Unveiling the nexus: exploring the relationship of e-satisfaction, e-trust, and online repurchase intentions among students in a developing country
Bibliographic record
Abstract
This study explores the influence of students’ repurchase intention in the online shopping environment, focusing on the importance of e-trust and e-customer satisfaction. It investigates the mediating role of e-trust and e-customer satisfaction on online repurchase intention, while considering the moderating effect of prior online experience in Bangladesh. The research adopts a quantitative approach, collecting data from 415 students who have engaged in online purchasing through an online Google survey and the collected data were analyzed using structural equation modeling (SEM) and for the robustness of the study, simultaneous approach of 2SLS method is utilized in this paper for examining the relationships between e-trust, e-satisfaction, and online repurchase intention. The findings reveal that e-customer satisfaction and e-trust significantly impact students’ online repurchase intention, with e-word of mouth exerting a stronger influence. Out of 16 hypotheses tested, two are unsupported, including one related to security and one involving e-trust mediating customer service quality and repurchase intention. Future researchers are encouraged to replicate the model across cultures and diverse product categories. This research provides valuable insights for e-commerce businesses, emphasizing the significance of establishing customer trust and satisfaction to promote online repurchase behavior, particularly among students who are a substantial segment of e-commerce consumers in Bangladesh.
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How this classification was reachedexpand
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 | 0.004 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".