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Record W2588919389 · doi:10.1111/1744-7941.12140

High‐performance work systems and employee engagement: empirical evidence from China

2017· article· en· W2588919389 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAsia Pacific Journal of Human Resources · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversity of Windsor
FundersFundamental Research Funds for the Central UniversitiesJiangnan University
KeywordsEmployee engagementJob satisfactionWork systemsMoodEmployee researchContext (archaeology)Human resource managementBusinessEmpirical evidenceAffect (linguistics)ChinaEmployee resource groupsPsychologyEmpirical researchPublic relationsSocial psychologyWork (physics)ManagementPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Employee engagement and commitment has been a very important issue in human resource managers’ agenda. The present study adds to the literature by examining the impact of high‐performance work systems ( HPWS ) on employee attitudes and on employee engagement in China in response to the increasing interest in the universalistic effects of HPWS in the globalized world market. With the data from 782 employees working in China's manufacturing and service sectors, this study shows that HPWS are positively related to employees’ positive mood and job satisfaction, and that job satisfaction and positive mood lead to high employee engagement. Moreover, employee's positive mood and job satisfaction also mediate the relationship between HPWS and employee engagement. The result helps explore one mechanism via which HPWS affect employee behaviors and provides empirical evidence for the applicability of HPWS in an international context.

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.

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score1.000

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 communication0.0010.002
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 head0.051
GPT teacher head0.281
Teacher spread0.229 · 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