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Record W2128085746 · doi:10.1177/0018726713516377

The role of negative affectivity in the relationships between pay satisfaction, affective and continuance commitment and voluntary turnover: A moderated mediation model

2014· article· en· W2128085746 on OpenAlexaff
Alexandra Panaccio, Christian Vandenberghe, Ahmed Khalil Ben Ayed

Bibliographic record

VenueHuman Relations · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsHEC MontréalConcordia University
Fundersnot available
KeywordsNegative affectivityPositive affectivityPsychologyOrganizational commitmentSocial psychologyTurnoverContinuanceMediationTurnover intentionAffective events theoryJob satisfactionModerated mediationJob attitudePersonalityManagementJob performance

Abstract

fetched live from OpenAlex

This study examines the mediating role of affective and continuance commitment in the relationship between pay satisfaction and voluntary turnover, and the moderating role of negative affectivity. Drawing from data collected at two points in time from a sample of human resource management professionals ( N = 509), we found that affective and continuance commitment mediated the negative relationship of pay satisfaction to turnover. Moreover, pay satisfaction’s indirect negative relationship with turnover via affective commitment was weaker among respondents high in negative affectivity, while its indirect negative relationship with turnover via continuance commitment was stronger among those with high negative affectivity. Finally, the residual negative relationship of pay satisfaction to turnover was stronger at high levels of negative affectivity. We discuss the implications of this study for our understanding of the role of affective commitment, continuance commitment and negative affectivity in the pay satisfaction–turnover relationship.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.029
Threshold uncertainty score0.705

Codex and Gemma teacher scores by category

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

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 head0.016
GPT teacher head0.234
Teacher spread0.218 · 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.

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

Citations49
Published2014
Admission routes1
Has abstractyes

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