3 THEORETICAL FRAMEWORK
CHAPTER 5: DATA PRESENTATION AND ANALYSIS
5.7 Factor discussion
of 0.6, suggesting that an exploratory factor analysis should be performed on the data.
Based on the eigenvalue criterion (eigenvalue larger than 1), two components were discovered in the analysis (Field 2013), accounting for 67.3 % of the total variance.
Nothing was loaded twice onto either component. The Cronbach's alpha coefficient was then used to evaluate the internal consistency (reliability) of each of the found components, with a threshold defined in the literature as 0,5 (acceptable), 0,6 (adequate for exploratory study), and 0,7 for previously deployed apparatus. The two factors that were found had Cronbach's alpha values of 0,931 and 0,825, respectively, which were deemed satisfactory.
Subsequently, two factor-based variables, labelled as “General economic and business impacts” and “Governmental and political impacts”, were calculated using the mean value across the items included in each factor.
• SecCF1 indicates “The impact of age, size and strategy of business on productivity and sustainability”.
• SecCF2 indicates “The impact of education, skills and experience of owner/manager on the business”.
• SecCF3 indicates “Resource availability for training”.
• ExfacF1 indicates “General economic and business impacts”.
• ExfacF3 indicates “Governmental and political impacts”.
The factor with the highest mean value (4,04) was “General economic and business impacts” and the factor with the lowest mean value (3.2) was “Resource availability for training”. All the skewness and kurtosis values fall between –2 and +2 (Metin et al.
2012). Therefore, the normality assumption holds for all seven of the newly identified factors. Correlation analysis was conducted in order to address research objective 4:
to identify whether there are interrelationships between attitudes towards access to technological production management tools and knowledge of internal and external factors of the business.
Correlation analysis quantifies the magnitude and orientation of the association between two variables. The direction of the correlation might exhibit either a positive or negative relationship, while the degree of the correlation is quantified on a scale ranging from 0 to 1. A value of 0 indicates no connection, whereas a value of 1 signifies a perfect correlation. A correlation value of 0.10 is commonly interpreted as indicating a weak or tiny relationship. A correlation coefficient of 0.30 is typically considered to signify a moderate correlation. On the other hand, a correlation coefficient of 0.50 or above is generally regarded as indicative of a strong or significant correlation.
Table 5.5: Correlation matrix
SecBF1 SecBF2 SecCF1 SecCF2 SecCF3 ExfacF1 ExfacF2 SecBF1
Pearson correlation
1
SecBF2 Pearson Correlation
0,480** 1
SecCF1 Pearson correlation
0,365** 0,432** 1
SecCF2 Pearson correlation
0,629** 0,420** 0,570** 1
SecCF3 Pearson correlation
0,272** –0,151* 0,104 0,200** 1
ExfacF1 Pearson correlation
0,363** 0,369** 0,644** 0,406** 0,092 1
ExfacF2 Pearson correlation
0,161* 0,050 0,306** 0,134* 0,309** 0,638** 1
* Indicates significance at the 5% level
** Indicates significance at the 1% level
The correlations between the seven factors were all positive, with the only exception being between “Challenges of introducing production management tools” and
“Resource availability for training”, which were negatively and weakly (less than 0,3) correlated. Strong correlations (above 0,5) were observed between Sec BF1 and SecCF2, Sec CF1 and SecCF2, SecCF1 and ExfacF1 ,SecCF2 and ExfacF2. Only three of the pairs did not show statistical significance and indicated very weak correlations of 0,104, 0,050 and 0,092, respectively. Therefore, interrelationships exist between 18 of the 21 pairs of factors. Descriptive statistics was conducted] in order to address research objective 5: to ascertain if there are variations between the groupings of (1) the age of the respondents, (2) the roles of respondents and (3) the education levels of respondents regarding their perceptions of access to technological production management tools and their knowledge of internal and external factors of
the business. In order to analyse respondents’ knowledge of internal and external factors of the business, parametric independent t-tests were conducted for 1) and 2) and the nonparametric Kruskal-Wallis test for 3), due to some groups having a small sample size.
Inferential analysis was performed, firstly to assess whether statistically significant differences exist with regard to the seven recognized factors between the groups as defined by the categories of role (only manager and owners, excluding supervisors) and age of respondent (20–39 years and 40–59 years, excluding the 60+ response).
The categories were chosen so as to contain enough responses to conduct the parametric test.
The statistical significance of the differences between these groups was determined from descriptive statistics for independent groups. The significance level was set at 5%.
Table 5.6: Mean and standard deviation of the seven factors per role group
Role_adj N Mean Std. Deviation
SecBF1 1 79 3,4824 0,79187
2 142 3,3537 0,75968
SecBF2 1 79 3,4620 0,95666
2 142 3,3187 0,79233
SecCF1 1 79 3,9283 0,84043
2 142 3,5822 0,76743
SecCF2 1 79 3,9430 0,79758
2 142 3,4489 0,87140
SecCF3 1 79 3,1709 1,22706
2 142 31937 1,02079
ExfacF1 1 79 4,2447 0,71841
2 142 3,9272 0,91987
ExfacF2 1 79 3,8059 0,95573
2 142 3,7418 1,00777
The results of the test are shown in Table 5.7.
Table 5.7: The Lavene and T test for equality of means
Levene's
Test Sig.
t-test for equality of
means df
Two- sided p SecBF1 Equal
variances assumed
1,253 0,264 1,189 219 0,236
Equal
variances not assumed
1,175 155,738 0,242
SecBF2 Equal variances assumed
4,216 0,041 1,195 219 0,233
Equal
variances not assumed
1,133 137,791 0,259
SecCF1 Equal variances assumed
1,427 0,234 3,105 219 0,002
Equal
variances not assumed
3,025 149,369 0,003
SecCF2 Equal variances assumed
0,227 0,634 4,162 219 0,000
Equal
variances not assumed
4,268 173,636 0,000
SecCF3 Equal variances assumed
4,303 0,039 –0,148 219 0,883
Equal
variances not assumed
–0,140 138,285 0,889
ExfacF1 Equal variances assumed
6,291 0,013 2,650 219 0,009
Equal
variances not assumed
2,841 195,301 0,005
ExfacF2 Equal variances assumed
0,057 0,811 0,462 219 0,645
Equal
variances not assumed
0,469 168,646 0,640
The results are shown in Table 5.8.
Table 5.8: Test statistics
SecBF1 SecBF2 SecCF1 SecCF2 SecCF3 ExfacF1 ExfacF2 Kruskal-
Wallis H
12,512 19,382 10,765 18,912 9,660 19,141 6,694
Df 4 4 4 4 4 4 4
Asymp. sig. 0,014 < 0,001 0,029 < 0,001 0,047 < 0,001 0,153 a. Kruskal-Wallis test
b. Grouping variable: Edu_adj
The results indicate that there is a statistically significant difference at the 5% level of significance between the education groups for all the factors (p values < 0,05), except for “Governmental and political impact” (ExfacF2). The mean ranks, presented in
Table 5.9, indicate the trend of responses. For example, for section BF1, those with a bachelor’s degree tend to agree the least while those with a postgraduate degree tend to agree the most with the statements.
Table 5.9: Ranks
Edu_adj N Mean rank
SecBF1 1,00 17 129,56
2,00 29 106,90
3,00 86 113,11
4,00 63 94,44
5,00 28 142,71
Total 223
SecBF2 1,00 17 86,26
2,00 29 113,83
3,00 86 111,49
4,00 63 98,67
5,00 28 157,27
Total 223
SecCF1 1,00 17 115,88
2,00 29 122,00
3,00 86 109,35
4,00 63 96,52
5,00 28 142,27
Total 223
SecCF2 1,00 17 131,18
2,00 29 121,07
3,00 86 111,84
4,00 63 87,75
5,00 28 146,02
Total 223
SecCF3 1,00 17 138,85
2,00 29 131,16