Big data analytics in sustainable humanitarian supply chain: barriers and their interactions
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Big data analytics in sustainable humanitarian supply chain: barriers and their interactions Surajit Bag1 · Shivam Gupta2
· Lincoln Wood3
Accepted: 9 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
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 analyticsdriven 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. Keywords Barriers · Big data analytics · Fuzzy total interpretive structural modeling · Humanitarian supply chain management · Sustainability
1 Introduction Disasters cause massive losses and disruptions to normal life (Altay and Green 2006; O’Brien et al. 2006; Behl and Dutta 2019). Disasters can be created by humans or nature with slow
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123
Annals of Operations Research
onset or sudden onset (Jabbour et al. 2017). There has been increasing focus on the preparadeness stages to manage disaster situations (Behl and Dutta 2019) with a particular emphasis on using data and analytics to support decision-making (Prasad et al. 2018; Gupta et al. 2019). Given how successesful big data analytics (BDA) applications have been in commercial supply chains (Wamba et al. 2015; Hazen et al. 2016a, b; Mishra et al. 2018; Papadopoulos et al. 2017a; Gunasekaran et al. 2017; Wamba et al. 2018), the relatively recent and slow uptake of BDA applications in humanita
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