Influence of big data and predictive analytics and social capital on performance of humanitarian supply chain

Abstract

Purpose The purpose of this paper is threefold: first, to investigate the role of big data and predictive analytics (BDPA) and social capital on the performance of humanitarian supply chains (HSCs); second, to explore the different performance measurement frameworks and develop a conceptual model for an HSC context that can be used by humanitarian organizations; and third, to provide insights for future research direction. Design/methodology/approach After a detailed review of relevant literature, grounded in resource-based view and social capital theory, the paper proposes a conceptual model that depicts the influence of BDPA and social capital on the performance of an HSC. Findings The study deliberates that BDPA as a capability improves the effectiveness of humanitarian missions to achieve its goals. It uncovers the fact that social capital binds people, organization or a country to form a network and has a critical role in the form of monetary or non-monetary support in disaster management. Further, it argues that social capital combined with BDPA capability can result in a better HSC performance. Research limitations/implications The proposed model integrating BDPA and social capital for HSC performance is conceptual and it needs to be empirically validated. Practical implications Organizations and practitioners may use this framework by mobilizing social capital, BDPA to enhance their abilities to help victims of calamities. Social implications Findings from study can help improve coordination among different stakeholders in HSC, effectiveness of humanitarian operations, which means lives saved and faster reconstruction process after disaster. Second, by implementing performance measurements framework recommended by study, donors and other stakeholders will get much desired transparency at each stage of HSCs. Originality/value The findings contribute to the missing link of social capital and BDPA to the existing performance of HSC literature, finally leading to a better HSC performance.

The emergent role of digital technologies in the context of humanitarian supply chains: a systematic literature review

“…Dubey et al ( 2018 ) Big Data Analytics Capability (BDAC) as an organisational culture positively impacts the collaborative performance and swift trust between military and civil organisations working together in disaster relief operations (Dubey et al, 2019 ; Dubey et al, 2019c ; Dubey et al 2019b ; Dubey et al, 2019a ) BDPA, as a capability, improves effectiveness of humanitarian operations to achieve its objectives, and combined with social capital can improve HSC performance. Jeble et al ( 2019 ) To predict crowd behaviour in extreme situations of evacuation. Bellomo et al ( 2016 ) BDA to leverage opportunities to generate RISE (rapid, impactful, sustained, and efficient) operations in humanitarian context.…”

“…Bellomo et al ( 2016 ), Drosio and Stanek ( 2016 ), Horita et al ( 2017 ), Ragini et al ( 2018 ) Efficiency and Responsiveness in Humanitarian Operations. Mulder et al ( 2016 ), Swaminathan et al (2018), Jeble et al ( 2019 ) Resource allocation in DM. Grabowski et al ( 2016 ) Supply chain resilience.…”

“…BDPA, as a capability, improves effectiveness of humanitarian operations to achieve its objectives, and combined with social capital can improve HSC performance. Jeble et al ( 2019 )…”

“…Efficiency and Responsiveness in Humanitarian Operations. Mulder et al ( 2016 ), Swaminathan et al (2018), Jeble et al ( 2019 )…”

“…The adoption and integration of DTs is essential in delivering more value to HSC (Rodríguez-Espíndola et al, 2020 ). A variety of individual domains of HSC has been discussed in the literature from a technological perspective: risk and need assessment (Dmitry, Dolgui and Sokolov, 2019), data generation and collection (Kane et al, 2014 ; Gunther et al, 2017 ), procurement and donation management (Lasi et al, 2014 ; Ülkü et al, 2015 ), agility (Schniedderjans and Hales 2016 ; Dubey et al, 2020 ), coordination and collaboration with other relief agencies (Kabra & Ramesh, 2015 ; Lu et al, 2018 ), capacity building of institutions and people (Chute & French, 2019 ), resilience in supply chain networks (Dubey et al, 2020a ; Papadopoulos et al, 2017 ), strategic planning for emergency relief (Gavidia., 2017 ), relief logistics (Delmonteil and Rancourt, 2017 ), improved forecasting and early warning systems (Jeble et al, 2019 ), inventory management (Ozguven & Ozbay, 2013 ), performance evaluation (Yang et al, 2011a , 2011b ) and continuous improvement in preparedness and response practices (Mesmar et al, 2016 ).…”