Feb 2026· Computers in Human Behavior· Vol abs/2602.17850· 0 citations· 58 references
Computer Science
TL;DR
It is suggested that chatbot communication style influences users' perceptions of conversational agents and may improve performance relative to less supportive chatbot designs, but the overall value of chatbot interaction depends on the task context.
Abstract
Conversational agents increasingly mediate everyday digital interactions, yet the effects of their communication style on user experience and task success remain insufficiently understood. Addressing this gap, we report a between-subject user study in which participants interacted with one of two versions of a chatbot called NAVI, which assisted them in an interactive map-based 2D navigation task. The two chatbot versions were designed to differ primarily in communication style: one used a friendly and supportive tone, while the other used a direct and task-focused tone. We also included a control condition where participants did not interact with a chatbot but received the step-by-step navigation instructions. The friendly chatbot significantly increased users'communication satisfaction and was associated with higher task success than the direct chatbot. However, participants in the control condition achieved the highest task success overall, suggesting that chatbot interaction may introduce overhead in tasks that can be completed effectively using straightforward instructions. We did not find significant evidence that gender moderated the effects of communication style, although exploratory gender-stratified analyses suggested patterns that warrant further investigation. Finally, we found limited evidence of global linguistic accommodation, with only selective feature-level alignment. These findings suggest that chatbot communication style influences users'perceptions of conversational agents and may improve performance relative to less supportive chatbot designs, but the overall value of chatbot interaction depends on the task context. The study highlights the need for task-sensitive, transparent and carefully evaluated communication-style choices in conversational-agent design.
This pilot study investigated the influence of gender of a chatbot and gender of users on the User Experience, along the dimensions of warmth/friendliness, competence, trust, human-likeness user satisfaction and helpfulness. When interacting with a chatbot, users often categorize the chatbot as female, gender-neutral o...
C. van Nimwegen, Lotte Consten· Proceedings of the 2026 Euro...· 0 citations
The results revealed that chatbot interactions were perceived as more effective for low-complexity, transactional service tasks because they reduced response times and increased convenience, and hybrid chatbot–human service models may provide the strongest effort–outcome balance by combining the efficiency of automatio...
Ioseb Gabelaia, Samuel Steiner· Journal of Marketing Researc...· 0 citations
Investigating EFL learners’ interaction patterns with ChatGPT as an out-of-class language partner and examines how these patterns and learner perceptions evolve over time suggests greater interactional engagement and growing familiarity with AI-mediated speaking practice.
Wen-Zheng Huang, Hao Zhou· Journal of China Computer-As...· 0 citations
Generative artificial intelligence (GenAI) has become a research focus in recent years, and has been widely applied in many fields. Due to its excellent natural language processing ability and its capability to simulate human conversation, the Chat Generative Pre-trained Transformer (ChatGPT) has attracted much attenti...
Zhong-Zhen Lin, Yu-Kai Li, Sha-Sha Li et al.· Acta Psychologica· 0 citations
This work adopts an interdisciplinary approach to investigate how interactions with conversational recommender systems (CRSs) can foster user trust, compared to faceted search interfaces commonly used in e-commerce. We present an empirical comparison between a system-driven CRS and a functionally equivalent faceted sea...
Laura Modre, Ahmadou Wagne, T. Kolb et al.· Proceedings of the 20th ACM...· 0 citations
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.