Examining supply chain relationships: Do buyer and supplier perspectives on collaborative relationships differ?
Bibliographic record
Abstract
Abstract Firms are building collaborative relationships with their supply chain partners in order to achieve efficiencies, flexibility, and sustainable competitive advantage. However, it is unclear if collaborative relationships provide benefits that compensate for the additional expense associated with such relationships. Further, it is unclear what factors promote successful collaborations. This research examines collaborative relationships in two separate studies using structural equation modeling: one study examines buyers’ perceptions and the second study examines suppliers’ perceptions. The two studies are then compared using invariance testing in order to determine economic and relational factors that drive satisfaction and performance from each party's perspective. Results show that collaborative activities, such as information sharing, joint relationship effort, and dedicated investments lead to trust and commitment. Trust and commitment, in turn, lead to improved satisfaction and performance. Results from the two independent studies exhibit similarities and differences; while the conceptual model is highly similar, certain paths vary in their significance and/or their importance across buyer and supplier firms such that buyers focus more on relationship outcomes while suppliers look to safeguard their transaction specific investments through information sharing and joint relationship effort. Managerial and theoretical implications of the findings are discussed.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".