Functional and dysfunctional personality dispositions as antecedents of practical wisdom
Bibliographic record
Abstract
Purpose This study aims to analyze functional and dysfunctional personality dispositions as antecedents of practical wisdom. Design/methodology/approach A survey was administered to 204 full-time employees recruited from the CloudResearch Connect crowdsourcing platform, and the data were analyzed by means of correlation analysis, linear regression, and fuzzy-set Qualitative Comparative Analysis (fsQCA). Findings Practical wisdom is not an esoteric notion, and it may be measured by means of conventional research techniques, including surveys. Practical wisdom is not correlated with age, organizational tenure, or overall work experience. Functional personality dispositions of getting along with and getting ahead of people cultivate practical wisdom, and dysfunctional personality dispositions of moving away from people suppress it. While these personality dispositions are important, wisdom development is a complex process driven by a combination of multiple factors rather than by a single determinant. FsQCA may be successfully used to obtain a more comprehensive picture of the phenomenon of interest. Practical implications Practitioners should remain open-minded and not automatically discard or renounce the very concept of practical wisdom. Managers should administer the practical wisdom survey used in this study to identify clusters of practical wisdom in their organizations. Organizations should also distribute the Hogan Personality Content Single-Items inventory to all prospective employees during the hiring process and pay attention to all functional personality dispositions, as well as the dysfunctional personality dispositions of moving away from people, because these promote and suppress the development of practical wisdom, respectively. Originality/value To the best of the author’s knowledge, this is the first study to empirically explore personality dispositions as antecedents of practical wisdom in the knowledge management domain.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".