Predicting Intentions to Purchase Sustainable Apparel in China: A Structural Equation Modeling Approach
Bibliographic record
Abstract
The present study aims at exploring psychological determinants of intention to purchase sustainable apparel within the framework of the Theory of Planned Behavior (TPB). A convenience sample of 784 university students studying in three major cities (Beijing, Shanghai, and Dalian) of China completed the anonymous surveys. All antecedents included in this study were significantly related to intention of sustainable apparel purchasing. Among them, the most important predictor of Intention to purchase sustainable apparel was individuals’ Attitude towards buying sustainable apparel, followed by Perceived Behaviour Control and Subjective Norm. The TPB was proved to be a reliable predictive model of intention to purchase sustainable apparel in the Chinese context. Findings from this study give readers an understanding of the magnitude and significance of relationships between antecedents and intention in the sustainable apparel consumption domain. These results lead to suggestions for policy makers, marketers and stakeholders involved in the sustainable apparel market.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".