Drivers of change and sudden disruptive transformations range from profound technological changes, emergence of new business models and major economic restructuring, environmental disruptions, to shifts in social norms, values and lifestyles.
if broadly conceived in technological, social, organisational and institutional terms. The scale and direction of innovation are determined by a mix of factors, many of them national in their nature,
forecasting and technology assessment and thus it is not a discipline with solid, widely accepted theoretical foundations.
or to enable technology observers to determine the current life cycle stage of a particular technology of interest
They typically involve complex and systemic relationships within and between social, technological, economic, environmental, and value systems.
Systemic action is required for a collective transformation through the coordinated application of scientific/technological, social and business innovation simultaneously supported by political will.
which appears to be suitable for aligning scientific/technological and social innovations to achieve a structural transformation. 6. Papers in this special issue The papers in this special issue of TFSC discuss various methodological aspects of FTA APPROACHES as well as some advances needed in practice to assist us in comprehending transformations.
whereas Gao et al. 7 propose the development of a new forecasting approach to analysing technology life cycle of a particular technology of interest.
Shaper-Rinkel 13 analyses future-oriented governance of emerging technologies in the USA and in Germany,
and perspectives from the outset of an endeavour in order to properly foster nanotechnology by establishing governance structures able to coordinate interactions of relevant actors.
successful cases and good practices to build trust,(e) creation of technological and methodological interfaces between QL and QT approaches,(f) setting up of multidisciplinary teams from the very beginning of an exercise
Gao et al. 7 proposes an approach to enable technology managers to determine the current life cycle stage of a particular technology.
which is currently the major forecasting approach to analyse technology life cycle (TLC), they propose a model to calculate the TLC for a technology based on multiple patent-related indicators.
The right understanding where a certain technology is in its TLC is important to estimate its future development,
and thus decide whether to invest in it or not. The authors claim that the first step for devising a technology strategy is to decide
if the technology is worth investing in by better understanding how such technology might develop in the future.
In this context, the proposed model focuses on devising and assessing patent-based TLC indicators using a Nearest Neighbour Classifier,
which is used widely in pattern recognition, to measure the technology life cycle stage of the selected technology.
Clearly, different types of technologies may have different developing patterns, especially for those technologies close to basic science, such as biotechnology,
and future research should take this into account to test the validity of the proposed model.
there is a major limitation of this method to assess a given technology. It is an oftenobseerve fact that technologies change their course because of (unpredictable) changes in the broader socioeconomic context (fluctuations in demand
changes in regulation, changing/stronger ethical concerns, scarcity of natural resources, environmental issues, etc. as well as due to new combinations of existing and/or emerging technologies.
Technology assessment activities part of the FTA family can also influence technological trajectories. Two papers from the same school Hamarat et al. 11 and Kwakkel and Pruit 12 address the need for novel methods and techniques to support adaptive policy-making.
They analyse whether models can be used at all in decision-making under uncertainty. In this context they claim that Exploratory Modelling
Shaper-Rinkel 13 analyses future-oriented governance of emerging technologies. She explores the role that different types of FTA played in the development of nanotechnology governance in the USA and in Germany.
In the USA FTA was used to create visionary concepts and to promote co-operation between various actors.
In both countries, public policy activities to foster nanotechnology were accompanied by efforts to establish governance structures to coordinate interactions between actors of the innovation system.
The FTA TOOLS used to develop governance frameworks for nanotechnology in these two countries differ along time.
In both countries, early FTA envisioned innovative future nanotechnologies, but did not support guidance either for future innovative governance or for using nanotechnology for disruptive innovation in order to address grand societal challenges.
Comparing these two countries, the main difference lies in the existence of an umbrella organisation in the USA that pools heterogeneous stakeholders
Hence, the implication for future emerging technologies is that the methodology and practice of FTA should consider the governance dimension from the beginning by acknowledging that monitoring
The notion of FTA addressing research and innovation policy through priority-setting and articulation of demand has shifted to the search of breakthrough science
influences on future-oriented technology analysis, Tech. Anal. Strateg. Manag. 24 (8)( 2012) 753 767.3 L. Georghiou, J. C. Harper, Rising to the challenges Reflections on Future-oriented technology analysis, Technol.
7 L. Gao, A l. Porter, J. Wang, S. Fang, X. Zhang, T. Ma, W. Wang, L. Huang, Technology life cycle analysis method
13 P. Shaper-Rinkel, The role of future-oriented technology analysis in the governance of emerging technologies: The example of nanotechnology, Technol.
Forecast. Soc. Chang. 80 (3)( 2013) 444 452 (this issue. 14 E. Schirrmeister, P. Warnke, Envisioning structural transformation lessons from a foresight project on the future of innovation, Technol.
and is now a senior advisor of STI (Science, Technology and Innovation policy and strategy at CGEE.
and practice in RTDI (Research, Technology development and Innovation), business strategy and sustainability, environment management, cleaner production and foresight.
(which comprises Foresight, Forecasting and Technology assessment), 1 foresight practitioners have concentrated traditionally on participatory methods based on qualitative data,
and that emerging changes in the socioeconomic and technological landscapes need to be taken into account.
Another part of the FTA COMMUNITY, constituted by Forecasting and Technology assessment practitioners, holds an opposite standpoint, considering qualitative and participatory approaches as a second best option, to which we are compelled somehow to refer until adequate quantitative methods arise.
complex and adaptive nature of the systems we are dealing with today are moving from one technological era to another.
Cunningham and van der Lei 28 use such an approach for models providing support to decision-making on the selection of new technologies and discuss the issue of providing equilibrium between different groups of experts and stakeholders.
or for the identification of potential disruptive technologies 5 10 years ahead. Shibata et al. 33 distinguish between incremental
/Technological forecasting & Social Change 80 (2013) 386 397 identification of emerging clusters analysing citations and keywords for a particular technology field,
Järvanpää et al. 34 analyse the use of bibliometric data for distinguishing between science-based and conventional technologies,
giving experts an opportunity to focus on particular technology areas using relevant qualitative methods. Thorleuchter et al. 35 demonstrate that patent-based quantitative approaches to cross-impact analysis for the identification of relationships between technologies can be used instead of,
or in combination with, traditional qualitative methods based on literature reviews. Identification of trends and wildcards: Quantitative methods can also be used to identify outliers (outstanding observations) which could be revised further by experts as potential wild cards.
to monitor the convergence of adjacent technologies 37, or to identify Emerging s&t areas 38. Comparison of outcomes of qualitative and quantitative approaches Participants at the 2011 International Seville Conference on FTA raised the potential of the use of qualitative and quantitative methods for identifying
thus limiting the scale of failures (with a focus on market pull vis-à-vis the technology push approach).
In the NEEDS project (www. needs-project. org), the acceptability of future energy technology options was submitted to a multi-criteria assessment involving a panel of stakeholders, the results
new technologies such as web 2. 0 can be used by FTA to streamline operations by increasing interactive participation of stakeholders, speeding-up the provision of information and feedbacks and integrating data of different sorts (pictures
Scientists (particularly natural scientists and technologists) often tend to consider subjectivity, e g. experts opinions as a disturbance to be avoided,
and addressing different types of innovation. 9 A good example is the contribution that FTA can provide to policy and decision makers in charge of the prioritisation of alternative technological options.
The long term prospects of emerging technologies are usually scrutinised through the lens of e g. Cost Benefit Analysis
and benefits that can be expected from the diffusion of the technologies under scrutiny (how will their diffusion affect quality of life, the conservation of natural resources, landscape integrity, ecosystem services etc.).
The appraisal of the expected future performance (and the ranking) of alternative technological options therefore explicitly incorporates information that inherently reflects the subjectivity of social players,
new technology foresight, forecasting & assessment methods, in: JRC Technical Report, EUR 21473 EN, European commission, 2004, Available at:
Strategic intelligence for an Innovative economy, Springer verlag, Berlin and Heidelberg, 2008.28 S w. Cunningham, T. E. van der Lei, Decision-making for new technology:
Change 78 (2011) 274 282.34 H. M. Järvanpää, S. J. Mäkinen, M. Seppänen, Patent and publishing activity sequence over a technology's life cycle, Technol.
and patent-based cross-impact analysis for identifying relationships between technologies, Technol. Forecast. Soc. Change 77 (2010) 1037 1050.36 R. Popper, Foresight methodology, in:
Change 78 (2011) 256 273.38 P. Lee, H. Su, F. Wu, Quantitative mapping of patented technology the case of electrical conducting polymer composite, Technol.
12th International Command and Control Research and Technology Symposium, US Naval War College, Newport R i. USA, June 19 21,2007, 2007, Available at:
This function is studying technological and societal trends and events, which may affect future European public policies by applying horizon scanning and foresight.
He received his engineering degree at Ecole Centrale (Paris). His key qualifications are Sustainability Policy analysis and impact assessment, and foresight studies.
Technology life cycle analysis method based on patent documents Lidan Gao a b,, Alan L. Porter c, Jing Wang d, Shu Fang a, Xian Zhang a, Tingting Ma e, Wenping Wang e, Lu Huang e
School of Public policy, Georgia Institute of technology, Atlanta, GA 30332-0345, USA d College of Computer science & Technology, Huaqiao University, Xiamen, 361021, PR China e
Received 14 may 2011 Received in revised form 22 june 2012 Accepted 23 august 2012 Available online 28 november 2012 To estimate the future development of one technology
one needs to know the current stage of its technology life cycle (TLC). The dominant approach to analysing TLC uses the S-curve to observe patent applications over time.
But using the patent application counts alone to represent the development of technology oversimplifies the situation.
In this paper, we build a model to calculate the TLC for an object technology based on multiple patent-related indicators.
The model includes the following steps: first, we focus on devising and assessing patent-based TLC indicators.
Then we choose some technologies (training technologies) with identified life cycle stages, and finally compare the indicator features in training technologies with the indicator values in an object technology (test technology) using a nearest neighbour classifier,
which is used widely in pattern recognition to measure the technology life cycle stage of the object technology.
Such study can be used in management practice to enable technology observers to determine the current life cycle stage of a particular technology of interest
and make their R&d strategy accordingly. 2012 Elsevier Inc. All rights reserved. Keywords: Technology life cycle Patent Indicator Cathode ray tube Thin film transistor liquid crystal display Nano-biosensor 1. Introduction The rapidly changing economic environment
and increasingly fierce competition require companies to be innovative, both in their products andmarketing strategies,
if they are to flourish. A successful productmust balance three components: technology, marketing, and user experience 1. Technology plays a key role among these three components 2. Before the product strategy is formulated,
a technology strategy must be developed to provide competitive products, materials, processes, or system technologies 3. The first step for devising a technology strategy is to decide
if the technology is worth the investment. Howwill the technology develop in the future? Will the technology flourish in the future
or will it decline? To answer these questions, one should know the current life cycle stage of the technology
in order to estimate future development trends to make informed decisions on whether to invest in it or not.
Within the Future-oriented technology analysis (FTA), technology forecasting traces back to the 1950's 4. One of its half dozen
or so basic techniques, dating from that time at least, is trend analysis. This includes both historical time series analyses
and fitting of growth models to project possible future trends 5. Most trend projection is naïve i e.,
, fitting a curve to the historical data under the assumption that whatever forces are collectively driving the trend will continue into the future unabated.
It follows that such projection becomes increasingly precarious as the future horizon is extended beyond a few years.
Another important technology forecasting technique 6 is the use of analogies. Herein, one anticipates growth in an emerging technology based on the pattern of growth observed in a somewhat related technology.
The stronger that relationship, the more likely the pattern will pertain. Technological forecasting & Social Change 80 (2013) 398 407 Corresponding author at:
& Social Change Another important predecessor approach upon which we draw is the identification of Technology Readiness Levels (TRLS).
The U s. military, especially the Air force, has made use of this categorization of technology development to help identify current status and future prospects.
When a complex technical system incorporates a number of emerging technologies, use of TRLS has proven helpful in designing a viable new system.
but precise anticipation of when a given advanced technology will be ready for application is precarious.
The concept of the technology life cycle (TLC) was presented by Arthur 9 to measure technological changes. It includes two dimensions the competitive impact and integration in products or process and four stages.
According to Arthur's definition, the characteristic of the emerging stage is a new technology with low competitive impact and low integration in products or processes.
In the growth stage, there are pacing technologies with high competitive impact that have not yet been integrated in new products or processes.
In the maturity stage, some pacing technologies turn into key technologies, are integrated into products or processes, and maintain their high competitive impact.
As soon as a technology loses its competitive impact it becomes a base technology. It enters the saturation stage
and might be replaced by a new technology. According to this definition, Ernst 10 developed a map to illustrate TLC (Fig. 1). The dominant approach to analysing TLC with an S-curve is to observe technological performance,
either over time or in terms of cumulative R&d expenditures. But using one indicator only to present technological performance would be problematic.
A research team from MIT 11 studied the development trends of power transmission technology and aero-engine technology by S-curve modelling.
The results showed that the S-curve with a single indicator was not reliable and might lead the research in the wrong direction.
They suggested considering multiple indicators to measure technological development and to make business decisions. Usually, patent application activity is tracked as a TLC indicator for the S-curve analysis 10,12, 13.
But using patent application counts alone to represent the development of technology oversimplifies the situation. Accordingly, some multiple indicators are used to measure TLC.
Watts and Porter 14 have introduced nine indicators that look at publications of different types during the technology life cycle.
Reinhard et al. 15 tested seven indicators related to patents. Table 1 shows the indicators listed in the two papers.
These papers studied the indicators that would have different performance based on the changes of technology.
Separately the indicators can serve to measure technological changes. In this paper, we focus on combining multiple indicators to calculate the life cycle stages for an object technology
and hope that would help decision makers estimate its future development trends. 2. Methodology The model that we build to calculate the TLC for an object technology includes the following steps:
first, we focus on devising and assessing patent-based TLC indicators, then we choose some technologies (training technologies) with identified life cycle stages,
and finally we compare the indicator features in training technologies with the indicator values in an object technology Fig. 1. The S-curve concept of technology life cycle. 399 L. Gao et al./
/Technological forecasting & Social Change 80 (2013) 398 407 (test technology) via the nearest neighbour classifier,
which is used widely in pattern recognition, in order to measure the technology's life cycle stages. The research framework is designed as follows (Fig. 2). 2. 1. Indicators
and data source The most fundamental and challenging task is to select suitable indicators and data sources.
In a recent work 16, we have compiled candidate patent indicators from multiple sources. Thirteen indicators are selected for TLC assessment (Table 2). All the data of the indicators are extracted by priority year (the first filing date year for a patent application
except the first indicator. In this research, we choose the Derwent Innovation Index (DII) as the data source and Vantagepoint (VP) for data cleaning and extraction.
Matlab 2010b is used for implementing the algorithms. 2. 1. 1. Application and priority Usually, three kinds of dates are included in the DII database:
Currently, most of TLC related literatures are based on application year 15,17 20. But the priority year presents the first time an invention has been disclosed.
So in this paper, we choose the other two indicators to measure the development of technology:
we count the number of patents Table 1 Technology life cycle indicators by former researchers. Author Indicator Robert J Watts, Alan L Porter 14 Number of items in databases such as Science Citation Index number of items in databases such as Engineering
Index number of items in databases such as U s. patents Number of items in databases such as Newspaper Abstracts Daily Issues raised in the Business
and Popular Press abstracts Trends over time in number of items Technological needs noted Types of topics receiving attention Spin-off technologies linked Reinhard Haupt, Martin Kloyer
. Framework of TLC analysis. 400 L Gao et al.//Technological forecasting & Social Change 80 (2013) 398 407 in DII by application year for the Application indicator and count the number of patents in DII by priority year for the Priority indicator
We count the respective numbers for each of these two indicators in DII by priority year. 2. 1. 3. Inventor This indicator indicates the amount of human resources invested in R&d of one particular technology.
Number of Inventors has been used as indicator to measure the TLC of RFID 21. We count the number of unique individual inventors of each year by priority year. 2. 1. 4. Citation Two major types of cited references are given in a patent:
Backward citations to science literature indicate a linkage between science and the patented technology. Backward citations to other patents may indicate a linkage between other technologies and the patented technology.
The number of these two kinds of references can be found on the front page of the patent documents.
established by the Strasbourg Agreement 1971, is the most widely used hierarchical classification system of patents based on the different areas of technologies to
The number of IPC codes represents howmany fields are involved in the development of a technology.
Generally, the top 5 or top 10 IPCS represent the main technology subjects. IPC code has been used as an indicator to measure the technology life cycle 26.
We count the number of IPCS 4-digit) in DII by priority year for the IPC indicator;
The technology structure is also different: MC and IPC are used complementary codes in this paper to measure technology subjects.
We count the number of MCS in DII by priority year for the MC indicator;
Table 2 Technology life cycle indicators. No. Indicator Indicator description 1 Application Number of patents in DII by application year 2 Priority Number of patents in DII by priority year 3 Corporate Number
/Technological forecasting & Social Change 80 (2013) 398 407 2. 2. TLC stages of CRT and TFT-LCD It is better to choose a training technology with four TLC stages.
So we choose another similar technology, the Thin Film Transistor Liquid crystal display (TFT-LCD), as the second training technology.
Nano-biosensor (NBS) is chosen as the test technology. We then focus on CRT and TFT-LCD technologies and assess their life cycle stages.
We developed the questionnaires based on the concept of TLC given by Arthur D. Little 9. Ten experts in CRT,
TFT-LCD or display fields were asked to give the time periods of four stages for TFT-LCD and CRT.
We obtained four responses. By discussing with two of the experts who gave similar time periods for CRT
we finally determined the TLC stages of CRT and the stages of TFT-LCD based on one related paper 29.
Table 3 shows the TLC stages of CRT and TFT-LCD as given by the experts and literature. 2. 3. Search query The search terms for each technology are defined simply
We divide NBS technology into two parts: one is related a nano technology and the other is related a biosensor technology.
A query strategy for nanotechnology has been developed by TPAC at the Georgia Institute of technology 30. We refine our search terms for biosensors based on our earlier research 31
and add some keywords related to functions of biosensors, including test (or similar keywords, such as measure*,monitor*)and nucleic acid*(or some other bio-related keywords, such as lactate or cholesterol), and sensor*.
*After combining the nanotechnology search query with the biosensor terms, we obtain 1493 records for NBS.
All the records are downloaded from DII, and Vantagepoint software www. thevantagepoint. com is employed to extract, clean,
we develop a map for 13 indicators of each training technology. Numbers of inventors suggest very interesting changes in different stages.
Since the indicators show different trends in different stages, it might be better to combine all 13 indicators to measure the change of technology rather than using one single indicator.
Table 3 TLC stages of CRT and TFT-LCD. Stage Emerging Growth Maturity Decline Period (year)( CRT) 1897 1929 1930 1972 1973 2000 2001 2020 Period (year)( TFT-LCD
ð11þ Since we have the TLC stages of TFL-LCD and CRT, we can form the label set of training set L la la 1;
f ð12þ li represents TLC stages of TFT-LCD and CRT. For a training point aj O and test point bk, the distance between aj and bk is defined as dist aj;
vuutð13þ Table 4 Cross-correlation analysis for 13 indicators (r=0. 9). TLC stage Emerging Growth Maturity Decline Group 1 1, 2
that is the TLC stage information of NBS. 3. Results and implications for management Table 5 shows the label results for each test point of NBS.
And according to the definition of TLC, in a technology's growth stage, there are pacing technologies with high competitive impact that have not yet been integrated into new products or processes.
That means, some product-related technologies may be commercialised in the future; however, at the moment, these technologies need more work
in order to resolve key problems. The most successful commercial biosensor technology surface plasmon resonance does not have a very good limit of detection (LOD),
the nanoparticle based SPR (or local SPR) can provide excellent LOD. However, the current fabrication technology is expensive 32.
Therefore, the fabrication technology is one of the pacing technologies of NBS. In this stage, a lot of challenging problems must be overcome, such as enhancement of gene array and protein array,
and some new and promising technologies are still under research 33. Technology observers can make their R&d investment decision by using the proposed approach.
The result shows that NBS is in a growth stage. Itmeans that there are many technologies still in development,
including SPR. Technology managers might informtheir NBS R&d investments by analysing patent application data from 1997 to the present to identify hot research topics or technological gaps.
For somenbs related companies that have enough money for R&d, it is a good time to invest in NBS to pursue potentialmarkets. 4. Conclusions How might technology life cycle analysis based on patents contribute to FTA?
This approach to gauge a technology's growth trend provides a more robust projection. However, as mentioned in Section 1,
extrapolative technology trend approaches are not Fig. 5. An example for computing the distance between test point
and training points. 405 L. Gao et al.//Technological forecasting & Social Change 80 (2013) 398 407 definitive projections.
Indeed, explicit analyses of what factors and forces are apt to alter projected developmental trends are worthwhile note Ted Gordon's Trend Impact analysis (TIA) especially 34.
A thoughtful anonymous reviewer reminded us of the wide range of factors that could change a development trajectory,
including new combinations of technologies (existing and/or emerging), and many socioeconomic forces (e g.,, fluctuations in demand, regulations, ethical or environmental concerns.
or delayed impacts on society of introducing new technologies i e.,, technology assessment but that is beyond the scope of this study.
This study is based on patent documents; it adopts 13 indicators that can be quantified to measure the TLC stages of an object technology.
We introduce the nearest neighbour classifier, which is used commonly in pattern recognition and some other fields,
In this study, we take TFT-LCD and CRT as the training technologies and NBS as the test technology.
This means that there are pacing technologies with high competitive impact that have not yet been integrated into new products or processes.
This method can be used not only in NBS but also in other technology fields, since data of the all indicators can be downloaded from most patent databases.
First, only two technologies serve as the training technologies to calculate the similarity feature with the object technology (test technology.
This is due to the lack of ideal training technologies with four TLC stages. So, this study resembles a laboratory test.
we still need to find more technologies and obtain more data to validate the method.
Second, we did not consider the technology type. TFT-LCD and CRT are categorised as single-technology type,
but NBS is a multi-technology: it involves nanotechnology and biotechnology, with diverse application possibilities.
Different types of technologies may have different developing patterns, especially for those technologies close to basic science, such as biotechnology.
Future research should also take this into account. Third, the classifier we used in this paper is the nearest neighbour classifier.
For future study, we will test some other classifiers, such as nearest feature line (NFL) and Bayesian classifier, to assess
How might this TLC estimation method fit in with other FTA techniques? Porter 40 suggested considering the use of multiple FTA METHODS tailored to the type of foresight study.
TLC is intriguing in that it combines aspects of several of those: trend analyses (where it best fits),
More importantly, we suggest that TLC would be complemented by informal and/or formal expert opinion to check the results
and to identify factors apt to alter the course of development that TLC suggests. It is oriented mid-term (i e.,
Acknowledgement 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 no. 0531194) and the Science of Science policy Program Measuring and Tracking Research Knowledge Integration (Georgia Tech;
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Alan Porter is a Professor Emeritus of Industrial & Systems Engineering, and of Public policy, at Georgia Tech, where he remains Co-director of the Technology policy and Assessment Center.
He is also a Director of R&d for Search Technology, Inc.,Norcross, GA. He is the author of some 220 articles and books,
including Tech Mining (Wiley, 2005) and Forecasting and Management of Technology (Wiley, 2011. Jing Wang is an Associate professor of Huaqiao University.
His major is computer science and technology and he mainly focuses on Pattern Recognition, Neurocomputing et al.
He is the author of more than 20 articles. Shu Fang is a Professor and the Director of Chengdu Library of Chinese Academy of Sciences.
Her specialty is science and technology management, particularly the study of technology forecasting and assessment. She is focusing on a research on emerging science and technology topics. 407 L. Gao et al./
/Technological forecasting & Social Change 80 (2013) 398 407
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