A Data Mining Approach for Predicting Firm Innovation Performance after Coopetition

• Main Authors: Min-Luen Sun, 孫敏倫
• Other Authors: Chih-Ping Wei
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/75417352263123712888

碩士 === 國立臺灣大學 === 資訊管理學研究所 === 104 === There are about 20% coopetition in all alliances and more than 85% of companies who did coopetition is belong to high-tech industry. It shows coopetition plays an important role for contemporary business, especially in high-tech industries. Many researches show that coopetition brings positive effect on innovativeness (Belderbos, Carree and Lokshin, 2004; Quintana-Garcia and Benavides-Velasco, 2004). The appropriate selection of coopetition targets for a given bidder company constitutes a critical first step for an effective coopetition activity. Yet existing studies employ financial and network indicators when constructing inter-firm relationship prediction models (Tsakanos, Georgopoulos, & Siriopoulos, 2007; Schilling & Phelps, 2007). Even though some considered technological indicators, most of them performed qualitative researches (e.g., questionnaire and interviews) rather than quantitative research. For example, Hall & Ziedonis (2001) estimated the patenting behavior by structure questions and a follow-up survey. Due to the importance of coopetition and many researches show the effect of technological indicators on innovation performance, our study developed an automated prediction model for predicting the innovation performance resulting from a coopetition. Our evaluation results, on the basis of the coopetition cases between January 1990 to December 2015 that involve companies in high-tech industries (i.e., ICT, software and pharmaceutical industries). With defined technological, financial and network indicators, we developed an innovation performance prediction model with 0.863 correlation coefficient. We proved that the technological variables are effective for this prediction task, and we investigated the incorporation of network variables successfully improve the prediction effectiveness.