The Dual Impact of Video Content on OTT Viewership: Examining the Relationship between Video Marketer-Generated and Video User-Generated Content
Bibliographic record
Abstract
In the digital media landscape, YouTube has become crucial for promoting over-the-top (OTT) media content. This study investigates the effects of video marketer-generated content (VMGC) and video user-generated content (VUGC) on OTT viewership. Using Netflix's Top 10 movie viewership data and related YouTube content, we find that both VMGC and VUGC positively affect viewership. However, their interaction is negative, suggesting substitution. VUGC's positive effect is negatively moderated by video length and engagement, indicating a substitution effect, while VMGC's promotional effect is not. This difference might result from VMGC's careful design to promote without substituting the original content. Additional analysis reveals that spoilers do not drive VUGC's substitution effect. These findings highlight the importance of considering video content characteristics in understanding consumer behavior and demand for OTT content.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".