DETERMINANTS OF RECENT ONLINE PURCHASING AND THE PERCENTAGE OF INCOME SPENT ONLINE
Bibliographic record
Abstract
The recent stagnation of electronic commerce highlights the need to understand contemporary online consumer behavior. E-commerce’s slow growth has coincided with an explosion in the usage of Web 2.0 activities. These novel online venues have created many new channels for online retailers to reach buyers, yet these online activities have gone largely unstudied. This study incorporates current user demographics and Web 2.0 activities to dynamically model the determinants of two key measurements of recent online shopping, a recent purchase and the novel dependent variable, percentage of income spent online. Regression analysis is applied to a nationally representative 2007 survey of the U.S. online population. Determinants of a recent online purchase include, ownership of a credit card, PayPalTM account, listening to podcasts, participating in online auctions, and for the first time, female gender. In a second regression, positive determinants for the percentage of income spent online include, male gender, educational attainment, online auctions, instant messaging, and online dating. Online spending increases with time online, and appears to compete with other forms of online entertainment and social networking. These results produce snapshots of contemporary online shoppers that can be used by electronic retailers to determine which product characteristics to highlight for greatest impact, and to efficiently target specific Web 2.0 activities, such as entertainment, podcast and social network websites, to develop new and robust marketing platforms.
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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 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".