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Record W4413266451 · doi:10.24818/ea/2025/70/1152

The Role of Managerial Support in Influencing the Attitudes of Highly Educated Millennials in Serbia

2025· article· en· W4413266451 on OpenAlexfundno aff
Dimitrije Gašić, Vladimir Dženopoljac, Mladen Čudanov, József Poór, Nemanja Berber

Bibliographic record

VenueAmfiteatru Economic · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Spirituality and Leadership
Canadian institutionsnot available
FundersCanadian Mental Health AssociationUniversity of South FloridaH. Lee Moffitt Cancer Center and Research Institute
KeywordsPsychologySocial psychologyMarketingBusiness

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.301
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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