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Big data and predictive analytics in humanitarian supply chains: Enabling visibility and coordination in the presence of swift trust

Journal Article


Abstract


  • Purpose –The primary objective of this research is to understand how big data and predictive analytics (BDPA), as an organizational capability, can improve both visibility and coordination in humanitarian supply chains.

    Design/methodology/approach - We conceptualize a research model grounded in contingent resource-based view (CRBV), where we propose that BDPA capabilities affect visibility and coordination under the moderating effect of swift trust. Using Ordinary Least Squares Regression, we test the hypotheses using survey data collected from informants at 205 International Non-Government Organizations (NGOs).

    Findings - Results indicate that BDPA has a significant influence on visibility and coordination. Further, results suggest that swift trust does not have an amplifying effect on the relationships between BDPA and visibility and coordination. However, the mediation test suggests that swift trust act as a mediating construct. Hence, we argue that swift-trust is not the condition for improving coordination among the actors in humanitarian supply chains.

    Research limitations/ implications - The major limitation of the study is that we have used cross-sectional survey data to test our research hypotheses. Following Guide and Ketokivi (2015), we present arguments on how to address the limitations of cross-sectional data or use of longitudinal data that can address common method bias (CMB) or endogeneity related problems.

    Practical implications - Managers can use our framework, first, to understand how organizational resources can be used for creating BDPA and second, how BDPA can help to build swift trust and be used to improve visibility and coordination in the humanitarian supply chain.

    Originality/value- This is the first research that has empirically tested the anecdotal and conceptual evidence. The findings make notable contributions to existing humanitarian supply chain literature and may be useful to managers who are contemplating the use of BDPA to improve disaster relief related activities.

Authors


  •   Dubey, Rameshwar (external author)
  •   Luo, Zongwei (external author)
  •   Gunasekaran, Angappa (external author)
  •   Akter, Shahriar
  •   Hazen, Benjamin (external author)
  •   Douglas, Matthew A. (external author)

Publication Date


  • 2018

Citation


  • Dubey, R., Luo, Z., Gunasekaran, A., Akter, S., Hazen, B. T. & Douglas, M. A. (2018). Big data and predictive analytics in humanitarian supply chains: Enabling visibility and coordination in the presence of swift trust. The International Journal of Logistics Management, 29 (2), 485-512.

Scopus Eid


  • 2-s2.0-85047894904

Number Of Pages


  • 27

Start Page


  • 485

End Page


  • 512

Volume


  • 29

Issue


  • 2

Place Of Publication


  • United Kingdom

Abstract


  • Purpose –The primary objective of this research is to understand how big data and predictive analytics (BDPA), as an organizational capability, can improve both visibility and coordination in humanitarian supply chains.

    Design/methodology/approach - We conceptualize a research model grounded in contingent resource-based view (CRBV), where we propose that BDPA capabilities affect visibility and coordination under the moderating effect of swift trust. Using Ordinary Least Squares Regression, we test the hypotheses using survey data collected from informants at 205 International Non-Government Organizations (NGOs).

    Findings - Results indicate that BDPA has a significant influence on visibility and coordination. Further, results suggest that swift trust does not have an amplifying effect on the relationships between BDPA and visibility and coordination. However, the mediation test suggests that swift trust act as a mediating construct. Hence, we argue that swift-trust is not the condition for improving coordination among the actors in humanitarian supply chains.

    Research limitations/ implications - The major limitation of the study is that we have used cross-sectional survey data to test our research hypotheses. Following Guide and Ketokivi (2015), we present arguments on how to address the limitations of cross-sectional data or use of longitudinal data that can address common method bias (CMB) or endogeneity related problems.

    Practical implications - Managers can use our framework, first, to understand how organizational resources can be used for creating BDPA and second, how BDPA can help to build swift trust and be used to improve visibility and coordination in the humanitarian supply chain.

    Originality/value- This is the first research that has empirically tested the anecdotal and conceptual evidence. The findings make notable contributions to existing humanitarian supply chain literature and may be useful to managers who are contemplating the use of BDPA to improve disaster relief related activities.

Authors


  •   Dubey, Rameshwar (external author)
  •   Luo, Zongwei (external author)
  •   Gunasekaran, Angappa (external author)
  •   Akter, Shahriar
  •   Hazen, Benjamin (external author)
  •   Douglas, Matthew A. (external author)

Publication Date


  • 2018

Citation


  • Dubey, R., Luo, Z., Gunasekaran, A., Akter, S., Hazen, B. T. & Douglas, M. A. (2018). Big data and predictive analytics in humanitarian supply chains: Enabling visibility and coordination in the presence of swift trust. The International Journal of Logistics Management, 29 (2), 485-512.

Scopus Eid


  • 2-s2.0-85047894904

Number Of Pages


  • 27

Start Page


  • 485

End Page


  • 512

Volume


  • 29

Issue


  • 2

Place Of Publication


  • United Kingdom