Small Manufacturers vs. Large Retailers on RFID Adoption in the Apparel Supply Chain
Bibliographic record
Abstract
The apparel industry is one of the most rapidly growing sectors of the radio frequency identification (RFID) market, and within it, large retailers have been driving RFID adoption. However, the continuation of this industry’s fast-paced growth is questionable due to the uncertainty associated with how manufacturers, especially small ones, would react to the retailer-led RFID initiative. The literature suggests that the relationship between small manufacturers and large retailers could promote or inhibit RFID adoption among the manufacturers. In order to study the impact of the relationship between small manufacturers and large retailers on the small manufacturers’ RFID adoption decisions, this research develops a 2×2 (two-by-two) game model and conducts outcome stability analysis. The results show that, in the 2×2 game framework, (i) the retailer’s opportunistic behavior is unlikely to occur due to the strong stability associated with the manufacturer’s do-nothing option; (ii) the do-nothing option, however, may lead to missed opportunities for both parties; (iii) the retailer’s pressure tactic is not effective in persuading the small manufacturer to adopt RFID; and (iv) the retailer’s collaborative strategy also does not guarantee the manufacturer’s RFID adoption. The discussion of these results concludes with specific suggestions for how to encourage RFID adoption among the small apparel manufacturers.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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 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".