The role of service quality and marketing mix on customer satisfaction and repurchase intention of SMEs products
Bibliographic record
Abstract
The purpose of this study was to determine the effect of service quality and marketing mix on customer satisfaction and repurchase intention. The sampling method used in this research is non-probability sampling with purposive sampling technique. The total sample in this study was 212 respondents. Methods of data collection using an online questionnaire. The data analysis used is instrument validity and reliability test, classical assumption test, hypothesis test and path analysis using SPSS 25.0 for windows program. The results of this study indicate that all items for each variable are valid and reliable. Both structural models meet the criteria for the classical assumption test with no multicollinearity, heteroscedasticity, and normality assumption. Based on the results of the t test for the service quality variable, it has a significant effect on customer satisfaction. The marketing mix variable has a significant effect on customer satisfaction and repurchase intention. The service quality and the customer satisfaction also have significant effects on repurchase intention.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".