Modelling Theory of Planned Behavior on Health Concern and Health Knowledge towards Purchase Intention on Organic Products
Bibliographic record
Abstract
Organic products have been gaining popularity among consumers worldwide due to the environmental and health benefits they are associated with. As a result of this trend, organic industries have been flourishing and have been able to expand into a variety of consumer product/service categories. Looking to explore purchasing behavior related organic coconut cosmetic products, this study attempted to apply the theory of planned behavior (TPB), which is a method of predicting consumer behavior that has been used extensively in a variety of research areas in recent years. Based upon the literature review, an extended TPB model that incorporates health concerns and health knowledge, in addition to attitude, subjective norms and perceived behavior control was examined in this study. For the data collection, an online survey was issued to residents of Bangkok, Thailand; with a total of 613 respondents retuning the questionnaires. Structural equation modeling (SEM) was employed to analyze the data using SPSS AMOS 24. The results showed that attitude, subjective norms, perceived behavior control and health concerns positively affect purchase intention; however, health knowledge did not influence purchase intentions related to the organic coconut cosmetic products. Similar to the findings in most extant literature, attitude was found to exert the most influence on the purchase behavior in this study.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".