Differences in Investment Behavior – A Quantitative Study on the Effects of Robo-Advisors and Human Financial Advisors
Keywords:
business, economics, robo advisors, behavioral finance, affective trust, risk, investment, financial literacyAbstract
This study examines investment behavior differences between static and conversational robo-advisors and human financial advisors, focusing on affective trust, risk, and financial literacy. Using an online factorial experiment (n = 165), U.S. participants evaluated advisor scenarios under positive and negative message framing. MANOVA and Pearson correlations revealed no significant trust or risk differences across advisor types or framings. However, individuals with high financial literacy perceived conversational robo-advisors as riskier under negative framing, while lower-literacy participants trusted robo-advisors more. The study highlights the need for human-like design improvements in robo-advisors and enhanced financial education to support informed, confident decision-making.
References
Ahmad, M., & Shah, S.Z.A. (2022). Overconfidence heuristic-driven bias in investment decision-making and performance: Mediating effects of risk perception and moderating effects of financial literacy. Journal of Economic and Administrative Sciences, 38(1), 60–90. https://doi.org/10.1108/JEAS-07-2020-0116
Algarni, A. (2019). What Message Characteristics Make Social Engineering Successful on Facebook: The Role of Central Route, Peripheral Route, and Perceived Risk. Information, 10(6), 211. https://doi.org/10.3390/info10060211
Alsabah, H., Capponi, A., Lacedelli, O.R., & Stern, M. (2019). Robo-advising: Learning Investors’ Risk Preferences via Portfolio Choices. ArXiv.Org. https://doi.org/10.48550/arxiv.1911.02067
Ansari, Y., Albarrak, M.S., Sherfudeen, N., & Aman, A. (2022). A Study of Financial Literacy of Investors—A Bibliometric Analysis. International Journal of Financial Studies, 10(2), 36. https://doi.org/10.3390/ijfs10020036
Antoun, C., Zhang, C., Conrad, F.G., & Schober, M.F. (2016). Comparisons of online recruitment strategies for convenience samples: Craigslist, Google AdWords, Facebook, and Amazon Mechanical Turk. Field Methods, 28(3), 231–246. https://doi.org/10.1177/1525822X15603149
Ashraf, N., Camerer, C.F., & Loewenstein, G. (2005). Adam Smith, Behavioral Economist. The Journal of Economic Perspectives, 19(3), 131–145. https://doi.org/10.1257/089533005774357897
Baker, T., & Dellaert, B. (2018). Regulating Robo Advice Across the Financial Services Industry. Iowa Law Review, 103(2), 713–750.
Belanche, D., Casaló, L.V., & Flavián, C. (2019). Artificial Intelligence in FinTech: Understanding robo-advisors adoption among customers. Industrial Management + Data Systems, 119(7), 1411–1430. https://doi.org/10.1108/IMDS-08-2018-0368
Beketov, M., Lehmann, K., & Wittke, M. (2018). Robo Advisors: Quantitative methods inside the robots. Journal of Asset Management, 19(6), 363–370. https://doi.org/10.1057/s41260-018-0092-9
Eichler, K.S., & Schwab, E. (2024, October). Evaluating Robo-Advisors through Behavioral Finance: A Critical Review of Technology Potential, Rationality, and Investor Expectations. Frontiers in Behavioral Economics, 3(28). https://doi.org/10.3389/frbhe.2024.1489159
Caudill, L.E., & Diab, D.L. (2020). Digital Word of Mouth and Organizational Attraction: Focusing on Message Characteristics and Time. Corporate Reputation Review, 23(3), 170–180. https://doi.org/10.1057/s41299-019-00077-x
Chua, A.Y.K., Pal, A., & Banerjee, S. (2023). AI-enabled investment advice: Will users buy it? Computers in Human Behavior, 138, 107481. https://doi.org/10.1016/j.chb.2022.107481
Costa, D.F., Carvalho, F. de M., & Moreira, B.C. de M. (2019). Behavioral Economics and Behavioral Finance: A Bibliometric Analysis of the Scientific Fields. Journal of Economic Surveys, 33(1), 3–24. https://doi.org/10.1111/joes.12262
Daniel, K., Hirshleifer, D., & Subrahmanyam, A. (1998). Investor Psychology and Security Market Under- and Overreactions. The Journal of Finance (New York), 53(6), 1839–1885. https://doi.org/10.1111/0022-1082.00077
Darškuvienė, V., & Lisauskiene, 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
De Bondt, W.F.M., & Thaler, R.H. (1994). Financial Decision-Making in Markets and Firms: A Behavioral Perspective. NBER Working Paper Series, 4777. https://doi.org/10.3386/w4777
Dunkel, C.S., Nedelec, J., & van der Linden, D. (2023). Reevaluating the Dunning-Kruger effect: A response to and replication of Gignac and Zajenkowski (2020). Intelligence (Norwood), 96. https://doi.org/10.1016/j.intell.2022.101717
El Hedhli, K., & Zourrig, H. (2023). Dual routes or a one-way to persuasion? The elaboration likelihood model versus the unimodel. Journal of Marketing Communications, 29(5), 433–454. https://doi.org/10.1080/13527266.2022.2034033
Evans, J., & Stanovich, K.E. (2013). Dual-Process Theories of Higher Cognition: Advancing the Debate. Perspectives on Psychological Science, 8(3), 223–241. https://doi.org/10.1177/1745691612460685
Garg, G., Shamshad, M., Gauhar, N., Tabash, M.I., Hamouri, B., & Daniel, L.N. (2023). A Bibliometric Analysis of Fintech Trends: An Empirical Investigation. International Journal of Financial Studies, 11(2), 79–. https://doi.org/10.3390/ijfs11020079
Gier, N.R., Krampe, C., & Kenning, P. (2023). Why it is good to communicate the bad: Understanding the influence of message framing in persuasive communication on consumer decision-making processes. Frontiers in human neuroscience, 17, 1085810. https://doi.org/10.3389/fnhum.2023.1085810
Guresen, E., & Kayakutlu, G. (2011). Definition of artificial neural networks with comparison to other networks. Procedia Computer Science, 3, 426–433. https://doi.org/10.1016/j.procs.2010.12.071
Green, S.B., & Salkind, N.J. (2017). Using SPSS for Windows and Macintosh: Analyzing and Understanding Data (8th ed.). Upper Saddle River, NJ: Prentice Hall.
Henry, G.T. (1990). Practical sampling. SAGE. https://doi.org/10.4135/9781412985451
Hildebrand, C., & Bergner, A. (2021). Conversational robo advisors as surrogates of trust: Onboarding experience, firm perception, and consumer financial decision making. Journal of the Academy of Marketing Science, 49(4), 659–676. https://doi.org/10.1007/s11747-020-00753-z
Hwang, M.I., & Lin, J.W. (1999). Information dimension, information overload and decision quality. Journal of Information Science, 25(3), 213–218. https://doi.org/10.1177/016555159902500305
Johnson, D., & Grayson, K. (2005). Cognitive and affective trust in service relationships. Journal of Business Research, 58(4), 500–507. https://doi.org/10.1016/S0148-2963(03)00140-1
Jung, D., Dorner, V., Weinhardt, C., & Pusmaz, H. (2018). Designing a robo-advisor for risk-averse, low-budget consumers. Electronic Markets, 28(3), 367–380. https://doi.org/10.1007/s12525-017-0279-9
Kahneman, D. (2003). A Perspective on Judgment and Choice: Mapping Bounded Rationality. The American Psychologist, 58(9), 697–720. https://doi.org/10.1037/0003-066X.58.9.697
Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
Kalantari, B. (2010). Herbert A. Simon on making decisions: Enduring insights and bounded rationality. Journal of Management History, 16(4), 509–520. https://doi.org/10.1108/17511341011073988
Karlsson, N., Loewenstein, G., & Seppi, D. (2009). Ostrich effect: Selective attention to information. Journal of Risk and Uncertainty, 38(2), 95–115. https://doi.org/10.1007/s11166-009-9060-6
Kruger, J., & Dunning, D. (1999). Unskilled and Unaware of It: How Difficulties in Recognizing One’s Own Incompetence Lead to Inflated Self-Assessments. Journal of Personality and Social Psychology, 77(6), 1121–1134. https://doi.org/10.1037/0022-3514.77.6.1121
Lewis, D.R. (2018). The perils of overconfidence: Why many consumers fail to seek advice when they really should. Journal of Financial Services Marketing, 23(2), 104–111. https://doi.org/10.1057/s41264-018-0048-7
Lusardi, A., & Mitchell, O.S. (2011). Financial literacy around the world: An overview. Journal of Pension Economics & Finance, 10(4), 497–508. https://doi.org/10.1017/S1474747211000448
Maier-Paape, S., & Zhu, Q.J. (2018). A general framework for portfolio theory. Part I: Theory and various models. Risks (Basel), 6(2), 1–35. https://doi.org/10.3390/risks6020053
Markowitz, H.M. (1959). Portfolio selection: Efficient diversification of investments. Yale University Press. https://doi.org/10.12987/9780300191677
Mittal, S., Bhattacharya, S., & Mandal, S. (2022). Characteristics analysis of behavioural portfolio theory in the Markowitz portfolio theory framework. Managerial Finance, 48(2), 277–288. https://doi.org/10.1108/MF-05-2021-0208
OECD. (2011). Measuring Financial Literacy: Questionnaire and Guidance Notes for Conducting an Internationally Comparable Survey of Financial Literacy. Retrieved from https://www.oecd.org/finance/financial-education/49319977.pdf
Owusu, S.P., & Laryea, E. (2023). The impact of anchoring bias on investment decision-making: Evidence from Ghana. Review of Behavioral Finance, 15(5), 729–749. https://doi.org/10.1108/RBF-09-2020-0223
Pachur, T., & Bröder, A. (2013). Judgment: A cognitive processing perspective. Wiley Interdisciplinary Reviews. Cognitive Science, 4(6), 665–681. https://doi.org/10.1002/wcs.1259
Petty, R.E., & Cacioppo, J.T. (1979). Issue involvement can increase or decrease persuasion by enhancing message-relevant cognitive responses. Journal of Personality and Social Psychology, 37(10), 1915–1926. https://doi.org/10.1037/0022-3514.37.10.1915
Petty, R.E., & Cacioppo, J.T. (1990). Involvement and Persuasion: Tradition Versus Integration. Psychological Bulletin, 107(3), 367–374. https://doi.org/10.1037/0033-2909.107.3.367
Pietersen, J., Ferreira-Schenk, S., & Dickason, Z. (2021). Are Robots Taking Over? Technological Advancements and Investor Risk Tolerance. Acta Universitatis Danubius. Œconomica, 17(4), 7–27.
Raffaelli, R., Glynn, M.A., & Tushman, M. (2019). Frame flexibility: The role of cognitive and emotional framing in innovation adoption by incumbent firms. Strategic Management Journal, 40(7), 1013–1039. https://doi.org/10.1002/smj.3011
Raut, R.K. (2020). Past behaviour, financial literacy and investment decision-making process of individual investors. International Journal of Emerging Markets, 15(6), 1243–1263. https://doi.org/10.1108/IJOEM-07-2018-0379
Russell, J.A., & Barrett, L.F. (1999). Core Affect, Prototypical Emotional Episodes, and Other Things Called Emotion: Dissecting the Elephant. Journal of Personality and Social Psychology, 76(5), 805–819. https://doi.org/10.1037/0022-3514.76.5.805
Schwarz, G., Christensen, T., & Zhu, X. (2022). Bounded Rationality, Satisficing, Artificial Intelligence, and Decision‐Making in Public Organizations: The Contributions of Herbert Simon. Public Administration Review, 82(5), 902–904. https://doi.org/10.1111/puar.13540
Sent, E.-M. (2004). Behavioral Economics: How Psychology Made Its (Limited) Way Back Into Economics. History of Political Economy, 36(4), 735–760. https://doi.org/10.1215/00182702-36-4-735
Seo, M.-G., Goldfarb, B., & Barrett, L.F. (2010). Affect and the Framing Effect Within Individuals Over Time: Risk Taking in a Dynamic Investment Simulation. Academy of Management Journal, 53(2), 411–431. https://doi.org/10.5465/AMJ.2010.49389383
Statista. (2024, October 8). Share of Americans Investing Money in the Stock Market 1999-2024. Statista Research Department. Retrieved November 3, 2024, from www.statista.com/statistics/270034/percentage-of-us-adults-to-have-money-invested-in-the-stock-market/
Shefrin, H. (2009). Behavioralizing Finance. Foundations and Trends® in Finance, 4(1–2), 1–184. https://doi.org/10.1561/0500000030
Simon, H.A. (1955). A Behavioral Model of Rational Choice. The Quarterly Journal of Economics, 69(1), 99–118. https://doi.org/10.2307/1884852
Sironi, P. (2016). FinTech innovation: From robo-advisors to goal based investing and gamification. Wiley.
Statista. (2023). Robo-Advisors - Worldwide | Statista Market Forecast. Retrieved November 3, 2024, from www.statista.com/outlook/fmo/wealth-management/digital-investment/robo-advisors/worldwide
Statista Market Insights. (2023, August 1). Robo-Advisors – Worldwide. Statista Market Forecast. Statista. Retrieved from https://www.statista.com/outlook/fmo/wealth-management/digital-investment/robo-advisors/worldwide
Statista Research Department. (2024). Consumer Trust in AI Activities Globally in 2024. Statista. Retrieved from www.statista.com/statistics/1475638/consumer-trust-in-ai-activities-globally/#:~:text=When%20surveyed%20in%202024%2C%20more
Tan, G.K.S. (2020). Robo-advisors and the financialization of lay investors. Geoforum, 117, 46–60. https://doi.org/10.1016/j.geoforum.2020.09.004
Tepper, T., & Curry, B. (2021, December 1). Best Robo-Advisors of December 2021. Forbes Advisor. Retrieved from https://www.forbes.com/advisor/investing/best-robo-advisors/
Thaler, R.H. (2016). Behavioral Economics: Past, Present, and Future. The American Economic Review, 106(7), 1577–1600. https://doi.org/10.1257/aer.106.7.1577
Thomaz, F., Salge, C., Karahanna, E., & Hulland, J. (2020). Learning from the Dark Web: Leveraging conversational agents in the era of hyper-privacy to enhance marketing. Journal of the Academy of Marketing Science, 48(1), 43–63. https://doi.org/10.1007/s11747-019-00704-3
Tversky, A., & Kahneman, D. (1974). Judgment under Uncertainty: Heuristics and Biases. Science, 185(4157), 1124–1131. https://doi.org/10.1126/science.185.4157.1124
Tversky, A., & Kahneman, D. (1979). Prospect Theory: An Analysis of Decision under Risk. IDEAS Working Paper Series from RePEc.
Tversky, A., & Kahneman, D. (1986). Rational Choice and the Framing of Decisions. The Journal of Business (Chicago, Ill.), 59(4), S251–S278. https://doi.org/10.1086/296365
Upashi, R., & Kadakol, A.M. (2023). Impact of Behavioral Biases on Investment Decision Making: Evidence from the Review of Literature. Abhigyan (New Delhi), 41(1), 35–. https://doi.org/10.56401/Abhigyan/41.1.2023.35-49
Van’t Riet, J., Cox, A.D., Cox, D., Zimet, G.D., De Bruijn, G.J., Van den Putte, B., . . . Ruiter, R.A. (2014). Does perceived risk influence the effects of message framing? A new investigation of a widely held notion. Psychology & Health, 29(8), 933–949. https://doi.org/10.1080/08870446.2014.896916
Wexler, M.N., & Oberlander, J. (2021). Robo-advisors (RAs): the programmed self-service market for professional advice. Journal of Service Theory and Practice, 31(3), 351–365. https://doi.org/10.1108/JSTP-07-2020-0153
Wilcox, J. (2022). Human Judgment: How Accurate Is It, and How Can It Get Better? Springer.
Wu, P.C.S., & Wang, Y.-C. (2011). The influences of electronic word-of-mouth message appeal and message source credibility on brand attitude. Asia Pacific Journal of Marketing and Logistics, 23(4), 448–472. https://doi.org/10.1108/13555851111165020
Zhang, L., Pentina, I., & Fan, Y. (2021). Who do you choose? Comparing perceptions of human vs robo-advisor in the context of financial services. The Journal of Services Marketing, 35(5), 634–646. https://doi.org/10.1108/JSM-05-2020-0162
