Monetizing Fan Economy in Korean Entertainment

Leyu Fan

doi:10.54254/2754-1169/2025.LD28930

Advances in Economics, Management and Political SciencesOpen access

Monetizing Fan Economy in Korean Entertainment

Research Article

Open Access

Monetizing Fan Economy in Korean Entertainment

Leyu Fan ^1*

¹ Beijing No.4 High School

^*Corresponding author: fanleyu11@126.com

Published on 5 November 2025

AEMPS Vol.237

ISSN (Print): 2754-1177

ISSN (Online): 2754-1169

ISBN (Print): 978-1-80590-507-3

ISBN (Online): 978-1-80590-508-0

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Abstract

The K-pop fan economy currently holds a significant position through its unique operational model, yet quantitative analysis of its development trajectory and big data application research require further exploration. This research focuses on investigating the K-pop fan economy. Taking K-pop idol groups as the research subjects, this study analyzes relevant data from fan interaction platforms and social media by deriving the effects of strategies such as quantifying return cycles and releasing multiple album versions. This analysis combines big data tools including cluster analysis and time series methods. Findings reveal that high-frequency comebacks and merchandise marketing significantly boost fan enthusiasm and sales. Big data tools enable precise operations, providing robust support for the efficient development of the fan economy.

Keywords:

K-pop, Fan Economy, Development Pathways, Big Data Tools

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References

[1]. Soraya Sedkaoui; Mounia Khelfaoui, "Cluster Analysis, " in Sharing Economy and Big Data Analytics , Wiley, 2020, pp.195-214, doi: 10.1002/9781119695035.ch11.

[2]. Elinor Jones, Simon Harden, Michael J. Crawley. (2022). Time Series..

[3]. Francesca Lazzeri, (2020). Machine Learning for Time Series Forecasting with Python

[4]. Liu Shuang, Zhao Jingxiu, Yang Hongya, Xu Guanhua. (2018). Review of Text Sentiment Analysis. http: //f.wanfangdata.com.cn.https.zjlib.proxy.zyproxy.zjlib.cn/online/pc/periodical/rjdk201806001?transaction=%7B%22id%22%3Anull, %22transferOutAccountsStatus%22%3Anull, %22transaction%22%3A%7B%22id%22%3A%221964609473045721088%22, %22status%22%3A1, %22createDa

[5]. Teng, Z. Q., & Wang, Y. F. (2024). A study on the fan-idol identification mechanism from the perspective of fan economy: Based on the identity association theory. Journal of News Lovers, (11), 46-48. https: //doi.org/10.16017/j.cnki.xwahz.2024.11.013.

References

[1]. Soraya Sedkaoui; Mounia Khelfaoui, "Cluster Analysis, " in Sharing Economy and Big Data Analytics , Wiley, 2020, pp.195-214, doi: 10.1002/9781119695035.ch11.

[2]. Elinor Jones, Simon Harden, Michael J. Crawley. (2022). Time Series..

[3]. Francesca Lazzeri, (2020). Machine Learning for Time Series Forecasting with Python

Cite this article

Fan,L. (2025). Monetizing Fan Economy in Korean Entertainment. Advances in Economics, Management and Political Sciences,237,1-6.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of ICFTBA 2025 Symposium: Strategic Human Capital Management in the Era of AI

ISBN: 978-1-80590-507-3(Print) / 978-1-80590-508-0(Online)

Editor: Lukáš Vartiak, Anil Nguyen

Conference website: https://www.icftba.org/London.html

Conference date: 4 November 2025

Series: Advances in Economics, Management and Political Sciences

Volume number: Vol.237

ISSN: 2754-1169(Print) / 2754-1177(Online)

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