Chatbot research: Unveiling evolutionary trends and collaborative pathways through bibliometric analysis

L. Manigandan; Alur Sivakumar

doi:10.31893/multirev.2024045

Keywords:

chatbot

innovation

artificial intelligence

bibliometric

cocitation analysis

thematic

multiple corresponding analysis

Abstract

As technology continues to grow and rapidly transform, marketing techniques have adapted to meet the evolving desires and requirements of customers. The recent advancements in artificial intelligence (AI) and machine learning, coupled with the widespread popularity of the internet and messaging platforms, have led businesses to shift their focus toward chatbots. This study aims to contribute to the field of chatbot research by conducting a bibliometric analysis of chatbot papers available in the Scopus and Web of Science (WoS) databases. A comprehensive analysis of 360 articles was performed utilizing the Bibliometrix software package. The keywords "Chatbot" and "Artificial Intelligence" emerged as the most frequently employed terms in the majority of publications. Collaboration between the United States and China was found to be the most extensive in the field. However, limited studies have been conducted on marketing-related aspects of chatbots, such as anthropomorphism, customer satisfaction, and trust. This study reviews Scopus and WoS titles and abstracts to provide an in-depth view of chatbot research and recommends new areas of research.
References
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Manigandan, L., & Sivakumar, A. (2023). Chatbot research: Unveiling evolutionary trends and collaborative pathways through bibliometric analysis. Multidisciplinary Reviews, 7(3), 2024045. https://doi.org/10.31893/multirev.2024045

[1] Al-Zaman, M. (2021). A bibliometric and co-occurrence analysis of COVID-19–related literature published between December 2019 and June 2020. Science Editing, 8(1), 57–63.

[2] Araujo, T. (2018). Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions. Computers in Human Behavior, 85, 183–189.

[3] Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 958–975.

[4] Arnold, A. (2018). How chatbots feed into millennials’ need for instant gratification. (accessed October 31, 2020). In https:// www.forbes.com/sites/andrewarnold/2018/01/27/how-chatbots-feed-into-millennials-needfor-instant-gratification/?sh=79a719ac3675.

[5] Ask, J.A., Facemire, M., & Hogan, A. (2016). The state of chatbots. In Forrester.com Report.

[6] Asquith, M. (2019). Rule-based vs AI Chatbots. (accessed January 4, 2020).

[7] Bornmann, L., Haunschild, R., & Hug, S. E. (2018). Visualizing the context of citations referencing papers published by Eugene Garfield: A new type of keyword co-occurrence analysis. Scientometrics, 114(2), 427–437.

[8] Callon, M. C. (1983). From translation to problematic networks: an introduction to co-word analysis. Information (International Social Science Council), 569–590.

[9] CGS. (2019). CGS survey reveals consumers prefer a hybrid AI/human approach to customer service. Is there chatbot fatigue?

[10] Dale, R. (2016). The return of the chatbots. Natural Language Engineering, 22(5), 811–817. https://doi.org/10.1017/S1351324916000243

[11] Furstenau, L. B., Sott, M. K., Homrich, A. J. O., Kipper, L. M., Dohan, M. S., López-Robles, J. R., ... & Tortorella, G. L. (2021). An overview of 42 years of lean production: Applying bibliometric analysis to investigate strategic themes and scientific evolution structure. Technology Analysis & Strategic Management, 33(9), 1068-1087.

[12] Garfield, E. (2004). Historiographic Mapping of Knowledge Domains Literature. Journal of Information Science, 20(2), 119–145.

[13] Go, E., & Sundar, S. S. (2019). Humanizing chatbots: The effects of visual, identity and conversational cues on humanness perceptions. Computers in Human Behavior, 97, 304–316. https://doi.org/10.1016/j.chb.2019.01.020

[14] Greenacre, M. (2006). Multiple correspondence analysis and related methods: CRC press.

[15] Haan, D. H. (n.d.). Chatbot Personality and Customer Satisfaction. Information Science.

[16] Hagberg, J. S.-Z. (2016). The digitalization of retailing: An exploratory framework. International Journal of Retail & Distribution Management, 694–712.

[17] Han, M. (2019). Instant messaging chat bot: Your new best friend? In Smart marketing with the internet of things, ed. B. Barbosa, S. Filipe, C. A. Santos and D. Simoes, . Hershey, PA:IGI Global.

[18] Handarkho, Y. D. (2020). The intentions to use social commerce from social, technology, and personal trait perspectives: analysis of direct, indirect, and moderating effects. Journal of Research in Interactive Marketing, 14(3), 305–336.

[19] Higaki, A., Uetani, T., Ikeda, S., & Yamaguchi, O. (2020). Co-authorship network analysis in cardiovascular research utilizing machine learning (2009–2019). International Journal of Medical Informatics, 143.

[20] Hill, J., Ford, W. R., & Farreras, I. G. (2915). Real conversations with artificial intelligence: A comparison between human–human online conversations and human–chatbot conversations. Computers in Human Behavior, 49, 245–250.

[21] J.Weizenbaum. (1966). ELIZA — A Computer Program For the Study of Natural Language Communication Between Man And Machine. Commun. ACM, 9(1), 36–45.

[22] The innovative success that is Apple, Inc. Marshall University Marshall Digital Scholar, (2012).

[23] Kang, G. D. (2006). The hierarchical structure of service quality: Integration of technical and functional quality. ,. Managing Service Quality: An International Journal, 37–50.

[24] Kiesler, S., Powers, A., Fussell, S., & Torrey, C. (2008). Anthropomorphic interactions with a robot and robot-like agent. Social Cognition, 26(2), 169–181.

[25] Kim, S. H., Bae, J. H., & Jeon, H. M. (2019). Continuous intention on accommodation apps: integrated value-based adoption and expectation–confirmation model analysis. Sustainability, 11(6), 1578–1595.

[26] Koskinen, J. I. (2008). How to use bibliometric methods in evaluation of scientific research? An example from finnish schizophrenia research. Nordic Journal.

[27] Lim, H. J., & Suh, C. K. (2021). The Intellectual Structure of Business Analytics by Author Co-citation Analysis: 2002~ 2020. The Journal of Information Systems, 30(1), 21–44.

[28] Martínez, M.A., Cobo, M.J., Herrera, M. and Herrera-Viedma, E. (2015). Analyzing the scientific evolution of social work using science mapping. Research on SocialWork Practice, 25(2), 257–277.

[29] Nass, C. (2004). Etiquette equality: Exhibitions and expectations of computer politeness. Communications in Computer and Information Science, 47(4), 35–37.

[30] Pillai, R., & Sivathanu, B. (2020). Adoption of AI-based chatbots for hospitality and tourism. International Journal of Contemporary Hospitality Management, 32(10), 3199–3226. https://doi.org/10.1108/IJCHM-04-2020-0259

[31] Rahman, A. M., Al Mamun, A., & Islam, A. (2017). Programming challenges of chatbot: Current and future prospective. IEEE Region 10 Humanitarian Technology Conference, 10, 75–78.

[32] Rawlins. L. K. (2016). Facebook chat bots to replace call centres. iTWeb. http://www.itweb. co.za/index.php?option=com_content&view=article&id=151,438 (accessed August 08, 2018).

[33] Rodríguez-Sabiote, C. Ú.-S.-R. (2020). Active Learning in an Environment of Innovative Training and Sustainability. Mapping of the Conceptual Structure of Research Fronts through a Bibliometric.

[34] Shawar, B. A., and E. A. (2007). Chat bots: Are they really useful? LDV Forum, 22(1), 29–49.

[35] Shawar, B. A., & Atwell, E. S. (2005). Using corpora in machine-learning chatbot systems. International Journal of Corpus Linguistics, 10(4), 489–516. https://doi.org/10.1075/ijcl.10.4.06sha

[36] Small, H. (1999). Visualizing science by citation mapping. Journal of the American Society for Information Science, 799–813.

[37] Small, H. J. (1980). Co‐citation context analysis and the structure of paradigms.

[38] Tennekes, M. J. (2018). Tmap: Thematic Maps in R. 84(6), 1–39.

[39] Tsai, W.-H.S., Liu, Y. and Chuan, C.-H. (2021). How chatbots’ social presence communication enhances consumer engagement: the mediating role of parasocial interaction and dialogu. Journal of Research in Interactive Marketing, 15(3), 460–482.

[40] Turban, E., J. Outland, D. King, J. K. Lee, T. P. Liang, and D. C. T. (2018). Intelligent (smart) E-commerce. In Electronic Commerce 2018. Electronic Commerce, 249–283.

[41] Turing, M. (1950). Computing machinery and intelligence. Mind, 49, 433–460.

[42] Uliyar, A. (2017). Primer: Oracle Intelligent Bots. Powered by artificial intelligent. White Paper, 1–28.

[43] Wang, C., Wu, R., Deng, L., Chen, Y., Li, Y. and Wan, Y. (2020). A bibliometric analysis on noshow research: status, hotspots, trends and outlook. Sustainability, 12(10), 3997.

[44] Westerman, D., Cross, A. C., & Lindmark, P. G. (2019). I Believe in a Thing Called Bot: Perceptions of the Humanness of “Chatbots.” Communication Studies, 70(3), 295–312. https://doi.org/10.1080/10510974.2018.1557233

[45] Worldpay. (2018). Consumer data: How consumers use and perceive messaging and chatbots in eCommerce. (accessed October 31, 2020).

[46] Zhou, L., Gao, J., Li, D., & Shum, H. Y. (2020). The design and implementation of xiaoice, an empathetic social chatbot. Computational Linguistics, 46(1), 53–93. https://doi.org/10.1162/COLI_a_00368

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