ESSAYS ON TEXT MINING FOR IMPROVED DECISION MAKING ...

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information from the colloquial language. A successful comparing to the frequency baseline proves the feasibility of this approach. Study 7 contributes to previous research in multiple ways. The main contribution of the proposed approach is the new CCI index that identifies relative strength, relative weakness, plus parity of the organization's R&D activities in technology pairs. The second contribution is a method to determine the characteristics of relationships and to show whether two technologies are equally influencing one another (symmetry) or whether the impact of the first technology on the second is different from the impact of the second technology on the first (asymmetry). A third contribution is the presentation of a CCI network graph that shows the overall structure and the complex CCI relationships between several technologies. Finally, changes of the CCI are analyzed over time to discover trends regarding how the technology impact changes over time. They show which technology should receive more or less development and investment. Overall, the results testify to the ability of CCI to generate potentially useful insights for R&D decision makers of organizations. 7. Shortcomings & Further Research Future work concerning study 1 should focus on improving the prediction by adding further unstructured information from existing customers (e.g. e-mails) to the prediction model. Study 2 is evaluated concerning e-commerce companies. It can be enlarged by selecting further line of businesses e.g. automotive supplier companies. The parameters of study 3 can be optimized concerning the precision and recall values of the evaluation. Directions for future research of study 4 are given by the fact that nowadays there is a large amount of textual information available on the internet and this information probably contains many new technological ideas. Enlarging this approach to a web idea mining approach that automatically identifies problem solution ideas from the internet is an interesting topic for further research. Further work of study 5 should aim at enlarging and optimizing this approach e.g. by identifying further properties of innovative ideas. A second avenue of further research could _______________________________________________________________________ 20
take the granularity of the context information into account e.g. by using technologies rather than scientific categories. This also probably leads to an increasing precision and recall. Study 6 can be enlarged by optimizing the parameters concerning the precision and recall values of the evaluation. Future research for study 7 should aim at assigning R&D projects to technologies rather than technology areas. In the case study, internal R&D projects and patent data should be assigned to the 200 defense-based technologies from EDA taxonomy. Then, a more detailed view on the technological landscape in the ’defense’ application field could be provided. A second avenue of further research could take the occurrence of new technologies into account. This research focuses on computing the impacts between technologies or technology areas. It does not consider the computation of the occurrence probability of new technologies or technology areas. This could be an interesting topic for future research. 8. Literature [1] Abebe, A. J., Guinot, V., Solomatine, D. P. (2000). Fuzzy alpha-cut vs. Monte Carlo techniques in assessing uncertainty in model parameters. In: Proc. 4-th International Conference on Hydroinformatics, Iowa City. [2] Albers, S., Gassmann, O. (2005). Handbuch Technologie- und Innovationsmanagement: Strategie- Umsetzung- Controlling. p. 196, Gabler Verlag, Wiesbaden. [3] Allgeier, H., Albrecht, K. (2003). Campus Management. p. 394, Campus Verlag, Frankfurt. [4] Allison, P.D. (1999). Logistic Regression using the SAS System: Theory and Application. SAS Institute Inc., Cary, NC. [5] Annacchino, M.A. (2003). New Product Development: From Initial Idea to Product Management. p. 88 f., Butterworth-Heinemann, Oxford. [6] Berth R. (1997). Der große Innovations-Test: das Arbeitsbuch für Entscheider: Chancen erkennen, Flops vermeiden - Theorie und Praxis des Management of Change. Econ, Düsseldorf. [7] Bürgel, H.D., Haller, C., Binder, M. (1996). F&E-Management. p. 85., Vahlen, München. [8] Choi, C., Kim, S., Park, Y. (2007). A patent-based cross impact analysis for quantitative estimation of technological impact: The case of information and communication technology. Technol. Forecast. Soc. Change 74, 1296-1314. [9] Coussement, K., Van den Poel, D. (2008). Information & Management 45, 164-174. _______________________________________________________________________ 21
Page 1 and 2: ESSAYS ON TEXT MINING FOR IMPROVED
Page 3 and 4: Table of Contents 1. Acknowledgment
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: Study 3 shows that a semi-automatic
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
Page 53 and 54: Predicting E-Commerce Success using
Page 59 and 60: with , )} x T = the training set, }
Page 61 and 62: 4.3 Comparing predictive performanc
Page 63 and 64: 4.4 Results Predicting E-Commerce S
Page 69 and 70: 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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