PROFILE OF THE ELECTRONIC COMMERCE CONSUMER: ASTUDY WITH BRAZILIAN UNIVERSITY STUDENTS
Bibliographic record
Abstract
This work aimed to analyze the profile of electronic commerce costumers, as well as identify characteristics that differentiate costumers from non-costumers of electronic commerce. For this purpose, a quantitative-descriptive study was carried out with university students of a federal public university of the south-western region of Brazil, during the first half of 2011, using a structured questionnaire. Data were analyzed through descriptive statistics, logistic regression and cluster analysis. The results showed that more than 75% of the students had already made purchases over the Internet and that security and price were major factors in their decision. Men were the primary users of electronic commerce and this type of consumption was positively related to income and the use of credit cards. In addition, consumption preferences able to differentiate consumers of electronic commerce from non-consumers were: quick and practical shopping, risk perception and indifference towards testing the products before making purchases. The results also indicated that there were four different segments in habits, preferences and socio-demographic characteristics: controlled, young consumers, basic consumer and conventional buyers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".