Mining the R&D innovation performance processes for high-tech firms based on rough set theory

The research and development (R&D) innovation of firms continues to be viewed as an important source of competitive advantage to academics and practitioners. To explore and extract the R&D innovation decision rules, it is important to understand how the R&D innovation rule-base works. However, many studies have not yet adequately induced and extracted the decision rule of R&D innovation and performance based on the characteristics and components of the original data rather than on post-determination models. The analysis of this study is grounded in the taxonomy of induction-related activities using a rough set theory approach or rule-based decision-making technique to infer R&D innovation decision rules and models linking R&D innovation to sales growth. The rules developed using rough set theory can be directly translated into a path-dependent flow network to infer decision paths and parameters. The flow network graph and cause-and-effect relationship of decision rules are heavily exploited in R&D innovation characteristics. In addition, an empirical case of R&D innovation performance will be illustrated to show that the rough sets model and the flow network graph are useful and efficient tools for building R&D innovation decision rules and providing predictions. We will then illustrate that integrating the flow network graph with rough set theory can fully reflect the characteristics of R&D innovation, and, through the established model, we can obtain a more reasonable result than with artificial influence.