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Record W3198434430 · doi:10.1108/pr-05-2020-0326

Bullying and turnover intentions: how creative employees overcome perceptions of dysfunctional organizational politics

2021· article· en· W3198434430 on OpenAlexaff
Dirk De Clercq, Tasneem Fatima, Sadia Jahanzeb

Bibliographic record

VenuePersonnel Review · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsBrock University
Fundersnot available
KeywordsDysfunctional familyCreativityPoliticsOriginalitySocial psychologyPsychologyPerceptionOrganisation climatePublic relationsPolitical science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.267
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.000

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.

Opus teacher head0.023
GPT teacher head0.256
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations30
Published2021
Admission routes1
Has abstractyes

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