Factors Influencing Generation Z's Adoption of AI in Banking: An Extended Technology Acceptance Model Approach

Citation

Lim, Kah Boon and Lau, Kai Hui and Yeo, Sook Fern and Tan, Cheng Ling (2025) Factors Influencing Generation Z's Adoption of AI in Banking: An Extended Technology Acceptance Model Approach. Journal of Logistics, Informatics and Service Science. ISSN 24092665

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Abstract

This study examines the factors that influence Generation Z's intention to adopt artificial intelligence technology in banking services by extending the Technology Acceptance Model (TAM). Using quantitative methodology, we collected data from 192 Generation Z respondents in Malaysia through structured questionnaires that measured seven potential influencing factors: perceived ease of use, perceived usefulness, awareness, perceived risk, perceived trust, subjective norms and attitude toward AI in banking. Multiple linear regression analysis revealed that five factors—perceived usefulness, awareness, perceived trust, subjective norms and attitude—have statistically significant positive effects on adoption intention, explaining 58.3% of the variance. Interestingly, perceived ease of use and perceived risk did not demonstrate significant influence in this context, contrary to some previous research. These findings provide valuable theoretical contributions by validating an extended TAM specifically for AI banking adoption among Generation Z consumers, while offering practical guidance for financial institutions seeking to enhance AI technology acceptance among younger customers. Banks should focus on demonstrating practical benefits, increasing awareness through education, building trust through transparent implementation, leveraging social influence and fostering positive attitudes through engaging experiences. While our study is limited to the Malaysian context, it offers important insights for understanding the technology adoption behaviors of this digitally native generation that will increasingly shape the future of banking services.

Item Type: Article
Uncontrolled Keywords: Artificial Intelligence
Subjects: Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions: Faculty of Business (FOB)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 27 Aug 2025 04:22
Last Modified: 27 Aug 2025 04:22
URII: http://shdl.mmu.edu.my/id/eprint/14447

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