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

Mónika-Anetta Alt () and Vizeli Ibolya ()
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Mónika-Anetta Alt: Babeş-Bolyai University
Vizeli Ibolya: Babeş-Bolyai University

Tržište/Market, 2021, vol. 33, issue 2, 165-183

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.

Keywords: artificial intelligence (AI); chatbot; banking industry; technology acceptance; segmentation; customer segments (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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