Web site design, trust, satisfaction and e‐loyalty: the Indian experience
Bibliographic record
Abstract
Purpose With the rapid expansion of global online markets including India, researchers and practitioners are challenged to understand drivers of customer satisfaction, trust and loyalty towards web sites. The paper aims to focus on web site design, which is expected to influence whether customers revisit an online vendor. Design/methodology/approach Participants in India evaluated a local and foreign web site of the same online vendor. Surveys and interviews were used to collect the data. Findings The results indicate significant preference for the local web site in almost all design categories. Further, the local site instilled greater trust, satisfaction and loyalty. Data collected for this study are compared with parallel work conducted using the same procedures in four other countries. Research limitations/implications The current investigation is relevant for researchers who aim to expand knowledge concerning the impact of web site design related to user trust, satisfaction and loyalty. The work also has implications for web designers or managers who seek to enhance the market attraction and retention of online web sites. Limitations of the study are that both the local and foreign web sites used were Samsung web sites and that only a single task (searching for a cell phone) was used. Originality/value Few studies have examined web design in relation to user outcomes such as trust, satisfaction and loyalty in international markets.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".