Transforming regions into innovation ecosystems: A model for renewing local industrial structures
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Bibliographic record
Abstract
ABSTRACTThis article elaborates on how to overcome regional structural crises by transforming regions into innovation ecosystems. The article uses a literature review and case study methods to examine the structural change that many regions and cities all over the world are going through, and it investigates how to manage this change and support innovation as efficiently as possible. The ecosystem approach emphasizes the position and roles of local and public actors in developing innovation activity. The case study concentrates on Jyvaskyla, a small industrial city in the region of Central Finland. In 2009, the region faced an economic crisis when the mobile device manufacturer Nokia closed its research center in Jyvaskyla.The case study resulted in a model for building innovation ecosystems. The model consists of authentic dialogue, Triple Helix cooperation, the core organization, and futures studies. The article clarifies the concepts of the innovation ecosystem and hub, and shows how innovations require a special ecosystem where innovations emerge when different actors collaborate and co-create.The research has implications for innovation practices and studies. The results are relevant for many small cities and regions, especially ones with a strong industrial history, whose real challenge is how to transform their economies into innovation economies. The research adds to the studies on innovation environments and supports the creation of world-class innovation ecosystems through deep cooperation among local, regional, and national actors.Keywords: innovation, change management, innovation hub, regional development, structural change, systemic changeIntroductionBoth national innovation systems and regional developers are struggling to meet the demands of the constantly changing global competitive environment. Countries, regions, and cities all over the world are undergoing major structural changes as the economy shifts from manufacturing to services and as waves of sociotechnical development shape the innovation landscape. To manage the structural change and to support innovations as efficiently as possible, local innovation environments need to be developed and strengthened.In this article we elaborate on the concepts of the innovation ecosystem and the innovation hub and present a model for managing regional structural change and development. We have attempted to answer two research questions: 1) How regions and cities be systematically transformed into innovation ecosystems? and 2) How can local industrial structures be renewed? To answer these questions we explored the building process and the special characteristics of innovation ecosystems, and analyzed the changes in innovation activities and policies. As a result we present a systemic model for building modem innovation ecosystems. The next section consists of a conceptual review of innovation ecosystems, hubs, and systemic development. The latter sections consist of the empirical case study and a description of the model for building innovation ecosystems.As our overall aim, we investigate creative hubs in the global economy. We argue that innovations require a special ecosystem that includes top-level universities and research institutions, sufficient financing and a local market, a skilled labor force, specialization as well as cooperation among companies, and global networking. This kind of ecosystem requires the creation of world class innovation hubs where a high quality of life and excellent business possibilities are combined. Such a hub can be built through deep cooperation among local, regional, and national actors. However, in reality relatively few regions have exhibited this kind of renewal capability (Etzkowitz & Klofsten, 2005). Innovation tends to cluster in certain sectors or areas which grow faster and imply stmctural changes (Fagerberg, 2006). Similarly, regional development is shifting towards large clusters, cities, and metropolitan areas, while most of the value creation, R&D activities, and patenting happen in the global-level innovation hubs. …
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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