IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v283y2019i1d10.1007_s10479-017-2584-2.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-017-2584-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-017-2584-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Abhishek Behl & Pankaj Dutta, 2019. "Humanitarian supply chain management: a thematic literature review and future directions of research," Annals of Operations Research, Springer, vol. 283(1), pages 1001-1044, December.
    2. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.
    3. Deepa Mishra & Sameer Kumar & Elkafi Hassini, 2019. "Current trends in disaster management simulation modelling research," Annals of Operations Research, Springer, vol. 283(1), pages 1387-1411, December.
    4. S. Vijayakumar Bharathi, 2017. "Prioritizing and Ranking the Big Data Information Security Risk Spectrum," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 183-201, September.
    5. Ashrafi, Amir & Zare Ravasan, Ahad & Trkman, Peter & Afshari, Samira, 2019. "The role of business analytics capabilities in bolstering firms’ agility and performance," International Journal of Information Management, Elsevier, vol. 47(C), pages 1-15.
    6. Patrick Mikalef & Ilias O. Pappas & John Krogstie & Michail Giannakos, 2018. "Big data analytics capabilities: a systematic literature review and research agenda," Information Systems and e-Business Management, Springer, vol. 16(3), pages 547-578, August.
    7. Kovacs, Gyöngyi & Moshtari, Mohammad, 2019. "A roadmap for higher research quality in humanitarian operations: A methodological perspective," European Journal of Operational Research, Elsevier, vol. 276(2), pages 395-408.
    8. Hunt, Kyle & Narayanan, Adithya & Zhuang, Jun, 2022. "Blockchain in humanitarian operations management: A review of research and practice," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    9. Roßmann, Bernhard & Canzaniello, Angelo & von der Gracht, Heiko & Hartmann, Evi, 2018. "The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 135-149.
    10. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Roubaud, David & Fosso Wamba, Samuel & Giannakis, Mihalis & Foropon, Cyril, 2019. "Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain," International Journal of Production Economics, Elsevier, vol. 210(C), pages 120-136.
    11. Maria Besiou & Luk N. Van Wassenhove, 2020. "Humanitarian Operations: A World of Opportunity for Relevant and Impactful Research," Manufacturing & Service Operations Management, INFORMS, vol. 22(1), pages 135-145, January.
    12. Brinch, Morten & Gunasekaran, Angappa & Fosso Wamba, Samuel, 2021. "Firm-level capabilities towards big data value creation," Journal of Business Research, Elsevier, vol. 131(C), pages 539-548.
    13. Shivam Gupta & Nezih Altay & Zongwei Luo, 2019. "Big data in humanitarian supply chain management: a review and further research directions," Annals of Operations Research, Springer, vol. 283(1), pages 1153-1173, December.
    14. Ioannis Kougkoulos & M. Selim Cakir & Nathan Kunz & Doreen S. Boyd & Alexander Trautrims & Kornilia Hatzinikolaou & Stefan Gold, 2021. "A Multi‐Method Approach to Prioritize Locations of Labor Exploitation for Ground‐Based Interventions," Production and Operations Management, Production and Operations Management Society, vol. 30(12), pages 4396-4411, December.
    15. Courtney Blair & Erica Gralla & Finley Wetmore & Jarrod Goentzel & Megan Peters, 2021. "A Systems Framework for International Development: The Data‐Layered Causal Loop Diagram," Production and Operations Management, Production and Operations Management Society, vol. 30(12), pages 4374-4395, December.
    16. TURKEŠ, Renata & SÖRENSEN, Kenneth, 2018. "Case studies and random instances for the problem of pre-positioning emergency supplies," Working Papers 2018004, University of Antwerp, Faculty of Business and Economics.
    17. Pournader, Mehrdokht & Ghaderi, Hadi & Hassanzadegan, Amir & Fahimnia, Behnam, 2021. "Artificial intelligence applications in supply chain management," International Journal of Production Economics, Elsevier, vol. 241(C).
    18. Surajit Bag & Shivam Gupta & Lincoln Wood, 2022. "Big data analytics in sustainable humanitarian supply chain: barriers and their interactions," Annals of Operations Research, Springer, vol. 319(1), pages 721-760, December.
    19. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).
    20. CHEN, Helen S.Y., 2020. "Designing Sustainable Humanitarian Supply Chains," OSF Preprints m82ar, Center for Open Science.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:283:y:2019:i:1:d:10.1007_s10479-017-2584-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.