The Moderating Impacts of Self-efficacy, Self-confidence and Smartphone Addiction on the Relationships among Perceived Usefulness, Perceived Risks and Perceived Costs and Intention to Use Mobile Payment Services: A Study among Consumers in Viet Nam

This research was conducted with the aim of exploring factors affecting intention to use mobile payment services of consumers in Ho Chi Minh City, Vietnam and moderators on these relationships. The research model and eight hypotheses were developed from prior relevant research. The questionnaire was distributed to 333 Vietnamese respondents living in Ho Chi Minh City with 300 valid responses. Multiple linear regressions were used to assess the hypotheses. The result indicated that perceived usefulness was the most influential determinant, then perceived cost, perceived trust, and perceived risk; also, self-confidence played an important role in moderating the effect of perceived risk on intention to use mobile payment services. Managerial implications and directions for future research were also discussed.

mobile payment services, moderators, usefulness, risk, trust, cost

1. Adsota. (2020). Vietnam Digital Advertising Report 2019. Slideshare. https://www.slideshare.net/AdsotaAds/vietnam-digital-advertising-report-2019-228704700 [Google Scholar] [Crossref]

2. Ahmad, S. Z., & Khalid, K. (2017). The adoption of M-government services from the user’s perspectives: Empirical evidence from the United Arab Emirates. International Journal of Information Management, 37(5), 367–379. https://doi.org/10.1016/j.ijinfomgt.2017.03.008 [Google Scholar] [Crossref]

3. 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 [Google Scholar] [Crossref]

4. Allied Market Research. (2020). Mobile Payment Market by Payment Type, Transaction Mode, End User, Purchase Type, and Application: Opportunity Analysis and Industry Forecast, 2020–2027. Allied Market Research. https://www.alliedmarketresearch.com/mobile-payments-market [Google Scholar] [Crossref]

5. Amoroso, D. L., & Magnier-Watanabe, R. (2012). Building a research model for mobile wallet consumer adoption: The case of Mobile Suica in Japan. Journal of Theoretical and Applied Electronic Commerce Research, 7(1), 13–14. https://doi.org/10.4067/s0718-18762012000100008 [Google Scholar] [Crossref]

6. Arvidsson, N. (2014). Consumer attitudes on mobile payment services – results from a proof of concept test. International Journal of Bank Marketing, 32(2), 150–170. https://doi.org/10.1108/ijbm-05-2013-0048 [Google Scholar] [Crossref]

7. Bandura, A. (1997). Self-efficacy and health behaviour. In A. Baum, S. Newman, J. Wienman, R. West, & C. McManus (Eds.), Cambridge handbook of psychology, health and medicine (pp. 160–162). Cambridge University Press. [Google Scholar] [Crossref]

8. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall. [Google Scholar] [Crossref]

9. Bearden, W. O., Hardesty, D. M., & Rose, R. L. (2001). Consumer self-confidence: Refinements in conceptualization and measurement. Journal of Consumer Research, 28(1), 121–134. https://doi.org/10.1086/321951 [Google Scholar] [Crossref]

10. Bhattacherjee, A., & Sanford, C. (2006). Influence processes for information technology acceptance: An elaboration likelihood model. MIS Quarterly, 30(4), 805–825. https://doi.org/10.2307/25148755 [Google Scholar] [Crossref]

11. Cao, X., Yu, L., Liu, Z., Gong, M., & Adeel, L. (2018). Understanding mobile payment users’ continuance intention: A trust transfer perspective. Internet Research, 28(2), 456–476. https://doi.org/10.1108/intr-11-2016-0359 [Google Scholar] [Crossref]

12. Chong, A. Y. L., Chan, F. T., & Ooi, K. B. (2012). Predicting consumer decisions to adopt mobile commerce: Cross country empirical examination between China and Malaysia. Decision Support Systems, 53(1), 34–43. https://doi.org/10.1016/j.dss.2011.12.001 [Google Scholar] [Crossref]

13. Comfrey, A. L., & Lee, H. B. (1992). A first course in factor analysis. Lawrence Erlbaum Associates. [Google Scholar] [Crossref]

14. 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 [Google Scholar] [Crossref]

15. 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 [Google Scholar] [Crossref]

16. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982 [Google Scholar] [Crossref]

17. Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral dissertation). Sloan School of Management, Massachusetts Institute of Technology. [Google Scholar] [Crossref]

18. Dwyer, F. R., Schurr, P. H., & Oh, S. (1987). Developing buyer-seller relationships. Journal of Marketing, 51(2), 11–27. https://doi.org/10.1177/002224298705100202 [Google Scholar] [Crossref]

19. Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474. https://doi.org/10.1016/S1071-5819%2803%2900111-3 [Google Scholar] [Crossref]

20. Fernandes, D. V. H., & Santos, C. P. (2008). The behavioral consequences of the customer dissatisfaction. Revista de Administração Contemporânea, 12(spe), 147–174. https://doi.org/10.1590/S1415-65552008000500007 [Google Scholar] [Crossref]

21. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Addison-Wesley. [Google Scholar] [Crossref]

22. Griffiths, M., & Renwick, B. (2003). Misfortune or mismanagement: A study of consumer debt crisis. The University of Newcastle’s Digital Repository. [Google Scholar] [Crossref]

23. GSMA. (2017). State of the Industry Report on Mobile Money. GSMA. https://www.gsma.com/mobilefordevelopment/wp-content/uploads/2017/03/GSMA_State-of-the-Industry-Report-on-Mobile-Money_2016.pdf [Google Scholar] [Crossref]

24. Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford Publications. [Google Scholar] [Crossref]

25. Hung, M. C., Hwang, H. G., & Hsieh, T. C. (2007). An exploratory study on the continuance of mobile commerce: An extended expectation-confirmation model of information system use. International Journal of Mobile Communications, 5(4), 409–426. https://doi.org/10.1504/ijmc.2007.012788 [Google Scholar] [Crossref]

26. Islam, M., Khan, M. A., Ramayah, T., & Hossain, M. M. (2011). The adoption of mobile commerce service among employed mobile phone users in Bangladesh: Self-efficacy as a moderator. International Business Research, 4(2), 80–89. http://dx.doi.org/10.5539/ibr.v4n2p80 [Google Scholar] [Crossref]

27. Jaradat, M. I. R. M., & Faqih, K. M. S. (2014). Investigating the moderating effects of gender and self-efficacy in the context of mobile payment adoption: A developing country perspective. International Journal of Business and Management, 9(11), 147–168. https://doi.org/10.5539/ijbm.v9n11p147 [Google Scholar] [Crossref]

28. Kapoor, K. K., Dwivedi, Y. K., & Williams, M. D. (2015). Examining the role of three sets of innovation attributes for determining adoption of the interbank mobile payment service. Information Systems Frontiers, 17(5), 1039–1056. https://doi.org/10.1007/s10796-014-9484-7 [Google Scholar] [Crossref]

29. Kesharwani, A., & Singh Bisht, S. (2012). The impact of trust and perceived risk on internet banking adoption in India. International Journal of Bank Marketing, 30(4), 303–322. https://doi.org/10.1108/02652321211236923 [Google Scholar] [Crossref]

30. Kim, Y., Choi, B., & Choi, J. (2015). A study on the successful adoption of IOT services: Focused on iBeacon and nearby. Journal of the Korea Society of IT Services, 14(1), 217–236. https://doi.org/10.9716/KITS.2015.14.1.217 [Google Scholar] [Crossref]

31. Lee, K. C., & Chung, N. (2009). Understanding factors affecting trust in and satisfaction with mobile banking in Korea: A modified DeLone and McLean’s model perspective. Interacting with Computers, 21(5–6), 385–392. https://doi.org/10.1016/j.intcom.2009.06.004 [Google Scholar] [Crossref]

32. 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]

33. Liébana-Cabanillas, F., Japutra, A., Molinillo, S., Singh, N., & Sinha, N. (2020). Assessment of mobile technology use in the emerging market: Analyzing intention to use m-payment services in India. Telecommunications Policy, 44(9), 102009. https://doi.org/10.1016/j.telpol.2020.102009 [Google Scholar] [Crossref]

34. Likert, R. (1932). A technique for measurement of attitudes. Archives of Psychology, 140, 5–55. https://legacy.voteview.com/pdf/Likert_1932.pdf [Google Scholar] [Crossref]

35. Lin, H. F. (2010). An application of fuzzy AHP for evaluating course website quality. Computers & Education, 54(4), 877–888. https://doi.org/10.1016/j.compedu.2009.09.017 [Google Scholar] [Crossref]

36. Linck, K., Pousttchi, K., & Wiedemann, D. G. (2006). Security issues in mobile payment from the customer viewpoint. Paper presented at the 14th European Conference on Information Systems (ECIS 2006), Goteborg, Schweden. [Google Scholar] [Crossref]

37. Liu, J., Kauffman, R. J., & Ma, D. (2015). Competition, cooperation, and regulation: Understanding the evolution of the mobile payment technology ecosystem. Electronic Commerce Research and Applications, 14(5), 372–391. https://doi.org/10.1016/j.elerap.2015.03.003 [Google Scholar] [Crossref]

38. Lu, Y., Yang, S., Chau, P. Y., & Cao, Y. (2011). Dynamics between the trust transfer process and intention to use mobile payment services: A cross-environment perspective. Information & Management, 48(8), 393–403. https://doi.org/10.1016/j.im.2011.09.006 [Google Scholar] [Crossref]

39. Luarn, P., & Lin, H. H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in Human Behavior, 21(6), 873–891. https://doi.org/10.1016/j.chb.2004.03.003 [Google Scholar] [Crossref]

40. Lumpkin, J. R., & Hunt, J. B. (1989). Mobility as an influence on retail patronage behavior of the elderly: Testing conventional wisdom. Journal of the Academy of Marketing Science, 17(1), 1–12. https://doi.org/10.1007/bf02726348 [Google Scholar] [Crossref]

41. Madan, K., & Yadav, R. (2016). Behavioural intention to adopt mobile wallet: A developing country perspective. Journal of Indian Business Research, 8(3), 227–244. https://doi.org/10.1108/jibr-10-2015-0112 [Google Scholar] [Crossref]

42. Mallat, N. (2007). Exploring consumer adoption of mobile payments—A qualitative study. Journal of Strategic Information Systems, 16(4), 413–432. http://dx.doi.org/10.1016/j.jsis.2007.08.001 [Google Scholar] [Crossref]

43. Mallat, N., Rossi, M., & Tuunainen, V. K. (2004). Mobile banking services. Communications of the ACM, 47(5), 42–46. https://doi.org/10.1145/986213.986236 [Google Scholar] [Crossref]

44. Mallat, N., Rossi, M., Tuunainen, V. K., & Öörni, A. (2008). An empirical investigation of mobile ticketing service adoption in public transportation. Personal and Ubiquitous Computing, 12(1), 57–65. https://doi.org/10.1007/s00779-006-0126-z [Google Scholar] [Crossref]

45. Marafon, D. L., Basso, K., Espartel, L. B., Barcellos, M. D., & Rech, E. (2018). Perceived risk and intention to use internet banking: The effects of self-confidence and risk acceptance. International Journal of Bank Marketing, 36(2), 277–289. https://doi.org/10.1108/ijbm-11-2016-0166 [Google Scholar] [Crossref]

46. Mathieson, K., Peacock, E., & Chin, W. W. (2001). Extending the technology acceptance model: The influence of perceived user resources. DATA BASE for Advances in Information Systems, 32(3), 86–112. http://dx.doi.org/10.1145/506724.506730 [Google Scholar] [Crossref]

47. Mitchell, V. W. (1999). Consumer perceived risk: Conceptualisations and models. European Journal of Marketing, 33(1/2), 163–195. https://doi.org/10.1108/03090569910249229 [Google Scholar] [Crossref]

48. Natarajan, T., Balasubramanian, S. A., & Kasilingam, D. L. (2017). Understanding the intention to use mobile shopping applications and its influence on price sensitivity. Journal of Retailing and Consumer Services, 37, 8–22. https://doi.org/10.1016/j.jretconser.2017.02.010 [Google Scholar] [Crossref]

49. Nhan Dan News. (2020, February 27). Vietnam’s 3G and 4G subscribers outnumber 2G for first time. Nhan Dan. https://en.nhandan.com.vn/scitech/item/8442202-vietnam%E2%80%99s-3g-and-4g-subscribers-outnumber-2g-for-first-time.html [Google Scholar] [Crossref]

50. Ondrus, J., & Pigneur, Y. (2005). A disruption analysis in the mobile payment market. Paper presented at the 38th Annual Hawaii International Conference on System Sciences, Hawaii, USA. https://doi.org/10.1109/hicss.2005.9 [Google Scholar] [Crossref]

51. Ooi, K. B., & Tan, G. W. H. (2016). Mobile technology acceptance model: An investigation using mobile users to explore smartphone credit card. Expert Systems with Applications, 59, 33–46. https://doi.org/10.1016/j.eswa.2016.04.015 [Google Scholar] [Crossref]

52. Pham, T. T. T., & Ho, J. C. (2015). The effects of product-related, personal-related factors and attractiveness of alternatives on consumer adoption of NFC-based mobile payments. Technology in Society, 43, 159–172. https://doi.org/10.1016/j.techsoc.2015.05.004 [Google Scholar] [Crossref]

53. Phong, N. D., Khoi, N. H., & Nhat-Hanh Le, A. (2018). Factors affecting mobile shopping: A Vietnamese perspective. Journal of Asian Business and Economic Studies, 25(2), 186–205. https://doi.org/10.1108/jabes-05-2018-0012 [Google Scholar] [Crossref]

54. Phonthanukitithaworn, C., Sellitto, C., & Fong, M. (2015). User intentions to adopt mobile payment services: A study of early adopters in Thailand. The Journal of Internet Banking and Commerce, 20(1), 1–29. http://www.icommercecentral.com/open-access/user-intentions-to-adopt-mobile-payment-services-a-study-of-early-adopters-in-thailand.pdf [Google Scholar] [Crossref]

55. Phonthanukitithaworn, C., Sellitto, C., & Fong, M. W. L. (2016). A comparative study of current and potential users of mobile payment services. SAGE Open, 6(4), 1–14. https://doi.org/10.1177/2158244016675397 [Google Scholar] [Crossref]

56. Priya, R., Gandhi, A. V., & Shaikh, A. (2018). Mobile banking adoption in an emerging economy. Benchmarking: An International Journal, 25(2), 743–762. https://doi.org/10.1108/bij-01-2016-0009 [Google Scholar] [Crossref]

57. Qasim, H., & Abu-Shanab, E. (2015). Drivers of mobile payment acceptance: The impact of network externalities. Information Systems Frontiers, 18(5), 1021–1034. https://doi.org/10.1007/s10796-015-9598-6 [Google Scholar] [Crossref]

58. Rana, N. P., Dwivedi, Y. K., Williams, M. D., & Weerakkody, V. (2015). Investigating success of an e-government initiative: Validation of an integrated IS success model. Information Systems Frontiers, 17(1), 127–142. https://doi.org/10.1007/s10796-014-9504-7 [Google Scholar] [Crossref]

59. Schierz, P. G., Schilke, O., & Wirtz, B. W. (2010). Understanding consumer acceptance of mobile payment services: An empirical analysis. Electronic Commerce Research and Applications, 9(3), 209–216. https://doi.org/10.1016/j.elerap.2009.07.005 [Google Scholar] [Crossref]

60. Shaw, B., & Kesharwani, A. (2019). Moderating effect of smartphone addiction on mobile wallet payment adoption. Journal of Internet Commerce, 18(3), 291–309. https://doi.org/10.1080/15332861.2019.1620045 [Google Scholar] [Crossref]

61. Shaw, N. (2014). The mediating influence of trust in the adoption of the mobile wallet. Journal of Retailing and Consumer Services, 21(4), 449–459. https://doi.org/10.1016/j.jretconser.2014.03.008 [Google Scholar] [Crossref]

62. Shira, D. (2021). Q&A: All eyes on Vietnam – Why and how to enter the market. Vietnam Briefing News. https://www.vietnam-briefing.com/news/qa-all-eyes-on-vietnam-why-and-how-to-enter-the-market.html/ [Google Scholar] [Crossref]

63. Slade, E. L., Williams, M. D., & Dwivedi, Y. K. (2014). Devising a research model to examine adoption of mobile payments: An extension of UTAUT2. The Marketing Review, 14(3), 310–335. https://doi.org/10.1362/146934714x14024779062036 [Google Scholar] [Crossref]

64. Suls, J., & Wallston, K. A. (Eds.). (2008). Social psychological foundations of health and illness. John Wiley & Sons. [Google Scholar] [Crossref]

65. Tan, E., & Leby Lau, J. (2016). Behavioural intention to adopt mobile banking among the millennial generation. Young Consumers, 17(1), 18–31. https://doi.org/10.1108/YC-07-2015-00537 [Google Scholar] [Crossref]

66. Tonioni, F., D’Alessandris, L., Lai, C., Martinelli, D., Corvino, S., Vasale, M., Fanella, F., Aceto, P., & Bria, P. (2012). Internet addiction: Hours spent online, behaviors and psychological symptoms. General Hospital Psychiatry, 34(1), 80–87. https://doi.org/10.1016/j.genhosppsych.2011.09.013 [Google Scholar] [Crossref]

67. Tsu Wei, T., Marthandan, G., Yee-Loong Chong, A., Ooi, K., & Arumugam, S. (2009). What drives Malaysian m-commerce adoption? An empirical analysis. Industrial Management & Data Systems, 109(3), 370–388. https://doi.org/10.1108/02635570910939399 [Google Scholar] [Crossref]

68. Venkatakrishnan, V., & Eston, N. (2013). Mobile-phone money transfer services’ usage in Dodoma Urban, Tanzania. International Journal of Research in Management & Technology, 3(1), 31–36. [suspicious link removed] [Google Scholar] [Crossref]

69. 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 [Google Scholar] [Crossref]

70. Vijayasarathy, L. R. (2004). Predicting consumer intentions to use on-line shopping: The case for an augmented technology acceptance model. Information & Management, 41(6), 747–762. https://doi.org/10.1016/j.im.2003.08.011 [Google Scholar] [Crossref]

71. Wang, W., & Li, H. (2012). Factors influencing mobile services adoption: A brand-equity perspective. Internet Research, 22(2), 142–179. https://doi.org/10.1108/10662241211214548 [Google Scholar] [Crossref]

72. Wu, J., & Wang, S. (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information & Management, 42(6), 719–729. https://doi.org/10.1016/j.im.2004.07.001 [Google Scholar] [Crossref]

73. Yang, K. (2012). Consumer technology traits in determining mobile shopping adoption: An application of the extended theory of planned behavior. Journal of Retailing and Consumer Services, 19(5), 484–491. http://dx.doi.org/10.1016/j.jretconser.2012.06.003 [Google Scholar] [Crossref]

74. Zhou, T. (2013). An empirical examination of continuance intention of mobile payment services. Decision Support Systems, 54(2), 1085–1091. https://doi.org/10.1016/j.dss.2012.10.034 [Google Scholar] [Crossref]

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