Exploring the Effects of Cashless Mobile Payment Adoption in Thailand:  A Case of Silver Generation

Natinee Thanajaro; Wanlapa Hattakitpanitchakul

Authors

Natinee Thanajaro Faculty of Business Administration for Society, Srinakharinwirot University
Wanlapa Hattakitpanitchakul Faculty of Business Administration for Society, Srinakharinwirot University

Keywords:

technology adoption, aging society, user experience, satisfaction, behavioral intentions

Abstract

This study explores the adoption of cashless mobile payment systems among Thailand's silver generation (aged 60 and above), a demographic experiencing rapid growth in digital engagement yet facing unique adoption challenges. By integrating the Technology Acceptance Model (TAM) with user experience perspectives, the research investigates the influence of perceived ease of use, perceived usefulness, social influence, technology self-efficacy, and trust on user experience, which mediates satisfaction, adoption rates, and behavioral intentions. A quantitative survey of 480 elderly participants in Bangkok was conducted, with data analyzed using structural equation modeling (SEM). Key findings indicate that perceived ease of use and trust significantly enhance user experience, while social influence and technology self-efficacy moderately contribute. The study highlights user experience as a critical mediator, emphasizing the need for user-friendly interfaces, trust-building strategies, and digital literacy programs tailored for the elderly. These findings provide valuable insights for financial institutions, policymakers, and technology developers aiming to promote digital financial inclusion among older populations in emerging economies.

References

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T

Alexandru, A., Coardos, D., & Tudora, E. (2019). Acceptance of the technologies deployed for the development of online public services and systems used by elderly. In 2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) (pp. 1-4). IEEE.

Alsaad, A., & Al-Okaily, M. (2022). Acceptance of protection technology in a time of fear: The case of COVID-19 exposure detection apps. Information, Technology & People, 35(3), 1116–1135. https://doi.org/10.1108/ITP-10-2020-0719

Bandura, A. (1986). Social Foundations of Thought and Action: A Social Cognitive Theory. Prentice Hall.

Bell, E., Bryman, A., & Harley, B. (2022). Business research methods (6th edition). Oxford University Press.

Berkowsly, R.W., Sharit, J. and Czaja, S.J. (2017) Factors Predicting Decisions About Technology Adoption Among Older Adults. Innovation in Aging, 1(3), 1-12. https://doi.org/10.1093/geroni/igy002

Byrne, B. M. (2016). Structural equation modeling with AMOS: Basic concepts, applications, and programming (3rd edition). Routledge.

Chaveesuk, S., Wutthirong, P and Chaiyasoonthorn, W. (2018). The Model of Mobile Payment System Acceptance on Social Networks in Thailand. A Conceptual Framework. In ICIME 2018: Proceedings of the 2018 10th International Conference on Information Management and Engineering, 35-39. https://doi.org/10.1145/3285957.3285990

Chen, K., & Chan, A. H. S. (2011). A review of technology acceptance by older adults. Gerontechnology, 10(1), 1-12. https://doi.org/10.4017/gt.2011.10.01.006.00

Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211. https://doi.org/10.2307/249688

Compeau, D. R., Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS Quarterly, 23(2), 145–158. https://doi.org/10.2307/249749

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th edition). SAGE Publications.

Dahlberg, T., Guo, J., & Ondrus, J. (2015). A critical review of mobile payment research. Electronic Commerce Research and Applications, 14(5), 265-284. https://doi.org/10.1016/j.elerap.2015.07.006

Dahlberg, T., Mallat, N., Ondrus, J., & Zmijewska, A. (2008). Past, present and future of mobile payments research: A literature review. Electronic Commerce Research and Applications, 7(2), 165–181. https://doi.org/10.1016/j.elerap.2007.02.001

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Delone, W.H. and McLean, E.R. (2003). The Delone and McLean Model of Information Systems Success: A Ten-Year Update. Journal of Management Information Systems, 19(4), 9-30. https://doi.org/10.1080/07421222.2003.11045748

Ertz, M., Jo, M.-S., Kong, Y., & Sarigöllü, E. (2021). Predicting m-shopping in the two largest m-commerce markets: The United States and China. International Journal of Market Research, 64(2), 249-268. https://doi.org/10.1177/1470785321102303

Feng, K. K., Li, T. W., & Zhang, A. X. (2023, April). Understanding collaborative practices and tools of professional UX practitioners in software organizations. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-20). https://doi.org/10.1145/3544548.3581273

Gefen, D., & Straub, D. W. (2004). Consumer trust in B2C e-Commerce and the importance of social presence: Experiments in e-Products and e-Services. Omega, 32(6), 407–424. https://doi.org/10.1016/j.omega.2004.01.006

Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519

Gharaibeh, M.K., Arshad, M.R., & Gharaibeh, N.K. (2018). Using the UTAUT2 Model to Determine Factors Affecting Adoption of Mobile Banking Services: A Qualitative Approach. International Journal of Interactive Mobile Technologies, 12, 123-134. https://doi.org/10.3991/ijim.v12i4.8525

Gupta, P. and Hakhu, R. (2022). Impact of Perceived Security and Perceived Trust on Intention to Use Digital Payments – A Study on Indian Customers. Webology, 18(6), 169-181.

Hair, J. F., Black, W. C., Babin, B. J., and Anderson, R. E. (2019). Multivariate data analysis (8th edition). Cengage Learning.

Hassenzahl, M., & Tractinsky, N. (2006). User experience – a research agenda. Behaviour & Information Technology, 25(2), 91–97. https://doi.org/10.1080/01449290500330331

Hong, S.J., & Tam, K. Y. (2006). Understanding the Adoption of Multipurpose Information Appliances: The Case of Mobile Data Services. Information Systems Research, 17(2), 162–179. https://doi.org/10.1287/isre.1060.0088

Kapoor, A., Sindwani, R., Goel, M., & Shankar, A. (2022). Mobile wallet adoption intention amid COVID-19 pandemic outbreak: A novel conceptual framework. Computers & industrial engineering, 172, 108646. https://doi.org/10.1016/j.cie.2022.108646

Khalilzadeh, J., Ozturk, A. B., & Bilgihan, A. (2017). Security-related factors in extended UTAUT model for NFC-based mobile payment in the restaurant industry. Computers in Human Behavior, 70, 460-474. https://doi.org/10.1016/j.chb.2017.01.001

Kim, C., Mirusmonov, M., & Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3), 310-322. https://doi.org/10.1016/j.chb.2009.10.013

Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling (4th edition). Guilford Press.

Kraiwanit, T., Jangjarat, K., & Srijam, A. (2023). Factors Determining Online Activities and Technology Use Among Older Adults in Thailand. Social Sciences and Humanities, 31 (2), 803-816. https://doi.org/10.47836/pjssh.31.2.17

Law, E. L. C., Roto, V., Hassenzahl, M., Vermeeren, A. P., & Kort, J. (2009). Understanding, scoping and defining user experience: a survey approach. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 719–728. https://doi.org/10.1145/1518701.1518813

Lee, M. K., & Turban, E. (2001). A trust model for consumer internet shopping. International Journal of Electronic Commerce, 6(1), 75–91. https://doi.org/10.1080/10864415.2001.11044227

Lewis, J. R., & Sauro, J. (2023). Effect of Perceived Ease of Use and Usefulness on UX and Behavioral Outcomes. International Journal of Human-Computer Interaction, 1–8. https://doi.org/10.1080/10447318.2023.2260164

Lian, J., and Yen, D.C. (2014). Online shopping drivers and barriers for older adults: Age and gender differences. Computers in Human Behavior, 37, 133-143. https://doi.org/10.1016/j.chb.2014.04.028

McKnight, D. H., & Chervany, N. L. (2002). What trust means in e-commerce customer relationships: An interdisciplinary conceptual typology. International Journal of Electronic Commerce, 6(2), 35–59. https://doi.org/10.1080/10864415.2001.11044235

McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-Commerce: An integrative typology. Information Systems Research, 13(3), 334–359. https://doi.org/10.1287/isre.13.3.334.81

Msweli, N.T. and Mawela, T. (2021). Financial Inclusion of the Elderly: Exploring the Role of Mobile Banking Adoption. Acta Informatica Pragensia, 10(1), 1-21.

Naidoo, R., and Leonard, A. (2007). Perceived usefulness, service quality and loyalty incentives: Effects on electronic service continuance. South African Journal of Business Management, 38(3), 39-48.

Niehaves, B., & Plattfaut, R. (2014). Internet adoption by the elderly: employing IS technology acceptance theories for understanding the age-related digital divide. European Journal of Information Systems, 23(6), 708–726. https://doi.org/10.1057/ejis.2013.19

Nosike, R.C.J., Sandra, N.O. and Uju, N.C. (2024). The Imporatance of Digital Transformation in a Post-Pandemic World. Development Policy and Management Review. 4(1), 1-15. https://doi.org/10.61731/dpmr.v4i1.32718

Nunnally, J. C. (1978). Psychometric theory (2nd edition). McGraw-Hill.

Pan, S., & Jordan-Marsh, M. (2010). Internet use intention and adoption among Chinese older adults: From the expanded technology acceptance model perspective. Computers in Human Behavior, 26(5), 1111-1119. https://doi.org/10.1016/j.chb.2010.03.015

Park, I., Kim, D., Moon, J., Kim, S., Kang, Y., & Bae, S. (2022). Searching for new technology acceptance model under social context: analyzing the determinants of acceptance of intelligent information technology in digital transformation and implications for the requisites of digital sustainability. Sustainability, 14(1), 579. https://doi.org/10.3390/su14010579

Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–134. https://doi.org/10.1080/10864415.2003.11044275

Pavlou, P. A., & Fygenson, M. (2006). Understanding and predicting electronic commerce adoption: An extension of the theory of planned behavior. MIS Quarterly, 115–143. https://doi.org/10.2307/25148720

Puriwat, W., & Tripopsakul, S. (2017). Mobile banking adoption in Thailand: An integration of technology acceptance model and mobile service quality. Economic Research Studies Journal, 20(4B), 200-210.

Raj, L. V., Amilan, S., Aparna, K., & Swaminathan, K. (2023). Factors influencing the adoption of cashless transactions during COVID-19: an extension of enhanced UTAUT with pandemic precautionary measures. Journal of Financial Services Marketing, 1.

Razali, N. A., Tajudeen, F. P., & Baharudin, A. S. (2024). Developing a Model for Mobile Payment Adoption among Senior Citizens. Pakistan Journal of Life and Social Sciences, 22(2), 15049-15063.

Saadé, R. G., & Kira, D. (2009). Computer anxiety in e-learning: The effect of computer self-efficacy. Journal of Information Technology Education: Research, 8(1), 177–191.

Seo, J., Lim, H. Y., & Lee, J. (2023). The Power of Close Others: How Social Interactions Impact Older Adults’ Mobile Shopping Experience. https://doi.org/10.1145/3563657.3595993

Singh, N., Sinha, N., & Liébana‐Cabanillas, F.J. (2020). Determining factors in the adoption and recommendation of mobile wallet services in India: Analysis of the effect of innovativeness, stress to use and social influence. International Journal of Information Management, 50, 191-205. https://doi.org/10.1016/j.ijinfomgt.2019.05.022

Sinha, M., Majra, H., Hutchins, J., & Saxena, R. (2019). Mobile payments in India: The privacy factor. International Journal of Bank Marketing, 37(1), 192-209. https://doi.org/10.1108/IJBM-05-2017-0099

Srizongkhram, S., Shirahada, K., & Chiadamrong, N. (2018). Critical Factors for Adoption of Wearable Technology for the Elderly: Case Study of Thailand. In Portland International Conference on Management of Engineering and Technology (PICMET), 1-9.

Tsai, H. S., Shillair, R., & Cotten, S. R. (2015). Social Support and “Playing Around”: An Examination of How Older Adults Acquire Digital Literacy With Tablet Computers. Journal of Applied Gerontology, 36(1), 29-55. https://doi.org/10.1177/0733464815609440

Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926

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

Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412

Yang, H., Yu, J., Zo, H., & Choi, M. (2021). User acceptance of wearable devices: An extended perspective of perceived value. Telematics and Informatics, 63, 101609. https://doi.org/10.1016/j.tele.2015.08.007

Yang, C. C., Yang, S. Y., and Chang, Y. C. (2023). Predicting older adults' mobile payment adoption: An extended TAM model. International Journal of Environmental Research and Public Health, 20(2), 1391. https://doi.org/10.3390/ijerph20021391

Yuan, S., Liu, Y., Yao, R., and Liu, J. (2016). An investigation of users’ continuance intention towards mobile banking in China. Information Development, 32(1), 20-34. https://doi.org/10.1177/026666691452214

Zajicek, M. (2004). Successful and available: interface design exemplars for older users. Interacting with Computers, 16, 411–430. https://doi.org/10.1016/j.intcom.2004.04.003

Zhou, T. (2011). Understanding mobile Internet continuance usage from the perspectives of UTAUT and flow. Information Development, 27(3), 207–218. https://doi.org/10.1177/02666669114145