How Do Anticipated and Self Regulations and Information Sourcing Openness Drive Firms to Implement Eco-Innovation? Evidence from Korean Manufacturing Firms
Abstract
:1. Introduction
2. Theorizing and Formulating Hypotheses
2.1. Concepts of Eco-Innovation
2.2. Drivers of Eco-Innovation
2.2.1. External Drivers of Eco-Innovation
2.2.2. Internal Drivers of Eco-Innovation
3. Methods
3.1. Research Model
3.2. Data Sample
3.3. Descriptive Statstics
3.4. Analytical Model
4. Empirical Findings and Discussion
4.1. The Empirical Findings on the External Factors
4.2. The Empirical Findings on the Internal Factors
4.3. The Empirical Findings on the Control Variables
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Variables | Label | Definition | Type | Source | |
---|---|---|---|---|---|
IVs | Anticipated Regulation | Exp-Regu | Predicted environmental regulations or taxes | Binary | KIS 2010 |
Self-regulation | Self-Regu | Voluntary conventions or agreements within the industry | Binary | ||
Information Sourcing Openness | Breadth | The number of used external information sources for innovative activities | Count | ||
Importance | The average degree of importance of used information for innovative activities obtained from external sources (=Sum of the extent to which used information obtained from external sources is important/The number of used external sources) | Ratio | |||
CVs | Regulatory Pull/Push | Pre-Regu | Existing environmental regulations or taxes | Binary | |
Subsidy | Using subsidiaries from the government or financial benefits related to eco-innovation | Binary | |||
Market Pull | Mkt-Pull | Market demands for eco-innovation from the current or future consumers | Binary | ||
Energy Consumption | Eng-Cons | The average annual energy consumption of an industry in 2012 =Log (Total energy consumption of an industry in 109 kcal/The number of firms of an industry) | Log | NETIS | |
Innovation Capability | Inno-Capa | The number of innovations on products, processes, organizations, and marketing | Count | KIS 2010 | |
Technology Push | In-R&D | Log (0.1+% of internal R&D expense to sales volumes 2007~2009) | Log | ||
Ex-R&D | Log (0.1+% of external R&D expense to sales volumes 2007~2009) | Log | |||
Firm Size | Log (The number of full-time employees in 2007) | Log | |||
Firm Age | The age of the firm (=2010-the year of establishment) | Count | |||
DVs | Eco-Process Innovation | D1 | Reduction of material consumption per output unit | Binary | |
D2 | Reduction of energy consumption per output unit | Binary | |||
D3 | Reduction of CO2 emissions | Binary | |||
D4 | Replacement of polluting and hazardous materials with less environmentally harmful ones | Binary | |||
D5 | Reduction of soil, water, noise, and air pollution | Binary | |||
D6 | Recycle of waste, water and materials | Binary | |||
Eco-Product Innovation | D7 | Reduction of energy consumption | Binary | ||
D8 | Reduction of soil, water, noise, and air pollution | Binary | |||
D9 | Improvement in recyclability after product use | Binary | |||
Total Number | D10 | D1 + D2 + D3 + D4 + D5 + D6 + D7 + D8 + D9 | Count |
Variables | Observations | Mean | Std. Deviation | Max | Min |
---|---|---|---|---|---|
D1 | 1824 | 0.282 | 0.450 | 0 | 1 |
D2 | 1824 | 0.298 | 0.457 | 0 | 1 |
D3 | 1824 | 0.247 | 0.432 | 0 | 1 |
D4 | 1824 | 0.318 | 0.466 | 0 | 1 |
D5 | 1824 | 0.280 | 0.449 | 0 | 1 |
D6 | 1824 | 0.349 | 0.477 | 0 | 1 |
D7 | 1824 | 0.316 | 0.465 | 0 | 1 |
D8 | 1824 | 0.240 | 0.427 | 0 | 1 |
D9 | 1824 | 0.270 | 0.444 | 0 | 1 |
D10 | 1824 | 2.600 | 3.027 | 0 | 9 |
Exp-Regu | 1824 | 0.202 | 0.402 | 0 | 1 |
Self-Regu | 1824 | 0.165 | 0.371 | 0 | 1 |
Breadth | 1824 | 6.852 | 3.297 | 1 | 11 |
Importance | 1824 | 2.944 | 0.764 | 1 | 5 |
Pre-Regu | 1824 | 0.158 | 0.365 | 0 | 1 |
Subsidy | 1824 | 0.054 | 0.227 | 0 | 1 |
Mkt-Pull | 1824 | 0.338 | 0.473 | 0 | 1 |
Eng-Cons | 1824 | 0.545 | 1.712 | −2.440 | 7.688 |
Inno-Capa | 1824 | 2.746 | 1.122 | 0 | 4 |
In-R&D | 1824 | −2.050 | 0.360 | −2.303 | 2.565 |
Ex-R&D | 1824 | −2.259 | 0.126 | −2.303 | −0.640 |
Firm Size | 1824 | 4.346 | 1.406 | 1.131 | 10.292 |
Firm Age | 1824 | 19.730 | 14.034 | 4 | 94 |
ZIP | BIC = 5914.050 | AIC = 5803.874 | Prefer | Over | Evidence |
---|---|---|---|---|---|
vs. ZINB | BIC = 5858.568 | dif = 55.481 | ZINB | ZIP | Very strong |
AIC = 5742.884 | dif = 60.990 | ZINB | ZIP | ||
LRX2 = 62.990 | prob = 0.000 | ZINB | ZIP | p = 0.000 |
Number of obs = 1824 Wald chi2 (117) = 1983.06 Prob > chi2 = 0.000 Log likelihood = −5789.3644 | Eco-Process Innovation | |||||||
---|---|---|---|---|---|---|---|---|
D1 Material | D2 Energy | D3 CO2 | D4 Danger | D5 Soil/Water/Noise/Air | D6 Recycle | |||
IVs | External Factors | Exp-Regu | 0.6972 *** | 0.8866 *** | 0.8197 *** | 0.9260 *** | 0.8009 *** | 0.7427 *** |
(0.0813) | (0.0797) | (0.0812) | (0.0808) | (0.0791) | (0.0812) | |||
Self-Regu | 0.7235 *** | 0.7728 *** | 0.7790 *** | 0.6439 *** | 0.7787 *** | 0.8929 *** | ||
(0.0865) | (0.0845) | (0.0872) | (0.0876) | (0.0863) | (0.0862) | |||
Internal Factors | Breadth | 0.0257 * | 0.0221 * | 0.0193 | 0.0093 | 0.0213 | −0.0044 | |
(0.0136) | (0.0131) | (0.0136) | (0.0131) | (0.0131) | (0.0128) | |||
Importance | 0.0929 * | 0.1122 ** | 0.0767 | 0.0534 | −0.0082 | 0.0176 | ||
(0.0519) | (0.0502) | (0.0517) | (0.0498) | (0.0487) | (0.0480) | |||
CVs | External Factors | Pre-Regu | 0.6731 *** | 0.5427 *** | 0.5876 *** | 0.9656 *** | 1.0153 *** | 0.9943 *** |
(0.0902) | (0.0889) | (0.0897) | (0.0905) | (0.0876) | (0.0896) | |||
Subsidy | 0.8025 *** | 0.7789 *** | 0.7982 *** | 0.8422 *** | 0.8155 *** | 0.8056 *** | ||
(0.1347) | (0.1309) | (0.1374) | (0.1424) | (0.1342) | (0.1414) | |||
Mkt-Pull | 1.1959 *** | 1.0813 *** | 1.0398 *** | 1.0751 *** | 1.0348 *** | 1.0669 *** | ||
(0.0767) | (0.0744) | (0.0775) | (0.0753) | (0.0758) | (0.0736) | |||
Eng-Cons | 0.0228 | 0.0406* | 0.0637 *** | 0.0066 | 0.0565 *** | 0.0801 *** | ||
(0.0217) | (0.0211) | (0.0216) | (0.0214) | (0.0209) | (0.0207) | |||
Internal Factors | Inno-Capa | 0.1777 *** | 0.1023 *** | 0.0964** | 0.1787 *** | 0.0804** | 0.1313 *** | |
(0.040) | (0.0384) | (0.0398) | (0.0390) | (0.0386) | (0.0377) | |||
In-R&D | −0.0841 | 0.0917 | −0.0383 | −0.1206 | −0.1576 | −0.3301 *** | ||
(0.1143) | (0.0991) | (0.1131) | (0.1188) | (0.1167) | (0.1212) | |||
Ex-R&D | 0.0880 | −0.0703 | −0.1743 | 0.4359 | −0.0083 | 0.0728 | ||
(0.2924) | (0.2778) | (0.3082) | (0.3071) | (0.3073) | (0.2946) | |||
Firm Size | 0.0508 | 0.0323 | 0.1161 *** | 0.0454 | −0.0062 | 0.0124 | ||
(0.0342) | (0.0328) | (0.0337) | (0.0334) | (0.0327) | (0.0327) | |||
Firm Age | 0.0006 | 0.0071 ** | −0.0035 | 0.0011 | 0.0078 *** | 0.0021 | ||
(0.0030) | (0.0029) | (0.0030) | (0.0030) | (0.0029) | (0.0029) | |||
_cons | −2.8505 *** | −2.6194 *** | −3.3425 *** | −1.7839 *** | −2.5646 *** | −2.4832 *** | ||
(0.6849) | (0.6480) | (0.7128) | (0.6960) | (0.7059) | (0.6778) |
Number of obs = 1824 Wald chi2 (117) = 1983.06 Prob > chi2 = 0.000 Log likelihood = −5789.3644 | Eco-Product Innovation | Total Number Negative Binomial Regression Part (eco-innovation > 0) | ||||
---|---|---|---|---|---|---|
D7 Energy | D8 Soil/Water /Noise/Air | D9 Recycle | Number of obs = 1824 Nonzero obs = 1049 Zero obs = 775 LR chi2 (13) = 185.59 Prob > chi2 = 0.000 Inflation model = logit Log likelihood = −2846.924 | |||
IVs | External Factors | Exp-Regu | 0.4974 *** | 0.6616 *** | 0.5123 *** | 0.1671 *** |
(0.0807) | (0.0798) | (0.0788) | (0.0380) | |||
Self-Regu | 0.8165 *** | 0.6866 *** | 0.7677 *** | 0.1661 *** | ||
(0.0851) | (0.0833) | (0.0827) | (0.0392) | |||
Internal Factors | Breadth | 0.0203 | 0.0242 * | 0.0268 ** | 0.0297 *** | |
(0.0129) | (0.0133) | (0.0129) | (0.0069) | |||
Importance | −0.0255 | 0.0582 | 0.0319 | 0.0456 * | ||
(0.0487) | (0.0490) | (0.0481) | (0.0252) | |||
CVs | External Factors | Pre-Regu | 0.5879 *** | 0.6614 *** | 0.6534 *** | 0.1126 *** |
(0.0882) | (0.0882) | (0.0876) | (0.0420) | |||
Subsidy | 0.7525 *** | 0.6405 *** | 0.4983 *** | 0.2081 *** | ||
(0.1395) | (0.1328) | (0.1353) | (0.0575) | |||
Mkt-Pull | 1.2199 *** | 1.1177 *** | 1.0173 *** | 0.2477 *** | ||
(0.0731) | (0.0762) | (0.0726) | (0.0403) | |||
Eng-Cons | −0.0039 | 0.0286 | −0.0046 | 0.0166 | ||
(0.0212) | (0.0209) | (0.0205) | (0.0101) | |||
Internal Factors | Inno-Capa | 0.1519 *** | 0.1482 *** | 0.1322 *** | 0.0574 *** | |
(0.0376) | (0.0390) | (0.0375) | (0.0205) | |||
In-R&D | −0.1117 | −0.1193 | −0.4013 *** | −0.1354 ** | ||
(0.1143) | (0.1194) | (0.1261) | (0.0607) | |||
Ex-R&D | 0.1870 | 0.4169 | −0.2644 | 0.1298 | ||
(0.2963) | (0.3067) | (0.3310) | (0.1530) | |||
Firm Size | 0.0620 * | 0.0334 | −0.0622 * | 0.0281 * | ||
(0.0326) | (0.0327) | (0.0319) | (0.0154) | |||
Firm Age | −0.0020 | −0.0001 | 0.0014 | 0.0011 | ||
(0.0029) | (0.0029) | (0.0029) | (0.0014) | |||
_cons | −1.9993 *** | −1.9692 *** | −3.3432 *** | 0.4843 | ||
(0.6752) | (0.6965) | (0.7474) | (0.3520) | |||
Inflated (eco-innovation = 0) | Pre-Regu | −26.1298 | ||||
(19,138.8200) | ||||||
Subsidy | −25.6075 | |||||
(31,736.5000) | ||||||
Mkt-Pull | −26.3002 | |||||
(13,302.1500) | ||||||
Inno-Capa | −0.3999 *** | |||||
(0.0768) | ||||||
Firm Size | −0.1716 *** | |||||
(0.0642) | ||||||
_cons | 3.0180 *** | |||||
(0.3387) | ||||||
/lnalpha | −2.3352 *** | |||||
(0.1604) | ||||||
alpha | 0.0968 |
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Yu, C.; Park, J.; Hwang, Y.S. How Do Anticipated and Self Regulations and Information Sourcing Openness Drive Firms to Implement Eco-Innovation? Evidence from Korean Manufacturing Firms. Int. J. Environ. Res. Public Health 2019, 16, 2678. https://doi.org/10.3390/ijerph16152678
Yu C, Park J, Hwang YS. How Do Anticipated and Self Regulations and Information Sourcing Openness Drive Firms to Implement Eco-Innovation? Evidence from Korean Manufacturing Firms. International Journal of Environmental Research and Public Health. 2019; 16(15):2678. https://doi.org/10.3390/ijerph16152678
Chicago/Turabian StyleYu, Cheon, Junghoon Park, and Yun Seop Hwang. 2019. "How Do Anticipated and Self Regulations and Information Sourcing Openness Drive Firms to Implement Eco-Innovation? Evidence from Korean Manufacturing Firms" International Journal of Environmental Research and Public Health 16, no. 15: 2678. https://doi.org/10.3390/ijerph16152678