IDEAS home Printed from https://ideas.repec.org/a/spr/elmark/v28y2018i3d10.1007_s12525-017-0279-9.html
   My bibliography  Save this article

Designing a robo-advisor for risk-averse, low-budget consumers

Author

Listed:
  • Dominik Jung

    (Karlsruhe Institute of Technology (KIT), Institute of Information Systems and Marketing (IISM))

  • Verena Dorner

    (Karlsruhe Institute of Technology (KIT), Institute of Information Systems and Marketing (IISM))

  • Christof Weinhardt

    (Karlsruhe Institute of Technology (KIT), Institute of Information Systems and Marketing (IISM))

  • Hakan Pusmaz

    (Karlsruhe Institute of Technology (KIT), Institute of Information Systems and Marketing (IISM))

Abstract

Banks have reacted much more enthusiastically to the FinTech revolution than many of their customers. Robo-advisory, automated web-based investment advisory, in particular promises many advantages for both banks and customers - but consumer adoption has been slow so far. Recent studies suggest that this might be due to a mix of low trust in banks, high expectations of transparency and general inability or unwillingness to engage with investment questions. Research in decision support and guidance shows customers’ willingness to interact with a decision support tool depends greatly on its usability. We identify requirements for robo-advisory, derive design principles and evaluate them in two iterations with a real robo-advisor in a controlled laboratory study. The evaluation results confirm the validity of our identified design principles.

Suggested Citation

  • Dominik Jung & Verena Dorner & Christof Weinhardt & Hakan Pusmaz, 2018. "Designing a robo-advisor for risk-averse, low-budget consumers," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(3), pages 367-380, August.
    • Handle: RePEc:spr:elmark:v:28:y:2018:i:3:d:10.1007_s12525-017-0279-9
    DOI: 10.1007/s12525-017-0279-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12525-017-0279-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12525-017-0279-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ben Greiner, 2015. "Subject pool recruitment procedures: organizing experiments with ORSEE," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 1(1), pages 114-125, July.
    2. Trentin, Alessio & Perin, Elisa & Forza, Cipriano, 2012. "Product configurator impact on product quality," International Journal of Production Economics, Elsevier, vol. 135(2), pages 850-859.
    3. Senecal, Sylvain & Kalczynski, Pawel J. & Nantel, Jacques, 2005. "Consumers' decision-making process and their online shopping behavior: a clickstream analysis," Journal of Business Research, Elsevier, vol. 58(11), pages 1599-1608, November.
    4. Charles A. Holt & Susan K. Laury, 2005. "Risk Aversion and Incentive Effects: New Data without Order Effects," American Economic Review, American Economic Association, vol. 95(3), pages 902-912, June.
    5. Clayton Arlen Looney & Andrew M. Hardin, 2009. "Decision Support for Retirement Portfolio Management: Overcoming Myopic Loss Aversion via Technology Design," Management Science, INFORMS, vol. 55(10), pages 1688-1703, October.
    6. Philipp Nussbaumer & Inu Matter & Gian Reto à Porta & Gerhard Schwabe, 2012. "Designing for Cost Transparency in Investment Advisory Service Encounters," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 4(6), pages 347-361, December.
    7. Charness, Gary & Gneezy, Uri & Imas, Alex, 2013. "Experimental methods: Eliciting risk preferences," Journal of Economic Behavior & Organization, Elsevier, vol. 87(C), pages 43-51.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nourallah, Mustafa, 2023. "One size does not fit all: Young retail investors’ initial trust in financial robo-advisors," Journal of Business Research, Elsevier, vol. 156(C).
    2. Xusen Cheng & Fei Guo & Jin Chen & Kejiang Li & Yihui Zhang & Peng Gao, 2019. "Exploring the Trust Influencing Mechanism of Robo-Advisor Service: A Mixed Method Approach," Sustainability, MDPI, vol. 11(18), pages 1-20, September.
    3. Gerlach, Johannes M. & Lutz, Julia K.T., 2021. "Digital financial advice solutions – Evidence on factors affecting the future usage intention and the moderating effect of experience," Journal of Economics and Business, Elsevier, vol. 117(C).
    4. Bai, Zefeng, 2021. "Does robo-advisory help reduce the likelihood of carrying a credit card debt? Evidence from an instrumental variable approach," Journal of Behavioral and Experimental Finance, Elsevier, vol. 29(C).
    5. Mike K. P. So, 2021. "Robo-Advising Risk Profiling through Content Analysis for Sustainable Development in the Hong Kong Financial Market," Sustainability, MDPI, vol. 13(3), pages 1-15, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tamás Csermely & Alexander Rabas, 2016. "How to reveal people’s preferences: Comparing time consistency and predictive power of multiple price list risk elicitation methods," Journal of Risk and Uncertainty, Springer, vol. 53(2), pages 107-136, December.
    2. Gary Charness & Nir Chemaya & Dario Trujano-Ochoa, 2023. "Learning your own risk preferences," Journal of Risk and Uncertainty, Springer, vol. 67(1), pages 1-19, August.
    3. Jacob K Goeree & Bernardo Garcia-Pola, 2023. "A Non-Parametric Test of Risk Aversion," Papers 2308.02083, arXiv.org.
    4. Anwesha Bandyopadhyay & Lutfunnahar Begum & Philip J. Grossman, 2021. "Gender differences in the stability of risk attitudes," Journal of Risk and Uncertainty, Springer, vol. 63(2), pages 169-201, October.
    5. Galliera, Arianna, 2018. "Self-selecting random or cumulative pay? A bargaining experiment," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 72(C), pages 106-120.
    6. Pablo Brañas‐Garza & Matteo M. Galizzi & Jeroen Nieboer, 2018. "Experimental And Self‐Reported Measures Of Risk Taking And Digit Ratio (2d:4d): Evidence From A Large, Systematic Study," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(3), pages 1131-1157, August.
    7. Julija Michailova & Tadeusz Tyszka & Katarzyna Pfeifer, 2017. "Are People Interested in Probabilities of Natural Disasters?," Risk Analysis, John Wiley & Sons, vol. 37(5), pages 1005-1017, May.
    8. Alejandro Arrieta & Ariadna García‐Prado & Paula González & José Luis Pinto‐Prades, 2017. "Risk attitudes in medical decisions for others: An experimental approach," Health Economics, John Wiley & Sons, Ltd., vol. 26(S3), pages 97-113, December.
    9. Helland, Leif & Iachan, Felipe S. & Juelsrud, Ragnar E. & Nenov, Plamen T., 2021. "Information quality and regime change: Evidence from the lab," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 538-554.
    10. Hermansson, Cecilia, 2018. "Can self-assessed financial risk measures explain and predict bank customers’ objective financial risk?," Journal of Economic Behavior & Organization, Elsevier, vol. 148(C), pages 226-240.
    11. Francesca Gioia, 2017. "Peer effects on risk behaviour: the importance of group identity," Experimental Economics, Springer;Economic Science Association, vol. 20(1), pages 100-129, March.
    12. Volker Benndorf, 2018. "Voluntary Disclosure of Private Information and Unraveling in the Market for Lemons: An Experiment," Games, MDPI, vol. 9(2), pages 1-17, May.
    13. Stephen L. Cheung, 2020. "Eliciting utility curvature in time preference," Experimental Economics, Springer;Economic Science Association, vol. 23(2), pages 493-525, June.
    14. Birgit M Beisswingert & Keshun Zhang & Thomas Goetz & Urs Fischbacher, 2016. "Spillover Effects of Loss of Control on Risky Decision-Making," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-19, March.
    15. Ola Andersson & Håkan J. Holm & Jean-Robert Tyran & Erik Wengström, 2016. "Deciding for Others Reduces Loss Aversion," Management Science, INFORMS, vol. 62(1), pages 29-36, January.
    16. Giuseppe Attanasi & Nikolaos Georgantzís & Valentina Rotondi & Daria Vigani, 2018. "Lottery- and survey-based risk attitudes linked through a multichoice elicitation task," Theory and Decision, Springer, vol. 84(3), pages 341-372, May.
    17. Sven Grüner & Mira Lehberger & Norbert Hirschauer & Oliver Mußhoff, 2022. "How (un)informative are experiments with students for other social groups? A study of agricultural students and farmers," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 66(3), pages 471-504, July.
    18. Holzmeister, Felix & Stefan, Matthias, 2019. "The Risk Elicitation Puzzle Revisited: Across-Methods (In)consistency?," OSF Preprints pj9u2, Center for Open Science.
    19. Lönnqvist, Jan-Erik & Verkasalo, Markku & Walkowitz, Gari & Wichardt, Philipp C., 2015. "Measuring individual risk attitudes in the lab: Task or ask? An empirical comparison," Journal of Economic Behavior & Organization, Elsevier, vol. 119(C), pages 254-266.
    20. Sascha Füllbrunn & Wolfgang J. Luhan, 2015. "Am I my Peer‘s Keeper? Social Responsibility in Financial Decision Making," Ruhr Economic Papers 0551, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.

    More about this item

    Keywords

    Robo-advisory; Usability engineering; User-centric design;
    All these keywords.

    JEL classification:

    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles
    • G29 - Financial Economics - - Financial Institutions and Services - - - Other

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:elmark:v:28:y:2018:i:3:d:10.1007_s12525-017-0279-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.