Building high performance supply-chain relationships for dynamic environments
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore how different forms of integration interact with environmental dynamism to influence the outcomes of a buyer–supplier relationship (BSR). Specifically, the authors assess the impact of communication, operational process integration (OPI) and joint knowledge exploration (JKE) on the economic value and competitive differentiation generated by the BSR. Furthermore, the authors assess the moderating role of environmental dynamism in changing the performance implications of these different forms of integration. Design/methodology/approach The authors empirically test the theoretical model using survey data collected from North America. The authors apply techniques such as confirmatory factor analysis, regression and a variety of robustness checks to ensure the validity of the findings. Findings The results indicate that OPI and JKE are useful in generating higher value from key supply chain relationships. However, communication does not directly influence performance outcomes, rather it assists in the implementation of other forms of integration. In stable environments, better returns can be obtained from focusing on OPI, while in dynamic environments JKE becomes far more important. Originality/value This study shows that different aspects of integration have very different performance implications and that selective integration can outperform broad-based integration in some conditions. More importantly, the performance implications depend on environmental dynamism in unique ways, where greater integration is not always the best response to dynamic business conditions. The results allow managers to make better decisions regarding what forms of integration to establish in key supply chain relationships.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".