Website Quality and The Role of Customer Satisfaction Toward Repurchase Intention: A Study of Indonesian E-Commerce
Bibliographic record
Abstract
A survey by Katadata shows that during the COVID-19 Pandemic, the e-commerce sector of Indonesia showed a significant rise of visitors. In addition, the number of visits to e-commerce websites is very high in the third Quarter of 2020. Additionally, the e-commerce sector is one of the highest contributors towards Indonesia’s gross domestic product (GDP). Thus, increasing consumer satisfaction and repurchase intention is essential for companies, especially in fashion products. This study aims to measure website quality on repurchase intention through the role of consumer satisfaction in the context of e-commerce in Indonesia.Furthermore, website quality has dimensions such as information quality, system quality, and e-service quality. This study uses a descriptive quantitative approach that focuses on e-commerce in Indonesia. The data is collected through online questionnaire which is distributed to 333 consumers who used e-commerce to buy fashion products and used for further analysis. To analyze each variable, partial least squares structural equation modelling (PLS-SEM) is used to test the hypotheses. This study found that all dimensions of website quality and customer satisfaction are determining factors in repurchase intention. Lastly, this research provides guidelines in providing strategies for companies in the context of e-commerce websites in Indonesia
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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 | 0.003 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".