Proactive career behaviors and subjective career success: The moderating role of national culture
Bibliographic record
Abstract
Summary Although career proactivity has positive consequences for an individual's career success, studies mostly examine objective measures of success within single countries. This raises important questions about whether proactivity is equally beneficial for different aspects of subjective career success, and the extent to which these benefits extend across cultures. Drawing on Social Information Processing theory, we examined the relationship between proactive career behaviors and two aspects of subjective career success—financial success and work‐life balance—and the moderating role of national culture. We tested our hypotheses using multilevel analyses on a large‐scale sample of 11,892 employees from 22 countries covering nine of GLOBE's 10 cultural clusters. Although we found that proactive career behaviors were positively related to subjective financial success, this relationship was not significant for work‐life balance. Furthermore, career proactivity was relatively more important for subjective financial success in cultures with high in‐group collectivism, high power distance, and low uncertainty avoidance. For work‐life balance, career proactivity was relatively more important in cultures characterized by high in‐group collectivism and humane orientation. Our findings underline the need to treat subjective career success as a multidimensional construct and highlight the complex role of national culture in shaping the outcomes of career proactivity.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".