Abstract
This paper intends to demonstrate how open innovation systems could be developed by tackling the challenging knowledge management problems that are encountered when aiming at involving very large audiences. This is the case when generalizing open innovation approach beyond companies to a wider societal context like in the case of national innovation systems. The Open Innovation Banking System (OIBS) project, funded by the European Social Fund (ESF) and the participating higher education institutions in Finland, is used as a basis for our discussion. It specifically aims at bringing the largely underutilized creativity of students and senior citizens to play. Among several technologies to develop OIBS, mashups as hybrid web applications can play an important role in such constantly evolving system and contents. However, relying only on unstructured text inputs, the services of textual content sharing for OIBS would require intelligent text processing that far exceeds the capability of such applications. In this paper, we propose an “idealet”-centric solution for representing the data submitted by users, enabling concise description, refinement and linking of ideas as input for innovation processes. An idealet is defined as the core knowledge about an innovative idea. The relationships among idealets and essays can be represented in a semantic network in terms of their relationships. This scheme allows the mashup applications for OIBS to more effectively retrieve, process, extract, and deliver the most important knowledge from an ocean of information contributed by participating information composer, reviewers, and users. The paper also discusses how the idealet-centric approach can be employed for a functional open innovation system.
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Hämäläinen, M., Li, Q., Lin, Z., Tomoiaga, A., Wang, J. (2010). An Idealet-Centric Scheme for Large Scale Open Innovation Systems. In: Sharman, R., Rao, H.R., Raghu, T.S. (eds) Exploring the Grand Challenges for Next Generation E-Business. WEB 2009. Lecture Notes in Business Information Processing, vol 52. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17449-0_3
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DOI: https://doi.org/10.1007/978-3-642-17449-0_3
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