RESPONSIBLE ENTREPRENEURSHIP VISION DEVELOPMENT AND ETHICS

176 RESPONSIBLE ENTREPRENEURSHIP
this study, Statistical Package for Social Sciences (SPSS) will be used, together with AMOS
(Analysis of Moment Structures). In order to test the relationship between variables and in
order to find the statistical significance of the hypothesized proposed model, structural equation
modeling, more specifically confirmatory factor analysis will be used.
Results and data analysis
Exploratory factor analysis
The Exploratory Factor Analysis (EFA) has the scope of discovering the structure of a
relatively large set of variables and the initial assumption is that any indicator is associated
with any factor. The factor analysis initially identifies seven factors. The value of KMO is
0.939, very close to 1, so this means that the correlation models are relatively compact and
the factor analysis will establish trustworthy and distinct factors. If this would not have been
met, we could have said that distinct and reliable factors cannot be produced. Communalities
table reveals values smaller than 0.30.
Total Variance Explained table determines the number of significant factors. Only extracted
rotated values are meaningful for interpretation. The factors are arranged in the descending
order based on the most explained variance. We choose the rotation method Promax with Kaiser
Normalization.
The extraction refers to the process of determining how many factors best explain the
observed covariation matrix within the data set. 71.518% of the total variance is explained by
the seven factors, only the first factor is explaining 50.698% of the total variance, followed
by 5.485%, 4.889%, 2.194%, 2.423%, 2.391% and 2.437% (all the remaining six factors).
Pattern Matrix table indicates the factors which can be used in the study. After eliminating
the questions for which the average value is less than 0.70, we obtain four factors and
we start over the analysis. We apply Kaiser-Meyer-Olkin and Barlett tests. The KMO test
value is 0.945, very close to 1, so the correlation models are relatively compact and the factor
analysis will establish trustful and distinct factors. Sig. value is less than 0.01, so we can
say that the factor analysis is adequate in this case. Analyzing the Communalities table, we
observe that all the values in the second column are greater than 0.30, which is a good sign.
Afterwards we analyze Total Variance Explained table. Total variance explained indicates how
much of the variability in the data has been modeled by the extracted factors.
The first four factors are meaningful since they have Eigenvalues greater than 1. The Total
Variance Explained table shows the actual factors that were extracted. The section labeled
“Rotation Sum of Squared Lodings” show us only those factors that met our cut-off criterion
(the extraction method). In our case there were four factors with eigenvalues greater than
1. The “% of Variance column tells us how much of the total variability (in all of the variables
together) can be accounted for by each of the summary scale of factors. We observe
that 69.053% of the total variance is explained by the four factors, only the first factor explaining
58.372%, followed by 4.096%, 2.992% and 3.593% (the other three factors). The value
69.053% is less than in the case of the seven factors, but in this case we have a better and
cleaner Pattern Matrix, so we can continue with the study. In the second Pattern Matrix, we
can see that variables group into factors; more precisely they load onto factors. We can observe
a very clean factor structure in which convergent and discriminant validity are evident by the
high loadings within factors, and no cross-loadings between factors.