Business-to-Consumer Web Site Quality and Web Shoppers' Emotions: Exploring a Research Model
Bibliographic record
Abstract
Based on the literature on consumer behavior, psychology, and information systems, this paper explores relationships between Web site quality, the cognitive appraisal of situational state (a key cognitive antecedent to emotions) and a set of positive and negative emotions. A theoretical model is tested on data collected from 215 different Web shopping episodes. Results show that when shopping on business-to-consumer Web sites for low-touch products (music CDs and movies in DVD format), customers felt emotions, namely liking, joy, pride, dislike, frustration, and fear. Even though the mean intensity levels of these emotions is low to moderate, for a substantial number of shoppers (near a third of the sample population) the emotions of liking and joy were felt intensely. Results also indicate that Web site quality, measured by several Web site design components, has a positive impact on the cognitive appraisal of situational state, operationalized as the satisfaction level of the overall online shopping experience. In turn, this appraisal affects all emotions felt by shoppers except fear. This study is particularly addressed to designers and managers of B2C Web sites as it invites them to consider Web shoppers’ emotions while designing and developing their electronic platform.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".