CHAPTER 4 RESULTS AND DISCUSSION
4.5 Hypotheses Test
4.5.6 The Results of Sub-hypotheses Test
4.5.6.1 Testing the Impacts of Various Dimensions of External Environment on DT
As shown in Table 4.31,
1) For the sake of testing the impact of CN on DT, the Model 2 added CN variable to the Model 1, and the regression analysis results were F=10.539 (P<0.001), =0.359 (P<0.001), R²=0.284, indicating that CN can explain 28.4%
variation of DT, so the hypothesis H1a was valid.
2) For the sake of testing the impact of MC on DT, the Model 3 added MC variable to the Model 1, and the results of regression analysis were
F=10.955 (P<0.001), =0.339 (P<0.001), R²= 0.292, indicating that MC can explain 29.2% variation of DT, the hypothesis H1b was valid.
3) For the sake of testing the impact of GP on DT, the Model 4 added GP variable to the Model 1, and the results of regression analysis were F=11.672 (P<0.001), =0.345 (P<0.001), R²=0.305, indicating that MC can explain 30.5%
variation of DT, the hypothesis H1c was valid.
4) For the sake of testing the impact of TT on DT, the Model 5 added TT variable to the Model 1, and the results of regression analysis were F=10.208 (P<0.001), =0.318 (P<0.001), R²=0.277, indicating that TT can explain 27.7%
variation of DT, the hypothesis H1d was valid.
4.5.6.2 Testing the Impacts of Various Dimensions of External Environment on DI
As shown in Table 4.32,
1) For the sake of testing the impact of CN on DI, the Model 10 added CN variable to the Model 9, and the regression analysis results were F=9.817 (P<0.001), =0.291 (P<0.001), R²=0.270, indicating that CN can explain 27.0%
variation of DI, so the hypothesis H2a was valid.
2) For the sake of testing the impact of MC on DI, the Model 11 added MC variable to the Model 9, and the regression analysis results were F=9.131 (P<0.001), =0.230 (P<0.001), R²=0.256, indicating that MC can explain 25.6%
variation of DI, so the hypothesis H2b was valid.
3) For the sake of testing the impact of GP on DI, the Model 12 added GP variable to the Model 9, and the regression analysis results were F=10.959 (P<0.001), = 0.298 (P<0.001), R²=0.292, indicating that GP can explain 29.2%
variation of DI, so the hypothesis H2c was valid.
4) For the sake of testing the impact of TT on DI, the Model 13 added TT variable to the Model 9, and the regression analysis results were F=8.564 (P<0.001), = 0.198 (P<0.001), R²=0.244, indicating that TT can explain 24.4%
variation of DI, so the hypothesis H2d was valid.
4.5.6.3 Testing the Impacts of Various Dimensions of the Internal Conditions on DT
As shown in Table 4.33,
1) For the sake of testing the impact of DS on DT, the Model 17 added DS variable to the Model 16, and the regression analysis results were F=10.014 (P<0.001), = 0.315 (P<0.001), R²=0.274, indicating that DS can explain 27.4%
variation of DT, so the hypothesis H5a was valid.
2) For the sake of testing the impact of OC on DT, the Model 18 added OC variable to the Model 16, and the regression analysis results were F=17.945 (P<0.001), = 0.463 (P<0.001), R²=0.403, indicating that OC can explain 40.3% variation of DT, so the hypothesis H5b were valid.
3) For the sake of testing the impact of LS on DT, the Model 19 added LS variable to the Model 16, and the regression analysis results were F=10.995 (P<0.001), =0.348 (P<0.001), R²=0.292, indicating that LS can explain 29.2% variation of DT, so the hypothesis H5c was valid.
4.5.6.4 Testing the Impacts of Various Dimensions of Internal Conditions on DI……….
As shown in Table 4.34,
1) For the sake of testing the impact of DS on DI, the Model 24 added DS variable to the Model 23, and the regression analysis results were F=9.913 (P<0.001), =0.276 (P<0.001), R²=0.271, indicating that DS can explain 27.1%
variation of DI, so the hypothesis H4a was valid.
2) For the sake of testing the impact of OC on DI, the Model 25 added OC variable to the Model 23, and the regression analysis results were F=11.059 (P<0.001), = 0.296 (P<0.001), R²=0.294, indicating that OC can explain 29.4%
variation of DI, so the hypothesis H4b was valid.
3) For the sake of testing the impact of LS on DI, the Model 26 added LS variable to the Model 23, and the regression analysis results were F=12.613 (P<0.001), =0.374 (P<0.001), R²=0.322, indicating that LS can explain 32.2%
variation of DI, so the hypothesis H4c was valid.
4.5.6.5 Testing the Impacts of Each Dimension of the External Environment on Each Dimension of the Internal Conditions Based on the above hypotheses, the regression analysis was carried out with the four dimensions CN, MC, GP and TT in the external environment as independent variables and the three dimensions DS, OC and LS in the internal conditions as dependent variables. The analysis results are shown in Table 4.36.
Table 4.36 Regression Analysis Results of Each Dimension of the External Environment on Each Dimension of the Internal Conditions
Independent Variables DS
CN 0.199*** - - -
MC - 0.180*** - -
GP - - 0.203*** -
TT - - - 0.146***
F 15.423*** 14.555*** 21.316*** 9.433***
R² 0.036 0.034 0.049 0.220
Adjusted R² 0.034 0.032 0.047 0.020
D-W 1.599 1.621 1.675 1.615
Independent Variables OC
CN 0.417*** - - -
MC - 0.330*** - -
GP - - 0.338*** -
TT - - - 0.258***
F 66.681*** 42.946*** 51.752*** 25.062***
R² 0.130 0.094 0.111 0.057
Adjusted R² 0.128 0.092 0.109 0.055
D-W 1.585 1.572 1.690 1.562
Independent Variables LS
CN 0.275*** - - -
MC - 0.208*** - -
GP - - 0.259*** -
Independent Variables DS
TT - - - 0.169**
F 29.417*** 18.917*** 34.209*** 12.193**
R² 0.066 0.044 0.076 0.029
Adjusted R² 0.064 0.410 0.074 0.026
D-W 1.600 1.543 1.639 1.543
As shown in Table 4.36, the regression coefficients of CN for DS, OC and LS were 0.199 (P<0.001), 0.417 (P<0.001) and 0.275 (P<0.001), R² were 0.036, 0.220 and 0.066 respectively. It can be seen that CN had positive impact on DS, OC and LS, so the hypotheses H3a1, H3a2, H3a3 were valid.
The regression coefficients of MC for DS, OC and LS were 0.180 (P<0.001), 0.330 (P<0.001) and 0.208 (P<0.001), R² were 0.034, 0.094 and 0.044 respectively. It can be seen that MC had positive impact on DS, OC and LS, so the hypotheses H3b1, H3b2, H3b3 were valid.
The regression coefficients of GP for DS, OC and LS were 0.203 (P<0.001), 0.338 P<0.001) and 0.259 (P<0.001), R² were 0.049, 0.111, 0.076 respectively. It can be seen that GP had positive impact on DS, OC and LS, so the hypotheses H3c1, H3c2, H3c3 were valid.
The regression coefficients of TT for DS, OC and LS are 0.146 (P<0.001), 0.258 (P<0.001) and 0.169 (P<0.001), R² were 0.220, 0.057 and 0.029 respectively. It can be seen that TT had positive impact on DS, OC and LS, so the hypotheses H3d1, H3d2, H3d3 were valid.