Problem Description

Conversational Agents (CAs), such as chatbots, have become increasingly popular with technological advancements like speech recognition. Many modern CAs are designed to appear more human-like, leading to anthropomorphism—the tendency to attribute human traits and behaviors to non-human objects or entities. Despite knowing that they are interacting with non-living objects, users may alter their decision-making behavior based on the degree of anthropomorphism. For instance, users might invest more or less money depending on whether a CA appears more anthropomorphic than its alternatives. Anthropomorphism may also influence user perceptions of the interaction, such as perceived trust, satisfaction, or engagement.

Goal of the Thesis

This thesis aims to conduct a structured literature review on the influence of anthropomorphism in CAs on user decision-making. This includes identifying and analyzing theoretical frameworks that explain behavioral changes resulting from anthropomorphic design features in CAs. The review should aim to uncover key theories, behavioral effects, and practical implications for designing effective CAs.

Requirements

• Proficiency in conducting a systematic literature review or willingness to learn it independently.
• Ability to summarize and synthesize findings systematically.

Sources

• Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114–126. https://doi.org/10.1037/xge0000033
• Feine, J., Gnewuch, U., Morana, S., & Maedche, A. (2019). A Taxonomy of Social Cues for Conversational Agents. International Journal of Human-Computer Studies, 132, 138–161. https://doi.org/10.1016/j.ijhcs.2019.07.009
• Seeger, A.-M., Pfeiffer, J., & Heinzl, A. (2021). Texting with human-like conversational agents: Designing for anthropomorphism. Journal of the Association for Information Systems, 22(4), 931–967.