Bibliographic record
Abstract
In Turkey, as in many countries, online shopping has experienced modest changes in 90’s. In addition to the doubts about the quality of products purchased online, the lack of consumer confidence in financial transactions made on commercial sites account for this slow evolution. However, democratization of the Internet, development of social media and evolution of e-marketing has changed the purchasing habits of consumers. The purpose of this research is to analyze how the Internet as a tool has or may have a concrete effect on online purchasing behaviors of economic agents and to highlight the key factors influencing the decision process of e-consumer. To this end, we conducted a population survey of 1055 consumers. The data collected were analyzed using the SPSS factor analysis was then applied to the data. The results of our analysis indicate that, prior to any decision, consumers get information about the products they wish to acquire from not only the search engines, but also the decisions of other consumers. In fact, consumers trust substantially the past experiences of other consumers shared on the Internet. Noting that various discussion forums or websites can be used in the comparison of products and influence the decision of purchasing of e-customer. Our research indicates that low-price variable and/or special offers available only on the Internet are the real factor that encourages purchase online in Turkey.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".