Website Design, Technological Expertise, Demographics, and Consumer’s E-purchase Transactions
Bibliographic record
Abstract
This study investigates the association of e-retailer’s website design, the consumers’ technological expertise, and some demographic characteristics with e-purchase transactions. The study was conducted on a sample of 290 respondents of Saudi consumers who had online purchase. The findings revealed a statistically significant positive relationship between the consumers’ technological expertise and their e-purchase transactions. The study also demonstrated no relationship between the e-retailer’s website design and the consumers’ e-purchase transactions. Regarding demographics and consumers’ e-purchase transactions, the study found nonsignificant differences between males and females, as well as among the different levels of education, as opposed to significant differences among the consumer’s monthly income levels in favor of higher-income consumers, and among different age levels in favor of the age 35-45 category. To help both marketers and consumers to gain the benefits of e-purchase, the study recommended e-marketers to establish marketing activities that enhance the consumer adoption of e-shopping; giving more concern to order processing as an important strategy for differentiation and positioning. The study also recommended e-retailers to focus on entertaining and luxury products to attract higher-income consumers. Furthermore, the study advised e-retailers to extensively do consumer behavior research as a base to enhance the planning of e-marketing strategies and activities.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".