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Identifying Relevant Segments of Potential Banking Chatbot Users Based on Technology Adoption Behavior

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  • Mónika-Anetta Alt

    (Babeş-Bolyai University)

  • Vizeli Ibolya

    (Babeş-Bolyai University)

Abstract

Purpose – Chatbot technology is expected to revolutionize customer service in financial institutions. However, the adoption of customer service chatbots in banking remains low. Therefore, the aim of this paper is to identify relevant segments of potential banking chatbot users based on technology adoption behavior. Design/Methodology/Approach – Data for the research was collected through an online questionnaire in Romania using the non-probability sampling method. The 287 questionnaires were analyzed using hierarchical and k-means cluster analysis. Findings and implications – The analysis revealed three distinct segments: Innovators (26%), consisting of highly educated young women employed in the business sector; the Late Majority (55%), consisting of young women with higher education degrees who work in services-related fields; and Laggards (19%), consisting of educated middle-aged men employed in the business sector. New significant differences among demographic and banking behavior variables were observed across the profiles of potential banking chatbot user segments. Limitations – The study is based on a non-probability sample collected from only one country, with a rather small sample size. Originality – Technology acceptance variables (perceived usefulness, perceived ease of use), expanded to include constructs such as awareness of service, perceived privacy risk, and perceived compatibility, were found to be appropriate for customer segmentation purposes in the context of chatbot applications based on artificial intelligence. The study also revealed a new innovator demographic profile.

Suggested Citation

  • Mónika-Anetta Alt & Vizeli Ibolya, 2021. "Identifying Relevant Segments of Potential Banking Chatbot Users Based on Technology Adoption Behavior," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 33(2), pages 165-183.
    • Handle: RePEc:zag:market:v:33:y:2021:i:2:p:165-183
    DOI: 10.22598/mt/2021.33.2.165
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    References listed on IDEAS

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    1. Imtiaz Arif & Sahar Afshan & Arshian Sharif, 2016. "Resistance to Adopt Mobile Banking in a Developing Country: Evidence from Modified TAM Model," Journal of Finance and Economics Research, Geist Science, Iqra University, Faculty of Business Administration, vol. 1(1), pages 23-38, January.
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    4. Rese, Alexandra & Ganster, Lena & Baier, Daniel, 2020. "Chatbots in retailers’ customer communication: How to measure their acceptance?," Journal of Retailing and Consumer Services, Elsevier, vol. 56(C).
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    6. Andrea Lučić & Dajana Barbić & Marija Uzelac, 2020. "The Role of Financial Education in Adolescent Consumers’ Financial Knowledge Enhancement," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 32(SI), pages 115-130.
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    Cited by:

    1. Nitin Upadhyay & Aakash Kamble, 2024. "Why can’t we help but love mobile banking chatbots? Perspective of stimulus-organism-response," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 29(3), pages 855-872, September.
    2. Nicoleta Valentina Florea & Gabriel Croitoru & Georgiana Radu (Cârstea) & Daria Florea, 2024. "The Analysis of the Impact of Digital Product Innovation and Human Resources Specialists on Intention to Use Artificial Intelligence in Financial Banking System," Journal of Financial Studies, Institute of Financial Studies, vol. 16(9), pages 96-110, May.
    3. Won-Jun Lee, 2024. "Service Failure by Human, Service Recovery by AI Chatbot: The Impact of Justice, AI Efficacy on Recovery Effort," Virtual Economics, The London Academy of Science and Business, vol. 7(4), pages 48-63, December.
    4. Rob Kim Marjerison & Youran Zhang & Hanyi Zheng, 2022. "AI in E-Commerce: Application of the Use and Gratification Model to The Acceptance of Chatbots," Sustainability, MDPI, vol. 14(21), pages 1-16, November.

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