Do Conspicuous Consumption Motives Explain Eco?Friendly Consumption Decisions? Evidence from Social Media Exposure Effects
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".