Asia pacific journal of information systems

The Korea Society of Management Information Systems (한국경영정보학회)
권호 표지
DOI QR코드

DOI QR Code

Trends in the AI-based Banking Conversational Agents Literature: A Bibliometric Review

  • Received : 2023.05.16
  • Accepted : 2023.06.27
  • Published : 2023.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Artificial Intelligence (AI) and the technologies powered by AI fuel the fourth industrial revolution. Being the primary adopter of such innovations, banking has recently started using the most common AI-based technology, i.e., conversational agents. Although research extensively focuses on this niche area and provides bibliometric understanding for such agents in other industries, a similar review with scientometric insights of the banking literature concerning AI conversational agents is absent till date. Furthermore, in the era following the pandemic, banks are faced with the imperative to provide solutions that align with the changing landscape of remote consumer behavior. As a result, banks are proactively integrating technology-driven solutions, such as automated agents, to effectively address the growing demand for remote customer support. Hence more research is needed to perfect such agents. In order to bridge these existing gaps, the present study undertook a comprehensive examination of two decades' worth of banking literature. A meticulous review was conducted, analyzing approximately 116 papers published from 2003 to 2023. The aim was to provide a scientometric overview of the topic, catering to the research needs of both academic and industrial professionals. Holistically, the study seeks to present a macro-view about the existing trends in AI based banking conversational agents' literature while focusing on quantity, qualitative and structural indicators that are effectively necessary to offer new directions for the AI-based banking solutions. Our study, therefore, presents insights surrounding the literature, using selected techniques related to performance analysis and science mapping.

Keywords

References

  1. Abdulquadri, A., Mogaji, E., Kieu, T. A., and Nguyen, N. P. (2021). Digital transformation in financial services provision: A Nigerian perspective to the adoption of chatbot. Journal of Enterprising Communities: People and Places in the Global Economy, 15(2), 258-281. https://doi.org/10.1108/JEC-06-2020-0126
  2. Adam, M., Wessel, M., and Benlian, A. (2021). AI-based chatbots in customer service and their effects on user compliance. Electronic Markets, 31(2), 427-445. https://doi.org/10.1007/s12525-020-00414-7
  3. Adil, M., Sadiq, M., Jebarajakirthy, C., Maseeh, H. I., Sangroya, D., and Bharti, K. (2022). Online service failure: Antecedents, moderators and consequences. Journal of Service Theory and Practice, 32(6), 797-842. https://doi.org/10.1108/JSTP-01-2022-0019
  4. Agarwal, S., Agarwal, B., and Gupta, R. (2022). Chatbots and virtual assistants: a bibliometric analysis. Library Hi Tech, 40(4), 1013-1030. https://doi.org/10.1108/LHT-09-2021-0330
  5. Allegue, S., Abdellatif, T., and El Abed, H. (2021). SBM: A Smart Budget Manager in banking using machine learning, NLP, and NLU. In Concurrency and Computation: Practice and Experience, e6673.
  6. Au, C. D., Klingenberger, L., Svoboda, M., and Frere, E. (2021). Business model of sustainable robo-advisors: Empirical insights for practical implementation. Sustainability, 13(23), 13009. https://doi.org/10.3390/su132313009
  7. Bajwa, I. A., Ur Rehman, S., Iqbal, A., Anwer, Z., Ashiq, M., and Khan, M. A. (2022). Past, present and future of FinTech research: A bibliometric analysis. SAGE Open, 12(4), 21582440221131242.
  8. Baker, H. K., Kumar, S., and Pattnaik, D. (2021). Twenty-five years of the journal of corporate finance: A scientometric analysis. Journal of Corporate Finance, 66, 101572.
  9. Bawack, R. E., Wamba, S. F., Carillo, K. D. A., and Akter, S. (2022). Artificial intelligence in E-Commerce: A bibliometric study and literature review. Electronic markets, 32(1), 297-338. https://doi.org/10.1007/s12525-022-00537-z
  10. Bhatia, A., Chandani, A., and Chhateja, J. (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, 25, 100281.
  11. Burton, B., Kumar, S., and Pandey, N. (2020). Twenty-five years of The European Journal of Finance (EJF): A retrospective analysis. The European Journal of Finance, 26(18), 1817-1841. https://doi.org/10.1080/1351847X.2020.1754873
  12. Carrasco, R. A., Munoz-Leiva, F., SanchezFernandez, J., and Liebana-Cabanillas, F. J. (2012). A model for the integration of e-financial services questionnaires with SERVQUAL scales under fuzzy linguistic modeling. Expert Systems with Applications, 39(14), 11535-11547. https://doi.org/10.1016/j.eswa.2012.03.055
  13. Casanueva, I., Temcinas, T., Gerz, D., Henderson, M., and Vulic, I. (2020). Efficient intent detection with dual sentence encoders. arXiv preprint arXiv:2003.04807.
  14. Cheng, Y. M. (2020). 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
  15. 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
  16. Chong, T., Yu, T., Keeling, D. I., and de Ruyter, K. (2021). AI-chatbots on the services frontline addressing the challenges and opportunities of agency. Journal of Retailing and Consumer Services, 63, 102735. https://doi.org/10.1016/j.jretconser.2021.102735
  17. Chowdhary, K., and Chowdhary, K. R. (2020). Natural language processing. In Fundamentals of Artificial Intelligence. New Delhi: Springer.
  18. Cohen, S. B., and Smith, N. A. (2010). Covariance in unsupervised learning of probabilistic grammars. The Journal of Machine Learning Research, 11, 3017-3051.
  19. Creelman, V. (2022). "Thank you for reaching out:" Brand relationship management and the conversational human voice of customer care in online service encounters. Discourse, Context & Media, 46, 100572.
  20. Dang, C. N., Moreno-Garcia, M. N., and Prieta, F. D. L. (2021). An approach to integrating sentiment analysis into recommender systems. Sensors, 21(16), 5666. https://doi.org/10.3390/s21165666
  21. David, L. O., Nwulu, N. I., Aigbavboa, C. O., and Adepoju, O. O. (2022). Integrating fourth industrial revolution (4IR) technologies into the water, energy & food nexus for sustainable security: A bibliometric analysis. Journal of Cleaner Production, 363, 132522.
  22. Digalaki, E. (2019, December 17). The impact of artificial intelligence in the banking sector & how AI is being used in 2020. Business Insider. Retrieved from https://www.businessinsider.in/finance/news/the-impact-of-artificial-intelligence-in-the-bankingsector-how-ai-is-being-used-in-2020/articleshow/72860899.cms
  23. Dogra, N., Adil, M., Dhamija, A., Kumar, M., and Nasir, M. (2022). What makes a community sustainably developed? A review of 25 years of sustainable community tourism literature. Community Development, 53(5), 585-606. https://doi.org/10.1080/15575330.2021.2015606
  24. Doherty, D., and Curran, K. (2019, January). Chatbots for online banking services. In Web Intelligence (Vol. 17, No. 4, pp. 327-342). IOS Press. https://doi.org/10.3233/WEB-190422
  25. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., and Lim, W. M. (2021 a). 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. Donthu, N., Reinartz, W., Kumar, S., and Pattnaik, D. (2021 b). A retrospective review of the first 35 years of the International Journal of Research in Marketing. International Journal of Research in Marketing, 38(1), 232-269. https://doi.org/10.1016/j.ijresmar.2020.10.006
  27. Ellegaard, O. (2018). The application of bibliometric analysis: Disciplinary and user aspects. Scientometrics, 116(1), 181-202. https://doi.org/10.1007/s11192-018-2765-z
  28. Ellegaard, O., and Wallin, J. A., (2015). The bibliometric analysis of scholarly production: How great is the impact? Scientometrics, 105(3), 1809e1831. https://doi.org/10.1007/s11192-015-1645-z.
  29. Enholm, I. M., Papagiannidis, E., Mikalef, P., and Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24(5), 1709-1734. https://doi.org/10.1007/s10796-021-10186-w
  30. Eren, B. A. (2021). Determinants of customer satisfaction in chatbot use: Evidence from a banking application in Turkey. International Journal of Bank Marketing, 39(2), 294-311. https://doi.org/10.1108/IJBM-02-2020-0056
  31. Ertz, M., and Leblanc-Proulx, S. (2018). Sustainability in the collaborative economy: A bibliometric analysis reveals emerging interest. Journal of Cleaner Production, 196, 1073-1085. https://doi.org/10.1016/j.jclepro.2018.06.095
  32. Flavian, C., Perez-Rueda, A., Belanche, D., and Casalo, L. V. (2022). Intention to use analytical artificial intelligence (AI) in services-the effect of technology readiness and awareness. Journal of Service Management, 33(2), 293-320. https://doi.org/10.1108/JOSM-10-2020-0378
  33. Gallego-Losada, R., Montero-Navarro, A., Rodriguez-Sanchez, J. L., and Gonzalez-Torres, T. (2022). Retirement planning and financial literacy, at the crossroads. A bibliometric analysis. Finance Research Letters, 44, 102109.
  34. Gandrud, C., and Hallerberg, M. (2019). The measurement of real-time perceptions of financial stress: Implications for political science. British Journal of Political Science, 49(4), 1577-1589. https://doi.org/10.1017/S0007123417000291
  35. Garcia-Mendez, S., Fernandez-Gavilanes, M., Juncal-Martinez, J., Gonzalez-Castano, F. J., and Seara, O. B. (2020). Identifying banking transaction descriptions via support vector machine short-text classification based on a specialized labelled corpus. IEEE Access, 8, 61642-61655. https://doi.org/10.1109/ACCESS.2020.2983584
  36. Goel, P., Kaushik, N., Sivathanu, B., Pillai, R., and Vikas, J. (2022). Consumers' adoption of artificial intelligence and robotics in hospitality and tourism sector: Literature review and future research agenda. Tourism Review, 77(4), 1081-1096. https://doi.org/10.1108/TR-03-2021-0138
  37. Gomber, P., Kauffman, R. J., Parker, C., and 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
  38. Gonzalez-Gonzalez, J., Garcia-Mendez, S., De Arriba-Perez, F., Gonzalez-Castano, F. J., and Barba-Seara, O. (2022). Explainable automatic industrial carbon footprint estimation from bank transaction classification using natural language processing. IEEE Access, 10, 126326-126338. https://doi.org/10.1109/ACCESS.2022.3226324
  39. Goodell, J. W., Kumar, S., Lahmar, O., and Pandey, N. (2023). A bibliometric analysis of cultural finance. International Review of Financial Analysis, 85, 102442.
  40. Guggemos, J., Seufert, S., and Sonderegger, S. (2020). Humanoid robots in higher education: Evaluating the acceptance of Pepper in the context of an academic writing course using the UTAUT. British Journal of Educational Technology, 51(5), 1864-1883. https://doi.org/10.1111/bjet.13006
  41. Hancock, J. T., Naaman, M., and Levy, K. (2020). AI-mediated communication: Definition, research agenda, and ethical considerations. Journal of Computer-Mediated Communication, 25(1), 89-100. https://doi.org/10.1093/jcmc/zmz022
  42. Hari, H., Iyer, R., and Sampat, B. (2022). Customer brand engagement through chatbots on bank websites-Examining the antecedents and consequences. International Journal of Human-Computer Interaction, 38(13), 1212-1227. https://doi.org/10.1080/10447318.2021.1988487
  43. Hentzen, J. K., Hoffmann, A., Dolan, R., and Pala, E. (2022). Artificial intelligence in customer-facing financial services: A systematic literature review and agenda for future research. International Journal of Bank Marketing, 40(6), 1299-1336. https://doi.org/10.1108/IJBM-09-2021-0417
  44. Howarth, J. (2023, January 20). 57+ Incredible Fintech Stats (2023-2025). Exploding Topics. Retrieved from https://explodingtopics.com/blog/fintech-stats
  45. Huang, S. Y., and Lee, C. J. (2022). Predicting continuance intention to fintech chatbot. Computers in Human Behavior, 129, 107027.
  46. Huang, S. Y., Lee, C. J., and Lee, S. C. (2021). Toward a Unified Theory of Customer Continuance Model for Financial Technology Chatbots. Sensors, 21(17), 5687.
  47. Hwang, S., and Kim, J. (2021). Toward a chatbot for financial sustainability. Sustainability, 13(6), 3173.
  48. Io, H. N., and Lee, C. B. (2017, December). Chatbots and conversational agents: A bibliometric analysis. In 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 215-219). IEEE.
  49. Ivanova, O. V., Korobeinikova, L. S., Risin, I. E., and Sysoeva, E. F. (2020). The main directions and tools of banking digitalization. In Digital Economy: Complexity and Variety vs. Rationality 9 (pp. 510-516). Springer International Publishing.
  50. Jang, M., Jung, Y., and Kim, S. (2021). Investigating managers' understanding of chatbots in the Korean financial industry. Computers in Human Behavior, 120, 106747.
  51. Johari, N. M., Nohuddin, P. N., Baharin, A. H. A., Yakob, N. A., and Ebadi, M. J. (2022). Features requirement elicitation process for designing a chatbot application. IET Networks.
  52. Kasaraneni, H., and Rosaline, S. (2022). Automatic merging of scopus and web of science data for simplified and effective bibliometric analysis. Annals of Data Science, 1-18. https://doi.org/10.1007/s40745-022-00438-0
  53. Kaufman, D. (2020). Deep Learning: a Brazilian case. In Intelligent Systems and Applications: Proceedings of the 2019 Intelligent Systems Conference (IntelliSys) Volume 1 (pp. 832-847). Springer International Publishing.
  54. Kaur, B., Kiran, S., Grima, S., and Rupeika-Apoga, R. (2021). Digital banking in Northern India: The risks on customer satisfaction. Risks, 9(11), 209.
  55. Kaushal, V., and Yadav, R. (2023). Learning successful implementation of Chatbots in businesses from B2B customer experience perspective. Concurrency and Computation: Practice and Experience, 35(1), e7450. https://doi.org/10.1002/cpe.7450
  56. Kawamura, K., Kobashi, Y., Shizume, M., and Ueda, K. (2019). Strategic central bank communication: Discourse analysis of the Bank of Japan's Monthly Report. Journal of Economic Dynamics and Control, 100, 230-250. https://doi.org/10.1016/j.jedc.2018.11.007
  57. Kaya, O., Schildbach, J., AG, D. B., and Schneider, S. (2019). Artificial Intelligence in Banking. Artificial intelligence.
  58. Khan, A., Goodell, J. W., Hassan, M. K., and Paltrinieri, A. (2022). A bibliometric review of finance bibliometric papers. Finance Research Letters, 47, 102520. https://doi.org/10.1016/j.frl.2021.102520
  59. Kim, J., and Im, I. (2023). Anthropomorphic response: Understanding interactions between humans and artificial intelligence agents. Computers in Human Behavior, 139, 107512.
  60. Kim, J., Kang, S., and Lee, K. H. (2021). Evolution of digital marketing communication: Bibliometric analysis and network visualization from key articles. Journal of Business Research, 130, 552-563. https://doi.org/10.1016/j.jbusres.2019.09.043
  61. Kot, M., and Leszczynski, G. (2022). AI-activated value co-creation. An exploratory study of conversational agents. Industrial Marketing Management, 107, 287-299. https://doi.org/10.1016/j.indmarman.2022.10.013
  62. Kumpulainen, M., and Seppanen, M. (2022). Combining web of science and scopus datasets in citation-based literature study. Scientometrics, 127(10), 5613-5631. https://doi.org/10.1007/s11192-022-04475-7
  63. Kuo, H. K. J., Siohan, O., and Olive, J. P. (2003). Advances in natural language call routing. Bell Labs Technical Journal, 7(4), 155-170. https://doi.org/10.1002/bltj.10040
  64. Kuo, H. K., and Lee, C. H. (2003). Discriminative training of natural language call routers. IEEE Transactions on Speech and Audio Processing, 11(1), 24-35. https://doi.org/10.1109/TSA.2002.807352
  65. Lai, S. T., Leu, F. Y., and Lin, J. W. (2019). A banking chatbot security control procedure for protecting user data security and privacy. In Advances on Broadband and Wireless Computing, Communication and Applications: Proceedings of the 13th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA-2018) (pp. 561-571). Springer International Publishing.
  66. Lappeman, J., Marlie, S., Johnson, T., and Poggenpoel, S. (2022). Trust and digital privacy: willingness to disclose personal information to banking chatbot services. Journal of Financial Services Marketing, 1-21.
  67. Lee, C. T., Pan, L. Y., and Hsieh, S. H. (2022). Artificial intelligent chatbots as brand promoters: A two-stage structural equation modeling-artificial neural network approach. Internet Research, 32(4), 1329-1356. https://doi.org/10.1108/INTR-01-2021-0030
  68. Li, B., and Xu, Z. (2021). Insights into financial technology (FinTech): A bibliometric and visual study. Financial innovation, 7, 1-28. https://doi.org/10.1186/s40854-021-00285-7
  69. Li, B., and Xu, Z. (2022). A comprehensive bibliometric analysis of financial innovation. Economic Research-Ekonomska Istrazivanja, 35(1), 367-390. https://doi.org/10.1080/1331677X.2021.1893203
  70. Li, C. H., Yeh, S. F., Chang, T. J., Tsai, M. H., Chen, K., and Chang, Y. J. (2020, April). A conversation analysis of non-progress and coping strategies with a banking task-oriented chatbot. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-12).
  71. Li, X., Han, J., Zhang, S., Chen, K., Zhao, L., He, Y., and Liu, S. (2021). Artificial intelligence for screening Chinese electronic medical record and biobank information. Biopreservation and Biobanking, 19(5), 386-393. https://doi.org/10.1089/bio.2020.0151
  72. Lim, W. M., Kumar, S., Verma, S., and Chaturvedi, R. (2022). Alexa, what do we know about conversational commerce? Insights from a systematic literature review. Psychology & Marketing, 39(6), 1129-1155. https://doi.org/10.1002/mar.21654
  73. Linnenluecke, M. K., Marrone, M., and Singh, A. K. (2020). Conducting systematic literature reviews and bibliometric analyses. Australian Journal of Management, 45(2), 175-194. https://doi.org/10.1177/031289621987767
  74. Lothritz, C., Allix, K., Lebichot, B., Veiber, L., Bissyande, T. F., and Klein, J. (2021, June). Comparing multilingual and multiple monolingual models for intent classification and slot filling. In Natural Language Processing and Information Systems: 26th International Conference on Applications of Natural Language to Information Systems, NLDB 2021, Saarbrucken, Germany, June 23-25, 2021, Proceedings (pp. 367-375). Cham: Springer International Publishing.
  75. Mane, D., Patil, M., Chaudhari, V., Nayakavadi, R., and Pandhe, S. (2022, September). A Survey on Chatbot in Devanagari Language. In Proceedings of the 6th International Conference on Advance Computing and Intelligent Engineering: ICACIE 2021 (pp. 341-354). Singapore: Springer Nature Singapore.
  76. Manshad, M. S., and Brannon, D. C. (2022). Gender-based conversational interface preferences in live chat systems for financial services. Journal of Financial Services Marketing, 1-13. https://doi.org/10.1057/s41264-022-00175-8
  77. Mhlanga, D. (2022). Stakeholder capitalism, the fourth industrial revolution (4IR), and sustainable development: Issues to be resolved. Sustainability, 14(7), 3902.
  78. Mogaji, E., and Nguyen, N. P. (2022). Managers' understanding of artificial intelligence in relation to marketing financial services: Insights from a cross-country study. International Journal of Bank Marketing, 40(6), 1272-1298. https://doi.org/10.1108/IJBM-09-2021-0440
  79. Mogaji, E., Balakrishnan, J., Nwoba, A. C., and Nguyen, N. P. (2021). Emerging-market consumers' interactions with banking chatbots. Telematics and Informatics, 65, 101711.
  80. Mongeon, P., and Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: A comparative analysis. Scientometrics, 106, 213-228. https://doi.org/10.1007/s11192-015-1765-5
  81. Naeem, M. A., Karim, S., Rabbani, M. R., Bashar, A., and Kumar, S. (2022). Current state and future directions of green and sustainable finance: A bibliometric analysis. Qualitative Research in Financial Markets, (ahead-of-print).
  82. Narin, F., Olivastro, D., and Stevens, K. A. (1994). Bibliometrics/theory, practice and problems. Evaluation review, 18(1), 65-76. https://doi.org/10.1177/0193841X9401800107
  83. Neff, G. (2016). Talking to bots: Symbiotic agency and the case of Tay. International Journal of Communication, 10, 4915-4931.
  84. Ng, M., Coopamootoo, K. P., Toreini, E., Aitken, M., Elliot, K., and van Moorsel, A. (2020, September). Simulating the effects of social presence on trust, privacy concerns & usage intentions in automated bots for finance. In 2020 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) (pp. 190-199). IEEE.
  85. Northey, G., Hunter, V., Mulcahy, R., Choong, K., and Mehmet, M. (2022). Man vs machine: how artificial intelligence in banking influences consumer belief in financial advice. International Journal of Bank Marketing, (ahead-of-print).
  86. Ogonowski, A., Montandon, A., Botha, E., and Reyneke, M. (2014). Should new online stores invest in social presence elements? The effect of social presence on initial trust formation. Journal of Retailing and Consumer Services, 21(4), 482-491. https://doi.org/10.1016/j.jretconser.2014.03.004
  87. Palomino-Navarro, N., and Arbaiza, F. (2022). The Role of a Chabot Personality in the Attitude of Consumers Towards a Banking Brand. In Information Systems and Technologies: WorldCIST 2022 (Vol. 1 pp. 390-400). Cham: Springer International Publishing.
  88. Pisarovic, I., Darena, F., Prochazka, D., and Janis, V. (2022). Preprocessing of normative documents for interactive question answering. Expert Systems with Applications, 191, 116314.
  89. Pranckute, R. (2021). Web of Science (WoS) and Scopus: The titans of bibliographic information in today's academic world. Publications, 9(1), 12.
  90. Prentice, C., Dominique Lopes, S., and Wang, X. (2020). The impact of artificial intelligence and employee service quality on customer satisfaction and loyalty. Journal of Hospitality Marketing & Management, 29(7), 739-756. https://doi.org/10.1080/19368623.2020.1722304
  91. Pritchard, A. (1969). Statistical bibliography or bibliometrics. Journal of documentation, 25, 348.
  92. Quah, J. T., and Chua, Y. W. (2019). Chatbot assisted marketing in financial service industry. In Services Computing-SCC 2019: 16th International Conference, Held as Part of the Services Conference Federation, SCF 2019, San Diego, CA, USA, June 25-30, 2019, Proceedings 16 (pp. 107-114). Springer International Publishing.
  93. Rafiq, F., Dogra, N., Adil, M., and Wu, J. Z. (2022). Examining consumer's intention to adopt AI-chatbots in tourism using partial least squares structural equation modeling method. Mathematics, 10, 2190.
  94. Richad, R., Vivensius, V., Sfenrianto, S., and Kaburuan, E. R. (2019). Analysis of factors influencing millennial's technology acceptance of chatbot in the banking industry in Indonesia. International Journal of Civil Engineering and Technology, 10(4), 1270-1281. https://doi.org/10.34218/IJM.10.3.2019.011
  95. Richardson, M. L., Adams, S. J., Agarwal, A., Auffermann, W. F., Bhattacharya, A. K., Consul, N., Fotos, J. S., Kelahan, L. C., Lin, C., Lo, H. S., Nguyen, X. V., Salkowski, L. R., Sin, J. M., Thomas, R. C., Wassef, S., and Ikuta, I. (2021). Review of artificial intelligence training tools and courses for radiologists. Academic Radiology, 28(9), 1238-1252. https://doi.org/10.1016/j.acra.2020.12.026
  96. Rohm, P. (2018). Exploring the landscape of corporate venture capital: a systematic review of the entrepreneurial and finance literature. Management Review Quarterly, 68(3), 279-319. https://doi.org/10.1007/s11301-018-0140-z
  97. Rowe, M., Nicholls, D. A., and Shaw, J. (2022). How to replace a physiotherapist: artificial intelligence and the redistribution of expertise. Physiotherapy Theory and Practice, 38(13), 2275-2283. https://doi.org/10.1080/09593985.2021.1934924
  98. Sana, Chakraborty, S., Adil, M., and Sadiq, M. (2023). Ecotourism experience: A systematic review and future research agenda. International Journal of Consumer Studies, 1-26. https://doi.org/0.1111/ijcs.12902.
  99. Schildt, H. A., and Mattsson, J. T. (2006). A dense network sub-grouping algorithm for co-citation analysis and its implementation in the software tool Sitkis. Scientometrics, 67(1), 143-163. https://doi.org/10.1007/s11192-006-0054-8
  100. Secinaro, S., Brescia, V., Calandra, D., and Biancone, P. (2020). Employing bibliometric analysis to identify suiTable business models for electric cars. Journal of cleaner production, 264, 121503.
  101. Sihotang, M. K., and Hasanah, H. (2021, February). Islamic banking strategy in facing the new normal era during the Covid 19. In Proceeding International Seminar Of Islamic Studies (Vol. 2, No. 1, pp. 479-485).
  102. Solakis, K., Katsoni, V., Mahmoud, A. B., and Grigoriou, N. (2022). Factors affecting value co-creation through artificial intelligence in tourism: A general literature review. Journal of Tourism Futures, (ahead-of-print).
  103. Stevenson, M., Mues, C., and Bravo, C. (2021). The value of text for small business default prediction: A deep learning approach. European Journal of Operational Research, 295(2), 758-771. https://doi.org/10.1016/j.ejor.2021.03.008
  104. Suhel, S. F., Shukla, V. K., Vyas, S., and Mishra, V. P. (2020, June). Conversation to automation in banking through chatbot using artificial machine intelligence language. In 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) (pp. 611-618). IEEE.
  105. Taylor, J. J., Subramanian, A., Freitas, A., Ferreira, D. M., and Dickinson, C. M. (2023). What do individuals with visual impairment need and want from a dialogue-based digital assistant?. Clinical and Experimental Optometry, 1-10.
  106. Tenemaza, M., Lujan-Mora, S., de Antonio, A., Ramirez, J., and Zarabia, O. (2020). Ekybot: framework proposal for chatbot in financial enterprises. In Intelligent Human Systems Integration 2020: Proceedings of the 3rd International Conference on Intelligent Human Systems Integration (IHSI 2020): Integrating People and Intelligent Systems, February 19-21, 2020, Modena, Italy (pp. 254-259). Springer International Publishing.
  107. Tran, B. X., Vu, G. T., Ha, G. H., Vuong, Q. H., Ho, M. T., Vuong, T. T., La, V. P., Ho, M. T., Nghiem, K. C. P., Nguyen, H. L. T., Latkin, C. A., Tam, W. W. S., Cheung, N. M., Nguyen, H. K. T., Ho, C. S. H., and Ho, R. C. (2019). Global evolution of research in artificial intelligence in health and medicine: a bibliometric study. Journal of Clinical Medicine, 8(3), 360. https://doi.org/10.3390/jcm8030360
  108. Trivedi, J. (2019). Examining the customer experience of using banking chatbots and its impact on brand love: The moderating role of perceived risk. Journal of internet Commerce, 18(1), 91-111. https://doi.org/10.1080/15332861.2019.1567188
  109. Tut, D. (2023). FinTech and the Covid-19 pandemic: Evidence from electronic payment systems. Emerging Markets Review, 100999. https://doi.org/10.1016/j.ememar.2023.100999
  110. van de Sande, D., Van Genderen, M. E., Smit, J. M., Huiskens, J., Visser, J. J., Veen, R. E., van Unen, E., BA, O. H., Gommers, D., and van Bommel, J. (2022). Developing, implementing and governing artificial intelligence in medicine: a step-by-step approach to prevent an artificial intelligence winter. BMJ Health & Care Informatics, 29(1), e100495. https://doi.org/10.1136/bmjhci-2021-100495
  111. Wallin, J. A. (2005). Bibliometric methods: Pitfalls and possibilities. Basic & Clinical Pharmacology & Toxicology, 97(5), 261-275. https://doi.org/10.1111/j.1742-7843.2005.pto_139.x
  112. Wang, X., Wong, Y. D., Chen, T., and Yuen, K. F. (2021). Adoption of shopper-facing technologies under social distancing: A conceptualisation and an interplay between task-technology fit and technology trust. Computers in Human Behavior, 124, 106900.
  113. White, H. D., and Griffith, B. C. (1981). Author cocitation: A literature measure of intellectual structure. Journal of the American Society for information Science, 32(3), 163-171. https://doi.org/10.1002/asi.4630320302
  114. Wilkie, J., Jack, M. A., and Littlewood, P. J. (2005). System-initiated digressive proposals in automated human-computer telephone dialogues: The use of contrasting politeness strategies. International Journal of Human-Computer Studies, 62(1), 41-71. https://doi.org/10.1016/j.ijhcs.2004.08.001
  115. Xing, Z., Yu, F., Du, J., Walker, J. S., Paulson, C. B., Mani, N. S., and Song, L. (2019). Conversational interfaces for health: bibliometric analysis of grants, publications, and patents. Journal of Medical Internet Research, 21(11), e14672.
  116. Yoruk, T., Akar, N., and Ozmen, N. V. (2023). Research trends on guest experience with service robots in the hospitality industry: a bibliometric analysis. European Journal of Innovation Management, ahead-of-print. https://doi.org/10.1108/EJIM-09-2022-0530
  117. Zhang, D., Zhang, Z., and Managi, S. (2019). A bibliometric analysis on green finance: Current status, development, and future directions. Finance Research Letters, 29, 425-430. https://doi.org/10.1016/j.frl.2019.02.003
  118. Zhou, W., Chen, J., and Huang, Y. (2019). Co-Citation analysis and burst detection on financial bubbles with scientometrics approach. Economic Research-Ekonomska istrazivanja, 32(1), 2310-2328. https://doi.org/10.1080/1331677X.2019.1645716
  119. Zhu, J., and Liu, W. (2020). A tale of two databases: The use of Web of Science and Scopus in academic papers. Scientometrics, 123(1), 321-335. https://doi.org/10.1007/s11192-020-03387-8
  120. Zitouni, I. (2007). Constrained minimization and discriminative training for natural language call routing. IEEE Transactions on Audio, Speech, and Language Processing, 16(1), 208-215. https://doi.org/10.1109/TASL.2007.911056
  121. Zitouni, I., Kuo, H. K. J., and Lee, C. H. (2003). Boosting and combination of classifiers for natural language call routing systems. Speech Communication, 41(4), 647-661. https://doi.org/10.1016/S0167-6393(03)00103-1