The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Green supply chain management in China: pressures, practices and performance
Abstract
Abstract Purpose – Green supply chain management (GSCM) has emerged as a key approach for enterprises seeking to become environmentally sustainable. This paper aims to evaluate and describe GSCM drivers, practices and performance among various Chinese manufacturing organizations. Design/methodology/approach – Based on a literature review, four propositions are put forward. An empirical study using survey research was completed. The survey questionnaire was designed with 54 items using literature and industry expert input. An exploratory factor analysis was conducted to derive groupings of GSCM pressures, practice and performance from the survey data which included 314 responses. A categorical and descriptive nature of the results is then presented with an evaluation and comparative analysis with previous research findings. Findings – Chinese enterprises have increased their environmental awareness due to regulatory, competitive, and marketing pressures and drivers. However, this awareness has not been translated into strong GSCM practice adoption, let alone into improvements in some areas of performance, where it was expected. Research limitations/implications – The investigation and its findings are still relatively exploratory. Future research can investigate relationships identified in this work, as well as tease out mediating and moderating relationships. A more broadly‐based and random sample study across China would also provide a better picture of this GSCM situation. Practical implications – Efforts made by Chinese enterprises together with the Chinese government have established a good foundation for further development. After China's entry into the WTO, a win‐win relationship between foreign companies and Chinese manufacturers is still possible. Originality/value – This work is one of the few and pioneering efforts to investigate GSCM practices in China.
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Citations
Cites
Supply Loops and Their Constraints: The Industrial Ecology of Recycling and Reuse
Geyer, R., & Jackson, T. (2004) · California Management Review · doi:10.2307/41166210 · Cited by 281
Cited by
Green supply chain management: A review and bibliometric analysis
Fahimnia, B., Sarkis, J., & Davarzani, H. (2015) · International Journal of Production Economics · doi:10.1016/j.ijpe.2015.01.003 · Cited by 2,131
Self-citationZhu, Q., & Sarkis, J. (2007) · International Journal of Production Research · doi:10.1080/00207540701440345 · Cited by 1,235
Self-citationSustainable operations: Their impact on the triple bottom line
Gimenez, C., Sierra, V., & Rodon, J. (2012) · International Journal of Production Economics · doi:10.1016/j.ijpe.2012.01.035 · Cited by 1,019
An analysis of the drivers affecting the implementation of green supply chain management
Diabat, A., & Govindan, K. (2011) · Resources, Conservation and Recycling · doi:10.1016/j.resconrec.2010.12.002 · Cited by 938
A literature review on green supply chain management: Trends and future challenges
Tseng, M.-L., Islam, M. S., Karia, N., Fauzi, F. A., & Afrin, S. (2019) · Resources, Conservation and Recycling · doi:10.1016/j.resconrec.2018.10.009 · Cited by 820
Metadata
- Authors
- Zhu, Q., Sarkis, J., & Geng, Y.
- Venue
- International Journal of Operations & Production Management
- Publisher
- Emerald
- Published
- 2005-05
- Volume
- 25
- Issue
- 5
- Pages
- 449-468
- ISSN
- 0144-3577 (ISSN-L)
- OpenAlex
- W1968492666
- Cited by
- 1,506
- References
- 1
- FWCI
- 46.40
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhu, Q., Sarkis, J., & Geng, Y. (2005). Green supply chain management in China: pressures, practices and performance. International Journal of Operations & Production Management. https://doi.org/10.1108/01443570510593148
Data sources
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.