Distinctive personality traits of quality management personnel
Bibliographic record
Abstract
Purpose – The purpose of this paper is to investigate differences in personality and career satisfaction between quality managers and workers in other fields based on Person-Environment Fit theory. Design/methodology/approach – Field study: personality and career satisfaction data for 965 quality managers were compared with those for a sample of over 85,000 individuals in many different occupations and employment settings using multivariate analysis of variance (MANOVA) and t-tests. Findings – Quality managers were higher than other occupations in intrinsic motivation, tough-mindedness, and conscientiousness, but lower in career satisfaction, optimism, and assertiveness. Research limitations/implications – This paper does not contain any longitudinal study; there is also a lack of some demographic variables, including race/ethnicity, job tenure, and career tenure. Practical implications – The findings carry implications for career planning, recruiting, pre-employment testing, training, and helping quality managers navigate through their organizations and careers. Social implications – Overall, the authors provide a personality profile of quality managers and show that many quality managers have lower career satisfaction than other occupations. Originality/value – These findings provide an occupational profile of salient personality traits of QC managers which can be used in occupational classification, field identity, and career planning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".