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Table 5 Path coefficients

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

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
Mediation effect  
No. Path Beta t p Mediation
H7a CU → CA → ES 0.103 2.038 0.021 Mediation
H7b CO → CA → ES 0.094 1.905 0.029 Mediation
H7c TO → CA → ES 0.173 4.107 0.000 Mediation
H7d NO → CA → ES 0.111 1.979 0.024 Mediation
H7e IO → CA → ES 0.327 4.904 0.000 Mediation
  1. 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