Continuous Supplier Performance Improvement: Effects of Collaborative Communication and Control
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
Manufacturing firms seek continuous supplier performance improvement because this outcome makes them more competitive in downstream markets. Although manufacturing firms use a range of tools to effect continuous supplier performance improvement, the author focuses on two that are especially important—collaborative communication and control—and poses the following research questions: (1) How does collaborative communication foster continuous supplier performance improvement? and (2) What are the combined effects of collaborative communication and control? The results from a survey of 153 manufacturer–supplier dyads show that collaborative communication fosters continuous supplier performance improvement by enhancing supplier knowledge (of manufacturer needs) and by building supplier affective commitment (toward the manufacturer). With respect to the combined effects of communication and control, the results show that capability control enhances the positive effects of both supplier knowledge and supplier affective commitment on continuous supplier performance improvement, whereas process control undermines the effect of supplier knowledge on the outcome. This pattern of results suggests that manufacturing firms should emphasize capability control and deemphasize process control to foster continuous supplier performance improvement.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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