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

TL;DR

This study investigates how AI social features, specifically relational talk, can negatively impact consumer satisfaction in commercial interactions due to perceived awkwardness and expectancy violations.

Contribution

It reveals that AI relational talk can backfire in commercial settings, and introduces goal-relevant relational talk as a strategy to mitigate negative effects.

Findings

AI relational talk decreases satisfaction due to awkwardness and expectancy violation.

Goal-relevant relational talk reduces perceived awkwardness and improves satisfaction.

Perceived awkwardness is a key emotional barrier in human-AI commercial interactions.

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.