Causal Factors Influencing User Acceptance of Government Mobile Applications in Thailand
Article Sidebar
Main Article Content
Abstract
During the COVID-19 outbreak, the necessity for digital technology in service delivery intensified, prompting governmental agencies to develop numerous applications that the public has widely utilized. This research aimed to identify the causal factors influencing user acceptance of these government mobile applications in Thailand. The study utilized quantitative methods, to target individuals at least 18 years old who use government mobile apps. A proportional stratified sampling method was employed to collect data, which was then analyzed using a structural equation model. The results indicated that user attitudes had the most direct influence on the acceptance of government mobile applications. However, when considering the overall impact, perceptions of government mobile applications emerged as the most significant factor, followed by the quality of the applications, government policy, and user attitude, in that order. These findings enable application developers to understand user needs better and assist in developing efficient applications that align with these needs. Furthermore, the insights from this study aid the government sector in promoting technological innovation and service improvement by developing effective and widely accepted applications, thereby encouraging the adoption of new technologies to enhance application quality.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright: CC BY-NC-ND 4.0
References
Agyapong, A. (2017). Digital technology and economic growth: A case study of Ghana. England: University of Liverpool.
Alsharhan, D., & Ali, R. (2021). Assessing quality factors influencing users’ intention to adopt mobile applications in Kuwait. Journal of Information Technology Research, 14(3), 15-31.
Alsswey, A., Bin Umar, I. N., & Bervell, B. (2018). Investigating the acceptance of mobile health application user interface cultural-based design to assist Arab elderly users. International Journal of Advanced Computer Science and Applications, 9(8), 144-152.
Bhatia, S. S. (2015). Investigating the quality of mobile applications. University of Wollongong. Retrieved from https://ro.uow.edu.au/theses/4613
Charoenwiriyakul, C., & Sukkasame, K. (2015). The influence of marketing factors on e-commerce customer loyalty of business (in Thai). The Journal of Administration Development Research, 8(2), 11-39.
Cheng, W., & Xie, X. (2018). Factors affecting users’ intention to use mobile applications: A comparative study between China and the United States. Journal of Global Information Technology Management, 21(1), 34-54.
Chirapapaisarn, N. (2020). Understanding the role of government policy in digital technology adoption: The case of Thailand. Maryland: University of Maryland.
Cho, I., & Park, H. (2018). Designing an open government data platform for knowledge discovery: An empirical study in South Korea. Government Information Quarterly, 35(3), 385-396.
Chu, C. L. (2019). Assessing the accuracy of mobile applications for sleep monitoring. Florida: University of Florida.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 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, 982-1003.
Deakin, M. (2016). Open data and the future of public services. Journal of Innovation Management, 4(4), 96-107.
Department of Provincial Administration. (2022). Official statistics registration systems. Retrieved from https://stat.bora.dopa.go.th/stat/statnew/statMONTH/statmonth/#/displayData.
El-Gohary, H. (2012). Factors affecting e-marketing adoption and implementation in tourism firms: An empirical investigation of Egyptian small tourism organizations. Tourism Management, 33(5), 1256-1269.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Massachusetts: Addison-Wesley.
Fuentes-Enriquez, R., & Rojas-Romero, Y. (2013). Developing accountability, transparency and government efficiency through mobile apps: The case of Mexico (pp. 313-316). In Proceedings of the 7th International Conference on Theory and Practice of Electronic Governance. Seoul, Republic of Korea: The Ministry of Security and Public Administration of the Republic of Korea.
Gao, Q. (2019). Investigating the accuracy of mobile applications in transit performance measurement. Florida: University of South Florida.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th eds.). New Jersey: Prentice Hall.
Hayduk, L. A. (1996). LISREL: Issues, debates, and strategies. Maryland: Johns Hopkins University Press.
Himarat, T., & Khongmalai, O. (2018). A model to encourage financial technology adoption in the banking industry (in Thai). Suan Dusit Graduate School Academic Journal, 15(1), 209-219.
Jeong, B., & Yoon, E. (2013). An empirical on consumer acceptance of mobile banking services. Business and Management Research, 2(1), 31-40.
Jinthanon, P., & Rapeepisarn, K. (2020). Factors affecting the acceptance of the technology and software quality influencing on the intention to use e-wallet applications of undergraduate students in Hat Yai Municipality, Songkhla Province. RSU Library Journal, 26(2), 54-86.
Johnston, L., & Hawke, S. (2002). Effects of mobile phone use on perceived control, intimacy, and social identity. Journal of Community & Applied Social Psychology, 12(4), 265-275.
Karaket, W., & Distanont, A. (2018). Model to support fintech adoption in mobile payment for financial technology startup (in Thai). Suan Dusit Graduate School Academic Journal, 13(3), 97-105.
Kijpredarborisuthi, B. (1997). Social sciences research methodology. Bangkok, Thailand: Mahidol University.
Koul, S., & Eydgah, A. (2017). A systematic review of technology adoption frameworks and their applications. Journal of Technology Management & Innovation, 12(4), 106-113.
Lee, M. C. (2009). Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 8(3), 130-141.
Lee, S. M., & Trimi, S. (2021). Convergence Innovation in the digital age and in the COVID-19 pandemic crisis. Journal of Business Research, 123, 14-22.
Li, L. (2016). An analysis of government policy on mobile applications: A comparative study of the United States and China. Alabama: University of Alabama.
Liao, C., Tsou, C., & Huang, M. (2007). Factors influencing the usage of 3G mobile services in Taiwan. Online Information Review, 31(6), 759-774.
Likert, R. (1932). A technique for measurement of attitudes. Archives of Psychology, 140, 5-55.
Lule, I., Omwansa, T., & Mwololo, T. (2012). Application of technology acceptance model (TAM) in M-banking adoption in Kenya. International Journal of Computing and ICT Research, 6(1), 31-43.
Maddox, E. F. (2016). An analysis of the accuracy of smartphone heart rate measurement applications. Arkansas: University of Arkansas.
Maduku, D. K. (2014). Behavioral intention towards mobile banking usage by South African retail banking clients. Investment Management and Financial Innovations, 11(3),37-51.
Ministry of Digital Economy and Society. (2016). Digital economy and society development plan (1st ed.). Bangkok, Thailand: Ministry of Digital Economy and Society.
Mishra, A. K. (1996). Organizational responses to crisis: The centrality of trust (pp. 261-287). In Kramer, R. M., & Tyler T. R. (Eds.). Trust in organizations: Frontiers of theory and research. California: Sage.
Munn, N. L. (1971). Introduction to psychology. Massachusetts: Houghton Mifflin.
Nuq, P. A., & Aubert, B. (2013). Towards a better understanding of the intention to use eHealth services by medical professionals: the case of developing countries. International Journal of Healthcare Management, 6(4), 217-236
Office of the National Digital Economy and Society Commission. (2019). National digital economy and society development plan and policy 2018 - 2037. Bangkok, Thailand: Ministry of Digital Economy and Society.
Oliveira, T., Thomas, M. A., Baptista, G., & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior,61(2), 404-414.
Ooi, K.B., & Tan, G.W.H. (2016). Mobile technology acceptance model: An investigation using mobile users to explore smartphone credit card. Journal of Expert Systems with Applications, 59, 33-46.
Ren, S. J. F., Wamba, S. F., Akter, S., & Dubey, R. (2016). Modelling quality dynamics, business value and firm performance in a big data analytics environment. International Journal of Production Research, 55(17), 5011-5026.
Rogers, E. M. (1962). Diffusion of innovations. New York: Free Press.
Chuenyindee, T., Ong, A. K. S., Prasetyo, Y. T., Persada, S. F., Adiating, R., & Sittiwatethanasiri, T. (2022). Factors affecting the perceived usability of the COVID-19 contact tracing application “Thai Chana” during the early COVID-19 omicron period. International Journal of Environmental Research and Public Health, 19(7), 4383.
The Bureau of Registration Administration. (2023). Official statistics registration systems Retrieved from https://stat.bora.dopa.go.th/stat/statnew/statMenu/newStat/home.php
The Secretariat of the Prime Minister. (2017). Thailand 4.0 drives the future toward prosperity, stability, and sustainability. Thai Khu Fah Journal, 33(1), 4-17.
Thurstone, L. L., & Chave, E. J. (1966). The measurement of attitude: A psychophysical method and some experiments with a scale for measuring attitude toward the Church. Chicago: University of Chicago.
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.
Vicente, P., & Lopes, I. (2016). Attitudes of older mobile phone users towards mobile phones. Communications, 41(1), 71-86.
Wang, Y., Lin, H., & Luarn, P. (2006). Predicting consumer intention to use mobile service. Information System Journal, 16(2), 157-179.
Wu, J. H., Wang, S. C., & Tsai, H. H. (2017). What drives purchase intention for paid mobile apps? An expectation confirmation model with perceived value. Information & Management, 54(1), 92-102.
Yakea, S. (2020). E-payment system drive Thailand to be a cashless society. Review of Economics and Finance, 18(1), 87-91.
Yusuf, J. E., Mohamad, R., & Ahmad, A. R. (2019). The adoption of open data by government agencies: A literature review. Journal of Theoretical and Applied Information Technology, 97(19), 3079-3093.
Zhou, L., Xue, S., & Li, R., (2022). Extending the technology acceptance model to explore students’ intention to use an online education platform at a university in China. SAGE Open January-March 2022, 1-13.
Zhou, T. (2011). The effect of initial trust on user adoption of mobile payment. Information Development, 27(4), 290-300.
