International differences in employee silence motives: Scale validation, prevalence, and relationships with culture characteristics across 33 countries
Bibliographic record
Abstract
Summary Employee silence, the withholding of work‐related ideas, questions, or concerns from someone who could effect change, has been proposed to hamper individual and collective learning as well as the detection of errors and unethical behaviors in many areas of the world. To facilitate cross‐cultural research, we validated an instrument measuring four employee silence motives (i.e., silence based on fear, resignation, prosocial, and selfish motives) in 21 languages. Across 33 countries ( N = 8,222) representing diverse cultural clusters, the instrument shows good psychometric properties (i.e., internal reliabilities, factor structure, and measurement invariance). Results further revealed similarities and differences in the prevalence of silence motives between countries, but did not necessarily support cultural stereotypes. To explore the role of culture for silence, we examined relationships of silence motives with the societal practices cultural dimensions from the GLOBE Program. We found relationships between silence motives and power distance, institutional collectivism, and uncertainty avoidance. Overall, the findings suggest that relationships between silence and cultural dimensions are more complex than commonly assumed. We discuss the explanatory power of nations as (cultural) units of analysis, our social scientific approach, the predictive value of cultural dimensions, and opportunities to extend silence research geographically, methodologically, and conceptually.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".