Predicting the innovation capability of investment projects using the BIFPET algorithm: a framework and case study

Abstract

This paper proposes a framework based on fuzzy probability for the prediction of innovation capability of an investment project. The prediction of innovation capability is a difficult task due to the fact that there exist almost no information source, except for a project feasibility report reviewed before the investment in the project. The proposed framework integrates five clusters of factors, namely; human resources related factors, technology related factors, firm-features related factors, R&D factors and other factors categorized under the heading of miscellaneous factors. It uses an adapted version of the belief in fuzzy probability estimations of time (BIFPET) algorithm for synthesizing all factors into a cohesive prediction on innovation capability. A case study is presented to illustrate the applicability and effectiveness of the proposed framework. The results of this study show that the proposed framework can be used for the prediction of innovation capability of an investment project. It can be used by grant-giving institutions, governments or entrepreneurs to sort investment projects in descending order with respect to their innovation capability level.