運用資料探勘技術預測企業競合策略之創新績效

Abstract

競合策略佔一般企業聯盟的20%，且有85%以上的競合策略發生在高科技產業之中，如資訊科技產業、軟體產業、製藥產業，足見競合策略在近代商業環境中的重要性。許多研究指出，競合策略對企業的創新績效表現有正向影響 (Belderbos, Carree, & Lokshin, 2004; Quintana-Garcia & Benavides-Velasco, 2004)。選擇一個正確且合適的競合對象，無疑是競合策略成功的第一步，進而能為公司帶來良好的創新表現。然而，目前預測企業間聯盟關係的研究，多以經濟指標或網絡指標為主 (Tsakanos, Georgopoulos, & Siriopoulos, 2007; Schilling & Phelps, 2007)。即使部分研究考量了科技指標，大部分研究皆使用量化的方法為主（例如問卷或訪談），而非資料探勘的方式。例如，Hall與Ziedonis（2001）便是以結構化的問卷和後續的訪查來研究公司的專利行為。基於競合策略在現代商業環境的重要性，加上許多研究指出科技指標對創新績效表現有強烈相關性，我們開發了一個自動預測系統，用以預測企業在實施競合策略後的創新績效表現。我們分析1990年1月到2015年12月在高科技產業中參與競合策略的企業，並定義多個科技指標、財務指標與網絡指標，開發出與結果的相關係數達0.863的預測模型。研究指出，科技指標的確對預測競合策略的創新績效非常有效，且我們發現，科技指標搭配網絡指標能夠有更好的預測效果。

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.