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Record W3015819501 · doi:10.5267/j.msl.2020.4.007

The antecedent model of green awareness customer

2020· article· en· W3015819501 on OpenAlexvenueno aff
Doni Purnama Alamsyah, Rudy Aryanto, Iston Dwija Utama, Lita Sari Marita, Norfaridatul Akmaliah Othman

Bibliographic record

VenueManagement Science Letters · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsnot available
Fundersnot available
KeywordsAntecedent (behavioral psychology)Computer scienceBusinessPsychologyProcess managementOperations managementSocial psychologyEconomics

Abstract

fetched live from OpenAlex

This study aims to review the correlation of environmental knowledge, eco-label, and per-ceived quality of green awareness customers with environmentally friendly products. Research method is based on a survey on 100 customers of a supermarket who are familiar with environmentally friendly products. The analysis technique conducted uses linear regression with smart tools of SPSS for testing the hypotheses of the paper. The research finding demonstrates that environmental knowledge, eco-label, and perceived quality had positive correla-tions with green awareness customers. Out of all the factors that influence green awareness, eco-label of the customer maintains the most determining impact on green awareness improvement. Research finding emphasizes that antecedent of green awareness includes environmental knowledge, eco-label, and perceived quality. The results are useful information for stakeholders since they may use green marketing strategy and government in decision making to the green products.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0210.001

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 head0.129
GPT teacher head0.362
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations39
Published2020
Admission routes1
Has abstractyes

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