CHAPTER 5 DATA ANALYSIS AND PRESENTATION
5.3 QUANTITATIVE DATA ANALYSIS
5.3.8 Role of Policy in Mitigating the Effects of Globalization
5.3.8.1 Correlation Matrix
To increase objectivity in establishing the relationships within the 18 items that measured the role of government intervention in facilitating survival of the clothing and textile firms hit hard by the adverse effects of globalization, correlation analysis was conducted. Generally, correlation coefficients close to 1 show strong positive relationships, correlation coefficients close to -1 show strong negative correlation while values close to 0 suggests the non-existence of any correlation. An analysis of the correlation coefficents confirmed the categorization of the items. Some items exhibited high positive relationships between each other, while other items had high negative relationships. What this means is that the ways
131
through which the government could help in the resuscitation of the clothing and textile sector can be categorized based on how the respondents rated the different ways. More specifically, the results show that data reduction methods could be employed on the data to come up with broader perspectives of how government policy could be used as an enabling framework for the development of effective mrketing strategies to withstand the adverse effects of globalization.
As a follow-up to the above descriptive statistics, a factor analysis was conducted. However, this test was conducted only after the assumptions of normality, homoscedasticity, and linearity were checked (Hair et al, 2010). Therefore, this study used the Kaiser Meyer Olkin (KMO) measure of sampling adequacy, which indicates the inter-correlation among the variables and the validity of the variables to enter factor analysis. Table 5.29 shows the KMO measure of sampling adequacy that was used to determine whether factor analysis was an appropriate method (goodness of fit) to adopt in reducing the items into broad government policy interventions. A Kaiser-Meyer-Olkin value of 0.5 is generally considered the minimum, values between 0.7- 0.8 are deemed acceptable and those above 0.8 are considered superior.
Table 5.29: KMO and Bartlett's Test- Effect of National policy
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .882
Bartlett's Test of Sphericity
Approx. Chi-Square 2936.083
df 153
Sig. .000
Source: Primary Data
The results above show that the KMO measure of sampling adequacy was 0.882, which was way ahead of the minimum of 0.5. As a result, this was considered highly satisfactory, implying that undertaking data reduction using the factor analysis model was highly appropriate. The Principal Component Analysis extraction method was used to extract the principal components. To do so, a condition was set in SPSS such that only principal components with eigenvalues above one (1) could be extracted. The varimax method of rotation was set to establish the principal components. The results were as shown in Table 5.30.
132
Table 5.30: Total Variance Explained –Effect of national policy
Source: Primary Data
The results in Table 5.30 above show that 3 principal components were extracted. Before rotation, principal component 1, principal component 2 and principal component 3 had initial eigenvalues of 8.486, 3.165 and 1.656 respectively and initially accounted for 47.1%, 17.6% and 9.2% of the variation in the performance of clothing and textile firms in Zimbabwe at the time of conducting this study. The rotated sums of squared loadings show that principal component 1, principal component 2 and principal component 3 had eigenvalues of 5.753, 4.762 and 2.792 and accounted for 31.96%, 26.45% and 15.51%
of the variation in the performance of clothing and textile companies in Zimbabwe respectively. The cumulative percentage variation that these 3 extracted factors represented was 73.93%. The implications to the role of government policy in mitigating the effects of globalization in the clothing and textile sector was that the government could formulate its intervention policies guided by the 3 broad categories. The crafting of these policy frameworks would be expected to prove a conducive and supportive operating environment in which the marketing strategies of the clothing and textile sector companies could thrive.
To confirm the extraction of the 3 principal components from the 18 items initially used, the scree plot
133
shown in Figure 5.6 shows that the graph starts to flatten after the first three components in line with the extraction cutoff point of eigenvalues of at least 1 as was initially conditioned in SPSS. All the other principal components after the 3rd were statistically insignificant and were therefore discarded from the subsequent analysis.
Figure 5.6: Scree Plot- Effect of National Policy
Source: Primary Data
To decide on what these 3 extracted components represented so as to clarify tangible government policy dimensions to resuscitate the ailing clothing sector, the factor loadings of each item on the extracted components illustrated in Table 5.31 were used. Generally, the higher the absolute value of the loading, the more the item contributes to the principal component. It was therefore set in SPSS that factor loading
134
less than 0.7 be suppressed so that the 18 items could be easily associated with the extracted components where they had the greatest association.
Table 5.31: Rotated Component Matrix
a- Effect of national policy
Component
1 2 3
Offer tax holidays to the clothing and textile sector -.892
Provide funding to capacitate the sector .913
Enact stringent regulations to prevent the entry of cheap commodities .885 Increase tariffs for all imported clothing and textile products .728
Encourage the consumption of local products .875
Capacitate the industry through availing affordable loans .922 Formulate policies which are friendly to the industry .902
Apply policy consistently
Create a conducive environment for business .870
Encourage and support international marketing for the generation of foreign
currency -.885
Set standards which promote the production and selling of quality products .727
Regulate for the benefit of local industries
Offer strategic direction to companies .927
Promote the integration of companies with other large international companies Capacitate the entire value chain in the clothing and textile sector through
concessionary lending .921
Formulate policies in line with regional policies .860
Promote the creation of a level playing field in the face of globalization .918 Protect the clothing sector through appropriate legislation Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 4 iterations.
Source: Primary Data
The results show that 3 items namely, applying policy consistently, regulating for the benefit of the local clothing industry and protecting the clothing sector through appropriate legislation had factor loading less
135
than 0.7. These items were consequently discarded from the subsequent analysis to determine the latent policy dimensions represented by the extracted principal components.
The results also show that offering tax holidays to the clothing and textile sector, providing funding to capacitate the clothing sector, capacitating the industry through availing affordable loans, encouraging and supporting international marketing for the generation of foreign currency, offering strategic direction to companies and capacitating the entire value chain in the clothing and textile sector, were the items that loaded heavily on principal component 1. Considering that these factors were inclined towards the need for financial support, principal component 1 was referred to as the provision of financial assistance.
Items that loaded highly on principal component 2 were encouraging the consumption of local products, formulating policies which are friendly to the industry, creating a conducive environment for business, formulating policies in line with regional policies and promoting the creation of a level playing field in the face of globalization. Accordingly, principal component 2 was referred to as the ‘facilitation of a favourable operating environment’. This was due to the general theme generated by the measurements under this component which suggested the creation of a favourable operating environment.
Concerning principal component 3, Table 5.31 illustrates that enacting stringent regulations to prevent the entry of cheap commodities, increasing tariffs for all imported clothing and textile products and setting standards which promote the production and selling of quality products all had higher factor loadings on it. Consequently, principal component 3 was referred to as the adoption of protectionism to improve the performance of the clothing sector in Zimbabwe.
The role that government could play in the resuscitation of the clothing and textile industy in Zimbabwe is summarized in Table 5.32. The table illustates the rotation sums of the squared loadings that ranks the 3 broad policy intervention dimensions that the government could adopt.
136
Table 5.32: Total Variance Explained- Effect of national policy
Component
Rotation Sums of Squared Loadings Total % of Variance Cumulative %
Providing financial assistance 5.753 31.963 31.963
Creating a favourable operating environment 4.762 26.454 58.417
Adopting Protectionism 2.792 15.513 73.930
Extraction Method: Principal Component Analysis.
Source: Primary Data
The results above show that the major policy intervention is the provision of financial assistance which accounted for 31.96% of the variation in the business performance of the clothing and textile sector. The second policy intervention is the creation of a favourable operating environment accounting for 26.25%
of the variation in the business performance of the industry followed by the adoption of protectionist measures which accounted for 15.51% of the variation in the business performance of the sector. What this implies is that while protectionism is desirable in safeguarding the local industry from unfair trading practices such as the dumping of cheap and substandard products, this study infers that it cannot be the main pillar of policy intervention when addressing industry viability arising from the adverse effect of globalization.
While the above provided insightful information, further analysis was need in order to explain the nature of relationships between the predictor variable and measured variable in line with the research model and the five hypotheses formulated. A Structural Equation Modelling was done as detailed below.