Table 5 PLSpredict assessment of...

Table 5

PLSpredict assessment of indicators in the indirect model

PLSpredict assessment of indicators in the model mediated by the KMP construct
	PLS			LM			PLS – LM
Indicator	RMSE	MAE	Q²predict	RMSE	MAE	Q²predict	RMSE	MAE	Q²predict
CR1	0.823	0.606	0.560	0.829	0.606	0.554	−0.006	0.000	0.006
CR2	1.042	0.733	0.325	1.060	0.782	0.302	−0.018	−0.049	0.023
CR3	0.882	0.652	0.471	0.917	0.677	0.429	−0.035	−0.025	0.043
CR4	0.769	0.564	0.444	0.802	0.587	0.396	−0.032	−0.022	0.047
CR5	1.064	0.818	0.449	1.079	0.808	0.433	−0.016	0.010	0.016
PR1	0.917	0.710	0.537	0.931	0.721	0.523	−0.014	−0.011	0.014
PR2	0.937	0.719	0.505	0.939	0.731	0.503	−0.001	−0.012	0.001
PR3	0.860	0.653	0.437	0.906	0.685	0.375	−0.046	−0.032	0.062
PR4	0.909	0.696	0.374	0.942	0.712	0.328	−0.033	−0.016	0.047
PR5	0.980	0.755	0.433	0.986	0.786	0.426	−0.006	−0.031	0.007

PLSpredict assessment of indicators in the model mediated by the KMP construct
	PLS			LM			PLS – LM
Indicator	RMSE	MAE	Q²predict	RMSE	MAE	Q²predict	RMSE	MAE	Q²predict
CR1	0.823	0.606	0.560	0.829	0.606	0.554	−0.006	0.000	0.006
CR2	1.042	0.733	0.325	1.060	0.782	0.302	−0.018	−0.049	0.023
CR3	0.882	0.652	0.471	0.917	0.677	0.429	−0.035	−0.025	0.043
CR4	0.769	0.564	0.444	0.802	0.587	0.396	−0.032	−0.022	0.047
CR5	1.064	0.818	0.449	1.079	0.808	0.433	−0.016	0.010	0.016
PR1	0.917	0.710	0.537	0.931	0.721	0.523	−0.014	−0.011	0.014
PR2	0.937	0.719	0.505	0.939	0.731	0.503	−0.001	−0.012	0.001
PR3	0.860	0.653	0.437	0.906	0.685	0.375	−0.046	−0.032	0.062
PR4	0.909	0.696	0.374	0.942	0.712	0.328	−0.033	−0.016	0.047
PR5	0.980	0.755	0.433	0.986	0.786	0.426	−0.006	−0.031	0.007

Note(s): RMSE: root mean squared error. MAE: mean absolute error. PLS: partial least squares path model. LM: linear regression model. PR: people results. K = 4 subgroups, number of repetitions = 10

Source(s): Authors' own creation

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