Determinant factors of online purchase decision process via social commerce: An empirical study of organic black rice in Indonesia
Bibliographic record
Abstract
Organic black rice (OBR) is a healthy food that is environmentally friendly which is better than organic white rice and organic brown rice. However, the demand for OBR in Indonesia is still low. In addition, some people consider OBR as black sticky rice. Meanwhile, black rice has great potential to be developed in Indonesia because it has local varieties that are still rare, have a high selling value, and are suitable for cultivation based on the analysis of their farming. The rapid development of social media users in Indonesia causes organic black rice to be traded online via social commerce (s-commerce). There has been a lot of research on social commerce, but there is still very few social commerce research offering framework design. The purpose of this research is to develop a conceptual model (framework) of the online OBR purchasing decision process via s-commerce, and to identify the factors underlying consumer assessment of the process. As a result, the conceptual model shows consumers recognize the need for OBR through free platforms, namely Search Engine Optimization (SEO), Instagram, blogs, article sites, through friends and through family. The factors underlying consumers' assessment of the online OBR purchasing decision process were security in purchasing decisions, Internet, friends, satisfaction with the results, Instagram and other social media, and family factor. These factors can be used as important considerations in online OBR marketing via s-commerce.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".