Earticle

초록

영어

This research examines the impact of recommendation systems on user consumption behavior in the context of serialized content platforms. Unlike streaming (or short-form), which allows for continuous consumption, serialized formats such as digital comics (i.e., webtoons) impose a nested temporal structure characterized by mandatory waiting periods and recurring short-session consumptions. These temporal constraints compel users to make frequent consumption decisions under a limited budget and time, increasing cognitive fatigue and altering content engagement patterns. Leveraging a field experiment on a leading webtoon platform in Korea, we compare recommendation-based versus popularity-based content displays to assess their effects on user conversion behavior (i.e., paid or alternative content consumption). The serialized release structure, particularly the distinction between initial publication and subsequent availability, enables us to examine how the timing of algorithmic exposure moderates the effectiveness of recommendations. Preliminary findings suggest that recommendation systems enhance conversion by either reinforcing early-stage engagement or facilitating content switching as decision fatigue accumulates. These results highlight that recommendation systems serve not only as content-matching tools but also as adaptive behavioral interventions in environments requiring repeated decision-making, suggesting important implications for platform operators in designing recommendation strategies aligned with the temporal dynamics of serialized content.

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Abstract
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저자

• Bongjin Sohn [ Korea University Business School, Information Systems ]
• Antino Kim [ Indiana University Bloomington Kelly School of Business, Operations & Decision Technologies ]
• Gunwoong Lee [ Korea University Business School, Information Systems ]

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