The Interplay of Competition and Cooperation in the Innovation Process Between Established Organizations and Startups
Bibliographic record
Abstract
This study aims to analyze the dynamic process of coopetition between an established organization and startups to develop innovation. We conducted an exploratory, qualitative study, based on Grounded Theory. The Grounded Theory allows the development of a theory emerging from data that is simultaneously collected and analyzed, determining the categories to observe the core questions. It can be divided into two stages: initial coding (open and axial coding) and focused coding. In the open coding, are defined categories and subcategories that are reviewed in the axial coding to generate more precise explanations? Along with the focused coding, the data organized from initial coding is categorized for an analytical understanding of the phenomena. In the first stage, we conducted eight semi-structured interviews with a homogeneous sample. An interview guide addressing coopetition factors was developed. As a result, we developed a framework from the theoretical background. This framework was evaluated by three executives and professors with experience in coopetition between large corporations and startups. The snowball technique was used to recruit the participants. Our findings reveal that different factors – market increase, strategic alignment, and technological alignment – are associated. We observed that coopetition not only helps in developing new markets but also in understanding the user demands of these markets. Thus, coopetition is an accelerator of innovation, since it allows the identification of the resource complementarity and technological scale gains.
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How this classification was reachedexpand
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".