CHAPTER 5 DATA ANALYSIS AND PRESENTATION
5.4. S TRUCTURAL EQUATION MODELLING (SEM)
5.4.2. F INAL S TRUCTURAL MODEL
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representing the latent variable, ‘effect of conducive policy’ and its measurements. However, to test the fitness of the model, a test for goodness of fit was conducted as shown in Table 5.42 below.
Table 5.42: Goodness of fit (Effect of conducive national policy)
CD 0.993 Coefficient of determination
SRMR 0.031 Standardized root mean squared residual Size of residuals
TLI 0.998 Tucker-Lewis index
CFI 0.999 Comparative fit index Baseline comparison
BIC 2004.744 Bayesian information criterion
AIC 1919.418 Akaike's information criterion Information criteria
pclose 0.660 Probability RMSEA <= 0.05
upper bound 0.091 90% CI, lower bound 0.000
RMSEA 0.024 Root mean squared error of approximation Population error
p > chi2 0.000
chi2_bs(28) 918.248 baseline vs. saturated p > chi2 0.374
chi2_ms(14) 15.054 model vs. saturated Likelihood ratio
Fit statistic Value Description
. estat gof, stat(all)
Source: Primary Data
The results above show a good fit of the hypothesized construct and its measurements. This is reflected by high CFI and TLI indices of 0.999 and 0998 respectively. In addition, a Root Mean Square error of 0.024 further confirms a perfect fit. As suggested by the literature, values for the RMS range from zero to 1.0 with well-fitting models obtaining values less than 0.05 (Kline, 2005). In a well-fitting model the lower limit is close to 0 while the upper limit should be less than 0.08. The construct was thus suitable for further analysis in the final structural equation.
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G). The detailed path analysis table is attached as an appendix due to its size. However, a summary of the key findings are shown in Table 5.45.
154 Figure 5.12: Final Structural Equation Model
National Policies
1
Offer tax holidays to the clothing and textile sector 1.6
1 6.2e-02
Encourage and support international marketing for the generation of foreign curr 1.6
2 7.3e-02
Set standards which promote the production and selling of quality products 2.4
3 .79
Offer strategic direction to companies 2.1
4 .37 Sustainability
5 3.7e-02
Formulate policies in line with regional policies .73 2.8 Improve product quality
2.8 6 8.3e-03
Reduce costs through cost leadership 2.9 7 4.6e-03
Exploit value chains through collaboration 2.9 8 1.9e-02
Use of investment in advanced technology 2.9 9 5.6e-04
Improved internal working systems 2.9 104.6e-03
Marketing Program Standardization 1
It helps to reduce product cost 3.3 112.0e-02
Assists in the creation of competitive advantages 3 12.16
One sure way of overcoming the effects of globalisation 3.3 131.6e-02
Improved product quality and customer loyalty 3.2 145.8e-02
Results in signficant cost savings 3.3 159.2e-02
Results in the creation of sustainable competitive advantages 3.1 168.3e-02
Increases the company's ability to produce high quality products at a low cost 3.2
17.11
Allows companies to focus 3.2
18.11
Results in increased productivity 3.1
19.6
Promotes the marketing of quality products 3.2
208.1e-02
Vital in mitigating the effects of globalisation 2.5
21.45
Integrated Coalliances 1
Helps in knowledge transfer 2.6 22.26
Allows organisations to offset their weaknesses through collaboration 3.2
234.3e-04
Strengthens organisations
3.3 24
2.0e-02 Results in the creation of large competent companies
4 25 .32 Role of Technology
1
Improving product quality 2.7
26.54
Creating brand equity 2.2
27.64
.97
.96 -.46
.79 .21
-.45
.12 .84
1
1 .99
1 1
.99 .92 .99 .97 .95
.96
.94 .94
.63 .96
.74
.86
1
.99 .83 .68
.6
Source: Primary Data
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Support for the study hypotheses, which are labelled on their corresponding paths in Figure 5.12 above could be ascertained by examining the directionality (positive or negative) of the path coefficients and the significance of the p-values. The standardized path coefficients are expected to be at least 0.2 and preferably greater than 0.3 (Chin 1998).
Table 5.44: Goodness of fit for final Structural Equation Model
CD 0.952 Coefficient of determination
SRMR 0.017 Standardized root mean squared residual Size of residuals
TLI 1.000 Tucker-Lewis index
CFI 1.000 Comparative fit index Baseline comparison
BIC 1900.584 Bayesian information criterion
AIC 1829.479 Akaike's information criterion Information criteria
pclose 0.693 Probability RMSEA <= 0.05
upper bound 0.095 90% CI, lower bound 0.000
RMSEA 0.000 Root mean squared error of approximation Population error
p > chi2 0.000
chi2_bs(21) 1161.476 baseline vs. saturated p > chi2 0.459
chi2_ms(10) 9.788 model vs. saturated Likelihood ratio
Fit statistic Value Description
Table 5.44 above confirms a positive fit between the measurements and the construct which is evident the CFI and TLI indices of 1.000 and 1.000 respectively all showing a very good fit. A coefficient of determination of 0.952 indicates that the model can account for 95.2% of the variations; which indicates its suitability in concluding the decisions on the hypotheses. Since the above results represent the goodness of fit for the final structural model, a decision can now be made based on the path coefficients reflected on Figure 5.12 above and the p-values of the each of the four confirmed constructs in relationship with the measured variable (sustainability through marketing strategies). The summary results are presented on Table 5.45 below:
156 Table 5.45: Summary of Proposed hypothesis relationships
Proposed Hypothesis Relation Hypothesis Path
Coefficient p-value Rejected /Supported Sustainability < -
Marketing Program Standardization H1 -.450 0.000 Rejected??
Coordination Of Marketing Activities* H2 - - Rejected
Integrated Co-alliances H3 0.120 0.267 Supported
Effect of modern technology H4 0.840 0.000 Supported
Effect of national policy H5 0.210 0.230 supported
Notes: *the model did not fit the data at Confirmatory Factor Analysis (CFA)
The table above was drawn by extracting the path coefficients from the final Structural Equation Model (Figure 5.12). The p- values were obtained from the composite path analysis for the final model which is attached as an appendix (Appendix G). The results provide support for the proposed positive relationships as they appear in the research model (i.e., H1, H2, H3, H4 and H5). Figure 5.12 provides the path coefficients for H1, H3, H4 and H5. . The confirmed hypotheses are H3, H4 and H5 with positive path coefficients of 0.12., 0.840 and 0.210 respectively. With respect to H3, although the results point to a significant association between integrated co-alliances and sustainability through marketing strategies with a standardized path coefficient of 0.120, this path adds minimal value to the understanding of the relationship between marketing strategies through integrated co-alliances and their ability to sustain a company in the global environment. The reason is because the standardized path coefficient failed to meet the minimum benchmark for path strength. Chin (1998) proposes that standardized paths should be at least 0.20 and ideally above 0.30 in order to be considered meaningful.
Interpretation of results
The study concludes that two critical marketing strategies which should be used by companies is the use and application of modern technologies and being supported by conducive national policies. These results are in conformity with arguments in the current literature wherein an organization’s competitiveness in the industry must now include technological and policy issues. The existence of supporting national policies and modern technology are significantly associated with the Clothing and Textile Companies’
survival and sustainability in the face of globalization. While integrated marketing activities failed to create a strong effect on company survival as a marketing strategy, they must be considered on the strength of the other results obtained from descriptive and inferential statistical analysis in this study.
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