ESSAYS ON TEXT MINING FOR IMPROVED DECISION MAKING ...

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Doctoral Exam Committee Prof. Dr. Dirk Van den Poel Advisor, Ghent University Prof. Dr. Anita Prinzie Co-Advisor, Ghent University Prof. Dr. Aimé Heene Ghent University Prof. Dr. Mario Vanhoucke Ghent University Prof. Dr. Marc De Clercq Ghent University Prof. Dr. Patrick Van Kenhove Ghent University Prof. Dr. Bart Baesens Katholieke Universiteit Leuven, Belgium & University of Southampton, UK Prof. Dr. Horst Geschka University of Darmstadt, Germany _______________________________________________________________________ 2
Table of Contents 1. Acknowledgments .............................................................................................................4 2. Nederlandstalige Samenvatting ........................................................................................5 3. Deutsche Zusammenfassung ...........................................................................................8 4. Summary.........................................................................................................................11 5. Research Objectives .......................................................................................................12 5.1. Overview.................................................................................................................12 5.2. Study 1: Analyzing existing customers’ websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketing ....14 5.3. Study 2: Predicting E-Commerce Company Success by Mining the Text of Its Publicly-Accessible Website ...............................................................................14 5.4. Study 3: Finding New Technological Ideas and Inventions with Text Mining and Technique Philosophy.........................................................................................15 5.5. Study 4: Mining Ideas from Textual Information .....................................................15 5.6. Study 5: Mining Innovative Ideas to Support new Product Research and Development.......................................................................................................16 5.7. Study 6: Extracting Consumers Needs for New Products - A Web Mining Approach ............................................................................................................................16 5.8. Study 7: A compared R&D-based and patent-based cross impact analysis for identifying relationships between technologies...................................................17 6. Main Findings..................................................................................................................18 7. Shortcomings & Further Research..................................................................................20 8. Literature .........................................................................................................................21 Chapter 1: Study 1 - Analyzing existing customers’ websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketing .......................26 Chapter 2: Study 2 - Predicting E-Commerce Company Success by Mining the Text of Its Publicly-Accessible Website ……………………………...………………………………………..51 Chapter 3: Study 3 - Finding New Technological Ideas and Inventions with Text Mining and Technique Philosophy…….…………………………………………………………………………69 Chapter 4: Study 4 - Mining Ideas from Textual Information……………………………...…….81 Chapter 5: Study 5 - Mining Innovative Ideas to Support new Product Research and Development ……………………………………………………………………………………….103 Chapter 6: Study 6 - Extracting Consumers Needs for New Products - A Web Mining Approach ……………………………………………………………………………………………115 Chapter 7: Study 7:- A compared R&D-based and patent-based cross impact analysis for identifying relationships between technologies ………………………………………………...128 _______________________________________________________________________ 3
Page 1: ESSAYS ON TEXT MINING FOR IMPROVED
Page 5 and 6: 2. Nederlandstalige Samenvatting In
Page 7 and 8: ideeën zijn. Ten einde dit doel te
Page 9 and 10: Ein wesentlicher Erfolgsfaktor für
Page 11 and 12: 4. Summary Data mining is defined a
Page 13 and 14: from customers' and companies' webs
Page 15 and 16: of e-commerce companies divided int
Page 17 and 18: that provides commentary, news, and
Page 19 and 20: Study 3 shows that a semi-automatic
Page 21 and 22: take the granularity of the context
Page 23 and 24: [25] Herring, S.C., Scheidt, L.A.,
Page 25 and 26: [56] Thorleuchter, D., Van den Poel
Page 27 and 28: Table of Contents Analyzing existin
Page 29 and 30: 1 Introduction Analyzing existing C
Page 31 and 32: Analyzing existing Customers’ Web
Page 33 and 34: Fig. 2: Different steps in data col
Page 39 and 40: 3.4 Website clustering Analyzing ex
Page 41 and 42: 4 Empirical verification 4.1 Resear
Page 47 and 48: 5 Conclusion Analyzing existing Cus
Page 51 and 52: Chapter II Predicting E-Commerce Co
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Predicting E-Commerce Success using
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with , )} x T = the training set, }
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4.3 Comparing predictive performanc
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4.4 Results Predicting E-Commerce S
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Chapter III Finding new technologic
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Finding New Technological Ideas Fin
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Finding New Technological Ideas to
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Finding New Technological Ideas 3.
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p( Ξ raw δ ,.., δ ) 1 μ ⎧ raw
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Finding New Technological Ideas inv
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Chapter IV Mining Ideas from Textua
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Mining Ideas from Textual Informati
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1.2. Idea Definition Mining Ideas f
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3. Idea Mining Process Mining Ideas
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6. Results and Discussions Mining I
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Chapter V Mining Innovative Ideas t
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Mining Innovative Ideas to Support
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Mining Innovative Ideas idea is a n
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Mining Innovative Ideas identifies
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Mining Innovative Ideas categories
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9. Outlook Mining Innovative Ideas
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Chapter VI Extracting Consumers Nee
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Extracting Consumers Needs for New
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2. Rationale Behind this Approach E
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Bibliography Extracting Consumers N
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Table of Contents A Compared Cross
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A Compared Cross Impact Analysis Ke
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A Compared Cross Impact Analysis Th
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A Compared Cross Impact Analysis te
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A Compared Cross Impact Analysis In
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A Compared Cross Impact Analysis De
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A Compared Cross Impact Analysis A
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A Compared Cross Impact Analysis Ta
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A Compared Cross Impact Analysis Ea
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Table 3: Technology area pairs with
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A Compared Cross Impact Analysis A
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A Compared Cross Impact Analysis al
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A Compared Cross Impact Analysis As
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A Compared Cross Impact Analysis Ta
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A Compared Cross Impact Analysis Th
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A Compared Cross Impact Analysis [2
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