The Effect of Service Quality on Customer Complaints and Customer Loyalty Through Customer Satisfaction of ZALORA Indonesia E-commerce Website Users
Bibliographic record
Abstract
Indonesia has one of the world's highest concentrations of internet users. Internet can be utilized for various activities, one of which is doing business. E- commerce website visits in Indonesia have increased since the third quarter of 2019 to the second quarter of 2022. ZALORA Indonesia is one of the e-commerce websites in Indonesia that has been present since 2012. The purpose of this research is to look at the impact of service quality on customer complaints and customer loyalty for ZALORA Indonesia e-commerce website customers. The method used is a quantitative method with a causal approach. This research sample collection uses purposive non- probability sampling techniques. The data collection technique used is distributing questionnaires using Google Form which is then tested for validity and reliability using SPSS version 25 software, and data processing is carried out to obtain the expected results using SmartPLS software. The help of using G-Power software to determine the number of samples that must be obtained.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".