Issue

Vol 38 No 3 (2023): September

Date Log

Submitted
Nov 24, 2022
Accepted
May 3, 2023
Published
Sep 11, 2023
Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Copyright

Upon acceptance of an article, authors transfer copyright to the JIEB as part of a journal publishing agreement, but authors still have the right to share their article for personal use, internal institutional use, and for any use permitted under the CC BY-SA license

Open Access

Articles are freely available to the public without any subscription with permitted reuse. For open access articles, permitted third party (re)use is defined by the following Creative Commons user licenses: Creative Commons Attribution (CC BY-SA).

Managing Consumers’ Adoption of Artificial Intelligence-Based Financial Robo-Advisory Services: A Moderated Mediation Model

https://doi.org/10.22146/jieb.v38i3.6242
Dewan Mehrab Ashrafi
mehrab.ashrafi35@gmail.com
Department of Business Administration, ULAB School of Business, University of Liberal Arts Bangladesh, Dhaka, Bangladesh

Corresponding Author(s) : Dewan Mehrab Ashrafi

mehrab.ashrafi35@gmail.com

Journal of Indonesian Economy and Business, Vol 38 No 3 (2023): September

Abstract

Introduction/Main Objectives: This study investigates the determinants of willingness to use financial robo-advisory services. The study aims to identify the intertwined roles of perceived value, perceived risk, and perceived financial knowledge in consumers’ acceptance of financial robo-advisory services. Background Problem: Fintech and AI-based applications have opened up new prospects for financial management, but studies into the adoption and implementation of robo-advisors are limited and scant. Novelty: The study offers novel insights by exploring the direct and indirect effects of perceived value and risk on consumer deci­sions around adopting robo-advisory services. The study also identifies other major drivers of robo-advisory service adoption and formulates a comprehensive model. Research Methods: A quantitative method using a deductive approach was applied, with PLS-SEM performed on a sample of 285 respondents from Bangladesh. The sample was gathered using a purposive sampling method. Findings/Results: Findings revealed that while relative advantage and perceived innovativeness positively affected perceived value and adoption intention, complexity negatively impacted perceived value and adoption intention. The findings also highlighted that attitude had a negative effect on perceived risk and intention to adopt robo-advisory services. The mediating impact of perceived value and risk in predicting the relationship between relative advantage, attitude and behavioral intention to adopt robo-advisory services was also identified. Moreover, the study revealed that perceived financial knowledge moderated the relationship between perceived value and behavioral intention. Conclusion: This study contributes to the existing body of literature by showing the intertwined roles of perceived value, perceived risk, and perceived financial knowledge in consumer acceptance of robo-advisory services. The study provides meaningful insights for financial institutions, and policymakers seeking to make robo-advisory services more reliable and acceptable to consumers through innovative service design and positioning.

Keywords

robo-advisory services perceived value perceived risk financial knowledge artificial intelligence
Ashrafi, D. M. (2023). Managing Consumers’ Adoption of Artificial Intelligence-Based Financial Robo-Advisory Services: A Moderated Mediation Model. Journal of Indonesian Economy and Business, 38(3), 270-301. https://doi.org/10.22146/jieb.v38i3.6242
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. Abrardi, L., Cambini, C., & Rondi, L. (2022). Artificial intelligence, firms and consumer behavior: A survey. Journal of Economic Surveys, 36(4), 969-991.
  2. Acemoglu, D., & Restrepo, P. (2020). Robots and jobs: evidence from us labor markets. Journal of Political Economy, 128(6), 2188–2244. https://doi.org/10.1086/705716
  3. Ajzen, I. (1991). The theory of planned beha-vior. Organizational behavior and human decision processes, 50(2), 179-211.
  4. Al-Abdullatif, A. M., & Alsubaie, M. A. (2022). Using digital learning platforms for teaching Arabic literacy: A post-pandemic mobile learning scenario in Saudi Arabia. Sustai¬nability, 14(19), 11868.
  5. Alam, S. S., Masukujjaman, M., Susmit, S., Susmit, S., & Abd Aziz, H. (2022). Aug-mented reality adoption intention among travel and tour operators in Malaysia: Mediation effect of value alignment. Journal of Tourism Futures.
  6. Ali, M., Qamar, J., Asif, M., Khemta, N., & Jamil, H. (2016). How knowledge and financial self-efficacy moderate the relation¬ship between money attitudes and personal financial management behavior. European Online Journal of Natural And Social Sciences, 5(2), 296–308. www.european-science.com
  7. Arias-Oliva, M., Pelegrín-Borondo, J., & Matías-Clavero, G. (2019). Variables influencing cryptocurrency use: A techno¬logy acceptance model in Spain. Frontiers in Psychology, 10, 475. https://doi.org/10.3389/FPSYG.2019.00475/BIBTEX
  8. Ashrafi, D. M., & Easmin, R. (2022). Okay google, good to talk to you… examining the determinants affecting users’ behavioral intention for adopting voice assistants: Does technology self-efficacy matter?. Interna¬tional Journal of Innovation and Technology Management, 2350004.
  9. Ashrafi, D. M., & Easmin, R. (2023). The role of innovation resistance and technology readiness in the adoption of QR code payments among digital natives: A serial moderated mediation model. Int. Journal of Business Science and Applied Management, 18(1).
  10. Ashrafi, D. M., Alam, I., & Anzum, M. (2021). An empirical investigation of consumers’ intention for using ride-sharing applications: Does perceived risk matter?. International Journal of Innovation and Technology Management, 18(08), 2150040.
  11. Ashrafi, D. M., Dovash, R. H., & Kabir, M. R. (2022). Determinants of fintech service continuance behavior: moderating role of transaction security and trust. Journal of Global Business and Technology, 18(2), 35-59.
  12. Ashrafi, D. M., Habiba, W., & Alam, I. (2020). An assessment of the behavioural intention for using ride-sharing services: Empirical evidence from a developing country. Malay¬sian Journal of Consumer and Family Economics, 24(1), 36–62
  13. Atwal, G., & Bryson, D. (2021). Antecedents of intention to adopt artificial intelligence services by consumers in personal financial investing. Strategic Change, 30(3), 293-298.
  14. Aw, E. C. X., Basha, N. K., Ng, S. I., & Sambasivan, M. (2019). To grab or not to grab? The role of trust and perceived value in on-demand ridesharing services. Asia Pacific Journal of Marketing and Logistics, 31(5), 1442–1465. https://doi.org/10.1108/APJML-09-2018-0368/FULL/XML
  15. Bajwa, I. A., Ur Rehman, S., Iqbal, A., Anwar, Z., Ashiq, M., & Khan, M. A. (2022). Past, present and future of fintech research: A bibliometric analysis. Sage Open, 12(4), 21582440221131242.
  16. Beach, L. R., & Mitchell, T. R. (1978). A conti¬ngency model for the selection of decision strategies. Academy of Management Review, 3(3), 439–449. https://doi.org/10.5465/AMR.1978.4305717
  17. Belanche, D., Casaló, L. V., & Flavián, C. (2019). Artificial Intelligence in FinTech: understanding robo-advisors adoption among customers. Industrial Management & Data Systems.
  18. Bhatia, A., Chandani, A., Atiq, R., Mehta, M., & Divekar, R. (2021). Artificial intelligence in financial services: A qualitative research to discover robo-advisory services. Qualitative Research in Financial Markets.
  19. Brenner, L., & Meyll, T. (2020). Robo-advisors: a substitute for human financial advice?. Journal of Behavioral and Experimental Finance, 25, 100275.
  20. Bruckes, M., Westmattelmann, D., Oldeweme, A., & Schewe, G. (2019). Determinants and barriers of adopting robo-advisory services. Fortieth International Conference On Infor¬mation Systems, 2, 1–9. https://aisel.aisnet.org/icis2019/blockchain_fintech/blockchain_fintech/2
  21. Budiarani, V. H., Maulidan, R., Setianto, D. P., & Widayanti, I. (2021). The kano model: How the pandemic influences customer satisfaction with digital wallet services in Indonesia. Journal of Indonesian Economy and Business, 36(1), 62-82.
  22. Chang, H. H., Fu, C. S., & Jain, H. T. (2016). Modifying UTAUT and innovation diffusion theory to reveal online shopping behavior: Familiarity and perceived risk as mediators. Information Development, 32(5), 1757–1773. https://doi.org/10.1177/0266666915623317
  23. Chiu, C.-M., Wang, E. T. G., Fang, Y.-H., & Huang, H.-Y. (2014). Understanding custo¬mers’ repeat purchase intentions in B2C e-commerce: The roles of utilitarian value, hedonic value and perceived risk. Informa¬tion Systems Journal, 24(1), 85–114. https://doi.org/10.1111/J.1365-2575.2012.00407.X
  24. Cranmer, E. E., Papalexi, M., tom Dieck, M. C., & Bamford, D. (2022). Internet of things: Aspiration, implementation and contribu¬tion. Journal of Business Research, 139, 69-80.
  25. de Luna, I. R., Liébana-Cabanillas, F., Sánchez-Fernández, J., & Muñoz-Leiva, F. (2019). Mobile payment is not all the same: The adoption of mobile payment systems depending on the technology applied. Tech¬nological Forecasting and Social Change, 146, 931–944. https://doi.org/10.1016/J.TECHFORE.2018.09.018
  26. Dwivedi, Y. K., & Wang, Y. (2022). Guest editorial: Artificial intelligence for B2B marketing: Challenges and opportunities. Industrial Marketing Management, 105, 109-113.
  27. Etikan, I., Musa, S. A., & Alkassim, R. S. (2015). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical And Applied Statistics, 5(1), 1. https://doi.org/10.11648/J.AJTAS.20160501.11
  28. Fan, L., & Chatterjee, S. (2020). The utilization of robo-advisors by individual investors: An analysis using diffusion of innovation and information search frameworks. Journal of Financial Counseling and Planning, 31(1), 130–145. https://doi.org/10.1891/JFCP-18-00078
  29. Faqih, K. M. (2022). Factors influencing the behavioral intention to adopt a technological innovation from a developing country context: The case of mobile augmented reality games. Technology in Society, 69, 101958
  30. Feng, W., Tu, R., Lu, T., & Zhou, Z. (2018). Understanding forced adoption of self-service technology: The impacts of users’ psychological reactance. Behaviour & Infor¬mation Technology, 38(8), 820–832. https://doi.org/10.1080/0144929X.2018.1557745
  31. Figà-Talamanca, G., Tanzi, P. M., & D’Urzo, E. (2022). Robo-advisor acceptance: Do gender and generation matter?. PloS one, 17(6), e0269454.
  32. Fishbein, M. (1979). A theory of reasoned action: some applications and implications.
  33. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobser¬vable variables and measurement error. Journal of marketing research, 18(1), 39-50.
  34. Fu, F. Q., & Elliott, M. T. (2013). The moderating effect of perceived product innovativeness and product knowledge on new product adoption: An integrated model. Journal Of Marketing Theory and Practice, 21(3), 257-272.
  35. Gan, L. Y., Khan, M. T. I., & Liew, T. W. (2021). Understanding consumer’s adoption of financial robo-advisors at the outbreak of the COVID-19 crisis in Malaysia. Financial Planning Review,4(3), e1127. https://doi.org/10.1002/CFP2.1127
  36. Gawior, B., Polasik, M., & del Olmo, J. L. (2022). Credit card use, hedonic motiva-tions, and impulse buying behavior in fast fashion physical stores during covid-19: The sustainability paradox. Sustainability, 14(7), 4133.
  37. Glaser, F., Iliewa, Z., Jung, D., & Weber, M. (2019). Towards designing robo-advisors for unexperienced investors with experience sampling of time-series data. Lecture Notes In Information Systems and Organisation, 29, 133–138. https://doi.org/10.1007/978-3-030-01087-4_16
  38. Gordon, R., Dibb, S., Magee, C., Cooper, P., & Waitt, G. (2018). Empirically testing the concept of value-in-behavior and its rele¬vance for social marketing. Journal of Business Research, 82, 56–67. https://doi.org/10.1016/J.JBUSRES.2017.08.035
  39. Gupta, S., Ghardallou, W., Pandey, D. K., & Sahu, G. P. (2022). Artificial intelligence adoption in the insurance industry: Evidence using the technology–organization–environ¬ment framework. Research in International Business and Finance, 63, 101757.
  40. Gupta, N., Fischer, A. R., & Frewer, L. J. (2012). Socio-psychological determinants of public acceptance of technologies: A review. Public Understanding of Science, 21(7), 782-795.
  41. Hadi, F. (2017). Effect of emotional intelligence on investment decision making with a moderating role of financial literacy. Business Review, 16(2), 53–62. https://doi.org/10.17265/1537-1514/2017.02.002
  42. Hair, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106–121. https://doi.org/10.1108/EBR-10-2013-0128/FULL/XML
  43. Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2011). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science,40(3), 414–433. https://doi.org/10.1007/S11747-011-0261-6
  44. Hassanein, K., & Head, M. (2007). Manipulating perceived social presence through the web interface and its impact on attitude towards online shopping. International Journal of Human-Computer Studies, 65(8), 689–708. https://doi.org/10.1016/J.IJHCS.2006.11.018
  45. Henager, R., & Cude, B. J. (2016). Financial literacy and long- and short-term financial behavior in different age groups. Journal of Financial Counseling and Planning, 27(1), 3–19. https://doi.org/10.1891/1052-3073.27.1.3
  46. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/S11747-014-0403-8/FIGURES/8
  47. Higgins, E. T. (2006). Value from hedonic experience and engagement. Psychological Review, 113(3), 439–460. https://psycnet.apa.org/buy/2006-08257-001
  48. Hohenberger, C., Lee, C., & Coughlin, J. F. (2019). Acceptance of robo-advisors: Effects of financial experience, affective reactions, and self-enhancement motives. Financial Planning Review, 2(2), e1047. https://doi.org/10.1002/CFP2.1047
  49. Holbrook, M. B., & Batra, R. (1987). Assessing the role of emotions as mediators of consumer responses to advertising. Journal of Consumer Research, 14(3), 404–420. https://doi.org/10.1086/209123
  50. Hu, Z., Ding, S., Li, S., Chen, L., & Yang, S. (2019). Adoption intention of fintech services for bank users: An empirical examination with an extended technology acceptance model. Symmetry, 11(3), 340.
  51. Hwang, J., & Hyun, S. S. (2016). Perceived firm innovativeness in cruise travelers’ expe¬rience and perceived luxury value: The moderating effect of advertising effec¬tiveness. Asia Pacific Journal of Tourism Research, 21(1), S101-S128.
  52. Hwang, J., Lee, J. S., & Kim, H. (2019). Perceived innovativeness of drone food delivery services and its impacts on attitude and behavioral intentions: The moderating role of gender and age. International Journal of Hospitality Management, 81, 94-103.
  53. International Federation of Robotics. (2022). Executive summary world robotics 2022 service robots. https://ifr.org/free-downloads/
  54. Iskender, A., Sirakaya-Turk, E., Cardenas, D., & Hikmet, N. (2022). Restaurant patrons’ intentions toward QR code menus in the US during COVID-19: Acceptance of techno¬logy adoption model (ATAM). Journal of Foodservice Business Research, 1-26.
  55. Jiang, K., Qin, M., & Li, S. (2022). Chatbots in retail: How do they affect the continued use and purchase intentions of Chinese con¬sumers? Journal of Consumer Behaviour, 21(4), 756-772.
  56. Johnson, E. J., & Payne, J. W. (1985). Effort and accuracy in choice. Management science, 31(4), 395-414.
  57. Jung, D., Dorner, V., Glaser, F., & Morana, S. (2018). Robo-advisory: Digitalization and automation of financial advisory. Business and Information Systems Engineering, 60 (1), 81–86. https://doi.org/10.1007/s12599-018-0521-9
  58. Jünger, M., & Mietzner, M. (2020). Banking goes digital: The adoption of FinTech services by German households. Finance Research Letters, 34, 101260.
  59. Kavota, J. K., Kamdjoug, J. R. K., & Wamba, S. F. (2020). Social media and disaster management: Case of the north and south Kivu regions in the Democratic Republic of the Congo. International Journal of Infor¬mation Management, 52, 102068.
  60. Khan, A., & Hashim, F. (2021). Rise of Islamic robo advisory in the 21st century. In Artificial Intelligence and Islamic Finance, 193-209. Routledge.
  61. Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decision Support Systems, 44(2), 544–564. https://doi.org/10.1016/J.DSS.2007.07.001
  62. Kim, H. J., & Lee, J. M. (2022). The barriers and drivers of postponers' adoption intention of mobile-only banks. International Journal of Mobile Communications, 20(3), 309-331.
  63. Kim, H. W., Chan, H. C., & Gupta, S. (2007). Value-based adoption of mobile internet: An empirical investigation. Decision Support Systems, 43(1), 111–126. https://doi.org/10.1016/J.DSS.2005.05.009
  64. Kim, Young H., Goh, B. K., & Yuan, J. J. (2010). Development of a multi-dimen-sional scale for measuring food tourist motivations. Journal of Quality Assurance in Hospitality and Tourism, 11(1), 56–71. https://doi.org/10.1080/15280080903520568
  65. Kim, Young Hoon, Kim, D. J., & Wachter, K. (2013). A study of mobile user engagement (MoEN): Engagement motivations, per¬ceived value, satisfaction, and continued engagement intention. Decision Support Systems, 56(1), 361–370. https://doi.org/10.1016/J.DSS.2013.07.002
  66. Kleinschmidt, E. J., & Cooper, R. G. (1991). The impact of product innovativeness on performance. Journal of product innovation management, 8(4), 240-251.
  67. Konana, P., & Balasubramanian, S. (2005). The social–economic–psychological model of technology adoption and usage: An appli¬cation to online investing. Decision Support Systems, 39(3), 505–524. https://doi.org/10.1016/J.DSS.2003.12.003
  68. Kwon, D., Jeong, P., & Chung, D. (2022). An empirical study of factors influencing the intention to use robo-advisors. Journal of Information & Knowledge Management, 2250039.
  69. https://doi.org/10.1016/J.DSS.2010.02.008
  70. Laksamana, P., Suharyanto, S., & Cahaya, Y. F. (2022). Determining factors of continuance intention in mobile payment: Fintech industry perspective. Asia Pacific Journal of Marketing and Logistics, (ahead-of-print).
  71. Laukkanen, T., & Pasanen, M. (2008). Mobile banking innovators and early adopters: How they differ from other online users? Journal of Financial Services Marketing, 13(2), 86–94. https://doi.org/10.1057/PALGRAVE.FSM.4760077
  72. Lin, K. Y., Wang, Y. T., & Huang, T. K. (2020). Exploring the antecedents of mobile payment service usage: Perspectives based on cost–benefit theory, perceived value, and social influences. Online Information Review, 44(1), 299-318
  73. Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629-650.
  74. Lourenço, C. J. S., Dellaert, B. G. C., & Donkers, B. (2020). Whose algorithm says so: The relationships between type of firm, perceptions of trust and expertise, and the acceptance of financial robo-advice. Journal of Interactive Marketing, 49, 107–124. https://doi.org/10.1016/J.INTMAR.2019.10.003
  75. Lusardi, A., & Mitchell, O. S. (2017). Older women’s labor market attachment, retire-ment planning, and household debt. University of Chicago Press. Elsevier BV. https://doi.org/10.2139/SSRN.2835472
  76. Madhu, S., Soundararajan, V., & Parayitam, S. (2022). Online promotions and hedonic motives as moderators in the relationship between e-impulsive buying tendency and customer satisfaction: Evidence from India. Journal of Internet Commerce, 1-37
  77. Manrai, R., & Gupta, K. P. (2022). Investor’s perceptions on artificial intelligence (AI) technology adoption in investment services in India. Journal of Financial Services Marketing, 1-14.
  78. McLean, G., Osei-Frimpong, K., Wilson, A., & Pitardi, V. (2020). How live chat assistants drive travel consumers’ attitudes, trust and purchase intentions: The role of human touch. International Journal of Contempo¬rary Hospitality Management, 32(5), 1795–1812. https://doi.org/10.1108/IJCHM-07-2019-0605/FULL/XML
  79. Mehra, A., Paul, J., & Kaurav, R. P. S. (2021). Determinants of mobile apps adoption among young adults: theoretical extension and analysis. Journal of Marketing Communications, 27(5), 481-509.
  80. Mehra, B., Sikes, E. S., & Singh, V. (2020). Scenarios of technology use to promote community engagement: Overcoming marginalization and bridging digital divides in the Southern and Central Appalachian rural libraries. Information Processing & Management, 57(3), 102129.
  81. Meyer-Waarden, L., & Cloarec, J. (2022). “Baby, you can drive my car”: Psycho-logical antecedents that drive consumers’ adoption of AI-powered autonomous vehicles. Technovation, 109, 102348
  82. Moore, G. C., & Benbasat, I. (1991). Develop¬ment of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222. https://doi.org/10.1287/ISRE.2.3.192
  83. Moorthy, K., T’ing, L. C., Ming, K. S., Ping, C. C., Ping, L. Y., Joe, L. Q., & Jie, W. Y. (2018). Behavioral intention to adopt digital library by the undergraduates. International Information and Library Review, 51(2), 128–144. https://doi.org/10.1080/10572317.2018.1463049
  84. Muthukannan, P., Tan, B., Tan, F. T. C., & Leong, C. (2021). Novel mechanisms of scalability of financial services in an emerging market context: Insights from Indonesian Fintech Ecosystem. Interna-tional Journal of Information Management, 61, 102403.
  85. Nguyen, T. A. N. (2022). Does financial knowledge matter in using fintech services? Evidence from an emerging economy. Sustainability, 14(9), 5083.
  86. Nourallah, M., Öhman, P., & Amin, M. (2022). No trust, no use: How young retail investors build initial trust in financial robo-advisors. Journal of Financial Reporting and Accounting, (ahead-of-print).
  87. Nowlis, S. M., Dhar, R., & Simonson, I. (2010). The effect of decision order on purchase quantity decisions. Journal of Marketing Research, 47(4), 725-737.
  88. Patma, T. S., Fienaningsih, N., Rahayu, K. S., & Artatanaya, I. G. L. S. (2021). Impact of Information Quality on Customer Perceived Value, Experience Quality, and Customer Satisfaction from Using GoFood Aplication. Journal of Indonesian Economy and Business, 36(1), 51-61.
  89. Payne, J. W. (1982). Contingent decision behavior. Psychological bulletin, 92(2), 382.
  90. Pan, J., Ding, S., Wu, D., Yang, S., & Yang, J. (2019). Exploring behavioural intentions toward smart healthcare services among medical practitioners: A technology transfer perspective. International Journal of Pro¬duction Research, 57(18), 5801–5820. https://doi.org/10.1080/00207543.2018.1550272
  91. Park, J., Konana, P., Gu, B., Kumar, A., & Raghunathan, R. (2010). Confirmation bias, overconfidence, and investment perfor¬mance: Evidence from stock message boards. SSRN Electronic Journal. https://doi.org/10.2139/SSRN.1639470
  92. Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Com¬merce, 7(3), 101–134. https://doi.org/10.1080/10864415.2003.11044275
  93. Plouffe, C. R., Hulland, J. S., & Vandenbosch, M. (2001). Richness versus parsimony in modeling technology adoption decisions—understanding merchant adoption of a smart card-based payment system. Information systems research, 12(2), 208-222.
  94. Rahman, M., Ming, T. H., Baigh, T. A., & Sarker, M. (2021). Adoption of artificial intelligence in banking services: an empi-rical analysis. International Journal of Emerging Markets, (ahead-of-print).
  95. Reynolds, N., & Ruiz de Maya, S. (2013). The impact of complexity and perceived difficulty on consumer revisit intentions. Journal of Marketing Management, 29(5-6), 625-645.
  96. Risher, J., & Hair, J. F. (2017). The robustness of PLS across disciplines. Academy of Business Journal, 1, 47–55.
  97. Rogers, E. M. (1995). Diffusion of innovations. New York, 12.
  98. Roh, T., Seok, J., & Kim, Y. (2022). Unveiling ways to reach organic purchase: Green perceived value, perceived knowledge, attitude, subjective norm, and trust. Journal of Retailing and Consumer Services, 67, 102988.
  99. Rosman, C. (2018). Mad about Erica: Why a million people use bank of America’s chatbot. American Banker, New York, NY. www. americanbanker. com/news/mad-about-erica-why-amillion-people-use-bank-of-americas-chatbot
  100. Rühr, A., Berger, B., & Hess, T. (2019). Can I control my robo-advisor? Trade-offs in automation and user control in (digital) investment management. Twenty-Fifth Ame¬ricas Conference on Information Systems, 1–10. https://www.researchgate.net/publication/334625614
  101. Ryu, H. S. (2018). What makes users willing or hesitant to use Fintech?: The moderating effect of user type. Industrial Management and Data Systems, 118(3), 541–569. https://doi.org/10.1108/IMDS-07-2017-0325/FULL/HTML
  102. Sadiq, M. N., & Khan, R. A. A. (2019). Impact of personality traits on investment intention: The mediating role of risk behaviour and the moderating role of financial literacy. Journal of finance and Economics research, 4(1), 1-18. https://doi.org/10.20547/jfer1904101
  103. Shachak, A., Kuziemsky, C., & Petersen, C. (2019). Beyond TAM and UTAUT: Future directions for HIT implementation research. Journal of biomedical informatics, 100, 103315.
  104. Shaw, N., & Sergueeva, K. (2019). The non-monetary benefits of mobile commerce: Extending UTAUT2 with perceived value. International Journal of Information Management, 45, 44–55. https://doi.org/10.1016/J.IJINFOMGT.2018.10.024
  105. Siahaan, N. H., Martini, A. N., & Halim, M. (2022). The effect of cognitive and affective involvement on intention to use and word of mouth: the case of zoom users in indonesia. Journal of Indonesian Economy & Business, 37(2), 136-161
  106. Sirdeshmukh, D., Singh, J., & Sabol, B. (2002). Consumer trust, value, and loyalty in relational exchanges. Journal of marketing, 66(1), 15-37. https://doi.org/10.1509/JMKG.66.1.15.18449
  107. Skitka, L. J., Hanson, B. E., Morgan, G. S., & Wisneski, D. C. (2021). The psychology of moral conviction. Annual Review of Psycho¬logy, 72, 347–366. https://doi.org/10.1146/ANNUREV-PSYCH-063020-030612
  108. Slade, E. L., Dwivedi, Y. K., Piercy, N. C., & Williams, M. D. (2015). Modeling con-sumers’ adoption intentions of remote mobile payments in the united kingdom: Extending UTAUT with innovativeness, risk, and trust. Psychology and Marketing, 32(8), 860–873. https://doi.org/10.1002/MAR.20823
  109. Statista. (2022). Robo-advisors worldwide report. www.statista.com/outlook/337/100/%0Arobo-advisors/worldwide
  110. Susilo, G. F. A., Rani, U., & Khotijah, S. A. (2022). The trusting beliefs of users and the intention to continue making purchases via social commerce. Journal of Indonesian Economy and Business, 37(1), 1-14.
  111. Sweeney, J. C., Soutar, G. N., & McColl‐ Kennedy, J. R. (2011). The marketing practices‐performance relationship in professional service firms. Journal of service management, 22(3), 292-316.
  112. Talha, M., Wang, F., Maia, D., & Marra, G. (2022). Impact of information technology on accounting and finance in the digital health sector. Journal of Commercial Biotechnology, 27(2).
  113. Thakur, R., & Srivastava, M. (2014). Adoption readiness, personal innovativeness, perceived risk and usage intention across customer groups for mobile payment services in India. Internet Research, 24(3), 369–392. https://doi.org/10.1108/INTR-12-2012-0244/FULL/XML
  114. Tiberius, V., Gojowy, R., & Dabić, M. (2022). Forecasting the future of robo advisory: A three-stage Delphi study on economic, technological, and societal implications. Technological Forecasting and Social Change, 182, 121824.
  115. Todd, T. M., & Seay, M. C. (2020). Financial attributes, financial behaviors, financial-advisor-use beliefs, and investing charac-teristics associated with having used a robo-advisor. Financial Planning Review, 3(3), e1104. https://doi.org/10.1002/CFP2.1104
  116. Turel, O., Serenko, A., & Bontis, N. (2010). User acceptance of hedonic digital artifacts: A theory of consumption values perspective. Information & management, 47(1), 53-59.
  117. Venkatesh, V., & Brown, S. A. (2001). A long¬itudinal investigation of personal computers in homes: Adoption determinants and emerging challenges. MIS Quarterly: Mana¬gement Information Systems, 25(1), 71–98. https://doi.org/10.2307/3250959
  118. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly: Management Infor-mation Systems, 27(3), 425–478. https://doi.org/10.2307/30036540
  119. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of informa¬tion technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly: Management Information Systems, 36(1), 157–178. https://doi.org/10.2307/41410412
  120. Vu, H. T., & Lim, J. (2022). Effects of country and individual factors on public acceptance of artificial intelligence and robotics technologies: A multilevel SEM analysis of 28-country survey data. Behaviour & Information Technology, 41(7), 1515-1528.
  121. Wang, Y.-S., Lin, H.-H., & Luarn, P. (2006). Predicting consumer intention to use mobile service. Information Systems Journal, 16(2), 157–179. https://doi.org/10.1111/J.1365-2575.2006.00213.X
  122. Wang, Y., Meister, D., & Wang, Y. (2008). Relative advantage and perceived useful-ness: the adoption of competing ICTs. DIGIT 2008 Proceedings, 6.
  123. Wang, Y., Wang, S., Wang, J., Wei, J., & Wang, C. (2019). An empirical study of consu¬mers’ intention to use ride-sharing services: Using an extended technology acceptance model. Transportation 2018, 47(1), 397–415. https://doi.org/10.1007/S11116-018-9893-4
  124. Widodo, M., Irawan, M. I., & Sukmono, R. A. (2019). Extending UTAUT2 to explore digital wallet adoption in Indonesia. 2019 International Conference on Information and Communications Technology, ICOIACT 2019, 878–883. https://doi.org/10.1109/ICOIACT46704.2019.8938415
  125. Xie, Jianli, Ye, L., Huang, W., & Ye, M. (2021). Understanding fintech platform adoption: Impacts of perceived value and perceived risk. Journal of Theoretical and Applied Electronic Commerce Research., 16(5), 1893–1911. https://doi.org/10.3390/JTAER16050106
  126. Yeh, H. C., Yu, M. C., Liu, C. H., & Huang, C. I. (2022). Robo-advisor based on unified theory of acceptance and use of technology. Asia Pacific Journal of Marketing and Logistics, (ahead-of-print).
  127. Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2–22. https://doi.org/10.1177/002224298805200302
Read More
Author biographies is not available.
Download this PDF file
PDF
Statistic
Read Counter : 934 Download : 873