Determinants of an Effective Anti-Bribery and Anti-Corruption (ABAC) Culture in Malaysian Public Listed Companies: A Conceptual-Based Framework
Bibliographic record
Abstract
This paper integrates statutory provisions, regulatory guidance, corporate governance standards, and literatures to ascertain the determinants of an effective anti-bribery and anti-corruption (ABAC) culture within Malaysian Public Listed Companies (PLCs). The introduction of corporate liability via Section 17A of the Malaysian Anti-Corruption Commission (MACC) Act 2009 has catalyzed a shift from compliance-centric activities to a more profound examination of organizational culture. This study classifies the determinants of an effective ABAC culture into “hard” factors (legal, structural, and procedural) and “soft” factors (behavioral, cultural, and leadership). Drawing parallels from seminal management theories, this paper proposes a refined Dual-Factor ABAC Culture Model, which posits that while “hard” factors function as essential safeguards to prevent misconduct, “soft” factors are the primary drivers that cultivate a sustainable culture of integrity. The analysis culminates in a conceptual framework that integrates these cultural enablers with institutional safeguards, offering a governance-ready diagnostic instrument for PLCs, regulators, and practitioners aiming to embed integrity as a strategic asset.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".