Computer Science

Exploring the mechanism of sustained consumer trust in AI chatbots after service failures: a perspective based on attribution and CASA theories

C. Gu, Y. Zhang, et al.

This research, conducted by Chenyu Gu, Yu Zhang, and Linhao Zeng, unveils critical insights into maintaining consumer trust in AI chatbots after service failures. It integrates CASA theory with attribution theory, revealing how human-like qualities in chatbots can enhance trust, even in the face of failures. Don't miss out on these key findings for AI chatbot development!

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Playback language: English

Index

Abstract

This study investigates how to maintain consumer trust in AI chatbots after service failures. Integrating Computers as Social Actors (CASA) theory with attribution theory, a model is constructed and empirically tested using 462 survey responses. Results show that CASA factors (anthropomorphic characteristics, empathy, interaction quality) enhance trust after failures, mediated by attribution styles. AI anxiety negatively impacts trust and moderates the effect of internal attributions on sustained trust. Findings provide insights for AI chatbot development.

Publisher

Humanities and Social Sciences Communications

Published On

Oct 22, 2024

Authors

Chenyu Gu, Yu Zhang, Linhao Zeng

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