Bibliographic record
Abstract
Wojciech Woziwodzki, co-founder, president & CEO of Tequila Mobile SA, a mobile gamers developer, publisher and service provider, had to make some important strategic decisions. Tequila Mobile SA had already decided to shift to a new free2play revenue model but needed to decide whether to focus on building its business in markets where penetration of smartphone devices was high and the economy was developed, or in markets where the use of mobile devices was taking off but the economy was still developing. The other critical decision was whether to continue to invest in in-house game development or focus on being a platform providing tools for third-party developers. The mobile game industry had exploded in recent years with the introduction of smartphones, application (app) stores, and cell phone penetration into developing economies. It brought with it a significant increase in the number of mobile games being developed and published, and Woziwodzki wanted to differentiate Tequila Mobile SA from the growing number of players in the quickly-evolving industry.Learning Objective:To teach students about the competitive strategies of multi-sided platforms (MSPs) through analysis of strategic interactions on business models -- specifically how strategic decisions influence a firm's business model and how an analysis of a business model helps to support strategic decisions making.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.444 | 0.213 |
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".