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Record W3090141750 · doi:10.5267/j.msl.2020.9.012

Green value and sustainable transportation engagement: The mediating role of attitude

2020· article· en· W3090141750 on OpenAlexvenueno aff
Nurliyana Nasuha Razali, Marhana Mohamed Anuar, Abdul Hafaz Ngah

Bibliographic record

VenueManagement Science Letters · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsnot available
FundersUniversiti Malaysia Terengganu
KeywordsValue (mathematics)BusinessSustainable transportPsychologyMarketingEnvironmental economicsAdvertisingOperations managementSustainabilityEconomicsMathematicsStatistics

Abstract

fetched live from OpenAlex

This study aims to investigate the effect of green value on attitude and sustainable transportation engagement and the mediating role of attitude on the relationship between green value and sustainable transportation engagement. A survey was carried out on students in a public university. A sample of 107 students was obtained for this study. The data was analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings show that green value and attitude had a significant influence on sustainable transportation engagement. In addition, attitude played a mediating role in the relationship between green value and sustainable transportation engagement. The study provides valuable contributions for both theory and practice in the area of eco-campus. Theoretically, the study extends the value-attitudebehavior model in the context of sustainable transportation and adds to the body of literature of ecocampus. This is among the first studies conducted in Malaysia that investigates factors that contribute towards sustainable transportation engagement among students. In terms of managerial significance, the results provide guidance to policymakers to help them plan for strategies to enhance students' engagement in sustainable transportation in the future.

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 score0.324
Threshold uncertainty score0.491

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.008
GPT teacher head0.196
Teacher spread0.189 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

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