Millennials and Buy Now Pay Later Adoption in Sabah: Trends, Determinants, and Insights from the Unified Theory of Acceptance and Use of Technology 2 and Perceived Risk Framework

Buy Now, Pay Later (BNPL) has become a notable financial innovation, enabling consumers to defer payments for online and in‑store purchases. Although existing studies have centred largely on Generation Z, Millennials remain comparatively underexamined despite representing a significant proportion of global users. This group demonstrates higher economic stability, stronger financial literacy, and more mature digital habits, including hedonic and habitual motivations that shape their intention to adopt and continue using Buy Now, Pay Later services. Established behavioural frameworks such as the Theory of Planned Behaviour (TPB), the Technology Acceptance Model (TAM), and the Unified Theory of Acceptance and Use of Technology (UTAUT) provide useful foundations but insufficiently incorporate cost, experiential, behavioural, and risk considerations. UTAUT2 offers an extended approach yet remains limited in addressing multidimensional perceived risks, including performance, psychological, social, time, and delivery risks. This review synthesises studies published between 2023 and 2025, with a focus on adoption patterns in Malaysia, Indonesia, and India, and particular attention to Millennials and regional nuances such as Sabah. The integration of UTAUT2 with perceived risk theory provides a more comprehensive understanding of behavioural intention and highlights key gaps to inform policymakers, practitioners, and future research.

Buy Now Pay Later, Millennials, UTAUT2

1. Galetsi, P., Katsaliaki, K., & Kumar, S. (2023). Exploring benefits and ethical challenges in the rise of mHealth (mobile healthcare) technology for the common good: An analysis of mobile applications for health specialists. Technovation, 121. https://doi.org/10.1016/j.technovation.2022.102598 [Google Scholar] [Crossref]

2. Raj, V. A., Jasrotia, S. S., & Rai, S. S. (2024). Intensifying materialism through buy-now pay-later (BNPL): Examining the dark sides. International Journal of Bank Marketing, 42, 94–112. https://doi.org/10.1108/IJBM-08-2022-0343 [Google Scholar] [Crossref]

3. Spherical Insights & Consulting. (2023). Global Buy Now Pay Later market size to exceed USD 160.7 billion by 2032 | CAGR of 20.2%. https://finance.yahoo.com/news/global-buy-now-pay-later-110000720.html [Google Scholar] [Crossref]

4. Research and Markets. (2024). Malaysia Buy Now Pay Later business and investment opportunities databook – 75+ KPIs on BNPL market size, end-use sectors, market share, product analysis, business model, demographics – Q1 2024 update. https://www.prnewswire.com/news-releases/malaysia-buy-now-pay-later-business-and-investment-opportunities-report-2024-market-to-grow-by-17-5-to-reach-2-78-billion-in-2024---forecasts-to-2029--302077001.html [Google Scholar] [Crossref]

5. Statista. (2024). Number of Buy Now, Pay Later (BNPL) users in Asia in 2022, by selected country. https://www.statista.com/statistics/1420986/asia-number-of-bnpl-users-by-country-2022/ [Google Scholar] [Crossref]

6. Department of Statistics Malaysia. (2024). Current population estimates 2024. https://www.dosm.gov.my/portal-main/release-content/current-population-estimates-2024 [Google Scholar] [Crossref]

7. Malaysian Communications and Multimedia Commission. (2023, December). Communications and multimedia: Facts & figures, 3Q 2023. https://mcmc.gov.my [Google Scholar] [Crossref]

8. Department of Statistics Malaysia. (2022). Pendapatan isi rumah mengikut Negeri. OpenDOSM Data Catalogue. https://open.dosm.gov.my/ms-MY/data-catalogue/hh_income_state [Google Scholar] [Crossref]

9. Department of Statistics Malaysia. (2025). Household income survey report 2024 (Malaysia & States). https://www.dosm.gov.my/portal-main/release-content/household-income-survey-report--malaysia--states-2024 [Google Scholar] [Crossref]

10. Osman, I., Ariffin, N. A. M., Yuraimie, M. F. N. B. M., Ali, M. F. B., & Akbar, M. A. F. B. N. (2024). How Buy Now, Pay Later (BNPL) is shaping Gen Z's spending spree in Malaysia. Information Management and Business Review, 16, 657–674. https://doi.org/10.22610/imbr.v16i3(I)S.4092 [Google Scholar] [Crossref]

11. Wong, C. H., Khor, H. K., Wolor, C. W., Tan, S. T., & Toh, J. S. (2024). Determinants of intention to use Buy Now Pay Later (BNPL). Journal of Lifestyle and SDGs Review, 5. https://doi.org/10.47172/2965-730X.SDGsReview.v5.n01.pe02698 [Google Scholar] [Crossref]

12. Mat, A., Thay, N. I., Suhaimi, N. F. N. A., Zainudin, N. A. S., Azhar, N. A. Z., Norli, P. M., & Munir, Z. A. (2025). Buy Now, Pay Later: Understanding BNPL trends among generation Z. South East Asia Journal of Contemporary Business, Economics and Law, 34, 161–168. https://www.researchgate.net/profile/Azmi-Mat/publication/391774840_Buy_Now_Pay_Later_Understanding_BNPL_Trends_Among_Generation_Z/links/685e05bfe9b6c13c89e4bdf2/Buy-Now-Pay-Later-Understanding-BNPL-Trends-Among-Generation-Z.pdf [Google Scholar] [Crossref]

13. Ismail, N. D., Lazim, C. S. L. M., Zolkaply, Z., Othman, M. S., Abu Thahir, S. B. S., & Tazilah, M. D. A. K. (2025). A conceptual ttudy of understanding the moderating effects of financial struggles on impulsive online purchases with Buy Now Pay Later services among Gen Z in Malaysia. In Proceedings of the 12th International Conference on Business, Accounting, Finance and Economics (BAFE 2024) (pp. 567–577). https://www.atlantis-press.com/proceedings/bafe-24/126009242 [Google Scholar] [Crossref]

14. Huey, C. S., Cheng, K. J., Ying, I. P. Z., Seng, L. K., & Kai, L. Y. (2023). The rise of Buy-Now-Pay-Later (BNPL) service in Malaysia (Final Year Project). Universiti Tunku Abdul Rahman. http://eprints.utar.edu.my/5536/1/fyp_BF_2023_CSH.pdf [Google Scholar] [Crossref]

15. Min, L. H., & Cheng, T. L. (2023). Consumers' intention to use "Buy Now Pay Later" in Malaysia. In Proceedings of the Conference on Management, Business, Innovation, Education and Social Sciences (CoMBInES). https://journal.uib.ac.id/index.php/combines/article/view/7696 [Google Scholar] [Crossref]

16. Tang, M. B., Sa'adi, N., & Andam, J. L. (2025). Buy Now, Pay Later services: Adoption drivers among digitally fluent generations in Sarawak. Journal of Technology Management and Business, 12, 119–136. https://penerbit.uthm.edu.my/ojs/index.php/jtmb/article/view/19844 [Google Scholar] [Crossref]

17. Izham, H. I., Peng, K. P. Y., Cheng, A. Y., Loy, C. K., & Hansaram, S. K. (2025). The impact of Buy Now, Pay Later services on the impulsive buying behavior of Generation Z in Shah Alam, Malaysia. International Journal of Research and Innovation in Social Science, 9, 9349–9369. https://doi.org/10.47772/IJRISS.2025.909000769 [Google Scholar] [Crossref]

18. Raj, V. A., Jasrotia, S. S., & Rai, S. S. (2025). Role of perceived risks and perceived benefits on consumers' behavioural intention to use Buy-Now, Pay-Later (BNPL) services. Journal of Facilities Management, 23, 330–351. https://doi.org/10.1108/JFM-01-2023-0004 [Google Scholar] [Crossref]

19. Azzahra, K., Mardinawati, M., Zees, S. R., & Ayundyayasti, P. (2023). Factors influence the intention to use in paylater feature among the millennial generation. Applied Accounting and Management Review, 2, 31–39. https://doi.org/10.32497/aamar.v2i2.4613 [Google Scholar] [Crossref]

20. Chen, N. H., Habibullah, M. S., & Sapar, R. (2024). Intention to use FinTech payments: The perspectives of benefits, risks, and openness to change. Global Journal of Business Social Sciences Review, 12, 91–113. https://doi.org/10.35609/gjbssr.2024.12.3(1) [Google Scholar] [Crossref]

21. Susetyarini, E., & Fauzi, A. (2020). Trend of critical thinking skill researches in biology education journals across Indonesia: From research design to data analysis. International Journal of Instruction, 13, 535–550. https://doi.org/10.29333/iji.2020.13135a [Google Scholar] [Crossref]

22. 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, 157–178. https://doi.org/10.2307/41410412 [Google Scholar] [Crossref]

23. Marikyan, D., & Papagiannidis, S. (2025). Unified theory of acceptance and use of technology: A review. In S. Papagiannidis (Ed.). TheoryHub Book. https://open.ncl.ac.uk [Google Scholar] [Crossref]

24. Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59, 451–474. https://doi.org/10.1016/S1071-5819(03)00111-3 [Google Scholar] [Crossref]

25. Trinh, H. N., Tran, H. H., & Vuong, D. H. Q. (2020). Determinants of consumers' intention to use credit card: A perspective of multifaceted perceived risk. Asian Journal of Economics and Banking, 4, 105–120. https://doi.org/10.1108/AJEB-06-2020-0018 [Google Scholar] [Crossref]

26. Mahbob, N. N., Hasim, M. A., & Ishak, M. F. (2023). The influence of perceived risk factors on online purchase intention among millennials in Malaysia. Information Management and Business Review, 15, 50–58. https://doi.org/10.22610/imbr.v15i3(I).3516 [Google Scholar] [Crossref]

27. Christen, M., & Morgan, R. M. (2005). Keeping up with the Joneses: Analyzing the effect of income inequality on consumer borrowing. Quantitative Marketing and Economics, 3, 145–173. https://doi.org/10.1007/s11129-005-0351-1 [Google Scholar] [Crossref]

28. Putri, R. A., & Ishak, K. (2024). Strategies to improve financial technology literacy for millennials and Generation Z. JPS (Jurnal Perbankan Syariah), 5, 304–318. https://doi.org/10.46367/jps.v5i2.2017 [Google Scholar] [Crossref]

29. Nga, J. K., & Yien, K. L. (2013). The influence of personality trait and demographics on financial decision making among Generation Y. Young Consumers: Insight and Ideas for Responsible Marketers, 14, 230–243. https://doi.org/10.1108/YC-11-2012-00325 [Google Scholar] [Crossref]

30. Marjerison, R. K., Dong, H., & Kim, J. M. (2025). Generational investment behavior: The influence of risk tolerance and technology adoption in an evolving financial landscape. SAGE Open, 15(3). https://doi.org/10.1177/21582440251352342 [Google Scholar] [Crossref]

31. Simcock, P., Sudbury, L., & Wright, G. (2006). Age, perceived risk and satisfaction in consumer decision making: A review and extension. Journal of Marketing Management, 22(3–4), 355–377. https://doi.org/10.1362/026725706776861163 [Google Scholar] [Crossref]

32. Bauman, M. J., Velikova, N., Dodd, T., & Blankenship, T. (2020). Generational differences in risk perception and situational uses of wine information sources. International Journal of Wine Business Research, 32(2), 247–265. https://doi.org/10.1108/IJWBR-03-2019-0022 [Google Scholar] [Crossref]

33. Yang, Q., Pang, C., Liu, L., Yen, D. C., & Tarn, J. M. (2015). Exploring consumer perceived risk and trust for online payments: An empirical study in China’s younger generation. Computers in Human Behavior, 50, 9–24. https://doi.org/10.1016/j.chb.2015.03.058 [Google Scholar] [Crossref]

34. Umunadi, E. K. (2011). Perception of technical education students on the role of ICT in general studies programme (GSP) in university education. International Journal of Academic Research in Business and Social Sciences, 1(3), 190. https://doi.org/10.6007/ijarbss.v1i2.32 [Google Scholar] [Crossref]

35. Burstein, D. D. (2013). Fast future: How the millennial generation is shaping our world. Beacon Press. https://books.google.com.my/books?hl=en&lr=&id=JttmDwAAQBAJ&oi=fnd&pg=PP1&dq=35.%09Burstein,+D.+D.+(2013).+Fast+future:+How+the+millennial+generation+is+shaping+our+world.+Beacon+Press.&ots=FAtJX5mlMy&sig=g9vwk3xAkllgtizGTiT8JqyLZ9A&redir_esc=y#v=onepage&q&f=false [Google Scholar] [Crossref]

36. Kim, K. T., Anderson, S. G., & Seay, M. C. (2019). Financial knowledge and short-term and long-term financial behaviors of millennials in the United States. Journal of Family and Economic Issues, 40, 194–208. https://doi.org/10.1007/s10834-018-9595-2 [Google Scholar] [Crossref]

37. Gorman, P., Nelson, T., & Glassman, A. (2004). The Millennial generation: A strategic opportunity. Organizational Analysis, 12(3). [Google Scholar] [Crossref]

38. Thangavel, P., Pathak, P., & Chandra, B. (2019). Consumer decision-making style of Gen Z: A generational cohort analysis. Global Business Review, 23(3), 710–728. https://doi.org/10.1177/0972150919880128 [Google Scholar] [Crossref]

39. Ridwan, N. H., Musa, C. I., & Haeruddin, M. I. M. (2025). Decision-making behavior of Generation Z in online purchases: A systematic literature review. Multidisciplinary Reviews, 8(12), 2025384–2025384. https://doi.org/10.31893/multirev.2025384 [Google Scholar] [Crossref]

40. Dimitriou, C. K., & AbouElgheit, E. (2019). Understanding Generation Z’s travel social decision-making. Tourism and Hospitality Management, 25(2), 311–334. https://doi.org/10.20867/thm.25.2.4 [Google Scholar] [Crossref]

41. Halim, N. A., Salleh, S. M., Mustapa, S. N. S., Rozali, N., & Khairi, S. M. M. (2024). From instant gratification to long-term consequences: How buy now, pay later influences consumer behavior and financial stability. International Journal of Research and Innovation in Social Science, 8(9), 403–411. https://doi.org/10.47772/IJRISS.2024.809035 [Google Scholar] [Crossref]

42. Pokharel, J., & Maharjan, I. (2024). Financial behavior of Generation Z and millennials. Journal of Emerging Management Studies, 1, 148–170. https://doi.org/10.3126/jems.v1i2.71522 [Google Scholar] [Crossref]

43. Irawan, A. W., & Khoir, M. (2025). The impact of a consumptive lifestyle on the economic stability of millennials. Journal of Islamic Banking, 5(2), 22–30. https://www.journal.uaindonesia.ac.id/index.php/JIB/article/download/930/576 [Google Scholar] [Crossref]

44. Tolani, K., Shukla, J. V., Mohare, R., & Paralkar, T. A. (2025). Machine learning analysis of financial behavior: A study of Gen Y and Gen Z preferences. Multidisciplinary Science Journal, 7, 2025380–2025380. https://doi.org/10.31893/multiscience.2025380 [Google Scholar] [Crossref]

45. Leyshon, A., & Thrift, N. (1994). Access to financial services and financial infrastructure withdrawal: Problems and policies. Area, 26(3), 268–275. https://www.jstor.org/stable/20003457 [Google Scholar] [Crossref]

46. Kabakova, O., & Plaksenkov, E. (2018). Analysis of factors affecting financial inclusion: Ecosystem view. Journal of Business Research, 89, 198–205. https://doi.org/10.1016/j.jbusres.2018.01.066 [Google Scholar] [Crossref]

47. Aggarwal, R., & Goodell, J. W. (2014). Cross-national differences in access to finance: Influence of culture and institutional environments. Research in International Business and Finance, 31, 193–211. https://doi.org/10.1016/j.ribaf.2013.09.004 [Google Scholar] [Crossref]

48. Gotway, C. A., & Young, L. J. (2002). Combining incompatible spatial data. Journal of the American Statistical Association, 97(458), 632–648. https://doi.org/10.1198/016214502760047140 [Google Scholar] [Crossref]

49. Calderero, F., & Marques, F. (2010). Region merging techniques using information theory statistical measures. IEEE Transactions on Image Processing, 19(6), 1567–1586. https://doi.org/10.1109/TIP.2010.2043008 [Google Scholar] [Crossref]

50. De Leeuw, E. D. (2005). To mix or not to mix data collection modes in surveys. Journal of Official Statistics, 21(5), 233–255. [Google Scholar] [Crossref]

51. Fang, Y. X., Gill, S. S., Kunasekaran, P., Rosnon, M. R., Talib, A. T., & Abd Aziz, A. (2022). Digital divide: An inquiry on the native communities of Sabah. Societies, 12(6), 148. https://doi.org/10.3390/soc12060148 [Google Scholar] [Crossref]

52. Jones, P., & Lucas, K. (2012). The social consequences of transport decision-making: Clarifying concepts, synthesising knowledge and assessing implications. Journal of Transport Geography, 21, 4–16. https://doi.org/10.1016/j.jtrangeo.2012.01.012 [Google Scholar] [Crossref]

53. Van Kerkhoff, L., & Pilbeam, V. (2017). Understanding socio-cultural dimensions of environmental decision-making: A knowledge governance approach. Environmental Science & Policy, 73, 29–37. https://doi.org/10.1016/j.envsci.2017.03.011 [Google Scholar] [Crossref]

54. Wilkerson, M. L., Mitchell, M. G., Shanahan, D., Wilson, K. A., Ives, C. D., Lovelock, C. E., & Rhodes, J. R. (2018). The role of socio-economic factors in planning and managing urban ecosystem services. Ecosystem Services, 31, 102–110. https://doi.org/10.1016/j.ecoser.2018.02.017 [Google Scholar] [Crossref]

55. Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30, 607–610. https://doi.org/10.1177/001316447003000308 [Google Scholar] [Crossref]

56. Department of Statistics Malaysia. (2025). Kawasanku: Sabah — What does this population look like? https://open.dosm.gov.my/dashboard/kawasanku/Sabah [Google Scholar] [Crossref]

57. Niven, D. J., Mrklas, K. J., Holodinsky, J. K., et al. (2015). Towards understanding the de-adoption of low-value clinical practices: A scoping review. BMC Medicine, 13, 255. https://doi.org/10.1186/s12916-015-0488-z [Google Scholar] [Crossref]

58. Karim, K. S., Islam, M. E., Ibrahim, A. M., Pan, S. H., & Rahman, M. M. (2024). Online marketing trends and purchasing intent: Advances in customer satisfaction through PLS-SEM and ANN approach. Advances in Decision Sciences, 27(4), 1–31. https://doi.org/10.47654/v27y2024i4p24-54 [Google Scholar] [Crossref]

59. Oliva, R., & Kallenberg, R. (2003). Managing the transition from products to services. International Journal of Service Industry Management, 14(2), 160–172. https://doi.org/10.1108/09564230310474138 [Google Scholar] [Crossref]

60. Hage, E., Roo, J. P., van Offenbeek, M. A., et al. (2013). Implementation factors and their effect on e-health service adoption in rural communities: A systematic literature review. BMC Health Services Research, 13. https://doi.org/10.1186/1472-6963-13-19 [Google Scholar] [Crossref]

61. Sil, R. (2018). Triangulating area studies, not just methods: How cross-regional comparison aids qualitative and mixed-method research. In Comparative Area Studies: Methodological Rationales and Cross-Regional Applications (pp. 225–246). https://doi.org/10.1093/oso/9780190846374.003.0013 [Google Scholar] [Crossref]

62. Mair, C. A. (2024). Conceptualising contextual variation in older adults’ quality of life cross-nationally: Challenges and opportunities. In Handbook of Quality of Life Research. Edward Elgar Publishing. https://doi.org/10.4337/9781789908794.00036 [Google Scholar] [Crossref]

63. Ahram, A. I. (2011). The theory and method of comparative area studies. Qualitative Research, 11(1), 69–90. https://doi.org/10.1177/1468794110385297 [Google Scholar] [Crossref]

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