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An empirical study on the use of artificial intelligence in the banking sector of Indonesia by extending the TAM model and the moderating effect of perceived trust

Artificial intelligence technology is increasingly becoming integral in business, and banks need to implement this technology on a large scale for competitiveness. However, studies on artificial intelligence in the banking sector are limited, and customers are concerned about its implementation. The...

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Main Authors: Ikhsan, Ridho Bramulya, Fernando, Yudi, Prabowo, Hartiwi, Yuniarty, Y, Gui, Anderes, Kuncoro, Engkos Achmad
Format: Article
Language:English
Published: Elsevier 2025
Subjects:
HD28 Management. Industrial Management
HF Commerce
Online Access:http://umpir.ump.edu.my/id/eprint/43527/1/AI%20in%20banking%20sector.pdf
http://umpir.ump.edu.my/id/eprint/43527/
https://doi.org/10.1016/j.digbus.2024.100103
https://doi.org/10.1016/j.digbus.2024.100103
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spelling my.ump.umpir.435272025-01-13T02:57:25Z http://umpir.ump.edu.my/id/eprint/43527/ An empirical study on the use of artificial intelligence in the banking sector of Indonesia by extending the TAM model and the moderating effect of perceived trust Ikhsan, Ridho Bramulya Fernando, Yudi Prabowo, Hartiwi Yuniarty, Y Gui, Anderes Kuncoro, Engkos Achmad HD28 Management. Industrial Management HF Commerce Artificial intelligence technology is increasingly becoming integral in business, and banks need to implement this technology on a large scale for competitiveness. However, studies on artificial intelligence in the banking sector are limited, and customers are concerned about its implementation. Therefore, this study aims to measure the intention to continue adopting artificial intelligence in Indonesia’s banking sector. This study proposed nineteen hypotheses and used a technology acceptance model framework with the awareness of artificial intelligence, subjective norms, perceived risk, and perceived trust as extensions. The researchers surveyed 388 bank customers who have interacted with artificial intelligence. The survey results extended the technology acceptance model framework by accepting all the hypotheses. This study contributes to the banking industry of developing countries by generating artificial intelligence technology with a high level of security. Elsevier 2025-06-01 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/43527/1/AI%20in%20banking%20sector.pdf Ikhsan, Ridho Bramulya and Fernando, Yudi and Prabowo, Hartiwi and Yuniarty, Y and Gui, Anderes and Kuncoro, Engkos Achmad (2025) An empirical study on the use of artificial intelligence in the banking sector of Indonesia by extending the TAM model and the moderating effect of perceived trust. Digital Business, 5 (1). pp. 1-19. ISSN 2666-9544. (Published) https://doi.org/10.1016/j.digbus.2024.100103 https://doi.org/10.1016/j.digbus.2024.100103
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic HD28 Management. Industrial Management
HF Commerce
spellingShingle HD28 Management. Industrial Management
HF Commerce
Ikhsan, Ridho Bramulya
Fernando, Yudi
Prabowo, Hartiwi
Yuniarty, Y
Gui, Anderes
Kuncoro, Engkos Achmad
An empirical study on the use of artificial intelligence in the banking sector of Indonesia by extending the TAM model and the moderating effect of perceived trust
description Artificial intelligence technology is increasingly becoming integral in business, and banks need to implement this technology on a large scale for competitiveness. However, studies on artificial intelligence in the banking sector are limited, and customers are concerned about its implementation. Therefore, this study aims to measure the intention to continue adopting artificial intelligence in Indonesia’s banking sector. This study proposed nineteen hypotheses and used a technology acceptance model framework with the awareness of artificial intelligence, subjective norms, perceived risk, and perceived trust as extensions. The researchers surveyed 388 bank customers who have interacted with artificial intelligence. The survey results extended the technology acceptance model framework by accepting all the hypotheses. This study contributes to the banking industry of developing countries by generating artificial intelligence technology with a high level of security.
format Article
author Ikhsan, Ridho Bramulya
Fernando, Yudi
Prabowo, Hartiwi
Yuniarty, Y
Gui, Anderes
Kuncoro, Engkos Achmad
author_facet Ikhsan, Ridho Bramulya
Fernando, Yudi
Prabowo, Hartiwi
Yuniarty, Y
Gui, Anderes
Kuncoro, Engkos Achmad
author_sort Ikhsan, Ridho Bramulya
title An empirical study on the use of artificial intelligence in the banking sector of Indonesia by extending the TAM model and the moderating effect of perceived trust
title_short An empirical study on the use of artificial intelligence in the banking sector of Indonesia by extending the TAM model and the moderating effect of perceived trust
title_full An empirical study on the use of artificial intelligence in the banking sector of Indonesia by extending the TAM model and the moderating effect of perceived trust
title_fullStr An empirical study on the use of artificial intelligence in the banking sector of Indonesia by extending the TAM model and the moderating effect of perceived trust
title_full_unstemmed An empirical study on the use of artificial intelligence in the banking sector of Indonesia by extending the TAM model and the moderating effect of perceived trust
title_sort empirical study on the use of artificial intelligence in the banking sector of indonesia by extending the tam model and the moderating effect of perceived trust
publisher Elsevier
publishDate 2025
url http://umpir.ump.edu.my/id/eprint/43527/1/AI%20in%20banking%20sector.pdf
http://umpir.ump.edu.my/id/eprint/43527/
https://doi.org/10.1016/j.digbus.2024.100103
https://doi.org/10.1016/j.digbus.2024.100103
_version_ 1822924939020206080
score 13.232414

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