Pengaruh E-Service Quality Terhadap E-Repurchase Intention dengan E-Consumer Satisfaction sebagai Variabel Intervening pada E-Commerce Bukalapak
Bibliographic record
Abstract
Technological developments in the digital era are growing rapidly, with an important role played by technology in people's daily activities, including online shopping activities. The high number of e-commerce activities and the high level of visits to e-commerce sites represent the level of competition in the e-commerce industry and the high interest in buying on e-commerce platforms. Bukalapak has experienced a quite crucial decline, namely Bukalapak's monthly site visits which have continued to decline significantly since the first quarter of 2019. This study aims to determine the effect of E-Service Quality on E-Repurchase Intention mediated by E-Consumer Satisfaction on E- -Commerce Bukalapak. This type of research is quantitative research using descriptive analysis. The total number of respondents used in this study was 400 with the criteria of having made a purchase at Bukalapak at least once. The sampling technique used is non-probability sampling with purposive sampling and a Likert scale. The data analysis used was PLS (Partial Least Square) using SmartPLS 3.0 software. The results stated that E-Service Quality had a positive and significant influence on E-Repurchase Intention. E-Service Quality has a positive and significant influence on E-Consumer Satisfaction. E-Consumer Satisfaction has a positive and significant influence on E-Repurchase Intention. E-Service Quality has a positive and significant influence on E-Repurchase Intention through E-Consumer Satisfaction.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".