ScholarWorks@숙명여자대학교

초록

Web novels, created, distributed, and consumed via the internet, have significantly transformed the way contemporary audiences engage with literature. The rapid growth of the web novel market has enhanced its attractiveness while simultaneously intensifying competition, thereby highlighting the need for effective market segmentation strategies. However, prior research on web novels has primarily taken a humanities-oriented approach, focusing on narrative structures and genre characteristics. Most existing segmentation studies rely heavily on survey data, which often fail to reflect behavioral variables such as the value consumers seek. To address this gap, the present study employs topic modeling, a text mining technique, to analyze actual web novel reviews and segment the market based on consumers’ sought value. The analysis identifies five distinct segments in the Korean web novel market: ‘Self-Growth,’ ‘World-Building,’ ‘Relationship-Centered,’ ‘Women’s Narrative,’ and ‘Genre-Setting Fusion.’ Furthermore, this study evaluates the market potential of each segment by examining the expandability of web novels into other content forms (one source multi-use, OSMU). The OSMU potential of each topic is assessed using logistic regression, random forest, and SHAP analysis, with the results showing that the ‘Women’s Narrative’ segment has the highest content expansion potential. This study demonstrates that segmentation based on consumers’ sought value is more effective than conventional genre-based classification in assessing market potential. It offers new insights into the latent value of web novels and presents a consumer-oriented segmentation methodology that can contribute to the strategic development of the web novel industry.

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