Bibliographic record
Abstract
Although the rapid growth of the mobile phone market makes companies very excited about the great potential of m-commerce, after the dot.com crisis people are more sceptical and think it might just be more hype. There is still no single answer to justify either of these two opposite opinions. To achieve m-commerce success, people are looking for innovative killer or extensions of existing e-commerce applications in a mobile environment. It is not, however, the application but the business model behind the application that really determines the success. So far, we still do not fully understand what is the appropriate business model that could lead to the success of m-commerce. To search for a solution, we need to identify the fundamental technology differences between m-commerce and internet based e-commerce. Based on this understanding, we develop a framework to analyse m-commerce business models along two dimensions the key components and the taxonomy. We hope our framework can be used to help businesses in developing their m-commerce strategies and turning hype into real profit.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".