Navigating the Dark Side: Dark Triad and Time Theft in the Turkish Context
Bibliographic record
Abstract
Time theft, defined as employee engagement in non-work-related activities during work hours, imposes significant organizational costs; however, its underlying causes remain largely underexplored. This study addresses this theoretical gap by examining the combined influence of Dark Triad personality traits (psychopathy, Machiavellianism, and narcissism) and key situational factors (general loneliness, social media addiction, and workplace boredom) on three distinct dimensions of time theft: classic, technological, and social. The analysis, based on survey data from 264 private and public sector employees in Sakarya, Turkey, revealed that psychopathy and Machiavellianism initially correlated strongly with classic and technological time theft. Nevertheless, their predictive ability was significantly attenuated when situational variables were introduced into the full regression model. Specifically, workplace boredom and social media addiction emerged as robust predictors, exerting a dominant influence over the Dark Triad traits. Neither narcissism, general loneliness, nor standard demographic variables demonstrated a significant relationship with any dimension of time theft. The paper concludes by discussing the conceptual implications of these findings within Turkey's traditional collectivist culture and proposing avenues for future research and practical intervention strategies.
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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 | 0.000 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".