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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
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    References listed on IDEAS

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    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. 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).
    3. 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).
    4. Cardillo, Giovanni & Chiappini, Helen, 2024. "Robo-advisors: A systematic literature review," Finance Research Letters, Elsevier, vol. 62(PA).
    5. Zhu, Hui & Vigren, Olli & Söderberg, Inga-Lill, 2024. "Implementing artificial intelligence empowered financial advisory services: A literature review and critical research agenda," Journal of Business Research, Elsevier, vol. 174(C).
    6. Luo, Haohan & Liu, Xin & Lv, Xingyang & Hu, Yubei & Ahmad, Ali J., 2024. "Investors’ willingness to use robo-advisors: Extrapolating influencing factors based on the fiduciary duty of investment advisors," International Review of Economics & Finance, Elsevier, vol. 94(C).
    7. 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.
    8. 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.

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    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

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