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Record W3013044897 · doi:10.1108/ebhrm-04-2019-0037

Employee perceptions of HRM practices and their turnover intentions: evidence from South Korea

2020· article· en· W3013044897 on OpenAlexaff
Jinuk Oh

Bibliographic record

VenueEvidence-based HRM a Global Forum for Empirical Scholarship · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAutonomyJob satisfactionSocial exchange theoryTurnoverContext (archaeology)Job securityPsychologyPerceptionHuman resource managementOriginalityPerspective (graphical)Compensation (psychology)Affect (linguistics)Social psychologyBusinessKnowledge managementManagementPolitical scienceWork (physics)

Abstract

fetched live from OpenAlex

Purpose The study sought to provide insight into the affective mechanisms that underlie the relationship between HRM practices and employee turnover intentions from the perspective of Korean employees. The study drew on social exchange theory and used compensation satisfaction, perceived job security and job autonomy to explain how perceptions of HRM practices affect employee turnover intentions. Design/methodology/approach The data were generated from a survey questionnaire administered to both white-collar and knowledge workers in different organizations in the Seoul Capital Area. The final sample consisted of 310 full-time employees. Findings The results show that compensation satisfaction and perceived job security have significant indirect negative effects on employees' intentions to leave their organization in the Korean context, which supports previous studies in Western contexts. However, the indirect effects of job autonomy on employee turnover intention were not significant in the current study. Originality/value This study continues the conversation about the important role HRM practices play in retaining valuable employees. This study offers a nuanced view of the relationship between HRM practices and employee turnover in a distinctive research setting. This study also provides realistic and practical suggestions on HRM so that organizations in Korea are able to implement HRM practices that help them retain competent employees.

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)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.004
Open science0.0000.000
Research integrity0.0000.000
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 head0.137
GPT teacher head0.352
Teacher spread0.214 · 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

Citations23
Published2020
Admission routes1
Has abstractyes

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