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Semi-automatic Technology Roadmapping Composing Method for Multiple Science, Technology, and Innovation Data Incorporation

In: Anticipating Future Innovation Pathways Through Large Data Analysis

Author

Listed:
  • Yi Zhang

    (Centre for Quantum Computation & Intelligent Systems, University of Technology Sydney
    Beijing Institute of Technology)

  • Hongshu Chen

    (Centre for Quantum Computation & Intelligent Systems, University of Technology Sydney
    Beijing Institute of Technology)

  • Donghua Zhu

    (Beijing Institute of Technology)

Abstract

Since its first engagement with industry decades ago, technology roadmapping (TRM) is taking a more and more important role for competitive technical intelligence (CTI) in current R&D planning and innovation tracking. Important topics for both science policy and engineering management researchers involves with approaches that refer to real-world problems, explore value-added information from complex data sets, fuse analytic results and expert knowledge effectively and reasonable, and demonstrate to decision makers visually and understandably. The growing variety of Science, Technology, and Innovation (ST&I) data sources in the Big Data Age increases these challenges and opportunities. Addressing these concerns, this paper attempts to propose a semi-automatic TRM composing method to incorporate multiple ST&I data sources—we design an extendable interface for engaging diverse ST&I data sources and apply the fuzzy set to transfer vague expert knowledge to defined numeric values for automatic TRM generation. We focus on a case study on computer science-related R&D. Empirical data from the United States (US) National Science Foundation (NSF) Award data (innovative research ideas and proposals) and Derwent Innovation Index (DII) patent data source (technical and commercial information) affords vantage points at two stages of R&D process and also provide further capabilities for more ST&I data source incorporation. The understanding gained will also assist in description of computer science macro-trends for R&D decision makers.

Suggested Citation

  • Yi Zhang & Hongshu Chen & Donghua Zhu, 2016. "Semi-automatic Technology Roadmapping Composing Method for Multiple Science, Technology, and Innovation Data Incorporation," Innovation, Technology, and Knowledge Management, in: Tugrul U. Daim & Denise Chiavetta & Alan L. Porter & Ozcan Saritas (ed.), Anticipating Future Innovation Pathways Through Large Data Analysis, chapter 0, pages 211-227, Springer.
    • Handle: RePEc:spr:innchp:978-3-319-39056-7_12
    DOI: 10.1007/978-3-319-39056-7_12
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    Cited by:

    1. Hofmann, Peter & Keller, Robert & Urbach, Nils, 2019. "Inter-technology relationship networks: Arranging technologies through text mining," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 202-213.

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