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A compared R&D-based and patent-based cross impact analysis for identifying relationships between technologies

: Thorleuchter, D.; Poel, D. van den; Prinzie, A.

Postprint urn:nbn:de:0011-n-1460057 (402 KByte PDF)
MD5 Fingerprint: e4dda133b421c024d7e8c4933cf5f9ab
Erstellt am: 17.6.2011

Technological forecasting and social change 77 (2010), Nr.7, S.1037-1050
ISSN: 0040-1625
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer INT ()
compared cross impact; cross impact analysis; technological impact analysis; R & D; patent analysis; defence taxonomy; centroid vector; machine learning; multi label classification

The planning of technological research and development (R&D) is demanding in areas with many relationships between technologies. To support decision makers of a government organization with R&D planning in these areas, a methodology to make the technology impact more transparent is introduced. The method shows current technology impact and impact trends from the R&D of an organization's competitors and compares these to the technology impact and impact trends from the organization's own R&D. This way, relative strength, relative weakness, plus parity of the organization's R&D activities in technology pairs can be identified.
A quantitative cross impact analysis (CIA) approach is used to estimate the impact across technologies. Our quantitative CIA approach contrasts to standard qualitative CIA approaches that estimate technology impact by means of literature surveys and expert interviews. In this paper, the impact is computed based on the R&D information regarding the respective organization on one hand, and based on patent data representative regarding R&D information of the organization's competitors on the other hand. As an illustration, the application field 'defence' is used, where many interrelations and interdependencies between defence-based technologies occur. Firstly, an R&D-based and patent-based Compared Cross Impact (CCI) among technologies is computed. Secondly, characteristics of the CCI are identified. Thirdly, the CCI data is presented as a network to show the overall structure and the complex relationships between the technologies. Finally, changes of the CCI are analyzed over time. The results show that the proposed methodology has the potential to generate useful insights for government organizations to help direct technology investments.