ASSESSMENT OF THE “DISRUPT-O-METER” MODEL BY ORDINAL MULTICRITERIA METHODS

Abstract The objective of this article is to explore a potential diagnostic model, called “Disrupt-O-Meter”, about the Christensen's disruptive innovation theory. The diagnostic model was analyzed under multi-criteria decision aid (MCDA) methods. This diagnosis presents a typical data structure of multi-criteria ordinal problems. Different alternatives were evaluated under a set of criteria, using a scale of ordinal preferences. The steps of a MCDA problem were followed. The chosen methods were the Borda, the Condorcet and the Probabilistic Composition of Preferences (CPP). This article used a database from other research, about 3D printing technology startups. The results showed the best discrimination power by the CPP method, revealing the business category with the most disruptive potential, among other alternatives.