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請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/49841
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dc.contributor.advisor魏志平(Chih-Ping Wei)
dc.contributor.authorMin-Luen Sunen
dc.contributor.author孫敏倫zh_TW
dc.date.accessioned2021-06-15T11:51:53Z-
dc.date.available2016-08-24
dc.date.copyright2016-08-24
dc.date.issued2016
dc.date.submitted2016-08-11
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/49841-
dc.description.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。zh_TW
dc.description.abstractThere 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.en
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dc.description.tableofcontents誌謝 i
中文摘要 ii
Abstract iv
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivations and Objectives 3
1.3 Research Structure 6
Chapter 2 Literature Review 9
2.1 Definition and Theoretical Frameworks for Coopetition 9
2.2 Importance and Risks of Coopetition for Technological Innovation 11
2.3 Factors Affecting Innovation Performance Brought by an Inter-firm Relationship (Alliance, M&A, Coopetition) 15
2.3.1 Technological Characteristics 15
2.3.2 Financial Characteristics 19
2.3.3 Network Characteristics 20
Chapter 3 Method 23
3.1 Overall Design 23
3.2 Variables 25
3.2.1 Dependent Variable 25
3.2.2 Financial Related Independent Variables 27
3.2.3 Network Related Independent Variables 29
3.2.4 Technological Related Independent Variables 33
3.3 Inductive Learning Algorithms 44
Chapter 4 Empirical Evaluation 48
4.1 Data Collection 48
4.1.1 Initial Dataset 48
4.1.2 Coopetition Cases 50
4.2 Evaluation Results 53
4.2.1 Experiment 1: How is the Prediction Effectiveness with Technological Variables 54
4.2.2 Experiment 2: Effectiveness of Technological Variables Combining with Other Variables 56
4.2.3 Experiment 3: Effectiveness in Different Windows 58
Chapter 5 Conclusion and Future Research 63
5.1 Conclusion 63
5.2 Future Research 65
References 66
dc.language.isoen
dc.subject財務指標zh_TW
dc.subject網絡指標zh_TW
dc.subject科技指標zh_TW
dc.subject預測模型zh_TW
dc.subject創新績效zh_TW
dc.subject資料探勘zh_TW
dc.subject競合策略zh_TW
dc.subjectTechnological Indicatorsen
dc.subjectFinancial Indicatorsen
dc.subjectData Miningen
dc.subjectPrediction Modelen
dc.subjectCoopetitionen
dc.subjectInnovation Performanceen
dc.subjectNetwork Indicatorsen
dc.title運用資料探勘技術預測企業競合策略之創新績效zh_TW
dc.titleA Data Mining Approach for Predicting Firm Innovation Performance after Coopetitionen
dc.typeThesis
dc.date.schoolyear104-2
dc.description.degree碩士
dc.contributor.oralexamcommittee陳彥良,楊錦生,吳怡瑾
dc.subject.keyword競合策略,資料探勘,創新績效,預測模型,科技指標,網絡指標,財務指標,zh_TW
dc.subject.keywordCoopetition,Prediction Model,Innovation Performance,Data Mining,Technological Indicators,Network Indicators,Financial Indicators,en
dc.relation.page76
dc.identifier.doi10.6342/NTU201601701
dc.rights.note有償授權
dc.date.accepted2016-08-11
dc.contributor.author-college管理學院zh_TW
dc.contributor.author-dept資訊管理學研究所zh_TW
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