How to build consumer trust towards e-satisfaction in e-commerce sites in the covid-19 pandemic time?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it