Exploring Performance Determinants of China’s Cable Operators and OTT Service Providers in the Era of Digital Convergence—From the Perspective of an Industry Platform
Bibliographic record
Abstract
This paper investigates key determinants of business performance in China’s video industry in the era of digital convergence. Specifically, we analyze China’s OTT (over-the-top) service providers and cable operators based on the perspective of an industry platform, which acts as the core module of a business ecosystem and is capable of facilitating and coordinating interdependence among different agents. Panel data models are established to empirically explore what factors impact the performance of these two types of players. The findings demonstrate that both platform use and the size of an installed base are crucial for the determinants of the performance of OTT service providers and cable operators. An online video platform can also benefit from an increasing proportion of mobile viewers by implementing a multi-screen strategy. Further, an OTT service provider can profit from the interaction between its installed base and UGC (user-generated content), while cable operators can take advantage of positive feedback between their demand side and supply side.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".