Infocom business models innovation with the development of corporate universities
Bibliographic record
Abstract
Since the mid-90s, the infocom industry has gone into a period of sustained disruptive innovations which, combined with deregulation, led to a lot of turbulence and a sometimes difficult redefinition of business models. A Schumpeterian wave of innovation enhanced by competition leads to creation of wealth as an unprecedented investment boom occurs in the infocom sector sustained by overly optimistic and sometimes fraudulent forecasts of internet traffic. We provide a strategic framework by explaining which types of infocom business models are better adapted to cope with the challenges of the new developments related to the digital economy, in order to shape the corporate innovation strategy. We also analyse how the development of a corporate university must be linked to the development of such innovation strategy. The first section of the article deals with the wave of disruptive innovations in the communications industry over the last few years and pinpoints the ubiquity of IT in all aspects of the economy and society. The next section focuses on the search for profitable business models for infocom providers. The links between business model innovation and the development of corporate universities is analysed in the next section. It is followed by a discussion section.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".