Technological forecasting & Social Change 72 (2005) 1122 1136 at Sandia has its roots in the LDRD program.
K. W. Boyack, N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1123 IA leads were designed to not only benchmark the visualizations,
N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1124 3. 1. Data collection Two different sets of data were compiled from multiple sources
K. W. Boyack, N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1125 document matrix.
During our meeting with the CIS area leader, we first K. W. Boyack, N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1126 gathered information
K. W. Boyack, N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1127 greatest overlaps with CIS,
K. W. Boyack, N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1128 Another significant outcome of the meeting with the IA leader was his desire
K. W. Boyack, N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1129 extract the hidden relationships within the landscape visualization
N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1130 The first level of analysis identified a macroscale understanding of the overlaps as well as the unique competencies and capabilities that each IA possessed.
N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1131 areas of interest to Sandia since the map indicates that they are well outside our core competency areas.
The area inside the dashed box is explored further in Fig. 7. K. W. Boyack, N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1132 competencies,
K. W. Boyack, N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1133 rolling up all of the IAS to an overall Sandia category.
K. W. Boyack, N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1134 relationships.
K. W. Boyack, N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1135 References 1 K. Bfrner, C. Chen, K
K. W. Boyack, N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1136
This article was downloaded by: University of Bucharest On: 03 december 2014, At: 05:05 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number:
The position of these beacons should be checked regularly in relation to changes in the landscape and in relation to other‘navigators'.
is a partial structural openness that endorses flexibility in responding to the systemic flows, such as changes in the business environment or in the customer's innovation processes.
such as a change in the environment, emerges. In the context of systemic transformation capacities, the generic process of roadmapping is coarsely the following:(
In our ideal model, we have depicted, for example, disruptive futures (phenomena that change the name of the game),
and organisations when responding to system-level changes. First activation of the systemic transformation capacities is useful
and socio-technical change. Minna Halonen is a research scientist at VTT. Her research focusses on foresight and socio-technical change
especially on organisational learning theories, network development, and impact evaluation. She has an MSC in Applied Geography from La Sapienza University of Rome.
Johanna Kohl is a senior scientist and a team leader in Foresight and Socio-Technical change team at VTT.
Nina Wessberg is a senior scientist in Foresight and Socio-Technical change team at VTT. Her current research interests are especially in sustainable energy solutions at the society.
Insights about dynamics and change from sociology and institutional theory. Research policy 33, nos. 6 7: 897 920.
Technological forecasting & Social Change 71, no. 2: 141 59. Kostoff, R. N, . and R. R. Schaller. 2001.
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Technological forecasting & Social Change 76:39 49. Smits, R, . and S. Kuhlmann. 2004. The rise of systemic instruments in innovation policy.
The elements that appear in a TDS depiction change from application to application. For instance, Shi, Porter,
Technological forecasting & Social Change 74, no. 4: 413 32. Huang, L.,Z. C. Peng, Y. Guo,
Technological forecasting & Social Change 72, no. 9: 1094 112. O'Regan, B, . and M. Gratzel. 1991.
Technological forecasting & Social Change, doi: 10.1016/j. techfore. 2011.06.004. Robinson, D. K. R, . and T. Propp. 2008.
Technological forecasting & Social Change 75, no. 4: 517 38. Roper, A t.,S w. Cunningham, A l. Porter, T. W. Mason, F. A. Rossini,
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Keywords Foresight, Combination, Delphi method, Scenario, Innovation, Sustainable development, Forward planning Paper type Case study 1. Introduction The situation surrounding science and technology has undergone a radical change in recent years.
Foresight has changed its role according to these changes: it aims to provide an overview of future impacts on our society in broader contexts.
Their main role was to identify key or emerging technologies, looking into the development of science and technology and the expected changes in society.
considering changes on a global or national scale. Based on the discussion, four global or national challenges were set as the goals of science, technology and innovation.
With the discussion above and the dramatic changes occurring inside and outside Japan as a backdrop,
References Cachia, R.,Compano, R. and Da Costa, O. 2007),‘Grasping the potential of online social networks for foresight'',Technological forecasting and Social Change, Vol. 74 No. 8
an efficient,‘round-less',almost real time Delphi method'',Technological forecasting and Social Change, Vol. 73, pp. 321-33.
and how the necessary changes can be captured and measured in a management framework. They also discuss the contributions foresight can make to the management system at different stages of development (cf.
Strategic dialogues need to be flexible enough to cope with this kind of change Organizational learning In such models,
and their action in anticipation of potential research policy changes triggered by the dialogue (such as new funding programs) This can be a problem
Cagnin, C. and Loveridge, D. 2011),‘A business framework for building anticipatory capacity to manage disruptive and transformative change and lead business networks towards sustainable development,
theories and tools'',Global Environmental change, Vol. 16, pp. 170-81. Corresponding author Frauke Lohr can be contacted at:
Foresight and‘‘grand challenges''within research and innovation policies Martin Rhisiart Abstract Purpose The paper seeks to discuss how foresight is used to understand the implications of global changes for research and innovation policies.
and innovation system to assess the implications of new technologies and wider socioeconomic changes (Martin and Johnston, 1999;
which combined analysis of global changes with a participatory process involving national stakeholders. The exercise was designed to assess the implications of global changes for research.
A relatively novel aspect which evolved during the course of the exercise, was the focus on translating future-oriented knowledge (from drivers and trends) into grand challenges for the national research and innovation system.
and support in RTDI towards addressing grand challenges in areas such as energy, resources, demographic change, health and security. 3. Irish foresight project on global drivers and their implications for research and innovation:
5. increasing pace of change; and 6. energy security. Figure 1 shows the distribution of drivers and trends on an impact versus likelihood matrix.
growth Energy security Renewable energy Renewable energy Peak oil Global trade falters Converging technologies Increasing pace of change Increasing pace of change Open innovation
energy, food and demographic changes. The Lund Declaration and other initiatives have provided high-level impetus for actors in the research
and innovation communities to consider the impacts of changes in conditions, resources and other factors over different time horizons.
Experiences in Britain, Australia and New zealand'',Technological forecasting and Social Change, Vol. 60 No. 1, pp. 37-54.
a contextualist analysis and discussion'',Technological forecasting and Social Change, Vol. 74 No. 8, pp. 1374-93.
Russia also needs to implement relevant institutional changes, and Table III The interrelation of the projects Research focus Main results Questions for further investigation FS1 The evaluation of topics by the following criteria:
. Curran, C. S. and Leker, J. 2011),‘Patent indicators for monitoring convergence examples from NFF and ICT'',Technological forecasting and Social Change, Vol. 78, pp. 256-73.
resurrection and new paradigms'',Technological forecasting and Social Change, Vol. 60 No. 1, pp. 85-94. Hax, A. and Majluf, N. 1996), The Strategy Concept and Process:
implications for technological forecasting'',Technological forecasting and Social Change, Vol. 60 No. 3, pp. 237-45. Markides, C. and Williamson, P. 1994),‘Related diversificaton, core competences and corporate performance'',Strategic management Journal, Vol. 15, pp. 149-57.
a re-evaluation of research and theory'',Technological forecasting and Social Change, Vol. 39, pp. 235-51.
Shih, M. J.,Liu, D. R. and Hsu, M. L. 2010),‘Discovering competitive intelligence by mining changes in patent trends'',Expert Systems with Applications, Vol. 37
Turoff, M. 1970),‘The design of a policy Delphi'',Technological forecasting and Social Change, Vol. 2, pp. 149-71.
Received 14 may 2011 Accepted 18 september 2012 Available online 28 november 2012 This paper reflects on the potential of future-oriented analysis (FTA) to address major change
Dealing with disruptive changes and grand challenges in particular therefore, raises several conceptual, methodological and operational issues. Two of them are general,
FTA practices Fundamental change and transformations Grand challenges 1. Introduction Drawing upon a critical reflection on the selected papers for this special issue as well as on the discussions that took place at the fourth Seville International Conference on Future-oriented technology analysis,
this paper discusses the potential of future-oriented analysis (FTA) to address major change and to support decision-makers
Technological forecasting & Social Change 80 (2013) 379 385 Corresponding author at: Center for Strategic Studies andmanagement (CGEE), SCNQD 2, Bl.
Sciencedirect Technological forecasting & Social Change In this context, when analysing the potential of future-oriented technology analysis (FTA) to assist societies, decision-makers and businesses to tackle fundamental, disruptive transformations, in general,
and grand societal challenges, in particular, it is important to understand the very nature of change.
adapt and respond pro-actively to change. FTA has a potentially useful role to play in enabling a better understanding of complex situations and in defining effective policy responses
and policy discussions on tackling these major changes, as well as research and innovation agendas to support these dialogues and policy discussions.
but also to their adjustment, adaptability and ability to shape responses to fundamental changes. At the same time, FTA can contribute to building‘change'capacities that allow organisations to become capable of anticipating
and addressing continuous as well as disruptive change, and thus more adaptive or setting new trends and/or developing new modes of operation.
This can be achieved through regular FTA ACTIVITIES, assisting networking and co-operation within and across organisations,
/Technological forecasting & Social Change 80 (2013) 379 385 3. Combining quantitative and qualitative approaches FTA is an umbrella term to denote several decision-preparatory tools (technology foresight,
For example, simulation models can explore the repercussions of changes in major (external) parameters, as well as the outcome of policy options and other actions.
Forecasting e g. demographic or environmental changes is also highly relevant for certain policy needs or to enable technology observers to determine the current life cycle stage of a particular technology of interest
/Technological forecasting & Social Change 80 (2013) 379 385 are by nature complex and largely impervious to top-down rational planning approaches.
and accept recommendations possibly leading to fundamental changes e g. in terms of a radical redistribution of decision-making power?
inductive approach, visual inspiration, assessment of coverage of dimensions of change, and prolonged divergence. Finally, Georghiou and Harper 3 set the scene against which change is considered
and show the landscape that has formed the demand and influenced the practice of FTA to show that alignment of approaches, consideration of users'perspectives and divergence,
/Technological forecasting & Social Change 80 (2013) 379 385 In more detail, Haegeman et al. 4 depart from the methodological debate that has been a relevant element of the International Seville Conference series on Future-oriented technology analysis (FTA
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.
/Technological forecasting & Social Change 80 (2013) 379 385 more experimental approaches to creating new solutions
i) capture of indications for extrasysttemi change at a micro level instead of extrapolating seemingly dominant macro-trends, ii) mobilisation of tacit knowledge as well as support a creative spirit and an easy exchange of ideas among diverse stakeholders through
iii) rigorous assessment of coverage of dimensions of change to take into account possibly unrecognised/hidden structural changes,
and discontinuity much emphasised by grand challenges, transformations and disruptive changes that claim for adaptation and alignment as coping strategies.
which change is considered in the domain of innovation policy and in investigator-driven research they show the landscape that has formed the demand
/Technological forecasting & Social Change 80 (2013) 379 385 practice and assist in considering transformations that are going to take us closer to anticipating disruptive innovations and events.
/Technological forecasting & Social Change 80 (2013) 379 385
Quantitative and qualitative approaches in Future-oriented technology analysis (FTA: From combination to integration? Karel Haegeman a,, Elisabetta Marinelli b, Fabiana Scapolo c, Andrea Ricci d, Alexander Sokolov e a European commission, JRC-IPTS, Edificio Expo WTC, C/Inca
& Social Change 80 (2013) 386 397 The views expressed are purely those of the authors
Sciencedirect Technological forecasting & Social Change mathematical tools. A participatory method, regardless of the qualitative or quantitative data it uses, is one in
and that emerging changes in the socioeconomic and technological landscapes need to be taken into account.
/Technological forecasting & Social Change 80 (2013) 386 397 qualitative) as an imaginative projection of current knowledge in which formal methods and techniques play a subsidiary role (p. 753.
/Technological forecasting & Social Change 80 (2013) 386 397 identification of emerging clusters analysing citations and keywords for a particular technology field,
forecasting radical changes and thus limiting the scale of failures (with a focus on market pull vis-à-vis the technology push approach).
/Technological forecasting & Social Change 80 (2013) 386 397 Other tools and disciplines that can serve as interface to facilitate the use of qualitative and quantitative approaches and data Social network analysis:
linking ecosystem change and human well-being by combining qualitative storyline development and quantitative modelling through several iterations between both parts 44.
/Technological forecasting & Social Change 80 (2013) 386 397 are brought not always together in the analysis 62 and qualitative and quantitative tasks are carried out by different teams,
/Technological forecasting & Social Change 80 (2013) 386 397 Finally, in this debate, there is a tendency to equate qualitative with participatory.
/Technological forecasting & Social Change 80 (2013) 386 397 sciences, Cameron 71 developed the Five Ps Framework, 13 which provides a mixed-methods starter kit,
/Technological forecasting & Social Change 80 (2013) 386 397 reasonable representation of the systems being analysed, and that the intrinsic uncertainties associated with such representation are documented at best. 5. 2. 2. Lack of trust One aspect of trust is that it derives from perceived credibility,
/Technological forecasting & Social Change 80 (2013) 386 397 identification of the features that may help the organisers of FTA projects in the selection of the most appropriate set of tools (characterising
and shaping structural and systemic changes, Technol. Anal. Strateg. Manage. 24 (8)( 2012) 729 734.22 A. Eerola,
Change 76 (2009) 1037 1050.29 N. Agami, M. Saleh, H. El-Shishiny, A fuzzy logic based trend impact analysis method, Technol.
Change 77 (2010) 1051 1060.30 E. Kemp-Benedict, Converting qualitative assessments to quantitative assumptions: Bayes'rule and the pundits wager, Technol.
Change 77 (2010) 167 171.31 B. P. Bryant, R. J. Lempert, Thinking inside the box:
Change 77 (2010) 34 49.395 K. Haegeman et al.//Technological forecasting & Social Change 80 (2013) 386 397 32 D. Rossetti di Valdalbero, The Power of Science economic research and European decision-making:
the case of energy and environment policies, Peter Lang, 2010. ISBN 978-90-5201-586-6 pb. 33 N. Shibata, Y. Kajikawa, Y. Takeda,
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.
Change 77 (2010) 1037 1050.36 R. Popper, Foresight methodology, in: L. Georghiou, J. C. Harper, M. Keenan,
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.
Change 77 (2010) 466 478.39 K. Haegeman, C. Cagnin, T. Könnölä, D. Collins, Web 2. 0 foresight for innovation policy:
Recipes for Systemic change, Helsinki Design Lab Powered by Sitra, 2010, Available at: http://helsinkidesignlab. org/peoplepods/themes/hdl/downloads/In studio-Recipes for systemic change. pdf. Last accessed July 2012.44 J. Alcamo, D. van Vuuren, C. Ringler
/Technological forecasting & Social Change 80 (2013) 386 397 Fabiana Scapolo holds a Phd on foresight methodologies and practices from the Manchester University (UK).
/Technological forecasting & Social Change 80 (2013) 386 397
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
Technological forecasting & Social Change 80 (2013) 398 407 Corresponding author at: Chengdu Library of the Chinese Academy of Sciences, Chengdu 610041, PR China.
& Social Change Another important predecessor approach upon which we draw is the identification of Technology Readiness Levels (TRLS).
These papers studied the indicators that would have different performance based on the changes of technology.
/Technological forecasting & Social Change 80 (2013) 398 407 (test technology) via the nearest neighbour classifier,
/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
/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.
Numbers of inventors suggest very interesting changes in different stages. Fig. 3, which presents the emerging and growth stages, shows that the number of inventors is typically higher than that of all other indicators.
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.
/Technological forecasting & Social Change 80 (2013) 398 407 We propose a normalisation method with two steps to pre-process the original data.
/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
/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.
/Technological forecasting & Social Change 80 (2013) 398 407 2 H. X. G. Ming, W. F. Lu, C. F. Zhu, Technology challenges
Change 56 (1997) 25 47.15 R. Haupt, M. Kloyer, M. Lange, Patent indicators for the technology life cycle development, Res.
/Technological forecasting & Social Change 80 (2013) 398 407
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,
After some introductory thoughts in the first part, it is tried in the second part to summarize in five points some of the still missing pieces to complete the puzzle to developing a firmly based Evolutionary theory of technological change (ETTC.
Technological forecasting & Social Change 72 (2005) 1137 1152 promising approaches under way. The fourth part with conclusions closes the article,
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.
T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1139 quality control. Peter Corning 5 has pointed out that complexity in nature
These aspects may consist in some of the missing pieces to complete the puzzle of a firmly based evolutionary theory of technological change (ETTC for short),
The development of a working ETTC bears the correct understanding of three difficult-to-define concepts,
This barrier must be overcome to constructing a working ETTC. It does not make sense to develop an ETTC starting from the analogies
and/or disanalogies found between biological and techno-cultural evolution, or in other words, between the evolution of organisms and artifacts.
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.
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
T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1142 3. 2. To point 2:
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
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,
In the points below I try to resume some important aspects that were considered never consistently in the attempts of model building of an ETTC:
With this short collection of ideas I wish to suggest that a firmly conceptually based danthropology of techniquet is still lacking in the current attempts of model building and formal theorizing of an ETTC.
& Social Change 72 (2005) 1137 1152 1145 never correctly realized that Darwin in his second
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
or in other words, the basic process of Gene Culture Coevolution, which is the most appropriate approach to develop a firmly based ETTC.
T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1147 the coevolutionary complexity of managing two inheritance systems (the vertical, genetic,
and their dynamics (behavior over time) is defined via the change of their organization (or dstatet) as described by the system's differential equations.
T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1148 Although a consistent ETTC still not exists
David Goldberg 37, for instance, for studying the connection between the two basic processes of innovation, continual improvement and discontinuous change.
& 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.
and genetic programming as the most promising candidate for establishing the knowledge basis of a working Evolutionary theory of technological change,
As a first step toward a research agenda for future development of TFA I propose the realization of an international seminar in this field (Evolutionary theory of technological change) bringing together specialists in evolutionary model building and digital Darwinism to discuss the existing approaches
& 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,
Change 71 (2004) 287 303.2 H. A. Linstone, TF and SC: 1969 1999, Technol. Forecast.
Change 62 (1999) 1 8. 3 Bowonder, et al. Predicting the future: lessons from evolutionary theory, Technol.
Change 62 (1999) 51 62.4 T. L. Brown, Making Truth: The Roles of Metaphors in Science, University of Illinois Press, 2003.5 P. Corning, Nature's Magic:
Change 18 (1980) 257 282.11 T. Modis, Predictions: Society's Telltale Signature Reveals the Past and Forecasts the Future, Simon and Scuster, New york, 1992.12 T. Devezas, J. Corredine, The biological determinants of long-wave behavior in socioeconomic
Change 68 (2001) 1 57.13 H. De vries, Species and Varieties, Their Varieties by Mutations, Kegan Paul, Trench Trubner,
An Evolutionary theory of Economic Change, Beknap of Harvard university Press, Boston, 1982.17 G. Baslalla, The Evolution of Technology, Cambridge university Press, 1988.18 H. Sachsse, Anthropologie der Technik
Change 70 (2003) 819 859.26 S. Kauffman, At home in the Universe, Oxford university Press, New york, 1995.27 D. Strumsky, L. Lobo,
Change 68 (2001) 293 308. T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1151 34 J. Goldenberg, B. Libai, Y. Louzoun, D. Mazursky
, S. Solomon, Inevitably reborn: the reawakening of extinct innovations, Technol. Forecast. Soc. Change 71 (2004) 881 896.35 G. Silverberg, B. Verspagen, A percolation model of innovation in complex technology spaces, J. Econ.
Dyn. Control 29 (2005) 225 244.36 Z. Michalewicz, D. B. Vogel, How to Solve it:
Change 64 (2000) 7 12.38 J. Koza, Genetic Programming: On the Programming of Computers by Means of Natural selection, MIT Press, Boston, Ma, 1992.39 J. Koza, et al.
Modeling, Simulating and Forecasting Social Change, a Proposal of a Seminar to the Calouste Gulbenkian Foundation,
T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1152
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