Self-efficacy, perceived behavioral control and entrepreneurial intention among polish students in the context of industry 4.0: Assessing the effect of education level

rol (PBC) and entrepreneurial intention (EI) is 
provided. The result shows that the multiple correlation only reach at 0.233 (R=0.233)
and so the Adjust R Square is just 0.054 (Adjusted R2=0.054) which proves that only 
5.4 of variation of entrepreneurial intention can be explained by the model comprised of 
three variables (educational level, self-efficacy and perceived behavioral control). 
Table 7. ANOVAa
Model Sum of Squares df Mean Square F Sig. 
1 Regression 8.730 3 2.910 10.507 0.000b
Residual 152.053 549 0.277
Total 160.783 552
a. Dependent Variable: EI 
b. Predictors: (Constant), Educational Level, SE, PBC 
Table 6 (ANOVA) reports the significance of regression model. In this study, the Sig. 
associated with the F-Test is 0.000 which is highly significant, which confirms that the 
model can explain a significant amount of variation in the entrepreneurial intention. 
Moreover, the Mean Square column also confirms that very much of the variance is 
explained by the Regression line than by the Residual (2.910 compared to 0.277). This 
reinforces the conclusion that the model is rather good.
Table 8. Coefficientsa 
Model Unstandardized 
Coefficients 
Standardized 
Coefficients 
t
Sig. 
B Std. Error Beta
(Constant) 2.088 0.089 23.454 0.000
Educational 
Level
0.190 0.041 0.192 4.609 0.000
SE 0.006 0.034 0.010
0.170
0.865
PBC 0.062 0.033 0.113
1.879
0.061
a. Dependent Variable: EI 
Table 7 (Coefficients) represents the coefficients for the regression equation, which is: 
Entrepreneurial intention = 2.088 + 0.190* educational level + 0. 062* perceived 
behavioral control + 0.006 * self-efficacy.
224
Particularly, the Standardized Coefficients Beta reports the contribution each variable 
makes to the model. In this study, educational level is the most important: a variation of 
1 % in educational level would lead to a change of 19.0% in entrepreneurial intention 
(ߚଵ=0.190, p=0.000). Similarly, perceived behavioral control has the second strongest 
effect on entrepreneurial intention (ߚଶ=0.062, p=0.061> 0.005), followed by self-
efficacy (ߚଷ=0.006, p=0.865> 0.005). 
4.5. Chi-Square and Cramer’s V Tests 
Chi-Square and Cramer’s V Tests are employed to report the difference of educational level in 
self-efficacy, perceived behavioral control and entrepreneurial intention 
Chi-Square and Cramer’s V Tests for educational level difference in entrepreneurial 
self-efficacy 
Table 9. Chi-Square and Cramer’s V results for educational level difference in 
entrepreneurial self-efficacy 
 Chi-Square Tests Symmetric Measures 
Value df Asymp. Sig. (2-sided) Value Approx. Sig.
Pearson Chi-Square 98.912a 123 0.946 Phi 0.423 0.946
Likelihood Ratio 98.429 123 0.950 Cramer’s V 0.244 0.946
Linear-by-Linear Association 1.644 1 0.200
Note: N=553, a. 131 cells (78.0%) have expected count less than 5. The minimum expected count is 0.02
Source: Authors’ elaborations based on research study
The Chi-Square Tests in table 9 shows that the probability of differences this larger or 
larger occurring by chance is 0.946, which is higher than the normal 0.05 criterion level 
used (95% significance). Thus, there are no evidences of educational level difference in 
self-efficacy. Moreover, Cramer’s V change between 0 and 1, with 0 referring to no 
association and 1 showing the perfect association, the result in table 9 indicates that the 
value is 0.423, it means that the association between the educational level and 
entrepreneurial self-efficacy makes up the moderate level. 
Table 10. Chi-Square and Cramer’s V results for educational level difference in 
perceived behavioral control 
 Chi-Square Tests Symmetric Measures 
Value df Asymp. Sig. (2-sided) Value Approx. Sig.
Pearson Chi-Square 77.831a 105 0.978 Phi 0.375 0.978
Likelihood Ratio 89.636 105 0.858 Cramer’s V 0.217 0.978
Linear-by-Linear Association 2.176 1 0.140
Note: N=553, a. 109 cells (75.70%) have expected count less than 5. The minimum expected count is 0.02
Source: Authors’ elaborations based on research study
Analogously, table 10 presents that there is no difference between educational levels in 
perceived behavioral control and the association is moderate (Sig. = 0.978 > 0.05, φ 
=0.375).
225
Table 11. Chi-Square and Cramer’s V results for educational level difference in 
entrepreneurial intention 
 Chi-Square Tests Symmetric Measures 
Value df Asymp. Sig. (2-sided) Value Approx. Sig.
Pearson Chi-Square 112.473a 84 0.021 Phi 0.451 0.021
Likelihood Ratio 96.514 84 0.165 Cramer’s V 0.260 0.021
Linear-by-Linear Association 21.941 1 0.000
Note: N=553, a. 85 cells (73.3%) have expected count less than 5. The minimum expected count is 0.02
Source: Authors’ elaborations based on research study
Finally, the strong evidences in educational differences in entrepreneurial intention and 
the association is rather high (Sig. = 0.021 <0.05, φ =0.451).
5. Conclusion 
The objective of this study is to investigate the impacts of educational level, self-
efficacy, and perceived behavioral control on entrepreneurial intention among Polish 
students in the context of Industry 4.0. The research results show that the educational 
level, self-efficacy, perceived behavioral control have positive effects on entrepreneurial 
intention at the high level (p-value <0.05). Thus, the hypotheses including H1, H2, H4, 
H6 are accepted. In addition, educational level is seen as the most influential factor in
entrepreneurial intention, followed by perceived behavioral control, and self-efficacy. In 
addition, there are the strong evidence of educational level differences in entrepreneurial 
intention, but no education differences in self-efficacy and perceived behavior control. 
However, there are some restrictions. Firstly, the author only focuses on figuring out the 
direct effects of educational level, self-efficacy and perceived behavior control on 
entrepreneurial intention, the further researches should extend the research model by 
supplementing mediating variables, or using different variables. Secondly, the 
quantitative method through the availability sample can be seen as a restriction of this 
study, the further research should use the different approach to collect data in order to 
increase the significance level. 
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