CHAPTER 4 RESULTS AND DISCUSSION
4.3 The Impacts of Demographic Variables on DT
4.3.1 Staff Position
According to the descriptive data of employees in different positions on DT as shown in Table 4.12, the general staff scored lower on DT.
Table 4.12 The Descriptive Statistics of Employees in Different Positions on DT
Variable Staff Position Number of
Cases Mean Standard
Deviation
DT General Staff (1) 192 3.488 1.038
First-line Manager (2) 78 3.558 0.848
Middle Manager (3) 49 3.724 0.781
Senior Manager (4) 96 3.883 0.804
Total 415 3.620 0.936
The grouping comparison results are shown in Table 4.13. Firstly, the result of variance homogeneity test of position on enterprise DT was F=4.207，P=0.006<0.05
，which meant that the sample variance difference was significant and the variance was homogeneous. With 95% confidence, there were significant differences between staff positions and enterprise DT. Finally, the least significant difference (LSD) method was used for inter group comparison. The results showed that senior managers, middle managers and first-line managers had a higher impact on enterprise DT than general staffs. Therefore, the higher the staff position was, the greater the impacts of employees on the DT of enterprises were.
Table 4.13 Comparative Analysis of Variance of Employees in Different Positions on DT
Source Sum of Squares Df Mean Square F Sig. LSD
DT Group 10.8 3 3.6 4.207 0.006 1<2<3<4
Error 351.676 411 0.856 - -
Total 362.476 414 - - -
4.3.2 Region………..
According to the descriptive data of enterprises in different regions on DT as shown in Table 4.14, the West region scored lower on DT.
Table 4.14 Descriptive Statistics of Enterprises in Different Regions on DT
Variable Region Number of
Cases Mean Standard Deviation
DT Northeast (1) 133 3.639 0.895
East (2) 105 3.850 0.801
Central (3) 110 3.702 1.004
West (4) 67 3.090 0.912
Total 415 3.620 0.936
The grouping comparison results are shown in Table 4.15. Firstly, the result of variance homogeneity test of region on enterprise DT was F=10.236，P=0.000<0.05
，which meant that the sample variance difference was significant and the variance was homogeneous. With 95% confidence, there were significant differences between region and enterprise DT. Finally, the least significant difference (LSD) method was used for inter group comparison. The results showed that the eastern, northeast and central regions had a higher impact on the DT of enterprises than the western regions.
Therefore, the higher the economic development of the region was, the greater the impacts on the DT of enterprises were.
Table 4.15 Comparative Analysis of Variance of Enterprises in Different Regions on DT
Source Sum of Squares Df Mean Square F Sig. LSD DT Group 25.200 3 8.400 10.236 0.000 4<1<3<2
Error 337.276 411 0.821 - -
Total 362.476 414 - - -
4.3.3 Age………
According to the descriptive data of enterprises in different age on DT as shown in Table 4.16, enterprises older than 10 years scored the highest on DT.
Table 4.16 Descriptive Statistics of Enterprises in Different Ages on DT
Variable Age Number of Cases Mean Standard Deviation
DT <3year (1) 77 3.227 1.120
3-5year (2) 90 3.494 0.959
5-10year (3) 120 3.765 0.845
>10year (4) 128 3.811 0.792
Total 415 3.620 0.936
The grouping comparison results are shown in Table 4.17. Firstly, the result of variance homogeneity test of age on enterprise DT was F=8.192，P=0.000<0.05，
which meant that the sample variance difference was significant and the variance was homogeneous. With 95% confidence, there were significant differences between age and enterprise DT. Finally, the least significant difference (LSD) method was used for inter group comparison. The results showed that enterprises aged >10 years, 5-10 years and 3-5 years had a higher impact on DT than enterprises aged <3 years.
Table 4.17 Comparative Analysis of Variance of Enterprises in Different Ages on DT Source Sum of Squares Df Mean Square F Sig. LSD DT Group 20.451 3 6.817 8.192 0.000 1<2<3<4
Error 342.025 411 0.832 - -
Total 362.476 414 - - -
4.3.4 The Situation of DT
According to the descriptive data of enterprises in different situation on DT as shown in Table 4.18, the situation of “No, and there is no intention and plan for DT”
scored the highest.
Table 4.18 Descriptive Statistics of Enterprises in Different Situation on DT
Variable Situation of DT Number of
Cases Mean Standard
Deviation DT No, and there is no
intention and plan for DT
28 3.759 1.037
No, but there is a willingness and plan for DT
73 3.295 1.009
Yes, the DT project is in the early stage of
construction
123 3.614 0.984
Yes, the DT project has achieved certain results
191 3.729 0.832
Total 415 3.620 0.936
The grouping comparison results are shown in Table 4.19. Firstly, the result of variance homogeneity test of enterprises in different situation on DT was F=4.107，
P=0.007<0.05，which meant that the sample variance difference wa significant and the variance was homogeneous. With 95% confidence, there were significant differences between different situation of DT and enterprise DT. Finally, the least significant difference (LSD) method was used for inter group comparison. The results showed that the situation of “No, and there is no intention and plan for DT” had the greatest impact on the DT of enterprises. It can be seen from this that the intention and plan for DT to some extent determine whether enterprises will carry out DT. Otherwise, the situation of “the DT project has achieved certain results” had the second largest impact on the DT of enterprises. It can be seen that the achievements of enterprise DT will promote the development of enterprise DT to the higher level.
Table 4.19 Comparative Analysis of Variance of Enterprises in Different Situation on DT
Source Sum of Squares Df Mean Square F Sig. LSD DT Group 10.550 3 3.517 4.107 0.007 1>4>3>2
Error 351.926 411 0.856 - -
Total 362.476 414 - - -
4.3.5 Company Size
According to the descriptive data of company size on DT as shown in Table 4.20, the company size of “more than 2000 people” scored the highest on DT.
Table 4.20 Descriptive Statistics of Company Size on DT
Variable Company Size Number of
Cases Mean Standard
Deviation
DT Less than 100 people (1) 49 3.245 0.861
100-300 people (2) 79 3.354 0.889
300-2000 people (3) 155 3.708 0.844
More than 2000 people (4) 132 3.816 1.022
Total 415 3.620 0.936
The grouping comparison results are shown in Table 4.21. Firstly, the result of variance homogeneity test of age on enterprise DT was F=7.475，P=0.000<0.05，
which means that the sample variance difference was significant and the variance was homogeneous. With 95% confidence, there were significant differences between company size and enterprise DT. Finally, the least significant difference (LSD) method was used for the inter group comparison. The results showed that the company size of
“more than 2000 people” had the greatest impact on the DT of enterprises. It can be seen from this that the larger the company size was, the greater the impact of it on the enterprises’ DT was.
Table 4.21 Comparative Analysis of Variance of Company Size on DT
Source Sum of Squares Df Mean Square F Sig. LSD DT Group 18.754 3 6.251 7.457 0.000 1<2<3<4
Error 343.722 411 0.836 - -
Total 362.476 414 - - -
4.3.6 Ownership Type
As the ownership type of enterprises was divided into state-owned enterprises and private enterprises, the total samples can be divided into two independent samples.
Therefore, the ownership type can be analyzed by independent-sample T-test.
Table 4.22 Independent-Sample T-Test of Ownership Type
Variable Ownership
Type Mean
Levene Variance Equality Test T-Test for Equality of Means
F Sig. Homogeneous T Df Sig. Mean
Difference
DT State-owned 3.791 11.345 0.001 No 3.112 388.581 0.002 0.277
Private 3.514
If P value in Levene variance equality test is greater than 0.05, assuming that the two groups have the same overall variance, and it can explain the T-test according to the first row of data, that is, equal variance was assumed. If P value in Levene variance equality test is less than 0.05, not assuming that the two groups have the same overall variance, and it can explain the T-test according to the second row of data, that is, equal variance was not assumed. Therefore, as shown in Table 4.22, the T-test results of independent samples of ownership type were: T=3.112, P=0.002<0.05. It can be seen that the differences of ownership type can significantly affect the DT of enterprises.
There were significant differences in the mean value of different ownership types.
Combined with the mean value, it can be concluded that the mean value of state-owned enterprises was significantly higher than that of private enterprises. The comprehensive strength of state-owned enterprises often had more influence on the implementation of DT than that of private enterprises.
4.4 Construction of the SEM Model
SEM is also known as the covariance structure model, which is a significant multivariable analysis tool. The model is based on the covariance matrix of characteristic variables to analyze the relationship between characteristics (Lowry &
Gaskin, 2014). In this study, Amos23.0 was used to draw the model path map. The SEM model of influencing factors of pharmaceutical enterprises’ DT is shown in Figure 4.5.
Figure 4.5 The SEM Model of Influencing Factors of Pharmaceutical Enterprises’ DT
4.5 Hypotheses Test
4.5.1 Dummy Variable Processing
In the multiple regression analysis, the independent variable should be the measurement variable (isometric or proportional variable). If the independent variable is the discontinuous variable (nominal or sequential variable), it should be transformed into the dummy variable when the regression model is put into use. Most of the demographic variables in this study are nominal variables and sequential variables.
Therefore, before regression analysis, this study first virtualized the demographic variables.
The position variable took “General Staff” as the reference group, and position dummy variable P_2 was the comparison between “First-line Manager and General Staff”; Position dummy variable P_3 was the comparison between “Middle Manager and General staff”; Position dummy variable P_4 was the comparison between “Senior Manager and General Staff”.
The region variable took “Northeast” as the reference group, and the region dummy variable R_2 was the comparison between “East and Northeast”; Region dummy variable R_3 was the comparison between “Central and Northeast”; Region dummy variable R_4 was the comparison between “West and Northeast”.
Age variable took “<3 years” as the reference group, and age dummy variable A_2 was the comparison between “3-5 years and <3 years”; Age dummy variable A_3 was the comparison between “5-10 years and <3 years”; Age dummy variable A_4 was the comparison between “>10 years and <3 years”.
Type variable took “State-owned Enterprise” as the reference group, and type dummy variable T_2 was the comparison between “Private Enterprises and State- owned Enterprises”.
Action variable took “No, and there is no intention and plan for DT” as the reference group, and action dummy variable Action_2 was the comparison between
“No, and there is no intention and plan for DT” and “No, but there is a willingness and plan for DT”; Action dummy variable Action_3 was the comparison between “No, and there is no intention and plan for DT” and “Yes, the DT project is in the early stage of construction”. Action dummy variable Action_4 was the comparison between “No, and there is no intention and plan for DT” and “Yes, the DT project has achieved certain results”.
4.5.2 Correlation Analysis of Variables, Common Method Biases Test and Multicollinearity Test
4.5.2.1 Correlation Analysis of Variables
In this study, Pearson correlation coefficient analysis was conducted for the variable indicators in the regression model, and the results are shown in Table 4.23.
In terms of independent variables, firstly, the external environment and internal conditions were positively correlated (correlation coefficient=0.382, P<0.01), indicating that both variables were significantly correlated. Secondly, the variables of customer needs, market competition, government policy and digital technology in the external environment are significantly correlated with the variables of digital strategy, organization capability and leadership in the internal conditions. There was a positive correlation between the external environment and digital innovation (correlation coefficient=0.419, P<0.01), and there was a positive correlation between customer needs, market competition, government policy, digital technology variables in the external environment and digital innovation. There was a positive correlation between internal conditions and digital innovation (correlation coefficient=0.508, P<0.01), and there was a positive correlation between the internal conditions (e.g. digital strategy, organization capability, leadership) variables and digital innovation. Thirdly, there was
a positive correlation between the external environment and DT (correlation coefficient=0.522, P<0.01), and there was a positive correlation between the external environment (e.g. customer needs, market competition, government policy, digital technology)variables and DT; There was a positive correlation between internal conditions and DT (correlation coefficient=0.581, P<0.01), and there was a positive correlation between the internal conditions (e.g. digital strategy, organization capability, leadership) variables and DT. Fourthly, there was a positive correlation between digital innovation and DT (correlation coefficient =0.5537, P<0.01).
In terms of the moderating variables, there was a positive correlation between company size and digital innovation (correlation coefficient=0.209, P<0.01).
The increase of company size had a positive role in promoting enterprise digital innovation. There was a positive correlation between company size and DT (correlation coefficient =0.219, P<0.01). The enlargement of company size had a positive effect on DT.
Table 4.23 Correlation Analysis of Variables Variables12345678910111213141516 Position1- - - - - - - - - - - - - - - Region-.103*1- - - - - - - - - - - - - - Size0.043-0.0821- - - - - - - - - - - - - Age .130**-0.0880.0141- - - - - - - - - - - - Type 0.0120.031-0.022-.503** 1- - - - - - - - - - - Action0.087-.161** -0.006.612** -.496** 1- - - - - - - - - - CN.113*-0.034.098*0.070-0.0700.0151- - - - - - - - - MC0.074-0.052.177**.184**-.153**.144**.445**1- - - - - - - - GP.118*-.110*.109*.119*-.130**0.040.558**.553**1- - - - - - - TT0.029-0.049.153**0.081-0.0480.025.457**.537**.564**1- - - - - - DS.217**-0.0910.039.128**-0.0930.093.190**.185**.222**.149**1- - - - - OC.192**-0.0680.038.129**-.137**0.053.360**.307**.334**.239**.470**1- - - - LS.148**-0.0900.093.181**-.141**0.052.258**.209**.277**.169**.403**.477**1- - - EE.104*-0.078.168**.143**-.127**0.071.758**.794**.844**.801**.233**.387**.285**1- - IC.233**-.104*0.071.182**-.155**0.082.342**.296**.350**.236**.775**.828**.783**.382**1- DI.263**-0.070.209**.199**-.136**0.007.353**.311**.398**.277**.354**.404**.455**.419**.508**1 DT.171**-.155**.219**.227**-.144**.101*.404**.422**.451**.391**.389**.553**.438**.522**.581**.537** Note: *At the 0.05 level (two tailed), the correlation was significant. **At 0.01 level (two tailed), the correlation was significant.
4.5.2.2 Common Method Biases Test
When analyzing the relevant data of the SEM model, it is essential to test the common method biases first. The test method is usually the Harman single factor method. If the variation explained by the first factor is less than 40% of the critical standard, it indicates that there is no serious common method biases in the study (Harman, 1976).
Table 4.24 The Results of Common Method Biases Test
Component
Initial Eigenvalue Extraction Sums of Squared Loadings
Total % of variance
Cumulative
% Total % of
variance
Cumulative
%
1 10.941 35.292 35.292 10.941 35.292 35.292
2 3.732 12.037 47.329 3.732 12.037 47.329
3 1.750 5.646 52.976 1.750 5.646 52.976
4 1.633 5.267 58.242 1.633 5.267 58.242
5 1.491 4.809 63.051 1.491 4.809 63.051
6 1.412 4.556 67.607 1.412 4.556 67.607
7 1.276 4.115 71.722 1.276 4.115 71.722
8 1.056 3.405 75.127 1.056 3.405 75.127
9 1.015 3.275 78.402 1.015 3.275 78.402
This study used the single factor analysis to test the common method biases. As shown in Table 4.24, the factor analysis method was adopted. The factor analysis without rotation showed that there were 9 factors with characteristic root greater than 1, of which the factor with the largest characteristic root explained 35.292%
of the overall variation. The research shows that no single factor can explain most of
the variation. Therefore, there was no serious common method biases in the data of this study.
4.5.2.3 Multicollinearity Test
Before the multi-level regression analysis, it is essential to test whether there is multicollinearity among variables. Judging by the tolerance and variance inflation factor (VIF), when tolerance is greater than 0.1 and VIF is less than 10, there is no multicollinearity between variables (Mulyadi & Anwar, 2012).
In this study, the independent variables were put into the regression model for collinearity diagnosis. The values of tolerance and VIF of each variable are shown in Table 4.25, Table 4.26, Table 4.27 and Table 4.28. Since the tolerance of each variable was greater than 0.1 and VIF was less than 10, it indicated that there was basically no multicollinearity between each variable. Therefore, the regression analysis results of this study were reliable.
Table 4.25 Collinearity Diagnosis of the Impact of the External Environment on DT Independent VariablesStep1Step2Step3Step4Step5Step6Step7Step ToleranceVIFToleranceVIFToleranceVIFToleranceVIFToleranceVIFToleranceVIFToleranceVIFTolerance PositionP_20.8071.2400.8021.2470.8031.2450.8001.2490.8041.2440.8001.2510.7991.2520.798 P_30.8211.2190.7931.2620.8201.2200.8061.2400.8141.2290.8051.2420.8011.2480.787 P_40.8201.2190.8081.2380.8131.2300.8051.2430.8171.2240.8081.2370.8051.2420.763 RegionR_20.7161.3960.7001.4290.7071.4140.7121.4040.7091.4110.7021.4240.7021.4250.695 R_30.6881.4540.6521.5330.6791.4720.6811.4680.6711.490.6671.5000.6661.5020.665 R_40.7441.3440.7161.3970.7271.3760.7081.4120.7171.3950.7111.4070.7061.4170.704 AgeA_20.4362.2950.4352.2970.4332.3100.4262.3460.4282.3380.4292.3290.4282.3350.428 A_30.3153.1760.3133.1930.3123.2060.3103.2290.3063.2670.3083.2420.3083.2520.302 A_40.3063.2660.3063.2710.3023.3070.3033.3050.3023.3070.3023.3130.3013.3180.293 TypeT_20.5981.6720.5971.6740.5961.6770.5931.6870.5981.6730.5961.6780.5951.6800.592 ActionAction_20.3153.1700.3133.1970.3143.1810.3103.2280.3143.1850.3123.2010.3123.2010.311 Action_30.1995.0330.1985.0510.1985.0390.1945.1490.1975.070.1975.0880.1975.0890.195 Action_40.1596.2880.1586.3470.1596.3000.1556.4500.1576.3490.1576.3750.1576.3840.153 Size- - 0.9371.0670.9201.0870.9361.0680.9271.0790.9271.0790.9021.1090.877 CN- - 0.8531.173- - - - - - - - - - - MC- - - - 0.8951.118- - - - - - - - - GP- - - - - - 0.8691.151- - - - - - - TT- - - - - - - - 0.8751.143- - - - - EE- - - - - - - - - - 0.8301.2040.8241.2140.740 Zscore(Size)*Zscore(EE) - - - - - - - - - - - 0.9381.0660.922 DI- - - - - - - - - - - - - - 0.691
Table 4.26 Collinearity Diagnosis of the Impact of the Internal Conditions on DT Independent VariablesStep1Step2Step3Step4Step5Step6Step7 ToleranceVIFToleranceVIFToleranceVIFToleranceVIFToleranceVIFToleranceVIFTolerance PositionP_20.8071.2400.8041.2430.8011.2490.7951.2580.7961.2560.7951.2580.795 P_30.8211.2190.8041.2440.8011.2490.8141.2280.7981.2530.7981.2540.789 P_40.8201.2190.7831.2770.7891.2680.7971.2540.7721.2950.7721.2960.748 RegionR_20.7161.3960.7071.4150.6951.4390.7031.4220.6931.4440.6901.4490.688 R_30.6881.4540.6841.4620.6801.4710.6811.4690.6801.4710.6791.4730.676 R_40.7441.3440.7251.3800.7241.3810.7191.3900.7191.3920.7121.4040.711 AgeA_20.4362.2950.4342.3030.4352.3010.4352.3000.4342.3040.4332.3090.431 A_30.3153.1760.3113.2170.3123.2020.3083.2470.3083.2430.3073.2540.302 A_40.3063.2660.3053.2810.3043.2870.3003.3320.3023.3150.3003.3340.293 TypeT_20.5981.6720.5971.6750.5931.6870.5971.6760.5941.6820.5941.6820.592 ActionAction_20.3153.1700.3133.1950.3143.1870.3123.2020.3123.2030.3123.2040.311 Action_30.1995.0330.1985.0460.1985.0620.1965.1150.1965.0900.1965.0940.195 Action_40.1596.2880.1586.3220.1576.3530.1566.4200.1566.3980.1566.4140.152 Size- - 0.9391.0650.9391.0650.9361.0680.9391.0650.9391.0650.907 DS- - 0.8961.115- - - - - - - - - OC- - - - 0.8811.135- - - - - - - LS- - - - - - 0.8671.153- - - - - IC- - - - - - - - 0.8281.2070.8151.2270.667 Zscore(Size)*Zscore(IC) - - - - - - - - - - 0.9621.0400.952 DI- - - - - - - - - - - - 0.644
Table 4.27 Collinearity Diagnosis of the Impact of the External Environment on DI Independent Variables Step1Step2Step3Step4Step5Step6Step7 ToleranceVIFToleranceVIFToleranceVIFToleranceVIFToleranceVIFToleranceVIFTolerance PositionP_20.8071.2400.8021.2470.8031.2450.8001.2490.8041.2440.8001.2510.799 P_30.8211.2190.7931.2620.8201.2200.8061.2400.8141.2290.8051.2420.801 P_40.8201.2190.8081.2380.8131.2300.8051.2430.8171.2240.8081.2370.805 RegionR_20.7161.3960.7001.4290.7071.4140.7121.4040.7091.4110.7021.4240.702 R_30.6881.4540.6521.5330.6791.4720.6811.4680.6711.4900.6671.5000.666 R_40.7441.3440.7161.3970.7271.3760.7081.4120.7171.3950.7111.4070.706 Age A_20.4362.2950.4352.2970.4332.3100.4262.3460.4282.3380.4292.3290.428 A_30.3153.1760.3133.1930.3123.2060.3103.2290.3063.2670.3083.2420.308 A_40.3063.2660.3063.2710.3023.3070.3033.3050.3023.3070.3023.3130.301 Type T_20.5981.6720.5971.6740.5961.6770.5931.6870.5981.6730.5961.6780.595 ActionAction_20.3153.1700.3133.1970.3143.1810.3103.2280.3143.1850.3123.2010.312 Action_30.1995.0330.1985.0510.1985.0390.1945.1490.1975.0700.1975.0880.197 Action_40.1596.2880.1586.3470.1596.3000.1556.4500.1576.3490.1576.3750.157 Size - - 0.9371.0670.9201.0870.9361.0680.9271.0790.9271.0790.902 CN- - 0.8531.173- - - - - - - - - MC- - - - 0.8951.118- - - - - - - GP- - - - - - 0.8691.151- - - - - TT- - - - - - - - 0.8751.143- - - EE- - - - - - - - - - 0.8301.2040.824 Zscore(Size)*Zscore(EE)- - - - - - - - - - - - 0.938
Table 4.28 Collinearity Diagnosis of the Impact of the Internal Conditions on DI Independent Variables Step1Step2Step3Step4Step5Step6 ToleranceVIFToleranceVIFToleranceVIFToleranceVIFToleranceVIFTolerance PositionP_20.8071.2400.8041.2430.8011.2490.7951.2580.7961.2560.795 P_30.8211.2190.8041.2440.8011.2490.8141.2280.7981.2530.798 P_40.8201.2190.7831.2770.7891.2680.7971.2540.7721.2950.772 RegionR_20.7161.3960.7071.4150.6951.4390.7031.4220.6931.4440.690 R_30.6881.4540.6841.4620.6801.4710.6811.4690.6801.4710.679 R_40.7441.3440.7251.3800.7241.3810.7191.3900.7191.3920.712 Age A_20.4362.2950.4342.3030.4352.3010.4352.3000.4342.3040.433 A_30.3153.1760.3113.2170.3123.2020.3083.2470.3083.2430.307 A_40.3063.2660.3053.2810.3043.2870.3003.3320.3023.3150.300 Type T_20.5981.6720.5971.6750.5931.6870.5971.6760.5941.6820.594 ActionAction_20.3153.1700.3133.1950.3143.1870.3123.2020.3123.2030.312 Action_30.1995.0330.1985.0460.1985.0620.1965.1150.1965.0900.196 Action_40.1596.2880.1586.3220.1576.3530.1566.4200.1566.3980.156 Size - - 0.9391.0650.9391.0650.9361.0680.9391.0650.939 DS- - 0.8961.115- - - - - - - OC- - - - 0.8811.135- - - - - LS- - - - - - 0.8671.153- - - IC- - - - - - - - 0.8281.2070.815 Zscore(Size)*Zscore(IC) - - - - - - - - - - 0.962
4.5.3 The Results of Main Effect Test
In this study, Amos23.0 was used to process the mean value of the second-order factors model of the influencing factors of pharmaceutical enterprises’ DT. CN represents the mean value of customer needs, MC represents the mean value of market competition, GP represents the mean value of government policy, TT represents the mean value of digital technology, DS represents the mean value of digital strategy, OC represents the mean value of organization capability, and LS represents the mean value of leadership. Based on the data obtained from the questionnaire, parameter estimation and model fitting were carried out for the influencing factor model of pharmaceutical enterprises’ DT. The results are shown in Table 4.29, Table 4.30 and Figure 4.6.
Table 4.29 Fitting Indexes of the Influencing Factors Model of Pharmaceutical Enterprises’ DT
Fitting index χ２/df RMR GFI AGFI RMSEA NFI IFI CFI TLI
Criteria 1<χ２/df＜5 <0.1 ＞0.8 ＞0.8 ＜0.08 ＞0.9 ＞0.9 ＞0.9 ＞0.9 Actual value 1.680 0.061 0.903 0.886 0.041 0.925 0.968 0.968 0.965
Source: Mulaik et al. (1989).
As shown in table 4.29, χ２/df=1.680 (<5), RMR=0.061 (<0.1), RMSEA = 0.041 (0.08), and it showed that the model had good adaptability. GFI = 0.903, AGFI
= 0.886, NFI = 0.925, IFI = 0.968, CFI = 0.968, TLI = 0.965, so all the fitting indexes had reached the general criteria, indicating that the model established in this study was effective and had a good matching degree with the data collected from the questionnaire.
Table 4.30 Estimation of Latent Variable Parameters of the Influencing Factors Model of Pharmaceutical Enterprises’ DT
Hypothesis Hypothesis Paths
Unstandardized
Coefficients S.E. C.R. P Standardized Coefficients
H1 DT <--- EE 0.42 0.09 4.60 *** 0.27
H2 DI <--- EE 0.38 0.10 3.72 *** 0.24
H3 IC <--- EE 0.45 0.07 6.47 *** 0.52
H4 DI <--- IC 0.92 0.14 6.49 *** 0.50
H5 DT <--- IC 0.88 0.15 6.05 *** 0.49
H6 DT <--- DI 0.16 0.06 2.65 0.008 0.16
Note: *P＜0.05, **P＜0.01, ***P＜0.001
According to the results of path analysis in Table 4.30, the standardized path coefficient of the external environment on DT was 0.27 (T=4.60, P<0.001), indicating that the external environment had a significant effect on DT, so hypothesis H1 was valid; The standardized path coefficient of the external environment on digital innovation was 0.24 (T=3.72, P<0.01), indicating that the external environment had a significant effect on digital innovation, so the hypothesis H2 was valid;
The standardized path coefficient of the external environment on the internal conditions was 0.52 (T=6.47, P<0.001), indicating that the external environment had a significant effect on the internal conditions, so the hypothesis H3 was valid;
The standardized path coefficient of the internal conditions on digital innovation was 0.50 (T=6.49, P<0.001), indicating that the internal conditions had a significant effect on digital innovation, so the hypothesis H4 was valid;
The standardized path coefficient of the internal conditions on DT was 0.49 (T=6.05, P < 0.001), indicating that the internal conditions had a significant effect on DT, so the hypothesis H5 was valid;
The standardized path coefficient of digital innovation on DT was 0.16 (T=2.65, P <0.05), indicating that digital innovation had a significant effect on DT, so the hypothesis H6 was valid.
Figure 4.6 The SEM Model of the Influencing Factors of Pharmaceutical Enterprises’
DT (standardized)
4.5.4 The Results of Moderating Effect Test 4.5.4.1 Steps of Moderating Effect Test
The main steps of moderating effect test in this study include the following:
1) Have regression of the dependent variables on the independent variables and moderating variables;
2) Have regression of the dependent variables on the independent variables, moderating variable and the multiplicative term of the independent variables’ Zscore and the moderating variables’ Zscore. The regression coefficient of the multiplicative term is significant, and R2 is significantly improved, and the model interpretation ability is enhanced. It shows that the moderating variables have significant moderating effect on the dependent variables and independent variables;
3) Have regression of the mediating variables on the independent variables, moderating variables and the multiplicative term of the independent variables’ Zscore and moderating variables’ Zscore;
4) Have regression of the dependent variables on the independent variables, moderating variables and the multiplicative term of independent variable’s Zscore and moderating variables’ Zscore and the mediating variables. The regression coefficient of the mediating variables is significant, and R2 is significantly improved, and the explanatory ability of the model is enhanced. It shows that the mediating effect of the moderating variables is significant.
5) If the regression coefficient of the multiplication term in the step (3) is not significant, it is considered that the moderating effect of the moderating variables is entirely through the mediating variables.
4.5.4.2 The Moderating Effect Test of Company Size on the Relationship between the External Environment and DT For the sake of better testing the influence of moderating variables and avoid the interference from other variables, this study used the hierarchical regression method for testing. The Model 1 in Table 4.31 used control variables (e.g. Position, Region, Age, Type, Action) to perform regression analysis on DT.