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Record W2156154063 · doi:10.1080/09585192.2012.679950

The link between perceived human resource management practices, engagement and employee behaviour: a moderated mediation model

2012· article· en· W2156154063 on OpenAlexaff
Kerstin Alfes, Amanda Shantz, Catherine Truss, Emma Soane

Bibliographic record

VenueThe International Journal of Human Resource Management · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsModerated mediationSocial exchange theoryMediationPsychologyHuman resource managementEmployee engagementSocial psychologyPerceived organizational supportOrganizational citizenship behaviorLine managementOutcome (game theory)BusinessKnowledge managementPublic relationsOrganizational commitmentPolitical science

Abstract

fetched live from OpenAlex

This study contributes to our understanding of the mediating and moderating processes through which human resource management (HRM) practices are linked with behavioural outcomes. We developed and tested a moderated mediation model linking perceived HRM practices to organisational citizenship behaviour and turnover intentions. Drawing on social exchange theory, our model posits that the effect of perceived HRM practices on both outcome variables is mediated by levels of employee engagement, while the relationship between employee engagement and both outcome variables is moderated by perceived organisational support and leader–member exchange. Overall, data from 297 employees in a service sector organisation in the UK support this model. This suggests that the enactment of positive behavioural outcomes, as a consequence of engagement, largely depends on the wider organisational climate and employees' relationship with their line manager. Implications for practice and directions for future research 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)0.008
metaresearch head score (Gemma)0.020
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 score0.008
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.046
GPT teacher head0.303
Teacher spread0.257 · 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

Citations759
Published2012
Admission routes1
Has abstractyes

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