The impact of performance measurement on purchasing groups dynamics: the Canadian experience
Bibliographic record
Abstract
Purchasing groups were first created in the healthcare sector, which has faced unprecedented challenges in terms of cost control for over two decades. Purchasing groups are indeed supposed to generate additional savings and more efficient purchasing processes. However, although various aspects of purchasing groups have been studied since the early 2000s, both their performance measurement and the influence that this measurement has on inter-organizational dynamics have been neglected. In purchasing groups, the dynamics between the group itself and its members often results in tensions between both parties. Performance measurement within purchasing groups could alleviate those tensions, since “objective” data could then be used to improve communication. Based on a case study, this research sheds light on performance measurement in a purchasing group, on the dynamics between the group and its members, and on the interaction between performance measurement and inter-organizational dynamics. Results indicate that measuring performance impacts the dynamics between both parties, but that the relationship is also the other way around, and that the inter-organizational dynamics is quite complex. In addition, this paper proposes a framework summarizing the research findings.
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.
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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".