Setting Your Own Standards: Internal Corporate Governance Codes as a Response to Institutional Pressure
Bibliographic record
Abstract
This paper is concerned with organizational response to institutional pressure. We argue that when faced with externally imposed standards, organizations can sometimes respond by developing alternative standards for the same practices. This “substitution response” can shift the attention of stakeholders away from noncompliance with the original standards to adherence to the alternative standards. Empirically, we examine organizational response to the introduction of a government-sponsored but nonmandatory corporate governance code. Unable to comply with all of the requirements of this very specific and demanding code, many firms responded by developing their own internal corporate governance codes. We predict and show that adoption of these internal codes is driven by the visibility of a firm's corporate governance practices and by mimetic forces. We also find that internal governance codes differ in their degree of ceremoniality and that ceremoniality is inversely related to organizational dependence on stakeholders who value good corporate governance. These findings help us to understand when organizational responses to institutional pressure take a ceremonial as opposed to substantive form.
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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 | 0.010 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".