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Big data and disaster management: a systematic review and agenda for future research

Author

Listed:
  • Shahriar Akter

    (University of Wollongong)

  • Samuel Fosso Wamba

    (Toulouse Business School)

Abstract

The era of big data and analytics is opening up new possibilities for disaster management (DM). Due to its ability to visualize, analyze and predict disasters, big data is changing the humanitarian operations and crisis management dramatically. Yet, the relevant literature is diverse and fragmented, which calls for its review in order to ascertain its development. A number of publications have dealt with the subject of big data and its applications for minimizing disasters. Based on a systematic literature review, this study examines big data in DM to present main contributions, gaps, challenges and future research agenda. The study presents the findings in terms of yearly distribution, main journals, and most cited papers. The findings also show a classification of publications, an analysis of the trends and the impact of published research in the DM context. Overall the study contributes to a better understanding of the importance of big data in disaster management.

Suggested Citation

  • Shahriar Akter & Samuel Fosso Wamba, 2019. "Big data and disaster management: a systematic review and agenda for future research," Annals of Operations Research, Springer, vol. 283(1), pages 939-959, December.
  • Handle: RePEc:spr:annopr:v:283:y:2019:i:1:d:10.1007_s10479-017-2584-2
    DOI: 10.1007/s10479-017-2584-2
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    References listed on IDEAS

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    1. Shahriar Akter & Samuel Fosso Wamba, 2016. "Big data analytics in E-commerce: a systematic review and agenda for future research," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(2), pages 173-194, May.
    2. Ritu Agarwal & Vasant Dhar, 2014. "Editorial —Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research," Information Systems Research, INFORMS, vol. 25(3), pages 443-448, September.
    3. Anonymous, 2014. "Introduction to the Issue," Journal of Wine Economics, Cambridge University Press, vol. 9(1), pages 1-2, May.
    4. Hassini, Elkafi & Surti, Chirag & Searcy, Cory, 2012. "A literature review and a case study of sustainable supply chains with a focus on metrics," International Journal of Production Economics, Elsevier, vol. 140(1), pages 69-82.
    5. Nigel Scott & Simon Batchelor, 2013. "Real Time Monitoring in Disasters," IDS Bulletin, Blackwell Publishing, vol. 44(2), pages 122-134, March.
    6. E. W. T. Ngai & J. K. L. Poon & F. F. C. Suk & C. C. Ng, 2009. "Design of an RFID-based Healthcare Management System using an Information System Design Theory," Information Systems Frontiers, Springer, vol. 11(4), pages 405-417, September.
    7. Xihui Wang & Yunfei Wu & Liang Liang & Zhimin Huang, 2016. "Service outsourcing and disaster response methods in a relief supply chain," Annals of Operations Research, Springer, vol. 240(2), pages 471-487, May.
    8. Urmila Pyakurel & Tanka Nath Dhamala, 2017. "Continuous Dynamic Contraflow Approach for Evacuation Planning," Annals of Operations Research, Springer, vol. 253(1), pages 573-598, June.
    9. Martin K. Starr & Luk N. Van Wassenhove, 2014. "Introduction to the Special Issue on Humanitarian Operations and Crisis Management," Production and Operations Management, Production and Operations Management Society, vol. 23(6), pages 925-937, June.
    10. Adam Smith & Jessica Matthews, 2015. "Quantifying uncertainty and variable sensitivity within the US billion-dollar weather and climate disaster cost estimates," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 77(3), pages 1829-1851, July.
    11. Hara, Yusuke & Kuwahara, Masao, 2015. "Traffic Monitoring immediately after a major natural disaster as revealed by probe data – A case in Ishinomaki after the Great East Japan Earthquake," Transportation Research Part A: Policy and Practice, Elsevier, vol. 75(C), pages 1-15.
    12. Douglas Bish & Esra Agca & Roger Glick, 2014. "Decision support for hospital evacuation and emergency response," Annals of Operations Research, Springer, vol. 221(1), pages 89-106, October.
    13. Fosso Wamba, Samuel & Akter, Shahriar & Edwards, Andrew & Chopin, Geoffrey & Gnanzou, Denis, 2015. "How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study," International Journal of Production Economics, Elsevier, vol. 165(C), pages 234-246.
    14. Altay, Nezih & Green III, Walter G., 2006. "OR/MS research in disaster operations management," European Journal of Operational Research, Elsevier, vol. 175(1), pages 475-493, November.
    15. Anonymous, 2014. "Introduction to the Issue," Journal of Wine Economics, Cambridge University Press, vol. 9(2), pages 109-110, August.
    16. Akter, Shahriar & Wamba, Samuel Fosso & Gunasekaran, Angappa & Dubey, Rameshwar & Childe, Stephen J., 2016. "How to improve firm performance using big data analytics capability and business strategy alignment?," International Journal of Production Economics, Elsevier, vol. 182(C), pages 113-131.
    17. Hazen, Benjamin T. & Boone, Christopher A. & Ezell, Jeremy D. & Jones-Farmer, L. Allison, 2014. "Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications," International Journal of Production Economics, Elsevier, vol. 154(C), pages 72-80.
    18. Ngai, E.W.T. & Moon, Karen K.L. & Riggins, Frederick J. & Yi, Candace Y., 2008. "RFID research: An academic literature review (1995-2005) and future research directions," International Journal of Production Economics, Elsevier, vol. 112(2), pages 510-520, April.
    19. Galindo, Gina & Batta, Rajan, 2013. "Review of recent developments in OR/MS research in disaster operations management," European Journal of Operational Research, Elsevier, vol. 230(2), pages 201-211.
    20. Steven Ji-fan Ren & Samuel Fosso Wamba & Shahriar Akter & Rameshwar Dubey & Stephen J. Childe, 2017. "Modelling quality dynamics, business value and firm performance in a big data analytics environment," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5011-5026, September.
    21. Bouchard, Louise & Albertini, Marcelo & Batista, Ricardo & de Montigny, Joanne, 2015. "Research on health inequalities: A bibliometric analysis (1966–2014)," Social Science & Medicine, Elsevier, vol. 141(C), pages 100-108.
    22. Fahimnia, Behnam & Sarkis, Joseph & Davarzani, Hoda, 2015. "Green supply chain management: A review and bibliometric analysis," International Journal of Production Economics, Elsevier, vol. 162(C), pages 101-114.
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