Traditional vs Internet vs Mobile: Which is More Effective Way to Reach Potential Customers
Bibliographic record
Abstract
Smartphones have provided their users many niches, particularly for mobile commerce, known as mcommerce. As smartphone penetration around the globe, it has rapidly altered the phone users and the market places, as US mobile devices have penetrated more than 80% of the population in 2017. The average adult daily usage of mobile devices outpaced personal computers for the first time, and the users have conducted more commerce activities on their mobile devices than on their personal computers. As a result, predicted by eMarketer, US mcommerce will be a half of the total ecommerce by 2022. The marketers realize that they can better find their customers on the move, and enable them to better target these customers for their products. This research, through an empirical survey, focuses on the smartphone user behavior. The research results provide some useful insights for marketers in their future marketing endeavors.
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How this classification was reachedexpand
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".