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Record W4399595769 · doi:10.1177/07439156241264286

Is Sustainability a Liability? Green Marketing and Consumer Beliefs About Eco-Friendly Products

2024· article· en· W4399595769 on OpenAlexaff
Alexander Chernev, Sean Blair, Ulf Böckenholt, Himanshu Mishra

Bibliographic record

VenueJournal of Public Policy & Marketing · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsSustainabilityLiabilityVariety (cybernetics)MarketingArgument (complex analysis)Relevance (law)Product (mathematics)BusinessEnvironmental economicsEconomicsAccountingPolitical scienceComputer scienceLaw

Abstract

fetched live from OpenAlex

Prior research has suggested that consumers believe sustainable products tend to underperform compared with those made using traditional methods, a phenomenon referred to as the “sustainability liability.” Despite early conceptual justification and evidence supporting this argument, recent research has not attempted to validate this effect and assess its practical relevance. By employing a variety of scenarios adapted from prior studies, the authors quantify the magnitude of the sustainability-liability effect and show that it is relatively small and unlikely to be meaningful. This research also estimates the boundary conditions to identify scenarios in which a significant sustainability-liability effect might occur. Using archival data, the authors demonstrate that the association between sustainability and inferior product performance has decreased over time, explaining the discrepancy between their findings and prior research. These findings have important public policy implications, providing decision makers with empirical evidence that designing and promoting eco-friendly products can benefit society without detracting from the perceived performance of the company's offerings.

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)0.019
metaresearch head score (Gemma)0.032
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.398
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0000.001
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 head0.012
GPT teacher head0.258
Teacher spread0.246 · 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.

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

Citations8
Published2024
Admission routes1
Has abstractyes

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