An Effect of Product Quality, Price, and Word of Mouth on Buying Interest : A case of Tretes Porridge in Binjai
Bibliographic record
Abstract
Small and medium enterprises (SMEs) is one of the supporting economic growths of a region. Small and medium size business can be developed by increasing the buying interest of the people around. Buying interest is the desire that arises from consumers to a product as a result of the consumer's observation of a product. This research is quantitative research with a sample of 109 respondents. This study focuses on the effect of product quality, price, and word of mouth on interest buying : a case of Bubur Tretes Binjai. This research was conducted in Binjai . The population of this study is the customer of Bubur Tretes that is located in Binjai. The researcher concluded that taking a sample of 109 respondents. Primary data were gathered by using questionnaires. The data were analyzed by using structural equation modeling-partial least squares (SEM-PLS) with SmartPLS software. The result of this study indicated that the variable product quality (X1) partially had a significant effect on buying interest, the price variable (X2) partially had not significant effect on buying interest, and word of mouth variable (X3) partially had a significant effect on buying interest. Simultaneously, product quality, price , and word of mouth influence buying interest on the traditional product of Bubur Tretes in Binjai. From the results of data processing, it was found that the coefficient of determination was 69.2% which indicates that X1, X2, and X3 together were able to influence Y by 69.2% with a moderate category, the remaining 30.8% was influenced by other factors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".