Identifying and describing constituents of innovation ecosystems
Bibliographic record
Abstract
Purpose Innovation ecosystems have not been defined univocally. The authors compare the different approaches to innovation ecosystems in the literature, the link with open innovation, the value creating and value capturing processes in innovation ecosystems, and the need to orchestrate them properly. In this way, the purpose of this paper is to provide a highly needed, concise overview of the state of the art in innovation ecosystem thinking. Design/methodology/approach A systematic screening of the literature searching for publications focusing on innovation ecosystems is carried out in the paper. The authors found 30 publications and compared the different approaches to innovation ecosystems: the authors classify them according to industries, the level of analysis, their central focus on innovation ecosystems, whether frameworks are developed in the publications, the main actors, focus on SMEs or large companies, the success of innovation ecosystems and the role of the orchestrator. Findings The authors found different approaches to innovation ecosystems in the literature. Some papers look at the link with open innovation, and others at the value creating and value capturing processes in innovation ecosystems, the role of orchestrators, etc. The authors also provide an overview about the industries, the level of analysis, the central focus of the research, the main actors in the networks and the success factors. The authors observe that most publications have been written in Europe and apply to European ecosystems. The approach in Europe is, to some extent, also different from the main focus of leading American scholars. Research limitations/implications The authors compare different approaches to innovation ecosystems. This provides a highly needed understanding of the state of the art in innovation ecosystem thinking. There are some limitations as well: the paper only does a literature review, and the authors are not developing a new framework to study innovation ecosystems. Practical implications The literature overview is not primarily focused on practitioners, but the tables in the paper provide a quick overview of good management practices for setting up and managing innovation ecosystems. Social implications Innovations ecosystems are, in some cases, established to solve major societal problems such as changes in healthcare, energy systems, etc. Therefore, they require the interaction between different types of partners including universities, research institutes and governmental agencies. Studying innovation ecosystems is crucial to facilitate social or societal changes. Originality/value The paper presents a highly needed overview of the literature about innovation ecosystems and a concise examination of the different aspects that are studied so far.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".