Methodology for Evaluating Innovation Capabilities at University Institutions Using a Fuzzy System

Jakeline Serrano García, Jorge Robledo Velásquez

Abstract


This article proposes a methodology to evaluate Technological Innovation Capabilities at university institutions, seeking to strengthen innovation management and advance in the integration of said institutions in the dynamics of the innovation system. The Triple Helix Model is adopted to analyze the relationships of university institutions with their surroundings. The proposal is conceptually built on a based on the perspective of resources and capabilities and on to the systemic congruence model of the organization. A fuzzy inference system is developed as the mathematical support of the evaluation process of the Technological Innovation Capabilties. The methodology is experimentally applied to a university institution in Medellín – Colombia, demonstrating its consistency, viability and practical usefulness.

Keywords


Technological innovation capabilities, fuzzy logic, university institutions, Triple Helix, organizational congruence model.

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References


ABELLO, R. 2004. La universidad: un factor clave para la innovación tecnológica empresarial. Pensamiento y gestión, Universidad del Norte, Colombia, 16, 28 – 42.

ACS, Z., Anselin, L. y Varga, A. 2002. Patents and innovation counts as measures of regional production of new knowledge. Research Policy, 31, 1069–1085.

AZAGRA, M., Fragiskos, A., Gutiérrez, A. y Gracia, I. 2005. Faculty support for the objectives of university–industry relations versus degree of R&D cooperation: The importance of regional absorptive capability. Research Policy, 35, 37–55.

MINISTERIO DE EDUCACIÓN NACIONAL DE COLOMBIA. 2010. Plan Sectorial 2010-2014, Bogotá-Colombia, 1-110.

CALESTOUS, J. y Cheong, L. 2005. Task force on science, technology, and innovation. An millenniumproject 2005. Innovation: Applying Knowledge in Development.

CARVALHO, J. y Etzkowitz, H. 2008. New directions in Latin American university-industry-government Interactions. International Journal of Technology Management and Sustainable Development, 3, 193-204.

CASTELLANOS, O. y Jiménez, C. 2008. Desafíos en gestión tecnológica para las Universidades como generadoras de conocimiento. I Congreso Internacional de Gestión Tecnológica e Innovación, Bogotá – Colombia.

CATAÑO, G. y Botero P. 2007. Las Pymes: vínculos y redes de cooperación para la innovación en Antioquia, (un estudio en exploración). Revista Tecno-lógicas, 18, 11-42.

CHENG, J., Yam, R., Kam, C., y Ma, N. 2006. A study of the relationship between competitiveness and technological innovation capability based on DEA models. European Journal of Operational Research, 170, 971–986.

CHRISTENSEN, C. 1997. Making strategy: learning by doing. Harvard Business Review, 75(6), 141-156.

DEBACKERE, K. y Veugelers R. 2005. The role of academic technology transfer organizations in improving industry science links. Research Policy, 34, 321–342.

ETZKOWITZ, H. 2003. Research groups as ‘quasi-firms’: the invention of the entrepreneurial university. Research Policy, 32, 109-121.

HALL, B., Jaffe, A. y Trajtenberg, M. 2001. The NBER patent citations data file: lessons, insights and methodological tools. WP 8498 National Bureau of Economic Research.

KOSKO, B. 1994. Fuzzy systems as universal approximators. IEEE Transactions on Computers, 43, 1329-1333.

JANG, J., Mizutani, E. Y Sun, C. 1997. Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence. New York: Prentice Hall,

MARTÍN, B. & Sanz, A. 2002. Redes neuronales y sistemas difusos. México, D. F., Alfaomega Grupo Editor.

MEDINA, S. 2006. Estado de la cuestión acerca del uso de la lógica difusa en problemas financieros. Universidad Javeriana, Cuadernos Administración, 19, 195 – 223,

MEDINA, S. 2010. Modeling of operative risk using fuzzy expert systems. En M. Glykas (Ed.), Fuzzy Cognitive Maps Advances in Theory, Methodologies, Tools and Applications. Chios, Greece: Editorial Spinger, University of Aegean.

NADLER, D. y Tushman, M. 1980. A model for diagnosing organizational behavior. Organizational Dynamics, 35-51.

PEDRYCZ, W. y Gomide, F. 1998. An introduction to fuzzy sets, analysis and design. Cambrigde, Massachusetts, The MIT Press.

RASMUSSEN, E., Øystein, M., y Gulbrandsen, M. 2006. Initiatives to promote commercialization of university knowledge. Technovation, 26, 518–533.

ROBLEDO, J., López, C., Zapata, W., Pérez, J.D. 2010. Desarrollo de una metodología de evaluación de capacidades de innovación, Revista Perfil de Coyuntura Económica, agosto 2010, 15, 133-148.

SERRANO, J. 2010. Metodología para evaluar las capacidades de innovación tecnológica en una institución universitaria utilizando un sistema difuso, tesis de grado para optar al título de Magíster en Ingeniería Administrativa. Universidad Nacional de Colombia Sede Medellín.

TURA, T. y Harmaakorpi, V. 2005. Measuring regional innovative capability. 45th Congress of the European Regional Science Association Amsterdam, Netherlands, 23-27.

WANG, C. y Chuu, J. 2004. Group decision-making using a fuzzy linguistic approach for evaluating the flexibility in a manufacturing system. European Journal of Operational Research, 154, 563–572,

WANG, C., Lu, L., y Chen, C. 2008. Evaluating firm technological innovation capability under uncertainty. Technovation, 28, 349–363.




DOI: http://dx.doi.org/10.4067/S0718-27242013000300051



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