Accelerating Green Innovation Performance from the Relations of Network Potential, Absorptive Capacity, and Environmental Turbulence
Abstract
:1. Introduction
2. Theory Background and Hypotheses
2.1. Definition of Key Concepts
2.1.1. Network Potential
2.1.2. Green Innovation Performance (GIP)
2.1.3. Absorptive Capacity (AC)
2.1.4. Environmental Turbulence (ET)
2.2. Network Potential and GIP
- (1)
- NPC and GIP
- (2)
- NSR and GIP
- (3)
- NRC and GIP
2.3. The Mediating Role of AC
2.3.1. Network Potential and AC
- (1)
- NPC and AC
- (2)
- NSR and AC
- (3)
- NRC and AC
2.3.2. AC and GIP
2.4. The Moderating Role of ET
2.5. The Moderated Mediation Effect
2.6. The Theoretical Model
3. Research Design
3.1. Sample Selection and Data Collection
3.2. Variable Measurement
3.2.1. Independent Variable: Network Potential
3.2.2. Dependent Variable: Green Innovation Performance (GIP)
3.2.3. Mediating Variable: Absorptive Capacity (AC)
3.2.4. Moderating Variable: ET
3.2.5. Control Variable: Firm Age and Firm Scale
4. Empirical Analysis
4.1. Common Method Bias (CMB) Testing
4.2. Reliability and Validity Testing
- Content validity tests whether the subordinate items of each variable are reasonable. The scale of this study was modified regarding renowned mature scales and was pre-investigated and revised by experts; therefore, this scale had good content validity.
- Convergent validity tests the correlation between the subordinate items of each variable and the variable. It is tested using factor loading and average variance extracted (AVE); the closer the factor loading is to 1, the higher the convergent validity of the scale. It can be seen from Table 2 that the factor loadings of the three dimensions of network potential (NPC, NSR, and NRC), AC, ET, and GIP all exceeded 0.7, and all AVE values exceeded 0.5. Therefore, the variables in this scale had good convergent validity.
- Discriminant validity tests the degree of correlation between the subordinate items of each variable and other variables. Fornell and Larcker [100] proposed comparing the “average variance extracted (AVE) of each latent variable” with “the square of the correlation coefficient between the latent variable and other latent variables” to test the discriminant validity, namely, comparing the relative size of “the square root of a variable’s AVE” and “the correlation coefficient of this variable and other variables”. The data in parentheses are the square roots of AVE, and “other values” are the correlation coefficients between these variables and other variables. Because the square roots of AVE were all greater than “other values”, the variables of this scale had good discriminant validity (see Table 3).
4.3. Correlation Analysis
4.4. Hypotheses Testing
4.4.1. Direct Effect Testing
4.4.2. Mediating Effect Testing
4.4.3. Moderating Effect Testing
4.4.4. Moderated Mediation Effect Testing
5. Conclusions and Discussion
5.1. Conclusions
5.2. Theoretical Implications
5.3. Practical Implications
6. Limitations and Future Research Direction
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Variable | Item | Specific Expression |
---|---|---|
Network position centrality (NPC) | NPC1 | Our firm has a high reputation in the alliance network. |
NPC2 | Many firms are willing to cooperate with our firm. | |
NPC3 | The contact between partners is often through our firm. | |
NPC4 | When technical advice or technical support is needed, partners often seek help from our firm. | |
Network structure richness (NSR) | NSR1 | Our firm has more partners. |
NSR2 | The types of partners of our firm are relatively diverse. | |
NSR3 | The proportion of direct connections between our firm and its partners is relatively high. | |
NSR4 | More information and knowledge are flowing in the cooperation network of our firm. | |
Network relationship closeness (NRC) | NRC1 | The relationship between our firm and its partners is stable and involves mutual trust. |
NRC2 | Our firm has frequent exchanges with partners. | |
NRC3 | Partners will not take advantage of our firm’s weaknesses for profit. | |
NRC4 | Partners and our firm often solve problems together. | |
Green innovation performance (GIP) | GIP 1 | Our firm selects product materials that produce the least pollution for product development or design. |
GIP2 | Our firm chooses product materials that consume the least energy and resources for product development or design. | |
GIP 3 | When conventional methods fail, our firm will adopt new environmental management practices. | |
GIP 4 | During the production process, our firm can effectively reduce the discharge of harmful substances or waste. | |
GIP 5 | During the production process, our firm will recycle waste and emissions to process and reuse them. | |
Absorptive capacity (AC) | AC1 | Our firm can quickly and effectively obtain useful knowledge and information from the outside world. |
AC2 | Our firm can quickly analyze and understand newly acquired technologies and knowledge. | |
AC3 | Our firm can effectively integrate its existing relevant knowledge and technology with newly digested technology and knowledge. | |
AC4 | Our firm can quickly apply the new technologies and knowledge that it has mastered to actual research and development or production. | |
Environmental turbulence (ET) | ET1 | In the industry where our firm is located, the speed of technology change is very fast. |
ET2 | In the industry where our firm is located, it is difficult to predict the direction of technological development five years ahead. | |
ET3 | In the industry where our firm is located, products or services are updated quickly. | |
ET4 | In the industry where our firm is located, the needs and preferences of customers often change. | |
ET5 | In the industry where our firm is located, market competition is very fierce and the price is the main means of competition. |
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Characteristic | Category | Frequency | Percentage |
---|---|---|---|
Firm age | 3 to 5 years | 17 | 7.30% |
6 to 9 years | 35 | 15.02% | |
10 to 19 years | 105 | 45.06% | |
20 years and above | 76 | 32.62% | |
Industry | Pharmaceutical manufacturing | 46 | 19.74% |
Aviation, spacecraft, and equipment manufacturing | 32 | 13.73% | |
Manufacturing of electronic and communication equipment | 61 | 26.18% | |
Computer and office equipment manufacturing | 53 | 22.75% | |
Manufacturing of medical equipment and instruments | 29 | 12.45% | |
Manufacturing of information chemicals | 12 | 5.15% | |
Firm size | 99 people and below | 21 | 9.01% |
100 to 299 people | 71 | 30.47% | |
300 to 999 people | 84 | 36.05% | |
1000 to 2999 people | 31 | 13.30% | |
3000 people and above | 26 | 11.16% | |
R&D investment | Less than 3% | 26 | 11.16% |
3% (inclusive) to 5% | 88 | 37.77% | |
5% (inclusive) to 10% | 79 | 33.91% | |
10% and above | 40 | 17.17% | |
Number of alliances | 2 to 5 | 62 | 26.61% |
6 to 9 | 94 | 40.34% | |
10 to 19 | 51 | 21.89% | |
20 to 49 | 15 | 6.44% | |
50 and above | 11 | 4.72% | |
Area | Zhejiang (East China) | 56 | 24.03% |
Guangdong (South China) | 61 | 26.18% | |
Sichuan (West China) | 65 | 27.90% | |
Beijing (North China) | 51 | 21.89% |
Variable | Item | Factor Loading | Cronbach’s α | CR | AVE |
---|---|---|---|---|---|
NPC | NPC1 | 0.855 | 0.884 | 0.887 | 0.662 |
NPC2 | 0.816 | ||||
NPC3 | 0.806 | ||||
NPC4 | 0.775 | ||||
NSR | NSR1 | 0.758 | 0.831 | 0.861 | 0.607 |
NSR2 | 0.794 | ||||
NSR3 | 0.765 | ||||
NSR4 | 0.799 | ||||
NRC | NRC1 | 0.795 | 0.877 | 0.873 | 0.632 |
NRC2 | 0.830 | ||||
NRC3 | 0.809 | ||||
NRC4 | 0.742 | ||||
AC | AC1 | 0.829 | 0.882 | 0.919 | 0.739 |
AC2 | 0.861 | ||||
AC3 | 0.884 | ||||
AC4 | 0.864 | ||||
ET | ET1 | 0.780 | 0.849 | 0.894 | 0.628 |
ET2 | 0.784 | ||||
ET3 | 0.808 | ||||
ET4 | 0.793 | ||||
ET5 | 0.797 | ||||
GIP | GIP1 | 0.801 | 0.873 | 0.909 | 0.668 |
GIP2 | 0.829 | ||||
GIP3 | 0.830 | ||||
GIP4 | 0.854 | ||||
GIP5 | 0.769 |
Variable | M | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
---|---|---|---|---|---|---|---|---|---|---|
1. Firm age | 16.674 | 9.491 | — | |||||||
2. Firm size | 2.970 | 1.090 | 0.446 ** | — | ||||||
3. NPC | 3.600 | 0.887 | 0.099 | 0.128 | (0.814) | |||||
4. NSR | 3.846 | 0.733 | 0.090 | 0.008 | 0.360 ** | (0.779) | ||||
5. NRC | 3.762 | 0.773 | 0.086 | 0.102 | 0.445 ** | 0.269 ** | (0.795) | |||
6. AC | 3.845 | 0.765 | 0.111 | 0.117 | 0.413 ** | 0.350 ** | 0.580 ** | (0.860) | ||
7. ET | 3.786 | 0.754 | −0.061 | −0.022 | −0.145 * | −0.045 | −0.011 | 0.082 | (0.792) | |
8. GIP | 3.799 | 0.793 | −0.003 | 0.009 | 0.379 ** | 0.333 ** | 0.376 ** | 0.382 ** | 0.104 | (0.817) |
Variable | GIP (M1→M6) | AC (M7→M8) | |||||||
---|---|---|---|---|---|---|---|---|---|
M1 | M2 | M3 | M4 | M5 | M6 | M7 | M8 | ||
Control variable | Firm age | −0.009 (−0.123) | −0.053 (−0.820) | −0.057 (−0.887) | −0.038 (−0.553) | −0.033 (−0.481) | −0.031 (−0.463) | 0.074 (1.012) | 0.024 (0.420) |
Firm size | 0.013 (0.179) | −0.020 (−0.301) | −0.026 (−0.402) | −0.020 (−0.286) | −0.019 (−0.284) | −0.020 (−0.299) | 0.084 (1.153) | 0.040 (0.683) | |
Independent variable | NPC | 0.211 ** (3.107) | 0.189 ** (2.780) | 0.135 * (2.237) | |||||
NSR | 0.199 ** (3.153) | 0.171 ** (2.677) | 0.174 ** (3.096) | ||||||
NRC | 0.235 *** (3.594) | 0.160 * (2.179) | 0.467 *** (8.023) | ||||||
Mediating variable | AC | 161 * (2.168) | 0.389 *** (6.317) | 0.382 *** (6.194) | 0.432 *** (6.998) | ||||
Moderating variable | ET | 0.070 (1.136) | 0.052 (0.874) | ||||||
Interaction | AC*ET | 0.221 *** (3.621) | |||||||
Model summary | R2 | 0 | 0.235 | 0.250 | 0.149 | 0.153 | 0.200 | 0.018 | 0.395 |
F | 0.017 | 13.931 *** | 12.582 *** | 13.317 *** | 10.323 *** | 11.319 *** | 2.116 | 29.656 *** |
Path | Effect Type | Effect Value | BootSE | Bootstrap 95% CI | |
---|---|---|---|---|---|
LL | UL | ||||
NPC→AC→GIP | Total effect | 0.211 | 0.068 | 0.077 | 0.345 |
Direct effect | 0.189 | 0.068 | 0.055 | 0.323 | |
Indirect effect | 0.022 | 0.008 | 0.011 | 0.035 | |
NSR→AC→GIP | Total effect | 0.199 | 0.063 | 0.075 | 0.323 |
Direct effect | 0.171 | 0.064 | 0.045 | 0.297 | |
Indirect effect | 0.028 | 0.011 | 0.014 | 0.045 | |
NRC→AC→GIP | Total effect | 0.236 | 0.066 | 0.106 | 0.365 |
Direct effect | 0.161 | 0.074 | 0.015 | 0.306 | |
Indirect effect | 0.075 | 0.037 | 0.031 | 0.126 |
Independent Variable | Conditional Indirect Effect | Moderated Mediation Effect | |||||||
---|---|---|---|---|---|---|---|---|---|
Moderating Variable | Effect | BootSE | BootLLCI | BootULCI | INDEX | BootSE | BootLLCI | BootULCI | |
NPC | Low | 0.004 | 0.014 | −0.026 | 0.033 | 0.022 | 0.014 | 0.001 | 0.054 |
Mean | 0.026 | 0.015 | 0.001 | 0.059 | |||||
High | 0.048 | 0.025 | 0.005 | 0.102 | |||||
NSR | Low | 0.005 | 0.018 | −0.030 | 0.044 | 0.028 | 0.018 | 0.002 | 0.070 |
Mean | 0.033 | 0.021 | 0.004 | 0.082 | |||||
High | 0.062 | 0.034 | 0.011 | 0.143 | |||||
NRC | Low | 0.013 | 0.047 | −0.067 | 0.118 | 0.077 | 0.032 | 0.014 | 0.139 |
Mean | 0.090 | 0.043 | 0.018 | 0.185 | |||||
High | 0.166 | 0.059 | 0.065 | 0.297 |
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Song, S.; Hossin, M.A.; Yin, X.; Hosain, M.S. Accelerating Green Innovation Performance from the Relations of Network Potential, Absorptive Capacity, and Environmental Turbulence. Sustainability 2021, 13, 7765. https://doi.org/10.3390/su13147765
Song S, Hossin MA, Yin X, Hosain MS. Accelerating Green Innovation Performance from the Relations of Network Potential, Absorptive Capacity, and Environmental Turbulence. Sustainability. 2021; 13(14):7765. https://doi.org/10.3390/su13147765
Chicago/Turabian StyleSong, Shuizheng, Md Altab Hossin, Xiaohua Yin, and Md Sajjad Hosain. 2021. "Accelerating Green Innovation Performance from the Relations of Network Potential, Absorptive Capacity, and Environmental Turbulence" Sustainability 13, no. 14: 7765. https://doi.org/10.3390/su13147765