Chinese consumers’ perceptions, attitude, and purchase intention of organic products
Bibliographic record
Abstract
Organic agricultural production addresses environmental sustainability and food safety concerns that arise from contemporary industrial farming practices. This study focuses on urban China and aims to explore a behavioural model through which consumers' beliefs and attitudes toward organic food influence their consumption. The proposed model integrates the theory of planned behaviour (TPB) with the stimulus-organism-response (SOR) model. Over 1500 participants from Beijing, Shanghai, and Chongqing participated in an online survey, reporting their organic food consumption behaviours and preferences. The data were analyzed using a structural equation model. The results generally support the TPB model, suggesting that attitudes toward organic food, social norms, and the availability of products and information related to organic food predicted consumers' purchase intentions and consumption of organic products. The results are also consistent with the SOR model, revealing that participants' positive perceptions of organic food attributes, such as sensory properties, nutritional value, and ecological welfare, were positively associated with their attitudes toward hedonic, utilitarian, and ethical benefits. Moreover, attitudes regarding hedonic and utilitarian benefits correlated positively with purchase intention and consumption behaviour. Surprisingly, attitudes toward the ethical benefits of organic food did not show a direct relationship with purchase intention. However, a positive attitude toward ethical benefits was linked to enhanced attitudes toward hedonic and utilitarian benefits, thus indirectly influencing purchase intention. The results shed light on the strategy for promoting sustainable food consumption.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".