A Model to Support Decision Making in the Idea Management Domain

Authors

  • Marina Carradore Sérgio Federal University of Santa Catarina
  • Alexandre Leopoldo Gonçalves Federal University of Santa Catarina
  • João Artur de Souza Federal University of Santa Catarina

DOI:

https://doi.org/10.24023/FutureJournal/2175-5825/2015.v7i2.212

Keywords:

Innovation Management. Idea Management. Ontology. Cluster Analysis.

Abstract

Taking into account the global competitiveness, innovation has become a challenge for organizations. Idea management is an integral part of the innovation process and it is presented as an essential factor for achieving success. Due to the volume and sudden peaks in submissions of ideas, the appropriate analysis and the allocation of resources for investment are important issues to be addressed. The objective of this paper is to present a model for the management of ideas based on ontology and cluster analysis in order to maximize resources for investment in ideas. So as to demonstrate the model feasibility it was prepared a dataset comprised of fifty-five ideas collected from the Starbucks® site. These ideas were then stored in the domain ontology and were used as subsidies for the cluster analysis and for the building of a knowledge base. As a result, it was identified groups with similar ideas that, when analyzed, foster a greater potential for observation and may indicate patterns and trends that can assist in decision making.

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Author Biographies

Marina Carradore Sérgio, Federal University of Santa Catarina

Graduated in Information and Communication Technologies at the Federal University of Santa Catarina (UFSC), Arangua, Santa Catarina, Brazil, 2013. He is currently a Master's student in the Department of Engineering and Knowledge Management (EGC) in the University Knowledge Engineering area Federal de Santa Catarina, Brazil and researcher at the Higher Education Personnel Improvement Coordination. His main areas of research are: Ontology, Cluster Analysis, Ontology Engineering, Knowledge Discovery in Databases textual and Idea Management.

Alexandre Leopoldo Gonçalves, Federal University of Santa Catarina

Graduated in Computer Science from the Regional University of Blumenau (1997), Master and Doctor of the Federal University Production Engineering of Santa Catarina in 2000 and 2006. He is currently Associate Professor of the Department of Knowledge Engineering and permanent Teacher Program Post- degree in Engineering and Knowledge Management UFSC. It has experience in the areas of Computer Science and Knowledge Engineering, acting on the following topics: extraction and information retrieval, knowledge discovery and Ontology Engineering.

João Artur de Souza, Federal University of Santa Catarina

Degree in Mathematics from the Federal University of Santa Catarina (1989), Master in Mathematics and Scientific Computing at the Federal University of Santa Catarina (1993), PhD in Production Engineering from the Federal University of Santa Catarina (1999) and Post-Doctorate from the Federal University Santa Catarina (2000). She worked at the Federal University of Pelotas from 1993 to 2007 as a professor in the mathematics area, also acting in Distance Education. As a professor at the Federal University of Pelotas was Mathematics Course Coordinator Distance, working with virtual learning environment. He is currently a Professor at the Federal University of Santa Catarina Department of Knowledge Engineering. At Graduation works in the areas of Quantitative Research Methods, Innovation Management, Mathematical Logic and Techniques Knowledge Engineering. The Graduate acts as Professor of the Graduate Program in Engineering and UFSC of Knowledge Management in Knowledge Engineering area. His main areas of research are: Information Technology Management, Distance Education, Innovation, Innovation Management and Intelligence Innovation.

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Published

2015-12-19

How to Cite

Sérgio, M. C., Gonçalves, A. L., & Souza, J. A. de. (2015). A Model to Support Decision Making in the Idea Management Domain. Future Studies Research Journal: Trends and Strategies, 7(2), 118. https://doi.org/10.24023/FutureJournal/2175-5825/2015.v7i2.212

Issue

Vol. 7 No. 2 (2015): Future Studies Research Journal

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Artigos / Articles

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