The Acceptance of Using Information Technology for Disaster Risk Management: A Systematic Review
Open accessAbstract
The numbers of natural disaster events are continuously affecting human and the world economics.For coping with disaster, several sectors try to develop the frameworks, systems, technologies and so on.However, there are little researches focusing on the usage behavior of Information Technology (IT) for disaster risk management (DRM).Therefore, this study investigates the affecting factors on the intention to use IT for mitigating disaster's impacts.This study conducted a systematic review with the academic researches during 2011-2018.Two important factors from the Technology Acceptance Model (TAM) and others are used in describing individual behavior.In order to investigate the potential factors, the technology platforms are divided into nine types.According to the findings, computer software such as GIS applications are frequently used for simulation and spatial data analysis.Social media is preferred among the first choices during disaster events in order to communicate about situations and damages.Finally, we found five major potential factors which are Perceived Usefulness (PU), Perceived Ease of Use (PEOU), information accessibility, social influence, and disaster knowledge.Among them, the most essential one of using IT for disaster management is PU, while PEOU and information accessibility are more important in the web platforms.
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AI algorithms designed for human resource planning require well-structured decision objectives to provide good solutions.
“AI algorithms designed for human resource planning require well-structured decision objectives to provide good solutions (Meechang et al., 2020).”
From Unlocking the value of artificial intelligence in human resource management through AI capability framework (2023) · cites this paper as Meechang et al., 2020
The Introduction and §2.1 describe a review of human acceptance of IT for disaster risk management, not AI algorithms for human resource planning. Searches for human resource planning, AI algorithms, and decision objectives found no passage addressing whether well-structured objectives are needed to produce good solutions. (LLM, conf=0.76)
Experimental — accuracy not yet measuredAn AI algorithm might identify a relevant criterion or relationship affecting successful integration of employees into the company, but that relationship may not be evident for decision-makers, which could question the reliability of the results.
“An AI algorithm might identify a relevant criteria or relationship between criterion affecting the successful integration of employees into the company.However, that relationship may not be evident for decision-makers, which could question the reliability of the results (Meechang et al., 2020).”
From Unlocking the value of artificial intelligence in human resource management through AI capability framework (2023) · cites this paper as Meechang et al., 2020
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Experimental — accuracy not yet measuredSeveral decisions made by humans are based on judgement and intuition.
“In fact, several of the decisions made by humans are based on judgement and intuition (Meechang et al., 2020).”
From Unlocking the value of artificial intelligence in human resource management through AI capability framework (2023) · cites this paper as Meechang et al., 2020
Section 2.1 discusses models of human behavior, and section 4.6 describes reliance on past experience in disaster preparedness. Neither passage says that human decisions are based on judgment and intuition; searches for that phrase and a paraphrase found no passage addressing the claim. Page numbers were not available in our copy. (LLM, conf=0.72)
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1 of the 140 references couldn’t be matched to a known paper.
Research works printed but not linked: 9
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Sorna, U. S., Rahmat, S. R., Shan, F. P., & Firdaus, R. B. R. (2026) · Cogent Social Sciences · doi:10.1080/23311886.2026.2725276
- Authors
- Meechang, K., Leelawat, N., Tang, J., Kodaka, A., & Chintanapakdee, C.
- Affiliations
- Chulalongkorn University (TH)
- Keio University (JP)
- Venue
- Engineering Journal
- Published
- 2020-07-31
- Volume
- 24
- Issue
- 4
- Pages
- 111
- ISSN
- 0125-8281 (ISSN-L)
- OpenAlex
- W4239107822
- Semantic Scholar
- 165a82e2791c0c0b9998708e2a9c4284a586f19f
- Cited by
- 65
- References
- 140
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 16 · 2023: 8 · 2022: 12 · 2021: 10 · 2020: 7
- FWCI
- 1.78
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Engineering Journal (publishedVersion)
- Engineering Journal (Chulalongkorn University) (submittedVersion, cc-by-nc-nd)
- Funders
- Chulalongkorn University (763014)
- Japan International Cooperation Agency (JPMJSA1708)
- Japan Science and Technology Agency (JPMJSA1708)
- Science and Technology Research Partnership for Sustainable Development (JPMJSA1708)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Sociology and Political Science › Disaster Management and Resilience
- Social Sciences › Communication › Public Relations and Crisis Communication
- Keywords
- information technology adoption, disaster risk management, technology acceptance model, perceived usefulness, perceived ease of use, social media, geographic information systems, disaster knowledge, social influence, information accessibility
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