Beyond the first session: unraveling immersion’s dual effects on user retention in high-participation online fitness videos
Why this work is in the frame
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Bibliographic record
Abstract
Purpose This study aims to investigate the impact of immersion on user retention within high-participation online fitness contexts, where users are required to concurrently process visual instructions and execute physical movements. This dual-task requirement introduces additional complexity to user retention. Design/methodology/approach Using a BERT-based classifier and manual content coding, we analyzed user comments and video content from 550 Douyin (internationally known as TikTok) fitness videos. We investigated the direct influence of immersion on users’ intention to continue exercising by following creators’ fitness videos (ICEFV) and examined its moderating effect on the relationship between values of fitness videos (namely, fitness video-influenced fitness outcomes, entertainment, and co-participation experience) and ICEFV. Findings Immersion significantly enhances ICEFV. Furthermore, immersion positively moderates the relationship between fitness video-influenced fitness outcomes and ICEFV, but does not moderate the effects of entertainment and co-participation experience on ICEFV. Originality/value This study contributes to the literature on user retention in high-participation contexts by revealing a technology-driven dual pathway. We demonstrate that immersion not only directly fosters continued participation but also amplifies the impact of fitness outcomes on continued participation. By focusing on how immersion influences user retention beyond traditional content and influencer paradigms, our findings provide actionable insights for enhancing user participation in competitive online fitness markets.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it