The effect of transaction costs, payment terms and power on the level of raw materials inventories
Bibliographic record
Abstract
Abstract This paper proposes and tests an explanation for the level of raw materials inventories based on transaction cost economics theory and the role of power in a supply chain. According to this explanation, raw materials inventories are larger the higher a company's transaction costs and the lower its storage‐related production and management costs. Factors that affect these costs are the company's vulnerability to opportunism, whether the input becomes more or less costly to store and manage as it moves through the supply chain, payment terms and the company's power in relation to its supplier. This explanation for the level of raw materials inventories was tested on a large sample of customer industries matched to their main supplier industries. Consistent with this theory, the empirical results show that companies hold larger raw materials inventories the more money their suppliers spend on research and development and the less important the customers are to their suppliers. These results are important because they indicate companies must consider a wider range of factors than previously thought necessary when establishing inventory policy.
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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 | 0.005 | 0.064 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".