Modelling the significance of strategic orientation for competitive advantage and economic sustainability: the use of hybrid SEM–neural network analysis

Journal of Innovation and Entrepreneurship

A Systems View Across Time and Space

Table 5 Path coefficients

Hypo	Path	Beta	t	p	r²	f²	Q²	Decision
Direct effect
H1a	CU → CA	0.149	2.032	0.021		0.021		Supported
H1b	CU → ES	0.103	2.038	0.021
H2a	CO → CA	0.135	1.917	0.028	CA = 0.628	0.020	CA = 403	Supported
H2b	CO → ES	0.094	1.905	0.029
H3a	TO → CA	0.249	4.217	0.000	ES = 0.479	0.063	ES = 0.346	Supported
H3b	TO → ES	0.173	4.107	0.000
H4a	NO → CA	0.160	2.000	0.023		0.023		Supported
H4b	NO → ES	0.111	1.979	0.024
H5a	IO → CA	0.471	5.110	0.000		0.208		Supported
H5b	IO → ES	0.327	4.904	0.000
H6	CA → ES	0.693	21.950	0.000		0.926		Supported

CU customer orientation, CO competitor orientation, TO technology orientation, NO network orientation, IO innovation orientation, CA competitive advantage, ES economic sustainability, t t statistics, p probability/p value, beta path coefficient, R² R squared/determinant coefficient, f² effect size, Q² quality criteria model, decision decision of hypothesis testing