Table 10.

Prediction performance under different combinations of driving forces.

${\text{R}}_{\text{oos}}^2$	Multiple linear regression (1)	LASSO (2)	GBR (3)	RFR (4)
Benchmark	0.101436	0.031870	0.081122	0.166972
Benchmark + technology	0.105532	0.031870	0.271218	0.321959
Benchmark + organization	0.264684	0.117570	0.267906	0.334702
Benchmark + environment	0.203275	0.039836	0.462744	0.409237
Benchmark + industry	0.202921	0.031870	0.325166	0.380639
Benchmark + technology + organization	0.269030	0.117571	0.354623	0.391439
Benchmark + technology + environment	0.204882	0.039836	0.532292	0.438514
Benchmark + technology + industry	0.241420	0.031870	0.420074	0.475880
Benchmark + technology + environment	0.301312	0.136863	0.370195	0.459327
Benchmark + organization + industry	0.314237	0.117570	0.368417	0.415728
Benchmark + environment + industry	0.289889	0.039836	0.539554	0.508942
Benchmark + technology + organization + environment	0.303901	0.136863	0.423259	0.434396
Benchmark + technology + organization + industry	0.326098	0.117571	0.409223	0.427079
Benchmark + technology + environment + industry	0.323807	0.039836	0.551842	0.522093
Benchmark + organization + environment + industry	0.368127	0.136863	0.457802	0.451701
Benchmark + technology + organization + environment + industry	0.379480	0.136863	0.466834	0.474714