AI-based chatbots in customer service and their effects on user compliance

By Martin Adam, Michael Wessel, Alexander Benlian. Electronic Markets, 2020

971 citations recorded by the source corpus.

Communicating with customers through live chat interfaces has become an increasingly popular means to provide real-time customer service in many e-commerce settings. Today, human chat service agents are frequently replaced by conversational software agents or chatbots, which are systems designed to communicate with human users by means of natural language often based on artificial intelligence (AI). Though cost- and time-saving opportunities triggered a widespread implementation of AI-based chatbots, they still frequently fail to meet customer expectations, potentially resulting in users being less inclined to comply with requests made by the chatbot. Drawing on social response and commitment-consistency theory, we empirically examine through a randomized online experiment how verbal anthropomorphic design cues and the foot-in-the-door technique affect user request compliance. Our results demonstrate that both anthropomorphism as well as the need to stay consistent significantly increase the likelihood that users comply with a chatbot’s request for service feedback. Moreover, the results show that social presence mediates the effect of anthropomorphic design cues on user compliance.

Subfields: Artificial Intelligence, Social Psychology, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/4c33b3312875ce39d4475747e6e02f1672ff4886

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Sociology and Political Science

Citations in the corpus

Trustworthy artificial intelligence (2020, background): “Electronic markets, for example, are increasingly augmented with AI-based systems such as customer service chatbots (Adam et al. 2020).”
AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges (2025, neutral): “Their adoption has been reported across a wide range of application domains, including customer service automation [51,52], personal productivity assistance [53], organizational information retrieval [54,55], and decision support systems [56,57].”
Teachers’ attitudes towards chatbots in education: a technology acceptance model approach considering the effect of social language, bot proactiveness, and users’ characteristics (2021, background): “Agents who use social language get better responses from consumers than when they use a functional language (Adam, Wessel, and Benlian 2020).”
Trust in AI: progress, challenges, and future directions (2024, positive): “Electronic markets, for example, are increasingly augmented with AI-based systems such as customer service chatbots (Adam et al., 2021b).”
Privacy Regularization: Joint Privacy-Utility Optimization in LanguageModels (2021, background): “, 2010) have recently seen significant gains in capabilities, and are deployed at scale in several real-world scenarios (Chen et al., 2019; Adam et al., 2020).”