E – Retailing Attributes with Consumer Satisfaction, Trust and Repurchase Intension
Bibliographic record
Abstract
Being a key difference in a competitive market, consumer satisfaction and trust has come to be the fundamental part of business strategy.E-Retailing is becoming increasingly significant in establishing a new pattern of consumers shopping.The degree to which a retailer values its customers can have a big influence.To delight and keep customers, retailers must first understand their demands and lay the groundwork for an integrated and personalised experience.Additionally, when e-retailers have loyal customers, it benefits from free and very effective, optimistic word-of-mouth advertising.Hence, it is essential for retailers to adequately create consumer happiness which leads to repurchase intension.E-Retailers require reliable and accurate satisfaction indicators for this.As a result, retailers must recognize the expectations of customers in order to generate income and face market obstacles.The research was carried out using an internet-based questionnaire among respondents.To determine E-retailing attributes with consumer satisfaction and trust.The study was carried about among 206 online shopper and analysis such as percentage, descriptive statistics, Confirmatory factor analysis and SEM analysis.According to the study, there is a strong correlation between E-retailing qualities and consumer satisfaction, trust, and intent to repurchase.
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.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".