Factors influencing acceptance of Robo-Advisors for wealth management in Malaysia

References

• Abraham, F., Schmukler, S. L., & Tessada, J. (2019). Robo-Advisors: Investing Through Machines. World Bank Policy Research Working Paper, (134881).
   Google Scholar
• Au, C.-D., Klingenberger, L., Svoboda, M., & Frère, E. (2021). Business model of sustainable robo-advisors: Empirical insights for practical implementation. Sustainability, 13(13009), 13009. https://doi.org/10.3390/su132313009
   Google Scholar
• Baabdullah, A. M., Alalwan, A. A., Rana, N. P., Kizgin, H., & Patil, P. (2019). Consumer use of mobile banking (M-Banking) in Saudi Arabia: Towards an integrated model. International Journal of Information Management, 44(August 2018), 38–52, 44, 38–13. https://doi.org/10.1016/j.ijinfomgt.2018.09.002
   Web of Science ®Google Scholar
• Baek, T. H., & King, K. W. (2011). Exploring the consequences of brand credibility in services. Journal of Services Marketing, 25(4), 260–272. https://doi.org/10.1108/08876041111143096
   Web of Science ®Google Scholar
• Bagozzi, R. P., Baumgartner, H., & Yi, Y. (1992). State versus action orientation and the theory of reasoned action: An application to coupon usage. Journal of Consumer Research, 18(4), 505. https://doi.org/10.1086/209277
   Web of Science ®Google Scholar
• Bhatia, A., Chandani, A., Atiq, R., Mehta, M., & Divekar, R. (2021). Artificial Intelligence in financial services: Qualitative research to discover robo-advisory services. Quarterly Research in Financial Markets, 13(5), 632–654. https://doi.org/10.1108/QRFM-10-2020-0199
   Web of Science ®Google Scholar
• Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
   Web of Science ®Google Scholar
• Bruckes, M., Westmattelmann, D., Oldeweme, A., & Schewe, G. (2019). Determinants and barriers of adopting robo-advisory services. ICIS 2019 Proceedings, 2(2). https://aisel.aisnet.org/icis2019/blockchain_fintech/blockchain_fintech/2
   Google Scholar
• Chau, P. Y., & Hui, K. L. (1998). Identifying early adopters of new IT products: A case of Windows 95. Information and Management, 33(5), 225–230. https://doi.org/10.1016/S0378-7206(98)00031-7
   Web of Science ®Google Scholar
• Cheah, C. M., Teo, A. C., Sim, J. J., Oon, K. H., & Tan, B. I. (2011). Factors affecting Malaysian mobile banking adoption: An empirical analysis. International Journal of Network and Mobile Technologies, 2(3), 149–160. http://ijnmt.com/
   Google Scholar
• Chuang, L. M., Liu, C. C., & Kao, H. K. (2016). The adoption of FinTech service: TAM perspective. International Journal of Management and Administrative Sciences, 3(7), 1–15.
   Google Scholar
• Cruz, P., Neto, L. B. F., Murioz-Gallego, P., & Laukkanen, T. (2010). Mobile banking rollout in emerging markets: Evidence from Brazil. International Journal of Bank Marketing, 28(5), 342–371. https://doi.org/10.1108/02652321011064881
   Google Scholar
• D’Acunto, F., & Rossi, A. (2021). New frontiers of robo-advising: Consumption, saving, debt management, and Taxes. Working paper, Boston College (USA) and Georgetown University (USA). SSRN. https://ssrn.com/abstract=3778244
   Google Scholar
• Darskuviene, V., & Lisauskiené, N. (2021). Linking the Robo-advisors Phenomenon and behavioural biases in investment management: An interdisciplinary literature review and research Agenda. Organizations and Markets in Emerging Economies, 12(24), 459–477. https://doi.org/10.15388/omee.2021.12.65
   Google Scholar
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 133(3), 319. https://doi.org/10.2307/249008
   Google Scholar
• 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(03)00111-3
   Web of Science ®Google Scholar
• The Financial Industry Regulatory Authority FINRA (2016). Report on Digital Investment Advice. March issue. www.finra.org
   Google Scholar
• Forsythe, S., Liu, C., Shannon, D., & Gardner, L. C. (2006). Development of a scale to measure the perceived benefits and risk of online shopping. Journal of Interactive Marketing, 20(2), 55–75.
   Google Scholar
• Gerrard, P., & Cunningham, J. B. (2003). The diffusion of Internet banking among Singapore consumers. International Journal of Bank Marketing, 21(1), 16–28. https://doi.org/10.1108/02652320310457776
   Google Scholar
• Ghalandari, K. (2012). The effect of performance expectancy, effort expectancy, social influence and facilitating conditions on acceptance of e banking services in Iran: The moderating role of age and gender. Middle East Journal of Scientific Research, 12(6), 801–807. http://idosi.org/mejsr/mejsr12(6)12/8.pdf
   Google Scholar
• Grealish, A., & Kolm, P. (2021), “Robo-advisory: From investing principles and algorithms to future developments”, Chapter of book “Machine learning in financial markets: A guide to contemporary practice”, Cambridge University Press.
   Google Scholar
• Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (7th ed.). Prentice Hall.
   Google Scholar
• Hair, J. F., Jr., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). Pearson-Prentice Hall.
   Google Scholar
• Herbers, A. (2021). The pros and cons of being a data-driven advisor: Relying too much on data can make you miss what is really relevant in your business. Investment Advisor, May Issue, pp. 39–40.
   Google Scholar
• Hsbollah, H. M. (2009). E‐learning adoption: The role of relative advantages, trialability and academic specialisation. Campus-Wide Information Systems, 26(1), 54–70. https://doi.org/10.1108/10650740910921564
   Google Scholar
• Hu, P. J., Chau, P. Y., Sheng, O. R. L., & Tam, K. Y. (1999). Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of Management Information Systems, 162(2), 91–112. https://doi.org/10.1080/07421222.1999.11518247
   Google Scholar
• Ivantchev, C. B. (2022). Postasset management: Robo-advisory, longevity and postmoney life-cycle investment objectives. Journal of Futures Studies, 26(4), 69–78. https://jfsdigital.org/2022-2/vol-26-no-4-june-2022/postasset-management-robo-advisory-longevity-and-postmoney-life-cycle-investment-objectives/
   Web of Science ®Google Scholar
• Kim, G., Shin, B., & Lee, H. G. (2009). Understanding Dynamics between Initial Trust and Usage Intentions of Mobile Banking. Information Systems Journal, 19, 283–311.
   Web of Science ®Google Scholar
• KPMG. (2021). Digital wealth management in Asia pacific: A comparative analysis across eight key markets. https://home.kpmg/cn/en/home/insights/2021/03/digital- wealth wealthmanagementinasiapacific.html
   Google Scholar
• Lema, A. (2017). Factors influencing the adoption of mobile financial services in the unbanked population. Inkanyiso. Journal of Humanities and Social Sciences, 9(1), 37–51. https://doi.org/10.4314/IJHSS.V9I1
   Google Scholar
• Mainelli, M. (2015), “RegTech - worthy of Investment” (24 June 2015), IGTB.
   Google Scholar
• Mehta, A., Morris, N. P., Swinnerton, B., & Homer, M. (2019). The Influence of Values on e-Learning Adoption. Computers & Education, 141(1), 103617.
   Google Scholar
• Memon, M. A., Ting, H., Cheah, J. -H., Thurasamy, R., Chuah, F., & Cham, T. H. (2020). Sample Size for Survey Research: Review and Recommendations. Journal of Applied Structural Equation Modeling, 4(2), i–xx.
   Google Scholar
• Milani, A. (2019). The role of risk and trust in the adoption of Robo-advisory in Italy: An extension of the unified theory of acceptance and use of technology. PwC Italy, 4-26. https://www.pwc.com/it/it/publications/assets/docs/Report-Robo-advisors.pdf
   Google Scholar
• Moore, G. C., & Benbasat, I. (1991). Development of an Instrument to Measure the Perceptions of Adopting and Information Technology Innovation. Information Systems Research, 2(3), 192–222.
   Web of Science ®Google Scholar
• MyPF. (2021). Comparing between different robo-advisors available in Malaysia. https://mypf.my/2021/04/09/comparing-between-different-Robo
   Google Scholar
• Nicole, K. L., Morgan, M., Adrian, P., & Anita, L. Z. (2015). Enjoyment and Social Influence: Predicting Mobile Payment Adoption. The Service Industries Journal, 35(10), 537–554.
   Web of Science ®Google Scholar
• Phoon, K., & Koh, F. (2017). Robo-advisors and wealth management. The Journal of Alternative Investments, 20(3), 79–94. https://doi.org/10.3905/jai.2018.20.3.079
   Web of Science ®Google Scholar
• Reddavide, L. (2018). The Evolution of Wealth Management: Transformation and Innovation of Robo Advisory (pp. 27–32). Bachelor’s thesis, Università Ca’Foscari Venezia. http://hdl.handle.net/10579/13076
   Google Scholar
• Rogers, E. M. (1983). Diffusion of innovations. The Free Press.
   Google Scholar
• Rossi, A., & Utkus, S. (2020). The needs and wants in financial advice: Human versus Robo-advising, working paper, https://papers.ssrn.com
   Google Scholar
• Rühr, A., Berger, B., & Hess, T. (2019). Can I Control My Robo-Advisor? Trade-Offs in Automation and User Control in (Digital) Investment Management (Vol. 25). Twenty-fifth Americas Conference on Information Systems, Cancun, Mexico.
   Google Scholar
• Seo, B. I. (2016), Change in the asset management market led by Robo-Advisor. Seoul, South Korea: Eugene Investment & Securities.
   Google Scholar
• Shmueli, G., Ray, S., Estrada, J. M. V., & Chatla, S. B. (2016). The elephant in the room: Predictive performance of PLS models. Journal of Business Research, 69, 4552–4564.
   Web of Science ®Google Scholar
• Singh, I., & Kaur, N. (2017). Wealth management through Robo advisory. International Journal of Research-Granthaalayah, 5(6), 33–43. https://doi.org/10.29121/granthaalayah.v5.i6.2017.1991
   Google Scholar
• Slade, E. L., Dwivedi, Y. K., Piercy, N. C., & Williams, M. D. (2015). Modeling consumers’ 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
   Web of Science ®Google Scholar
• Suhr, D. (2006). Exploratory or Confirmatory Factor Analysis? Proceedings of the 31st Annual SAS? Users Group International Conference. Cary, NC: SAS Institute Inc., Cary, NC: 200–231.
   Google Scholar
• Talukder, M., Quazi, A., & Sathye, M. (2014). Mobile phone banking usage behaviour: An Australian perspective. Australasian Accounting, Business and Finance Journal, 8(4), 83–104. https://doi.org/10.14453/aabfj.v8i4.6
   Google Scholar
• Tan, Z. Y. (2021, January 25). StashAway achieves assets under management of over US$1b. The Edge Markets.
   Google Scholar
• Tan, G. W. H., Ooi, K. B., Chong, S. C., & T.S, H. (2014). NFC mobile credit card: The next frontier of mobile payment?. Telematics and Informatics, 31, 292–307.
   Web of Science ®Google Scholar
• Teo, T. S. H., & Pok, S. H. (2003). Adoption of WAP-Enabled Mobile Phones among Internet Users. Omega, 31, 483–498.
   Web of Science ®Google Scholar
• Thatcher, J. B., & Perrewe, P. L. (2002). An empirical examination of individual traits as antecedents to computer anxiety and computer self-efficacy. MIS Quarterly, 26(4), 381–396.
   Web of Science ®Google Scholar
• To, A. T., & Trinh, T. H. M. (2021). Understanding behavioral intention to use mobile wallets in Vietnam: Extending the tam model with trust and enjoyment. Cogent Business and Management, 8(1), 1. https://doi.org/10.1080/23311975.2021.1891661
   Web of Science ®Google Scholar
• Vanguard. (2017). Advice for real life. https://investor.vanguard.com/advice/personal-advisor
   Google Scholar
• 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.
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Venkatesh, V., & Zhang, X. (2010). Unified Theory of Acceptance and Use of. Journal of Global Information Technology Management, 13(1), 5–27. https://doi.org/10.1080/1097198X.2010.10856507
   Web of Science ®Google Scholar
• Yang, M., Al Mamun, A., Mohiuddin, M., Nawi, N. C., & Zainol, N. R. (2021). Cashless transactions: A study on intention and adoption of e-wallets. Sustainability (Switzerland), 13(2), 1–18. https://doi.org/10.3390/su13020831
   Web of Science ®Google Scholar
• Yang, S., Lu, Y., Gupta, S., Cao, Y., & Zhang, R. (2012). Mobile payment services adoption across time: An empirical study of the effects of behavioral beliefs. Social Influences, and Personal Traits. Computers in Human Behavior, [Online], 28(1), 129–142. https://doi.org/10.1016/j.chb.2011.08.019
   Web of Science ®Google Scholar