Paper Type

Short

Paper Number

PACIS2026-1429

Description

This study investigates how the source of AI disclosure—system-led versus social-led—shapes users’ psychological mechanisms and behavioral responses in a post-disclosure context. Drawing on Expectancy Violation Theory and Attribution Theory, this study hypothesizes that social-led disclosure triggers stronger negative reactions by evoking an integrity-based attribution, leading to greater expectancy violation, trust erosion, and a sense of betrayal, ultimately resulting in higher levels of Negative Word-of-Mouth toward the platform. To test the proposed model, a scenario-based between-subjects experiment was conducted via the CloudResearch platform. Preliminary findings from 60 participants indicate that social-led disclosure elicits greater trust erosion and a sense of betrayal compared to system-led disclosure. By conceptualizing the disclosure source as a distinct theoretical construct, this research contributes to the AI disclosure literature and offers strategic insights for platform governance and the implementation of AI transparency mandates.

Comments

03-EthicsSocietalImpact

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Jul 5th, 12:00 AM

How the Source of AI Disclosure Shapes Psychologcial Mechanisms and User Responses

This study investigates how the source of AI disclosure—system-led versus social-led—shapes users’ psychological mechanisms and behavioral responses in a post-disclosure context. Drawing on Expectancy Violation Theory and Attribution Theory, this study hypothesizes that social-led disclosure triggers stronger negative reactions by evoking an integrity-based attribution, leading to greater expectancy violation, trust erosion, and a sense of betrayal, ultimately resulting in higher levels of Negative Word-of-Mouth toward the platform. To test the proposed model, a scenario-based between-subjects experiment was conducted via the CloudResearch platform. Preliminary findings from 60 participants indicate that social-led disclosure elicits greater trust erosion and a sense of betrayal compared to system-led disclosure. By conceptualizing the disclosure source as a distinct theoretical construct, this research contributes to the AI disclosure literature and offers strategic insights for platform governance and the implementation of AI transparency mandates.