Big data in humanitarian supply chain networks: a resource dependence perspective

Sameer Prasad (), Rimi Zakaria () and Nezih Altay ()
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Sameer Prasad: University of Wisconsin - Whitewater
Rimi Zakaria: University of Wisconsin - Whitewater
Nezih Altay: DePaul University

Annals of Operations Research, 2018, vol. 270, issue 1, No 20, 383-413

Abstract: Abstract Humanitarian operations in developing world settings present a particularly rich opportunity for examining the use of big data analytics. Focal non-governmental organizations (NGOs) often synchronize the delivery of services in a supply chain fashion by aligning recipient community needs with resources from various stakeholders (nodes). In this research, we develop a resource dependence model connecting big data analytics to superior humanitarian outcomes by means of a case study (qualitative) of twelve humanitarian value streams. Specifically, we identify the nodes in the network that can exert power on the focal NGOs based upon the respective resources being provided to ensure that sufficient big data is being created. In addition, we are able to identify how the type of data attribute, i.e., volume, velocity, veracity, value, and variety, relates to different forms of humanitarian interventions (e.g., education, healthcare, land reform, disaster relief, etc.). Finally, we identify how the various types of data attributes affect humanitarian outcomes in terms of deliverables, lead-times, cost, and propagation. This research presents evidence of important linkages between the developmental body of knowledge and that of resource dependence theory (RDT) and big data analytics. In addition, we are able to generalize RDT assumptions from the multi-tiered supply chains to distributed networks. The prescriptive nature of the findings can be used by donor agencies and focal NGOs to design interventions and collect the necessary data to facilitate superior humanitarian outcomes.

Keywords: Resource dependence; Developmental sector; Humanitarian outcomes; Non-profit organizations; Big data (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (15)

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DOI: 10.1007/s10479-016-2280-7

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