Impact of internet of things (IoT) in disaster management: a task-technology fit perspective

Akash Sinha (), Prabhat Kumar (), Nripendra P. Rana (), Rubina Islam () and Yogesh K. Dwivedi ()
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Akash Sinha: National Institute of Technology Patna
Prabhat Kumar: National Institute of Technology Patna
Nripendra P. Rana: Swansea University
Rubina Islam: The University of Salford
Yogesh K. Dwivedi: Swansea University Bay campus

Annals of Operations Research, 2019, vol. 283, issue 1, No 31, 759-794

Abstract: Abstract Disaster management aims to mitigate the potential damage from the disasters, ensure immediate and suitable assistance to the victims, and attain effective and rapid recovery. These objectives require a planned and effective rescue operation post such disasters. Different types of information about the impact of the disaster are, hence, required for planning an effective and immediate relief operation. The IoT technology available today is quite mature and has the potential to be very useful in disaster situations. This paper analyzes the requirements for planning rescue operation for such natural disasters and proposes an IoT based solution to cater the identified requirements. The proposed solution is further validated using the task-technology fit (TTF) approach for analyzing the significance of the adoption of IoT technology for disaster management. Results from the exploratory study established the core dimensions of the task requirements and the TTF constructs. Results from the confirmatory factor analysis using PLS path modelling, further, suggest that both task requirements and IoT technology have significant impact on the IoT TTF in the disaster management scenario. This paper makes significant contributions in the development of appropriate constructs for modeling TTF for IoT Technology in the context of disaster management.

Keywords: Task-technology fit; Disaster management; Internet of things; IoT; TTF; Strategic value (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (21)

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DOI: 10.1007/s10479-017-2658-1

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