Repurchase intention behavior in B2C E-commerce
Bibliographic record
Abstract
The intention to buy back is one of the objectives of the business strategy. This study aims to analyze the effect of mediating customer satisfaction on e-service and repurchase intention. This analytical study was conducted on e-commerce that is widely used by Indonesia, namely Shopee.co.id. The rapid growth of e-commerce, both C2C and B2B, has made online retailers compete in the online retail business. The intention to buy back is no longer solely due to the quality of service like an offline business. The purpose of this study is to analyze the role of mediating customer satisfaction from the quality of E-Commerce on repurchase intention in E-Commerce that has implemented a combination of C2B and B2B. Quantitative methods with structural equation analysis (SEM) and path analysis were used to analyze data using LISREL. The questionnaire is distributed to respondents used as samples taken from the population for this research. The researched population is user of Shopee.co.id in Indonesia, whereas samples of the population are randomly taken. The samples of this research are 279 respondents. The results of this study found that there is no significant direct effect of electronic service quality on repurchases intentions, but when customer satisfaction acts as a mediating variable, it shows that electronic service quality affects repurchase intention significantly through customer satisfaction. This study will help online retailers to find out what factors influence customers to make repeat purchases.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".