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 3 Convergent validity and reliability

Constructs	No. items	Mean	SD	CA	DG rho	CR	AVE	VIF
CU	4	1.775	0.811	0.824	0.830	0.883	0.653	2.858
CO	3	4.22	0.804	0.855	0.860	0.912	0.775	2.517
TO	4	3.827	0.803	0.777	0.781	0.858	0.604	2.713
NO	5	3.895	0.951	0.861	0.870	0.900	0.642	3.002
IO	4	3.872	0.936	0.866	0.868	0.909	0.714	2.928
CA	7	3.946	0.822	0.910	0.910	0.929	0.652
ES	5	3.507	1.034	0.908	0.911	0.932	0.732

CU customer orientation, CO competitor orientation, TO technology orientation, NO network orientation, IO innovation orientation, CA competitive advantage, ES economic sustainability, DG’s rho Dillon–Goldstein’s rho, SD standard deviation, CA Cronbach’s alpha, CR composite reliability, AVE average variance extracted, VIF variance inflation factor