IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v270y2018i1d10.1007_s10479-016-2280-7.html
   My bibliography  Save this article

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

Author

Listed:
  • Sameer Prasad

    (University of Wisconsin - Whitewater)

  • Rimi Zakaria

    (University of Wisconsin - Whitewater)

  • Nezih Altay

    (DePaul University)

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.

Suggested Citation

  • Sameer Prasad & Rimi Zakaria & Nezih Altay, 2018. "Big data in humanitarian supply chain networks: a resource dependence perspective," Annals of Operations Research, Springer, vol. 270(1), pages 383-413, November.
  • Handle: RePEc:spr:annopr:v:270:y:2018:i:1:d:10.1007_s10479-016-2280-7
    DOI: 10.1007/s10479-016-2280-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-016-2280-7
    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/s10479-016-2280-7?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. Paul Hart & Carol Saunders, 1997. "Power and Trust: Critical Factors in the Adoption and Use of Electronic Data Interchange," Organization Science, INFORMS, vol. 8(1), pages 23-42, February.
    2. 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.
    3. Dursun Delen & Madhav Erraguntla & Richard Mayer & Chang-Nien Wu, 2011. "Better management of blood supply-chain with GIS-based analytics," Annals of Operations Research, Springer, vol. 185(1), pages 181-193, May.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ana Paula Pereira dos Passos & Jeferson Lana & Rosilene Marcon, 2023. "Social and political capabilities as nonmarket activities: What are they and how do firms develop them?," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 30(6), pages 2718-2730, November.
    2. Abhishek Behl & Pankaj Dutta & Zongwei Luo & Pratima Sheorey, 2022. "Enabling artificial intelligence on a donation-based crowdfunding platform: a theoretical approach," Annals of Operations Research, Springer, vol. 319(1), pages 761-789, December.
    3. Narayan Prasad Nagendra & Gopalakrishnan Narayanamurthy & Roger Moser, 2022. "Management of humanitarian relief operations using satellite big data analytics: the case of Kerala floods," Annals of Operations Research, Springer, vol. 319(1), pages 885-910, December.
    4. Shraddha Mishra & Surya Prakash Singh, 2022. "A stochastic disaster-resilient and sustainable reverse logistics model in big data environment," Annals of Operations Research, Springer, vol. 319(1), pages 853-884, December.
    5. Josip Marić & Carlos Galera-Zarco & Marco Opazo-Basáez, 2022. "The emergent role of digital technologies in the context of humanitarian supply chains: a systematic literature review," Annals of Operations Research, Springer, vol. 319(1), pages 1003-1044, December.
    6. Martijn Warnier & Vincent Alkema & Tina Comes & Bartel Walle, 2020. "Humanitarian access, interrupted: dynamic near real-time network analytics and mapping for reaching communities in disaster-affected countries," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 42(3), pages 815-834, September.
    7. 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.
    8. Rameshwar Dubey & David J. Bryde & Cyril Foropon & Gary Graham & Mihalis Giannakis & Deepa Bhatt Mishra, 2022. "Agility in humanitarian supply chain: an organizational information processing perspective and relational view," Annals of Operations Research, Springer, vol. 319(1), pages 559-579, December.
    9. Sachin Modgil & Rohit Kumar Singh & Cyril Foropon, 2022. "Quality management in humanitarian operations and disaster relief management: a review and future research directions," Annals of Operations Research, Springer, vol. 319(1), pages 1045-1098, December.
    10. 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).
    11. Koppiahraj Karuppiah & Bathrinath Sankaranarayanan & Syed Mithun Ali & Sanjoy Kumar Paul, 2021. "Key Challenges to Sustainable Humanitarian Supply Chains: Lessons from the COVID-19 Pandemic," Sustainability, MDPI, vol. 13(11), pages 1-20, May.
    12. Guo Fuli & Cyril Foropon & Ma Xin, 2022. "Reducing carbon emissions in humanitarian supply chain: the role of decision making and coordination," Annals of Operations Research, Springer, vol. 319(1), pages 355-377, December.
    13. Rodolfo Modrigais Strauss Nunes & Susana Carla Farias Pereira, 2022. "Intellectual structure and trends in the humanitarian operations field," Annals of Operations Research, Springer, vol. 319(1), pages 1099-1157, December.
    14. Carlos Bravo‐Laguna, 2023. "Examining the EU Reaction to a Humanitarian Emergency from a Network Perspective: The Response to Cyclones Idai and Kenneth," Journal of Common Market Studies, Wiley Blackwell, vol. 61(3), pages 673-691, May.
    15. Wu, Jie & Zahoor, Nadia & Khan, Zaheer & Meyer, Martin, 2023. "The effects of inward FDI communities on the research and development intensity of emerging market locally domiciled firms: Partial foreign ownership as a contingency," Journal of Business Research, Elsevier, vol. 156(C).

    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. Arunachalam, Deepak & Kumar, Niraj & Kawalek, John Paul, 2018. "Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 416-436.
    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. Pan Liu & Shu-ping Yi, 2018. "Investment decision-making and coordination of a three-stage supply chain considering Data Company in the Big Data era," Annals of Operations Research, Springer, vol. 270(1), pages 255-271, November.
    4. Benjamin T. Hazen & Joseph B. Skipper & Christopher A. Boone & Raymond R. Hill, 2018. "Back in business: operations research in support of big data analytics for operations and supply chain management," Annals of Operations Research, Springer, vol. 270(1), pages 201-211, November.
    5. Bin Shen & Hau-Ling Chan, 2017. "Forecast Information Sharing for Managing Supply Chains in the Big Data Era: Recent Development and Future Research," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(01), pages 1-26, February.
    6. 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.
    7. 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.
    8. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Papadopoulos, Thanos & Luo, Zongwei & Wamba, Samuel Fosso & Roubaud, David, 2019. "Can big data and predictive analytics improve social and environmental sustainability?," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 534-545.
    9. Biman Darshana Hettiarachchi & Stefan Seuring & Marcus Brandenburg, 2022. "Industry 4.0-driven operations and supply chains for the circular economy: a bibliometric analysis," Operations Management Research, Springer, vol. 15(3), pages 858-878, December.
    10. 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.
    11. Shuihua Han & Yufang Fu & Bin Cao & Zongwei Luo, 2018. "Pricing and bargaining strategy of e-retail under hybrid operational patterns," Annals of Operations Research, Springer, vol. 270(1), pages 179-200, November.
    12. Ionica Oncioiu & Ovidiu Constantin Bunget & Mirela Cătălina Türkeș & Sorinel Căpușneanu & Dan Ioan Topor & Attila Szora Tamaș & Ileana-Sorina Rakoș & Mihaela Ștefan Hint, 2019. "The Impact of Big Data Analytics on Company Performance in Supply Chain Management," Sustainability, MDPI, vol. 11(18), pages 1-22, September.
    13. Yu, Wantao & Chavez, Roberto & Jacobs, Mark A. & Feng, Mengying, 2018. "Data-driven supply chain capabilities and performance: A resource-based view," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 371-385.
    14. Raut, Rakesh D. & Mangla, Sachin Kumar & Narwane, Vaibhav S. & Dora, Manoj & Liu, Mengqi, 2021. "Big Data Analytics as a mediator in Lean, Agile, Resilient, and Green (LARG) practices effects on sustainable supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    15. Venkatesh Mani & Catarina Delgado & Benjamin T. Hazen & Purvishkumar Patel, 2017. "Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain," Sustainability, MDPI, vol. 9(4), pages 1-21, April.
    16. Hazen, Benjamin T. & Weigel, Fred K. & Ezell, Jeremy D. & Boehmke, Bradley C. & Bradley, Randy V., 2017. "Toward understanding outcomes associated with data quality improvement," International Journal of Production Economics, Elsevier, vol. 193(C), pages 737-747.
    17. Gunasekaran, Angappa & Papadopoulos, Thanos & Dubey, Rameshwar & Wamba, Samuel Fosso & Childe, Stephen J. & Hazen, Benjamin & Akter, Shahriar, 2017. "Big data and predictive analytics for supply chain and organizational performance," Journal of Business Research, Elsevier, vol. 70(C), pages 308-317.
    18. Deepa Mishra & Angappa Gunasekaran & Thanos Papadopoulos & Stephen J. Childe, 2018. "Big Data and supply chain management: a review and bibliometric analysis," Annals of Operations Research, Springer, vol. 270(1), pages 313-336, November.
    19. Gupta, Shivam & Chen, Haozhe & Hazen, Benjamin T. & Kaur, Sarabjot & Santibañez Gonzalez, Ernesto D.R., 2019. "Circular economy and big data analytics: A stakeholder perspective," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 466-474.
    20. 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.

    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:270:y:2018:i:1:d:10.1007_s10479-016-2280-7. 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.