Purpose: This bibliometric research article aimed to provide a comprehensive overview of the academic literature on robo-advisory services in finance, identifying key trends, influential factors, and potential avenues for future research. Design/Methodology/Approach : A systematic bibliometric analysis was conducted for scholarly articles published on the topic of robo-advisory between 2016 and 2022. The metadata was sourced from two premier research databases : SCOPUS and Web of Science. The metadata of 210 documents was analyzed using the Bibliometrix package of R. Results : A substantial growth in research on robo-advisory services in finance over the past decade was found. The application of robo-advisory in terms of asset allocation and portfolio optimization was seen. While most of the research focused on the technology enablers of robo-advisory, the applications offer further areas of study. Practical

Implications: The findings suggested promising avenues for future research, including the further integration of artificial intelligence and machine learning techniques in financial decision-making.

Originality: The study attempted to touch upon different aspects of this topic in terms of its enablers, technology, applications, and challenges faced. This research tried to design a path for further work in this field by suggesting a few bases that have not been studied much yet.

1. Abraham, F., Schmukler, S. L., & Tessada, J. (2019). Robo-advisors: Investing through machines. SSRN. https://ssrn.com/abstract=3360125
2. Anderson, E. W., & Sullivan, M. W. (1993). The antecedents and consequences of customer satisfaction for firms. Marketing Science, 12(2), 125–143. https://doi.org/10.1287/mksc.12.2.125
3. Annapurna, R., & Basri, S. (2024). Role of emotions in stock investment decisions: A critical review of the literature. Indian Journal of Finance, 18(5), 50–65. https://doi.org/10.17010/ijf/2024/v18i5/173842
4. Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
5. Arora, A. K., Kumar, S., & Kansal, A. (2024). Analysis of retail investors' attitudes toward IPO investments. Indian Journal of Finance, 18(9), 42–57. https://doi.org/10.17010/ijf/2024/v18i9/174459
6. Arya, S., Eckel, C., & Wichman, C. (2013). Anatomy of the credit score. Journal of Economic Behavior & Organization, 95, 175–185. https://doi.org/10.1016/j.jebo.2011.05.005
7. 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. https://doi.org/10.1108/IMDS-08-2018-0368
8. Bhatia, A., Chandani, A., Divekar, R., Mehta, M., & Vijay, N. (2022). Digital innovation in wealth management landscape: The moderating role of robo advisors in behavioural biases and investment decision-making. International Journal of Innovation Science, 14(3–4), 693–712. https://doi.org/10.1108/IJIS-10-2020-0245
9. Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
10. Brenner, L., & Meyll, T. (2020). Robo-advisors: A substitute for human financial advice? Journal of Behavioral and Experimental Finance, 25, Article ID 100275. https://doi.org/10.1016/j.jbef.2020.100275
11. Britton, B. L., & Atkinson, D. G. (2017). An investigation into the significant impacts of automation in asset management. Economics World, 5(5), 418–428. https://doi.org/10.17265/2328-7144/2017.05.004
12. Cao, L., Yuan, G., Leung, T., & Zhang, W. (2020). Special issue on AI and FinTech: The challenge ahead. IEEE Intelligent Systems, 35(2), 3–6. https://doi.org/10.1109/MIS.2020.2983494
13. Cheng, Y.-M. (2021). Will robo-advisors continue? Roles of task-technology fit, network externalities, gratifications and flow experience in facilitating continuance intention. Kybernetes, 50(6), 1751–1783. https://doi.org/10.1108/K-03-2020-0185
14. Cheng, Y.-M. (2023). How can robo-advisors retain end-users? Identifying the formation of an integrated post-adoption model. Journal of Enterprise Information Management, 36(1), 91–122. https://doi.org/10.1108/JEIM-07-2020-0277
15. Cheong, D., Kim, Y. M., Byun, H. W., Oh, K. J., & Kim, T. Y. (2017). Using genetic algorithm to support clustering-based portfolio optimization by investor information. Applied Soft Computing, 61, 593–602. https://doi.org/10.1016/j.asoc.2017.08.042
16. Chung, K. C., & Lin, C.-H. (2023). Drivers of financial robot continuance usage intentions: An application of self-efficacy theory. Journal of Internet Technology, 24(2), 401–410. https://jit.ndhu.edu.tw/article/view/2879
17. Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011a). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. Journal of Informetrics, 5(1), 146–166. https://doi.org/10.1016/j.joi.2010.10.002
18. Cobo, M. J., López‐Herrera, A. G., Herrera‐Viedma, E., & Herrera, F. (2011b). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7), 1382–1402. https://doi.org/10.1002/asi.21525
19. Collins, J. M. (2012). Financial advice: A substitute for financial literacy? SSRN. https://doi.org/10.2139/ssrn.2046227
20. D'Acunto, F., & Rossi, A. G. (2021). New frontiers of robo-advising: Consumption, saving, debt management, and taxes. SSRN. https://doi.org/10.2139/ssrn.3778244
21. D'Acunto, F., Prabhala, N., & Rossi, A. G. (2019). The promises and pitfalls of robo-advising. The Review of Financial Studies, 32(5), 1983–2020. https://doi.org/10.1093/rfs/hhz014
22. Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48, 24–42. https://doi.org/10.1007/s11747-019-00696-0
23. de Almeida, B. J., Neves, R. F., & Horta, N. (2018). Combining support vector machine with genetic algorithms to optimize investments in forex markets with high leverage. Applied Soft Computing, 64, 596–613. https://doi.org/10.1016/j.asoc.2017.12.047
24. de Lange, P. E., Melsom, B., Vennerød, C. B., & Westgaard, S. (2022). Explainable AI for credit assessment in banks. Journal of Risk and Financial Management, 15(12), 556. https://doi.org/10.3390/jrfm15120556
25. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
26. Faloon, M., & Scherer, B. (2017). Individualization of robo-advice. The Journal of Wealth Management, 20(1), 30–36. https://doi.org/10.3905/jwm.2017.20.1.030
27. Fisch, J. E., Labouré, M., & Turner, J. A. (2019). Chapter 2: The emergence of the robo-advisor. In J. Agnew, & O. S. Mitchell (eds.), The disruptive impact of FinTech on retirement systems (pp. 13–37). Oxford University Press. https://doi.org/10.1093/oso/9780198845553.003.0002
28. Gai, K., Wu, Y., Zhu, L., Xu, L., & Zhang, Y. (2019). Permissioned blockchain and edge computing empowered privacy-preserving smart grid networks. IEEE Internet of Things Journal, 6(5), 7992–8004. https://doi.org/10.1109/JIOT.2019.2904303
29. 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. D. Davis, R. Riedl, J. vom Brocke, P.-M. Léger, & A. B. Randolph (eds.), Information systems and neuroscience (Vol. 29, pp. 133–138). Springer. https://doi.org/10.1007/978-3-030-01087-4_16
30. Gökgöz, F., & Atmaca, M. E. (2012). Financial optimization in the Turkish electricity market: Markowitz's mean-variance approach. Renewable and Sustainable Energy Reviews, 16(1), 357–368. https://doi.org/10.1016/j.rser.2011.06.018
31. Gomber, P., Kauffman, R. J., Parker, C., & Weber, B. W. (2018). On the fintech revolution: Interpreting the forces of innovation, disruption, and transformation in financial services. Journal of Management Information Systems, 35(1), 220–265. https://doi.org/10.1080/07421222.2018.1440766
32. Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D. (2021). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance, 32, Article ID 100577. https://doi.org/10.1016/j.jbef.2021.100577
33. Greineder, M., Riasanow, T., Böhm, M., & Krcmar, H. (2020). The generic InsurTech ecosystem and its strategic implications for the digital transformation of the insurance industry. In 40 Years EMISA 2019 (pp. 119–132). Bonn. https://dl.gi.de/items/d1a7e6d0-117c-49f5-bcf7-29c38e930f43
34. Hasanah, E. N., Wiryono, S. K., & Koesrindartoto, D. P. (2023). Financial robo-advisor: Learning from academic literature. Jurnal Minds: Manajemen Ide Dan Inspirasi, 10(1), 17–40. https://doi.org/10.24252/minds.v10i1.33428
35. Hohenberger, C., Lee, C., & Coughlin, J. F. (2019). Acceptance of robo‐advisors: Effects of financial experience, affective reactions, and self‐enhancement motives. Financial Planning Review, 2(2), Article ID e1047. https://doi.org/10.1002/cfp2.1047
36. Hua, X., Huang, Y., & Zheng, Y. (2019). Current practices, new insights, and emerging trends of financial technologies. Industrial Management & Data Systems, 119(7), 1401–1410. https://doi.org/10.1108/IMDS-08-2019-0431
37. Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007
38. Ji, M. (2017). Are robots good fiduciaries? Regulating robo-advisors under the Investment Advisers Act of 1940. SSRN. https://doi.org/10.2139/ssrn.3036722
39. Joseph, D., Girish, S., & Suresh, G. (2023). FinTech and financial capability, what do we know and what we do not know: A scoping review. Indian Journal of Finance, 17(12), 40–55. https://doi.org/10.17010/ijf/2023/v17i12/170910
40. Jung, D., Dorner, V., Glaser, F., & Morana, S. (2018). Robo-advisory: Digitalization and automation of financial advisory. Business & Information Systems Engineering, 60, 81–86. https://doi.org/10.1007/s12599-018-0521-9
41. Jung, D., Dorner, V., Weinhardt, C., & Pusmaz, H. (2018). Designing a robo-advisor for risk-averse, low-budget consumers. Electronic Markets, 28, 367–380. https://doi.org/10.1007/s12525-017-0279-9
42. Khongwir, C. M., & Sharmiladevi, J. C. (2023). A bibliometric analysis on financial inclusion and financial literacy–Analyzing the current research trends. Indian Journal of Finance, 17(2), 27–53. https://doi.org/10.17010/ijf/2023/v17i2/172638
43. Kim, M., Kim, S., & Kim, J. (2019). Can mobile and biometric payments replace cards in the Korean offline payments market? Consumer preference analysis for payment systems using a discrete choice model. Telematics and Informatics, 38, 46–58. https://doi.org/10.1016/j.tele.2019.02.003
44. Kotishwar, A. (2020). Impact of blockchain technology on efficiency of financial transactions. Indian Journal of Finance, 14(3), 36–44. https://doi.org/10.17010/ijf/2020/v14i3/151074
45. Kou, G., Akdeniz, Ö. O., Dinçer, H., & Yüksel, S. (2021). Fintech investments in European banks: A hybrid IT2 fuzzy multidimensional decision-making approach. Financial Innovation, 7, Article no. 39. https://doi.org/10.1186/s40854-021-00256-y
46. Kuzmina-Merlino, I., & Saksonova, S. (2018). The knowledge and competencies required for the fintech sector. In new challenges of economic and business development–2018: Productivity and economic growth (pp. 388–395). University of Latvia, Faculty of Business, Management and Economics (NCEBD).
47. Lee, I., & Shin, Y. J. (2018). Fintech: Ecosystem, business models, investment decisions, and challenges. Business Horizons, 61(1), 35–46. https://doi.org/10.1016/j.bushor.2017.09.003
48. Lin, C.-P., & Bhattacherjee, A. (2008). Elucidating individual intention to use interactive information technologies: The role of network externalities. International Journal of Electronic Commerce, 13(1), 85–108. https://doi.org/10.2753/JEC1086-4415130103
49. Lin, K.-Y., & Lu, H.-P. (2011). Why people use social networking sites: An empirical study integrating network externalities and motivation theory. Computers in Human Behavior, 27(3), 1152–1161. https://doi.org/10.1016/j.chb.2010.12.009
50. Linge, A. A., Jiwani, A., & Kakde, B. B. (2024). Factors affecting risk attitude and investors' happiness of newly employed individuals. Indian Journal of Finance, 18(5), 66–80. https://doi.org/10.17010/ijf/2024/v18i5/173843
51. Lohan, S., Sidhu, A., & Kakran, S. (2023). Impact of investors' attention on the global stock market: A bibliometric analysis. Indian Journal of Finance, 17(10), 21–42. https://doi.org/10.17010/ijf/2023/v17i10/170881
52. Lusardi, A., & Mitchell, O. S. (2014). The economic importance of financial literacy: Theory and evidence. Journal of Economic Literature, 52(1), 5–44. https://doi.org/10.1257/jel.52.1.5
53. Mariani, M. M., Machado, I., & Nambisan, S. (2023). Types of innovation and artificial intelligence: A systematic quantitative literature review and research agenda. Journal of Business Research, 155(Part B), Article ID 113364. https://doi.org/10.1016/j.jbusres.2022.113364
54. Muñoz-Leiva, F., Climent-Climent, S., & Liébana-Cabanillas, F. (2017). Determinants of intention to use the mobile banking apps: An extension of the classic TAM model. Spanish Journal of Marketing - ESIC, 21(1), 25–38. https://doi.org/10.1016/j.sjme.2016.12.001
55. Murakami, T., Fujita, R., Ohki, T., Kaga, Y., Fujio, M., & Takahashi, K. (2019). Cancelable permutation-based indexing for secure and efficient biometric identification. IEEE Access, 7, 45563–45582. https://doi.org/10.1109/ACCESS.2019.2908456
56. Owens, E., Sheehan, B., Mullins, M., Cunneen, M., Ressel, J., & Castignani, G. (2022). Explainable artificial intelligence (XAI) in insurance. Risks, 10(12), 230. https://doi.org/10.3390/risks10120230
57. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamsheer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, A., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S.,…Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372. https://doi.org/10.1136/bmj.n71
58. Page, W. H., & Lopatka, J. E. (1999). Network externalities. Encyclopedia of Law and Economics, 760, 952–980.
59. Pal, A., Tiwari, C. K., & Behl, A. (2021). Blockchain technology in financial services: A comprehensive review of the literature. Journal of Global Operations and Strategic Sourcing, 14(1), 61–80. https://doi.org/10.1108/JGOSS-07-2020-0039
60. Pemer, F. (2020). Enacting professional service work in times of digitalization and potential disruption. Journal of Service Research, 24(2), 249–268. https://doi.org/10.1177/1094670520916801
61. Pinochet, L. H., Diogo, G. T., Lopes, E. L., Herrero, E., & Bueno, R. L. (2019). Propensity of contracting loans services from FinTech's in Brazil. International Journal of Bank Marketing, 37(5), 1190–1214. https://doi.org/10.1108/IJBM-07-2018-0174
62. Renduchintala, T., Alfauri, H., Yang, Z., Di Pietro, R., & Jain, R. (2022). A survey of blockchain applications in the fintech sector. Journal of Open Innovation: Technology, Market, and Complexity, 8(4), 185. https://doi.org/10.3390/joitmc8040185
63. Riasanow, T., Flötgen, R. J., Setzke, D. S., Böhm, M., & Krcmar, H. (2018). The generic ecosystem and innovation patterns of the digital transformation in the financial industry. In Proceedings of the Pacific Asia Conference on Information Systems. https://aisel.aisnet.org/pacis2018/77/
64. Riedel, A., Mulcahy, R., & Northey, G. (2022). Feeling the love? How consumer's political ideology shapes responses to AI financial service delivery. International Journal of Bank Marketing, 40(6), 1102–1132. https://doi.org/10.1108/IJBM-09-2021-0438
65. Ruf, C., Back, A., Bergmann, R., & Schlegel, M. (2015). Elicitation of requirements for the design of mobile financial advisory services--Instantiation and validation of the requirement data model with a multi-method approach. In 2015 48th Hawaii International Conference on System Sciences (pp. 1169–1178). IEEE. https://doi.org/10.1109/HICSS.2015.142
66. 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, Article ID 101490. https://doi.org/10.1016/j.ribaf.2021.101490
67. Singh, A. K., Shrivastav, R. K., & Chapagain, R. (2024). Sustainable financing initiatives and bank performance: A bibliometric analysis. Indian Journal of Finance, 18(8), 22–43. https://doi.org/10.17010/ijf/2024/v18i8/174241
68. Sokolov, A., Caverly, K., Mostovoy, J., Fahoum, T., & Seco, L. (2021). Weak supervision and Black-Litterman for automated ESG portfolio construction. The Journal of Financial Data Science, 3(3), 129–138. https://doi.org/10.3905/jfds.2021.1.070
69. Song, M., Xing, X., Duan, Y., Cohen, J., & Mou, J. (2022). Will artificial intelligence replace human customer service? The impact of communication quality and privacy risks on adoption intention. Journal of Retailing and Consumer Services, 66, Article ID 102900. https://doi.org/10.1016/j.jretconser.2021.102900
70. Stoilov, T., Stoilova, K., & Vladimirov, M. (2022). Decision support for portfolio management by information system with Black–Litterman model. International Journal of Information Technology & Decision Making, 21(02), 643–664. https://doi.org/10.1142/S0219622021500589
71. Sunil, N., & Sathish, K. B. (2025). Financial literacy and fintech: A double-edged sword for millennial investors' behavioral biases. Indian Journal of Finance, 19(3), 44–63. https://doi.org/10.17010/ijf/2025/v19i3/174849
72. Suryono, R. R., Budi, I., & Purwandari, B. (2020). Challenges and trends of financial technology (Fintech): A systematic literature review. Information, 11(12), 590. https://doi.org/10.3390/info11120590
73. Tan, G. K. (2020). Robo-advisors and the financialization of lay investors. Geoforum, 117, 46–60. https://doi.org/10.1016/j.geoforum.2020.09.004
74. Uhl, M. W., & Rohner, P. (2018). Robo-advisors versus traditional investment advisors: An unequal game. The Journal of Wealth Management, 21(1), 44–50. https://doi.org/10.3905/jwm.2018.21.1.004
75. Wang, X., Xu, X., Feagan, L., Huang, S., Jiao, L., & Zhao, W. (2018). Inter-bank payment system on enterprise blockchain platform. In 2018 IEEE 11th International Conference on Cloud Computing (CLOUD) (pp. 614–621). IEEE. https://doi.org/10.1109/CLOUD.2018.00085
76. Wang, X., Xue, H., Liu, X., & Pei, Q. (2019). A privacy-preserving edge computation-based face verification system for user authentication. IEEE Access, 7, 14186–14197. https://doi.org/10.1109/ACCESS.2019.2894535
77. Wattal, S., Racherla, P., & Mandviwalla, M. (2010). Network externalities and technology use: A quantitative analysis of intraorganizational blogs. Journal of Management Information Systems, 27(1), 145–174. https://doi.org/10.2753/MIS0742-1222270107
78. Wilson, H. J., & Daugherty, P. R. (2018). Collaborative intelligence: Humans and AI are joining forces. Harvard Business Review. https://hbr.org/2018/07/collaborative-intelligence-humans-and-ai-are-joining-forces
79. 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. https://doi.org/10.1108/JSM-05-2020-0162
80. Zhu, S.-S., Li, D., & Wang, S.-Y. (2004). Risk control over bankruptcy in dynamic portfolio selection: A generalized mean-variance formulation. IEEE Transactions on Automatic Control, 49(3), 447–457. https://doi.org/10.1109/TAC.2004.824474