Luiz Octávio Gavião , Fernando Toledo Ferraz , Gilson Brito Alves Lima , Annibal Parracho Sant’Anna
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引用次数: 5
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.
本文的目的是探索一个潜在的诊断模型，称为“破坏- o - meter”，关于克里斯滕森的破坏性创新理论。采用多准则决策辅助(MCDA)方法对诊断模型进行分析。该诊断给出了一个典型的多准则有序问题的数据结构。不同的选择是在一套标准下评估的，使用顺序偏好的尺度。遵循MCDA问题的步骤。选择了Borda、Condorcet和概率组合偏好(Probabilistic Composition of Preferences, CPP)三种方法。本文使用了来自其他数据库的研究，关于3D打印技术的创业公司。结果显示，CPP方法的辨别能力最好，揭示了在其他备选方案中最具颠覆潜力的业务类别。
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