CHAPTER 5 DATA ANALYSIS - Shodhgangashodhganga.inflibnet.ac.in/bitstream/10603/31875/14/14_chapter...
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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
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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
Reliability Statistics
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.
Table 5.9: Reliability value of factor 2
Reliability Statistics
Cronbach's Alpha Cronbach's Alpha Based on Standardized
Items
N of Items
.828 .833 5
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All the values under head of Cronbach’s alpha is item deleted ranges between 0.766 to 0.820
that is below the overall reliability value 0.828 of factor 2; implying that removal of any item
will not increase the overall reliability of factor.
Reliability of Factor 3 (Product): Alpha value for Factor 3; Price consisting of 5 items
comes out as 0.772.
Table 5.10: Reliability value of factor 3
Reliability Statistics
Cronbach's Alpha Cronbach's Alpha Based on Standardized
Items
N of Items
.772 .772 5
All the values under head of Cronbach’s alpha is item deleted ranges between 0.689 to 0.750
that is below the overall reliability value 0.772 of factor 3; implying that removal of any item
will not increase the overall reliability of factor.
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
Reliability Statistics
Cronbach's Alpha Cronbach's Alpha Based on Standardized
Items
N of Items
.706 .717 3
All the values under head of Cronbach’s alpha is item deleted ranges between 0.596 to 0.648
that is below the overall reliability value 0.706 of factor 4; implying that removal of any item
will not increase the overall reliability of factor.
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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Table 5.12: Reliability value of factor 5
Reliability Statistics
Cronbach's Alpha Cronbach's Alpha Based on Standardized
Items
N of Items
.716 .718 3
All the values under head of Cronbach’s alpha is item deleted ranges between 0.548 to 0.662
that is below the overall reliability value 0.716 of factor 5; implying that removal of any item
will not increase the overall reliability of factor.
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
Table 5.13: Reliability value of factor 6
Reliability Statistics
Cronbach's Alpha Cronbach's Alpha Based on Standardized
Items
N of Items
.689 .688 3
All the values under head of Cronbach’s alpha is item deleted ranges between 0.570 to 0.627
that is below the overall reliability value 0.689 of factor 6; implying that removal of any item
will not increase the overall reliability of factor.
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
Reliability Statistics
Cronbach's Alpha Cronbach's Alpha Based on Standardized
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
CO4 <--- Customer Orientation .755
CO3 <--- Customer Orientation .864
CO2 <--- Customer Orientation .855
CO1 <--- Customer Orientation .724
PR5 <--- Product .803
PR4 <--- Product .808
PR3 <--- Product .788
PR2 <--- Product .756
PR1 <--- Product .657
PL3 <--- Place .748
PL2 <--- Place .790
PL1 <--- Place .700
PM3 <--- Promotion .775
PM2 <--- Promotion .812
PM1 <--- Promotion .761
SH3 <--- Structural Hierarchy .750
SH2 <--- Structural Hierarchy .749
SH1 <--- Structural Hierarchy .554
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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
CO4 <--- Customer .755
CO3 <--- Customer .864
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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
CO2 <--- Customer .855
CO1 <--- Customer .724
PR5 <--- Product .803
PR4 <--- Product .808
PR3 <--- Product .788
PR2 <--- Product .756
PR1 <--- Product .657
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
Error variance PE4 = 1 – (0.813)² = 1 – 0.66= 0.33
Error variance PE5 = 1 – (0.708)² = 1 – 0.50= 0.49
Error variance PE6 = 1 – (0.842)² = 1 – 0.70= 0.29
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Error variance PE7 = 1 – (0.813)² = 1 – 0.66= 0.33
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
Error variance CO2 = 1 – (0.855)² = 1 – 0.731 = 0.26
Error variance CO3 = 1 – (0.864)² = 1 – 0.746 = 0.25
Error variance CO4 = 1 – (0.755)² = 1 – 0.570 = 0.42
Error variance CO1 = 1 – (0.748)² = 1 – 0.559 = 0.44
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
Error variance PR2 = 1 – (0.756)² = 1 – 0.571 = 0.42
Error variance PR3 = 1 – (0.758)² = 1 – 0.574 = 0.42
Error variance PR4 = 1 – (0.808)² = 1 – 0.652 = 0.34
Error variance PR5 = 1 – (0.803)² = 1 – 0.644 = 0.35
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
Error variance PL2 = 1 – (0.79)² = 1 – 0.624 = 0.37
Error variance PL3 = 1 – (0.748)² = 1 – 0.559 = 0.44
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
Error variance PM2 = 1 – (0.812)² = 1 – 0.659 = 0.34
Error variance PM3 = 1 – (0.775)² = 1 – 0.60 = 0.39
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
Error variance SH2 = 1 – (0.749)² = 1 – 0.561 = 0.43
Error variance SH3 = 1 – (0.75)² = 1 – 0.56 = 0.43
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.