Earticle

초록

영어

While visual cues in online reviews are vital, the impact of human faces in user-generated photos (UGPs) is complex. This study analyzes 152,320 Amazon clothing reviews to determine how facial characteristics (presence, quantity, clarity, expression) affect helpfulness across positive and negative reviews. Our findings reveal that while UGPs with faces are generally more helpful, the effect is nuanced. The presence of a face has a greater impact on negative reviews, whereas the general benefit of a UGP is more pronounced in positive reviews. We also find that a single, clear face is superior to multiple faces, though this reverses for negative reviews with high-quality photos. Non-smiling faces are more helpful in negative contexts. This work advances theories on media richness and negativity bias, providing actionable insights for consumers writing reviews and for platforms seeking to improve their review systems.

목차

Abstract
Introduction
Literature Review
Theory and Hypotheses
Media Richness and Visual Cues in Reviews
The Moderating Role of Review Valence and Negativity Bias
Internal Consistency of Facial Expressions and Valence
Methods
Results
Descriptive Statistics
Hypothesis Testing
Robustness Analyses
Discussion and Conclusion
Key Findings
Theoretical and Practical Contributions
Limitations and Future Research
References

저자

• Yan Sun [ School of Management, Kyung Hee University ]
• Sung-Byung Yang [ School of Management, Kyung Hee University ]

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