Perceived Overqualification Under the Microscope: Dimensionality, Profiles, and Outcomes
Bibliographic record
Abstract
Increasingly, many employees feel their jobs underutilize their qualifications. Management research in this realm has focused on the role of perceived overqualification–employee self-perceptions that they possess higher levels of knowledge, skills, education, or experience than required by their job. Past research finds mixed effects of perceived overqualification (POQ), including undesirable outcomes such as negative attitudes, poorer employee health and well-being, and increased turnover intentions. But at other times, POQ spurs innovative behavior, increased performance, and enhanced relationships. We investigate whether a more fine-grained approach to measuring perceived overqualification can help disentangle these mixed results. Most research to date relies on a single unidimensional measure of POQ. However, we explore its multidimensionally, finding evidence for three dimensions: Education, Experience, and Skills/Abilities. We then use a person-centered approach to reveal how POQ dimensions cluster together, creating distinct profile groups. These profiles reveal complexities in relative levels of POQ dimensions and help to explain differences in employee outcomes, including person-job fit, job satisfaction, turnover intentions, and thriving. Our work problematizes existing measurement practices with the goal of better understanding the diversity of the POQ experience. We establish the value of taking a multidimensional approach in future research and provide practical implications for managers seeking to maximize the well-being and performance of overqualified workers.
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 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".