A SURVEY OF ONLINE PURCHASING DECISION FACTORS AND SHOPPING AND PURCHASING BEHAVIORS OF UNIVERSITY STUDENTS
Bibliographic record
Abstract
Shopping and purchasing on retailers’ websites continues to expand. According to ACNielsen’s 2005 report, more than one tenth of the world population (627 million) have shopped online, and more than half of them have shopped (over 325 million) online within a month of the report’s release (ACNielsen, 2005). Compared to the third quarter 2004, the third quarter of 2005 showed a 26.7% increase in online retail sales in the U.S., generating $22.3 billion for online merchants (U.S. Census Bureau, 2005). Marketers and researchers have verified that university students are one of the most “wired” demographic groups of online users and continue to investigate the characteristics of shopping/purchasing behaviors among these students. The aim of this investigation is to further examine factors that drive university students’ online shopping and purchasing decisions, and the types of items they shop for and purchase online.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".