Examining the impact of Culture's consequences: A three-decade, multilevel, meta-analytic review of Hofstede's cultural value dimensions.
Bibliographic record
Abstract
Using data from 598 studies representing over 200,000 individuals, we meta-analyzed the relationship between G. Hofstede's (1980a) original 4 cultural value dimensions and a variety of organizationally relevant outcomes. First, values predict outcomes with similar strength (with an overall absolute weighted effect size of rho = 0.18) at the individual level of analysis. Second, the predictive power of the cultural values was significantly lower than that of personality traits and demographics for certain outcomes (e.g., job performance, absenteeism, turnover) but was significantly higher for others (e.g., organizational commitment, identification, citizenship behavior, team-related attitudes, feedback seeking). Third, cultural values were most strongly related to emotions, followed by attitudes, then behaviors, and finally job performance. Fourth, cultural values were more strongly related to outcomes for managers (rather than students) and for older, male, and more educated respondents. Fifth, findings were stronger for primary, rather than secondary, data. Finally, we provide support for M. Gelfand, L. H. Nishii, and J. L. Raver's (2006) conceptualization of societal tightness-looseness, finding significantly stronger effects in culturally tighter, rather than looser, countries.
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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 | 0.027 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".