Law‐abiding organizational climates in developing countries: The role of institutional factors and socially responsible organizational practices
Bibliographic record
Abstract
Abstract The institutional environment of developing countries may lead firms to engage in unlawful firm conduct, which is a pervasive problem in this context. Our paper examines the effectiveness of organizational practices for ensuring that firms adhere to the law in the light of pressures from the institutional environment to be unlawful. Using the lens of anomie theory, we investigate: (a) the negative effect of aspects of the institutional context—regulatory burden and lack of industry munificence—on a law‐abiding climate, a type of organizational climate related to unlawful conduct, and (b) the role of socially responsible organizational practices in combating these negative effects. Survey data were collected from 118 firms and analysed using OLS moderated regression. Our results indicate that a manager's perceptions of regulatory burden and lack of industry munificence are negatively related to the extent to which the firm has a law‐abiding climate. Furthermore, our findings shed light on the ability of socially responsible practices to countervail this effect. While the negative effect of perceived regulatory burden on law‐abiding climate weakens when codes of ethics are used more extensively by a firm, it strengthens when firms hold a CSR certification. The latter finding may be due to the lack of enforcement associated with the specific certification considered in our study.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".