How does justice matter in achieving buyer–supplier relationship performance?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This study presents an analysis exploring how four types of justice (distributive, procedural, interpersonal, and informational) influence dyadic relationship performance in the buyer–supplier context. Underpinned by loose coupling theory, we build a mediating framework in which we propose that a high level of justice (or fairness) as mutually perceived by both parties drives buyer–supplier relationship performance through bolstered coupling links in mutual knowledge sharing, continuous commitment, and relationship investment. Our survey of 216 paired manufacturers (suppliers) and distributors (buyers) in China generally supports this argument, leading to a conclusion that justice is not a direct determinant of buyer–supplier performance but a critical conduit that nourishes mid‐range coupling behaviors, which in turn promotes a successful relationship. Based on findings from this study, firms are encouraged to endorse all four kinds of justice in managing supply chain relationships. However, when constrained by resources, the recommendation for managers is to focus on achieving a high level of perceptual convergence on procedural justice and informational justice with the exchange partner, because mutual perceptions of procedural and informational justice have the strongest effects on coupling behaviors and buyer–supplier relationship performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 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 it