The influence of e-CRM, e-WOM, and e-service quality on the e-loyalty of online consumers
Bibliographic record
Abstract
The purpose of this study is to analyze the relationship between e-CRM (Electronic Customer Relationship Management) variables on e-Loyalty of online shop customers, e-WOM (electronic word-of-mouth) on e-Loyalty of online shop customers, and e-service quality on e-Loyalty of online shop customers. This study uses a quantitative approach. The population consists of all online shop consumers, and the sample of this study is 765 online shop consumers. The sampling technique used is simple random sampling. The research instrument is a questionnaire with a 7-point Likert scale. The research variables include e-CRM (Electronic Customer Relationship Management), e-WOM (electronic word-of-mouth), e-service quality, and e-Loyalty. Data were analyzed using Partial Least Square – Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0. The analysis consists of two stages: Outer Model (Measurement Model): Testing convergent validity, discriminant validity, and reliability. Inner Model (Structural Model): Testing path coefficients, R² values, and direct effects or hypothesis testing. The results of this study are E-CRM (Electronic Customer Relationship Management) has a positive relationship on e-Loyalty of online shop Customers, e-WOM (electronic word-of-mouth) has a positive relationship on e-Loyalty of online shop Customers, E-service quality has a positive relationship on e-Loyalty of online shop Customers. Optimal implementation of E-CRM, e-WOM and E-service quality through applications or websites can improve the overall user experience, which will ultimately encourage e-loyalty.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.004 | 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".