Innovation and Ontologies

1. Front Matter

   Pages I-6

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2. Roadmap for the Fuzzy Front End

   1. Front Matter

      Pages 7-9

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   2. Innovation

      Pages 10-32

   3. Roadmap for the Fuzzy Front End

      Pages 33-128

   4. Summary of FFE Roadmap

      Pages 129-131

3. Ontology

   1. Front Matter

      Pages 133-133

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   2. Concept Formation

      Pages 134-142

   3. Deployment in Business

      Pages 143-159

   4. Potential of Ontologies

      Pages 160-161

   5. Elements of Ontologies

      Pages 162-171

   6. Classification — The OntoCube

      Pages 172-195

4. Design Science Ontology Lifecycle

   1. Front Matter

      Pages 197-198

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   2. Multimethodological Background

      Pages 199-228

   3. Empirical Approach

      Pages 229-252

5. Results: The OntoGate

   1. Front Matter

      Pages 253-255

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   2. Results of Prepare

      Pages 256-258

   3. Results of Develop/ Build

      Pages 259-323

   4. Results of Justify/ Evaluate

      Pages 324-333

6. Back Matter

   Pages 335-407

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Foreword Innovation equally requires rational problem solving and interpretive space (Lester & Piore, 2004). It is a key challenge for innovation management to overcome this obvious dilemma, especially in the early stages of the innovation process. Ontologies as logical representations of knowledge bear the potential to enable the required balance between structure and flexibility. They provide a promising, yet largely unexplored approach for significantly improving corporate innovation processes. During the early stages of the innovation process, the so-called Fuzzy Front End of innovation management, decision-making in corporate practice is often “uncertain fumbling at minimal sight” (Hauschildt & Salomo, 2007), i.e. the existing idea selection processes tend to be ad hoc and somewhat intuitive. Due to this unsatisfying situation, in which new ideas are difficult to compare, inadequate risks are taken and projects are stopped too late, a generic framework for the early stages of innovation management is necessary. Ontologies, used to represent knowledge, provide powerful modeling potential. Developed by researchers from the field of Artificial Intelligence, ontologically based applications recently been widely applied in online business to business solutions. The so-called semantic web is also based on ontologies. Its high flexibility and easy adaptability recommend ontological modeling for broader use and application in a business context. The few existing examples in the field of HR management as well as in the field of knowledge management indicate the potential of corporate ontologies.

• Innovation
• Innovationsmanagement
• SME
• assessment
• business administration
• fuzzy Front end
• innovation management
• technology roadmap

Dr. Angelika C. Bullinger promovierte bei Prof. Dr. Prof. h.c. Dr. h.c. Ralf Reichwald am Institut für Information, Organisation und Management der Technischen Universität München. Sie ist wissenschaftliche Mitarbeiterin bei Prof. Dr. Kathrin Möslein am Lehrstuhl für Information der Universität Nürnberg-Erlangen.

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