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Record W4283360547 · doi:10.3390/su14084420

Determinants of Pro-Environmental Behaviour in the Workplace

2022· article· en· W4283360547 on OpenAlexaff
Bob Foster, Zikri Muhammad, Mohd Yusoff Yusliza, Juhari Noor Faezah, Muhamad Deni Johansyah, Jing Yi Yong, Adnan ul Haque, Jumadil Saputra, Olawole Fawehinmi

Bibliographic record

VenueSustainability · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsYorkville University
Fundersnot available
KeywordsEnvironmental consciousnessCausality (physics)Sustainable developmentSample (material)BusinessIdentification (biology)Resource (disambiguation)Process (computing)Structural equation modelingData collectionMarketingPsychologyConsciousnessPolitical scienceSociologyEcology

Abstract

fetched live from OpenAlex

The primary notion of sustainable development is to maintain a promising future for the planet and the next generation by raising the awareness of sustainable development of people around the world. This study seeks to foster and enhance more sustainable behaviour in households, workplaces, schools, and higher educational institutions; previous research has placed increasing attention on the identification of factors of pro-environmental behaviour. Accordingly, this study aims to examine the elements influencing the pro-environmental behaviour of employees in the workplace. A survey was performed from January to February 2020 on 150 public employees of an organisation in Terengganu. Out of 150 employees, only 84 participated and had their responses collected by using convenience sampling. Smart PLS-SEM was used in analysing the relationships between the variables. The result of this study found that green lifestyles have a significant positive effect on pro-environmental behaviour. However, the impacts of environmental commitment, environmental consciousness, green self-efficacy, and green human resource management were insignificant. This study provides data that were developed using a cross-sectional design; the assessment of causality among the constructs was a risky process. Furthermore, the study collected data from a single source, namely the employees, which would enhance the relationships through common method bias. The findings of this study also offered several managerial implications for green 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.006
GPT teacher head0.262
Teacher spread0.255 · 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 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

Citations80
Published2022
Admission routes1
Has abstractyes

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