MétaCan
Menu
Back to cohort
Record W2972329944 · doi:10.1177/0022243719865898

Media Coverage of Climate Change and Sustainable Product Consumption: Evidence from the Hybrid Vehicle Market

2019· article· en· W2972329944 on OpenAlexaff
Yubo Chen, Mrinal Ghosh, Liu Yong, Liang Zhao

Bibliographic record

VenueJournal of Marketing Research · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsAmbrose UniversityInstitute on Governance
FundersShanghai University of Finance and EconomicsTianjin UniversityNational Natural Science Foundation of China
KeywordsConsumption (sociology)Sustainable consumptionClimate changeContext (archaeology)BusinessMarketingProduct (mathematics)Consumer behaviourMass mediaAdvertisingMedia coverageGlobal warmingEconomicsMicroeconomicsSociologyProduction (economics)

Abstract

fetched live from OpenAlex

As sustainable consumption becomes increasingly important, firms must better understand the drivers behind the consumption of these products. This article examines the effects of mass media in the context of the U.S. hybrid vehicle market. Drawing on monthly sales data, the authors provide evidence that the general coverage of climate change or global warming by major media outlets exerts an overall positive impact on the sales of hybrid vehicles. This impact mainly comes from the media reports that assert that climate change is occurring. In contrast, media coverage that either denies climate change or holds a neutral stance on the issue has little impact. The authors provide preliminary evidence that a social norm advocating for environmentally friendly consumption plays an important role in how media coverage affects consumer purchase. They provide implications for theory and practice and call for future research that examines the causal impact of media in general on consumer decisions, especially in domains that are crucial for the society.

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)0.002
metaresearch head score (Gemma)0.017
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.082
GPT teacher head0.281
Teacher spread0.199 · 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

Citations104
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Marketing ResearchSame topicEnergy, Environment, Economic GrowthFrench-language works237,207