Skill underutilization and collective turnover in a professional service firm
Bibliographic record
Abstract
Purpose – The purpose of this paper is to examine skill underutilization and collective turnover in a large professional service firm (PSF). The authors hypothesize that skill underutilization is positively related to collective turnover, that skill underutilization is greater among professionals than nonprofessionals, and that the positive relationship between skill underutilization and collective turnover is stronger for professionals than for nonprofessionals. Design/methodology/approach – Using survey data from a large PSF, the authors test these predictions across 191 groups (professional and nonprofessional) in 80 offices. Collective turnover rates were taken from company records one year after the survey was administered. Findings – The authors find support for the prediction that skill underutilization is positively related to collective turnover. In addition, skill underutilization is greater among professionals than nonprofessionals within a PSF. However, the relationship between skill underutilization and collective turnover did not differ between professionals and nonprofessionals. Research limitations/implications – While the authors find that skill underutilization is positively related to collective turnover, future research is needed to measure the group processes that occur among group members and lead to collective turnover. Limitations of this study include the inability to validate the aggregation of data from the individual level to the group level, and the generalizability of findings to other PSFs or to involuntary turnover situations. Practical implications – Understanding the antecedents of collective turnover is of particular concern to PSFs, as they are composed of highly skilled, intrinsically motivated professionals, who generate value for the firm. These findings are particularly timely, given the significant levels of underemployment in countries throughout the world. Originality/value – In addition to extending skill underutilization and collective turnover research to the occupational group level, the findings highlight the importance of providing development opportunities for employees during difficult economic conditions in order to minimize collective turnover.
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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 | 0.001 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".