Do Conspicuous Consumption Motives Explain Eco Friendly Consumption Decisions? Evidence from Social Media Exposure Effects
Bibliographic record
Abstract
While past research suggests that conspicuous consumption does not support the goals of sustainable consumer behavior, emerging evidence indicates that it can drive green product choices in online contexts. However, the online mechanisms that support this process remain unclear. This study employs a Partial Least Squares Structural Equation Modelling approach to examine how conspicuous consumption motives influence green purchase intentions when consumers are exposed to green content on social media. It hypothesizes that social media exposure, Fear of Missing Out (FOMO), and social comparison act as antecedents to green conspicuous consumption motives, with perceived quality moderating these relationships. Data was collected from 346 social media users in the U.S., U.K., and Canada. Results indicate that conspicuous consumption motives significantly mediate the relationship between FOMO, social comparison, and green purchase intentions. While perceived quality enhances the effect of social comparison on conspicuous consumption, it does not significantly impact the relationship between FOMO and conspicuous consumption. These findings highlight the role of social media in shaping green purchase decisions and suggest that businesses and policymakers can leverage conspicuous consumption motives to promote sustainable consumer behavior. By positioning green products as both environmentally responsible and status-enhancing, marketers can appeal to consumers’ desire for social validation.
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 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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.
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".