Asymmetry, heterogeneity and inter‐firm relationships
Bibliographic record
Abstract
Purpose The purpose of this paper is to organize the theoretical landscape surrounding explanations of the impact asymmetry and heterogeneity on inter‐firm relationships, especially alliances. Design/methodology/approach A conceptual framework integrating the resource‐based view, transaction cost economics and industrial organization is put forth to better understand asymmetry and heterogeneity in alliances. Findings It is argued that low asymmetry and low heterogeneity are best addressed from an industrial organization perspective. Transaction cost economics best explains alliances in high asymmetry and low heterogeneity situations while the resource‐based view is most appropriate for high heterogeneity and low asymmetry alliances. In the case of high asymmetry and high heterogeneity, the tension between the resource‐based view and transaction costs economics is reconciled. Research limitations/implications Researchers gain an original re‐framing of the theoretical landscape that will assist in generating new insights for future theory development. Practical implications The paper lays the ground for new research directions while leaving practitioners with a better understanding of the lenses through which they should examine their firms' cooperative endeavours. Originality/value Previous literature seldom addressed the categorization of various theoretical approaches along the notions of asymmetry and heterogeneity in inter‐firm relationships.
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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".