ALONE OR IN COOPERATION: WHAT IS THE BEST STRATEGY FOR THE PERFORMANCE OF RADICAL PRODUCT INNOVATION IN THE VIDEO GAME INDUSTRY?
Bibliographic record
Abstract
The aim of this research is to study the impact of inter-organisational strategies on performance of radical product innovation. We distinguish three kinds of strategies: (1) individual strategy, (2) cooperation with non-rivals strategy, and (3) coopetition strategy. We study innovation at the product level, and we analyse the market performance. We develop and test the hypotheses comparing the effects of these three strategies on the market performance of radical product innovation. An empirical research is carried out to study the video game publishing industry. We perform a quantitative analysis on a sample of 100 video games that involve radical innovations, identified among 822 video games launched between 2006 and 2011. The main results show that coopetition is the most fruitful strategy for developing a radical innovation. In this process, a direct competitor becomes the best and the most viable partner for that type of innovation.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".