Return Visits: A Review of how Web Site Design Can Engender Visitor Loyalty
Bibliographic record
Abstract
Both the use of Web sites and the empirical knowledge as to what constitutes effective Web site design has grown exponentially in recent years. The aim of the current article is to outline the history and key elements of Web site design in an e-commerce context - primarily in the period 2002-2012. It was in 2002 that a Special Issue of ISR was focused on ‘Measuring e-Commerce in Net-Enabled Organizations.’ Before this, work was conducted on Web site design, but much of it was anecdotal. Systematic, empirical research and modeling of Web site design to dependent variables like trust, satisfaction, and loyalty until then had not receive substantial focus - at least in the information systems domain. In addition to an overview of empirical findings, this article has a practical focus on what designers must know about Web site elements if they are to provide compelling user experiences, taking into account the site's likely users. To this end, the article elaborates components of effective Web site design, user characteristics, and the online context that impact Web usage and acceptance, and design issues as they are relevant to diverse users including those in global markets. Web site elements that result in positive business impact are articulated. This retrospective on Web site design concludes with an overview of future research directions and current developments.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".