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Record W1544218999 · doi:10.1111/1467-8551.12052

Fairness Perceptions of Work−Life Balance Initiatives: Effects on Counterproductive Work Behaviour

2014· article· en· W1544218999 on OpenAlexfundno aff
T. Alexandra Beauregard

Bibliographic record

VenueBritish Journal of Management · 2014
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCounterproductive work behaviorPsychologyPerfectionism (psychology)Organizational justiceSocial psychologyPerceptionContext (archaeology)Work (physics)Work–life balanceSocial exchange theoryEquity (law)Organizational commitmentPolitical scienceOrganizational citizenship behavior

Abstract

fetched live from OpenAlex

This study examined the impact of employees' fairness perceptions regarding organizational work−life balance initiatives on their performance of counterproductive work behaviour (CWB). Moderating effects of adaptive and maladaptive perfectionism were also explored. Quantitative data collected from 224 public sector employees demonstrated significant main and moderating effects of informational justice, adaptive perfectionism and maladaptive perfectionism onCWB. Adaptive perfectionism weakened the link between informational justice andCWB, while maladaptive perfectionism strengthened it. Qualitative data collected from 26 employees indicate that both the social exchange and job stress models are useful frameworks for understandingCWBin the context of work−life balance initiatives;CWBemerged as both a negative emotional reaction to unfairness and as a tool used by employees to restore equity in the exchange relationship with their employer. Theoretical and practical implications are discussed.

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)0.004
metaresearch head score (Gemma)0.014
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 score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.273
Teacher spread0.264 · 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

Citations97
Published2014
Admission routes1
Has abstractyes

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