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Cited by 499

The DOI matches its Crossref record; 81 of the 81 references it cites were found in a registry or an index.

Green innovation to respond to environmental regulation: How external knowledge adoption and green absorptive capacity matter?

Zhang, Jianming, Liang, Gongqian, Feng, Taiwen, Yuan, Chunlin, Jiang, Wenbo·2020·Business Strategy and the Environment
DOI 10.1002/bse.2349
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Abstract

Although environmental regulations have been considered as important forces of conducting green innovation, how and under what conditions they affect green innovation are still unclear. Drawing from institutional theory, this study used survey data from 237 manufacturing firms in China to investigate how two dimensions of environmental regulations (i.e., command and control regulation and market‐based regulation) affect green product innovation and green process innovation. Further, this article examined the mediating role of external knowledge adoption and the moderating role of green absorptive capacity. Our results indicate that both command and control regulation and market‐based regulation have positive influences on external knowledge adoption. External knowledge adoption fully mediates these positive relationships. In addition, green absorptive capacity only strengthens the positive impact of market‐based regulation on external knowledge adoption. Our study contributes to institutional theory and green innovation literature.

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Metadata

Authors
Zhang, J., Liang, G., Feng, T., Yuan, C., & Jiang, W.
Venue
Business Strategy and the Environment
Publisher
Wiley
Published
2019-06-25
Volume
29
Issue
1
Pages
39-53
ISSN
  • 0964-4733 (ISSN-L)
  • 1099-0836
DOI
10.1002/bse.2349
OpenAlex
W2955882674
Cited by
499
References
81
FWCI
41.15
Type
journal article
Language
en
Open access
closed

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.