Bullying and turnover intentions: how creative employees overcome perceptions of dysfunctional organizational politics
Bibliographic record
Abstract
Purpose This study seeks to unpack the relationship between employees' exposure to workplace bullying and their turnover intentions, with a particular focus on the possible mediating role of perceived organizational politics and moderating role of creativity. Design/methodology/approach The hypotheses are tested with multi-source, multi-wave data collected from employees and their peers in various organizations. Findings Workplace bullying spurs turnover intentions because employees believe they operate in strongly politicized organizational environments. This mediating role of perceived organizational politics is mitigated to the extent that employees can draw from their creative skills though. Practical implications For managers, this study pinpoints a critical reason – employees perceive that they operate in an organizational climate that endorses dysfunctional politics – by which bullying behaviors stimulate desires to leave the organization. It also reveals how this process might be contained by spurring employees' creativity. Originality/value This study provides novel insights into the process that underlies the connection between workplace bullying and quitting intentions by revealing the hitherto overlooked roles of employees' beliefs about dysfunctional politics and their own creativity levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".