Achieving Technology Dominance for Startups: Illustrative Evidence of the Importance of Establishing Inter-Organizational Networks
Why this work is in the frame
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Bibliographic record
Abstract
In this paper we study the influence of the network of actors on the chances that a start-up will be successful in commercializing a platform. Scholars that study platform wars have conducted multiple studies of factors that influence platform dominance where the focus lies on factors that can be influenced by the firm (firm-level factors) and factors that are given in specific industries which can hardly be influenced by firms (environmental factors). Few studies have included views from platforms and entrepreneurship to understand the successful commercialization of new platform. A common research approach in both fields of platforms and entrepreneurship is network analyses. In this paper we conceptualize how entrepreneurs can apply network strategies to successfully commercialize their new products in an emerging market in the first phases of the technology dominance process. We illustrate our ideas by means of several examples.
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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 | 0.000 |
| 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 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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