How to build consumer trust towards e-satisfaction in e-commerce sites in the covid-19 pandemic time?
Bibliographic record
Abstract
The Covid-19 pandemic has limited freedom of movement. E-commerce sites are growing rapidly and are becoming the main choice today when the virus becomes more prevalent. Trust is the key in creating online shopping satisfaction on e-commerce sites. This research was conducted to determine the effect of E-Service Quality and E-Security on Trust towards E-Satisfaction in the largest e-commerce site in Indonesia called Tokopedia. The research method used is quantitative. The population in this research is Tokopedia customers who are members of the Facebook group with a sample size of 400 people. The results of the study show that there was a relationship between the E-Service Quality and Trust, there was no relationship between the E-Service Quality and E-Satisfaction, there was a relationship between the E-Security variable and Trust, there was a relationship between the E-Security variable and E-Satisfaction. There was also a joint influence of E-Service Quality and E-Security variables on Trust, there was a relationship between the Trust variable and E-Satisfaction. This research is a development from the previous research where there was an effect of E-Service Quality and E-Security on E-Satisfaction. By adding the Trust variable as a moderate variable and E-Satisfaction as the dependent variable, the researchers found that the impact of Trust on E-Satisfaction was greater than the direct effect between E-Service Quality and E-Security on E-Satisfaction. Based on these results, it can be seen that the level of trust can increase E-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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".