Open Innovation Ecosystem

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The term "Open Innovation Ecosystem (OIE)"[1] consists of two main parts that decribe the foundations of the approach of innovation:

  • (Ecosystem) The term ecosystem defines a community of living organisms in conjunction with the nonliving components of their environment (things like air, water and mineral soil), interacting as a system. It refers to both biotic factors as well as abiotic factors.[2] An ecosystem is self supporting[3](see Wikipedia:ecosystem). In OIE the term ecosystem defines also a community of people with heterogeneous background and expertise in conjunction with the resources of their environment (things like tools, devices, content, ...). In the OIE people are interacting as a system and drive the innovation. OIE refers to
  • technical,
  • cultural,
  • social,
  • scientific,
  • ...

factors. For sustainable development these factors are regarded as linked together in an evolutionary process. Dead ends of developments are not regarded as failure but instead the are treated and documented as lessons learned that contribute to the evolution of innovations[4]. The innovation in the OIE is driven by the network of interactions among participants and between participants and their environment, in which problem solving takes place[5]. The overall value of the ecosystem is more than that of its individual participants. Fasnacht states that the value captured from a network of multiple points within an ecosystem and the linear value chain of individual participants create a new delivery model, i.e. value constellation.[6]

Just like Biodiversity in an ecosystem the diversity of expertise in an Open Innovation Ecosystem affects the capabilities to respond in divers way. Diversity of expertise in an OIE is equivalent to the diversity to tools in a toolbox. The diversity in an OIE is especially valuable in Complex Dynamic Systems[7]. The system changes in space in time, expose to disturbances and response options that seem be useless before becomes a perfect response option in the altered environmental and systems condition in which innovation is needed.

Learning Environment and Implementation

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A methodology of OIE can be applied on

OIE as Learning Environment allows interaction with the environment the learner is currently in (e.g. Experimental Archaeology with students). Real World Labs can trigger innovation from global systems thinking to local activities that are supported in the lab[8]. A Living Lab is mainly regarded as user-driven innovation in science and in an environment for products and services[9].

The integration of research in and around the lab is performed to quantify benefits and derive general implications for innovation support in specific domains. These results can help to transfered findings to other settings that share the same requirements and constraints for learning and developement.

Scientific and Technical Conference Management and OIE

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Classical Conference as Scientific and Technical Learning Environments for Innovation

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  • Conferences are events in which e.g. scientists
    • can present new results,
    • discuss the results with other scientists,
    • identify new collaborations,
    • etc. ... and
    • get a peer-reviewed paper published, which is a scientific reward for the scientific work
  • conferences can be regarded as a learning environment for the scientific community, to learn about the progress and build the next scientific or technical progress on the results
  • build new things by recombining existing technology or new scientific results are one key element of innovation (similar principle can be found in genetic recombination).
  • transfer of a conference in a Wikiversity environment has some challenges

Maximize the personal benefit/outcome/reward for the new knowledge I found!

  • looking at the concept of minimal publishable unit from an educational perspective, then decomposition of ideas into slices of knowledge creates more workload for the learner, because the learner had to aggregate the slices and synthesize the decompositions back into the original idea of the scientist.

Wikiversity Conference as Scientific and Technical Learning Environments for Innovation

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In contrast to classical conferences Wikiversity conferences (see EFG-SGH Pilot the Wikiversity environment has an ansynchronous workflow with no fixed conference dates. It is a continuous evolution of an learning environment. Conference events can speed up development and can create releases rather than versions.

Learning Task

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  • (Genetic Algorithms) Genetic Algorithms are a methodology in w:Computer Science, that are used for optimization. Explore the concept of genetic algorithm and identify similarities and differences in the approach to an Open Innovation Ecosystem. Start with linking the candidate solutions to solutions produced by community members in an Open Innovation Ecosystem. In genetics the candidate solutions are called individuals, creatures, or phenotypes. Diversity in the genetic pool of candidate solutions produce solutions that evolve with recombination of ideas. Each candidate solution has a set of properties (its chromosomes or genotype) which can be mutated and altered; traditionally, solutions are represented by selection of possible solution generate in the innovation ecosystem. Describe a summary of your comparison of
  • (Appreciation of a well-documented Failure Case) Analyse the consequences of "We learn from failure, not from success!"[10][11] for the problem solving environment of an OIE.
  • (Trust) Explain the role of trust for building an Open Innovation Ecosystem
  • (Swarm Intelligence and Swarm Ignorance) Analyse the concept of Swarm Intelligence and explain how a collaborative development assures that the benefit of the collaborative work is shared in the community even 99% prototypes that are developed by different community members were not successful and only 1% of the distributed collaborative development was successful?

See also

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  1. Traitler, H., Watzke, H. J., & Saguy, I. S. (2011). Reinventing R&D in an open innovation ecosystem. Journal of food science, 76(2).
  2. Exsers (2017), p 22
  3. Tansley (1934); Molles (1999), p. 482; Chapin et al. (2002), p. 380; Schulze et al. (2005); p. 400; Gurevitch et al. (2006), p. 522; Smith & Smith 2012, p. G-5
  4. Joung, W., Hesketh, B., & Neal, A. (2006). Using “war stories” to train for adaptive performance: Is it better to learn from error or success?. Applied psychology, 55(2), 282-302.
  5. Terwiesch, C., & Xu, Y. (2008). Innovation contests, open innovation, and multiagent problem solving. Management science, 54(9), 1529-1543.
  6. Fasnacht, Daniel (2018). Open Innovation Ecosystems: Creating New Value Constellations in the Financial Services (in en). Management for Professionals. Cham: Springer International Publishing. pp. 134. doi:10.1007/978-3-319-76394-1_5. ISBN 9783319763941. 
  7. Abraham, R. H. (1986). Complex dynamical systems. Mathematical modelling in science and technology, 82-86.
  8. Kefalas, A. G. (1998). Think globally, act locally. Thunderbird International Business Review, 40(6), 547-562.
  9. Gassmann, O., Enkel, E., & Chesbrough, H. (2010). The future of open innovation. R&d Management, 40(3), 213-221.
  10. Starbuck, W. H., & Hedberg, B. (2001). How organizations learn from success and failure.
  11. Joung, W., Hesketh, B., & Neal, A. (2006). Using “war stories” to train for adaptive performance: Is it better to learn from error or success?. Applied psychology, 55(2), 282-302.