| dc.description.abstract | This study aims to quantify the capacity that criteria used during the selection
and prioritization stage of innovation projects have in predicting the future performance
of projects. For the development of the study, the existing works on the subject were
analyzed, through a systematic review of the literature, which later supported the
construction of the methodological procedures used. The predictive capacity was
quantified first by means of simple regression models, and later by means of multiple
regression models, which had as input data from twenty-one evaluations performed
during the screening of new ideas, and as outputs data from the ideas’ performance
evaluations, which were done after their development and subsequent market
introduction. The screening data used fall into four categories: impact, viability,
economic and risk. The performance metrics were developed and collected during the
execution of this work, having been considered four different performance metrics:
financial, knowledge acquisition, brand enhancement and ecosystem strengthening.
The results obtained evidenced that, except for knowledge acquisition, the
performance of an innovation project can be partially predicted through the criteria
used during the screening stage. The study also found that only a minority of the criteria
used is necessary for the construction of the multiple regression models with the best
predictive capacity. Moreover, the study presents quantitative evidence of the
relationship between the radicality of an idea and its expected performance. | en |
