Table 7 PLSp redict results Construct...

Table 7

PLSpredict results

Construct	PLSpredict
Construct	Item	Q²predict	PLS-SEM_RMSE	LM_RMSE	Decision
RT	RT1	0.309	0.782	0.765	Medium predictive power
	RT2	0.235	0.916	0.919
	RT3	0.184	0.877	0.884
	RT4	0.243	0.818	0.835
NC	NC1	0.229	0.707	0.703
	NC2	0.243	0.759	0.762
	NC 3	0.221	0.747	0.765
	NC4	0.200	0.747	0.752
SCR	SCR1	0.164	0.878	0.882
	SCR2	0.224	0.902	0.903
	SCR3	0.189	1.091	1.070
	SCR4	0.192	0.810	0.812
	SCR5	0.282	0.808	0.804
RP	RP1	0.160	0.832	0.847
	RP2	0.173	0.788	0.787
	RP3	0.227	0.868	0.869
	RP4	0.187	0.758	0.764
	RP5	0.196	0.831	0.840

Construct	PLSpredict
Construct	Item	Q²predict	PLS-SEM_RMSE	LM_RMSE	Decision
RT	RT1	0.309	0.782	0.765	Medium predictive power
	RT2	0.235	0.916	0.919
	RT3	0.184	0.877	0.884
	RT4	0.243	0.818	0.835
NC	NC1	0.229	0.707	0.703
	NC2	0.243	0.759	0.762
	NC 3	0.221	0.747	0.765
	NC4	0.200	0.747	0.752
SCR	SCR1	0.164	0.878	0.882
	SCR2	0.224	0.902	0.903
	SCR3	0.189	1.091	1.070
	SCR4	0.192	0.810	0.812
	SCR5	0.282	0.808	0.804
RP	RP1	0.160	0.832	0.847
	RP2	0.173	0.788	0.787
	RP3	0.227	0.868	0.869
	RP4	0.187	0.758	0.764
	RP5	0.196	0.831	0.840

Note(s): Q²predict = Predictive relevance; LM = Linear model; RMSE = Root mean square error; RT = Relational trust; NC = Network capability; SCR = Supply chain resilience; RP = Relationship performance

Source(s): Authors’ own work

[ViewLarge]

Sharing Unavailable