Perceived Risk in Apparel Online Shopping: A Multi Dimensional Perspective
Bibliographic record
Abstract
The purpose of this study, drawing on marketing and psychometric paradigms, is to investigate the effect of risk perception dimensions on apparel internet purchase intention among Saudi consumers. A web-based survey was conducted to measure consumers’ perception of the six types of risk associated with apparel online shopping and their influence on purchase intention. Three hundred responses were collected. Results showed that not all the considered risk constructs have the same influences on apparel internet purchasing intention. Specifically, time and performance risks have the most significant influence followed by privacy and social risks. Key words: Consumer behavior; Apparel; Internet shopping; Saudi ArabiaResume: Le but de cette etude, dessinant sur le marketing et les paradigmes psychometriques,est d'etudier l'effet des dimensions de perception de risque sur l'intention d'achat d’habillement sur l’Internet parmi les consommateurs saoudiens. Une enquete basee sur le WEB a ete menee pour mesurer consommateurs des six types du risque lies aux achats en ligne d'habillement et de leur influence sur l'intention d'achat. Trois cents reponses ont ete rassemblees. Les resultats ont prouve que non toutes les constructions considerees de risque ont la meme influence sur l'Internet d'habillement achetant l'intention. Specifiquement, le temps et les risque de representation ont l'influence la plus significative suivie de l'intimite et des risques sociaux. Mots cles: Comportement du consommateur; Habillement; Achats d'Internet; Arabie Saoudite
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".