Success Factors for Innovation: A Bayesian Network Approach

Abstract

The purpose of this study is to understand how innovation factors affect innovation and, at the same time, how the process of innovation factors leading to innovation can explain. This study used Bayesian Network Analysis (BNA) to examine the relationship between innovation factors such as information acquisition, research and development, government support system, product innovation and business process innovation using the 2020 Korean Innovation survey (KIS) data. As a result of the analysis, it confirmed that various factors and innovation had a direct or indirect relationship. For example, it found Government tax support, collaborative R&D and information from private research institutes to play an essential role in innovation, and other factors indirectly affect innovation through these factors. The conclusion section discusses the implications and limitations of research results and suggestions for future research.