.

5.8.2 NOMOLOGICAL VALIDATION

Nomological validity refers to degree that summated scale makes accurate predictions about

other concepts in a theoretically based model [122]. It gives an overall idea that up to what

degree results of research is consistent with the theory. For proving nomological validity,

given measure should show high correlation in a way supported by theory, with measure of

different but related constructs.

It has been suggested in literature that marketing flexibility is associated with market

orientation [136, 137]. Nomological validity of marketing flexibility measure is tested by

evaluating its relation with market orientation measure developed by Narver and Slater [138].

Both AUTOFLEX and Market Orientation scale has been administered on sample 3

respondents. For this a joint survey form was prepared with Part A and Part B. Part A of the

form consists of AUTOFLEX scale items while Part B is made up of Market orientation

measure variables. A total of 145 forms were distributed in six companies across three

regions. Out of total 145, 110 were found completed and hence used for nomological

validation. Please refer Appendix C for questionnaire details.

Correlation coefficient is usually considered for proving the nomological validity. A

significant positive correlation shows strong support for nomological validity. Scores of the

respondents for both these measures is calculated in order to calculate correlation coefficient.

Value of r; correlation coefficient comes out as 0.65 significant at p < 0.01 suggesting

sufficient correlation between these two measures. Further the correlation value is not as high

as 0.9 or 0.85 to hint that both the constructs are same.

Documents

CHAPTER 5 DATA ANALYSIS - Shodhgangashodhganga.inflibnet.ac.in/bitstream/10603/31875/14/14_chapter 5.pdfSPSS version 16.0 software is used for conducting exploratory factor analysis