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Big data analytics in sustainable humanitarian supply chain: barriers and their interactions

  • S.I. : Design and Management of Humanitarian Supply Chains
  • Published:
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Abstract

Big data analytics research in humanitarian supply chain management has gained popularity for its ability to manage risks. While big data analytics can predict future events, it can also concentrate on current events and support preparation for future events. Big data analytics-driven approaches in humanitarian supply chain management are complicated due to the presence of multiple barriers. The current study aims to identify the leading barriers; further categorize them and finally develop the contextual interrelationships using the Fuzzy Total Interpretive Structural Modeling (TISM) approach. Sustainable humanitarian supply chain management research is in nascent stage and therefore, Fuzzy TISM is used in this study for theory building purpose and answering three key questions-what, how and why. Fuzzy TISM shows some key transitive links which provides enhanced explanatory framework. The TISM model shows that the fifteen barriers achieved eight levels and decision-makers must aim to remove the foundational barriers to apply big data analytics in sustainable humanitarian supply chain management. Fuzzy TISM were synthesized to develop a conceptual model and this was statistically validated considering a sample of 108 responses from African based humanitarian organizations. Findings suggest that organizational focus is required on implementing modern management practices; second, more emphasis is required on infrastructure development and lastly, improvement is required on quality of information sharing as these variables can influence sustainable humanitarian supply chain management. Finally, the conclusions and future research directions were outlined which may help stakeholders in sustainable humanitarian supply chain management to eliminate the BDA barriers.

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Authors and Affiliations

  1. Department of Transport and Supply Chain Management, School of Management, College of Business and Economics, University of Johannesburg, Johannesburg, South Africa

    Surajit Bag

  2. Department of Information Systems, Supply Chain and Decision Making, NEOMA Business School, 59 Rue Pierre Taittinger, 51100, Reims, France

    Shivam Gupta

  3. Department of Management, University of Otago, Dunedin, 9054, New Zealand

    Lincoln Wood

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  1. Surajit Bag
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  2. Shivam Gupta
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  3. Lincoln Wood
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Correspondence to Shivam Gupta.

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Appendix

Appendix

See Tables 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 and 31.

Table 26 Operationalization of constructs
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Table 27 Combined loadings and cross-loadings
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Table 28 Correlations among latent variables with square root of AVEs
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Table 29 Latent variable coefficients
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Table 30 Model fit and quality indices
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Table 31 Causality assessment indices
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Bag, S., Gupta, S. & Wood, L. Big data analytics in sustainable humanitarian supply chain: barriers and their interactions. Ann Oper Res 319, 721–760 (2022). https://doi.org/10.1007/s10479-020-03790-7

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