Text Mining of Information Resources to Inform Forecasting of Innovation Pathways Lu Huang, 1 Ying Guo, 1 TingTing Ma, 1,2 Alan L. Porter 2,3 1 School of Management and Economics, Beijing Institute of Technology, Beijing , P. R. China 2 Technology Policy and Assessment Center, Georgia Tech, Atlanta, GA, USA 3 Search Technology, Inc., Norcross, GA, USA The 4th International Seville Conference on Future-Oriented Technology Analysis (FTA) 12 & 13 May 2011

Text Mining of Information Resources to Inform Forecasting of Innovation Pathways Introduction We seek to provide usable intelligence, not only to get a handle on the discontinuous development of NEST’s, but also on the pertinent contextual forces and factors affecting possible technological innovation. Technology Forecasting of Incrementally Advancing Technologies Future-oriented Technology Aanalyses (FTA) Our endeavours: withine the context of FTA Tools develop New & Emerging Science & Technologies (NESTs) …… NESTs high uncertainty high dynamics FTA tools for NESTs pose notable challenges

Text Mining of Information Resources to Inform Forecasting of Innovation Pathways Data and Methods We have devised a four-stage approach to Forecast Innovation Pathways (“FIP”). This integrates a) heavily empirical “Tech Mining” with b) heavily expert- based Multipath Mapping. The four FIP stages blend empirical and expert knowledge. Stage 1 – Understand the NEST and its critical environment Stage 2 – Tech Mine Stage 3 – Forecast likely innovation paths Stage 4 – Synthesize and report To operationalize these stages, we break them down into 10 steps. We label these A through J, but should emphasize that forecasting innovation pathways is not a once- through, linear process.

Text Mining of Information Resources to Inform Forecasting of Innovation Pathways

Data and Methods We exemplify our approach for a particular NEST case Dye-Sensitized Solar Cells (“DSSCs”) DSSC: one type of nano-enabled solar cells with special promise, are made of low-cost materials and are less equipment-intensive than other solar cell technologies. This analysis treats DSSC abstract records through 2010 based on searches in three databases: 4104 documents (including 3134 articles) appearing in the Science Citation Index (SCI) of the Web of Science (fundamental research emphasis) 3730 documents from EI Compendex (journal and conference articles) 3097 patent families from the Derwent World Patent Index (DWPI)

Text Mining of Information Resources to Inform Forecasting of Innovation Pathways Results----Profile R&D Dye-sensitized solar cells publication & patent trends

Text Mining of Information Resources to Inform Forecasting of Innovation Pathways Results----Profile R&D DSSC Science Overlay Map DSSC research involves many fields It concentrates in Materials Science and Chemistry Could help locate expertise

Text Mining of Information Resources to Inform Forecasting of Innovation Pathways Results----Profile R&D Geo-map of DSSC Research Organizations in China (based on SCI) Geo-map for China locating DSSC research activity Note several hotbeds

Text Mining of Information Resources to Inform Forecasting of Innovation Pathways Results----Profile innovation actors & activities Cites Share thru 2008 Cites Share 2009 on Pubs Share thru 2008 Pubs Share 2009 on Chinese Acad Sciences (CAS)6.0%19.9%19.5%25.3% Swiss Fed Inst Technol (EPFL)49.3%28.6%20.5%18.5% AIST (Japan)7.7%4.4%11.1%7.2% Uppsala University8.1%4.7%5.7%9.5% Korea Inst Sci & Technol1.9%5.1%6.3%8.2% Korea University2.3%10.3%6.1%8.2% Natl Taiwan Univeristy1.5%5.2%5.8%7.2% Imperial College, London6.9% 6.2%6.4% Royal Inst Technol3.0%8.0%6.8%4.5% Kyoto University3.3%5.5%5.8%3.5% NREL (U.S.)10.0%1.2%6.1%1.4% Leading DSSC Research Institutions [Showing Percentages within these 11 organizations]

Text Mining of Information Resources to Inform Forecasting of Innovation Pathways Results----Profile innovation actors & activities Cross-Data Analyses: Leading Industry “Actors” Organizations’ activity across these 4 databases varies a lot E.g., Samsung leads in publishing & patenting, but evidences little business activity in Factiva Dainippon Printing patents extensively, but does not publish Looking across different data types gains perspective

Text Mining of Information Resources to Inform Forecasting of Innovation Pathways Results----Determine potential applications Focused DSSC Cross-Charting: Tracking Materials to Technology to Functions to Applications New technique – “cross-charting” to link technical attributes to functional advantages – to potential applications To help focus attention from “technology push” through “market pull”

Text Mining of Information Resources to Inform Forecasting of Innovation Pathways Results----Lay out alternative innovation pathways This stage was completed in two rounds. The first round involved face-to- face interviews with researchers at the Georgia Institute of Technology (US), which provided input to allow a first evaluation of our analyses. The second round entailed a campus workshop (~10 participants including ~5 with particular knowledge about nano-enhanced solar cells). This focused on mapping likely innovation avenues.

Text Mining of Information Resources to Inform Forecasting of Innovation Pathways Results----Lay out alternative innovation pathways Ingredients for the Multi-path Exploration Consolidate our empirical information Present in a chart showing To stimulate workshop discussion of Future Innovation Pathways o Timeline (X axis) o Innovation progression (Y axis)

Text Mining of Information Resources to Inform Forecasting of Innovation Pathways Results----Lay out alternative innovation pathways Multi-Path Map for Dye Sensitized Solar Cells Depicts plausible innovation paths Identifies notable obstacles & opportunities along the paths Use to further discussion of this NEST and what to do to manage its innovation prospects

Text Mining of Information Resources to Inform Forecasting of Innovation Pathways Discussion We have worked to various degrees at Forecasting Innovation Pathways (FIP) for several NESTs, including nano biosensors, deep brain stimulation, and nano-enhanced solar cells. This paper pursues FTA pertaining to the development of dye-sensitized solar cells (DSSCs). - With Doug Robinson, we have tried “FIP” on several topics o Nano biosensors o Deep brain stimulation o Nano enhanced solar cells (here focusing on DSSCs) - Tingting Ma, in a related paper, investigates DSSCs through patent analyses of key technology components - Here we share tools to identify major actors in the NEST development and to discern alternative development pathways for technology management and policy

Text Mining of Information Resources to Inform Forecasting of Innovation Pathways Discussion - Nimble R&D profiling - Challenge to identify key actors and innovation steps - “Cross-charting” is our novel technique, still being refined to help do so - 10-step process for FIP – Forecasting Innovation Pathways - Integrates multiple empirical resources with expert contributions - We invite your reactions?

Text Mining of Information Resources to Inform Forecasting of Innovation Pathways Acknowledgements This research was undertaken at Georgia Tech drawing on support from the National Science Foundation (NSF) through the Center for Nanotechnology in Society (Arizona State University; Award Numbers and ); and the Science of Science Policy Program—“Measuring and Tracking Research Knowledge Integration” (Georgia Tech; Award No ). 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. We thank Douglas Robinson and Chen Xu for their contributions.

THANK YOU FOR YOUR ATTENTION Lu Huang