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Demographic and Socio-Economic Factors as Barriers to Robo-Advisory Acceptance in Poland
Abstract
Theoretical background: One manifestation of the use of artificial intelligence technology in financial services is robo-advisory. Automated assistants are used in the area of communication with consumers and the sale of financial products. The development of robo-advisory services may contribute to increasing the availability of financial services and the cost efficiency of banks’ operations. So far, however, robo-advisory has not been widely used in bank services, and the reasons for this can be seen in the lack of wide acceptance of robo-advisory by bank customers, among other things.
Purpose of the article: The aim of this paper is to identify barriers to the acceptance of robo-advisory in the services of banks operating in Poland. Variables relating to the demographic and socio-economic characteristics of consumers were analysed. Knowledge in this area can provide banks with a practical guideline for activities aimed at increasing acceptance of artificial intelligence technology and wider use of robo-advisory in financial services.
Research methods: The paper uses the results of a survey conducted in October 2020 regarding the application of artificial intelligence technology in the banking sector in Poland. The survey included a representative sample of 911 Polish citizens aged 18–65. A multinomial logit model was employed to identify variables that represent significant barriers to robo-advisory acceptance in financial services.
Main findings: The conducted research helped identify the barriers to acceptance of robo-advisory among consumers in Poland. A low propensity to use robo-advisory in bank services is characteristic of respondents from older age groups, as well as those who do not show a predilection for testing new technological solutions. Lack of experience in using investment advisory services and customer concerns about the misuse of personal data by banks are also significant barriers.
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Abraham, F., Schmukler, S.L., & Tessada, J. (2019). Robo-advisors: Investing through machines. Retrieved from https://documents1.worldbank.org/curated/en/275041551196836758/pdf/Robo-Advisors-Investing-through-Machines.pdf
Australian Council for Educational Research. (2016). A global measure of digital and ICT literacy skills. Retrieved from https://unesdoc.unesco.org/ark:/48223/pf0000245577
Bahrammirzaee, A. (2010). A comparative survey of artificial intelligence applications in finance: Artificial neural networks, expert system and hybrid intelligent systems. Neural Computing & Applications, 19, 1165–1195. doi:10.1007/s00521-010-0362-z
Balasubramnian, B., & Brisker, E.R. (2016). Financial adviser users and financial literacy. Financial Services Review, 25(2), 127–155.
Balcewicz, J. (2019). Sztuczna Inteligencja godna zaufania – rekomendacje ekspertów KE. Retrieved from https://cyberpolicy.nask.pl/sztuczna-inteligencja-godna-zaufania-rekomendacje-grupy-ekspertow-komisji-europejskiej/
Bartlett, M. (2021). Beyond privacy: Protecting data interests in the age of artificial intelligence. Law, Technology and Humans, 3(1), 96–108. doi:10.5204/lthj.1595
Bąk, A., & Bartłomowicz, T. (2014), Wielomianowe modele logitowe wyborów dyskretnych i ich implementacja w pakiecie DiscreteChoice programu R. Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, 327, 85–94.
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. doi:10.1108/IMDS-08-2018-0368
Black, N.J., Lockett, A., Ennew, C., Winklhofer, H., & McKechnie, S. (2002). Modelling consumer choice of distribution channels: An illustration from financial services. International Journal of Bank Marketing, 20(4), 161–173. doi:10.1108/02652320210432945
Brenner, L., & Meyll, T. (2020). Robo-advisors: A substitute for human financial advice? Journal of Behavioral and Experimental Finance, 25, 1–18. doi:10.1016/j.jbef.2020.100275
Bruckes, M., Westmattelmann, D., Oldeweme, A., & Schewe, G. (2019). Determinants and barriers of adopting robo-advisory services. ICIS 2019 Proceedings, 2. Retrieved from https://aisel.aisnet.org/icis2019/blockchain_fintech/blockchain_fintech/2
Buvat, J., Yardi, A., Girard, S., KVJ, S., Taylor, M., Thieullent, A.-L., Gadri, G., Sengupta, A., & Khemka, Y. (2018). The secret to winning customers’ hearts with artificial intelligence. Add human intelligence. Retrieved from https://www.capgemini.com/in-en/wp-content/uploads/sites/6/2018/07/DTI-AI-in-CX_V06-3.pdf
Calcagno, R., & Monticone, C. (2015). Financial literacy and the demand for financial advice. Journal of Banking and Finance, 50, 363–380. doi:10.1016/j.jbankfin.2014.03.013
Calzolari, G. (2021). Artificial intelligence market and capital flows. Artificial intelligence and the financial sector at crossroads. Retrieved from http://www.europarl.europa.eu/supporting-analyses
Cameron, A.C., & Trivedi, P.K. (2009). Microeconometrics. Methods and Applications. New York: Cambridge University Press.
Deloitte. (2020). Digital Banking Maturity 2020. Jaka jest reakcja banków na (r)ewolucję cyfrową? Retrieved from https://www2.deloitte.com/pl/pl/pages/financial-services/articles/digital-banking-maturity-2020.html
Dutot, V. (2015). Factors influencing near field communication (NFC) adoption: An extended TAM approach. Journal of High Technology Management Research, 26, 45–57. doi:10.1016/j.hitech.2015.04.005
Dziawgo, T. (2018). Wealth management market in China. Opportunities and challenges. Copernican Journal of Finance & Accounting, 7(4), 47–57. doi:10.12775/CJFA.2018.019
FSB. (2017). Artificial Intelligence and Machine Learning in Financial Services. Retrieved from https://www.fsb.org/2017/11/artificial-intelligence-and-machine-learning-in-financial-service/
Fulk, M., Grable, J.E., Watkins, K., & Kruger, M. (2018). Who uses robo-advisory services, and who does not? Financial Services Review, 27, 173–188.
Füller, J., Hutter, K., Wahl, J., Bilgram, V., & Tekic, Z. (2022). How AI revolutionizes innovation management – perceptions and implementation preferences of AI-based innovators. Technological Forecasting and Social Change, 178, 1–22. doi:10.1016/j.techfore.2022.121598
Gan, L.Y., Khan, M.T.I., & Liew, T.W. (2021). Understanding consumer’s adoption of financial robo-advisors at the outbreak of the COVID-19 crisis in Malaysia. Financial Planning Review, 4, 1–18. doi:10.1002/cfp2.1127
GDPR. (2016). Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). Retrieved from https://eur-lex.europa.eu/eli/reg/2016/679/oj
Gerlach, J.M., & Lutz, J.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, 117, 106009. doi:10.1016/j.jeconbus.2021.106009
Go, E.J., Moon, J., & Kim, J. (2020). Analysis of the current and future of the artificial intelligence in financial industry with big data techniques. Global Business & Finance Review, 25(1), 102–117. doi:10.17549/gbfr.2020.25.1.102
Harlow, W., Brown, K.C., & Jenks, S.E. (2020). The use and value of financial advice for retirement planning. Journal of Retirement, 7(3), 46–79. doi:10.3905/jor.2019.1.060
Hohenberger, Ch., Lee, Ch., & Coughlin, J. (2019). Acceptance of robo‐advisors: Effects of financial experience, affective reactions, and self‐enhancement motives. Financial Planning Review, 2, 1–14. doi:10.1002/cfp2.1047
Huang, M-H., Rust, R., & Maksimovic, V. (2019). The feeling economy: Managing in the next generation of artificial intelligence (AI). California Management Review, 61(4), 43–65. doi:10.1177/0008125619863
Jung, D., Dorner, V., Weinhardt, C., & Pusmaz, H. (2018). Designing a robo-advisor for risk-averse, low-budget consumers. Electronic Markets, 28, 367–380. doi:10.1007/s12525-017-0279-9
Karjaluoto, H., Mattila, M., & Pento, T. (2002). Factors underlying attitude formation towards online banking in Finland. International Journal of Bank Marketing, 20(6), 261–272. doi:10.1108/02652320210446724
Kidd, Ch., & Saxena, B. (2021). NLP vs NLU: What’s the difference? Retrieved from https://www.bmc.com/blogs/nlu-vs-nlp-natural-language-understanding-processing/
Klapper, L., Lusardi, A., & van Oudheusden, P. (2015). Financial Literacy Around the World: Insights from The Standard & Poor’s Ratings Services Global Financial Literacy Survey. Retrieved from https://gflec.org/wp-content/uploads/2015/11/3313-Finlit_Report_FINAL-5.11.16.pdf?x28160
Königsheim, Ch., Lukas, M., & Nöth, M. (2017). Financial knowledge, risk preferences, and the demand for digital financial services. Schmalenbach Business Review, 18(4), 343–375. doi:10.1007/s41464-017-0040-0
Königstorfer, F., & Thalmann, S. (2020). Applications of artificial intelligence in commercial banks – a re-search agenda for behavioral finance. Journal of Behavioral and Experimental Finance, 27, 1–15. doi:10.1016/j.jbef.2020.100352
Krasonikolakis, I., Tsarbopoulos, M., & Eng, T.-Y. (2020). Are incumbent banks bygones in the face of digital transformation? Journal of General Management, 46, 60–69. doi:10.1177/0306307020937883
Lachance, M.-E., & Tang, N. (2012). Financial advice and trust. Financial Services Review, 21, 209–226.
Laukkanen, T., & Pasanen, M. (2008). Mobile banking innovators and early adopters: How they differ from other online users? Journal of Financial Services Marketing, 13(2), 86–94. doi:10.1057/palgrave.fsm.4760077
Lee, C., Ward, C., Raue, M., D’Ambrosio, L., & Coughlin, J.F. (2017). Age differences in acceptance of self-driving cars: A survey of perceptions and attitudes. In J. Zhou & G. Salvendy (Eds.), Human aspects of IT for the Aged Population. Aging, Design and User Experience. ITAP 2017. Lecture Notes in Computer Science, 10297. Cham: Springer. doi:10.1007/978-3-319-58530-7_1
Liang, D., Lau, N., Baker, S.A., & Antin, J.F. (2020). Examining senior drivers’ attitudes toward advanced driver assistance systems after naturalistic exposure. Innovation in Aging, 4(3), 1–12. doi:10.1093/geroni/igaa017
Liao, S.-Ch., Chou, T.-Ch., & Huang, Ch.-H. (2022). Revisiting the development trajectory of the digital divide: A main path analysis approach, Technological Forecasting and Social Change, 179, 121607. doi:10.1016/j.techfore.2022.121607
Lourenco, C.J., Dellaert, B.G., & Donkers, B. (2020). Whose algorithm says so: The relationships between type of firm, perceptions of trust and expertise, and the acceptance of financial robo-advice. Journal of Interactive Marketing, 49, 107–124. doi:10.1016/j.intmar.2019.10.003
Mhlanga, D. (2020). Industry 4.0 in finance: The impact of artificial intelligence (AI) on digital financial inclusion. International Journal of Financial Studies, 8(3), 1–14. doi:10.3390/ijfs8030045
Muravyeva, E., Janssen, J., Specht, M., & Custers, B. (2020). Exploring solutions to the privacy paradox in the context of e-assessment: Informed consent revisited. Ethics and Information Technology, 22, 223–238. doi:10.1007/s10676-020-09531-5
Nowak, K. (2017). Low cost retirement solutions based on robo-advisors and exchange traded funds. Copernican Journal of Finance & Accounting, 6(3), 75–94. doi:10.12775/ CJFA.2017.018
OECD. (2021). Artificial Intelligence, Machine Learning and Big Data in Finance: Opportunities, Challenges, and Implications for Policy Makers. Retrieved from https://www.oecd.org/finance/artificial-intelligence-machine-learning- big-data-in-finance.htm
Polasik, M., & Piotrowski, D. (2016). Payment innovations in Poland: A new approach of the banking sector to introducing payment solutions. Ekonomia i Prawo, 15(1), 103–131. doi:10.12775/EiP.2016.007
Powers, D.A., & Xie, Y. (2008). Statistical Methods for Categorical Data Analysis. Bingley: Emerald.
Pramanik, H.S., Kirtania, M., & Pani, A.K. (2019). Essence of digital transformation – manifestations at large financial institutions from North America. Future Generation Computer Systems, 95, 323–343. doi:10.1016/j.future.2018.12.003
Rogers, E. (2003). Diffusion of Innovations. New York: The Free Press.
Rogowski, W. (2017). Świt wirtualnego doradztwa finansowego (robo-advisor). E-mentor, 4(71), 53–63. doi:10.15219/em71.1315
Rühr, A. (2020). Robo-advisor configuration: An investigation of user preferences and the performance-control dilemma. Research Papers, 94. Retrieved from https://aisel.aisnet.org/ecis2020_rp/94
Schöler, J., Ostern, N., & Moormann, J. (2020). Toward voice-enabled robotic advisory for personalized wealth management. Banking & Information Technology, 21(2), 45–55.
Seiler, V., & Fanenbruck, K.M. (2021). Acceptance of digital investment solutions: The case of robo advisory in Germany. Research in International Business and Finance, 58, 1–14. doi:10.1016/j.ribaf.2021.101490
Sinha, I., & Mukherjee, S. (2016). Acceptance of technology, related factors in use of off branch e-banking: An Indian case study. The Journal of High Technology Management Research, 27(1), 88–100. doi:10.1016/j.hitech.2016.04.008
UKNF. (2020). Stanowisko Urzędu Komisji Nadzoru Finansowego w sprawie świadczenia usługi robo-doradztwa. Projekt. Retrieved from https://www.knf.gov.pl/knf/pl/komponenty/img/Stanowisko_UKNF_ws_robo-doradztwa_projekt__69671.pdf
Van Raaij, W.F. (2017). Explaining customer experience of digital financial advice. Economics World, 5(1), 69–84. doi:10.17265/2328-7144/2017.01.007
Van Rooy, D., & Bus, J. (2010). Trust and privacy in the future internet – a research perspective. Identity in the Information Society, 3, 397–404. doi:10.1007/s12394-010-0058-7
Waliszewski, K. (2020). Robo-doradztwo jako przykład fin-techu – problem regulacji i funkcjonowania. Business Law Journal, LXXIII(7), 12–20. doi:10.33226/0137-5490.2020.7.2
Waliszewski, K., & Warchlewska, A. (2020). Socio-demographic factors determining expectation experienced while using modern technologies in personal financial management (PFM and robo-advice): A Polish case. European Research Studies Journal, XXIII(2), 893–904. doi:10.35808/ersj/1904
Warchlewska, A., & Waliszewski, K. (2020). Who uses robo-advisors? The Polish case. European Research Studies Journal, XXIII(1), 97–114. doi:10.35808/ersj/1748
Yang, K., & Forney, J.C. (2013). The moderating role of consumer technology anxiety in mobile shopping adoption: differential effects of facilitating conditions and social influences. Journal of Electronic Commerce Research, 14, 334–347.
ZBP. (2020). Sztuczna inteligencja w bankowości. Retrieved from https://alebank.pl/wp-content/uploads/2020/06/Raport-SZTUCZNA-INTELIGENCJA.pdf
DOI: http://dx.doi.org/10.17951/h.2022.56.3.109-126
Date of publication: 2022-12-09 11:19:10
Date of submission: 2022-05-01 15:03:08
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