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Table 3 Convergent validity and reliability

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

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  
  1. 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