User Adoption of Generative AI for Government Information Services in Thailand
Article Sidebar
Main Article Content
Abstract
Recent advancements in generative AI have gained significant attention from both academic and industrial sectors. With the ability to generate new content like text, images, and audio from user inputs, generative AI has demonstrated considerable potential in enhancing organizational efficiency, improving service delivery, and automating complex tasks. In the government sector, generative AI offers opportunities to automate citizen inquiries, enhance administrative processes, and provide more personalized public services. This paper aimed to study the factors influencing user adoption of generative AI for government information services in Thailand. The sample included individuals, focusing on their intentions regarding the use of generative AI for accessing government information. Participants were selected using a convenience sampling method, and data was collected through 400 questionnaires. The data was then analyzed using descriptive statistics and a structural equation model (SEM) to test the hypotheses. The research results indicate that the factors influencing user adoption of generative AI for government information services in Thailand are social influence and user experience, which include perceived usefulness, perceived ease of use, and trust. The overall model explains 48.5% of the variance in the intention to use generative AI for government information services (R² = 0.485). The suggestions are proposed to increase adoption rates by developing strategies that involve engaging influencers and advocates, promoting community engagement and education, and improving the overall user experience. These findings provide valuable insights for government agencies and policymakers in Thailand on effectively promoting the adoption of generative AI, contributing to more efficient and accessible government services.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Views and opinions appearing in the Journal it is the responsibility of the author of the article, and does not constitute the view and responsibility of the editorial team.
References
Abdulkareem, A. K. (2024). E-Government in Nigeria: Can generative AI serve as a tool for civic engagement?. Public Governance, Administration and Finances Law Review, 9(1), 75–90. https://doi.org/10.53116/pgaflr.7068
Baabdullah, A. M. (2024). Generative conversational AI agent for managerial practices: The role of IQ dimensions, novelty seeking and ethical concerns. Technological Forecasting and Social Change, 198, 122951. https://doi.org/10.1016/j.techfore.2023.122951
Bouwman, H., Haaker, T., & Vos, H. (2007). Mobile service bundles: The example of navigation services. Electronic Markets, 17(1), 20-28. DOI:10.1080/10196780601136757
Charness, N., Yoon, J., Souders, D., Stothart, C., & Yehnert, C. (2018). Predictors of attitudes toward autonomous vehicles: The roles of age, gender, prior knowledge, and personality. Frontiers in Psychology, 9. https://doi.org/10.3389/fpsyg.2018.02589
Chatterjee, S., Rana, N. P., Dwivedi, Y. K., & Baabdullah, A. M. (2021). Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model. Technological Forecasting and Social Change, 170, 120880. https://doi.org/10.1016/j.techfore.2021.120880
Chen, Z.-G., Wang, J.-T., & Deng, X.-H. (2014). A trust model based on semantic distance for pervasive environments. Security and Communication Networks, 7(9), 1322-1330. https://doi.org/10.1002/sec.789
Cudjoe, D., Zhang, H., & Wang, H. (2023). Predicting residents’ adoption intention for smart waste classification and collection system. Technology in Society, 75, 102381. https://doi.org/10.1016/j.techsoc.2023.102381
Cummings, R. E., Monroe, S. M., & Watkins, M. (2024). Generative AI in first-year writing: An early analysis of affordances, limitations, and a framework for the future. Computers and Composition, 71, 102827. https://doi.org/10.1016/j.compcom.2024.102827
Das, K., Pattanaik, M., Basantia, S., Mishra, R., Das, D., Sahoo, K., & Paital, B. (2023). Informatics on a social view and need of ethical interventions for wellbeing via interference of artificial intelligence. Telematics and Informatics Reports, 11, 100065. https://doi.org/10.1016/j.teler.2023.100065
Gansser, O. A., & Reich, C. S. (2021). A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application. Technology in Society, 65, 101535. https://doi.org/10.1016/j.techsoc.2021.101535
Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE.
Hampton, C. (2015). Estimating and reporting structural equation models with behavioral accounting data. Behavioral Research in Accounting, 27(2), 1-34-34. https://doi.org/10.2308/bria-51226
Hőgye-Nagy, Á., Kovács, G., & Kurucz, G. (2023). Acceptance of self-driving cars among the university community: Effects of gender, previous experience, technology adoption propensity, and attitudes toward autonomous vehicles. Transportation Research Part F: Traffic Psychology and Behaviour, 94, 353-361. https://doi.org/10.1016/j.trf.2023.03.005
Hwang, J., & Oh, S. (2023). A brief survey of watermarks in generative AI. In The 14th International Conference on Information and Communication Technology Convergence (ICTC).
Kline, R. B. (2011). Principles and practice of structural equation modeling. Guilford.
Liew, T. W., Tan, S.-M., Yoo, N. E., Gan, C. L., & Lee, Y. Y. (2023). Let's talk about sex!: AI and relational factors in the adoption of a chatbot conveying sexual and reproductive health information. Computers in Human Behavior Reports, 11, 100323.
https://doi.org/10.1016/j.chbr.2023.100323
Lim, J.S., & Zhang, J. (2022). Adoption of AI-driven personalization in digital news platforms: An integrative model of technology acceptance and perceived contingency. Technology in Society, 69, 101965. https://doi.org/10.1016/j.techsoc.2022.101965
Limayem, M., Khalifa, M., & Chin, W. W. (2004). Factors motivating software piracy: a longitudinal study. IEEE Transactions on Engineering Management, 51(4), 414-425. https://doi.org/10.1109/TEM.2004.835087
Lothery, E.J. (2024). Transformative governance: Integrating generative artificial intelligence in state and local government operations. Organization Development & Change D.O.D.C. Dissertations, 20. https://scholarworks.bgsu.edu/odc_diss/20
Nees, M. (2016). Acceptance of self-driving cars: an examination of idealized versus realistic portrayals with a self-driving car acceptance scale. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 60, 1449-1453. https://doi.org/10.1177/ 1541931213601332
Oh, S., & Shon, T. (2023). Cybersecurity issues in generative AI. In International Conference on Platform Technology and Service (PlatCon), 16-18 Aug. 2023.
Pandey, J.K. (2024). Unlocking the power and future potential of generative AI in government transformation. Transforming Government: People, Process and Policy. https://doi.org/10.1108/TG-01-2024-0006
Pavlou, P. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7, 101-134.
Puspitasari, N., Firdaus, M. B., Haris, C. A., & Setyadi, H. J. (2019). An application of the UTAUT model for analysis of the adoption of an integrated license service information system. Procedia Computer Science, 161, 57-65. https://doi.org/10.1016/j.procs. 2019.11.099
Rajak, M., & Shaw, K. (2021). An extension of the technology acceptance model for mHealth user adoption. Technology in Society, 67, 101800. https://doi.org/10.1016/j.techsoc.2021.101800
Sætra, H. S. (2023). Generative AI: Here to stay, but for good?. Technology in Society, 75, 102372. https://doi.org/10.1016/j.techsoc.2023.102372
Saif, N., Khan, S. U., Shaheen, I., Alotaibi, F. A., Alnfiai, M. M., & Arif, M. (2024). Chat-GPT; Validating the Technology Acceptance Model (TAM) in the education sector via ubiquitous learning mechanisms. Computers in Human Behavior, 154, 108097. https://doi.org/10.1016/j.chb.2023.108097
Sheng, M. L., & Fauzi, A. A. (2023). Institutional behavior mechanism: Exploring the impacts of macro-environmental stimuli on continued digital payment adoption behavior. Computers in Human Behavior, 149, 107923. https://doi.org/10.1016/j.chb.2023.107923
Vaishya, R., Misra, A., & Vaish, A. (2023). Chat-GPT: Is this version good for healthcare and research?. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 17(4), 102744. https://doi.org/10.1016/j.dsx.2023.102744
Wang, Y., & Zhang, W. (2023). Factors influencing the adoption of generative AI for art designing among Chinese generation Z: A structural equation modeling approach. IEEE Access, 11, 143272-143284. https://doi.org/10.1109/ACCESS.2023.3342055