The Influence of Digital Retail Environment on Consumer Purchasing Behavior: A Causal Study
Bibliographic record
Abstract
As e-commerce continues to evolve, understanding consumer behavior in the context of online shopping becomes increasingly challenging. This study investigates the impact of purchasing attitudes among Saudi customers within the online clothing environment. By collecting 410 responses from residents of Riyadh, Saudi Arabia, the study emphasizes the convenience aspect of online and virtual markets and employs advanced statistical techniques, specifically SPSS-AMOS software, including confirmatory factor analysis, path analysis, and structural equation modeling. The findings reveal several key insights: perceived entertainment, perceived ease of use, and perceived usefulness significantly and positively influence the user experience on websites. The research highlights the importance of website quality indicators, such as perceived entertainment, ease of use, and usefulness, in shaping the user experience within the online clothing environment. It confirms that website quality, encompassing indicators of the user experience, exhibits a significant positive association with online impulse buying behavior. Additionally, the flow experience, which refers to the immersive and enjoyable state when browsing a website, also demonstrates a statistically significant and positive effect on online impulse buying behavior. These findings offer valuable insights for businesses and marketers seeking to understand and adapt to the evolving landscape of e-commerce, specifically concerning online clothing purchases.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".