The relationship between culture and corruption: a cross‐national study
Bibliographic record
Abstract
Purpose This paper aims to investigate the relationship between the GLOBE (Global Leadership and Organizational Behaviour Effectiveness) project national cultural dimensions of values and practices and the Corruption Perception Index (CPI). Design/methodology/approach Most empirical research on culture dimensions and corruption is based on Hofstede's dataset of culture conducted more than 25 years ago. Evidence from a more recent dataset of culture dimensions is needed before current generalizations can be made. The GLOBE project is based on the perceptions of 18,000 individuals. Findings The results provide empirical support for the influence of uncertainty avoidance values, human orientation practices, and individual collectivism practices on the level of corruption after controlling for economic and human development, which, in turn, adds to the efforts to build a general theory of the culture perspective of corruption. Research limitations/implications The findings offer valuable insights on why cultural values and cultural practices should be distinguished as they relate to corruption. Practical implications International policy makers as well as managers at multinational corporations can benefit from the findings of this research study. Originality/value The research reported is among the first to investigate the issue of corruption from the perspective of national cultural values and practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".