The Role of Managerial Support in Influencing the Attitudes of Highly Educated Millennials in Serbia
Bibliographic record
Abstract
This study explores the influence of managerial support on the attitudes of highly educated (BSc, MSc, and PhD) millennials in Serbia, focusing on job satisfaction, turnover intention, and job stress.Understanding this relationship is crucial for enhancing employee retention and organisational efficiency given the evolving workplace dynamics.Managerial support, encompassing emotional, instrumental, and informational assistance, is hypothesised to positively impact job satisfaction while reducing turnover intention and job stress.Data were collected through an online survey from 367 respondents in two waves (the first wave in January 2024 and the second in July 2024) and analysed using partial least squares structural equation modeling.The findings confirm that managerial support significantly enhances job satisfaction and mitigates turnover intention and job stress, aligning with existing literature.This research provides empirical evidence on the direct effects of managerial support in Serbia, offering valuable insights for organisations aiming to improve employee well-being and optimise leadership strategies.By emphasising the role of managerial support, the study highlights its significance in fostering a healthier work environment, increasing employee engagement, and enhancing overall organisational sustainability.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".