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Green Consumption Values and Consumer Behavior

2022· book-chapter· en· W4226184181 on OpenAlexaff
Stella Tan Char Ern, Omkar Dastane, Herman Fassou Haba

Bibliographic record

VenuePractice, progress, and proficiency in sustainability · 2022
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsGreen marketingConsumption (sociology)Consumer behaviourGreen consumptionMarketingIBMAdvertisingConceptual modelMarketing communicationEmpirical researchConsumer researchBusinessEconomicsSociologyMathematicsMicroeconomicsStatisticsSocial scienceComputer scienceProduction (economics)

Abstract

fetched live from OpenAlex

In the quest of fostering green consumer behavior, companies are developing green marketing communications. However, do green consumer behaviors gets influenced by such marketing practices or it is result of green consumption values? The research-based chapter answers this question by developing a conceptual model against the background of green consumer behavior theories. The explanatory research design with quantitative research method was employed. Empirical data was collected using self-administered online questionnaire from 234 Singaporean consumers of green products. Collected data was then subjected to a range of analysis techniques using IBM SPSS AMOS 24. The findings suggest that green consumption values have stronger impact on consumer behavior as compared to that of marketing communication. However, impact of both independent variables was found to be positive and significant. In addition, it was identified that no mediating effect of marketing communication exists in relationship between green consumption values and consumer behavior.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.018
GPT teacher head0.281
Teacher spread0.262 · 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 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

Citations5
Published2022
Admission routes1
Has abstractyes

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