Risk Governance for Improving Urban Disaster Risk Reduction

Choudhary, Chiranjiv; Neeli, Srinivasa Rajamani

doi:10.1007/978-981-10-3310-0_11

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…From the literature, we know that, when dealing with natural disasters, most government agencies have either experienced similar situations in the past or are psychologically affected by the current one (Choudhary and Neeli, 2018). An in-depth discussion with the respondents revealed that most of them had made significant contributions to DRO in the past, with 83% of them having been involved in technological advances in their current role during the previous decade, and either being decision-makers themselves or having worked closely with someone responsible for the processes involved in the adoption of technology.…”

Section: Discussion Of the Resultsmentioning

confidence: 99%

“…While Weng et al (2015) found that experience contributes to a government's perceptions of advances into e-governance, our study did not confirm such findings, as DRO are mostly conducted in isolation from other departments and, while there is traction among other government departments toward the adoption of AI, DRO-driven departments generally exhibit delays. Choudhary and Neeli (2018) asserted that good governance also involves the timely launch of schemes and policies and the involvement of government bodies wherever required. Our study adds to the findings as AI portals were launched to support DRO functions and help government firms to function better (Str€ omblad and Bengtsson, 2017;Shi et al, 2010).…”

Section: Artificial Intelligence For Disaster Reliefmentioning

confidence: 99%

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The role of organizational culture and voluntariness in the adoption of artificial intelligence for disaster relief operations

Behl

Chavan²,

Jain

et al. 2021

IJM

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PurposeThe study explores the readiness of government agencies to adopt artificial intelligence (AI) to improve the efficiency of disaster relief operations (DRO). For understanding the behavior of state-level and national-level government agencies involved in DRO, this study grounds its theoretical arguments on the civic voluntarism model (CVM) and the unified theory of acceptance and use of technology (UTAUT).Design/methodology/approachWe collected the primary data for this study from government agencies involved in DRO in India. To test the proposed theoretical model, we administered an online survey questionnaire to 184 government agency employees. To test the hypotheses, we employed partial least squares structural equation modeling (PLS-SEM).FindingsOur findings confirm that resources (time, money and skills) significantly influence the behavioral intentions related to the adoption of AI tools for DRO. Additionally, we identified that the behavioral intentions positively translate into the actual adoption of AI tools.Research limitations/implicationsOur study provides a unique viewpoint suited to understand the context of the adoption of AI in a governmental context. Companies often strive to invest in state-of-the-art technologies, but it is important to understand how government bodies involved in DRO strategize to adopt AI to improve efficiency.Originality/valueOur study offers a fresh perspective in understanding how the organizational culture and perspectives of government officials influence their inclinations to adopt AI for DRO. Additionally, it offers a multidimensional perspective by integrating the theoretical frameworks of CVM and UTAUT for a greater understanding of the adoption and deployment of AI tools with organizational culture and voluntariness as critical moderators.

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Section: Discussion Of the Resultsmentioning

confidence: 99%