Modelling the relationship between prior entrepreneurial exposure, entrepreneurship education and entrepreneurial action using neural networks

Figures & data

Figure 1. Construct associations in a linear model supported by relevant theories.

Figure 2. Construct associations in a non-linear model supported by relevant theories.

Table 1. Descriptive statistics and correlation of the main variables in this study.

	Entrepreneurial exposure	Entrepreneurial action	Entrepreneurial education
Mean	3.443	2.275	3.831
Std. deviation	1.170	1.354	0.688
Skewness	−0.541	0.436	−0.897
Kurtosis	−0.792	−1.518	1.920
Entrepreneurial exposure	1
Entrepreneurial action	0.217*	1
Entrepreneurial education	0.233**	0.183*	1

Note: *p < 0.05, **p < 0.01.

Table 2. Multiple linear regression results.

	Model 1
Prior entrepreneurial exposure Entrepreneurial education	0.184* 0.140
Adjusted R²	0.05*
F (p value)	4.27 (0.016)

Note: Standardised Beta-coefficients are presented. *p < 0.05, **p < 0.01.

Table 3. Training and testing sample size.

		N	Percent
Sample	Training	89	71.2%
	Testing	36	28.8%
Valid		125	100.0%

Table 4. Network information.

Input layer	Covariates	1	NewEntExp
		2	EntEdu
	Number of units^a		2
Hidden layer(s)	Number of hidden layers		1
	Number of units in hidden layer 1^a		3
	Activation function		Hyperbolic tangent
Output layer	Dependent variables	1	NewEntAction
	Number of units		1

Figure 3. Artificial network architecture.

Table 5. Importance of inputs (independent variables).

	Importance	Normalized importance
New entrepreneurial exposure	0.457	84.2%
Entrepreneurial education	0.543	100.0%