Bibliographic record
Abstract
The development of the internet raises opportunities for the marketing of a product and bring new forms for retail transactions, one of which is online shopping. Furthermore with the Internet, online consumers more easily gain access to information and they offered a wide variety of products and services that can be selected at competitive prices. The purpose of this study is to determine whether there is influence of E-Service Quality, Price Perception and Experiential Marketing to Repurchase Intention which mediated by Customer Satisfaction in On-line Shopping. The amount of samples is 180 respondents. Questionnaires were distributed to respondents who have shopped using online shopping with random sampling method. This study uses data analysis of Structural Equation Modeling by using Lisrel software. The result showed that there is the influence of e-service quality to customer satisfaction and to repurchase intention, while repurchase intention has negative influence occurs. Furthermore, price has no influence to customer satisfaction but has an influence to repurchase intention. Experiential marketing has no influence to customer satisfaction and repurchase intention. Customer satifaction has positive effect on repurchase intention. The effect of e-service quality and experiential marketing through customer satisfaction as mediation variable has no influence to repurchase intention, while price perception influence 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".