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Acceptance of digital investment solutions: The case of robo advisory in Germany

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  • Seiler, Volker
  • Fanenbruck, Katharina Maria

Abstract

The financial services sector is undergoing substantial change due to technological innovation and digitalization. Traditional banks face intensifying competition through the market entry of digital investment platforms that make use of automated investment advisory, so-called robo advisors. Based on replica of two German robo advisors, a sample of 96 participants assessed their intention to use such digital investment services. The results obtained using partial least squares (PLS) path modelling indicate that perceived usefulness and privacy are the most decisive factors with a one percent higher perceived usefulness (higher privacy) increasing usage intentions by 0.57 % (0.25 %). The results are robust to various socio-demographic and FinTech-related controls as well as alternative estimation procedures such as generalized structured component analysis (GSCA).

Suggested Citation

  • Seiler, Volker & Fanenbruck, Katharina Maria, 2021. "Acceptance of digital investment solutions: The case of robo advisory in Germany," Research in International Business and Finance, Elsevier, vol. 58(C).
  • Handle: RePEc:eee:riibaf:v:58:y:2021:i:c:s0275531921001112
    DOI: 10.1016/j.ribaf.2021.101490
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    as
    1. Thakor, Anjan, 2020. "Corrigendum to: Fintech and Banking: What Do We Know?," Journal of Financial Intermediation, Elsevier, vol. 43(C).
    2. Jeremy Burke & Angela A. Hung, 2015. "Trust and Financial Advice," Working Papers WR-1075, RAND Corporation.
    3. D’Hondt, Catherine & De Winne, Rudy & Ghysels, Eric & Raymond, Steve, 2020. "Artificial Intelligence Alter Egos: Who might benefit from robo-investing?," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 278-299.
    4. Francesco D’Acunto & Nagpurnanand Prabhala & Alberto G Rossi, 2019. "The Promises and Pitfalls of Robo-Advising," Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1983-2020.
    5. Bolton, Patrick & Freixas, Xavier & Shapiro, Joel, 2007. "Conflicts of interest, information provision, and competition in the financial services industry," Journal of Financial Economics, Elsevier, vol. 85(2), pages 297-330, August.
    6. Bhatia, Ankita & Chandani, Arti & Chhateja, Jagriti, 2020. "Robo advisory and its potential in addressing the behavioral biases of investors — A qualitative study in Indian context," Journal of Behavioral and Experimental Finance, Elsevier, vol. 25(C).
    7. Xu, Hai-Chuan & Zhou, Wei-Xing, 2018. "A weekly sentiment index and the cross-section of stock returns," Finance Research Letters, Elsevier, vol. 27(C), pages 135-139.
    8. Peter Gomber & Jascha-Alexander Koch & Michael Siering, 2017. "Digital Finance and FinTech: current research and future research directions," Journal of Business Economics, Springer, vol. 87(5), pages 537-580, July.
    9. Necmi K. Avkiran, 2018. "Rise of the Partial Least Squares Structural Equation Modeling: An Application in Banking," International Series in Operations Research & Management Science, in: Necmi K. Avkiran & Christian M. Ringle (ed.), Partial Least Squares Structural Equation Modeling, chapter 0, pages 1-29, Springer.
    10. Carin Cruijsen & Jakob Haan & David-Jan Jansen, 2016. "Trust and Financial Crisis Experiences," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 127(2), pages 577-600, June.
    11. Thakor, Anjan V., 2020. "Fintech and banking: What do we know?," Journal of Financial Intermediation, Elsevier, vol. 41(C).
    12. Nada Mselmi & Amine Lahiani & Taher Hamza, 2017. "Financial distress prediction: The case of French small and medium-sized firms," Post-Print hal-03529325, HAL.
    13. Shanmuganathan, Manchuna, 2020. "Behavioural finance in an era of artificial intelligence: Longitudinal case study of robo-advisors in investment decisions," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    14. 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).
    15. Jeremy Burke & Angela Hung & Jack Clift & Steven Garber & Joanne K. Yoong, 2015. "Impacts of Conflicts of Interest in the Financial Services Industry," Working Papers 1076, RAND Corporation.
    16. Brenner, Lukas & Meyll, Tobias, 2020. "Robo-advisors: A substitute for human financial advice?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 25(C).
    17. Tao, Ran & Su, Chi-Wei & Xiao, Yidong & Dai, Ke & Khalid, Fahad, 2021. "Robo advisors, algorithmic trading and investment management: Wonders of fourth industrial revolution in financial markets," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    18. Lucey, Brian M. & Vigne, Samuel A. & Ballester, Laura & Barbopoulos, Leonidas & Brzeszczynski, Janusz & Carchano, Oscar & Dimic, Nebojsa & Fernandez, Viviana & Gogolin, Fabian & González-Urteaga, Ana , 2018. "Future directions in international financial integration research - A crowdsourced perspective," International Review of Financial Analysis, Elsevier, vol. 55(C), pages 35-49.
    19. Mselmi, Nada & Lahiani, Amine & Hamza, Taher, 2017. "Financial distress prediction: The case of French small and medium-sized firms," International Review of Financial Analysis, Elsevier, vol. 50(C), pages 67-80.
    20. Brooks, Chris & Sangiorgi, Ivan & Hillenbrand, Carola & Money, Kevin, 2019. "Experience wears the trousers: Exploring gender and attitude to financial risk," Journal of Economic Behavior & Organization, Elsevier, vol. 163(C), pages 483-515.
    21. Lee Cronbach, 1951. "Coefficient alpha and the internal structure of tests," Psychometrika, Springer;The Psychometric Society, vol. 16(3), pages 297-334, September.
    22. Dashan Huang & Fuwei Jiang & Jun Tu & Guofu Zhou, 2015. "Investor Sentiment Aligned: A Powerful Predictor of Stock Returns," Review of Financial Studies, Society for Financial Studies, vol. 28(3), pages 791-837.
    23. Jünger, Moritz & Mietzner, Mark, 2020. "Banking goes digital: The adoption of FinTech services by German households," Finance Research Letters, Elsevier, vol. 34(C).
    24. Nada Mselmi & Amine Lahiani & Taher Hamza, 2017. "Financial distress prediction: The case of French small and medium-sized firms," Post-Print hal-03380580, HAL.
    25. Danilov, Anastasia & Biemann, Torsten & Kring, Thorn & Sliwka, Dirk, 2013. "The dark side of team incentives: Experimental evidence on advice quality from financial service professionals," Journal of Economic Behavior & Organization, Elsevier, vol. 93(C), pages 266-272.
    26. Necmi K. Avkiran & Christian M. Ringle (ed.), 2018. "Partial Least Squares Structural Equation Modeling," International Series in Operations Research and Management Science, Springer, number 978-3-319-71691-6, December.
    27. Hirsch, Bernhard & Nitzl, Christian & Schoen, Matthias, 2018. "Interorganizational trust and agency costs in credit relationships between savings banks and SMEs," Journal of Banking & Finance, Elsevier, vol. 97(C), pages 37-50.
    28. Jeremy Burke & Angela A. Hung & Jack Clift & Steven Garber & Joanne K. Yoong, 2015. "Impacts of Conflicts of Interest in the Financial Services Industry," Working Papers WR-1076, RAND Corporation.
    29. Gregor Dorfleitner & Lars Hornuf & Matthias Schmitt & Martina Weber, 2017. "FinTech in Germany," Springer Books, Springer, number 978-3-319-54666-7, November.
    30. Xinshu Zhao & John G. Lynch & Qimei Chen, 2010. "Reconsidering Baron and Kenny: Myths and Truths about Mediation Analysis," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 37(2), pages 197-206, August.
    31. Adusei, Michael & Gyapong, Eddie Yaw, 2017. "The impact of macroeconomic variables on exchange rate volatility in Ghana: The Partial Least Squares Structural Equation Modelling approach," Research in International Business and Finance, Elsevier, vol. 42(C), pages 1428-1444.
    32. Heungsun Hwang & Yoshio Takane, 2004. "Generalized structured component analysis," Psychometrika, Springer;The Psychometric Society, vol. 69(1), pages 81-99, March.
    33. Jeremy Burke & Angela Hung, 2015. "Trust and Financial Advice," Working Papers 1075, RAND Corporation.
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    More about this item

    Keywords

    FinTech; Robo advisory; Technology acceptance; Disruption; Partial least squares (PLS); Generalized structured component analysis (GSCA);
    All these keywords.

    JEL classification:

    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets
    • O14 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Industrialization; Manufacturing and Service Industries; Choice of Technology
    • O16 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Financial Markets; Saving and Capital Investment; Corporate Finance and Governance
    • O39 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Other

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