The role of Big Data in explaining disaster resilience in supply chains for sustainability

Thanos Papadopoulos, Angappa Gunasekaran, Rameshwar Dubey, Nezih Altay, Stephen J. Childe, Samuel Fosso-Wamba

Research output: Contribution to journalArticlepeer-review

459 Scopus citations


The purpose of this paper is to propose and test a theoretical framework to explain resilience in supply chain networks for sustainability using unstructured Big Data, based upon 36,422 items gathered in the form of tweets, news, Facebook, WordPress, Instagram, Google+, and YouTube, and structured data, via responses from 205 managers involved in disaster relief activities in the aftermath of Nepal earthquake in 2015. The paper uses Big Data analysis, followed by a survey which was analyzed using content analysis and confirmatory factor analysis (CFA). The results of the analysis suggest that swift trust, information sharing and public–private partnership are critical enablers of resilience in supply chain networks. The current study used cross-sectional data. However the hypotheses of the study can be tested using longitudinal data to attempt to establish causality. The article advances the literature on resilience in disaster supply chain networks for sustainability in that (i) it suggests the use of Big Data analysis to propose and test particular frameworks in the context of resilient supply chains that enable sustainability; (ii) it argues that swift trust, public private partnerships, and quality information sharing link to resilience in supply chain networks; and (iii) it uses the context of Nepal, at the moment of the disaster relief activities to provide contemporaneous perceptions of the phenomenon as it takes place.

Original languageEnglish (US)
Pages (from-to)1108-1118
Number of pages11
JournalJournal of Cleaner Production
StatePublished - Jan 20 2017

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • General Environmental Science
  • Strategy and Management
  • Industrial and Manufacturing Engineering


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