Investigating the effect of e-service quality on customer loyalty within the online marketplace during the covid-19 pandemic
Bibliographic record
Abstract
This study examines the effect of the quality of e-services provided by e-commerce on customer satisfaction and loyalty during the COVID-19 pandemic. This study used a quantitative approach to involve 118 respondents who traded in online markets. The sampling method used is purposive sampling. The data were analyzed using a structural equation model using the partial least squares technique. Research results indicate that out of the five hypothetical relationships raised by the researcher, one relationship related to remuneration was found to lack a statistically significant positive effect. Besides that, the remaining four hypothetical relationships, including efficiency, confidentiality, accountability, and customer satisfaction, demonstrated positive and statistically significant. The implication of this study highlights the need to cultivate and improve the quality of e-services in the online market, especially in the context of the COVID-19 pandemic. In this way, businesses can provide consumers with a rich shopping experience, enhancing customer satisfaction and laying the foundation for long-term customer 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 | 0.004 | 0.019 |
| 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 | 0.002 |
| 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".