The hollow corporation revisited: Can governance mechanisms substitute for technical expertise in managing buyer‐supplier relationships?
Bibliographic record
Abstract
AbstractThis article considers how a firm's system of exchange skills including internal technical expertise and supplier governance mechanisms influence supplier performance, both independently and jointly. The core question is whether inter‐firm governance mechanisms, including both relational and contractual mechanisms, can substitute for a firm's internal technical skills in maintaining supplier performance or, alternatively, whether a firm risks hollowing itself out by de‐emphasizing internal expertise when it outsources. The arguments build on the capabilities, inter‐organizational governance, and supply management literatures. We find that internal technical expertise influences multiple dimensions of supplier performance, including cooperation, price, quality, delivery, and communication, while relational governance also affects supplier performance though in a more focused way. In turn, combinations of technical expertise, relational governance, and contractual agreements jointly affect supplier performance. Thus, firms generate superior supplier performance if they retain internal technical skills as well as increase their use of external governance mechanisms to manage buyer‐supplier 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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".