Dominant factors influencing consumer satisfaction with the online purchase decision process through social commerce: A study of organic black rice in Indonesia
Bibliographic record
Abstract
The rapid increase in social media users in Indonesia has caused organic black rice (OBR) to be traded online via social commerce (s-commerce). OBR is an environmentally friendly functional food that meets the requirements for sustainable agricultural products. Unfortunately, in Indonesia the demand is still low, so some producers are reluctant to continue the OBR business. Therefore, it is important to study consumer satisfaction. Previous studies have primarily focused on satisfaction with choices and results of the decision process, not satisfaction with the decision process. Satisfaction in the perspective of the decision process has not been widely studied. This paper aims to identify the dominant factors that influence satisfaction with the online OBR purchase decision process via s-commerce. The research design is quantitative with a survey technique of 200 online consumers drawn by stratified random and convenience sampling. Data analysis using Exploratory Factor Analysis and Path Analysis. The results showed that the dominant factors were security in purchasing decisions, Instagram and other social media, friends, satisfaction with the results, Internet, references of friends and family as well as consideration of product taste and aroma, attractiveness, and disease treatment. These eight dominant factors can be used as important considerations in online OBR business through 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".