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Record W1640775974

Understanding High Performance Work System (HPWS) as Related to creativity and Job Engagement in Kurdistan and Canada

2015· article· en· W1640775974 on OpenAlexaboutno aff
Shirzad Mohammed Mahdi Rafia Sourchi, Liao Jian-qiao

Bibliographic record

VenueEuropean Journal of Business and Management · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsCreativityHuman resourcesWork systemsWork engagementOrganizational performanceKnowledge managementWork (physics)Job performanceHuman resource managementIdentification (biology)PsychologyValue (mathematics)Employee engagementManagementBusinessOperations managementMarketingSocial psychologyComputer scienceJob satisfactionStatisticsEngineeringEconomicsMathematics
DOInot available

Abstract

fetched live from OpenAlex

To understand the high performance Work system, the study determined the impact of high performance system in the organizational setup in Kurdistan and Canada. The research was quantitative in nature where participants were selected randomly from the different companies in Kurdistan and Canada. A total of 318 (Kurdistan) and 293 (Canada) data was collected where analysis was conducted with the SPSS 21. The study found out that there is a significant strong positive relationship between Human resource practices and creativity with the Pearson correlation coefficients r = 0.903 with the p-value <0.01. On the other hand the study found out that there is negative relationship between human resource practices and job engagement. The interaction effects of the high performance work systems human practices and the job engagement, organizational identification, and creativity was significant with the p-value <0.01. Keywords: High performance, organizational performance, workplace system

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 score0.065
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.037
GPT teacher head0.209
Teacher spread0.172 · 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

Citations10
Published2015
Admission routes1
Has abstractyes

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