| Title |
Institutional pressures as drivers of circular economy in firms: A machine learning approach |
| ID_Doc |
1637 |
| Authors |
Arranz, CFA; Sena, V; Kwong, C |
| Title |
Institutional pressures as drivers of circular economy in firms: A machine learning approach |
| Year |
2022 |
| Published |
|
| DOI |
10.1016/j.jclepro.2022.131738 |
| Abstract |
This paper investigates how institutional pressures affect the development of Circular Economy (CE) in firms. Using Institutional Entrepreneurship as a theoretical framework, this paper considers three different levels of institutional pressures (coercive, normative, and mimetic) to examine the effect of each pressure and their interactions on the development of CE. Seeking to clarify the debate on the effect of institutional pressures, this paper considers that the main limitation arises from the fact that previous research has analysed the relationship between institutional pressures without considering the interaction between them and the non-linearity of the processes. Deviating from previous papers, our analysis combines regression methods with Machine learning (i.e. Artificial Neural Networks), and employs data from the EU survey on Public Consultation on the Circular Economy. This research finds that while coercive pressures have a compulsory effect on the development of CE, mimetic and normative pressures do not have an effect by themselves, but only in interaction with coercive pressures. Moreover, this paper shows that the application of machine learning tools has an important contribution in solving interaction problems. From the perspective of environmental policy, this means that a comprehensive policy is required, which implies the coexistence or interaction of the three types of pressures. |
| Author Keywords |
Institutional pressures; Circular economy; Machine learning; ANN Model |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000805764100002 |
| WoS Category |
Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences |
| Research Area |
Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology |
| PDF |
https://doi.org/10.1016/j.jclepro.2022.131738
|