Examining sustainable supply chain management of SMEs using resource based view and institutional theory

K. T. Shibin, Rameshwar Dubey, Angappa Gunasekaran, Benjamin Hazen, David Roubaud, Shivam Gupta, Cyril Foropon

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

Abstract

The long-term viability of an organization hinges on social, environmental, and economic measures. However, based on extensive review of the literature, we have observed that measuring and improving the sustainable performance of supply chains is complex. We have grounded our theoretical framework in institutional theory and resource-based view and drawn thirteen hypotheses. We developed our instrument scientifically to validate our model and test our research hypotheses. The data was collected from the Indian auto components industry following Dillman’s total design test method. We gathered 205 usable responses. Following Peng and Lai’s (J Oper Manag 30(6):467–480, 2012) arguments, we have tested our model using variance-based structural equation modeling (PLS-SEM). We found that the constructs used for building our theoretical model possess construct validity and further satisfy the specified criteria for goodness of fit. The hypotheses test further suggests that coercive pressures under the mediation effect of top management belief and participation have significant influence on resource selection (i.e. supply chain connectivity and supply chain information sharing). The supply chain connectivity and supply chain information sharing have significant influence on environmental performance. Contrary to our belief, the normative and mimetic pressures have no significant influence on top management participation. The managerial implications of the findings are also discussed.

Original languageEnglish (US)
Pages (from-to)301-326
Number of pages26
JournalAnnals of Operations Research
Volume290
Issue number1-2
DOIs
StatePublished - Jul 1 2020

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Management Science and Operations Research

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