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Record W2940842860 · doi:10.5539/ibr.v12n5p86

Reviewing the Concept of Green HRM (GHRM) and Its Application Practices (Green Staffing) with Suggested Research Agenda: A Review from Literature Background and Testing Construction Perspective

2019· review· en· W2940842860 on OpenAlexvenueno aff
Safaa Shaban

Bibliographic record

VenueInternational Business Research · 2019
Typereview
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsnot available
Fundersnot available
KeywordsHuman resource managementSustainabilityBusinessStaffingPerspective (graphical)Human resourcesShareholderWork (physics)Knowledge managementManagementCorporate governanceEngineeringComputer scienceMechanical engineeringEconomics

Abstract

fetched live from OpenAlex

Green HRM has become one of the most critical topics in the Business world and sustainability. Many researchers and studies indicate that environmental green staffing associated strongly with the success of financial and marketplace components. The Green Human resource in green-oriented organisations plays a significant part in shaping the culture of suitability in their organisation. Shaping the practices and applications of the HR with the green view and applications will have an effect on all HR decisions and through all the activity of shareholders viewpoint. Currently, all the work gives more attention to the relationship between GHRM and organisation sustainability. GHRM help in creating, developing and implementing the strategy of sustainable business within the organisation. Although green HRM is still with ground-breaking, unclear define concept and its applications facing some difficulties. The purposes of this study in to present a full theoretical framework for GHRM practices and to test the perspective of the GHRM concept in construction companies in Egypt and the UK. The main finding is the perspective of the UK is higher than Egypt in realizing GHRM important to the organisations.

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)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.886
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0000.002
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 head0.220
GPT teacher head0.438
Teacher spread0.217 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations44
Published2019
Admission routes1
Has abstractyes

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