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Research Article

From Affective Satisfaction to Habit: Reinforcement Dynamics in Short-Form Drama Engagement

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DOI:

10.3791/71303

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August 4th, 2026

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In This Article

Summary

Short-form drama environments provide dense reinforcement conditions through ultra-short episodes and frequent re-entry. Using an S-O-R model and PLS-SEM, this study examined continuance intention among short-form drama users. The findings indicate that affective satisfaction was positively associated with habit, and habit was positively associated with continuance intention.

Abstract

Short-form drama environments have emerged as structurally distinct digital environments characterized by ultra-short episodes, high-frequency re-entry, and algorithmically reinforced exposure. While prior continuance research has examined satisfaction and habit as parallel predictors of sustained use, little is known about how compressed reinforcement cycles embedded in short-form ecosystems may strengthen the association between affective responses and habitual engagement under conditions of dense reinforcement. Drawing on the stimulus-organism-response (S-O-R) framework, this study proposes a structurally reinforced habit-formation model in which environmental stimuli (access convenience, passing time, and content quality) influence affective satisfaction, which, in turn, is associated with habitual engagement and continuance intention. Using cross-sectional survey data from 260 short-form drama users, analyzed using partial least squares structural equation modeling (PLS-SEM), the findings reveal that affective satisfaction is significantly associated with habit formation, and that habit emerges as a dominant driver of continuance intention. Quantitatively, content quality (β = 0.448, p < 0.001) and passing time (β = 0.385, p < 0.001) were the strongest predictors of affective satisfaction, which strongly predicted habit (β = 0.686, p < 0.001) and continuance intention (β = 0.496, p < 0.001). Habit further contributed to continuance intention (β = 0.393, p < 0.001), with the model explaining 61.0% and 68.9% of the variance in affective satisfaction and continuance intention, respectively. Theoretically, this study advances structurally compressed engagement research by conceptualizing short-form structural features as contextual reinforcement conditions that strengthen the association between satisfaction and habit. Practically, the results suggest that micro-episodic design and rapid re-entry mechanisms play a critical role in stabilizing behavioral continuity in short-form drama ecosystems. Because the study used a cross-sectional survey design, the findings should be interpreted as correlational evidence of associations among digital experience factors, affective satisfaction, habit, and continuance intention rather than evidence of causal or temporal change.

Introduction

The global digital entertainment landscape has undergone a profound transformation with the rapid expansion of short-form content platforms. Fueled by mobile-first consumption habits, algorithmic recommendation systems, and increasingly fragmented patterns of media use, short-form dramas have emerged as one of the fastest-growing entertainment formats worldwide1,2. Unlike traditional streaming services that rely on extended viewing sessions, short-form drama platforms deliver ultra-short episodes designed for rapid consumption and frequent re-entry throughout the day. As these platforms become deeply embedded ....

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Protocol

All procedures involving human participants were conducted in accordance with the ethical standards for human-subject research at aSSIST University and Dong-A University. As all responses were anonymized at the time of collection and no personally identifiable information was obtained at any stage, the Institutional Review Board of aSSIST University determined that the study qualified for exemption from full review under the set forth in Article 15 of the Bioethics and Safety Act of the Republic of Korea and the requirements for exemption from review set forth in Article 13 of its Enforcement Rule (https://public.irb.or.kr/pt/pt01/PT0103/PT0103R01.do). The exemption a....

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Results

Sample characteristics

Table 4 shows a well-balanced sample of 260 respondents across key demographic categories. By age, the largest group is participants in their 20s or younger (38.8%), followed by those in their 30s (32.3%). Respondents in their 40s (14.2%) and 50s (14.6%) are represented in smaller but similar proportions. Gender is almost evenly distributed, with 49.2% male and 50.8% female participants. In terms of education, university graduates make u.......

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Discussion

This study aimed to explain continuance intention in short-form drama contexts by integrating digital experience factors, affective satisfaction, and habit formation within the S-O-R framework. The findings provide strong empirical support for the proposed sequential mechanism and suggest that digital continuance is best understood as a dynamic affective–behavioral reinforcement process. Digital experience factors—access convenience, passing time, and content quality—significantly influence affective sa.......

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Disclosures

The authors declare no conflicts of interest.

Acknowledgements

This work was supported by research funding from aSSIST University.

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
Chatgpt 5.2OpenAIRRID:SCR_023775Enhancing readability and grammar check,  https://www.chatgpt.com/
Prolific (Online Participant Recruitment Platform)Prolific Academic LtdNot available / Not identifiedOnline platform used to recruit survey participants. Participants were screened based on eligibility criteria (e.g., current employment in an organization with AI adoption experience) and compensated according to Prolific's fair-pay guidelines. https://www.prolific.com/
Qualtrics (Online Survey Platform)Qualtrics, LLCRRID:SCR_016728Web-based survey platform used for questionnaire design, distribution, and response data collection. All measurement items were administered via Qualtrics using a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). https://www.qualtrics.com/
SmartPLS 4.0 (PLS-SEM Software)SmartPLS GmbHRRID:SCR_022040Used for all Partial Least Squares Structural Equation Modeling (PLS-SEM) analyses, including outer model assessment (composite reliability, AVE, HTMT), inner model path analysis (bootstrapping, 5,000 samples), MICOM 3-step compositional invariance testing (permutation, 1,000 draws), and PLS-MGA between-group difference tests. https://smartpls.com/

References

  1. He Z, et al. User immersion-aware short video recommendation. ACM Trans Inf Syst. 2025;44(1):1-33.
  2. Zhou R. Understanding the impact of TikTok's recommendation algorithm on user engagement. Int J Comput Sci Inf Technol. 2024;3(2):201-208.
  3. Bhattacherjee A. Understanding information systems continuance: An expectation-confirmation model. MIS Q. 2001;25(3):351-370.
  4. Oulasvirta A, Rattenbury T, Ma L, Raita E. Habits make smartphone use more pervasive. Pers Ubiquitous Comput. 2012;16(1):105-114.
  5. Peters H, et al. Social media use is predictable from app sequences: Using LSTM and transformer neural networks to model habitual behavior. Comput Hum B....

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Habit FormationContinuance IntentionStructural Equation ModelingDigital EngagementContent QualityBehavioral ContinuityS-O-R Framework