Determinants influencing the adoption of artificial intelligence technology in non-life insurers
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Thi Hai Duong Nguyen 
, Xuan Tiep Nguyen , Tran Ha Trang Le, Quynh Anh Bui
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
Although artificial intelligence (AI) technology has been widely used in the insurance industry at a global scale, studies examining the adoption of AI technology in emerging markets are few and far between. This paper fills this gap by using Cronbach’s alpha, exploratory factor analysis, confirmatory factor analysis, and structural equation model (SEM) to discover significant factors affecting their behavioral intentions to adopt AI technology in Vietnam, a developing country. Data is collected from nearly 470 employees in Vietnamese non-life insurance firms. Empirical findings show that the most important determinant influencing the adoption of AI technology in Vietnamese non-life insurers is attitudes toward adoption. Attitudes toward adoption are positively related to the perceived ease of use and perceived usefulness, consistent with Gupta et al. (2022). Although perceived risk has a negative influence on the behavioral intention to adopt AI technology, it is not a serious issue for insurance companies.
Keywords: Artificial Intelligence, Emerging Market, Non-Life Insurance, Organizational Behavior, Technology Acceptance Model
Authors’ individual contribution: Conceptualization — T.H.D.N.; Methodology — X.T.N.; Validation — T.H.T.L.; Writing — Review & Editing — T.H.T.L. and Q.A.B.; Visualization — X.T.N.; Supervision — T.H.D.N.
Declaration of conflicting interests: The Authors declare that there is no conflict of interest.
JEL Classification: G22, O32, O33
Received: 17.08.2023
Accepted: 13.02.2024
Published online: 16.02.2024
How to cite this paper: Nguyen, T. H. D., Nguyen, X. T., Le, T. H. T., & Bui, Q. A. (2024). Determinants influencing the adoption of artificial intelligence technology in non-life insurers. Corporate Governance and Organizational Behavior Review, 8(1), 205–212. https://doi.org/10.22495/cgobrv8i1p17
