Table 6

Interconnectedness of AI technologies in logistics and eco-innovation (Hypothesis 5)

SizeCoefOLSOLS HC0SLXSAR
Small(Intercept)98.199 [8.428] <0.00198.199 [6.379] <0.001103.375 [25.745] <0.00193.845 [36.596] 0.010
AI_log_S19.761 [9.955] 0.05819.761 [6.053] 0.00318.853 [11.008] 0.10019.706 [9.599] 0.040
lag.AI_log_S−8.615 [40.399] 0.833
rho0.042 [0.324] 0.897
R2 Adj.0.136/0.1020.138/0.066
Shapiro–WilkW = 0.95956, p-value = 0.3611
Breusch–PaganBP = 0.062897, df = 1, p-value = 0.802
Moran IMoran I = 0.54667, p-value = 0.2923
Medium(Intercept)91.899 [8.454] <0.00191.899 [6.874] <0.00192.996 [29.399] 0.00484.512 [35.460] 0.017
AI_log_M11.125 [3.910] 0.00911.125 [2.731] <0.00111.084 [4.125] 0.01311.117 [3.763] 0.003
lag.AI_log_M−0.715 [18.309] 0.969
rho0.071 [0.316] 0.822
R2 Adj.0.245/0.2140.245/0.182
Shapiro–WilkW = 0.92112, p-value = 0.04201
Breusch–PaganBP = 0.0013101, df = 1, p-value = 0.9711
Moran IMoran I = 0.63942, p-value = 0.2613
Large(Intercept)79.442 [7.807] <0.00179.442 [6.503] <0.00163.329 [19.534] 0.00385.404 [33.372] 0.010
AI_log_L5.532 [1.143] <0.0015.532 [1.188] <0.0015.567 [1.148] <0.0015.553 [1.100] <0.001
lag.AI_log_L3.304 [3.670] 0.377
rho−0.058 [0.305] 0.848
R2 Adj.0.484/0.4630.500/0.459
Shapiro–WilkW = 0.97362, p-value = 0.6991
Breusch–PaganBP = 1.9859, df = 1, p-value = 0.1588
Moran IMoran I = −0.40411, p-value = 0.6569

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