Impact of Buying Groups on Buyer–Supplier Relationships: Group–Dyad Interactions in Business-to-Business Markets
Bibliographic record
Abstract
This article examines the impact of business-to-business (B2B) buying groups on buyer–supplier relationships. The authors outline two distinct initiatives—monitoring and community building—through which buying groups govern buyer–supplier exchange and impact supplier performance toward buyers. Two boundary conditions are identified for the performance efficacy of the buying group's governance efforts: the buyer's own governance of the supplier and the dependence relations among the focal parties. Analyses using two-wave primary data collected from the U.S. health care sector and replications using secondary outcomes indicate support for the proposed framework. The authors conduct a conjoint experiment to further enhance the generalizability of the findings and report that a buying group's monitoring of suppliers enhances supplier performance both by itself and with increasing supplier monitoring by the buyer firm. Further, the supplier's dependence on the buying group and the buyer's dependence on the supplier both sharpen the efficacy of the buying group's monitoring program. However, the performance effects of community building are weakened as the buyer–supplier relationship becomes stronger and as the buyer grows more dependent on the supplier. The authors thus uncover novel interplays between B2B buying groups and B2B dyads and document their consequences for firm performance.
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How this classification was reachedexpand
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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".