Why Firms Want to Organize Efficiently and what Keeps Them from Doing So: Inappropriate Governance, Performance, and Adaptation in a Deregulated Industry
Bibliographic record
Abstract
This paper integrates content-based predictions of transaction cost economics with process-based predictions of organizational change to understand adaptation to deregulation in the for-hire trucking industry. We predict and find that firms whose governance of a core transaction is poor (according to transaction cost reasoning) will realize lower profits than their better-aligned counterparts and that these firms will attempt to adapt so as to better align their transactions. Results show that several organizational features affect the rate of adaptation: (1) firms with large investments in specialized assets adapt less readily than firms that rely on generic assets, (2) firms with unions adapt less readily than firms without unions, (3) firms that must replace employee drivers with owner-operators adapt less readily than firms that must replace owner-operators with employee drivers, and (4) entrants adapt more quickly than incumbent carriers. There is evidence of institutional isomorphism in that although carriers move systematically to reduce misalignment, they do so less assiduously when this will make their governance of drivers look less like that of nearby, similar carriers. Finally, our results indicate that firms that ultimately exited adapted more quickly than firms that survived.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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 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".