When Chatbots Meet Process Mining: Conversation Mining in the Era of Digital Trace Data

Christian Schuler (), Dominik Hauser (), Christoph Zehendner () and Maren Gierlich-Joas ()
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Christian Schuler: Universität Hamburg
Dominik Hauser: Universität Hamburg
Christoph Zehendner: Technische Universität Dresden
Maren Gierlich-Joas: Copenhagen Business School

Chapter Chapter 11 in Digital Trace Data Research in Information Systems, 2026, pp 251-274 from Springer

Abstract: Abstract This chapter presents a specific application of digital trace data research in the context of conversational agents (CAs). CAs are becoming more common in industry and everyday life, creating a need for reliable evaluation methods. Building on the literature on CAs and process mining, we follow a design science research (DSR) approach. We develop a travel recommendation chatbot using an open-source framework, conduct a survey with 45 participants, and collect log data from their interactions with the chatbot. We use process mining techniques to analyze this data and conclude that conversation mining can improve the chatbot’s performance. This research demonstrates that conversation mining can supplement existing evaluation strategies, providing a more comprehensive understanding of the user experience. We suggest that this approach could be used to evaluate other chatbots and could help improve the design and implementation of future systems. Thereby, this work is a showcase example and a critical reflection of how to use digital trace data for conversation mining.

Keywords: Chatbot; Process mining; User interaction (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prochp:978-3-032-05497-5_11

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DOI: 10.1007/978-3-032-05497-5_11

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