Impact of Inter-firm Relationship Fairness in Strategic Alliance on Relationship Commitment -- Mediating Effects of Inter-firm Trust
Bibliographic record
Abstract
As one of the core influencing factors of inter-firm relationship, relationship commitment has an important effect on the continuity of the inter-firm cooperative relationship and the enhancement of cooperative performance. By selecting 230 enterprises in Jiangsu as the study samples, collecting data through questionnaires and using an intermediary model, the impact imposed by inter-firm relationship fairness on the relationship commitment is studied and the mediating effect of inter-firm trust is testified in this paper. The results show that a route by which the relationship fairness affects the relationship commitment does exist in the sector of inter-firm cooperative relationship in China. Among them, distributive fairness can not only promote affective commitment directly, but also in the meantime bring in an indirect effect on the affective commitment through talent trust, while procedural fairness imposes positive impacts on affective commitment mainly by talent trust, the mediating variable. Besides, the improvement of interaction fairness can directly reduce the level of inter-firm calculative commitment on the one hand, and meanwhile helps to improve the inter-firm benevolent trust level and indirectly affects the calculative commitment on the other hand. Key words: Relationship fairness; Relationship commitment; Inter-firm trust; Mediating effect
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".