The effects of bargaining power on trade credit in a supply network
Bibliographic record
Abstract
We develop a multi-tier supply network model, rooted in social network theory, to evaluate the effect of bargaining power on trade credit and to track the effect of buyers' trade credit on suppliers' trade credit. We apply social network analysis to measure companies' bargaining power in the supply network of Hennes & Mauritz AB (H&M, the Swedish clothing retailer). The results show that the buyer's bargaining power significantly affects the choice of trade credit, and that the supplier's “upstreamness” is significantly associated with its trade credit. We find limited evidence to support the notion of a financial bullwhip effect, a result that merits further research, since this study is limited to the network of one company up to its fourth tier of suppliers in one financial year. Our results can be applied by companies seeking to control their cash flow and, therefore, the financial pressure within their supply network. This study contributes to the literature by bringing social network measures into the buyer–supplier financial flow, as well as offering one of the first empirical examinations of the propagation of financial pressure in a multi-tier supply network.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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 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".