Table 5 The PLSpredict assessment Item Q...

Table 5

The PLSpredict assessment

Item	Q²predict	PLS-SEM_RMSE	LM_RMSE	PLS-SEM_RMSE–LM_RMSE	Decision
GPI1	0.353	0.745	0.758	−0.013	High predictive power
GPI2	0.321	0.720	0.733	−0.013
GPI3	0.403	0.689	0.704	−0.015
GPI4	0.322	0.713	0.724	−0.011
GPI5	0.399	0.749	0.756	−0.007
PRS1	0.427	0.788	0.790	−0.002	Medium predictive power
PRS2	0.414	0.847	0.857	−0.010
PRS3	0.463	0.715	0.707	0.008
PRS4	0.447	0.782	0.788	−0.006

Item	Q²predict	PLS-SEM_RMSE	LM_RMSE	PLS-SEM_RMSE–LM_RMSE	Decision
GPI1	0.353	0.745	0.758	−0.013	High predictive power
GPI2	0.321	0.720	0.733	−0.013
GPI3	0.403	0.689	0.704	−0.015
GPI4	0.322	0.713	0.724	−0.011
GPI5	0.399	0.749	0.756	−0.007
PRS1	0.427	0.788	0.790	−0.002	Medium predictive power
PRS2	0.414	0.847	0.857	−0.010
PRS3	0.463	0.715	0.707	0.008
PRS4	0.447	0.782	0.788	−0.006

Note(s): GPI = Green purchase intention; PRSs = Price sensitivity to green products. Q²predict = Predictive relevance; PLS-SEM = Partial least squares structural equation modelling; LM = Linear model; RMSE = Root mean squared error; PLS-SEM_RMSE must produce smaller values than LM_RMSE, thus generating negative values in PLS-SEM_RMSE–LM_RMSE

Source(s): Authors’ own work

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