Personality disorders as a predictor of counterproductive knowledge behavior: the application of the Millon Clinical Multiaxial Inventory-IV
Bibliographic record
Abstract
Purpose This study investigates the role of personality disorders in the context of counterproductive knowledge behavior. Design/methodology/approach Data were collected through a survey administered to 120 full-time employees recruited from Amazon’s Mechanical Turk. Personality disorders were measured by means of the Millon Clinical Multiaxial Inventory-IV. Findings Personality disorders play an important role in the context of counterproductive knowledge behavior: employees suffering from various personality disorders are likely to hide knowledge from their fellow coworkers and engage in knowledge sabotage. Of particular importance are dependent, narcissistic and sadistic personality disorders as well as schizophrenic and delusional severe clinical syndromes. There is a need for a paradigm shift in terms of how the research community should portray those who engage in counterproductive knowledge behavior, reconsidering the underlying assumption that all of them act deliberately, consciously and rationally. Unexpectedly, most personality disorders do not facilitate knowledge hoarding. Practical implications Organizations should provide insurance coverage for the treatment of personality disorders, assist those seeking treatment, inform employees about the existence of personality disorders in the workplace and their impact on interemployee relationships, facilitate a stress-free work environment, remove social stigma that may be associated with personality disorders and, as a last resort, reassign workers suffering from extreme forms of personality disorders to tasks that require less interemployee interaction (instead of terminating them). Originality/value To the best of the authors’ knowledge, this work represents one of the first attempts to empirically investigate the notion of personality disorders in the context of knowledge management.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".