Artificial intelligence and customers’ intention to use robo-advisory in banking services

Authors

DOI:

https://doi.org/10.24136/eq.2023.031

Keywords:

robo-advisory, artificial intelligence in banking, financial advisory, machine learning, business ethics

Abstract

Research background: Robo-advisory is a modern and rapidly developing area of implementing artificial intelligence to support customer decision-making. The current significance of robo-advisory to the financial sector is minor or marginal, and boils down to formulating recommendations and implementing investment strategies. However, the ongoing digital transformation of the economy leads us to believe that in the near future this technology will also be much more widely used with banking products. This makes it necessary for banks and other financial institutions to be prepared to offer this service to their customers. 

Purpose of the article: The aim of this paper is to identify factors significantly influencing bank customers’ intention to use robo-advisory. Identification of robo-advisory acceptance factors may increase the effectiveness of banks' promotional activities regarding such a service.

Methods: Empirical data was obtained through a survey conducted on a representative sample of 911 Polish respondents aged 18–65. Using a multilevel ordered logit model and methods based on machine learning algorithms, the authors identified variables relating to the demographic and socio-economic characteristics, behaviors, and attitudes of consumers that primarily determine respondents’ adoption of robo-advisory.

Findings & value added: The results of the study indicate that the variables regarding the respondents' attitude towards the use of artificial intelligence in banking services turned out to be the most important from the point of view of acceptance of robo-advisory. Next in terms of importance were the variables presenting respondents' assessments of the ethics of financial services. An important finding is that experience in using basic financial services is not a significant factor when accepting robo-advisory. From the practical perspective, the article provides recommendations on the use of artificial intelligence technology in finance and ethical aspects of the provision of such services by banks.

Downloads

Download data is not yet available.

Author Biography

Witold Orzeszko, Nicolaus Copernicus University in Torun

ORZESZKO WITOLD received a Ph.D. degree in Economics from the Nicolaus Copernicus University in Toruń, Poland, in 2005. He is currently a professor at the Faculty of Economic Sciences and Management, Nicolaus Copernicus University and head of the Department of Applied Informatics and Mathematics in Economics. His principal areas of research are financial econometrics, nonparametric statistics and machine learning techniques.

References

Adamek, J., & Solarz, M. (2023). Adoption factors in digital lending services offered by FinTech lenders. Oeconomia Copernicana, 14(1), 169–212.

DOI: https://doi.org/10.24136/oc.2023.005
View in Google Scholar

Ahmed, S., Alshater, M., Ammari, A., & Hammami, H. (2022). Artificial Intelligence and machine learning in finance: A bibliometric review. Research in International Business and Finance, 61, 101646.

DOI: https://doi.org/10.1016/j.ribaf.2022.101646
View in Google Scholar

Alsabah, H., Capponi, A., Lacedelli, O. R., & Stern. M. (2021). Robo-advising: Learning investors’ risk preferences via portfolio choices. Journal of Financial Econometrics, 19(2), 369–392.

DOI: https://doi.org/10.1093/jjfinec/nbz040
View in Google Scholar

Bejger, S., & Fiszeder, P. (2021). Forecasting currency covariances using machine learning tree-based algorithms with low and high prices. Przegląd Statystyczny. Statistical Review, 68(3), 1–15.

DOI: https://doi.org/10.5604/01.3001.0015.5582
View in Google Scholar

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: https://doi.org/10.1108/IMDS-08-2018-0368
View in Google Scholar

Better Finance (2020). Robo-advice 5.0: can consumers trust robots? Retrieved from https://betterfinance.eu/wp-content/uploads/Robo-Advice-Report-2020-2501202 1.pdf (12.06.2023).
View in Google Scholar

Breiman, L. (1996). Bagging predictors. Machine Learning, 24, 123–140.

DOI: https://doi.org/10.1007/BF00058655
View in Google Scholar

Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.

DOI: https://doi.org/10.1023/A:1010933404324
View in Google Scholar

Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017). Classification and regression trees. Routledge.

DOI: https://doi.org/10.1201/9781315139470
View in Google Scholar

Brenner, L., & Meyll, T. (2020). Robo-advisors: A substitute for human financial advice? Journal of Behavioral and Experimental Finance, 25, 100275.

DOI: https://doi.org/10.1016/j.jbef.2020.100275
View in Google Scholar

Bruckes, M., Westmattelmann, D., Oldeweme, A., & Schewe, G. (2019). Determinants and barriers of adopting robo-advisory services. Retrieved from https://aisel.aisnet.org/icis2019/blockchain_fintech/blockchain_fintech/2 (28.05.2023).
View in Google Scholar

Bühlmann, P. (2012). Bagging, boosting and ensemble methods. In J. Gentle, W. Härdle & Y. Mori (Eds.). Handbook of computational statistics. Springer handbooks of computational statistics (pp. 985–1022). Heidelberg: Springer.

DOI: https://doi.org/10.1007/978-3-642-21551-3_33
View in Google Scholar

Bühlmann, P., & Yu, B. (2002). Analyzing bagging. Annals of Statistics, 30(4), 927–961.

DOI: https://doi.org/10.1214/aos/1031689014
View in Google Scholar

Cheng, X., Guo, F., Chen, J., Li, K., Zhang, Y., & Gao, P. (2019). Exploring the trust influencing mechanism of robo-advisor service: A mixed method approach. Sustainability, 11(18), 4917.

DOI: https://doi.org/10.3390/su11184917
View in Google Scholar

Cottrell, A., & Lucchetti, R. (2022). Gretl user’s guide. Gnu regression, econometrics and time-series library. Retrieved from http://gretl.sourceforge.net/gretl-help/gretl-guide.pdf (5.06.2023).
View in Google Scholar

D’Acunto, F., Prabhala, N., & Rossi, A. G. (2019). The promises and pitfalls of robo-advising. Review of Financial Studies, 32(5). 1983–2020.

DOI: https://doi.org/10.1093/rfs/hhz014
View in Google Scholar

D'Acunto, F., & Rossi, A. G. (2021). Robo‐advising. In R. Rau, R. Wardrop & L. Zingales (Eds.). The Palgrave handbook of technological finance (pp. 725–749). Cham: Palgrave Macmillan.

DOI: https://doi.org/10.1007/978-3-030-65117-6_26
View in Google Scholar

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

DOI: https://doi.org/10.2307/249008
View in Google Scholar

Day, M., Cheng T., & Li, J. (2018). AI robo-advisor with Big Data analytics for financial services. Retrieved from https://doi.org/10.1109/ASONAM.2018.8508854 (28.05.2023)

DOI: https://doi.org/10.1109/ASONAM.2018.8508854
View in Google Scholar

Doumpos, M., Zopounidis, C., Gounopoulos, D., Platanakis, E., & Zhang, W. (2023). Operational research and artificial intelligence methods in banking. European Journal of Operational Research, 306(1), 1–16. doi: 10.1016/j.ejor.2022.04.027.

DOI: https://doi.org/10.1016/j.ejor.2022.04.027
View in Google Scholar

European Supervisory Authorities (2015). Joint Committee discussion paper on automation in financial advice. Retrieved from https://www.eba.europa.eu/ sites/default/documents/files/documents/10180/1299866/b7e305c8-9383-4c46-a80 0-b0deb1e5b2a2/JC%202015%20080%20Discussion%20Paper%20on%20automat ion%20in%20financial%20advice.pdf?retry=1 (28.05.2023).
View in Google Scholar

Fan, L., & Chatterjee, S. (2020). The utilization of robo-advisors by individual investors: an analysis using diffusion of innovation and information search frameworks. Journal of Financial Counseling and Planning, 31(1). 130–145.

DOI: https://doi.org/10.1891/JFCP-18-00078
View in Google Scholar

Fernandes, T., & Oliveira, E. (2021). Understanding consumers’ acceptance of automated technologies in service encounters: Drivers of digital voice assistants adoption. Journal of Business Research, 122, 180–191.

DOI: https://doi.org/10.1016/j.jbusres.2020.08.058
View in Google Scholar

Ferreira, A. J., & Figueiredo, M. A. T. (2012). Boosting algorithms: A review of methods, theory, and applications. In C. Zhang & Y. Ma (Eds.). Ensemble machine learning: Methods and applications (pp. 35–85). New York: Springer.

DOI: https://doi.org/10.1007/978-1-4419-9326-7_2
View in Google Scholar

Fiszeder, P., & Orzeszko, W. (2021), Covariance matrix forecasting using support vector regression. Applied Intelligence, 51, 7029–7042.

DOI: https://doi.org/10.1007/s10489-021-02217-5
View in Google Scholar

Foerster, S., Linnainmaa, J. T., Melzer, B. T., & Previtero, A. (2017). Retail financial advice: Does one size fit all? Journal of Finance, 72(4), 1441–1482.

DOI: https://doi.org/10.1111/jofi.12514
View in Google Scholar

Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of online-learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.

DOI: https://doi.org/10.1006/jcss.1997.1504
View in Google Scholar

Fulk, M., Grable, J. E., Watkins, K., & Kruger, M. (2018). Who uses robo-advisory services, and who does not? Financial Services Review, 27(2), 173–188.

DOI: https://doi.org/10.61190/fsr.v27i2.3390
View in Google Scholar

Gallego-Losada, M-J., Montero-Navarro, A., García-Abajo, E., & Gallego-Losada, R. (2023). Digital financial inclusion. Visualizing the academic literature. Research in International Business and Finance, 64, 101862.

DOI: https://doi.org/10.1016/j.ribaf.2022.101862
View in Google Scholar

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: https://doi.org/10.1002/cfp2.1127
View in Google Scholar

Gerlach, J. M., & Lutz, J. K. (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: https://doi.org/10.1016/j.jeconbus.2021.106009
View in Google Scholar

Glaser, F., Iliewa, Z., Jung, D., & Weber, M. (2019). Towards designing robo-advisors for unexperienced investors with experience sampling of time-series data. In F. Davis, R. Riedl, J. vom Brocke, P. M. Léger & A. Randolph (Eds.). Information systems and neuroscience. Lecture notes in information systems and organisation (pp. 133–138). Cham: Springer.

DOI: https://doi.org/10.1007/978-3-030-01087-4_16
View in Google Scholar

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. New York: Springer.

DOI: https://doi.org/10.1007/978-0-387-84858-7
View in Google Scholar

Harasim, J. (2021). FinTechs, BigTechs and structural changes in capital markets. In A. Marszk & E. Lechman (Eds.). The digitalization of financial markets the socioeconomic impact of financial technologies (pp. 81–100). New York: Routledge.

DOI: https://doi.org/10.4324/9781003095354-5
View in Google Scholar

Helms, N., Hölscher, R., & Nelde, M. (2021). Automated investment management: Comparing the design and performance of international robo-managers. European Financial Management, 28(4), 1028–1078.

DOI: https://doi.org/10.1111/eufm.12333
View in Google Scholar

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: https://doi.org/10.1002/cfp2.1047
View in Google Scholar

Hussain, S., Gul, R., & Ullah, S. (2023). Role of financial inclusion and ICT for sustainable economic development in developing countries. Technological Forecasting and Social Change, 194. 122725.

DOI: https://doi.org/10.1016/j.techfore.2023.122725
View in Google Scholar

James, G., Witten D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning with applications in R. New York: Springer.

DOI: https://doi.org/10.1007/978-1-0716-1418-1
View in Google Scholar

Jung, D., Dorner, V., Weinhardt, Ch., & Pusmaz, H. (2018). Designing a robo-advisor for risk-averse, low-budget consumers. Electronic Markets, 28(3), 367–380.

DOI: https://doi.org/10.1007/s12525-017-0279-9
View in Google Scholar

Jung, D., Glaser, F., & Köpplin, W. (2019). Robo-advisory: Opportunities and risks for the future of financial advisory. In V. Nissen (Ed.). Advances in consulting research. Recent findings and practical cases (pp. 405–427). Cham: Springer.

DOI: https://doi.org/10.1007/978-3-319-95999-3_20
View in Google Scholar

Kordela, D. (2018). Robo-advisors in asset management – the experience from Germany. European Journal of Service Management, 28(1), 151–157.

DOI: https://doi.org/10.18276/ejsm.2018.28/1-19
View in Google Scholar

Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. New York: Springer.

DOI: https://doi.org/10.1007/978-1-4614-6849-3
View in Google Scholar

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: https://doi.org/10.1016/j.techfore.2022.121607
View in Google Scholar

Lourenço, C. J. S., Dellaert, B. G. C., & 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: https://doi.org/10.1016/j.intmar.2019.10.003
View in Google Scholar

Maume, P. (2021). Robo-advisors: How do they fit in the existing EU regulatory framework, in particular with regard to investor protection? Retrieved from https://www.europarl.europa.eu/RegData/etudes/STUD/2021/662928/IPOL_STU(2021)662928_EN.pdf (5.06.2023).
View in Google Scholar

Matuszewska-Janica, A., & Witkowska, D. (2021). Differences between determinants of men and women monthly wages across fourteen European Union states. Equilibrium. Quarterly Journal of Economics and Economic Policy, 16(3), 503–531.

DOI: https://doi.org/10.24136/eq.2021.018
View in Google Scholar

Małkowska, A., Urbaniec, M., & Kosała, M. (2021). The impact of digital transfor-mation on European countries: Insights from a comparative analysis. Equilibrium. Quarterly Journal of Economics and Economic Policy, 16(2), 325–355.

DOI: https://doi.org/10.24136/eq.2021.012
View in Google Scholar

Mehdiabadi, A., Shahabi, V., Shamsinejad, S., Amiri, M., Spulbar, C., & Birau, R. (2022). Investigating industry 5.0 and its impact on the banking industry: Requirements, approaches and communications. Applied Sciences, 12(10), 5126.

DOI: https://doi.org/10.3390/app12105126
View in Google Scholar

Milani, A. (2019). The role of risk and trust in the adoption of robo-advisory in Italy. PricewaterhouseCoopers Advisory. Retrieved from https://www.pwc.com/it/it/ publications/assets/docs/Report-robo-advisors.pdf (28.05.2023).
View in Google Scholar

Ngo-Ye, T. L., Choi, J. J., & Cummings, M. (2018). Modeling the robo-advisor ecosystem: Insights from a simulation study. Issues in Information Systems, 19(1), 128–138.
View in Google Scholar

Nguyen, T. P. L., Chew, L. W., Muthaiyah, S., Teh, B. H., & Ong, T. S. (2023). Factors influencing acceptance of robo-advisors for wealth management in Malaysia. Cogent Engineering, 10, 2188992,

DOI: https://doi.org/10.1080/23311916.2023.2188992
View in Google Scholar

Niszczota, P., & Kaszás, D. (2020). Robo-investment aversion. PLoS ONE, 15(9), e0239277.

DOI: https://doi.org/10.1371/journal.pone.0239277
View in Google Scholar

Nourallah, M. (2023). One size does not fit all: Young retail investors’ initial trust in financial robo-advisors. Journal of Business Research, 156, 113470.

DOI: https://doi.org/10.1016/j.jbusres.2022.113470
View in Google Scholar

Nourallah, M., Öhman, P., & Amin, M. (2023). No trust, no use: How young retail investors build initial trust in financial robo-advisors. Journal of Financial Reporting and Accounting, 21(1), 60–82.

DOI: https://doi.org/10.1108/JFRA-12-2021-0451
View in Google Scholar

Olejnik, S., Mills J., & Keselman, H. (2000). Using Wherry's adjusted R2 and Mallow's Cp for model selection from all possible regressions. Journal of Experimental Education, 68(4), 365–380.

DOI: https://doi.org/10.1080/00220970009600643
View in Google Scholar

Orzeszko, W. (2021). Nonlinear causality between crude oil prices and exchange rates: Evidence and forecasting. Energies, 14, 6043.

DOI: https://doi.org/10.3390/en14196043
View in Google Scholar

Papík, M., & Papíková, L. (2021). Application of selected data mining techniques in unintentional accounting error detection. Equilibrium. Quarterly Journal of Economics and Economic Policy, 16(1), 185–201.

DOI: https://doi.org/10.24136/eq.2021.007
View in Google Scholar

Phoon, K., & Koh, F. (2018). Robo-advisors and wealth management. Journal of Alternative Investments, 20(3), 79–94.

DOI: https://doi.org/10.3905/jai.2018.20.3.079
View in Google Scholar

Piotrowski, D. (2022). Demographic and socio-economic factors as barriers to robo-advisory acceptance in Poland. Annales Universitatis Mariae Curie-Skłodowska, section H – Oeconomia, 56(3), 109–126.

DOI: https://doi.org/10.17951/h.2022.56.3.109-126
View in Google Scholar

Purewal, K., & Haini, H. (2022). Re‑examining the effect of financial markets and institutions on economic growth: Evidence from the OECD countries. Economic Change and Restructuring, 55, 311–333.

DOI: https://doi.org/10.1007/s10644-020-09316-2
View in Google Scholar

Rodrigues, A., Ferreira, F., Teixeira, F., & Zopounidis, C. (2022). Artificial intelligence, digital transformation and cybersecurity in the banking sector: A multi-stakeholder cognition-driven framework. Research in International Business and Finance, 60, 101616.

DOI: https://doi.org/10.1016/j.ribaf.2022.101616
View in Google Scholar

Rogers, E. M. (2003). Diffusion of innovations. New York: Free Press.
View in Google Scholar

Rühr, A. (2020). Robo-advisor configuration: An investigation of user preferences and the performance-control dilemma. Retrieved from https://aisel.aisnet.org/ ecis2020_rp/94 (26.05.2023).
View in Google Scholar

Rühr, A., Berger, B., & Hess, T. (2019). Can I control my robo-advisor? Trade-offs in automation and user control in (digital) investment management. Proceedings of the 25th Americas Conference on Information Systems (AMCIS 2019). Retrieved from https://aisel.aisnet.org/amcis2019/cognitive_in_is/cognitive_in_is/2 (5.06.2023).
View in Google Scholar

Sabir, A. A., Ahmad, I., Ahmad, H., Rafiq, M., Khan, M. A., & Noreen, N. (2023). Consumer acceptance and adoption of AI robo-advisors in fintech industry. Mathematics, 11(6), 1311.

DOI: https://doi.org/10.3390/math11061311
View in Google Scholar

Seiffert, C., Khoshgoftaar, T. M., Van Hulse, J., & Napolitano, A. (2010). RUSBoost: A hybrid approach to alleviating class imbalance. IEEE Transactions on Systems Man and Cybernetics – Part A Systems and Humans, 40(1), 185–197.

DOI: https://doi.org/10.1109/TSMCA.2009.2029559
View in Google Scholar

Seiler, V., & Fanenbruck, K. (2021). Acceptance of digital investment solutions: The case of robo advisory in Germany. Research in International Business and Finance, 58. 101490.

DOI: https://doi.org/10.1016/j.ribaf.2021.101490
View in Google Scholar

Statista (2023). Assets under management of robo-advisors worldwide from 2018 to 2027. Retrieved from https://www.statista.com/forecasts/1262614/robo-advisors-managing-assets-worldwide (15.06.2023).
View in Google Scholar

Śliwiński, P. (2023). Endogenous money supply, global liquidity and financial transactions: Panel evidence from OECD countries. Equilibrium. Quarterly Journal of Economics and Economic Policy, 18(1), 121–152.

DOI: https://doi.org/10.24136/eq.2023.004
View in Google Scholar

Tanha, J., Abdi Y., Samadi, N., Razzaghi, N., & Asadpour, M. (2020). Boosting methods for multi‑class imbalanced data classification: An experimental review. Journal of Big Data, 7(70), 1–47.

DOI: https://doi.org/10.1186/s40537-020-00349-y
View in Google Scholar

Tao, R., Su, Ch-W., Xiao, Y., Dai, K., & Khalid, F. (2021). Robo advisors, algorithmic trading and investment management: Wonders of fourth industrial revolution in financial markets. Technological Forecasting and Social Change, 163(3), 120421.

DOI: https://doi.org/10.1016/j.techfore.2020.120421
View in Google Scholar

U. S. Securities and Exchange Commission (2017). Investor bulletin: Robo-Advisers. Retrieved from https://www.investor.gov/introduction-investing/general-resour ces/news-alerts/alerts-bulletins/investor-bulletins-45 (26.05.2023).
View in Google Scholar

Uhl, M. W., & Rohner, P. (2018). Robo-advisors versus traditional investment advisors: An unequal game. Journal of Wealth Management, 21(1), 44–50.

DOI: https://doi.org/10.3905/jwm.2018.21.1.044
View in Google Scholar

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

DOI: https://doi.org/10.2307/30036540
View in Google Scholar

Waliszewski, K. (2022). Managing personal finance by robo-advice users during the Covid-19 pandemic and in the post-pandemic period. A comparative analysis of Poland and Slovakia. Scientific Papers of Silesian University of Technology. Organization and Management Series, 158, 623–645.

DOI: https://doi.org/10.29119/1641-3466.2022.158.41
View in Google Scholar

Waliszewski, K., Cichowicz, E., Gębski, Ł., Kliber, F., Kubiczek, J., Niedziółka, P., Solarz, M., & Warchlewska, A. (2023). The role of the Lendtech sector in the consumer credit market in the context of household financial exclusion. Oeconomia Copernicana, 14(2), 609–643.

DOI: https://doi.org/10.24136/oc.2023.017
View in Google Scholar

Warchlewska, A., & Waliszewski, K. (2020). Who uses robo-advisors? The Polish case. European Research Studies Journal, XXIII(1), 97–114.

DOI: https://doi.org/10.35808/ersj/1748
View in Google Scholar

Warmuth, M. K., Liao, J., & Rätsch, G. (2006). Totally corrective boosting algorithms that maximize the margin. In W. W. Cohen & A. Moore (Eds.). ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning (pp. 1001–1008). Madison: Association for Computing Machinery.

DOI: https://doi.org/10.1145/1143844.1143970
View in Google Scholar

Wirtz, J., Patterson, P., Kunz, W., Gruber, T., Lu, V., Paluch, S., & Martins, A. (2018). Brave new world: Service robots in the frontline. Journal of Service Management, 29(5), 907–931.

DOI: https://doi.org/10.1108/JOSM-04-2018-0119
View in Google Scholar

Yi, T. Z., Rom, N. A. M., Hassan, N. M., Samsurijan, M. S., & Ebekozien, A. (2023). The adoption of robo-advisory among millennials in the 21st century: Trust, usability and knowledge perception. Sustainability, 15(7), 6016.

DOI: https://doi.org/10.3390/su15076016
View in Google Scholar

Zhang, L., Pentina, I., & Fan, Y. (2021). Who do you choose? Comparing perceptions of human vs robo-advisor in the context of financial services. Journal of Services Marketing, 35(5), 634–646.

DOI: https://doi.org/10.1108/JSM-05-2020-0162
View in Google Scholar

Downloads

Published

2023-12-30

How to Cite

Piotrowski, D., & Orzeszko, W. (2023). Artificial intelligence and customers’ intention to use robo-advisory in banking services. Equilibrium. Quarterly Journal of Economics and Economic Policy, 18(4), 967–1007. https://doi.org/10.24136/eq.2023.031

Issue

Section

Articles

Similar Articles

1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.