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Record W3214846571 · doi:10.18280/ijsdp.160613

Green Human Resource Management and Environmental Innovativeness

2021· article· en· W3214846571 on OpenAlexvenueno aff
Sufaid Ali, Anees Janee Ali, Khurram Ashfaq, Jamshed Khalid

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessScope (computer science)Green innovationStructural equation modelingKnowledge managementHuman resource managementSituatedResource (disambiguation)Organizational cultureResource-based viewIndustrial organizationMarketingCompetitive advantageManagementEconomicsComputer science

Abstract

fetched live from OpenAlex

Drawing upon the resource-based view and the situated learning theory, this study examined the effect of green human resource management (HRM) practices on the firm environmental innovativeness. The moderating role of organizational innovative culture on the relationship between green HRM and firm environmental innovativeness was also assessed. A survey of 212 furniture manufacturing companies in Malaysia was analyzed using structural equation modeling. Results from the data analysis suggest that green HRM practices are positively associated with the firm environmental innovativeness. The positive effect of green training and green compensation on firm environmental innovativeness was found to be increased by moderating the role of organizational innovative culture. The present study clarifies key green HRM practices that can assist the environmental innovativeness in Malaysian furniture manufacturing firms and advances related research by proposing and examining an overarching model to enlighten such synergies and the moderating role of organizational innovative culture. The findings further extend the scope of green HRM research to promote innovation in the manufacturing firms. The theoretical and practical implications of green HRM are presented to enhance the environmental innovativeness.

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)0.000
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 score0.374
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.011
GPT teacher head0.224
Teacher spread0.213 · 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

Citations28
Published2021
Admission routes1
Has abstractyes

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