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Do Conspicuous Consumption Motives Explain Eco?Friendly Consumption Decisions? Evidence from Social Media Exposure Effects

2024· other· en· W6958533205 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2024
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicInnovations in Aquaponics and Hydroponics Systems
Canadian institutionsnot available
Fundersnot available
KeywordsConspicuous consumptionConsumption (sociology)Social mediaProsocial behaviorGreen consumptionContext (archaeology)Quality (philosophy)

Abstract

fetched live from OpenAlex

While previous studies underscore that conspicuous consumption is antithetical to prosocial consumer behaviour, there is new evidence suggesting that conspicuous consumption motives may actually motivate green product choices. Despite this claim, little is known about the mechanisms explaining conspicuous consumption intentions of green products in the online context. Although social media’s impact on conspicuous consumption through online social dynamics such as Social Comparison and FOMO have been established, their effect in the context of green conspicuous consumption remains unclear. To address this gap, this study employed a Partial Least Squares Structural Equation Modelling approach to understand how conspicuous consumption motives drive green purchase intentions when consumers are exposed to green content on social media. The study hypothesizes Social Media Exposure to green content, FOMO, and Social Comparison as antecedents to green conspicuous consumption motives, with perceived quality as a moderator. Data was collected from 346 US, UK and Canada social media users. Findings suggest that conspicuous consumption motives are salient in mediating the relationship between FOMO, Social comparison and green purchase intentions. While perceived quality positively moderated the relationship between social comparison and conspicuous consumption, it was insignificant for the FOMO and conspicuous consumption relationship. Theoretical and Managerial implications are discussed.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.471
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.2340.025

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 head0.087
GPT teacher head0.295
Teacher spread0.208 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2024
Admission routes1
Has abstractyes

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