CHAPTER IV QUANTITATIVE METHOD
4.9 Measurement of Model Assessment
This research comprises of 17 observed variables and four latent constructs.
Following section will describes the measurement model assessment, structural model assessment, and final section presents the hypothesis testing.
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4.9.1 Investigation of Correlation Matrix between Main variables Table 35 KMO and Bartlette’s Test
Statistics Value
Kaiser-Meyer- Olkin Measure of Sampling Adequacy 0.776 Bartlette’s test of Sphericity
Approx. chi-square 957.766
df 135
Sig 0.000
Statistics used to verify the correlation matrix are Bartlette’s test of Sphericity and Kaiser- Meyer- Olkin Measure of Sampling Adequacy (MSA). The Kaiser- Meyer-Olkin Measure of Sampling Adequacy is a statistic that indicates the proportion of variance in variables that might be caused by underlying factors. High values (close to 1.0) generally indicate that a factor analysis may be useful with the data. If the value is less than 0.50, the results of the factor analysis probably won't be very useful. Bartlett's test of sphericity tests the hypothesis that your correlation matrix is an identity matrix, which would indicate that your variables are unrelated and therefore unsuitable for structure detection. Small values (less than 0.05) of the significance level indicate that a factor analysis may be useful with your data.
From Table 35, an initial analysis was run to obtain eigenvalues for each factor in the data. The Kaiser-Meyer-Olkin Measure verified the sampling adequacy for the analysis, KMO=.714 which is above Kaiser’s recommended threshold of 0.6 (Kaiser, 1974). Bartlett’s test of sphericity, χ2 (136) = 957.766, p < .000, indicated that correlations between items were sufficiently large for EFA.
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4.9.2 Investigation of the Exploratory Factor Analysis (EFA)
Exploratory factor analysis (EFA) is a statistical method used to uncover the underlying structure of a relatively large set of variables (Norris and Lecavalier, 2010). Overarching goal is to increase the reliability of the scale by identifying inappropriate items that can be removed and the dimensionality of constructs by examining the existence of relationships between items and factors when the information of the dimensionality is limited (Netemeyer and Bearden and Sharma, 2003).
Table 36 Loading of All Variables in This Study Construct Firm
Performance
Proactiveness Competitive Aggressiveness
Absorptive Capacity
PER1 0.933 0.432 0.201 0.192
PER2 0.924 0.369 0.080 0.190
PER3 0.916 0.394 0.049 0.191
PER4 0.893 0.486 0.152 0.238
PER5 0.843 0.456 0.064 0.201
PER6 0.822 0.437 0.102 0.176
PRO1 0.454 0.823 0.267 0.404
PRO2 0.415 0.782 0.370 0.564
PRO3 0.399 0.634 0.449 0.458
COM1 0.143 0.333 0.771 0.235
COM2 0.134 0.413 0.858 0.345
COM3 0.070 0.327 0.875 0.439
ACAP1 0.113 0.403 0.546 0.727
ACAP2 0.150 0.435 0.377 0.778
ACAP3 0.027 0.280 0.164 0.911
ACAP4 0.157 0.298 0.152 0.898
ACAP5 0.227 0.514 0.264 0.816
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EFA assumes that any indicator/measured variable may be associated with any factor. When developing a scale, researchers should use EFA first before moving on to confirmatory factor analysis (Worthington and Whittaker, 2006). This research, EFA was conducted on the 17 items with a varimax rotation using SPSS 22. In this study, the four factors (proactiveness, competitive aggressiveness, knowledge absorptive capacity and firm performance) were used to determine the pattern of the structure in the 17 items.
From Table, 36 the factor loading of all 17 variables is ranging from 0.634 to 0.933 which is more than 0.7. Therefore, there are 6 measured variables (FPER1, PFER2, FPER3, FPER4, FPER5 and FPER6) under firm performance construct (FPER), 3 measured variables (PRO1, PRO2, PRO3) underlying proactiveness construct (PRO), 3 measured variables (COM1, COM2, COM3) underlying competitive aggressiveness construct (COM), and 5 measured variables (ACAP1, ACAP2, ACAP3, ACAP4, and ACAP5) underlying knowledge absorptive capacity construct (ACAP)
Table 37 Eigen Value of Exploratory Factor Analysis
Factor Initial Eigen Value Extraction Sums of Squared Loading
Rotation Sums of Squared Loadings Total % of
Variance
Cumulative
%
Total % of Variance
Cumulative
%
Total % of Variance
Cumulative
%
1 6.11 35.96 35.96 6.114 35.965 35.965 5.027 29.57 29.57
2 3.64 21.39 57.35 3.636 21.389 57.354 2.480 14.59 44.16
3 1.63 9.61 66.97 1.634 9.612 66.966 2.178 12.81 56.97
4 1.17 6.86 73.82 1.167 6.862 73.828 2.125 12.50 69.47
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Figure 7 Scree Plot
From Table 37, four factors had eigenvalues greater than one, as the scree plot clearly illustrates in Figure 7. The initial 17 items structure explained 69.47 % of the variance in the pattern of relationships among the items. The percentages explained by each factor were 29.57 % (firm performance), 14.588% (competitive aggressiveness), 12.81 % (knowledge ACAP), and 12.50 % (proactiveness), respectively.
4.9.3 Investigation of the Confirmatory Factor Analysis (CFA)
Confirmatory factor analysis (CFA) allows the researcher to test the hypothesis that a relationship between the observed variables and their underlying latent factor(s)/construct(s) exists. Factor loadings are numerical values that indicate the strength and direction of a factor on a measured variable. Factor loadings indicate how strongly the factor influences the measured variable.
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As suggested by (Hair et al., 2011), factor loading of the items could be used to confirm the content validity of the measurement model. More specifically, all the items meant to measure a particular construct should load highly on the construct they were designed to measure. If some items load on some other factors higher than their respective construct, these items will be candidate for deletion. Further, all the measures of the construct should be significantly loaded on their respective construct.
As illustrated in Table 36, all the items load highly and significantly on the constructs they were designed to measure. Thus, the content validity of the measurement, outer, model was confirmed.
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4.9.3 Investigation of Confirmatory Factor Analysis
Figure 8 Confirmatory Factor Analysis of Proactiveness and CompettitiveAggressiveness Model
0.004 0.476 0.490
0.988 0.863
FPER
0.717
0.831
0.728 0.715
0.744 0.687
0.735 0.726 0.776
0.816 0.788
PRO1 PRO2 PRO3
COM1
COM2 COM3
ACAP1
ACAP2
ACAP5 ACAP4 ACAP3
FPER5 FPER4 FPER3 FPER2 FPER1
FPER6
0.731
0.785
0.819
0.977
PRO
COM
ACAP
0.641
0.476
0.240
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Table 38 Rules of Thumb of PLS-SEM Measurement model
Rules of Thumb Statistic Criterion
Indicator reliability Factor Loading >0.7 (Hair et al., 2011) Convergent validity AVE >0.5 (Hair et al., 2011)
Discriminant validity
AVE AVE of each latent construct should higher than the construct’s highest squared correlation with any other latent construct (Fornell-Larker criterion)
Heterotrait-Monotrait Ratio of Correlation (HTMT)
To assess discriminant validity. If the HTMT value is < 1.0 ( Hair et al., 2011)0.90, discriminant validity has been established between two reflective constructs.
Cross loadings An indicator loadings should be higher than all of its cross loadings (Hair et al., 2011).
Internal consistency reliability
Composite reliability >0.7 ( Hair et al., 2011)
Cronbach’s Alpha >0.7 (Nunnally and Bernstein, 1994) Dijkstra- Henseler’s
(rho)
>0.7 (Hair et al., 2011) Structural model
Rules of Thumb Statistic Criterion
Coefficient of determination
R2 0.25 = weak
0.50= moderate
0.75= substantial (Hair et al., 2011)
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Table 39 Statistical Value of Measurement Model Assessment
Exogenous Variable
Factor Loading
AVE CR Dijkstra-
Henseler’s (rho)
Cronbach’s Alpha ()
Proactiveness (PRO)
-
0.607 0.822 0.824 0.823
PRO1 0.731
PRO2 0.785
PRO3 0.819
Competitive Aggressiveness (COM)
-
0.630 0.836 0.837 0.835
COM1 0.776
COM2 0.778
COM3 0.816
Absorptive Capacity (ACAP)
-
0.424 0.781 0.801 0.786
ACAP1 0.726
ACAP2 0.735
ACAP3 0.687
ACAP4 0.715
ACAP5 0.744
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Table 39 (contd.) Exogenous Variable
Factor Loading
AVE CR Dijkstra-
Henseler’s (rho)
Cronbach’s Alpha ()
Proactiveness (PRO)
-
0.772 0.952 0.967 0.955
FPER1 0.988
FPER2 0.863
FPER3 0.717
FPER4 0.831
FPER5 0.728
FPER6 0.977
In table 39, the factor loading for all 17 items is raging from 0.687 to 0.977.
ACAP3 has factor loading equal 0.687 which is still acceptable (Hulland, 2002).
According to Hulland (2002), if it is an exploratory research, 0.4 or higher is acceptable. Items should above 0.7 (Hair et al., 2011) indicates the indicator adequate indicator reliability in 17 items.
In Table 39, average variance extract (AVE) are as follow; proactiveness = 0.607, competitive aggressiveness = 0.630, knowledge ACAP = 0.424 and firm performance = 0.772. It above 0.5 (Hair et al., 2011) which indicates the indicator convergent validity reliability in following variables, proactiveness (PRO), competitive aggressiveness (COM), and firm performance (FPER). However, AVE for knowledge absorptive capacity (ACAP) equal 0.424 is still adequate. According to (Fornell and Larcker, 2006), the cut-off value of AVE 0.40 is acceptable in case of composite reliability is higher than 0.6, the convergent validity of the construct is still
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adequate. For this case, the composite reliability equal 0.781. Hence, the AVE of the PRO, COM, ACAP and FPER construct indicate adequate convergent validity.
In Table 39, composite reliability (CR) are as follow; proactiveness = 0.822, competitive aggressiveness = 0.836, knowledge ACAP = 0.781 and firm performance
= 0.952. It above 0.7 (Hair et al., 2011) which indicates the construct’s internal consistency in following variables, proactiveness (PRO), competitive aggressiveness (COM), and firm performance (FPER). Hence, all constructs indicate adequate construct’s internal consistency.
Dijkstra-Henseler’s rho (rhoA) was estimation of data consistency provides a more accurate estimation of data consistency (Dijkstra and Henseler, 2015). In Table 39, Rho A are as follow; proactiveness = 0.824, competitive aggressiveness = 0.837, knowledge ACAP = 0.801 and firm performance = 0.967. It above the value of 0.7 (Hair et al., 2011) which indicate reliability coefficient in following variables, proactiveness (PRO), competitive aggressiveness (COM), and firm performance (FPER). Hence, the values indicate that the items loaded on all construct are reliable.
Cronbach’s Alpha designates the degree of internal consistency between the multiple variables (Hair et al., 2010). In Table 39, Cronbach’s alpha values are as follow; proactiveness = 0.823, competitive aggressiveness = 0.835, knowledge ACAP
= 0.786 and firm performance = 0.955. It above 0.70 (Nunnally and Bernstein, 1994) in following variables; proactiveness (PRO), competitive aggressiveness (COM), and firm performance (FPER). Hence, all constructs indicate adequate internal consistency between the multiple items (Hair et al., 2010).
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