67
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Discovering Latent Topics and Trends in Digital Technologies and Disaster Management Research: A Structural Topic Modeling Approach

, PhD, , PhD & , PhD
Published online: 22 May 2024
 

Abstract

The application of Digital Technologies (DTs) in disaster management has recently received much attention. It is imperative to summarize the existing literature in this area to identify the key research topics and possible future directions. Our study is the first of its kind to use an efficient, scalable, data-driven review approach using structural topic modeling to identify the links between DTs and disaster management. The paper has analyzed peer-reviewed Scopus-indexed journal articles on DTs and disaster management published between 2011 and 2023. Nine key research topics were identified, including topics such as technology awareness and education in disaster management, Use of social media in crisis communication, Disaster management interventions through autonomous systems, Communication networks and data applications in disasters, and Disaster management modelling. The topics and future directions represent areas where research and exploration make an important contribution to disaster management by leveraging DTs for better preparedness, response, and recovery in crisis situations.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Gaurav Kabra

Gaurav Kabra is a distinguished scholar and researcher with a diverse background in Information Technology and Supply Chain Management. He earned his PhD from the Department of Management Studies at the Indian Institute of Technology Roorkee, specializing in the interconnected disciplines of information technology and supply chain management. He holds a B.Tech (Information Technology) and MBA from the ABV-Indian Institute of Information Technology and Management Gwalior (ABV-IIITM Gwalior), India. Dr. Kabra’s research is featured in reputable national and international journals that are indexed by Scopus/ABDC/ABS, including Production Planning and Control, Journal of Enterprise Information Management, American Journal of Business, International Journal of Disaster Risk Reduction, Telematics and Informatics, and Neural Computing and Applications.

Vinit Ghosh

Vinit Ghosh has completed his PhD in OB/HR from the Indian Institute of Technology (IIT), Guwahati. He has worked for over 8 years in various multinational firms, such as TCS, Cognizant Technology Solutions (CTS), and HCL Technologies as a business process management consultant. His research has been published in The International Journal of Human Resource Management, Human Resource Development International, Journal of Environmental Management, to name a few. Dr. Ghosh’s expertise lies in the people-centric technology design and creativity. He is currently working as an Assistant Professor in the OB-HR area at T A Pai Institute of Management, Manipal, India.

Rohan Mukherjee

Rohan Mukherjee is an Associate Professor of Management information Systems and Analytics at IMI-Kolkata. He completed his MS and PhD from Indian Institute of Technology Kharagpur in visual information processing. Prior to joining IMI Kolkata, he has worked at IIM Bodh Gaya, IIM Jammu and Goa Institute of Management. Rohan has published consistently in reputed journals and his current research interest includes digital transformation and machine learning and their application in business.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 77.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.