Mobile device access: Effect on online purchases
Bibliographic record
Abstract
In this work we test if Internet access device impacts the online purchase probability of a consumer. We also test if the online purchase probability of consumers increases over time. Our data is from the Canadian Internet Use Survey over four years, consisting of at least 23,178 participants per year, relative to previous studies this is a very large dataset. We segment the participants into two groups: 1) those that use only their personal computer (PC) to access the Internet and 2) those that use their PC and a mobile device (mobile phone, tablet, etc.) to access the Internet. For each group we determine the purchase probabilities of consumers for 15 different product category types. Using 95% confidence intervals about the determined purchase probabilities we test if the purchase probability of one group is higher than the other. We find that mobile devices lead to a statistically greater purchase probability in only 4 of the 15 product categories. In addition, the purchase probability of households using mobile devices and PCs to access the Internet only increases between years 2005 and 2007, and is statistically unchanged between all other years. However, for households that use only a PC to access the Internet, the purchase probability increases each year the survey was conducted.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".