The influence of dark triad on knowledge hiding behavior with workplace spirituality as a moderator in higher education institutions
Bibliographic record
Abstract
Purpose In the knowledge management (KM) literature, there are umpteen discussions on knowledge sharing; however, the scholarly community still faces a dearth of literature on knowledge hiding behavior (KHB) and its determinants. The current study aims to examine the direct effect of dark triad (DT) personality dimensions (machiavellianism, narcissism and psychopathy) on KHB dimensions (rationalized hiding, evasive hiding and playing dumb). Drawing on social control theory, this study also explores the moderating effect of workplace spirituality (WS) on the direct relationship between DT and KHB. Design/methodology/approach Using purposive sampling, 281 matched-pair datasets from faculty members working with higher education institutions (HEI) in India have been obtained. The direct relationship has been tested through regression analysis and moderation analysis has been performed using the PROCESS macro for SPSS. Findings The study has successfully mapped DT dimensions with KHB dimensions, and it is observed that machiavellians mostly use evasive hiding, narcissists believe in rationalized hiding and paying dumb is mostly used by psychopaths. Workplace spirituality (WS) weakens the direct relationship between DT and KHB. Practical implications HEIs are advised to foster a climate conducive to WS by getting faculty to realize that their job is something larger than themselves through developing a sense of community among faculty members. Originality/value This empirical study extends the KM literature and expands the scope of bridging the gaps on KHB. It is one of the few studies to examine the impact of DT on KHB with WS as a moderator in HEIs.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".