Using Dark Patterns in Online Service Chatbots
(A2025-126223)

AUTHORS

Jan-Lukas Selter, University of Siegen; Julian Schmitz, University of Siegen; Hanna Schramm-Klein, University of Siegen

ABSTRACT

The purpose of this study is to examine three different dark patterns in the context of a live chat service interaction. The three dark patterns are social proof, pricing, and shortage. In addition, the difference between a human chat partner and an AI-based chatbot will be examined. The goal is to investigate trust and purchase intention in more detail. Results show that human chat partners in a service interaction lead to higher trust and higher purchase intention than AI-based chatbots. Dark pattern pricing was able to achieve the highest trust and purchase intention. Dark patterns may be an appropriate way for businesses to persuade consumers to purchase and make online commerce more efficient. However, care must be taken to ensure that dark patterns are not perceived as manipulative. While AI-based chatbots are already an effective alternative to live human chat, there is still an advantage of human chat interactions.