Earticle

초록

영어

Online platforms increasingly employ AI-generated review summaries (AIGS) to reduce information overload, but the implications of AI selection for individual reviews remain unclear. Using hotel review data from TripAdvisor, this study examines which reviews are chosen by AI for summarization and how this selection relates to review helpfulness. Logistic regression results show that reviews with a higher proportion of hotel-related words are more likely to be selected by AI, whereas reviews with greater lexical diversity or length are less likely to be chosen. Negative binomial and Tobit models reveal a significant negative relationship between AI selection and helpful votes, suggesting that content favored by AI may differ from what readers find most helpful. These findings highlight a potential mismatch between algorithmic curation and human evaluation, offering practical implications for both reviewers and platforms in designing AI–human co-curation systems.

목차

Abstract
Introduction
Related Literatures
AI-Generated Review Summaries (AIGS)
Review Helpfulness
Empirical Analysis
Empirical Setting
Data
Variable Definition
Method
Results
Discussion and Conclusion
References

키워드

저자

• SUNG JUN LIM [ Korea University ]
• Taekyung Kim [ Kyung Hee University ]
• Dongwon Lee [ Korea University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2025

• 십진분류

  KDC 325 DDC 658