Corporate Integrity Culture and Compliance: A Study of the Pharmaceutical Industry*
Bibliographic record
Abstract
ABSTRACT This study examines corporate integrity culture—that is, a firm's shared values and behaviors related to compliance, trustworthiness, and ethics. Different from prior research that associates culture measures with general firm‐level outcomes, we evaluate the pervasiveness of the integrity culture within an organization across two disparate business functions: operations and financial reporting. We first develop a measure of corporate integrity culture based on firms' internal control environments and show that, as predicted, weak integrity culture contributes to both operational and financial non‐compliance. We next document the predicted positive contemporaneous association between operational and financial non‐compliance, controlling for the integrity culture reflected in the internal control environment. Given the organizational and physical distances and lack of day‐to‐day interactions between the two business functions, we infer that management's “tone at the top” likely affects non‐compliance in both functions. Finally, for firms with existing operational non‐compliance, we find more negative market reactions to accounting restatements and higher CEO turnover propensities following restatements. These results indicate that top management must consistently reinforce a culture of compliance and integrity, lest it decay throughout the organization. Our results also imply that regulators evaluating compliance in specific functions could benefit from reviewing compliance in other functions within the firm.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".