Table 6 PLSpredict assessment of...

Table 6

PLSpredict assessment of indicators KMP construct

	PLS			LM			PLS – LM
Indicator	RMSE	MAE	Q²predict	RMSE	MAE	Q²predict	RMSE	MAE	Q²predict
Creation K	620.997	503.175	0.572	647.235	520.487	0.535	−26.237	−17.312	0.037
Storage K	715.254	579.718	0.468	716.967	561.269	0.465	−1.713	18.449	0.003
Transfer K	684.028	532.263	0.538	703.292	537.693	0.512	−19.264	−5.430	0.026
Apply K	668.485	461.061	0.538	694.093	480.112	0.501	−25.608	−19.051	0.036

	PLS			LM			PLS – LM
Indicator	RMSE	MAE	Q²predict	RMSE	MAE	Q²predict	RMSE	MAE	Q²predict
Creation K	620.997	503.175	0.572	647.235	520.487	0.535	−26.237	−17.312	0.037
Storage K	715.254	579.718	0.468	716.967	561.269	0.465	−1.713	18.449	0.003
Transfer K	684.028	532.263	0.538	703.292	537.693	0.512	−19.264	−5.430	0.026
Apply K	668.485	461.061	0.538	694.093	480.112	0.501	−25.608	−19.051	0.036

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

Source(s): Authors' own creation

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