The Influence of Personality and Technology Acceptance on the Decision to Use ChatGPT

Main Article Content

Nuttraipat Patphornchanat

Abstract

This research aims to study 1) the influence of ChatGPT users' personality factors on technology acceptance, 2) the influence of technology acceptance factors on attitudes toward using ChatGPT, and 3) the influence of attitudes toward using ChatGPT on the most significant factors that lead to the decision to use ChatGPT. This research employed a quantitative methodology. The data collection instrument was a questionnaire developed based on the Technology Acceptance Model framework, collecting data from a sample of 403 ChatGPT users obtained through a non-probability sampling method. The statistical method used for data analysis was multiple regression analysis. The research findings revealed that: 1) users’ personality factors, especially openness to new experiences, technology confidence, adaptability, and enjoyment of technology use, had a statistically significant positive influence on technology acceptance in terms of perceived usefulness and ease of use, 2) technology acceptance factors, both in terms of perceived usefulness and ease of use, had a statistically significant positive influence on attitudes toward using Chat GPT, with users who had high perceived usefulness and ease of use having positive attitudes toward usage, and 3) attitudes toward using Chat GPT had a statistically significant positive influence on the most critical factors that lead to the decision to use Chat GPT, which are continuous usage intention and recommendation to others. This research has significant academic implications and practical applications for developing strategies to promote the acceptance of AI chatbot technology, considering users' personality factors and perceptions.

Article Details

How to Cite
Patphornchanat, N. (2025). The Influence of Personality and Technology Acceptance on the Decision to Use ChatGPT. Arts of Management Journal, 9(3), 286–303. retrieved from https://so02.tci-thaijo.org/index.php/jam/article/view/278607
Section
Research Articles

References

Almogren, A. S., Al-Rahmi, W. M., & Dahri, N. A. (2024). Exploring factors influencing the acceptance of ChatGPT in higher education: A smart education perspective. Heliyon, 10(11), e31887. https://doi.org/10.1016/j.heliyon.2024.e31887

Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Prentice-Hall.

Chuttur, M. (2009). Overview of the technology acceptance model: origins, developments and future directions. All Sprouts Content, 290. https://aisel.aisnet.org/sprouts_all/290

Cronbach, L. J. (1970). Essentials of psychological testing. Harper & Row.

Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information system: theory and result[Doctoral dissertation, Massachusetts Institute of Technology].

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35, 982-1003. http://dx.doi.org/10.1287/mnsc.35.8.982

Fitzsimons, G. J., & Morwitz, V. G. (1996). The effect of measuring intent on brand-level purchase behavior. Journal of Consumer

Research, 23(1), 1-11. https://doi.org/10.1086/209462

Howard, J. A. (1994). Buyer behavior in marketing strategy. Prentice-Hall.

Ibrahim, F., Münscher, J-C., Daseking, M., & Telle, N-T. (2025) The technology acceptance model and adopter type analysis in the context of artificial intelligence. Frontiers in Artificial Intelligence, 7, 1496518. DOI: 10.3389/frai.2024.1496518

Kongernkrang, W., Sasithanakornkaew, S., & Kumsubha, B. (2024). Factors influencing the acceptance and continuous use behavior of ChatGPT artificial intelligence. Journal of MCU Nakhondhat, 11(2), 120-129. https://so03.tci-thaijo.org/index.php/JMND/article/view/275311/182358

McCrae, R. R., & Costa Jr., P. T. (1997). Conceptions and correlates of openness to experience. In R. Hogan, J. A. Johnson, & S. R. Briggs (Eds.), Handbook of personality psychology (pp. 825-847). https://doi.org/10.1016/B978-012134645-4/50032-9

Mowen, J. C., & Minor, M. (1998). Consumer Behavior. Prentice-Hall.

Neuendorf, Y., & Valdiseri, A. (2016). Consumer acceptance of online banking: an extension of the technology acceptance model. Internet Research, 14(3), 224-235.

Phetsri, S. (2023). Attitudes, intentions, and behaviors of ChatGPT usage in the work context of generation Y[Master’s thesis, Thammasat University].

Phuthong, T. (2018). The influence of personality and technology acceptance on the willingness to use electronics book. E-Journal, Silapakorn University, 11(2). 3179-3193. https://he02.tci-thaijo.org/index.php/Veridian-E-Journal/article/view/147257

Sirisook, P., Akkarapornprom, N., Changchot, S., Rattanawijit, J., & Niruttikul, N. (2024). Factor impact to intention to use ChatGPT application of marketer. Journal of Management and Development Ubon Ratchathani Rajabhat University, 11(2), 256-281. https://so06.tci-thaijo.org/index.php/JMDUBRU/article/view/280403/187056

Venkatesh, V., & Davis, F. D. (1996). A model of antecedents of perceived ease of use: development and test. Decision Science, 27(3), 451-481. https://doi.org/10.1111/j.1540-5915.1996.tb00860.x

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

Zhou, T. (2011). Understanding mobile Internet continuance usage from the perspectives of UTAUT and flow. Information Development, 27(3), 207-218. DOI:10.1177/0266666911414596