Determinants of Buy Now, Pay Later Acceptance among Baby Boomers in Rural Malaysia: An Extended Technology Acceptance Model
Authors
Fakulti Pengurusan Teknologi dan Teknousahawanan, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka (Malaysia)
Sekolah Asasi Teknikal dan Pengajian Diploma, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka (Malaysia)
Sekolah Asasi Teknikal dan Pengajian Diploma, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka (Malaysia)
Fakulti Pengurusan Teknologi dan Teknousahawanan, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka (Malaysia)
Sekolah Asasi Teknikal dan Pengajian Diploma, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka (Malaysia)
TM Technology Services Sdn Bhd, TM Global, Level 52, Menara TM, Jalan Pantai Baharu, 50672 Kuala Lumpur (Malaysia)
Article Information
DOI: 10.47772/IJRISS.2026.100800887
Subject Category: Management
Volume/Issue: 10/8 | Page No: 12989-13000
Publication Timeline
Submitted: 2026-09-07
Accepted: 2026-09-12
Published: 2026-09-19
Abstract
Buy Now, Pay Later (BNPL) has become a widely used digital payment innovation, yet most empirical studies on its acceptance focus on younger, urban, and digitally active consumers, leaving it unclear whether Technology Acceptance Model (TAM) determinants operate similarly among older adults in rural settings. This study examined BNPL acceptance among baby boomers (born 1946-1964, spanning their early sixties to eighties at the time of data collection) residing in rural regions of Malaysia using an extended TAM incorporating perceived usefulness (PU), perceived ease of use (PEOU), perceived risk (PR), and digital financial literacy (DFL). Data were collected from 302 respondents through an online and on-site questionnaire survey and analysed using descriptive statistics, Pearson correlation, and multiple linear regression in SPSS version 29. Most respondents (91.7%) had already used a BNPL service, indicating that the findings speak primarily to continuance rather than first-time adoption among this cohort. The four predictors jointly explained 69.9% of the variance in BNPL acceptance (R² = .699, F(4, 297) = 172.009, p < .001), and were associated with three of the four hypothesised paths. Perceived ease of use showed the strongest association (β = .442, p < .001), followed by digital financial literacy (β = .267, p < .001) and perceived usefulness (β = .177, p = .013); perceived risk was not significant (β = .023, p = .503). These results suggest that usability and digital competence, more than risk avoidance, are associated with continued BNPL acceptance among rural baby boomers, pointing to intuitive application design and targeted digital-financial-literacy support as potentially more effective than risk-reassurance or benefit-focused messaging for this demographic.
Keywords
Buy Now Pay Later; Baby Boomers; Technology Acceptance Model; Digital Financial Literacy; Rural Malaysia
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References
1. Department of Statistics Malaysia. (2023). ICT use and access by individuals and households survey report, Malaysia 2022. DOSM. [Google Scholar] [Crossref]
2. Malaysia Digital Economy Corporation (MDEC). (2023). Malaysia digital economy blueprint: Progress and updates. MDEC. [Google Scholar] [Crossref]
3. Bank for International Settlements (BIS). (2023, December). Buy now, pay later: The digital credit reshaping consumer payments. BIS Quarterly Review. [Google Scholar] [Crossref]
4. Consumer Credit Oversight Board Task Force. (2024). Buy Now Pay Later (BNPL) industry in Malaysia 2023-2024. CCOB. [Google Scholar] [Crossref]
5. Dadra, R., Sonavane, A., Bachwani, S., & Behera, C. (2024). Beyond credit cards: A TEP perspective on buy-now-pay-later adoption. Journal of Global Scholars of Marketing Science. https://doi.org/10.1080/21639159.2024.2391282 [Google Scholar] [Crossref]
6. Ishak, N., Nasah, C. R. J., & Salleh, M. S. (2025). The rise of Buy Now, Pay Later (BNPL): Financial innovation or a new avenue to debt? A systematic and bibliometric analysis. International Journal of Electronic Commerce Studies. https://doi.org/10.7903/IJECS.2596 [Google Scholar] [Crossref]
7. Gazali, H. M., & Jumadi, J. (2022). The acceptance of digital finance (DiFi) among Muslim consumers in Malaysia. Labuan Bulletin of International Business and Finance, 20(2), 107-119. https://doi.org/10.51200/lbibf.v20i2.3544 [Google Scholar] [Crossref]
8. Ting, H., Lim, T.-Y., de Run, E. C., & Koh, H. (2017). Are we Baby Boomers, Gen X and Gen Y? A qualitative inquiry into generation cohorts in Malaysia. Kasetsart Journal of Social Sciences, 38(3), 329-339. https://doi.org/10.1016/j.kjss.2017.06.004 [Google Scholar] [Crossref]
9. Department of Statistics Malaysia. (2020). Population distribution and basic demographic characteristics report 2020. DOSM. [Google Scholar] [Crossref]
10. Malaysian Communications and Multimedia Commission (MCMC). (2021). Internet Users Survey 2021. MCMC. [Google Scholar] [Crossref]
11. Schomburgk, L., & Hoffmann, A. O. I. (2023). Buy now, pay later ... on your credit card. Journal of Behavioral and Experimental Finance. https://doi.org/10.1016/j.jbef.2023.100788 [Google Scholar] [Crossref]
12. Browne, M. (2021). Fintech and responsibility: Buy-now-pay-later arrangements. Australian Journal of Management. https://doi.org/10.1177/03128962211032448 [Google Scholar] [Crossref]
13. Malay Mail. (2021, March 29). Boost joins DuitNow QR system to support economic regeneration in a contactless way. Malay Mail. [Google Scholar] [Crossref]
14. Ahmad, N. A., Mohd Ali, M. A., & Tengku Shahdan, T. S. (2025). Intergenerational instructional strategies and elderly preferences for digital applications: A Malaysian case study. PLOS ONE, 20(8), e0328481. https://doi.org/10.1371/journal.pone.0328481 [Google Scholar] [Crossref]
15. Czaja, S. J., Charness, N., Fisk, A. D., Hertzog, C., Nair, S. N., Rogers, W. A., & Sharit, J. (2006). Factors predicting the use of technology: Findings from the CREATE project. Psychology and Aging, 21(2), 333-352. https://doi.org/10.1037/0882-7974.21.2.333 [Google Scholar] [Crossref]
16. Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-481. https://doi.org/10.1111/j.1540-5915.1996.tb00860.x [Google Scholar] [Crossref]
17. Reisenwitz, T. H., & Iyer, R. (2007). A comparison of younger and older Baby Boomers: Investigating the viability of cohort segmentation. Journal of Consumer Marketing, 24(4), 202-213. https://doi.org/10.1108/07363760710755918 [Google Scholar] [Crossref]
18. Choi, Y., Han, S., & Lee, C. (2024). Exploring drivers of fintech adoption among elderly consumers. Technology in Society, 78, 102669. https://doi.org/10.1016/j.techsoc.2024.102669 [Google Scholar] [Crossref]
19. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. [Google Scholar] [Crossref]
20. 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 [Google Scholar] [Crossref]
21. Bauer, R. A. (1960). Consumer behavior as risk taking. In R. S. Hancock (Ed.), Dynamic marketing for a changing world (pp. 389-398). American Marketing Association. [Google Scholar] [Crossref]
22. Lyons, A. C., & Kass-Hanna, J. (2021). A methodological overview to defining and measuring “digital” financial literacy. Financial Planning Review, 4(2), e1113. [Google Scholar] [Crossref]
23. 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. https://doi.org/10.1016/j.elerap.2008.11.006 [Google Scholar] [Crossref]
24. Vuori, S., & Holmlund-Rytkönen, M. (2005). 55+ people as internet users. Marketing Intelligence & Planning, 23(1), 58-76. https://doi.org/10.1108/02634500510577408 [Google Scholar] [Crossref]
25. Kumar, A., Salo, J., & Bezawada, R. (2024). Buy Now, Pay Later: Impact of installment payments on customer purchases. Journal of Marketing. https://doi.org/10.1177/00222429241282414 [Google Scholar] [Crossref]
26. Raj, V. A., Jasrotia, S., & Rai, S. (2024). Role of perceived risks and perceived benefits on consumers' behavioural intention to use Buy-Now, Pay-Later services. Journal of Facilities Management. https://doi.org/10.1108/JFM-01-2023-0004 [Google Scholar] [Crossref]
27. Relja, R., Ward, P., & Zhao, A. (2023). Understanding the psychological determinants of buy-now-pay-later in the UK: A user perspective. International Journal of Bank Marketing. https://doi.org/10.1108/IJBM-07-2022-0324 [Google Scholar] [Crossref]
28. Wulandari, R. T., & Damayanti, S. M. (2022). The importance of digital financial literacy to anticipate impulsive buying behaviour in buy-now-pay-later mode. International Journal of Business and Economy, 4(3), 170-182. [Google Scholar] [Crossref]
29. Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications. [Google Scholar] [Crossref]
30. Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1-4. https://doi.org/10.11648/j.ajtas.20160501.11 [Google Scholar] [Crossref]
31. Tongco, M. D. C. (2007). Purposive sampling as a tool for informant selection. Ethnobotany Research and Applications, 5, 147-158. https://doi.org/10.17348/era.5.0.147-158 [Google Scholar] [Crossref]
32. Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607-610. [Google Scholar] [Crossref]
33. van Teijlingen, E., & Hundley, V. (2001). The importance of pilot studies. Social Research Update, (35), 1-4. [Google Scholar] [Crossref]
34. Hao Lao, U., Lee, L. E., Lew, D. K., Loo, L. S., Ooi, S. X., Lee, V. H., Loh, X. M., & Tan, G. W. H. (2021). Mobile payment adoption: Barriers for baby boomers in Malaysia [Preprint]. EasyChair Preprints. https://easychair.org/publications/preprint/2dgw [Google Scholar] [Crossref]
35. Hassan, Z. A., Schattner, P., & Mazza, D. (2006). Doing a pilot study: Why is it essential? Malaysian Family Physician, 1(2-3), 70-73. [Google Scholar] [Crossref]
36. Pallant, J. (2020). SPSS survival manual: A step by step guide to data analysis using IBM SPSS (7th ed.). Routledge. [Google Scholar] [Crossref]
37. Evans, J. D. (1996). Straightforward statistics for the behavioral sciences. Brooks/Cole. [Google Scholar] [Crossref]
38. Heale, R., & Twycross, A. (2015). Validity and reliability in quantitative studies. Evidence-Based Nursing, 18(3), 66-67. https://doi.org/10.1136/eb-2015-102129 [Google Scholar] [Crossref]
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