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Record W3091405717 · doi:10.5267/j.msl.2020.9.031

Multidimensional factors that influence the intention to practice segregation-at-source of solid waste: An empirical study

2020· article· en· W3091405717 on OpenAlexvenueno aff
Kai Wah Cheng, Syuhaily Osman, Zuroni Md Jusoh, Jasmine Leby Lau

Bibliographic record

VenueManagement Science Letters · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsnot available
Fundersnot available
KeywordsMunicipal solid wastePsychologyEmpirical researchBusinessSocial psychologyStatisticsWaste managementMathematicsEngineering

Abstract

fetched live from OpenAlex

Solid waste generation in Malaysia is one of the challenging environmental issues which are often neglected by local households. The main purpose of this research is to ascertain the mediating effect of environmental concerns (i.e., egoistic, altruistic, and biospheric concerns) between households' descriptive norm and injunctive norm to practice the segregation-at-source of solid waste. A total of 400 residents living in townships in nine districts in the state of Selangor were selected via a multistage sampling method. A self-administrated bilingual questionnaire was used to collect the research data. As for the mediation test, the present research found that egoistic concern and altruistic concern mediated the significant relationship between injunctive norm and intention to practice the segregation-at-source of solid waste among the households in Selangor. The present research concludes with research implications and provides several avenues for future research in order to create a comprehensive understanding of the segregation-at-source of solid waste policy among Malaysian citizens.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.478
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.003
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 head0.029
GPT teacher head0.303
Teacher spread0.274 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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