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Based on the two recent consecutive Korean Innovation Surveys in 2014 and 2016, this research empirically identifies the influencing factors and overall behavior of innovation success and failure in the manufacturing industry by using decision-making tree analysis (DT). The influencing factors and behavior of a successful innovator are also investigated from the perspectives of financial contribution, innovation activity, and research and development (R&amp;D) activity. By using DT, this study acquires comprehensive knowledge of the arguments on innovation factors and behaviors in different contexts over time while dealing with all the factors in a single statistical framework based on the Oslo manual. Results with around 80% predictive accuracy show that the role of R&amp;D is crucial for innovation success. The larger the firm size and the older the firm, the higher the success achieved by the firm will be. Firms in a low-technology industry prefer other innovation activities rather than R&amp;D. Concerning a successful innovator’s behavior, target market characteristics that drive a firm to seek market needs influence innovation behavior and the use of information for innovation. Firms prefer implementing low-cost R&amp;D activities across sectors, but firms in low-technology sectors prefer non-R&amp;D activities. Regional characteristics of well-established business environments help firms to focus on R&amp;D activities and reduce costly non-R&amp;D activities. Most firms having R&amp;D institutes focus on conducting in-house R&amp;D using their own information. Cooperative R&amp;D is conducted for closing capability gaps, but absorptive capacity is required to complement cooperative R&amp;D. These empirical findings reaffirm the arguments on innovation behavior and arrange them in the overall perspective; they also provide managerial and political implications. Establishing and strengthening private or public R&amp;D support programs to increase the capability of both in-house and cooperative R&amp;D through funding as well as leveling up the information environment on technology and the market is crucial to the national innovation system.

Identification of Overall Innovation Behavior by Using a Decision Tree: The Case of a Korean Manufacturer