Abstract
In recent years, the Triple Helix model has identified feasible approaches to measuring relations among universities, industries, and governments. Results have been extended to different databases, regions, and perspectives. This paper explores how bibliometrics and text mining can inform Triple Helix analyses. It engages Competitive Technical Intelligence concepts and methods for studies of Newly Emerging Science & Technology (NEST) in support of technology management and policy. A semantic TRIZ approach is used to assess NEST innovation patterns by associating topics (using noun phrases to address subjects and objects) and actions (via verbs). We then classify these innovation patterns by the dominant categories of origination: Academy, Industry, or Government. We then use TRIZ tags and benchmarks to locate NEST progress using Technology Roadmapping. Triple Helix inferences can then be related to the visualized patterns. We demonstrate these analyses via a case study for dye-sensitized solar cells.
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Notes
Dr. Chen Xu, who graduated in Materials Science and Engineering, Georgia Tech and works in IBM; Ms. Tingting Ma, PhD student at the School of Management and Economics, Beijing Institute of Technology, who has been analyzing DSSCs for three years; and Dr. Jud Ready, principal research engineer in nanotechnology and materials engineering, Georgia Tech Research Institute, who reviewed and agreed with our results finally.
Abbreviations
- CAS:
-
Chinese Academy of Science
- CTI:
-
Competitive technical intelligence
- DII:
-
Derwent innovations index
- DSSCs:
-
Dye-sensitized solar cells
- FTA:
-
Future-oriented technology analysis
- NEST:
-
Newly Emerging Science & Technology
- NLP:
-
Nature language processing
- P&S:
-
Problem & solution
- PCA:
-
Principle components analysis
- SCI:
-
Science citation index
- SME:
-
Small and medium enterprises
- ST&I:
-
Science, technology & innovation
- THM:
-
Triple Helix model
- TRM:
-
Technology roadmapping
- WoS:
-
Web of science
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Acknowledgments
We acknowledge support from the US National Science Foundation (Award #1064146—“Revealing Innovation Pathways: Hybrid Science Maps for Technology Assessment and Foresight”). The findings and observations contained in this paper are those of the authors and do not necessarily reflect the views of the National Science Foundation.
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Zhang, Y., Zhou, X., Porter, A.L. et al. Triple Helix innovation in China’s dye-sensitized solar cell industry: hybrid methods with semantic TRIZ and technology roadmapping. Scientometrics 99, 55–75 (2014). https://doi.org/10.1007/s11192-013-1090-9
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DOI: https://doi.org/10.1007/s11192-013-1090-9