Socially Fluent, Socially Awkward: Artificial Intelligence Relational Talk Backfires in Commercial Interactions

2026-04-13

AI Summary

This study challenges the prevailing assumption that greater social fluency in AI necessarily enhances user satisfaction, demonstrating instead that the use of irrelevant relational language by AI in transactional contexts significantly reduces consumer satisfaction by violating user expectations and eliciting perceived embarrassment. Through four behavioral experiments integrating mediation and moderation analyses, the research identifies embarrassment as a critical affective barrier in human–AI interaction—a finding not previously established in the literature. Importantly, the negative impact is mitigated when relational utterances align with users’ task-oriented goals, highlighting the contextual dependency of social cues in AI communication. These results provide both theoretical grounding and practical guidance for designing AI language that balances social expressiveness with functional relevance.

Technology Category

• Humans and AI: User Experience and Usability
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Application Domains: Humanities & Computational Social Science

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Advancements in Artificial Intelligence (AI) technologies' social fluency are being integrated into commercial interactions. As tools such as OpenAI's assistant are integrated into platforms such as Shopify, Klarna, and Visa, understanding consumer responses to AI social features become essential. One such feature is relational talk, an informal and non-obligatory social communication embedded in transactional exchanges. Across four experiments, we find: 1) a negative main effect of AI relational talk on satisfaction, mediated by expectancy violation and perceived interaction awkwardness, and 2) goal-relevant relational talk to attenuate this effect. This paper extends the literature by challenging the assumption that increased social fluency will improve satisfaction, and highlights the complexity of integrating social features into AI systems. It also identifies awkwardness as a key emotional response and barrier to effective human-AI interaction, showing that even in the absence of real social repercussions, perceived awkwardness in AI-led commercial interactions can elicit negative responses.

Problem

• AI relational talk
• consumer satisfaction
• perceived awkwardness
• expectancy violation
• human-AI interaction

Innovation

• relational talk
• social fluency
• perceived awkwardness
• expectancy violation
• human-AI interaction