Table 5 Indicator prediction summary...

Table 5

Indicator prediction summary

Indicator	PLS_SEM		LM	PLS_SEM-LM
	RMSE	*Q² predict	RMSE	RMSE
[BEN2: Access to new markets]	0.808	0.321	0.815	−0.007^a
[BEN4: Improved communication]	0.679	0.406	0.682	−0.003^a
[BEN5: New business lines]	0.750	0.409	0.745	0.005^b
[BEN6: More productivity]	0.678	0.505	0.638	0.040^b
[BEN8: New customers]	0.831	0.368	0.833	−0.002^a
[BEN9: Time optimization]	0.714	0.417	0.712	0.002^b
[BEN11: Effectiveness decisions]	0.781	0.348	0.787	−0.006^a
[BEN12: Employee satisfaction]	0.778	0.287	0.781	−0.003^a

Indicator	PLS_SEM		LM	PLS_SEM-LM
	RMSE	*Q² predict	RMSE	RMSE
[BEN2: Access to new markets]	0.808	0.321	0.815	−0.007^a
[BEN4: Improved communication]	0.679	0.406	0.682	−0.003^a
[BEN5: New business lines]	0.750	0.409	0.745	0.005^b
[BEN6: More productivity]	0.678	0.505	0.638	0.040^b
[BEN8: New customers]	0.831	0.368	0.833	−0.002^a
[BEN9: Time optimization]	0.714	0.417	0.712	0.002^b
[BEN11: Effectiveness decisions]	0.781	0.348	0.787	−0.006^a
[BEN12: Employee satisfaction]	0.778	0.287	0.781	−0.003^a

Note(s): 1. *Q² predict >0; all the indicators of the model studied have a Q² > 0

2. RMSE: All values < 1 are symmetric according to Hair et al. (2021)

3. LM: shows the predictive capabilities of the indicators

4. PLS_SEM-LM<0 The results referenced with “a” should have a lower prediction error, In comparison with the LM outcomes

5. For n = 500 subsamples based on distribution t (499) of one-tagged student: *p < 0.05 (t (0.05, 499) = 1.64791345); **p < 0.01 (t (0.01, 499) = 2.333843952); ***p < 0.001 (t (0.001; 499) = 3.106644601)

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