SMEs repurchase intention and customer satisfaction: Investigating the role of utilitarian value and service quality
Bibliographic record
Abstract
The purpose of this study was to determine the effect of utilitarian value and service quality on customer satisfaction to increase repurchase. The population in this study were SMEs consumers and the sampling technique used was non-probability sampling, while the non-probability sampling technique used was purposive sampling. The number of samples in this study were 128 respondents. The instrument used to obtain data was by using a questionnaire. The research method was quantitative, the data obtained were based on answers from respondents to the questionnaire, analyzed by statistical techniques of multiple linear regression analysis, the regression model was tested with classical assumptions in order to meet the requirements and was feasible to use to predict the effect of independent variables on the dependent variable. The results of the regression calculations were tested by t-test and coefficient of determination, while the results of mediation calculations were tested by path analysis and Sobel tests with the help of the SPSS for Windows version 25.0 program. After analyzing the data, the following results and conclusions were obtained: (1) Utilitarian Value has a positive and significant effect on Customer Satisfaction (2) Service Quality has a positive and significant effect on Customer Satisfaction (3) Utilitarian Value has no effect on Repurchase Intention (4) Service Quality has no positive and significant effect on Repurchase Intention, (5) Customer Satisfaction has a positive and significant effect on Repurchase Intention,(6) Utilitarian Value through Customer Satisfaction has a significant effect on Repurchase Intention. (7) Service Quality through Customer Satisfaction has a significant effect to 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 | 0.001 |
| 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".