PROFILE OF THE ELECTRONIC COMMERCE CONSUMER: ASTUDY WITH BRAZILIAN UNIVERSITY STUDENTS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it