Supplier evaluations: communication strategies to improve supplier performance
Bibliographic record
Abstract
Abstract As firms increasingly emphasize cooperative relationships with critical suppliers, executives of buyer firms are using supplier evaluations to ensure that their performance objectives are met. Supplier evaluations, one type of supplier development program (SDP), are an attempt to meet current and future business needs by improving supplier performance and capabilities. The purpose of this study was to determine how suppliers perceive the buying firm’s supplier evaluation communication process and its impact on suppliers’ performance. Three communication strategies (indirect influence strategy, formality and feedback) were tested separately and one in unison (collaborative). Using structural equation modeling (SEM) and data collected from 139 first‐tier North American automotive suppliers, the results of this research have shown that, contrary to the SDP literature from the buying firm’s perspective, the supplier’s perceptions of the buying firm’s communication does not directly influence suppliers’ performance. Specifically, the supplier evaluation communication process does not ensure improved supplier performance unless the supplier is committed to the buying firm. Buying firms can influence the supplier’s commitment through increased efforts of cooperation and commitment. The results also indicate that when a buying firm utilizes collaborative communication, the supplier perceives a positive influence on the buyer–supplier relationship.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".