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Record W2976547124 · doi:10.1108/pr-07-2018-0241

Time-related work stress and counterproductive work behavior

2019· article· en· W2976547124 on OpenAlexaff
Dirk De Clercq, Inam Ul Haq, Muhammad Umer Azeem

Bibliographic record

VenuePersonnel Review · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsBrock University
Fundersnot available
KeywordsCounterproductive work behaviorPsychologyExtant taxonSocial psychologyOriginalityPersonalityWork (physics)Work engagementWork stressBig Five personality traitsApplied psychologyOrganizational commitmentOrganizational citizenship behavior

Abstract

fetched live from OpenAlex

Purpose With a basis in the conservation of resources theory, the purpose of this paper is to investigate the relationship between employees’ experience of time-related work stress and their engagement in counterproductive work behavior (CWB), as well as the invigorating roles that different deviant personality traits might play in this process. Design/methodology/approach Two-wave survey data with a time lag of three weeks were collected from 127 employees in Pakistani organizations. Findings Employees’ sense that they have insufficient time to do their job tasks spurs their CWB, and this effect is particularly strong if they have strong Machiavellian, narcissistic or psychopathic tendencies. Originality/value This study adds to extant research by identifying employees’ time-related work stress as an understudied driver of their CWB and the three personality traits that constitute the dark triad as triggers of the translation of time-related work stress into CWB.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.225
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations87
Published2019
Admission routes1
Has abstractyes

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