Investment strategies of information‐provision technology in the platform‐based supply chain
Bibliographic record
Abstract
Abstract On retailing platforms, several information‐provision technologies are adopted to gain profit, such as production video ads service, live streaming service, and virtual reality/augmented reality tech. In this article, we focus on the investment strategies of information‐provision tech and its impact on the platform‐based supply chain. To this end, we develop a general model under which the platform invests in information‐provision tech for homogenous sellers and consumers search for products on the platform. Our results show that the platform should adopt a higher investment level in information‐provision tech for the products with the unit search cost or products' information uncertainty degree being medium. Also, a more competitive environment can lead to a lower platform's investment level in information‐provision tech when the number of browsing products is sufficiently large. Interestingly, we find that for a large unit search cost or small uncertainty degree of products' information, investing in information‐provision tech can benefit the platform's and sellers' profit. In addition, if the number of browsing products is large, investing in information‐provision tech can increase the consumer surplus and social welfare. Lastly, our results hold for a broad class of distributions of products' information uncertainty value and other practical cases. Our studies can help the platform to understand the roles of information‐provision tech and provide some practical management insights.
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How this classification was reachedexpand
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".