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Main Authors: Dharmaputri, Stephanie Kwari, Nagpal, Anish, Nyilasy, Greg, Lei, Jing
Format: Preprint
Published: 2026
Subjects:
Online Access:https://arxiv.org/abs/2604.12206
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author Dharmaputri, Stephanie Kwari
Nagpal, Anish
Nyilasy, Greg
Lei, Jing
author_facet Dharmaputri, Stephanie Kwari
Nagpal, Anish
Nyilasy, Greg
Lei, Jing
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2604_12206
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Socially Fluent, Socially Awkward: Artificial Intelligence Relational Talk Backfires in Commercial Interactions
Dharmaputri, Stephanie Kwari
Nagpal, Anish
Nyilasy, Greg
Lei, Jing
Human-Computer Interaction
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.
title Socially Fluent, Socially Awkward: Artificial Intelligence Relational Talk Backfires in Commercial Interactions
topic Human-Computer Interaction
url https://arxiv.org/abs/2604.12206