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Record W3115922788 · doi:10.18280/ijsdp.150810

The Factors Affecting Household Electricity Saving Behavior: A Study in Vietnam

2020· article· en· W3115922788 on OpenAlexvenueno aff
Nguyễn Ngọc Hiền, Pham Hoang

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsTheory of planned behaviorElectricityStructural equation modelingPromotion (chess)Consumption (sociology)Ho chi minhNorm (philosophy)QuestionnaireBusinessEnvironmental economicsMarketingPublic economicsControl (management)EconomicsSocioeconomicsEngineeringPolitical scienceSociologyPoliticsComputer science

Abstract

fetched live from OpenAlex

In the context of dramatically increase in electricity demand, Vietnam's potential for power supply remains limited. Research and promotion of electricity saving behavior of households become significant to reduce electricity consumption and protect ecological environment. This study incorporates elements of planned behavior theory (TPB) and norm activation model (NAM) as the basis for developing and extending key assumptions. In addition, expansion TPB is used to study influence factors affecting electricity saving behavior. Through a sample of 395 randomly selected residents in Tay Ninh Province and Ho Chi Minh city in Vietnam, the proposals were checked using a structural equation model (SEM). The results showed that the factors in TPB and NAM (such as perceived behavioral control, subjective norm, attitude, personal moral norm) and additional factor (perceived benefit) are the important factors that influence resident's intention of saving electricity. Moreover, electricity saving behavior is strongly influenced by the intention of saving electricity, perceived benefit, policy and social propaganda. Based on these results, some inferences are drawn, and recommendations are made for policy makers and further research proposals are discussed.

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 score0.027
Threshold uncertainty score0.286

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.025
GPT teacher head0.249
Teacher spread0.224 · 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

Citations17
Published2020
Admission routes1
Has abstractyes

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