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
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 100081, P. R. China
2 Technology Policy and Assessment Center, Georgia Tech, Atlanta, GA, USA
3 Search
Technology, Inc., Norcross, GA, USA
Text Mining of Information Resources to Inform Forecasting of Innovation Pathways
Introduction
Our endeavours:
withine the context of
Future-oriented Technology Aanalyses (FTA)
Technology Forecasting of Incrementally Advancing Technologies
FTA
develop
Tools
New & Emerging Science &
Technologies (NESTs)
……
high uncertainty
NESTs
high dynamics
FTA tools for NESTs pose
notable challenges
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.
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 oncethrough, linear process.
Text Mining of Information Resources to Inform Forecasting of Innovation Pathways
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 for China
locating DSSC
research activity
• Note several
hotbeds
Geo-map of DSSC Research Organizations in China (based on SCI)
Text Mining of Information Resources to Inform Forecasting of Innovation Pathways
Results----Profile innovation actors & activities
Leading DSSC Research Institutions [Showing Percentages within these 11 organizations]
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 University
8.1%
4.7%
5.7%
9.5%
Korea Inst Sci & Technol
1.9%
5.1%
6.3%
8.2%
Korea University
2.3%
10.3%
6.1%
8.2%
Natl Taiwan Univeristy
1.5%
5.2%
5.8%
7.2%
Imperial College, London
6.9%
6.9%
6.2%
6.4%
Royal Inst Technol
3.0%
8.0%
6.8%
4.5%
Kyoto University
3.3%
5.5%
5.8%
3.5%
NREL (U.S.)
10.0%
1.2%
6.1%
1.4%
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-toface 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
o Timeline (X axis)
o Innovation progression (Y axis)
• Present in a chart showing
• To stimulate workshop discussion of Future Innovation Pathways
Text Mining of Information Resources to Inform Forecasting of Innovation Pathways
Results----Lay out alternative innovation pathways
• 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
Multi-Path Map for Dye Sensitized Solar Cells
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 0531194 and 0937591);
and the Science of Science Policy Program—“Measuring and Tracking
Research Knowledge Integration” (Georgia Tech; Award No. 0830207). 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
huanglu628@163.com