Technology Acceptance Affecting the Intention to Use Artificial Intelligence (AI) in the Work of Personnel of Valaya Alongkorn Rajabhat University Under the Royal Patronage
Keywords:
Technology Acceptance, Artificial Intelligence, intention to Use TechnologyAbstract
This research article aims to study technology acceptance factors influencing the intention to use artificial intelligence (AI) technology in the workplace among academic staff at Valaya Alongkorn Rajabhat University under the Royal Patronage. The sample consisted of 250 academic personnel, selected through simple random sampling. The research instrument was a questionnaire, and the data were analyzed using descriptive statistics, including mean and standard deviation, and multiple regression analysis.
The analysis revealed that perceived ease of use, perceived usefulness, attitude toward usage, organizational support for AI adoption, and experience had a significantly positive impact. Upon further examination, it was found that attitude toward usage and organizational support for the adoption of AI technology had the highest average score of 4.69. This was followed by experience with using AI technology, with an average score of 4.64. Perceived usefulness and intention to use AI technology both had an average score of 4.63, while the lowest score was observed in perceived ease of use, with an average of 4.58.
References
กองนโยบายและแผน. (2566). สารสนเทศ ประจำปีการศึกษา 2566. มหาวิทยาลัยราชภัฏวไลยอลงกรณ์ ในพระบรมราชูปถัมภ์.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
Autry, C. W., Skinner, L. R., & Lamb, C. W. (2008). Interorganizational citizenship behaviors: An empirical study. Journal of Business Logistics, 29(1), 53–74.
Boontamnun, T. (2019). Factors affecting the acceptance of AI (Machine Learning) technology in the leasing industry: A case study of a private company in Bangkok. [Master’s thesis]. Thammasat University.
Borgman, H. P., Bahli, B., Heier, H., & Schewski, F. (2013). Cloudrise: Exploring cloud computing adoption and governance with the TOE framework. In Proceedings of the 46th Hawaii International Conference on System Sciences (pp. 1-10). IEEE. https://doi.org/10.1109/HICSS.2013.174
Chatterjee, S., Chaudhuri, R., Vrontis, D., Thrassou, A., & Ghosh, S. K. (2021). Adoption of artificial intelligence-integrated CRM systems in agile organizations in India. Technological Forecasting and Social Change, 168, 120783. https://doi.org/10.1016/j.techfore.2021.120783
Damerji, H. (2020). Technology readiness impact on artificial intelligence technology adoption by accounting students. [Doctoral dissertation]. University of La Verne.
Davis, F. D. (1985). A technology acceptance model for empirically testing new end- user information systems. [Doctoral dissertation]. Massachusetts Institute of Technology.
_______. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
Fishbein, M., & Ajzen, I. (2010). Predicting and changing behavior: The reasoned action approach. Psychology.
Francis, J., LaFond, R., Olsson, P., & Schipper, K. et al. (2004). Cost of capital and earnings transparency. Journal of Accounting and Economics, 79(4), 967-1010.
Gammanee, S., Rattananonsathien, N., & Klaprabchone, K. (2023). Model development to increase skills in using artificial intelligence to add value to work and workability of workers in Kanchanaburi Province. Journal of Administrative and Management Innovation, 11(1), 45–53.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson Education.
Handayani, N., & Prahartiwi, L. I. (2024). Analisis penerimaan teknologi e-wallet GoPay dengan technology acceptance model (TAM). Indonesian Journal on Information System (IJIS), 9(1). 47-57.
Ikongngam, W. (2018). Factors using information technology affecting administration of educational institutions under the Office of Samutprakarn Education. [Master’s thesis]. Rajamangala University of Technology Thanyaburi.
Kardes, F. R. (2002). Consumer behavior and managerial decision making (2nd ed.). Pearson Education.
Klein, L. (2016). Design for voice interface. O'Reilly Media.
Konerding, U. (1999). Formal models for predicting behavioral intentions in dichotomous choice situations. Methods of Psychological Research, 4(2), 1–32.
Kongernkrang, W., Sasithanakornkaew, S., & Kumsubha, B. (2024). Factors influencing the acceptance and continuous use behavior of Chat GPT artificial intelligence. Journal of MCU Nakhondhat, 11(2), 120–129.
Locke, E. A. (1976). The nature and causes of job satisfaction. In M. D. Dunnette (Ed.), Handbook of industrial and organizational psychology (pp. 129–168). Ran McNally.
Maneewong, P. (2020). Factors affecting the intention in using the EXPRESS program of accountants in higher education. Journal of the Association of Researchers, 25(1), 475-493.
Nakprathum, W., & Porananond, D. (2021). Knowledge and understanding, and attitudes affecting readiness in applying artificial intelligence (AI) technology in project management. MUT Journal of Business Administration, 18(2), 1–24.
Park, C. L. (2010). Making sense of the meaning literature: An integrative review of meaning making and its effects on adjustment to stressful life events. Psychological Bulletin, 136(2), 257–301.
Phoonsawat, G., Wanpeng, R., Homsai, S., & Pongsophon, P. (2022). Artificial intelligence and science teaching. Journal of Industrial Education, 21(1), c1-c8.
Scupola, A. (2009). SMEs' e‐commerce adoption: Perspectives from Denmark and Australia. Journal of Enterprise Information Management, 22(1/2), 152-166.
Seethongkam, K. (2021). Attitudes and impacts of private organization employees in Bangkok on the adoption of digital technology in organizations. [Master’s thesis]. Mahidol University.
Shroff, R. H., & Christopher C. (2011). Analysis of the technology acceptance model in examining students' behavioral intention to use an e-portfolio system. Australasian Journal of Educational Technology, 27(4), 600-618.
Somsamran, S., & Pacharadit, S. (2021). The effect of artificial intelligence and preparation of educational system. Academic Journal of Curriculum and Instruction Sakon Nakhon Rajabhat, 13(37), 263–268.
Suriyapaitool, W. (2017). The effects of perceived usefulness and ease of use on attitude and consumers' purchased intention of fashion products via M-commerce. [Master’s thesis]. Prince of Songkhla University.
Thanakun, T. (2019). A study of personal characteristics, knowledge of economy 4.0 and work process affecting to the desirable characteristics of the accountants for entering of artificial intelligence during Thailand 4.0 of Thai accountants in Bangkok. [Master’s thesis]. Thammasat University.
Tunjaya, P. (2018). User experience design of artificial intelligent technology. EAU Heritage Journal Science Technology, 12(1), 39-46.
Vărzaru, A. (2022). Assessing artificial intelligence technology acceptance in managerial accounting. Electronics, 11(14), 2256.
Yamane, T. (1973). Statistics: An introductory analysis. Harper & Row.
