Because of the flexibility and complexity of Newly Emerging Science and Technologies (NESTs), traditional statistical analysis fails to capture technology evolution in detail. Tracking technology evolution pathways supports industrial, governmental, and academic decisions to guide future development trends. Patents are one of the most important NESTs data sources and are pertinent to developmental paths. This paper draws upon text analyses, augmented by expert knowledge, to identify key NESTs sub-domains and component technologies. We then complement those analyses with patent citation analysis to help track developmental progressions. We identify key sub-domain patents, associated with particular component technology trajectories, then extract pivotal patents via citation analysis. We compose evolutionary pathways by combining citation and topical intelligence obtained through term clumping. We demonstrate our approach with empirical analysis of dye-sensitized solar cells (DSSCs), as an example of a promising NESTs, contributing to the remarkable growth in the renewable energy industry. The systematic approach we proposed not only offers a macro-perspective covering technology development levels and future trends, but also makes R&D information accessible for micro-level probes as needed. We work to uncover developmental trends and to compile mentions of possible applications, and this study informs NESTs management by spotting prime opportunities for innovation.
Author(s): Ying Huang, Yi Zhang, Jing Ma, Alan L. Porter, Xuefeng Wang, Ying Guo
Organization(s): Beijing Institute of Technology, University of Technology Sydney
Source: Anticipating Future Innovation Pathways Through Large Data Analysis pp 153-172
The forecasting innovation pathways (FIP) approach combines empirical tech mining with expert opinion. To date, FIP has been devised for relatively immature emerging technologies. This study extends the FIP methodology to work for a more advanced and complicated technology. It does so through a case analysis of hybrid and electric vehicles (HEVs). We retain the ten-step FIP process, augmenting several steps to deal with this more complex technology and technology delivery system (TDS). In particular, it is vital to address TDS sub-systems and attendant technical and market infrastructures. The key method to explore future prospects for the technology in question is an interactive workshop. Splitting into multiple workshop sub-groups proved constructive in addressing target markets and regional variations in innovation systems and policy options. The paper derives methodological suggestions to enrich FIP to address more complex technologies regarding scoping, sub-systems analyses, and ways to systematise key operations.
Author(s): Alan L. Porter, Scott W. Cunningham, Alejandro Sanz
Organization(s): Georgia Tech, Delft University of Technology
Source: International Journal of Technology Management
The transition of energy systems moving from non-renewable fossil-nuclear to renewable sources is a key challenge of climate mitigation and sustainable development. Green energy technologies can contribute to solutions of global problems such as climate change, growth of energy consumption, depletion of natural resources, negative environmental impacts, and energy security. In this article the prospective directions of technology development in green energy are studied and analyzed using a combination of qualitative and quantitative methods. Qualitative research involves participation of key experts in the field of green energy, while quantitative analysis includes collecting and processing data from different information sources (scientific publications, patents, news, Foresight projects, conferences, projects of international organizations, dissertations, and presentations) with a help of Vantage Point software. In addition, key challenges for green energy as well as its relationships with other technological and non-technological areas are identified and briefly described on the basis of expert and analytical results.
Author(s): S. Filippov, N. Mikova, and A. Sokolova
Organization(s): Energy Research Institute of the Russian Academy of Sciences and Higher School of Economics
Source: International Journal of Social Ecology and Sustainable Development (IJSESD)
“Global technological competitiveness” is widely acknowledged, but the challenge is to go beyond this recognition to develop empirical indicators of important transitions. These may concern particular technologies, the competitive position of particular organizations, or national/regional shifts. For decades, the US has been the world leader in biomedical technologies, with attendant implications for organizational priorities in terms of R&D location and market targeting. Recent years have seen a tremendous acceleration in Asian research in most domains, including biomedical, particularly visible in China. This paper investigates comparative patterns between the US and China in a promising emerging area of biotechnology — Nano-Enhanced Drug Delivery. It then explores indicators of, and implications for, future transitions at the national level — an approach we label “Forecasting Innovation Pathways.” http://www.sciencedirect.com/science/article/pii/S0040162514000900 Highlights
- Tech mining generates indicators for future national technological competitiveness.
- The case is a promising emerging area of biotechnology — Nano-Enhanced Drug Delivery.
- We investigate comparative patterns between the US and China.
- Results can provide insight into the approach of “Forecasting Innovation Pathways.”
Author(s): Ying Guo, Xiao Zhou, Alan L. Porter, and Douglas K.R. Robinson Organization(s): Beijing Instititute of Technology, Georgia Institute of Technology, Université de Paris-Est Source: Technological Forecasting & Social Change Year: 2015
Highly uncertain dynamics of New and Emerging Science and Technologies (NEST) pose special challenges to traditional forecasting tools. This paper explores the systematisation of the ‘Forecasting Innovation Pathways’ analytical approach through the application of Tech Mining. Continue reading Text mining of information resources to inform Forecasting Innovation Pathways
For “Newly Emerging Science & Technologies” (“NESTs”), uncertainty is the major challenge. Technological innovation for NESTs faces many kinds of risks that dramatically affect their development paths. This chapter combines methods of risk utility theory and technology path research and explores a new innovation risk path modeling method for NEST development. Continue reading Identifying innovation risk path for a newly emerging science & technology: Dye-sensitized solar cells