Research Article

Technology Spillover Perception and Knowledge Network Trap in Cross-Industry Innovation: An Empirical Examination from Unmanned Aerial Vehicle (UAV)

Table 3

Regression results.

Dependent variableStagesModelEPTLCTSP-WidthCTSP-DepthKCKPCTSP-Width KCCTSP-Depth KCCTSP-Width KPCTSP-Depth KPAdjusted

CIPFlat periodModel 10.001-0.1550.0870.041
Model 2-0.027-0.0930.2400.0630.4680.4120.381
Model 3-0.027-0.0930.2400.0630.009-0.0030.4680.3790.000
Model 4-0.024-0.0870.2380.044-0.0120.2840.4710.3830.003
Model 5-0.030-0.0830.2730.090-0.025-0.0790.4820.3950.011
Model 6-0.029-0.0640.2530.108-0.037-0.0700.4800.393-0.002
Low-speed climbing periodModel 7-0.120-0.0110.1010.076
Model 8-0.076-0.0030.2310.3030.2410.1970.140
Model 9-0.065-0.0050.0930.3040.669-0.4410.2570.1920.016
Model 10-0.066-0.0830.0140.4520.960-2.0480.2700.2060.013
Model 11-0.055-0.0690.4450.3150.295-1.2220.2860.2230.016
Model 12-0.054-0.0950.1820.9120.608-1.7660.3830.3290.097
High-speed growing periodModel 13-0.131-0.0050.3790.362
Model 14-0.0870.0560.0370.2740.4920.4650.113
Model 15-0.0880.0870.0780.2100.214-0.1850.5310.4930.039
Model 16-0.0770.052-0.0760.5530.337-0.6480.5840.5500.053
Model 17-0.0890.0680.1340.298-0.010-0.3360.5000.459-0.084
Model 18-0.0640.0650.0060.7540.306-1.5780.5510.5140.051

, , and suggest that the parameter estimates are significant at 0.1%, 1%, and 5%, respectively.