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Record W3131235881 · doi:10.1108/pr-08-2020-0603

Getting nowhere, going elsewhere: the impact of perceived career compromises on turnover intentions

2021· article· en· W3131235881 on OpenAlexaffabout
Dirk De Clercq

Bibliographic record

VenuePersonnel Review · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsBrock University
Fundersnot available
KeywordsCompromiseAutonomyPsychologySocial psychologyHuman resource managementOriginalityMaterialismPerceptionIdealismValue (mathematics)TurnoverPublic relationsMarketingBusinessManagementSociologyPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Purpose The purpose of this article is to investigate the unexplored relationship between employees' perceptions that they have made compromises in their careers (i.e. perceived career compromise) and their turnover intentions, as well as how it might be moderated by two personal factors (materialism and idealism) and two contextual factors (abusive supervision and decision autonomy). Design/methodology/approach Survey data were collected among employees who work in the education sector in Canada. Findings Employees' frustrations about unwanted career adjustments lead to an enhanced desire to leave their organization. This process is more likely among employees who are materialistic and suffer from verbally abusive leaders, but it is less likely among those who are idealistic and have more decision autonomy. Practical implications For human resource managers, these results provide novel insights into the individual and contextual circumstances in which frustrations about having to compromise career goals may escalate into the risk that valuable employees quit. Originality/value This study contributes to human resource management research by detailing the conditional effects of a hitherto overlooked determinant of employees' turnover intentions, namely, their beliefs about a discrepancy between their current career situation and their personal aspirations.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0040.000

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 head0.040
GPT teacher head0.289
Teacher spread0.249 · 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.

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

Citations14
Published2021
Admission routes2
Has abstractyes

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