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Record W2801590351 · doi:10.1108/jcm-09-2016-1941

Does “sharing” mean “socially responsible consuming”? Exploration of the relationship between collaborative consumption and socially responsible consumption

2018· article· en· W2801590351 on OpenAlexaff
Myriam Ertz, Fabien Durif, Agnès François-Lecompte, Caroline Boivin

Bibliographic record

VenueJournal of Consumer Marketing · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSharing Economy and Platforms
Canadian institutionsUniversité de SherbrookeUniversité du Québec à MontréalUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsConsumption (sociology)Sustainable consumptionOriginalityMarketingVariance (accounting)BusinessConsumer behaviourValue (mathematics)SustainabilitySharing economyAdvertisingPsychologyEconomicsSocial psychologyMicroeconomicsPolitical scienceSociologyProduction (economics)

Abstract

fetched live from OpenAlex

Purpose The purpose of this study is to investigate the extent to which collaborative consumption (CC) enthusiasts are significantly more likely to engage into specific forms of socially responsible consumption (SRC), in contrast to regular consumers. Design/methodology/approach The authors administered an online questionnaire survey to a panel of 1,006 consumers. A cluster analysis combined with analyses of variance then determined the extent to which CC enthusiasts were more likely to engage in the focal SRC behaviors as opposed to others. Findings CC enthusiasts differ positively from other consumers concerning sustainable transportation, citizen consumption and composting but negatively from other consumers concerning recycling; they do not differ significantly with regard to environmental, animal protection and local consumption. Originality/value Conflating CC and SRC remains debatable. This study provides some preliminary evidence about the complex associations that exists between the two constructs.

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)0.004
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0030.003
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.068
GPT teacher head0.290
Teacher spread0.222 · 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 designQualitative
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

Citations63
Published2018
Admission routes1
Has abstractyes

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