Operational decisions of live-streaming platform supply chain based on control power: considering the dual preferences of consumers
Bibliographic record
Abstract
As the problem of live streaming with goods ‘overturned’ has also emerged, it is crucial for supply chain members to make optimal decisions to maintain long-term cooperation relationship. Based on this background, we construct a new supply chain structure composed of a fresh food supplier, an online celebrity live-streaming base (OCLB) and a platform channel, and establish three game-theoretic models: the centralised decision model (CDM), two decentralised decision models (DDMs) led by fresh food supplier (P-S-L model) and OCLB (P-L-S model) to further explore the optimal decisions. The results show that, when consumers have higher dual preferences of freshness and live-streaming recommendation level, in the P-S-L model, it is in the best interest of the supply chain as a whole to pursue ‘neutral’ control power and choose a ‘moderate pricing and decent quality’ strategy, but in the P-L-S model, the OCLB as the dominant party can pursue higher control power, increase the selling price, or relax its preservation technology upgrading program, which may compensate for the loss of high preservation costs. This study follows the popular topic of live-streaming with goods, opens up new ideas to improve the high profitability of supply chain enterprises.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".