Perceived Overqualification and Job Outcomes: The Moderating Role of Manager Envy
Bibliographic record
Abstract
In this study, we suggest that manager envy will moderate the relationship between perceived overqualification and job-related outcomes (employee turnover, job satisfaction, and performance evaluation). We examined our hypotheses using a sample of 322 employees working in five-star hotels in the United Arab Emirates (UAE), gathered across five time periods. Web-based questionnaires were utilized to collect the data due to the COVID-19 pandemic and in order to obtain results more quickly. We gathered data from June 2021 to February 2022 from superiors at T1 and T4 and subordinates at T2 and T3 in five periods. We left a gap of two weeks between each period, and the same respondents were utilized for all phases. The findings indicate that perceived overqualification was more strongly and negatively related to employee job satisfaction when managers reported high envy. Furthermore, when envy was high, employee overqualification was positively related to job turnover. Promotion had no direct or moderated effects. The implications for the literature on overqualification and envy were addressed. The findings suggest that group-level implications on how perceived overqualification influences employees should be investigated. Perceived overqualification as a result of reporting to envious supervisors had a detrimental impact on the perceived performance and achievement of individuals who were overqualified. The findings also emphasize the relevance of examining overqualification at many levels of analysis, as well as the need to look into manager-level moderators.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".