Investigating the role of brand image and halal labels on purchase decisions: An empirical study of rice processing firms
Bibliographic record
Abstract
This study aims to examine the effect of halal labels on purchasing decisions with brand image as an intervening variable in rice processing companies. The study investigates the effect of halal labels on purchasing decisions, the effect of the halal label on brand image and the effect of brand image on purchasing decisions. The research method is quantitative and carried out using an online questionnaire distributed through social media. Determination of the research sample used a purposive sampling method, with the criteria of consumers of rice processing companies domiciled in the last year. 345 filled questionnaires were collected and processed using structural equation modeling (SEM). The results show that the halal label and brand image had a significant effect on purchasing decisions, brand image also played a mediating variable between the effect of halal labels and purchasing decisions. The study contributes to the theory that halal labels and brand image had a positive impact on purchase intentions. Company managers apply a halal label and create a brand image to increase purchase intention. Meanwhile for the industry, the research can be an important reference for consumer responses. Moreover, for academics, the results of this study become a reference for knowledge related to factors related to consumer purchasing decisions in consuming rice including the halal label, price, and brand image aspects.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".