time & dates

ART76.pdf

Mapping of future technology themes in sustainable energy Hai-Chen Lin, Te-Yi Chan and Cheng-Hua Ien Abstract Purpose To anticipate science

. Introduction Foresight and competitive intelligence (CI) are two fields that seek to address future-oriented environmental scanning (Calof and Smith, 2010.

Also, the rule of foresight has changed from the previous explorative forecasting to more be come more oriented to strategic planning (Martin, 1995.

or create PAGE 54 jforesight j VOL. 15 NO. 1 2013, pp. 54-73, Q Emerald Group Publishing Limited, ISSN 1463-6689 DOI

who contributed significantly to improving the quality of this paper. certain paths of development (Gavigan and Cahill, 1997;

Grupp and Linstone, 1999. Therefore, many countries not only use foresight as a tool to improve anticipatory intelligence but also use it as a priority-setting tool.

foresight results have been used for the implementation of policy measures (Aichholzer, 2001. Competitive intelligence is a systematic way to collect

and emphasizes the importance of a resource-based view of an organization (Powell and Bradford, 2000;

Barney, 1986; Grant, 1991; Markides and Williamson, 1994. According to the resource-based theory, competitive advantage occurs only when there is a situation of resource heterogeneity and resource immobility (Barney, 1991.

With this perception, conventional strength, weaknesses, opportunities and threats (SWOT) analyses are informed by the need to maintain these core competences in the face of the development by competitors of their own core competences and key assets (Hax and Majluf, 1996.

Therefore, the ability to access new markets can only be prepared if knowledge about competitor's intention and capabilities can be monitored.

Without such knowledge the capacity to access existing and new markets and to identify and maintain the basis of competitive advantage will be limited (Powell and Bradford, 2000).

In addition to the resource-based theory, the modern emphasis is on network approaches to industrial strategy

and the need for partnering approaches to manage these networks of buyers, suppliers and peer companies, knowing the capabilities and intents of other organization for noncompetitive purposes.

Competitive intelligence is also important to retain a dynamic understanding of the technology trajectories of the surrounding industrial environment (Nelson, 1997.

Therefore, it is not possible generate a viable and appropriate technology strategy without a perception of the changing technical capability of our own industry and that of related industries (Powell and Bradford, 2000.

and popularized (Calof and Smith, 2010). In general, CTI is competitive intelligence within the R&d arena (Herring, 1993;

Ashton et al. 1994). ) It has been defined as:..business sensitive information on external scientific or technological threats, opportunities,

or developments that have the potential to affect a company's competitive situation. Ashton and Klavans (1997) also defined three basic objectives for CTI activities:

1. to provide early warning of external technical developments or company moves that represent potential business, threats or opportunities;

competitor profiling, early warning assessment, scientometrics, science mapping, scenarios, network analysis and so forth (Calof and Smith, 2010).

a combination of CTI and Strategic technology foresight (STF) was proposed recently by Calof and Smith (2010).

where the emphasis VOL. 15 NO. 1 2013 jforesight jpage 55 is not only on the technologies of the future

they are of a shorter duration to foresight and are more of a predictive nature than STF.

2006). ) In FISTERA, many national foresight exercise reports were scanned and important ISTS were listed and scouted. In Delphi Austria, an analysis of the Japanese, German, French, British Delphi studies was conducted to separate

and evaluate worldwide technology trends during the preparatory studies (Aichholzer, 2001). In addition, after the Delphi Austria foresight process, the results of the Austria Technology Delphi were reclassified also according to the standard classifications of industry

and policy makers and to propose an explanation of the old structures/high performance paradox (Tichy, 1999;

Orwat, 2003. Another famous example is the foresight activity mapping used by the European foresight monitoring Network (EFMN),

A specially designed taxonomy is used for mapping (Popper, 2009. The mapping dimension of EFMN, especially for the science and technology field

and niches within technology trends where Austria might find opportunities to achieve leadership within the next 15 years (Aichholzer, 2001).

and interactions. 2. Methodological approach 2. 1 Delphi method background Delphi was developed in the 1950s by the US RAND Corporation

Delphi is a subjective-intuitive research method that aims at a consensus PAGE 56 jforesight jvol. 15 NO. 1 2013 on a particular topic among a group of experts,

1970). ) Previous publications have proved that the technique is established an method for foresight activities and that Delphi outperforms other group formats such as statistical groups or standard interacting groups in terms of effectiveness (Rowe and Wright, 1999).

It is assumed commonly that the method makes better use of group interaction (Rowe et al. 1991), whereby the questionnaire is the medium of interaction (Martino, 1983.

The Delphi method is especially useful for long-range forecasting (2030 years), as expert opinions are the only source of information available.

Meanwhile, the communication effect of Delphi studies and the value of the process are acknowledged also. 2. 2 Basic information for the scanned Delphi topics The Delphi topics used for sustainable energy are chosen from foresight reports from Japan, South korea and China.

& future technology of Korea-challenges and opportunities (Korea 2030) China's Report of Technology foresight 2004 Report year 2005 2005 2004 Project promoter/initiator Ministry of Education

and Technology policy Research institute (STEPI) Technology foresight Research team, National research Center for Science and Technology for Development Time horizon 2035 2030 2020 Original category Energy and resources Energy

NO 1 2013 jforesight jpage 57 intellectual property (IP) system that rewards creativity, stimulates innovation

and a new version of the IPC is published regularly by the WIPO (World Intellectual Property Organization, 2011).

and therefore the classification of patents is based on technologies or products that use specific technologies (Schmoch, 2008).

There is long history in economics of the use of patent data to understand the process of invention and innovation (Griliches, 1990;

Also, patent documents are used widely as a source for technology forecasting, CTI and for analysis of technology convergence (Kayal, 1999;

2010; Curran and Leker, 2011. Based on the IPC code given to each patent document, statistics regarding the code or advanced analysis can be done easily to compare development or the trajectory among different technology domains.

Therefore, the IPC code, as a classification system for the state-of-the-art technology, provides a route for linking mid-to long-term technology,

as described in the technology vision of a Delphi topic, to the present or near future technological trajectory.

In addition, the content of Delphi topics depends on the time horizon of the foresight activities. For example, the foresight activity of Japan has up to a 30-year time horizon,

and hence the technology trends provided by the scanned Delphi survey will provide a long-term view while the result from patent analysis for CTI or for forecasting provide a short-term view.

while patent analysis provides a more evidence-based view (Popper, 2008. 2. 3. 2 Overall procedures of the mapping.

or social network analysis PAGE 58 jforesight jvol. 15 NO. 1 2013 For converting and aggregating to the 35 WIPO technology classifications in Step 6,

Schmoch (2008) VOL. 15 NO. 1 2013 jforesight jpage 59 code C08g covering‘‘polymer electrolyte''is identified,

Schmoch (2008) Figure 1 Example for Delphi topic mapping PAGE 60 jforesight jvol. 15 NO. 1 2013 3. Results, impacts and policy options

and implications Since the time horizons surveyed in the three scanned foresight report are different, the Delphi topics need to be screened again based on their realization time for cross-country comparison.

The time horizons surveyed for Delphi topics in Japan are‘‘time of technological realization''and‘‘time of social application''.

''The time horizons surveyed for Delphi topics in South korea are‘‘time of international realization''and‘‘time for domestic realization''.

''The time horizon is not specific in the Chinese Delphi topics but the forecasting time horizon for the whole foresight activity was set at up to the year 2020, by

which technology that can be realized before the year 2020 is preselected. Therefore, Delphi topics with a realization time before the year 2020,

which is‘‘time of technological realization before 2020''for Japan, ‘‘time of international realization before 2020''for South korea and all Delphi topics from China, are used as the main target for analysis and for comparison.

Twenty-six Delphi topics in Japan which counts for 74.3 percent of the total Delphi topics,

were regarded as having realization before the year 2020. The remaining six and three Delphi topics are regarded as having realization between the years 2021 and 2030 and after the year 2031, respectively.

In South korea's case, most of the Delphi topics were regarded by experts as having realization before the year 2020;

only one topic regarded as having realization between the years 2021 and 2030. In China, all the topics were regarded as having realization before the year 2020.

Table V summarizes the realization time distribution of Delphi topics in Japan, South korea and China. 3. 1 Mapping technology interactions in Delphi topics 3. 1. 1 Summary result of the mapping in three countries.

The mapping results are demonstrated using the 35 technology fields suggested by WIPO. The summary results of the mapping for technology interaction prospected by Japan,

South korea and China is shown in Figure 2. The y axis denotes the source technology and the x axis denotes the application technology.

Interactions between source and application technology that can be identified in Delphi topics in either the Japan,

South korea or China reports are labeled with symbols. Different symbol represent different meanings. For example,‘‘O''means the technology interaction can be identified simultaneously in Japan (JP), South korea (KR) and China (CN),

'''means the technology interaction can be identified only in Japan (JP) and China (CN), and so on. Interactions within the technology field are shown not in Figure 2. By viewing the union result of the mapping from the application technology side, conventional energy technology 1 (Electrical machinery, apparatus,

energy) is a hot technology application before the year 2020, where the possible source technologies comprise technologies 7 (IT methods for management),

Technology 19 (Basic materials chemistry) is prospected also by these three countries as second hot application technology before the year 2020,

The third hot application technology before the year 2020 is technology 32 (Transport), and the possible Table V Realization time distribution of Delphi topic in Japan, South korea and China Before 2020 2021-2030 After 2031 Time horizon Topics Ratio (percent

) Topics Ratio (percent) Topics Ratio (percent) Japan 26 74.3 6 17.1 3 8. 6 South korea 75 98.7 1 1. 3

0 0 China 83 100 0 0 0 0 VOL. 15 NO. 1 2013 jforesight jpage 61 source technologies are derived from technologies 1

From the source technology side, technology 27 (Engines, pumps, turbines) is the hottest source technology for application to other technologies before 2020,

technology 15 (Biotechnology) to technology 1 Figure 2 Summary result of the mapping in three countries PAGE 62 jforesight jvol. 15 NO. 1 2013 (Electrical machinery

hybrid power system/vehicle use China Hybrid power system VOL. 15 NO. 1 2013 jforesight jpage 63 As shown in Figure 3, technology 1 (Electrical machinery, apparatus,

energy) is targeted the main application technology by other technologies before the year 2020. The source technologies comprise technologies 12 (Control), 15 (Biotechnology), 17 (Macromolecular chemistry, polymers), 19 (Basic materials chemistry), 20 (Materials, metallurgy), 24 (Environmental

The second most intensive linkage of the interaction is source technology 17 (Macromolecular chemistry, polymers) to application technology 1 (Electrical machinery, apparatus, energy.

the technology interactions of the Delphi topics before the year 2020 are much more divergent.

interactions across WIPO technologies for Delphi topics from Japan PAGE 64 jforesight jvol. 15 NO. 1 2013 (Civil engineering).

Figure 4 Technology interactions across WIPO technologies for Delphi topics from South korea VOL. 15 NO. 1 2013 jforesight jpage 65 engineering;

this may provide a CTI value. Therefore, the top 25 percent important Delphi topics before 2020 are selected according to the foresight result in each country,

and the deduced linkages for the selected topics are demonstrated also by a directional social network analysis (SNA).

jforesight jvol. 15 NO. 1 2013 energy. The important source technologies comprise technologies 15 (Biotechnology), 17 (Macromolecular chemistry, polymers), 19 (Basic materials chemistry), 24 (Environmental technology) and 35 (Civil engineering.

VOL. 15 NO. 1 2013 jforesight jpage 67 cells'',Solar and fuel cell power system in practical use,

and Figure 7 Technology interactions across WIPO technologies in top 25 percent important Delphi topics from South korea PAGE 68 jforesight jvol. 15 NO. 1 2013

technologies in top 25 percent important Delphi topics from China VOL. 15 NO. 1 2013 jforesight jpage 69 technology of solar cells,

this PAGE 70 jforesight jvol. 15 NO. 1 2013 kind of capability gap identification becomes easier.

in order to assign IPC codes to relevant records since January 2010 (Vivavip, 2010). In addition, since the IPC code provides a hierarchical framework for mapping future technology themes,

which can be accessed at http://nodexl. codeplex. com/References Aichholzer, G. 2001),‘Delphi Austria: an example of tailoring foresight to the needs of a small country'',available at:

www. oeaw. ac. at/ita/pdf/ita 01 02. pdf (accessed March 8, 2010. Ashton, B. W. and Klavans, R. A. 1997), Keeping Abreast of Science and Technology:

Technical intelligence for Business, Batelle Press, Columbus, OH. Ashton, B. W.,Johnson, A. H. and Stacey, G. S. 1994),‘Monitoring science and technology for competitive advantage'',Competitive intelligence Review, Vol. 5 No. 1, pp. 5-16.

Barney, J. B. 1986),‘Strategic factor market: expectations, luck, and business strategy'',Management Science, Vol. 32 No. 10, pp. 1231-41.

Barney, J. B. 1991),‘Firm resources and sustained competitive advantage'',Journal of Management, Vol. 17 No. 1, pp. 99-120.

Calof, J. and Smith, J. 2010),‘The integrative domain of foresight and competitive intelligence and its impact on R&d management'',R&d Management, Vol. 40 No. 1, pp. 31-9

. 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.

Fujii, A. 2006),‘Foresight on information society technologies in Europe''''Science & Technology trends Quarterly Review, No. 18, available at:

www. nistep. go. jp/achiev/ftx/eng/stfc/stt018e/qr18pdf/STTQR1802. pdf (accessed March 8, 2011.

Gavigan, J. P. and Cahill, E. 1997),‘Overview of recent European and non-European national technology foresight studies'',Technical Report No.

TR97/02, Institute for Prospective Technological Studies, Seville. Grant, R. 1991),‘The resource-based theory of competitive advantage:

implications for strategy formulation'',California Management Review, Vol. 33 No. 3, pp. 114-35. VOL. 15 NO. 1 2013 jforesight jpage 71 Griliches, Z. 1990),‘Patent statistics as economic indicators:

a survey'',Journal of Economic Literature, Vol. 28 No. 4, pp. 1661-707. Grupp, H. and Linstone, H. A. 1999),‘National technology foresight activities around the globe:

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:

A Pragmatic Approach, Prentice hall, London. Herring, J. P. 1993),‘Scientific and technical intelligence: the key to R&d'',Journal of Business strategy, Vol. 15, pp. 10-12.

Kayal, A. 1999),‘Measuring the pace of technological progress: 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.

Martin, B. R. 1995),‘Foresight in science and technology'',Technology analysis & Strategic management, Vol. 7 No. 2, pp. 139-68.

Martino, J. P. 1983), Technological forecasting for Decision making, 2nd ed.,North Holland, Amsterdam. Nelson, R. 1997), Why Do Firms Differ

and How Does It Matter? Resources, Firms and Strategies: A Reader in the Resource-based Perspective, Oxford university Press, Oxford.

Orwat, C. 2003),‘WP 1 Review and analysis of national foresight; D1. 1a Case study: Austria Delphi Austria'',FZK-ITAS Forschungszentrum Karlsruhe Gmbh in der Helmholtz-Gemeinschaft, Institut fu r Technikfolgenabscha tzung und Systemanalyse, available at:

www. itas. fzk. de/eng/projects/fistera/wp1case/d1. 1a-0304. htm (accessed November 8, 2010.

Popper, R. 2008),‘How are selected foresight methods'',Foresight, Vol. 10 No. 6, pp. 62-89. Popper, R. 2009),‘Mapping foresight:

revealing how Europe and other world regions navigate into the future'',Publications Office of the European union, European commission, Luxembourg, available at:

http://ec. europa. eu/research/social-sciences/pdf/efmn-mapping-foresight en. pdf (accessed March 8, 2011. Powell, J. H. and Bradford, J. P. 2000),‘Targeting intelligence gathering in a dynamic competitive environment'',International Journal of Information management, Vol. 20, pp. 181-95.

Rowe, G. and Wright, G. 1999),‘The Delphi technique as a forecasting tool: issues and analysis'',International Journal of Forecasting, Vol. 15, pp. 353-75.

Rowe, G.,Wright, G. and Bolger, F. 1991),‘Delphi: a re-evaluation of research and theory'',Technological forecasting and Social Change, Vol. 39, pp. 235-51.

Schmoch, U. 2008),‘Concept of a technology classification for country comparisons'',Final Report to the World Intellectual Property Organization (WIPO), Fraunhofer Institute for Systems and Innovation research, Karlsruhe

, available at: www. wipo. int/edocs/mdocs/classifications/en/ipc ce 41/ipc ce 41 5-annex1. pdf (accessed November 8, 2010.

Schmookler, J. 1966), Invention and Economic growth, Harvard university Press, Cambridge, MA. 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

, pp. 2882-90. Tichy, G. 1999),‘The innovation potential and thematic leadership of Austrian industries:

an interpretation of the technology Delphi with regard to the old structure/high-performance paradox'',Institute of technology Assessment, Vienna, available at:

www. oeaw. ac. at/ita/ebene5/GTDEL99IND. pdf (accessed March 8, 2011. Turoff, M. 1970),‘The design of a policy Delphi'',Technological forecasting and Social Change, Vol. 2, pp. 149-71.

Vivavip (2010),‘Inspec IPC codes have been added back to 1969'',Free Pint Limited, available at: http://web. vivavip. com/forum/Wire/read. php?

i 87550 World Intellectual Property Organization (2011),‘Preface to the International Patent Classification (IPC)''World Intellectual Property Organization, available at:

www. wipo. int/classifications/ipc/en/general/preface. html (accessed February 10, 2010. PAGE 72 jforesight jvol. 15 NO. 1 2013 About the authors Hai-Chen Lin is now an Associate Researcher at Science and Technology policy Research and Information Center (STPI),

which is under the National Applied research Laboratories (NARL). Her research interests include foresight, technology roadmap, and patent analysis.

Hai-Chen Lin is the corresponding author and can be contacted at: hclin@stpi. narl. org. tw Dr Te-Yi Chan received BS, MS and Phd degrees in Computer Engineering from Chung Yuan Christian University

in 2000,2002 and 2007, respectively. He is now an Associate Researcher at Science and Technology policy Research and Information Center (STPI),

which is under the National Applied research Laboratories (NARL). He works on data processing and text mining, and adopts these mechanisms to conduct research into science and technology development trends.

Cheng-Hua Ien received A MS degree in Food Science and Technology from Taiwan University in 1983.

VOL. 15 NO. 1 2013 jforesight jpage 73 To purchase reprints of this article please e-mail:

ART77.pdf

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

These present various methodological aspects of FTA APPROACHES as well as some advances needed in practice to assist FTA practitioners

and in tackling the so-called grand challenges. 2012 Elsevier Inc. All rights reserved. Keywords: 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,

These present some advances needed in practice to assist FTA practitioners and stakeholders in comprehending transformations

Technological forecasting & Social Change 80 (2013) 379 385 Corresponding author at: Center for Strategic Studies andmanagement (CGEE), SCNQD 2, Bl.

its geographical scope, time horizon, themes, methods, participants, budget, and other resources, target audience, communication strategy, etc.

and what cannot be at the time 2, p. 765. The information, knowledge and interpretation and resultant subjective opinion of FTA participants are decisive when dealing with grand challenges 3. Thus,

/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,

and also use scientific methods when analysing the past and the present in order to consider future options

or predict the future. Besides scientific methods, various other techniques are used also. The main objective of FTA projects is to assist decision-makers with relevant analyses,

the time horizon to be considered; the desire of their sponsors/clients; the number, experience, analytical and methodological skills and value system of their participants;

time and other resources available for FTA, etc. In other words, it would be a mistake to search for a fixed set of methods in the sense of one size fits all (or best practice.

when it is not too costly in terms of time, human resources and funds required to conduct an FTA project.

/Technological forecasting & Social Change 80 (2013) 379 385 are by nature complex and largely impervious to top-down rational planning approaches.

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.

/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

) since its launch in 2004. They claim that current trends in FTA and the increasing policy demand for robust evidence for decision-making indicate that there may be a momentum for pushing FTA towards integrating qualitative (QL) and quantitative (QT) approaches,

as well as provide guidance for the identification of the features that may help the selection of the appropriate set of tools

Kwakkel and Pruit 12 present three applications of EMA, using different modelling approaches, in three different technical domains and related to three different grand challenges, grounded in a system perspective.

/Technological forecasting & Social Change 80 (2013) 379 385 more experimental approaches to creating new solutions

The FTA TOOLS used to develop governance frameworks for nanotechnology in these two countries differ along time.

and that ensures the organisational continuity to use the experience and knowledge gained in distributed FTA ACTIVITIES.

and practice of FTA should consider the governance dimension from the beginning by acknowledging that monitoring

implies the need for an organisational structure that includes a variety of actors and perspectives from the outset.

/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.

Defending Against the Unknown, the Uncertain & the Unexpected, Presidents & Prime ministers, vol. 11, Issue 2, 2002, pp. 33 36,(Mar/Apr 2 D. Loveridge, O. Saritas

Manag. 24 (8)( 2012) 753 767.3 L. Georghiou, J. C. Harper, Rising to the challenges Reflections on Future-oriented technology analysis, Technol.

Chang. 80 (3)( 2013) 467 470 (this issue. 4 K. Haegeman, E. Marinelli, F. Scapolo, A. Ricci, A. Sokolov, Quantitative and qualitative approaches in Future-oriented technology analysis (FTA:

Chang. 80 (3)( 2013) 386 397 (this issue. 5 P. De Smedt, K. Borch, T. Fuller, Future scenarios to inspire innovation, Technol.

Chang. 80 (3)( 2013) 432 443 (this issue. 6 M. Weber, A. Havas, D. Schartinger, Exploring the Potential impact of FLA on National Innovation systems.

The 4th International Seville Conference on Future-oriented technology analysis (FTA), 2011,12 13 may. 7 L. Gao, A l. Porter, J. Wang, S. Fang, X. Zhang, T. Ma, W. Wang, L. Huang, Technology life cycle analysis method

Chang. 80 (3)( 2013) 398 407 (this issue. 8 H. Ritteland, M. Weber, Dilemmas in a general theory of planning, Policy Sci. 4 (1973) 155 169.9 C. Cagnin, E. Amanatidou, M

Public policy 39 (2012) 140 152.10 M. Boden, C. Cagnin, V. Carabias, K. Haegeman, T. Konnola, Facing the Future:

Time for the EU to Meet Global Challenges, EUR 24364 EN, Publications Office of the European union, Luxembourg, 2010.

bhttp://ftp. jrc. es/EURDOC/JRC55981. pdf>,accessed Oct 2011. 11 C. Hamarat, J. H. Kwakkel, E. Pruyt, Adaptive Robust Design under deep uncertainty, Technol.

Chang. 80 (3)( 2013) 408 418 (this issue. 12 J. H. Kwakkel, E. Pruyt, Exploratory Modeling and Analysis, an approach for model-based foresight under deep uncertainty, Technol.

Chang. 80 (3)( 2013) 419 431 (this issue. 13 P. Shaper-Rinkel, The role of future-oriented technology analysis in the governance of emerging technologies:

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.

Chang. 80 (3)( 2013) 453 466 (this issue. Cristiano Cagnin (Phd) used to work as a scientific officer at JRC-IPTS

Attila Havas (Phd, 1997) is a Senior Research fellow at the Institute of Economics Research centre for Economic and Regional Studies, Hungarian Academy of Sciences (http://econ. core. hu/english/inst/havas. html),

In 1997 2000 he was the Programme Director of TEP, the Hungarian technology foresight programme. He has contributed to international research projects on STI policies, innovation,

/Technological forecasting & Social Change 80 (2013) 379 385

ART78.pdf

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

Received 14 may 2011 Received in revised form 9 july 2012 Accepted 3 september 2012 Available online 8 november 2012 The FTA COMMUNITY relies on a set of disciplines and methods,

since the beginning, provided an avenue to debate methodological aspects and this paper summarises and furthers the discussion developed during the 2011 edition,

building on the debates at the conference and between members of the conference Scientific Committee, to which the authors of this paper belong.

combining research and practice, to overcome such barriers. 2012 Elsevier Inc. All rights reserved. Keywords: Qualitative Quantitative Barriers Combination Integration FTA Epistemological divide 1. Introduction The methodological debate has been a relevant element of the International Seville Conference series on Future-oriented technology analysis (FTA

) since its first edition in 2004. For the 2011 edition, the Scientific Committee decided to focus specifically on the combination of quantitative and qualitative methodologies.

This article, drawing on a position paper written for the conference, reports and expands on the lessons learnt during the event.

& Social Change 80 (2013) 386 397 The views expressed are purely those of the authors

Nevertheless in the discussions at the 2011 FTA Conference some trends were identified suggesting that methodological combination may potentially become more common amongst FTA scholars and practitioners.

complex and adaptive nature of the systems we are dealing with today are moving from one technological era to another.

the 2011 FTA Scientific Committee argued that the exclusive use of qualitative methods can lead to partial views on possible futures,

/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.

A wide variety of information about the past and the present can contribute to this

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,

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

and methods of social scanning and prediction markets could be used to improve professional forecasting and foresight in an era of complex phenomena and disruptive events with high level of uncertainties.

/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:

Social network analysis has attracted attention in the past years allowing quantitative analysis on relationships and links that make up various social processes.

During the 2011 International Seville Conference on FTA, the use of images and visualisation techniques was suggested as a tool,

One example constitutes the Millennium Ecosystem Assessment scenarios, linking ecosystem change and human well-being by combining qualitative storyline development and quantitative modelling through several iterations between both parts 44.

This section elaborates on these aspects by discussing some of the barriers that may hamper a deeper integration of quantitative and qualitative FTA METHODS. 4. 1. Barriers stemming from an epistemological divide in social sciences The main barrier to a full methodological integration is epistemological

and sustainability 43 (See http://www. sitra. fi/en/articles/2012/strategic-design-finlandsneewapproach-problem-solving).

/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,

The shortage of examples of full methodological integration (as shown in Section 3 may also reflect deeply ingrained cultural differences hindering good communication between different epistemological communities.

is therefore of the essence. 9 8 During the 2011 FTA Conference a lively discussion was devoted to the shift of FTA usage from exploring potential risks to inspiring sustainable innovation.

/Technological forecasting & Social Change 80 (2013) 386 397 Finally, in this debate, there is a tendency to equate qualitative with participatory.

On the other hand, qualitative approaches have been adopted for many decades (e g. scenarios) with no other involvement than that of the FTA EXPERTS.

/Technological forecasting & Social Change 80 (2013) 386 397 sciences, Cameron 71 developed the Five Ps Framework, 13 which provides a mixed-methods starter kit,

In particular, from the start of an FTA PROCESS, it is necessary to clarify the definition of terms used and the basic set of assumptions, on which the exercise (in its quantitative and qualitative components) rests.

and differences in cultural backgrounds requires a longer time horizon and below we suggest some of the steps that could be taken in such direction. 5. 2. 1. Lack of identified good practices In light of the growing volume and variety of FTA practices,

time horizon15) and provide both methodological and evaluation guidelines. The experience of the European foresight Platform (www. foresight-platform. eu) could provide some relevant inputs towards this endeavour.

and thus tailoring foresight phases to different foresight functions. 15 Typically, quantitative models present higher credibility for shorter time horizons,

/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,

The different and highly heterogeneous contributions to the 2011 International Seville Conference on Future-oriented technology analysis in this area share a common bottom line:

/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

establishing teams combining experts and practitioners from both communities from the start, and devoting more attention to communication processes.

and both the Scientific Committee and the participants of the 2011 Seville Conference on Future-oriented technology analysis for the fruitful discussions that helped shaping and refining it.

JRC Technical Report, EUR 21473 EN, European commission, 2004, Available at: http://forera. jrc. ec. europa. eu/fta/Proceedings%20short%20version. pdf. Last accessed July 2012.2 P. Bazeley, Issues in mixing qualitative and quantitative approaches

to research, in: R. Buber, J. Gadner (Eds. Applying Qualitative methods to Marketing Management Research, Palgrave Macmillan, Hampshire, United kingdom, 2004.3 J. Brannen, Mixed methods research:

Unpublished, 2005. Available at: http://eprints. ncrm. ac. uk/89/1/Methodsreviewpaperncrm-005. pdf, last accessed July 2012.4 J. E. M. Sale, L. H. Lohfeld,

K. Brazil, Revisiting the quantitative qualitative debate: implications for mixed-methods research, Qual. Quant. 36 (1)( 2002) 43 53.5 C. Teddlie, A. Tashakkori, Foundations of Mixed Methods Research:

Integrating Quantitative and Qualitative approaches in the Social and Behavioral Sciences, Sage Publications, 2009.6 M. Wood,

Online 5 (1)( 2010) 56 71.7 M. Cooke, N. Buckley, Web 2. 0, social networks and the future of market research, Int. J. Mark.

Res. 50 (2)( 2008) 267 292.8 S. Mathison, Why triangulate? Educ. Res. 17 (2)( 1988) 13 17.9 G. B. Rossman, B. L. Wilson, Numbers and words revisited:

being shamelessly eclectic, Qual. Quant. 28 (3)( 1994) 315 327.10 V. J. Caracelli, J. C. Greene, Crafting mixed-method evaluation designs, in:

J. C. Greene, V. J. Caracelli (Eds. Advances in Mixed-method Evaluation: The Challenges and Benefits of Integrating Diverse Paradigms, Jossey-Bass, San francisco, 1997.11 P. Goodwin, Why hindsight can damage foresight, Int. J. Appl.

Forecast. 17 (2010) 5 7. 12 H. A. Linstone, Three eras of technology foresight, keynote speech, in:

Fourth International Seville Conference on Future-oriented technology analysis, May 12 13 (2011) Seville, 2011, Available at: http://foresight. jrc. ec. europa. eu/fta 2011/documents/download/PRESENTATIONS/Keynotes/FTA%202011%20%205-9%20%20hl%20%20

Accessed July 2012.13 H. A. Linstone, Three eras of technology foresight, Technovation 31 (2011) 69 76.14 I. Tuomi, Foresight in an unpredictable world, Technol.

Manage. 24 (8)( 2012) 735 751.15 J. Calof, J. E. Smith, Critical success factors for government-led foresight, Sci.

Public policy 37 (1)( 2010) 31 40.16 L. Georghiou, M. Keenan, Evaluation and impact of foresight, in: L. Georghiou, J. Cassingena Harper, M. Keenan,

Eval. 19 (2)( 2010) 91 104.18 FTA Scientific Committee, Call for papers, in: The 4th International Seville Conference on Future-oriented technology analysis (FTA:

FTA and Grand Societal Challenges Shaping and Driving Structural and Systemic transformations, 2011, Available at: http://foresight. jrc. ec. europa. eu/fta 2011/FTA2011 CALL FINAL. pdf. Accessed August 2012.19 H. Van Lente,

Navigating foresight in a sea of expectations: lessons from the sociology of expectations, Technol. Anal. Strateg. Manage. 24 (8)( 2012) 769 782.20 D. Loveridge, O. Saritas, Ignorance and uncertainty:

influences on FTA, Technol. Anal. Strateg. Manage. 24 (8)( 2012) 753 767.21 K. Haegeman, K. M. Weber,

T. Könnölä, Preparing for grand challenges: the role of future-oriented technology analysis in anticipating and shaping structural and systemic changes, Technol.

Manage. 24 (8)( 2012) 729 734.22 A. Eerola, I. Miles, Methods and tools contributing to FTA:

a knowledge-based perspective, Futures 43 (2011) 265 278.23 For-Learn, Online foresight guide, European foresight platform.

www. foresight-platform. eu/community/foresightguide/2010accessed August 2012.24 J. E. Smith, O. Saritas, Science and technology foresight baker's dozen:

a pocket primer of comparative and combined foresight methods, Foresight 13 (2)( 2011) 79 96.25 R. Popper, How are selected foresight methods, Foresight 10 (6)( 2008) 62 89.26 In:

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,

I. Sakata, K. Matsushima, Detecting emerging research fronts in regeneration medicine by the citation network analysis of scientific publications, Technol.

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 78 (2)( 2011) 283 293.35 D. Thorleuchter, D. Van den Poel, A. Prinzie, A compared R&d-based

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:

management, Policy Pract. 14 (3)( 2012) 449 469.40 F. K. Jin, W. R. Fah, N d. En, L. M. Wei, L

12th International Command and Control Research and Technology Symposium, US Naval War College, Newport R i. USA, June 19 21,2007, 2007, Available at:

http://www. dodccrp. org/events/12th iccrts/CD/html/papers/053. pdf. Accessed July 2012.41 A. Soojung-Kim Pang, Future 2. 0:

rethinking the discipline, Foresight 12 (1)( 2010) 5 20.42 Y. Nugroho, O. Saritas, Incorporating network perspectives in foresight:

a methodological proposal, Foresight 11 (6)( 2009) 21 41.43 B. Boyer, J. W. Cook, M. Steinberg, in:

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

(Coordinating lead authors), Methodology for Developing the MA Scenarios, in: S. R. Carpenter, P. L. Pingali, E. M. Bennett and M. B. Zurek (Eds), Ecosystems and Human Well-being:

Scenarios, Volume 2, Millennium Ecosystem Assessment, Washington: ISLAND PRESS, 2005.45 D. T. Campbell, A Study of Leadership Among Submarine Officers, Ohio State university Res.

Res. 17 (1988) 10 16.53 K. R. Howe, Getting over the quantitative qualitative debate, Am.

J. Educ. 100 (1992) 236 256.54 T. D. Jick, Process and Impacts of a Merger:

Econ. 405 (1996) 422.56 W. K. Olsen, Triangulation in social research: qualitative and quantitative methods can really be mixed, in:

A Realist Approach, Routledge, London, 1992. orig. 1984. 58 A. Baban, Reconceptualisation of the division between quantitative and qualitative researchmethods, cognition, brain, behavior, Interdiscip.

J. XII (4)( 2008) 337 343.59 F. G. Castro, J. G. Kellison, S. J. Boyd, A. Kopak, A methodology for conducting integrative mixed

methods research and data analyses, J. Mixed Methods Res. 4 (4)( 2010) 342 360.60 R. B. Johnson, A j. Onwuegbuzie, Mixed methods research:

a research paradigm whose time has come, Educ. Res. 33 (7)( 2004) 14 26.61 A. Tashakkori, C. Teddlie, Mixed Methodology, Combining Qualitative and Quantitative approaches, Applied Social research Methods Series, 46, Sage

Publications, 1998.62 A. Bryman, Barriers to integrating quantitative and qualitative research, J. Mixed Methods Res. 1 (1)( 2007) 8 22.63 J. Mahoney, G. Goertz

, A tale of two cultures: contrasting qualitative and quantitative research, Pol. Anal. 14 (2006) 227 249.64 M. K. B. Lüdeke, Bridging Qualitative and Quantitative Methods in Foresight, Potsdam Institute for Climate Impact Research, 2006.

Available at http://www. pik-potsdam. de/luedeke/lit/springer12 ch4. pdf. Last accessed July 2012.65 A j. Reiss, Stuff and nonsense about social surveys and participant observation, in:

H. S. Becker, B. Geer, D. Riesman, R. S. Weiss (Eds. Institutions and the Person:

Papers in Memory of Everett C. Hughes, Aldine, Chicago, 1968.66 L. Georghiou, J. C. Harper, Rising to the challenges Reflections on Future-oriented technology analysis, Technol.

Change 80 (3)( 2013) 467 470 (this issue. 67 T. Wehnert, W. Jörß, Evaluation paper:

Deliverable 3. 1 of EFONET Energy Foresight Network, 2009, Available at: http://www. efonet. org/index. php?

Accessed July 2012.68 R. Popper, M. Keenan, I. Miles, M. Butter, G. Sainz, Global Foresight outlook 2007:

mapping foresight in Europe and the rest of the world, in: The EFMN Annual Mapping Report 2007, Report to the European commission, University of Manchester, TNO, Manchester/Delft, 2007.69 R. Popper, Mapping Foresight revealing how Europe

and other world regions navigate into the future, in: EUR 24041 EN, European commission, Directorate-General for Research, Socioeconomic Sciences and Humanities, 2009, Available at:

http://ec. europa. eu/research/social-sciences/pdf/efmnmapppingforesight en. pdf, last accessed July 2012.70 M. Rader, A l. Porter, Fitting future-oriented technology analysis methods to study

types, in: C. Cagnin, M. Keenan, R. Johnson, F. Scapolo, R. Barré (Eds. Future-oriented technology analysis: Strategic intelligence for an Innovative economy, Springer verlag, Berlin and Heidelberg, 2008.71 R. Cameron, Mixed methods research:

Methods 9 (2)( 2011) 96 108.72 O. Da Costa, P. Warnke, C. Cagnin, F. Scapolo, The impact of foresight on policy-making:

Manage. 20 (3)( 2008) 369 387.73 B b. Hughes, P. D. Johnston, Sustainable futures: building policy options into a scenario for development in a global knowledge society, Futures 37 (2005) 813 831.74 R. Johnston, Developing the capacity to assess the impact of foresight, Foresight 14 (1

)( 2012) 56 68.75 H. White, Combining quantitative and qualitative approaches in poverty analysis, World Dev. 30 (3)( 2002) 511 522.76 C. Ansell, A. Gash

, Collaborative governance in theory and practice, J. Public Adm. Res. Theory 18 (4)( 2008) 543 571.

Karel Haegeman is a scientific officer at JRC-IPTS (European commission. He has masters in business economics and in marketing,

/Technological forecasting & Social Change 80 (2013) 386 397 Fabiana Scapolo holds a Phd on foresight methodologies and practices from the Manchester University (UK).

and Innovative Approaches) and EFONET (Energy Foresight Network) and is rapporteur of the EC Working group Global Europe 2030 2050.

/Technological forecasting & Social Change 80 (2013) 386 397

< Back - Next >