The evolution of financial technology: A comprehensive bibliometric review of robo-advisors

Liance Mathew; Viswan M. Govindan; Aravind Jayakumar; Umesh Unnikrishnan; Jobin Jose

doi:10.31893/multirev.2024274

Keywords:

automated investment

fintech

bibliometric analysis

biblioshiny

VOSviewer

citespace

Abstract

This bibliometric study delves into the rapidly evolving domain of robo-advisors—digital platforms that offer automated, algorithm-driven financial planning services with minimal human intervention. Utilizing a robust dataset extracted from Scopus, the research employs the PRISMA flow chart methodology for meticulous screening, inclusion, and exclusion of relevant studies. Advanced bibliometric analysis tools such as Biblioshiny, VOSviewer, and CiteSpace are employed to conduct the analysis, facilitating a multifaceted examination of the literature. The findings of this study are extensive and informative, covering various aspects of the research on robo-advisors. It highlights the annual scientific production, identifying trends and growth patterns within the field. The study also spotlights the most productive authors and sources, shedding light on the leading contributors to the discourse on robo-advisors. Furthermore, it provides insights into the most globally cited documents, author cocitations, and keyword co-occurrences, revealing the core themes and discussions shaping this area of study. Significantly, the research identifies keywords with the strongest citation bursts and offers network visualizations of cited authors, showcasing the dynamic interactions within the academic community. Timezone and timeline views of cited journals and country collaborations offer a geographical and temporal perspective on the research landscape. Through these analyses, the study uncovers identified research gaps and practical implications, guiding future investigations and the practical application of robo-advisors in the financial industry.
References
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7. Baker, T., & Dellaert, B. (2018). Regulating robo advice across the financial services industry. Iowa Law Review, 103(2), 713–750.
8. Belanche, D., Casaló, L. V., Flavián, M., & Loureiro, S. M. C. (n.d.). Benefit versus risk: A behavioral model for using robo-advisors. The Service Industries Journal, 1–28. https://doi.org/10.1080/02642069.2023.2176485
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19. Figà-Talamanca, G., Tanzi, P. M., & DUrzo, E. (2022). Robo-advisor acceptance: Do gender and generation matter? PLoS ONE, 17(6). https://doi.org/10.1371/journal.pone.0269454
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22. Glaser, F., Iliewa, Z., Jung, D., & Weber, M. (2019). Towards designing robo-advisors for unexperienced investors with experience sampling of time-series data. In Lecture Notes in Information Systems and Organisation (Vol. 29, pp. 133–138). https://doi.org/10.1007/978-3-030-01087-4_16
23. Godin, B. (2006). On the origins of bibliometrics. Scientometrics, 68(1), 109–133. https://doi.org/10.1007/s11192-006-0086-0
24. Guo, L. (2020). Regulating Investment Robo-Advisors in China: Problems and Prospects. European Business Organization Law Review, 21(1), 69–99. https://doi.org/10.1007/s40804-020-00187-8
25. Harzing, A.-W., & Alakangas, S. (2016). Google Scholar, Scopus and the Web of Science: A longitudinal and cross-disciplinary comparison. Scientometrics, 106, 787–804.
26. Hong, X., Pan, L., Gong, Y., & Chen, Q. (2023). Robo-advisors and investment intention: A perspective of value-based adoption. Information and Management, 60(6). https://doi.org/10.1016/j.im.2023.103832
27. Hyun Baek, T., & Kim, M. (2023). Ai robo-advisor anthropomorphism: The impact of anthropomorphic appeals and regulatory focus on investment behaviors. Journal of Business Research, 164. https://doi.org/10.1016/j.jbusres.2023.114039
28. Imerman, M. B., & Fabozzi, F. J. (2020). Cashing in on innovation: A taxonomy of FinTech. In Journal of Asset Management (Vol. 21, Issue 3, pp. 167–177). https://doi.org/10.1057/s41260-020-00163-4
29. Joseph, J., Thomas, B., Jose, J., & Pathak, N. (2024). Decoding the growth of multimodal learning: A bibliometric exploration of its impact and influence. Intelligent Decision Technologies, 18(1), 151–167. https://doi.org/10.3233/IDT-230727
30. Komperda, R. (2017). Likert-type survey data analysis with R and RStudio. In ACS Symposium Series (Vol. 1260, pp. 91–116). https://doi.org/10.1021/bk-2017-1260.ch007
31. Ku, Y.-C., & Wang, H.-X. (2022). The Factors Influencing the Willingness of Investors to Use Robo-Advisors. 13327 LNCS, 286–299. https://doi.org/10.1007/978-3-031-05544-7_22
32. Kwon, D., Jeong, P., & Chung, D. (2022). An empirical study of factors influencing the intention to use robo-advisors. Journal of Information & Knowledge Management, 21(03), 2250039.
33. Mahmoud, M. I., Surwanti, A., & Pribadi, F. (2024). Bibliometric analysis of trends and patterns in capital structure research: A decade-long review (2012-2022). Multidisciplinary Reviews, 7(2). https://doi.org/10.31893/multirev.2024025
34. Maryanti, R., Nandiyanto, A. B. D., Hufad, A., Sunardi, S., Al Husaeni, D., & Al Husaeni, D. (2023). A computational bibliometric analysis of science education research using vosviewer. Journal of Engineering Science and Technology, 18(1), 301–309.
35. Merlin, J. I., & Prabakar, S. (2024). Trends and patterns: A bibliometric analysis of emotional intelligence and its implications for strategic leadership. Multidisciplinary Reviews, 7(4). https://doi.org/10.31893/multirev.2024063
36. 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(1). https://doi.org/10.1080/23311916.2023.2188992
37. Niazi, M. A. (2016). Review of “CiteSpace: A Practical Guide For Mapping Scientific Literature” by Chaomei Chen. Complex Adaptive Systems Modeling, 4(1), 23. https://doi.org/10.1186/s40294-016-0036-5
38. Puhle, M. (2019). THE PERFORMANCE and ASSET ALLOCATION of German ROBO-ADVISORS. Society and Economy, 41(3), 331–351. https://doi.org/10.1556/204.2019.41.3.4
39. Racine, J. S. (2012). RStudio: A platform-independent IDE for R and Sweave. JSTOR.
40. Ryu, H.-S., & Ko, K. S. (2020). Sustainable development of Fintech: Focused on uncertainty and perceived quality issues. In Sustainability (Switzerland) (Vol. 12, Issue 18). https://doi.org/10.3390/su12187669
41. Savchenko, S., & Kobets, V. (2022). Development of Robo-Advisor System for Personalized Investment and Insurance Portfolio Generation. 1635 CCIS, 213–228. https://doi.org/10.1007/978-3-031-14841-5_14
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43. Thomas, B., Joseph, J., & Jose, J. (2023). Explorative Bibliometric Study of Medical Image Analysis: Unveiling Trends and Advancements. Scientific Visualization, 15(5), 35–49. https://doi.org/10.26583/sv.15.5.04
44. Uzkiyyah, N. U., Qamari, I. N., & Rahayu, M. K. P. (2023). Seven years of research tracing on turnover intention: Bibliometric analysis. Multidisciplinary Reviews, 6(3). https://doi.org/10.31893/multirev.2023025
45. Van Eck, N. J., & Waltman, L. (2009). VOSviewer: A computer program for bibliometric mapping. In 12th International Conference on Scientometrics and Informetrics, ISSI 2009 (pp. 886–897). https://www.scopus.com/inward/record.uri?eid=2-s2.0-84901992309&partnerID=40&md5=2b19eafa3759e96b197c3ec7535a731f
46. Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
47. Wang, S., & Pradhan Dr, S. (2020). Exploring factors influencing older adults’ willingness to use robo-advisors.
48. Xia, H., Zhang, Q., Zhang, J. Z., & Zheng, L. J. (2023). Exploring investors’ willingness to use robo-advisors: Mediating role of emotional response. Industrial Management & Data Systems, 123(11), 2857–2881. https://doi.org/10.1108/IMDS-07-2022-0400
49. Yang, J., Cheng, C., Shen, S., & Yang, S. (2017). Comparison of complex network analysis software: Citespace, SCI 2 and Gephi. 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), 169–172.
50. Zhang, M. (2023). Research on Hot Topics and Trends of Tutor’s Guidance Style: Citespace-based Bibliometric Analysis. In ACM International Conference Proceeding Series (pp. 84–91). https://doi.org/10.1145/3591139.3591147

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Mathew, L., Govindan, V. M., Jayakumar, A., Unnikrishnan, U., & Jose, J. (2024). The evolution of financial technology: A comprehensive bibliometric review of robo-advisors. Multidisciplinary Reviews, 7(11), 2024274. https://doi.org/10.31893/multirev.2024274

[1] Afaq, A., & Gaur, L. (2021). The Rise of Robots to Help Combat Covid-19. In Proceedings of International Conference on Technological Advancements and Innovations, ICTAI 2021 (pp. 69–74). https://doi.org/10.1109/ICTAI53825.2021.9673256

[2] Ali, I., Balta, M., & Papadopoulos, T. (2022). Social media platforms and social enterprise: Bibliometric analysis and systematic review. International Journal of Information Management. https://doi.org/10.1016/j.ijinfomgt.2022.102510

[3] Amaliasita, M., & Rahayu, M. K. P. (2024). A bibliometric analysis – Green human resource management on green employee behavior. Multidisciplinary Reviews, 7(3). https://doi.org/10.31893/multirev.2024044

[4] Archambault, É., Campbell, D., Gingras, Y., & Larivière, V. (2009). Comparing bibliometric statistics obtained from the Web of Science and Scopus. Journal of the American Society for Information Science and Technology, 60(7), 1320–1326.

[5] Au, C.-D., Klingenberger, L., Svoboda, M., & Frère, E. (2021). Business model of sustainable robo-advisors: Empirical insights for practical implementation. Sustainability (Switzerland), 13(23). https://doi.org/10.3390/su132313009

[6] Babu, S., & Thomas, B. (2022). Bibliometric Analysis and Visualization of Scientific Literature on Random Forest Regression. Scientific Visualization, 14(5).

[7] Baker, T., & Dellaert, B. (2018). Regulating robo advice across the financial services industry. Iowa Law Review, 103(2), 713–750.

[8] Belanche, D., Casaló, L. V., Flavián, M., & Loureiro, S. M. C. (n.d.). Benefit versus risk: A behavioral model for using robo-advisors. The Service Industries Journal, 1–28. https://doi.org/10.1080/02642069.2023.2176485

[9] Belanche, D., Casaló, L. V., Flavián, M., & Loureiro, S. M. C. (2023a). Benefit versus risk: A behavioral model for using robo-advisors. Service Industries Journal. https://doi.org/10.1080/02642069.2023.2176485

[10] Belanche, D., Casaló, L. V., Flavián, M., & Loureiro, S. M. C. (2023b). Benefit versus risk: A behavioral model for using robo-advisors. Service Industries Journal. https://doi.org/10.1080/02642069.2023.2176485

[11] Brenner, L., & Meyll, T. (2020a). Robo-advisors: A substitute for human financial advice? Journal of Behavioral and Experimental Finance, 25. https://doi.org/10.1016/j.jbef.2020.100275

[12] Brenner, L., & Meyll, T. (2020b). Robo-advisors: A substitute for human financial advice? Journal of Behavioral and Experimental Finance, 25. https://doi.org/10.1016/j.jbef.2020.100275

[13] Brunen, A.-C., & Laubach, O. (2022). Do sustainable consumers prefer socially responsible investments? A study among the users of robo advisors. Journal of Banking and Finance, 136. https://doi.org/10.1016/j.jbankfin.2021.106314

[14] Cardillo, G., & Chiappini, H. (2024). Robo-advisors: A systematic literature review. Finance Research Letters, 62. https://doi.org/10.1016/j.frl.2024.105119

[15] Chen, S., Ding, Q., & Liang, K. (2023). Research on Green Port Based on LDA Model and CiteSpace Bibliometric Analysis. In Proceedings of SPIE - The International Society for Optical Engineering (Vol. 12725). https://doi.org/10.1117/12.2679115

[16] Cull, M. (2022). The Growing Role of FinTech and Robo-advisors. In De Gruyter Handbook of Personal Finance (pp. 529–550). https://doi.org/10.1515/9783110727692-030

[17] Deng, B., & Chau, M. (2021). Anthropomorphized financial robo-advisors and investment advice-taking behavior. 27th Annual Americas Conference on Information Systems, AMCIS 2021. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118625396&partnerID=40&md5=38fd0a0a65ac096324a9903636d13a2b

[18] Ellegaard, O., & Wallin, J. A. (2015). The bibliometric analysis of scholarly production: How great is the impact? Scientometrics, 105(3), 1809–1831. https://doi.org/10.1007/s11192-015-1645-z

[19] Figà-Talamanca, G., Tanzi, P. M., & DUrzo, E. (2022). Robo-advisor acceptance: Do gender and generation matter? PLoS ONE, 17(6). https://doi.org/10.1371/journal.pone.0269454

[20] Fischer, M. (2021). Fintech business models: Applied canvas method and analysis of venture capital rounds. In Fintech Business Models: Applied Canvas Method and Analysis of Venture Capital Rounds. https://doi.org/10.1515/9783110704907

[21] Gavel, Y., & Iselid, L. (2008). Web of Science and Scopus: A journal title overlap study. Online Information Review, 32(1), 8–21.

[22] Glaser, F., Iliewa, Z., Jung, D., & Weber, M. (2019). Towards designing robo-advisors for unexperienced investors with experience sampling of time-series data. In Lecture Notes in Information Systems and Organisation (Vol. 29, pp. 133–138). https://doi.org/10.1007/978-3-030-01087-4_16

[23] Godin, B. (2006). On the origins of bibliometrics. Scientometrics, 68(1), 109–133. https://doi.org/10.1007/s11192-006-0086-0

[24] Guo, L. (2020). Regulating Investment Robo-Advisors in China: Problems and Prospects. European Business Organization Law Review, 21(1), 69–99. https://doi.org/10.1007/s40804-020-00187-8

[25] Harzing, A.-W., & Alakangas, S. (2016). Google Scholar, Scopus and the Web of Science: A longitudinal and cross-disciplinary comparison. Scientometrics, 106, 787–804.

[26] Hong, X., Pan, L., Gong, Y., & Chen, Q. (2023). Robo-advisors and investment intention: A perspective of value-based adoption. Information and Management, 60(6). https://doi.org/10.1016/j.im.2023.103832

[27] Hyun Baek, T., & Kim, M. (2023). Ai robo-advisor anthropomorphism: The impact of anthropomorphic appeals and regulatory focus on investment behaviors. Journal of Business Research, 164. https://doi.org/10.1016/j.jbusres.2023.114039

[28] Imerman, M. B., & Fabozzi, F. J. (2020). Cashing in on innovation: A taxonomy of FinTech. In Journal of Asset Management (Vol. 21, Issue 3, pp. 167–177). https://doi.org/10.1057/s41260-020-00163-4

[29] Joseph, J., Thomas, B., Jose, J., & Pathak, N. (2024). Decoding the growth of multimodal learning: A bibliometric exploration of its impact and influence. Intelligent Decision Technologies, 18(1), 151–167. https://doi.org/10.3233/IDT-230727

[30] Komperda, R. (2017). Likert-type survey data analysis with R and RStudio. In ACS Symposium Series (Vol. 1260, pp. 91–116). https://doi.org/10.1021/bk-2017-1260.ch007

[31] Ku, Y.-C., & Wang, H.-X. (2022). The Factors Influencing the Willingness of Investors to Use Robo-Advisors. 13327 LNCS, 286–299. https://doi.org/10.1007/978-3-031-05544-7_22

[32] Kwon, D., Jeong, P., & Chung, D. (2022). An empirical study of factors influencing the intention to use robo-advisors. Journal of Information & Knowledge Management, 21(03), 2250039.

[33] Mahmoud, M. I., Surwanti, A., & Pribadi, F. (2024). Bibliometric analysis of trends and patterns in capital structure research: A decade-long review (2012-2022). Multidisciplinary Reviews, 7(2). https://doi.org/10.31893/multirev.2024025

[34] Maryanti, R., Nandiyanto, A. B. D., Hufad, A., Sunardi, S., Al Husaeni, D., & Al Husaeni, D. (2023). A computational bibliometric analysis of science education research using vosviewer. Journal of Engineering Science and Technology, 18(1), 301–309.

[35] Merlin, J. I., & Prabakar, S. (2024). Trends and patterns: A bibliometric analysis of emotional intelligence and its implications for strategic leadership. Multidisciplinary Reviews, 7(4). https://doi.org/10.31893/multirev.2024063

[36] 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(1). https://doi.org/10.1080/23311916.2023.2188992

[37] Niazi, M. A. (2016). Review of “CiteSpace: A Practical Guide For Mapping Scientific Literature” by Chaomei Chen. Complex Adaptive Systems Modeling, 4(1), 23. https://doi.org/10.1186/s40294-016-0036-5

[38] Puhle, M. (2019). THE PERFORMANCE and ASSET ALLOCATION of German ROBO-ADVISORS. Society and Economy, 41(3), 331–351. https://doi.org/10.1556/204.2019.41.3.4

[39] Racine, J. S. (2012). RStudio: A platform-independent IDE for R and Sweave. JSTOR.

[40] Ryu, H.-S., & Ko, K. S. (2020). Sustainable development of Fintech: Focused on uncertainty and perceived quality issues. In Sustainability (Switzerland) (Vol. 12, Issue 18). https://doi.org/10.3390/su12187669

[41] Savchenko, S., & Kobets, V. (2022). Development of Robo-Advisor System for Personalized Investment and Insurance Portfolio Generation. 1635 CCIS, 213–228. https://doi.org/10.1007/978-3-031-14841-5_14

[42] Souza de Cursi, E. (2023). Some Tips to Use R and RStudio. In E. Souza de Cursi (Ed.), Uncertainty Quantification using R (pp. 1–108). Springer International Publishing. https://doi.org/10.1007/978-3-031-17785-9_1

[43] Thomas, B., Joseph, J., & Jose, J. (2023). Explorative Bibliometric Study of Medical Image Analysis: Unveiling Trends and Advancements. Scientific Visualization, 15(5), 35–49. https://doi.org/10.26583/sv.15.5.04

[44] Uzkiyyah, N. U., Qamari, I. N., & Rahayu, M. K. P. (2023). Seven years of research tracing on turnover intention: Bibliometric analysis. Multidisciplinary Reviews, 6(3). https://doi.org/10.31893/multirev.2023025

[45] Van Eck, N. J., & Waltman, L. (2009). VOSviewer: A computer program for bibliometric mapping. In 12th International Conference on Scientometrics and Informetrics, ISSI 2009 (pp. 886–897). https://www.scopus.com/inward/record.uri?eid=2-s2.0-84901992309&partnerID=40&md5=2b19eafa3759e96b197c3ec7535a731f

[46] Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3

[47] Wang, S., & Pradhan Dr, S. (2020). Exploring factors influencing older adults’ willingness to use robo-advisors.

[48] Xia, H., Zhang, Q., Zhang, J. Z., & Zheng, L. J. (2023). Exploring investors’ willingness to use robo-advisors: Mediating role of emotional response. Industrial Management & Data Systems, 123(11), 2857–2881. https://doi.org/10.1108/IMDS-07-2022-0400

[49] Yang, J., Cheng, C., Shen, S., & Yang, S. (2017). Comparison of complex network analysis software: Citespace, SCI 2 and Gephi. 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), 169–172.

[50] Zhang, M. (2023). Research on Hot Topics and Trends of Tutor’s Guidance Style: Citespace-based Bibliometric Analysis. In ACM International Conference Proceeding Series (pp. 84–91). https://doi.org/10.1145/3591139.3591147

Abstract

References

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