RFID adoption strategy and the operational performance of a pharmaceutical supply chain: The role of hospital competition
Bibliographic record
Abstract
Amid hospitals’ efforts to enhance operational efficiency and slash costs, addressing inventory inaccuracies in hospital pharmaceutical supply chain has gained urgency. Although radio frequency identification (RFID) technology effectively reduces such inaccuracies, its introduction in vendor-managed inventory (VMI) remains debatable for vendors given the impact of competitive inter-hospital environment and corresponding implementation costs. This study develops a three-level Stackelberg game model to analyze the vendor’s strategy regarding RFID adoption within the pharmaceutical supply chain, taking into account different types of competition between hospitals (i.e., public vs. public and public vs. private). We find that the vendor’s RFID adoption strategy under both forms of competition is counter-intuitive, with RFID being adopted even when the intensity of RFID in reducing inventory inaccuracy is low, provided the reimbursement rate for public hospital is high. Furthermore, homogeneous competition increases the vendor’s need for higher RFID intensity, while heterogeneous competition heightens the requirement for a favorable reimbursement rate. Finally, both homogeneous and heterogeneous competition can facilitate RFID adoption, with the preference depending on diagnosis cost, reimbursement rate and RFID intensity. This study offers fresh perspectives on the intricacies of vendors’ decision-making processes for RFID adoption in healthcare, emphasizing the significance of competitive dynamics and strategic management of medical consumables. • We analyze how vendors strategize with RFID in various types of hospital competition. • The vendor’s RFID strategy is counter-intuitive under both types of competition. • both homogeneous and heterogeneous competition can promote the vendor to adopt RFID. • This paper studies competitive dynamics and strategic management of medical consumables.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".