Abstract
Based on the resource-based view and the thematic analysis of digital humanitarianism, information and Communication Technology (ICT) success, and the potential value of Crowd Sourcing (CS), this study proposes a Digital Humanitarianism Capability (DHC) model. The study extends the above research streams by examining the direct effects of DHC on Disaster Risk Reduction (DRR), as well as the mediating effects of process-oriented dynamic capabilities (PODC) on the relationship between DHC and DRR. To test our proposed research model, we used an online survey to collect data from 150 District Magistrates (DMs) of India who is handling the COVID-19 Pandemic Management. The findings confirm the value of the entanglement conceptualization of the hierarchical DHC model, which has both direct and indirect impacts on DRR. The results also confirm the strong mediating role of PODC in improving insights and enhancing DRR. Finally, implications for practice and research are discussed.
Supported by organization IMT Nagpur.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abushaikha, I., Schumann-Bölsche, D.: Mobile phones: established technologies for innovative humanitarian logistics concepts. Procedia Eng. (2016). https://doi.org/10.1016/j.proeng.2016.08.157
Author, F., Author, S.: Title of a proceedings paper. In: Editor, F., Editor, S. (eds.) Conference 2016. LNCS, vol. 9999, pp. 1–13. Springer, Heidelberg (2016). https://doi.org/10.10007/1234567890
Balcik, B., Beamon, B.M., Krejci, C.C., Muramatsu, K.M., Ramirez, M.: Coordination in humanitarian relief chains: practices, challenges and opportunities. Int. J. Prod. Econ. (2010). https://doi.org/10.1016/j.ijpe.2009.09.008
Barrett, P.: Structural equation modelling: adjudging model fit. Pers. Ind. Differ. 42(5), 815–824 (2007)
Bastos, M.A.G., Campos, V.B.G., de Mello Bandeira, R.A.: Logistic processes in a post-disaster relief operation. Procedia – Soc. Behav. Sci. (2014). https://doi.org/10.1016/j.sbspro.2014.01.152
Dasaklis, T.K., Pappis, C.P., Rachaniotis, N.P.: Epidemics control and logistics operations: a review. Int. J. Prod. Econ. (2012). https://doi.org/10.1016/j.ijpe.2012.05.023
Dave, A.: Digital humanitarians: how big data is changing the face of humanitarian response. J. Bioeth. Inq. 14(4), 567–569 (2017). https://doi.org/10.1007/s11673-017-9807-8
Djalante, R., Shaw, R., DeWit, A.: Building resilience against biologicalhazards and pandemics: COVID-19 and its implications for the Sendai Framework. Progress Disaster Sci. (2020). https://doi.org/10.1016/j.pdisas.2020.100080
Dwiputranti, M.I., Oktora, A., Okdinawati, L., Fauzan, M.N.: Acceptance and use of information technology: understanding information systems for Indonesia’s humanitarian relief operations. Gadjah Mada Int. J. Bus. (2019). https://doi.org/10.22146/gamaijb.39199
Fernandez-Luque, L., Imran, M.: Humanitarian health computing using artificial intelligence and social media: a narrative literature review. Int. J. Med. Inform. (2018). https://doi.org/10.1016/j.ijmedinf.2018.01.015
HolguÃn-Veras, J., Jaller, M., Van Wassenhove, L.N., Pérez, N., Wachtendorf, T.: On the unique features of post-disaster humanitarian logistics. J. Oper. Manag. (2012). https://doi.org/10.1016/j.jom.2012.08.003
Madianou, M.: Humanitarianism: myths and realities. AoIR Sel. Pap. Internet Res. 6 (2018). https://journals.uic.edu/ojs/index.php/spir/article/view/8541
Madianou, M.: Technocolonialism: digital innovation and data practices in the humanitarian response to refugee crises. Soc. Media Soc. (2019). https://doi.org/10.1177/2056305119863146
Meier, P.: Digital humanitarians: how big data is changing the face of humanitarian response. In: Digital Humanitarians: How Big Data Is Changing the Face of Humanitarian Response (2015). https://doi.org/10.1201/b18023
Nagendra, N.P., Narayanamurthy, G., Moser, R.: Management of humanitarian relief operations using satellite big data analytics: the case of Kerala floods. Ann. Oper. Res. (2020). https://doi.org/10.1007/s10479-020-03593-w
Nikbakhsh, E., Zanjirani Farahani, R.: Humanitarian logistics planning in disaster relief operations. Logist. Oper. Manag. (2011). https://doi.org/10.1016/B978-0-12-385202-1.00015-3
O’Sullivan, T.L., Phillips, K.P.: From SARS to pandemic influenza: the framing of high-risk populations. Nat. Hazards (2019). https://doi.org/10.1007/s11069019-03584-6
Oberski, D.: lavaan.survey: an R package for complex survey analysis of structural equation models. J. Stat. Softw. 57(1), 1–27 (2014)
Olanrewaju, O.G., Dong, Z.S., Hu, S.: Supplier selection decision making in disaster response. Comput. Ind. Eng. (2020). https://doi.org/10.1016/j.cie.2020.106412
Perakslis, E.D.: Using digital health to enable ethical health research in conflict and other humanitarian settings Chesmal Siriwardhana and Donal O’mathuna. Conflict Health (2018). https://doi.org/10.1186/s13031-018-0163-z
Rabta, B., Wankmüller, C., Reiner, G.: A drone fleet model for last-mile distribution in disaster relief operations. Int. J. Disaster Risk Reduct. (2018). https://doi.org/10.1016/j.ijdrr.2018.02.020
Rahman, M.T., Comes, T., Majchrzak, T.A.: Understanding decision support in large-scale disasters: challenges in humanitarian logistics distribution. In: Dokas, I.M., Bellamine-Ben Saoud, N., Dugdale, J., DÃaz, P. (eds.) ISCRAM-med 2017. LNBIP, vol. 301, pp. 106–121. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67633-3_9
RodrÃguez-EspÃndola, O., Albores, P., Brewster, C.: Disaster preparedness in humanitarian logistics: a collaborative approach for resource management in floods. Eur. J. Oper. Res. (2018). https://doi.org/10.1016/j.ejor.2017.01.021
Rosseel, Y., et al.: Package ‘lavaan.’ (2017). Accessed 17 June 2017
Runge, M.C., et al.: Assessing the risks posed by SARS-CoV-2 in and via North American bats—decision framing and rapid risk assessment. Open-File Rep. (2020). https://doi.org/10.3133/ofr20201060
Tatham, P., Houghton, L.: The wicked problem of humanitarian logistics and disaster relief aid. J. Hum. Logist. Supply Chain Manag. (2011). https://doi.org/10.1108/20426741111122394
Thomas, A.: Humanitarian logistics: enabling disaster response. Fritz Institute (2005)
Timperio, G., Panchal, G.B., Samvedi, A., Goh, M., De Souza, R.: Decision support framework for location selection and disaster relief network design. J. Hum. Logist. Supply Chain Manag. (2017). https://doi.org/10.1108/JHLSCM-11-2016-0040
Tomasini, R.M., Van Wassenhove, L.N.: From preparedness to partnerships: case study research on humanitarian logistics. Int. Trans. Oper. Res. (2009). https://doi.org/10.1111/j.1475-3995.2009.00697.x
Tomaszewski, B.M., MacEachren, A.M., Pezanowski, S., Xiaoyan, L., Turton, I.: Supporting humanitarian relief logistics operations through online geocollaborative knowledge management. In: ACM International Conference Proceeding Series (2006). https://doi.org/10.1145/1146598.1146701
Wisetjindawat, W., Ito, H., Fujita, M., Eizo, H.: Planning disaster relief operations. Procedia – Soc. Behav. Sci. (2014). https://doi.org/10.1016/j.sbspro.2014.01.1484
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 IFIP International Federation for Information Processing
About this paper
Cite this paper
Kumar, A., Vishwakarma, N.K., Upadhyay, P. (2020). Digital Humanitarianism in a Pandemic Outbreak: An Empirical Study of Antecedents and Consequences. In: Sharma, S.K., Dwivedi, Y.K., Metri, B., Rana, N.P. (eds) Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation. TDIT 2020. IFIP Advances in Information and Communication Technology, vol 618. Springer, Cham. https://doi.org/10.1007/978-3-030-64861-9_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-64861-9_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-64860-2
Online ISBN: 978-3-030-64861-9
eBook Packages: Computer ScienceComputer Science (R0)
