ONLINE RETAILING: RELATIONSHIP AMONG E-TAILING SYSTEM QUALITY, E-SATISFACTION, E-TRUST AND
Bibliographic record
Abstract
The purpose of this study is to propose and demonstrate an integrative model of e-tailing system quality, e-satisfaction, e-trust and commitment in China. Customer’s commitment is fundamental to the survival and profitability of e-tailing. Perhaps, study variables have considerable importance on e-tailers performance. The data were collected from a sample of 383 students at universities in China during the first quarter of 2014. We used confirmatory factor analysis (CFA) and structural equation modeling (SEM) to evaluate the hypotheses about the relationship among model constructs. Thus all the hypotheses developed in the study were positively confirmed except one, reinforcing the theory and previous research in this field. The result indicates that system quality is positively associated with e-trust and e-satisfaction, similarly e-trust influences positively to both types of commitment. However, e-satisfaction has positive effect on affective commitment, but it has not significant positive connection with continuance commitment in our study. This study reveals interesting implications that are useful to both academicians and practitioners.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".