The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Analyzing the relationship between green innovation and environmental performance in large manufacturing firms
Open accessAbstract
Extant literature suggests that green intellectual capital (GIC), green human resource management (GHRM), and green innovation (GI) impacts the environmental performance of firms. In this paper, we argue that the relationship between GIC, GHRM, GI and environmental performance is more complex than previously suggested. We propose that neither GIC nor GHRM are directly related to environmental performance. We argue instead that GI mediates the relationships between GIC, GHRM, and environmental performance. Further, we suggest that environmental strategies are directly related to environmental performance, while also moderating the relationship between GI and environmental performance. We tested our proposed model on a sample of 244 large manufacturing firms. The results of a structural equation modeling analysis provide support for most of our hypotheses.
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Cites
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Cited by
Sahoo, S., Kumar, A., & Upadhyay, A. (2023) · Business Strategy and the Environment · doi:10.1002/bse.3160 · Cited by 606
Badghish, S., & Soomro, Y. A. (2024) · Sustainability · doi:10.3390/su16051864 · Cited by 261
Khan, P. A., Johl, S. K., & Akhtar, S. (2021) · Journal of Risk and Financial Management · doi:10.3390/jrfm14120605 · Cited by 128
Metadata
- Authors
- Rehman, S. U., Kraus, S., Shah, S. A., Khanin, D., & Mahto, R. V.
- Venue
- Technological Forecasting and Social Change
- Publisher
- Elsevier BV
- Published
- 2021-02
- Volume
- 163
- Pages
- 120481
- ISSN
- 0040-1625 (ISSN-L)
- OpenAlex
- W3106819321
- Cited by
- 773
- References
- 32
- FWCI
- 78.42
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Rehman, S. U., Kraus, S., Shah, S. A., Khanin, D., & Mahto, R. V. (2021). Analyzing the relationship between green innovation and environmental performance in large manufacturing firms. Technological Forecasting and Social Change. https://doi.org/10.1016/j.techfore.2020.120481
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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