4.6 Article

Contextual and organizational factors in sustainable supply chain decision making: grey relational analysis and interpretative structural modeling

Journal

ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
Volume 23, Issue 8, Pages 12056-12076

Publisher

SPRINGER
DOI: 10.1007/s10668-020-01157-3

Keywords

Sustainable supply chain; Low-carbon decisions; Grey relational analysis (GRA); Interpretative structural model (ISM)

Funding

  1. Natural Science Foundation of Shaanxi Province, China [2020JM-201]

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The study uses mathematical analyses to identify critical factors that enterprises need to focus on for sustainable supply chains, guiding them to make more sustainable decisions with limited resources. Future research may enhance the generalizability of findings by validating results across multiple countries.
Sustainable supply chain emerges as a major business trend essential to long-term competitive advantage. Relevant corporate decisions concern a broad range of factors and require novel analytical models for critical control. This study conducts mathematical analyses to identify the factors that are vital yet receiving insufficient attention from researchers and practitioners. Valid survey observations were collected from 113 enterprises in China, the biggest emerging economy that faces the dilemma between development and sustainability. Grey relational analysis (GRA) and interpretative structural modeling (ISM) assess the importance levels of contextual and organizational factors and explore their joint effects. Validated with conventional expert interviews, the results prioritize the factors that play crucial roles in sustainable supply chains. In particular, enterprises should pay close attention to three factors: corporate collaboration, clean production and supplier selection, which provide useful clues on the best practices of formulating low-carbon decisions. With a better understanding of critical factors, enterprises may make supply chains more sustainable with limited resources. To enhance the generalizability of findings, future studies may collect more observations from multiple countries and validate the results in the settings of global supply chains.

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