Compliance Programs as Organizational Solutions to Corruption: A Literature Review
Bibliographic record
Abstract
Compliance programs are promoted as effective solutions to corruption in organizations. Compliance programs, also known as ethics and compliance programs, ethics programs, and integrity programs, are widely adopted by organizations across the world, but their ability to effectively curb corruption is largely disputed. Current research mainly focuses on compliance programs as sets of organizational self-regulatory practices dissociated from wider country and sectoral anti-corruption initiatives. This article reviews and critiques the multidisciplinary literature on compliance programs. The literature reveals that anti-corruption regulation is the main driver of compliance programs, but little attention is paid to corruption as the organizational issue that compliance programs are expected to solve. This review discusses how compliance practices are informed by two dominant theoretical approaches to explaining corruption (based on principal-agent theory and collective action theory) and the implications for research and organizational practice. This review concludes by proposing a shift towards situated, problem-driven organizational strategies that take into account the challenge to tackling specific instances of corruption, the processual character of compliance programs, and the interplay of social structure and individual agency in different phases of the process of design, implementation, and maintenance of such programs.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".