technologies

ART16.pdf

which technologies are advancing the new systems, structures and capabilities that will take us forward to 2020,2035 and 2050?

FTA provides a common umbrella for the foresight, forecasting and technology assessment communities. These closely related communities play an important role in guiding policy

The challenge of joining forces to develop more robust future-oriented support to decision making has been addressed in the series of International Seville FTA Conference organized by the Institute of Prospective Technological Studies, one of the Joint research Centers

and provoke the traditional complacency of policy makers who tend to treat technology as an externality,

The essence of Cunningham's model is that the application of hierarchical random graphs of technology characteristics to questions as complex as:

enables a probability model to be constructed that anticipates novel combinations of technologies. Using this model he identified a range of technology changes associated with new standards for accessible internet applications within 100days of their emergence and without prior reference to the individual technology morphologies pathways progression.

Imagining the prospects if this technique can be developed more widely conjures up exciting possibilities for the anticipation of future innovation system developments.

another Finnish team, bring this novel focus on tools further into the interface with policy approaches in their timely paper on the Role of Technology barometer in Assessing Past and Future development of National Innovation system.

and thus the capacity to know one's own technological position relative to others represents a new FTA capability with real world predictive performance capacity.

the Technology barometer can be regarded as a new tool for managing strategic investments in R&d, as well as in other areas such as new skills acquisition and patents management etc.

The existence of the Technology barometer is itself a provocative approach to innovation policy futures. From Germany

A Case for Critical systems Thinking in Nanotechnology; examines how vitally important the foresight objective of inclusiveness in the embracement of diverse stakeholders is for the credibility of an innovation process.

Using contemporary examples associated with the challenges of nanotechnology, they develop the case for ensuring that foresight offers a democratic rather than just a technocratic input to the future and to the policy processes

and advance technology in ways that are responsive to society's needs and concerns through the definition of problems and boundaries that must be respected.

An application to prospecting futures of the responsible development of nanotechnology, a research project exploring potential co-evolutions of nanotechnology and governance arrangements.

This involved the inclusion of pre-engagement analysis of potential co-evolutions in the form of scenarios into interactive workshop activities, with the aim of enabling multi-stakeholder anticipation of the complexities of co-evolution.

In addition to the key papers, the technical note of Greg Tegart on Energy and nanotechnologies: Priority areas for Australia's future features an excellent case example of the importance and learning being experienced from the application of novel FTA METHODOLOGIES to explore the possibilities offered by the use of nanotechnologies to contribute to new and improved approaches to energy conversion,

storage and distribution in Australia. 1136 Technological forecasting & Social Change 76 (2009) 1135 1137 We conclude with the observation of Scott Cunningham

Therefore new techniques are needed for analyzing technology architecture. This implies a renewed dedication to alternative exploratory modeling,

Totti Könnölä is a research fellow at the Institute for Prospective Technological Studies of the Joint research Centre in the European commission.

He is also an adjunct professor of operations and technology management at the IE Business school. He has supported coordinated

He holds a Dr. Tech. and a Lic. Tech. in systems analysis from the Helsinki University of Technology and MSC in environmental economics from the University of Helsinki.

Jack E. Smith is Senior Advisor Foresight and Innovation strategy, Defence R&d Canada, and Chair of the Foresight Synergy Network of Canada.

She was the leader of the VTT Technology foresight and Technology assessment in 1999 2008 being also the Deputy Technology manager of the knowledge center since 2007.

She holds a Phd from the Helsinki Swedish School of economics and Business administration. Her Lic. Tech. and M. Sc. degrees are from Helsinki University of Technology.

Totti Könnölä1 Institute for Prospective Technological Studies (IPTS), JRC-European commission, Edificio Expo, C/Inca Garcilaso, 3, E-41092 Seville, Spain Corresponding author.

E-mail address: totti. konnola@ec. europa. eu. Jack Smith Defence RD, Ottawa, Canada Annele Eerola VTT Technical research Centre of Finland, Espoo, Finland 1

ART17.pdf

Analysis for radical design Scott W. Cunningham Policy analysis Section, Faculty of technology, Policy and Management, Delft University of Technology, Postbus 5015,2600 GA Delft, The netherlands a r

and analyzing systemic change in technology. Technological changes increasingly stem from the novel recombination of existing technologies. Changes are multitudinous.

Therefore, new techniques are needed for analyzing technology architecture. A literature review of related work in the field of technology opportunities analysis is presented.

We consider a possible, radically decentralized context for the conduct of future design. A case study of new technology architecture in the information technology domain is presented.

An analytical method involving mining weighted graphs from technology archives is presented. The role of this new method in a context of distributed decision-making and design is presented. 2009 Elsevier Inc. All rights reserved.

Keywords: Hierarchical random graphs Architectural innovation Technology forecasting Design 1. Introduction This paper examines a technique suitable for monitoring

and analyzing systemic change in technology. Technological changes increasingly stem from the novel recombination of existing technologies. Therefore, new techniques are needed for analyzing technology architecture.

The work is significant because the analysis of a very significant feature of technological change the recombination of existing components is not being supported by most technology opportunity analysis techniques.

A literature review of related work in the field of technology opportunities analysis is presented. A case study of new technology architecture in the information technology domain is presented.

An analytical method involving mining weighted graphs from technology archives is presented. The role of this new method in a context of distributed decision-making and design is presented.

A prospective analysis of new technology fundamentally hinges on the concept of novelty. It is the newest and most novel of technologies which presents the greatest challenges for technological forecasting.

Fundamental uncertainty surrounds the exploitation and development of new technologies. Much has been made about the convergence of new technologies, particularly in the information,

biotechnology and material sectors 1. The forces impelling convergence at the time are seen as radical, revolutionary,

and deeply uncertain. One recent study, for instance, investigated uncertainty and the emergence of dominant designs in aircraft 2. While in retrospect

the design seemed assured, the actual choices at the time appeared divergent and highly contested.

One approach to the management of technological uncertainty has been to initiate the technological forecasting process only once a dominant design has emerged 3. Once a dominant design has been selected,

uncertainty is reduced fundamentally; processes of organizational and sectoral learning then assist in securing a niche for the new technology.

Trend extrapolation approaches, for instance, are based on tracking the emergence of new technologies only once a dominant design is secured 4. This solution of tracking dominant designs neglects some of the fundamental uncertainty associated with technological evolution.

Deep uncertainty characterizes many domains of decision-making in science and technology. In particular, under deep uncertainty, there is little agreement or consensus about system structure.

Thus, exploratory modeling is used to explore Technological forecasting & Social Change 76 (2009) 1138 1149 E-mail address:

10.1016/j. techfore. 2009.07.014 Contents lists available at Sciencedirect Technological forecasting & Social Change alternative views of the future,

seeking decisions robust under a variety conflicting forces 5. Uncertainty in new design arises in at least two areas 6. Technological design is an inherently uncertain process

which is therefore subject to epistemic uncertainty 7. Technological design entails the recombination of components in new and often unexpected fashion.

New techniques for managing the fundamental uncertainty in technological design and evolution are needed therefore. Previous work has provided technology analysts with a set of techniques for both integrating and decomposing new technologies.

Relevant research has approached the problem of forecasting radical technological change with methods for supporting analysis for both decomposition and integration of new technologies.

For the decomposition of technologies, morphological analysis has long been practiced as a technique for recognizing component technologies.

Patent studies have used TRIZ to investigate the character of innovative activity 8, 9. Integrative methods also allow for the anticipation of converging technology.

Swanson explored knowledge discovery by exploring database links 10. Swanson demonstrates integrative capability by demonstrating new links between technologies, inherent in the data,

which were not readily apparent to the respective scientific communities. In this paper we examine techniques for exploring emergent structures or architectures of technology.

Consider a knowledge base of technology where components of technologies are described and linkages between the technologies are identified.

This description of a knowledge base describes many repositories of scientific and technological information, including: the Internet, science and technology databases, patent databases, newswires,

and potentially also newsgroups or other online collaborative environments. This work is similar in spirit to that of Swanson

and Smalheiser reviewed earlier 10. It extends and elaborates upon the procedures described by these authors for discovering new linkages of knowledge through use of a structured representation of science and technology,

which facilitates finding the integrative terms responsible for novel recombination of component technologies. In addition, the paper outlines several important caveat about the use of these models in forecasting new technology:

the model lacks a model of the actor; full validation of the model requires a longitudinal analysis;

missing links may signal poor quality source materials; and content scoring remains a subjective process. 2. Application to distributed design environments Our purpose in exploring this topic is to better consider the information needs of designers.

Designers may soon be positioned in a new and radically decentralized environment. In this section, the paper explicates the social and technological organizational structures which may permit a new era of open innovation.

Chesborough 11 describes a new paradigm of open innovation involving the design of technological systems which, in technological requirements, transcends the boundaries of a single firm.

Likewise, in terms of knowledge production, researchers form multi-disciplinary teams devoted to specific problems and specific contexts 12.

These authors go further: rather than simply describing a new and distributed environment, they prescribe the manner in

which innovative organizations can create an open and porous environment by which to participate in this anticipated new mode of innovation.

Such networks require special technological and infrastructural capabilities to succeed in this emerging environment. In the following paragraphs some ideas about the organization of technological knowledge is described;

this knowledge is coupled with the institutional environment of distributed knowledge production. This review suggests an important avenue for research in this article, and in future research:

creating software solutions to help innovative organizations develop new technologies within an open innovation environment. Knowledge is structured hierarchically.

Hierarchies are one form of technological structure confirmed by theories and practice. A hierarchy is one structure of many that have been used for technological integration 14.

The International Institute for Applied Systems analysis (IIASA) and others have examined the hierarchical embedding of infrastructure systems

Clark 16 considers how market forces shape technological hierarchies, and how such hierarchies in turn shape the market

Thus, there is a rich basis of theoretical support for structuring technological component data in a hierarchical format.

the nodes are technologies, and the edges are the component relationships that are present between the respective technologies.

The challenge of the technology analyst is to usefully structure this information to anticipate change. The technology designer has a similar challenge in exploring new, heretofore unforeseen, combinations of new technologies.

The network data in the raw is not useful for this purpose. A structured representation of technology is needed for multiple reasons:

interpretation, theory, robustness and also the production of actionable results. An unstructured network contains many parameters,

which are hard to visualize and interpret. The technology analyst requires structured information, conforming to theories about the organization of science and technology.

Without a theory of the data the technology analyst cannot distinguish between meaningful structure and possibly accidental corruption of the knowledge base.

Therefore, without a generative model of the data, the interpretation of the data may not be robust.

The technology analyst needs to anticipate change. A structured representation of the data provides a principled account of where technological change is most likely to occur. 1139 S w. Cunningham/Technological forecasting & Social Change 76 (2009) 1138 1149 The article

which follows argues that there is a sizeable amount of open source information which is shared between distributed communities of designers and researchers.

This knowledge is stored in databases of science and technology. New future-oriented technology analysis techniques, such as the approach suggested here,

may contribute to the process and management of radical innovation 17,18. Radical innovation establishes a new dominant design,

and thereby creating a new set of design concepts and a new configuration of components technologies.

The express purpose of these science and technology databases is to research specific existing technologies, and yet the biggest promise of these sources of information may be the diffuse and distributed information they contain about the current state of the knowledge of the community.

but decentralized, knowledge of science and technology is something which can be tested through the use of machine learning techniques.

These concepts are explored further in this paper with a case exploring a software technology known as AJAX.

If the routine process of exploring for new technological components can be automated, then the designer will be free to spend more time at value-enhancing activities.

that many users of technology roadmapping exist in a vertically integrated environment where a few big players have the interests and capabilities to assist in technological coordination.

The description of distributed design in this article is, perhaps, somewhat at odds with the stated premise of technology roadmapping.

analysts should avail themselves of a wide variety of techniques appropriate for the task at hand. 3. Methodology In the following section we develop an analytical method for the representation of emerging technologies in the form of a hierarchical graph.

D). In this technology analysis application these nodes represent four component technologies of a system. These four nodes may be connected in sixtyfoou possible networks,

representing various combinations of the component technologies. Rather than a full enumeration of links, any observed network of these four component technologies can be represented compactly by introducing three parent nodes, each with their own associated probabilities of linkage.

These parent nodes which cannot be observed directly in the data, represent morphological principles actively at work in structuring the data.

& Social Change 76 (2009) 1138 1149 4. Results In this results section we apply the methodology described in the previous section to a specific system of new technologies.

Our previous example of four technologies will be expanded considerably to the analysis of forty-one technologies within an information technology design context.

Ajax is not so much a single technology; rather it is an architecture of technological components.

Ajax is therefore a particularly good test bed to test new techniques for anticipating architectural innovation. Recognition of this architecture grew only once the component technologies were given the Ajax name 26.

In this regard of being a novel recombination of source technologies Ajax is not unlike many other modern technologies.

Other examples, drawn just from the information technology sector, include grid computing, the ipod and iphone, virtualization and LAMPP. 4. 1. Data collection and comparative analysis For the case study we collect data about Ajax and component technologies from the Internet.

We use Wikipedia as a test-bed by mining a series of pages and hyperlinks starting from a seed page.

At this disambiguation page AJAX the computing technology has been distinguished from other meanings of the word Ajax.

Many of these nodes in this expanded network are now very remote in content from Internet technologies.

Fitting the data The component technologies of Ajax may be represented in hierarchical random graph form. We apply the Monte carlo simulation procedure of Clauset 21 to fit the 41 pages within one hop of Ajax (Programming) into a hierarchical random graph.

along with general purpose technologies such as web servers, Javascript and object-oriented computer science. The hierarchical random graph also productively groups related technologies such as mobile phones and personal digital assistants.

Another example of effective groupings of technology induced by the network structure is the combination of search engines and web crawler.

At the lowest leaves of the tree are functional groupings of technologies including: scripting languages (Activex, Java Applets and Visual basic Script), document models (DOM and XHTML), alternative implementations of Ajax (using JSON and IFRAME),

and web application technologies (including key phrases of rich Internet applications, style sheets, web applications). The Monte carlo simulation procedure evaluated a range of competing solutions to the model.

There are many common features shared among the set of best solutions of the algorithm. It is useful to examine a consensus diagram showing the major shared features shown across multiple solutions.

The consensus diagram makes it clear that there are two sets of technologies technologies which are external

and technologies which may be considered internal to the Ajax paradigm. The internal technologies constitute a hierarchy in Fig. 6,

while external technologies do not reveal much hierarchical structure, at least in this sample of the data. One challenge to classification revealed by Figs. 5 and 6 is the placement of the various web browsers.

Consider that Safari, Internet explorer, Internet explorer 5, and Mozilla all various kinds of Internet browsers are placed in different locations in the hierarchical random graph.

On the face of it, these related technologies should probably be grouped together. However, this is not an artifact of the algorithm

It may be that the various browsers become closely associated with specific innovations in media technology. 4. 3. Interpreting the results Fig. 6 does not label the parent nodes with probabilities because of graphic visibility concerns.

Nonetheless, the principal virtue of this hierarchical graph approach is the ability to use this probability model to anticipate novel combinations of technologies.

which seem logical given the induced structure of the technological system but which have not yet been realized.

As noted earlier the parent probabilities provide an explicit hypothesis about the nature of technological linkages.

but not yet observed in the Wikipedia knowledge base are shown below in Table 2. Fig. 6. Consensus diagram for Ajax Technologies. 1145 S w. Cunningham/Technological forecasting

These technology linkages are associated with a 70%likelihood. Some additional research into the first item reveals that there are indeed developments here:

and manipulate these web components through a range of alternative assistive technologies. An example of an assistive technology is screen readers;

screen readers convert plain text HTML messages to audible speech or Braille output. This new standard for rich Internet applications was incorporated in a recent beta version of Internet explorer 8. 5. Policy impacts These developments in ARIA are less than a year old at the time this paper was written the W3c posted a working draft

In summary, the hierarchical random graph did seem to anticipate new technological changes in the area of new standards for accessible rich Internet applications.

It recognized these new changes without explicit linkages in the knowledge base of technologies. Thus, the hierarchical random graph approach may provide a new forecasting, analysis and design technique for architectural innovation.

We have argued in this paper that many previous technology forecasting techniques have focused only on incremental and dominant designs.

high costs, high uncertainty, technological inexperience, business inexperience, lengthy time to market, and the general destruction of firm competence 17,29, 30.

and technology databases using techniques such as hierarchical random graphs Knowledge claims are heterogenous in character Derrida 32 Networks built upon science

and technology databases are very heterogeneous in character Technologists have a wealth of tacit knowledge, built upon practice,

which they struggle to encode within the network of scientific progress Polanyi 35 Changes in technology in this case are manifested in changes in network structure Knowledge is built upon the configuration of knowledge claims,

and not associated with single individuals Table 2 The most likely new combinations of technology predicted by the graph.

Common understanding of technological architecture as provided by machine learning models and delivered by decision support systems, may contribute to an open innovation paradigm where firms work together as part of an extended technological network 11.

A final note from these results might be directed to assisting innovation theorists. This technological network clearly demonstrated technologies internal and external to the core technology network.

The disassortative character of this network means that architectural innovation is much likely to occur from external technologies.

Other technological architectures may be very different: these might be assortative networks which favor the use of technologies which are internal,

and therefore already present within the system. The author suggests that the original conception of architectural change,

as specified by Henderson and Clark 29, is predicated on external sources of innovation. Certainly, this is the class of technological innovations

and case studies. 6. Interpretations from the philosophy and sociology of science The hierarchical random graph is one possible model of science, technology and innovation data.

The role of scientists, engineers, and innovators is to enhance the coherence of this network.

Derrida's ideas informed Callon and others who developed actor network theory as a vehicle for research in science and technology 33.

Knowledge about science and technology may come in two forms. Explicit knowledge entails knowledge about specific claims.

the existence of the claim can be verified through recourse to knowledge bases of science and technology.

Polanyi's account of science and technology has technologists laboring at the interface of claims,

and technology information, is likely to be material as well as semiotic in character. 1147 S w. Cunningham/Technological forecasting

The alternative approach would be to expressly encode the configuration within the database of science and technology.

a hierarchical random graph, is a useful way for structuring diffuse knowledge bases of science and technology.

as well as presaging a significant reorganization of the science and technology database to better match technological progress.

of which may be monitored by means of science and technology databases. This technique (and other techniques like it) may see application in open innovation,

and possible future research which might be performed to strengthen the method for technology analysis. The hierarchical random graph approach structured evidence of an existing technological network

recognizing the development of new technological linkages shortly after they actually occurred in the market.

Even better would have been to anticipate change before it occurs, rather than recognize change shortly after.

Nonetheless revealing undocumented links still provides a useful stimulus for technology monitoring efforts. The procedure proposed in this paper provides an objective method of predicting new technological linkages.

It remains subjective in two regards: the character of nodes, and the interpretation of new links.

For instance, the nodes used in this study ranged from specific technologies, to people (Jesse James Garrett), to institutions (W3c.

While a mix of node types might be desirable (for instance technology as well as process), it may be difficult to establish a uniform definition of the technological components of the network.

The case study recognized impending change in nodes related to W3c standard setting, rich Internet technologies, and Internet explorer.

Finding supporting evidence for change was ease. Nonetheless, interpreting the meaning of these changes introduced a component of subjectivity.

Such subjectivity may be hard to remove given the epistemic character of uncertainty in new technology. It is important also to acknowledge that this is only a first demonstration of concept on a relatively limited sample.

governance of converging new technologies integrated from the nanoscale, Paper Presented at the Portland International Conference on Management of Engineering and Technology, Portland, 2007.2 A. De Haan, K. Mulder, Sustainable air transport:

identifying possibilities for technological regime shifts in aircraft construction, Int. J. Innov. Manag. 6 (3)( 2002) 301 318.3 P. Anderson, M. Tushman, Technological discontinuities and dominant designs:

a cyclical model of technological change, Adm. Sci. Q. 35 (1990) 6045 6633.4 A l. Porter, A t. Roper, T. W. Mason, F. A. Rossini, J. Banks, Forecasting and Management of Technology

, John Wiley & Sons, New york, 1991.5 R. J. Lempert, S. Popper, S w. Bankes, Shaping the next one hundred years:

new methods for quantitative long-term policy analysis, Santa monica: The RAND Pardee Center, 2003.6 L. Fleming, Recombinant uncertainty in technological search, Manage.

Sci. 47 (1)( 2001) 117 132.7 W. E. Walker, P. Harremoes, J. Rotmans, J. P. Van der Sluis, M. B. A

. Van Asselt, P. Janssen, M. P. Krayer Von Kraus, Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support, Integrated Assessment 4 (1)( 2003) 5 17.8 G. S. Altshuller, Creativity as an Exact Science

, 1979.16 K. M. Clark, The interaction of design hierarchies and market concepts in technological evolution, Res.

Policy 14 (1985) 235 251.17 J. P. Dismukes, Technologies of thinking'seen key to accelerated radical innovation, Res.

Tech mining to accelerate radical innovation, PICMET 2007 Proceedings, 2007.19 Y. Yasunaga, M. Watanabe, M. Korenaga, Outline of the strategic technology roadmap of METI (Ministry of Trade and Industry

of Japan) and trial approaches for technology convergence with the methodology of technology roadmapping, PICMET 2007 Proceedings, Portland, Oregon:

the reconfiguration of existing product technologies and the failure of established firms, Adm. Sci. Q. 35 (1990) 9 30.30 S g. Green, M. B. Gavin, L. Aiman-Smith, Assessing a multidimensional measure of radical technological innovation, IEEE Trans.

Scott Cunningham received a Ph d. in Science, Technology and Innovation policy from the Science policy Research Unit.

developed patents in the fields of pricing and promotion algorithms, been a research fellow at the Technology policy Assessment Center of Georgia Tech,

He currently works for the Faculty of technology, Policy and Management, of the Delft University of Technology,

His book on Tech Mining co-authored with Alan Porter, was published in 2001 with Wiley. 1149 S w. Cunningham/Technological forecasting & Social Change 76 (2009) 1138 1149

ART18.pdf

, A. Klinke A j. Markard A m. Maurer b, A. Ruef a a Department Innovation research in Utility Sectors at The swiss Federal Institute of Aquatic Science and Technology (Eawag), Switzerland

b Department Urban Water management Research at Eawag, Switzerland c Institute of environmental Engineering at ETH Zurich, Switzerland d Competence Center Sustainability and Infrastructure Systems

at the German Fraunhofer Institute for Systems and Innovation research ISI, Germany e Technology and Society Unit of The swiss Federal Institute of Materials Science and Technology (Empa

value considerations and available technological alternatives. However currently, strategic infrastructure planning is carried often out in a very narrow perspective.

technology and innovation policy 6. Foresight is however much less well developed in strategic planning contexts as it often misses the link between analyzing uncertainties to assessing options

newly emerging technological solutions and potential future interest conflicts associated with the implementation of specific system configurations.

A similar approach has been presented by Dominguez et al. 2. They utilize infrastructure foresight to identify technological and organizational capability deficits.

Due to the long life time of their key technical components and the strong coupling between technological and institutional structures they exhibit strong path dependencies 11.

They consist of rules, standards, scientific knowledge, engineering practices, technologies and skills that determine a stable context in which highly complex system configurations can develop 12.

Socio-technical regimes create advantages for system development as they tend to reduce the costs of knowledge generation and the political costs of decision making 13.

the range of possible technological system alternatives has been substantially expanding. Due to ICT, miniaturization of components, new technological solutions like membrane technology or new measuring and control devices, radically different system configurations might become available with grossly enhanced performance characteristics.

And thirdly, the criteria by which the optimality of system configurations is assessed have been becoming more diverse and disputed.

a more elaborate and open approach to strategic planning seems very timely. 2. 2. Foresight supporting infrastructure planning Foresight methods have been developed over the past decades to explicitly address substantial uncertainties in technology development.

regions, sectors and companies mainly by focusing on perspectives associated with new technology development. Furthermore, its standard case of application has been to direct

and prioritize science, technology and innovation policy measures. While the earlier approaches tended to be techno-deterministic,

the later applications more explicitly address the co-evolution of technology and society 19. In line with this shift of attention, foresight was conceived mainly as an informing policy task until the 1970s,

or transport technology scenarios without explicitly assessing adequate strategies 39 43. Others include the assessment of options

consulting engineers and regional and national regulatory bodies. The highly localized and fragmented character of this organizational structure is illustrated by the fact that each of the 2500 communities runs their sewer systems

Weaknesses incorporate the uncertainty about system reliability of the technology and uncertainties associated with service reliability,

and incorporated early on in a robust decision procedure able to respond to emerging information (e g. concerning decentralized technologies),

Pol. 35 (5)( 2006) 609 625.12 A. Rip, R. Kemp, Technological change, in: S. Rayner, E. L. Malone (Eds.

Human choice and climate change resources and technology, Battelle Press, Columbus, 1998.13 M. S. Jørgensen, U. Jørgensen, C. Clausen, The social shaping approach to technology foresight

An Investigation into the Long-term challenges and Opportunities for the UK's Strategic Highway Network, Highway Agency for England, London, 2003.42 Office of Science and Technology, Intelligent Infrastructure Futures, Foresight Directorate

/Technological forecasting & Social Change 76 (2009) 1150 1162 49 K. M. Hillman, B. A. Sandén, Exploring technology paths:

A comparison of alternative technologies to decarbonize Canada's passenger transportation sector, Technol. Forecast. Soc.

Overview and Interpretative Framework, European Science and Technology observatory (ESTO), Paris, 2001.57 I. Miles, Appraisal of Alternative methods and Procedures for Producing Regional foresight, EU Kommission, Brüssel, 2002.58 R. Popper,

Eckhard Störmer is a project leader at the Social science Research Department Cirus (innovation research in utility sectors) at The swiss Federal Institute of Aquatic Science and Technology (Eawag.

Hans Kastenholz is a senior researcher at the Technology and Society unit of The swiss Federal Institute of Materials Science and Technology (Empa.

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