Earticle

초록

영어

This study investigates how digital misconduct in AI-mediated platforms influences reputational risk and stakeholder responses. Drawing on attribution theory and cognitive dissonance theory, we propose a multi-level framework linking misconduct types, psychological discomfort, and behavioral outcomes. Using longitudinal ESG controversy data from RepRisk (2014–2023), Study 1 examines the temporal dynamics of reputational risk following privacy violations, misleading communication, and greenwashing. Study 2 complements these findings through a scenario-based experiment examining how AI attribution shapes stakeholder reactions. The results show that privacy violations generate the strongest and most persistent reputational damage. Psychological discomfort mediates stakeholder responses, reducing investment intention and increasing negative word-of-mouth. These findings highlight the importance of accountability governance and transparent communication in managing reputational risk in AI-driven digital platforms.

목차

Abstract
Introduction
Theoretical Background and Hypotheses
Digital Misconduct and Reputational Risk in AI-Mediated Platforms
Psychological Discomfort and Stakeholder Response
Accountability Framing in AI-Mediated Platforms
Methods
Study 1: Longitudinal Reputational Analysis
Study 2: Experimental Design
Analytical Strategy
Results
Reputational Dynamics of Digital Misconduct
Discussion
References

키워드

저자

• 김주영 [ 연세대학교 미래캠퍼스 글로벌창의융합대학 경영학부 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2026

• 십진분류

  KDC 325 DDC 658