Does the relationship between sustainable human resource management and organizational identification vary by culture? Evidence from 35 countries based on GLOBE framework
Bibliographic record
Abstract
Purpose The article discusses the relationships between sustainable HRM and organizational identification, conceptualized at the individual level, and the moderating role of cultural dimensions conceptualized at the country level (described in GLOBE’s framework). The study’s theoretical model based on social exchange theory proposes that sustainable HRM practice increases organizational identification. However, the strength of this identification depends on the dimensions of national culture. Thus, we assumed national culture functions as a second-level moderator in the relationship between sustainable HRM and organizational identification. Design/methodology/approach We conducted the study with data from 10,421 employees across 35 countries. We used a multilevel modeling approach for data analysis. Findings The study revealed the cross-level interaction effects of national culture on the relationship between sustainable HRM practice and organizational identification. Specifically, the results indicate that sustainable HRM strengthens employees’ organizational identification more in cultures with higher levels of gender egalitarianism and lower levels of humane orientation. Originality/value This study demonstrates that the relationship between sustainable HRM practices and employees’ organizational identification is culturally sensitive. It highlights the need to consider cultural context when assessing the impact of sustainable HRM practices on employee outcomes. Furthermore, it shows that certain cultural dimensions can enhance the effect of sustainable HRM practices.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".