| Title |
Exploring drivers of eco-innovation in manufacturing firms' circular economy transition: an awareness, motivation, capability perspective |
| ID_Doc |
4125 |
| Authors |
Liu, ZY; Han, SH; Yao, MQ; Gupta, S; Laguir, I |
| Title |
Exploring drivers of eco-innovation in manufacturing firms' circular economy transition: an awareness, motivation, capability perspective |
| Year |
2023 |
| Published |
|
| DOI |
10.1007/s10479-023-05473-5 |
| Abstract |
Considering the crucial role of eco-innovation in the circular economy (CE) transition, the burgeoning literature from multiple disciplines explores factors that drive pro-CE eco-innovation (circular EI). However, mixed findings hamper us from understanding circular EI drivers comprehensively. Drawing upon the awareness-motivation-capability framework, we leverage deep learning techniques to identify a firm's social engagement sourced from its digital CSR communications on social media (CSR social engagement) as an awareness variable, and apply stochastic frontier analysis to evaluate technological innovation efficiency as the capability one. Then, we argue that these two drivers directly influence a firm's circular EI developments. Further, we also propose that environmental regulations and industrial competition, as the external institutional motivations, can moderate the direct effects of CSR social engagement and technological innovation efficiency on circular EI. Based on a unique dataset of 181 listed manufacturing firms in China from 2014 to 2019, our empirical findings suggest that CSR social engagement and technological innovation efficiency facilitate circular EI developments. Surprisingly, environmental regulation and industrial competition have distinct moderating effects on these positive relationships. Ultimately, this study provides manifold theoretical and managerial insights for policy-makers and manufacturing firms to tackle circular EI issues. |
| Author Keywords |
Eco-innovations; Circular economy; Digital CSR communications; AMC Framework; Deep learning techniques |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001050297100003 |
| WoS Category |
Operations Research & Management Science |
| Research Area |
Operations Research & Management Science |
| PDF |
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