A structured literature review of personality traits research in the knowledge behavior context: synthesis of the findings and practical recommendations
Bibliographic record
Abstract
Purpose This paper aims to present a structured literature review of personality traits research in the context of knowledge behavior. Design/methodology/approach A total of 200 empirical articles published in knowledge management-centric journals and other journals indexed by Google Scholar were discovered and analyzed. Findings It was found that many knowledge management researchers are inadequately aware of the personality psychology literature. More than two-thirds of the proposed nomological networks exclude trait-relevant situational cues, without which the trait–behavior relationship may not exist. Consequently, their conclusions on the predictive power of many personality traits are contradictory and inconclusive. Particularly unclear is the role of the Big Five traits, performance-approach/avoidance goal orientation and personal motivation traits. Personality trait constructs cannot simply be blindly borrowed from the psychology literature and recklessly added to knowledge management causal models. Practical implications Journal editorial teams should declare a moratorium on publishing empirical studies in which researchers carelessly add personality trait constructs to their causal models without proper conceptualizing that uses relevant theories and cites the original sources. Practitioners need to exercise caution when applying the recommendations of the studies reporting the impact of employee personality traits on knowledge behavior. Organizations should favor employees possessing emotional intelligence, learning-approach goal orientation and prosocial cooperative value orientation – and avoid those with the Dark Triad traits. Managers should explore their workplace and understand what situational cues activate the desirable and undesirable personality traits of their workers. Originality/value This study draws the attention of various stakeholders of the knowledge management discipline to a vital, yet possibly derailed, research area.
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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 | 0.015 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.033 | 0.022 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".