years

ART79.pdf

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

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

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

Within the Future-oriented technology analysis (FTA), technology forecasting traces back to the 1950's 4. One of its half dozen

It follows that such projection becomes increasingly precarious as the future horizon is extended beyond a few years.

Technological forecasting & Social Change 80 (2013) 398 407 Corresponding author at: Chengdu Library of the Chinese Academy of Sciences, Chengdu 610041, PR China.

/Technological forecasting & Social Change 80 (2013) 398 407 (test technology) via the nearest neighbour classifier,

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

application year, priority year, and basic year. The basic year has no legal meaning, but only represents the year in

which DII obtained the patent documents. 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

/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.

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:

We count the number of literature citations and the number of patent citations in DII by priority year. 2. 1. 5. IPC (four-digit) The International Patent Classification (IPC) system,

We count the number of IPCS 4-digit) in DII by priority year for the IPC indicator;

count the number of patents among the top 5 IPCS in DII by priority year for the IPC top 5 indicator;

and count the number of patents among the top 10 IPCS in DII by priority year for the IPC top 10 indicator. 2. 1. 6. MCS The Derwent manual code (MC

We count the number of MCS in DII by priority year for the MC indicator;

count the number of patents among the top 5 MCS in DII by priority year for the MC top 5 indicator;

and count the number of patents among the top 10 MCS in DII by priority year for the MC top 10 indicator.

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

of corporates in DII by priority year 4 Non-corporate Number of non-corporates in DII by priority year 5 Inventor Number of inventors in DII by priority year

6 Literature citation Number of backward citations to literatures in DII by priority year 7 Patent citation Number of backward citations to patents in DII by priority year

8 IPC Number of IPCS (4-digit) in DII by priority year 9 IPC top 5 Number of patents of top 5 IPCS in DII by priority

year 10 IPC top 10 Number of patents of top 10 IPCS in DII by priority year 11 MC Number of Manual Codes (MCS) in DII by priority year 12 MC top 5 Number of patents of top 5 MCS

in DII by priority year 13 MC top 10 Number of patents of top 10 MCS in DII by priority year 401 L. Gao et al./

/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.

From the literature, we find that the Cathode Ray Tube (CRT) has been developed for more than 100 years

and is now in the decline stage 27,28. But the patent information in the early years is unavailable (patent data in DII covers 1963 to the present.

So we choose another similar technology, the Thin Film Transistor Liquid crystal display (TFT-LCD), as the second training technology.

but slightly increases in the following years. The number of inventors is less than some other indicators, such as application numbers and priority application numbers in the maturity and decline stages.

and imported into MS Excel 13 rows of indicators, 30 columns (years) for TFT-LCD (from 1978 to 2007), 36 columns (years) for CRT (from 1972 to 2008),

and 24 columns (years) for NBS (from 1985 to 2008. 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

) 1976 1990 1991 2007 2008 402 L. Gao et al.//Technological forecasting & Social Change 80 (2013) 398 407 We propose a normalisation method with two steps to pre-process the original data.

The first step is data smoothing by calculating three-year moving averages. The original data are defined as A A1;

A2: ð1þ Here A1, A2 represent the original data of TFT-LCD and CRT respectively.

A2 h i ð2þ 0 500 1000 1500 2000 2500 3000 3500 1978 1979 1980 1981 1982 1983 1984 1985 1986

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

2007 2008 2009 Application Priority Corporate Non-corporate Inventor Literaturecitation Patentcitation IPC IP-CTOP10 MC MC-TOP5 IP-CTOP5 MC

0 500 1000 1500 2000 2500 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

2006 2007 2008 2009 Application Priority Corporate Non-corporate Inventor Literaturecitation Patentcitation IPC IPC-TOP5 IPC-TOP10 MC MC-TOP5

/Technological forecasting & Social Change 80 (2013) 398 407 A1 i; j ð Þ A1 i;

/Technological forecasting & Social Change 80 (2013) 398 407 For each test point bk, we compute the distance between bk

The label information of the first 12 test points (1985 1996) of NBS can be matched with that in the emerging stage of TFT-LCD

and the label information of the second 12 test points (1997 2008) of NBS can be matched with that in the growth stage of TFT-LCD.

Therefore, NBS is still in its growth stage (1997 to the present. 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.

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.

/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.

, 2 10 years in the future) to provide a more robust sense of likely developmental trajectory than does single variable trend projection.

Online at, http://www. nngroup. com/reports/life cycle of tech. html 1998. Table 5 TLC stages of NBS. 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 lb 1 1

1 1 1 1 1 1 1 1 1 1 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

2007 2008 lb 2 2 2 2 2 2 2 2 2 2 2 2 So the TLC stages estimated for NBS are:

Emerging stage (lb=1: 1985 1996. Growth stage (lb=2: 1997 present. 406 L. Gao et al./

/Technological forecasting & Social Change 80 (2013) 398 407 2 H. X. G. Ming, W. F. Lu, C. F. Zhu, Technology challenges

for product lifecycle management, Technical Report, STR/04/058/SP, Singapore Institute of Manufacturing Technology, 2004.3 T. A. Vijay, Challenges in product strategy

, product planning and technology development for product life cycle, CIRP Ann. Manuf. Technol. 43 (1)( 2008) 157 162.4 J. P. Martino, Technological forecasting for Decision making, 3rd Edition Mcgraw-hill, New york, NY, 1993.5 A t. Roper, S w

. Cunningham, A l. Porter, T. W. Mason, F. A. Rossini J. Banks, Forecasting and Management of Technology, 2nd Edition John Wiley, New york, NY, 2011.6 A l. Porter, M. Rader, Fitting future-oriented technology analysis methods to study

Strategic intelligence for an Innovative economy, Springer, Berllin, 2008, pp. 149 162.7 W. L. Nolte, B c. Kennedy, R. J. Dziegiel, Technology readiness level calculator

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Policy 36 (2007) 387 398.16 X. Zhang, S. Fang, C. Tang, G. H. Xiao, Z. Y. Hu, L. D. Gao

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Inf. 9 (2006) 160 166.18 C. M. Chu, Using technology life cycle to analysis the developing trend of thin-film photovoltaic industry, Ph d. dissertation, National Central

Serv. 11 (2009) 59 63.20 H. L. Yu, Analysis of the particleboard technology based on TRIZ and S-Curve technique evolution law, Forest.

Technol. 34 (4)( 2009) 57 60.21 Y. C. Wu, T. C. Yen, RFID technology innovations:

Policy 29 (2000) 409 434.23 D. Hicks, A. Breitzman, K. Hamilton, F. Narin, Research excellence and patented innovation, Sci.

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. dissertation, National University of Tainan Institutional Repository, Taiwan, 2007.27 C. H. Yeh, A comparative analysis of Taiwan's CRT and TFT-LCD industries based on the viewpoints of industrial

J. Liquid crystals Displays 12 (3)( 1997) 153 160.29 H. J. Lai Study on the technique development of TFT-LCD industry-based on patent analysis and life cycle theory, Ph d. dissertation, Chun Yuan Christian University, Taiwan, 2003.30 A l. Porter, J

Res. 10 (2008) 715 728.31 L. Huang, Z. C. Peng, Y. Guo, A l. Porter, Identifying the emerging roles of nanoparticles in biosensors, J. Bus. Chem

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Nanofluid. 4 (1 2)( 2008) 33 52.33 G. A. Urban, Micro-and nanobiosensors state of the art and trends, Meas.

Technol. 20 (2009) 1 18.34 T. J. Gordon, Trend impact analysis, in: J. C. Glenn, T. J. Gordon (Eds.

Futures research methodology Version 3. 0.,Millennium Project, WFUNA, WASHINGTON DC, 2009, Chapter 8. 35 E t. Popper, B. D. Buskirk, Technology life cycles in industrial markets, Ind.

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Policy 6 (1/2/3)( 2010) 36 45. Lidan Gao is an Associate professor of Chengdu Library of The Chinese Academy of Sciences.

including Tech Mining (Wiley, 2005) and Forecasting and Management of Technology (Wiley, 2011. Jing Wang is an Associate professor of Huaqiao University.

/Technological forecasting & Social Change 80 (2013) 398 407

ART8.pdf

Evolutionary theory of technological change: State-of-the-art and new approaches Tessaleno C. Devezas Technological forecasting and Innovation theory Working group, University of Beira Interior, Covilha, Portugal Received 13 may 2004;

accepted 6 october 2004 Abstract It is well known the fact that the world of technology is full of biological metaphors,

as for instance, evolution, mutation, selection, life cycle, survival of the fittest, etc. One of the most powerful technological forecasting tools, the logistic equation, has its origin in the biological realm

Technological forecasting & Social Change 72 (2005) 1137 1152 promising approaches under way. The fourth part with conclusions closes the article,

D 2005 Elsevier Inc. All rights reserved. Keywords: Technology evolution; Technological change; Complex systems; Universal Darwinism 1. Introductory thoughts The main objective of this seminar concerns the exploitation of the powerful new capabilities provided by the Information technology Era to advance Future-oriented technology analysis (TFA), both product and process.

much in accordance with the perspective envisioned by Harold Linstone in 1999 2, following his optimistic view of a strong, confident technology-driven scenario,

and tools that have grown explosively in recent years related to the biosciences, bioinformatics and evolutionary approaches. Among the needs for TFA envisioned by the TFA Methods Working group we find the questioning about the validity of the analogy between technological evolution and biological evolution (Ref. 1, pp. 299:

In the previously cited 30-year anniversary issue of TF and SC (1999) Bowonder et al. 3 have reviewed briefly this topic,

mainly focusing some of the lessons learned from evolutionary theory involved in anticipating changes in evolutionary trajectories,

whether it T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1138 can ever be achieved.

and Fig. 1. Technological forecasting in perspective presented by Linstone in the 30-year anniversary issue of TF and SC (1999).

T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1139 quality control. Peter Corning 5 has pointed out that complexity in nature

became in last years the most common figures permeating social and natural sciences as well. The metaphorical language provides the means for understanding

the amount of practical work using simulation methods is still a dwarf T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1140 one.

The mathematical tools that began to be employed in economics (as well as in technological forecasting) starting in the 1970s had been developed by mathematical biologists in the 1920s

and 1930s and were known widely. The widespread availability of computers (and of computer literacy) has contributed undoubtedly for the rapid diffusion of the usage of such mathematical tools,

Formalization of evolutionary thinking in biology in algorithmic terms began in 1930 when R. A. Fisher 6 published his opus bthe Genetical Theory of Natural selection,

T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1141 Yet in 1925 the American biologist and demographer Raymond Pearl 8 in his seminal book

It was not until the 1970s that the Volterra Lotka equations have found numerous applications in the world of business

T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1142 3. 2. To point 2:

On the Internet, a Google search yields the following results (April 2004) 2, 800,000 hits for globalization, 6, 600,000 for complexity,

if we substitute the words dgenetic underpinningst by building blocks T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1143 (following John Holland's 14 original

when we compare these contributions with texts published in the 1980s, as for instance the very often cited books of Nelson and Winter 16 and Basalla 17.

T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1144 Such a bridge could be offered by a better-developed danthropology of technique,

& Social Change 72 (2005) 1137 1152 1145 never correctly realized that Darwin in his second

and for more than fifteen years there has been the djournal for Evolutionary Economicst (Springer), devoted particularly to this topic.

as envisioned by Richard Dawkins 21 in his Universal Darwinism in 1983. Hodgson 20 stated that Darwinism provides a compelling ontology

beginning with Donald Campbell 23 in the T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1146 1960s (who coined the term Evolutionary Epistemology to characterize Popper

's epistemology) and conducting to some conceptual breakthroughs like Richard Dawkins'24 memes in the 1970s and more recently Daniel Dennet's 25 Darwin's Dangerous Idea (the idea that all the fruits of evolution,

T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1147 the coevolutionary complexity of managing two inheritance systems (the vertical, genetic,

since the 1950s, is btop-downq in character (so called because it views the system from above, as a whole).

Its origin remounts to the 1970s with the emergence of gaming simulation. Theoretically and methodologically this approach makes possible the construction of models from the level of processes that are immediately and empirically observable, namely the local interactions of single units (agents) governed by local rules.

T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1148 Although a consistent ETTC still not exists

or evolutionary computation, were invented by John Holland 14 in the 1960s and were developed by Holland and his students at the University of Michigan in the 1970s.

In technology and science GAS have been used as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems,

& Social Change 72 (2005) 1137 1152 1149 sophisticated simulations due to the simplicity of its basic assumptions and limitations that must be imposed in the rules governing interactions between agents.

or more generally speaking to genetic programming (a refinement of GAS developed in 1987 by John Koza 38),

They claim to have reproduced din silicot 15 previously patented inventions in the field of electronics (6 of them patented after January 2000)

& Social Change 72 (2005) 1137 1152 1150 recent proposal of this author with George Modelski for a seminar on Globalization as Evolutionary Process 40 to be held in the spring of 2005 in Paris,

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Econ. 12 (2002) 259 281.21 R. Dawkins, Universal Darwinism, in: D. S. Bendall (Ed.),Evolution from Molecules to Men, Cambridge university Press, 1983, pp. 403 425.22 K. Popper, Objective Knowledge:

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2003 February) 52 59.40 T. Devezas, G. Modelski, Globalization as Evolutionary Process: Modeling, Simulating and Forecasting Social Change, a Proposal of a Seminar to the Calouste Gulbenkian Foundation,

to be held in Laxenburg (IIASA), April 2006 (http://www. tfit-wg. ubi. pt/globalization). Tessaleno Devezas is Associate professor with Habilitation in the Faculty of engineering, University of Beira Interior (UBI), P-6200-001 Covilha,

T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1152

ART80.pdf

Adaptive Robust Design under deep uncertainty Caner Hamarat, Jan H. Kwakkel, Erik Pruyt Delft University of Technology policy Analysis Department, PO BOX 5015,2600 GA Delft

Received 14 may 2011 Received in revised form 2 july 2012 Accepted 27 august 2012 Available online 8 november 2012 Developing strategies,

Characteristic for these techniques is that they aim at charting the Technological forecasting & Social Change 80 (2013) 408 418 Corresponding author.

/Technological forecasting & Social Change 80 (2013) 408 418 Fig. 1 shows a framework that operationalizes the high level outline of adaptive policy-making.

/Technological forecasting & Social Change 80 (2013) 408 418 operationalizing the Adaptive Policy-making Framework is structured through workshops 35.

/Technological forecasting & Social Change 80 (2013) 408 418 explicitly considers the opportunities that uncertainties can present.

lifetime of a technology Parametric Varying between 15 and 50 years for different technologies Delay orders of lifetimes Orders of the decommissioning delays Categorical 1st, 3rd, 10th,

and constructing new capacity for a technology Parametric Varying between 1 and 5 years for different technologies Progress ratios Ratio for determining cost reduction due to learning curve Parametric Varying

rate Parametric Randomly fluctuating between-0. 01 and 0. 035 (smoothed concatenation of 10-year random growth values) Investment preference structures Preference criteria

/Technological forecasting & Social Change 80 (2013) 408 418 In order to explore the problem and the uncertainties of energy transitions,

These results show that the fraction of new technologies seems to be concentrated around 60%of total generation capacity by the simulated year 2100,

which means that over the 100 year simulation time, the fraction of new technologies remains below 60%for about half of the runs.

/Technological forecasting & Social Change 80 (2013) 408 418 fraction of new technologies for the no policy ensemble (in blue) and the basic policy ensemble (in green) as well as the KDES of the end states of all

/Technological forecasting & Social Change 80 (2013) 408 418 this signpost. Using this trigger, the corrective action would be to stop investing in Technology 2

Each of the economic growth parameters indicated in the third region corresponds to the value of economic development for ten years

and together they constitute the overall behavior of economic development over 100 years. Although it is difficult to interpret the combination of these economic growth parameters,

a 20%cost reduction of the new technologies is triggered over a period of 10 years. To further address this vulnerability, we also add a hedging action to the basic policy in the form of additional commissioning of Technologies 3 and 4 in their early years.

These actions together aim at making the sustainable technologies more cost efficient once their costs are reasonably affordable levels

and to promote the transition toward new technologies in their early years. The economic action is successful in promoting sustainable technologies

and increasing the total fraction after the first 10 years (around 2020). The adoption of the new technologies in later years is also higher than under the basic policy,

suggesting that these cost reductions are effective. To improve the performance of the adaptive policy even further,

/Technological forecasting & Social Change 80 (2013) 408 418 4. Discussion and implications for Future-oriented technology analysis (FTA) In this paper we proposed an iterative computational approach for designing adaptive policies that are robust

Fig. 5 shows that these actions are effective in the early years, but lose their effect after 2020 due to the time-restricted nature of the hedging action.

However it is not possible to conclude that this reduction in effectiveness is caused only by the nature of the hedging action.

To reveal the underlying mechanism leading to a decline after 2020, it is necessary to identify those runs that improve around 2020 and then collapse.

A modified classification in combination with PRIM could be utilized for such an analysis. This study also has implications for Future-oriented technology analysis (FTA.

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, Monterey, CA, 1984.417 C. Hamarat et al.//Technological forecasting & Social Change 80 (2013) 408 418 Caner Hamarat is a Phd researcher at the Faculty of technology, Policy and Management of Delft University of Technology.

He obtained an MSC degree in Industrial Engineering from Sabanci University. His research interests are exploration and analysis of dynamically complex systems under deep uncertainty.

/Technological forecasting & Social Change 80 (2013) 408 418

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