Blending fealty and fiat: How industry associations further the shared interests of rival firms
Bibliographic record
Abstract
When organized through industry associations, firms can gain significant influence over their institutional environments. But how do industry associations organize members’ efforts to achieve institutional influence? We draw from rich primary data uncovered through a five-year ethnography to describe in detail how an industry association navigates the difficult process of maintaining cooperative relationships amongst rivalrous firms while pursuing its broader mission of achieving favorable influence on the institutional environment in which the industry and its member firms operate. We find that this industry association pursued three interrelated sets of activities, relating to practice, policy, and representation, and that the pursuit of each required the industry association to manage a complex mix of competitive and collaborative tensions. Such a delicate balance on multiple fronts proves difficult to consistently sustain, which helps explains why, despite the benefits that cooperation can bring, there is such variation in the breadth and intensity of activities that industry associations pursue. We conclude by discussing the implications of these findings for research on industry associations, institutional change, and business strategy more broadly.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.025 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| 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".