“I buy green products for my benefits or yours”: understanding consumers' intention to purchase green products
Bibliographic record
Abstract
Purpose This research aims to conduct a comprehensive conceptual model and empirical validation of the integration of negative (ego-centric) and positive (altruistic and ego-centric) drivers of green buying based on social dilemma theory and psychological egoism theory. Design/methodology/approach Data were collected using a self-administered questionnaire survey and analyzed with hierarchical regression analysis. Findings The results indicate that moral obligations, green self-identity, environmental concern and social pressure are positively related to green purchase intention, while perceived cost of green purchasing and price sensitivity are negatively. Meanwhile, social pressure positively moderates the relationship between price sensitivity and consumers' green purchasing intention, but negatively moderates the relationship between perceived cost of green purchasing and consumers' green purchasing intention. Originality/value First, this study is a comprehensive model of the concept and empirical validation of the integration of negative (ego-centric) and positive (altruistic and ego-centric) drivers of green buying. Specially, this study emphasizes the neglected importance of positive ego-centric factors of green consumer behavior. Second, this study explicitly incorporated the moderating effect of social pressure in the context of China. Since green buying is a social dilemma, the changes in this social dilemma after being affected by social pressure under the Chinese collectivist culture are also worth noting. Third, little is known about what motivates green consumption in emerging economies, and this has been hampered by a lack of cultural knowledge, conceptual richness and behavioral research that critically analyses consumer behavior.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".