What Factors Contributed to the Success of Apple's iPhone?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Unknown to most North American consumers, a mobile data and Internet service in Japan called i-mode has been highly successful in that country for the past decade. Unfortunately, mobile data services in North America have lagged behind many European and Asian countries. However, the situation changed rapidly with the iPhone, launched in the US in June 2007. Consumers lined up for days for the chance to purchase one, and over 500, 000 units sold on the first weekend. Since that time, over 42 million iPhones have been sold, arguably making it one of the most successful mobile phone products ever launched. What is it that makes the iPhone such a success? In this paper we define a set of success criteria to investigate the success of the iPhone and propose a comprehensive success model. The success model can be used by both academics and practitioners to understand the reasons why, and ways to ensure that mobile data and commerce services become successful.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it