Earticle

초록

영어

Electronic word-of-mouth (WOM) plays a crucial role in movie-going decisions, yet spoiler content in reviews remains controversial. Although spoilers may potentially harm enjoyment, they may also alleviate uncertainty, thereby enhancing the viewing experience. Despite such tension, there has been a lack of research on which types of spoilers have either detrimental or informative effects on decision-making. To address this gap, we propose a framework to classify spoiler reviews as either detrimental or informative using natural language processing techniques. Based on the classification results, we conducted econometric analyses to assess their distinct impacts on box office revenue by using data from 322 movies. Our overall findings show that informative spoilers increases box office revenue whereas detrimental spoilers have no significant effect. However, moderating analyses reveal important context effects. Detrimental spoilers reduce revenue for narrative-driven or long-duration films and during weekends but show null effects in other contexts. Similarly, informative spoilers do not always increase demand, with their positive effects diminishing under certain conditions. This study offers valuable insights for review platforms and studios by highlighting the need for nuanced spoiler management to enhance audience engagement and revenue.

목차

ABSTRACT
Ⅰ. Introduction
Ⅱ. Conceptual Background
2.1. Spoiler Reviews
2.2. Prior Literature on Spoiler Effects
2.3. Characteristics of Spoilers: Detrimental and Informative
Ⅲ. Data
3.1. Data Collection
3.2. Variable Construction
Ⅳ. Spoiler Review Classification: Detrimental vs. Informative
4.1. Proxy for Detrimental Spoiler Review
4.2. Classification Results
Ⅴ. Impact of Spoiler Reviews on Box Office Revenue
5.1. Empirical Model
5.2. Empirical Findings
Ⅵ. Discussion and Implications
6.1. Discussion of Findings
6.2. Limitations and Future Research
6.3. Implications for Research
6.4. Implications for Practice
Ⅶ. Conclusion

저자

• Junho Bae [ AI Research Engineer, SK C&C, Korea ]
• Woosik Shin [ Assistant Professor, School of Liberal Arts and Sciences, Musashi University, Japan ]
• Hee-Woong Kim [ Professor, Graduate School of Information, Yonsei University, Korea ] Corresponding Author

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