Academic Journal

Modeling Trust Dimensions and Dynamics in Human-Agent Conversation: A Trajectory Epistemic Network Analysis Approach.

التفاصيل البيبلوغرافية
العنوان: Modeling Trust Dimensions and Dynamics in Human-Agent Conversation: A Trajectory Epistemic Network Analysis Approach.
المؤلفون: Li, Mengyao1 (AUTHOR) mengyao.li@wisc.edu, Kamaraj, Amudha V.1 (AUTHOR), Lee, John D.1 (AUTHOR)
المصدر: International Journal of Human-Computer Interaction. Jul2024, Vol. 40 Issue 14, p3571-3582. 12p.
مصطلحات موضوعية: TRUST, CONVERSATION analysis, CONVERSATION
مستخلص: Human-AI conversation provides a natural, unobtrusive, yet under-explored way to investigate trust dynamics in human-AI teams (HATs). In this paper, we modeled dynamic trust evolution in conversations using a novel method, trajectory epistemic network analysis (T-ENA). T-ENA captures the multidimensional aspect of trust (i.e., analytic and affective), and trajectory analysis segments conversations to capture temporal changes of trust over time. Twenty-four participants performed a habitat maintenance task assisted by a conversational agent and verbalized their experiences and feelings after each task. T-ENA showed that agent reliability significantly affected people's conversations in the analytic process of trust, t (38.88) = 15.18 , p < 0.001 , Cohen ′ s d = 4.72 , such as discussing agents' errors. The trajectory analysis showed that trust dynamics manifested through conversation topic diversity and flow. These results showed trust dimensions and dynamics in conversation should be considered interdependently and suggested that an adaptive conversational strategy for managing trust in HATs. [ABSTRACT FROM AUTHOR]
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قاعدة البيانات: Business Source Index
الوصف
تدمد:10447318
DOI:10.1080/10447318.2023.2201555