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Record W2179618945 · doi:10.5539/ass.v11n28p139

The Implementation of High Performance Work System in Public Organizations: Implication for Organizational Performance.

2015· article· en· W2179618945 on OpenAlexvenueno aff
Solomon Ozemoyah Ugheoke

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsnot available
Fundersnot available
KeywordsOrganizational performanceLikert scaleWork systemsArgument (complex analysis)Public sectorBusinessWork (physics)Competitive advantagePsychologyKnowledge managementMarketingPolitical scienceComputer scienceEngineering

Abstract

fetched live from OpenAlex

<p>Abstract</p> <p>A principal concern express among organizational researchers is to understand why some organizations irrespective of size, location and sector outperform others. High performance work system (HPWS) offers an explanation for this phenomenon. The implementation of unique practices leads some organizations to outperform others and give organizations the competitive advantage over others. While it has been well established that HPWS practices affect organizational performance within a large and complex organizations, less have been empirically established if they also create benefit for public organizations and this has generated concerns among researchers in the field of HPWS. Following this argument, this study examines this theoretical gap with a survey data collected from employees in the public sector. Overall, three dimensions of HPWS were identified by the researchers and the level of awareness was assessed on a seven point Likert scale. We found that two out of the three dimensions of HPWS identified in this have a positive relationship with organizational performance.</p> <p>Keywords: HPWS, organizational performance, selective training and development, PMS, individual role.</p> <p> </p>

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.882
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
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 head0.021
GPT teacher head0.265
Teacher spread0.244 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

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