Getting nowhere, going elsewhere: the impact of perceived career compromises on turnover intentions
Bibliographic record
Abstract
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.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".