Abstract
Abstract Higher education systems evolved in recent decades. Universities must not only provide society with capable professionals but also act in the market for technologies, knowledge, and ideas to promote technological development. This paper discusses the motivational performance evaluation system for technology transfer process, specifically the patterns’ evaluation of academic units considering micro-cultures and idiosyncrasies’ analysis, in the academic context of autonomy. Based on action research, the existing performance evaluation system was assessed, and multivariate cluster analysis was proposed and tested as a method to enable micro cultures’ identification and evaluation. The analysis proposed enabled a tool for reflexive discussion regarding the effectiveness of the institutional innovation system in academic units and Engineering Education, and its implications for social and technological development of industry and society enabled action proposals for improvement in the university’s technology transfer management process.
Highlights
Higher education systems have evolved in the last decades
Effective technological development will not occur unless research institutions and its administrative and academic units have been structured for effective Technology transfer (TT)
The institutional innovation system effectiveness construct was better described one-dimensional by other derived indexes: (i) the amount of registered patents/Amount of R&D Project (Patent_RDproj); (ii) patents, including multidisciplinary patents, with joint ownership with the productive sector/ amount of patents (P.JOPatent); and (iii) Amount of patents registered with involvement of at least one other academic unit (P.MDiscPatent)
Summary
Higher education systems have evolved in the last decades. Universities provide society with qualified professionals but must play a role in the market for technology, knowledge, and ideas to promote technological development. Technology transfer (TT) from universities comprises the main mechanism for regional development. Largely used (Bubela & Caulfield, 2010), performance assessment (PA) based purely on the amount of generated patents, spin-offs, and TT projects is limited (Agrawal & Henderson, 2002). They are macro and aggregated indicators, and, they enable comparison between institutions, are not effective to reveal researchers’ interaction with industry and its impact on research output This work discusses data analysis methods for monitoring the technology transfer (TT) process, rescuing decision theories and performance management systems (PMS), related the discussion to evaluate the applicability of multivariate methods ( cluster analysis)
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