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Centre, Institute for Prospective and Technological Studies (IPTS), Seville, Spain b Center for Strategic Studies and Management (CGEE), SCN Quadra 2, Bloco A, Ed. Corporate
Cristiano Cagnin & Denis Loveridge (2012) A framework, with embedded FTA, to enable business networks to evolve towards sustainable development, Technology analysis & Strategic management, 24:8, 797-820, DOI:
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8 september 2012,797 820 A framework, with embedded FTA, to enable business networks to evolve towards sustainable development Cristiano Cagnina, b*and Denis Loveridgec adg Joint research Centre, Institute for Prospective and Technological
Cagnin 2005), namely social (S), spatial-technological (ST), economic (E), ecological (Ec), political (P) and values-norms (V)( acronym SSTEEPV.
spatial-technological, institutional-political and cultural-values. Cooperation and dynamic partnerships (Holliday, Schmidheiny, and Watts 2002) are the cornersstone of networked sustainability.
and the intangible assets underlying sustainable development are partnerships, strategy, communication, competencies, motivation, technology and operations. These are needed the activities for the creation of value in sustainable development (Cagnin 2005.
Both forecasting and technology assessmeen provide a more factual and numerical understanding of a business's financial and technical risks
and objectives-Based on feedback loops and information persistence-Interpersonal and technological channels in use-Based on cross boundary learning and knowledge flow-Intuition
level sustainability net Technology-Focus on manual individdua routine automatiion craft development character-Embryonic-Databased, IT used to build systems that cross functions
spatial-technological, social, economic, environmmenta or ecological, political-institutional and cultural-values. Living systems share matter, information and energy with their external environments:
and an inclusive dialogue throughout the network Competences Core competences definition and review Communication Communication channels and processes definition and review Technology Technology infrastructure definition and review Operations Operations
and leveraging Technology Existing IT, systems (IS), strategic, managerial and operational technologies analysis and selection Operations Marketing and/or commercial;
production; procurement; financial; human resources; legal Run the business Implementing the vision of sustainability Business Sustainability Maturity Model Business Path to Sustainability Comparing present performance (as it is) with the business
and leveraging Technology Technology selection; building and leveraging Operations Internal operations and network relationships, performance reporting Sustain the business Achieve the identified vision of sustainability
Through its components (foresight, forecasting and technology assessment), FTA has an undeniably arduous role to play.
and is now a senior advisor of STI (Science, Technology and Innovation policy and strategy at CGEE.
energy matters and corporate venturing to create new high-technology businesses, large and small, relating to long-term directions of change in the business environment.
He has negotiated technology licences based on intellectual property, for business growth and business advantage. References Accountability. 1999.
Engineering Management Journal 11, no. 1: 7 14. Entovation International. 2004. The characteristics of 5th generation management. http://www. entovation. com/assessment/fifthgen. htm (accessed December 2004.
Journal of Management in Engineering, 18, no. 3: 150 5. Larsen, A. H. 2003. Finding and re-finding transparency at Aarstiderne.
Technology and competitive advantage. The Journal of Business strategy 5, no. 3: 60 78. Porter, M. E. 1991.
Proceedings of the fourth international Seville conference on futureorieente technology analysis, Seville, Spain. Downloaded by University of Bucharest at 05:04 03 december 2014 A framework, with embedded FTA,
and build a common set of values Technology An effective integration of social and environmental strategies can be supported strongly by theuse of IT.
Hence, technology (i e. IT and IS) plays a critical role in building and enabling an infrastructure to collect,
Recently, information visualization techniques have been used with corporate data to map several LDRD investment areas for the purpose of understanding strategic overlaps and identifying potential opportunities for future development outside of our current technologies.
and techniques hold great promise for aiding the future direction of the science and technology enterprise.
Much of the technology that has been developed 0040-1625/$-see front matter D 2004 Elsevier Inc. All rights reserved. doi:
Computational and Information sciences (CIS), Engineering sciences (ES), Electronics and Photonics (EP), Materials Science and Technology (MST),
Then, internal teams of experts in the technologies comprising each IA review the short ideas
The purpose of these visualizations was to identify past and present technological competencies and overlaps of competencies, within the IAS.
, person, company, technology, product, university, etc. and relationships from unstructured textual sources. Using rules to define categories,
and categorize technology terms and organization terms (e g.,, CIS, MST, PP, ES, and EP.!Technology and organization terms were linked together on a document basis and visualized in a network or link analysis map.
Both types of visualizations, the landscapes and the link analyses, were used for both the Sandiaspeccifi and DOE LDRD analyses,
The two largest perceived overlaps were between CIS and the Engineering sciences (ES) and Materials Science and Technology (MST) areas,
Several smaller regions of overlap between CIS and MST, mostly in the lower part of the graph, are all related to microsystems and related technologies.
For instance, a portion of the EP portfolio dealing with microelectromechanical systems (MEMS) technology has shifted from component integration to applications.
However, specific information indicating the relationships between technology and IA and the explicit nature of the relationships between the technologies is hidden still.
In order to Fig. 4. Scatterplot of the five Sandia LDRD IAS using the same map coordinates as shown in the Vxinsight map of Fig. 3. Overlaps between the CIS IA
The link analysis map was crucial in portraying to the IA leads the direct and indirect relationships that occurred between technologies within their IAS,
The first level of analysis consisted of identifying relationships between technologies and multiple IAS. The relationships exposed by this analysis were intended to reveal potential overlapping
or complementary technology spaces that can be leveraged jointly in future LDRD calls. Fig. 5 is an example of the link analysis visualizations that were created
Common technologies that indirectly link two (or more) IAS appear between the IAS, showing direct links between a technology and the associated IAS.
Technologies that are unique to an IA are depicted by the collection of links that extend out from each IA label.
Fig. 5. Clearforest link analysis map of specific technology linkages between the five IAS. Thicker lines indicate stronger relationships.
Fig. 5 indicates that each IA has a robust set of unique technologies indicated by the unlabeled lines extending out from the IA markers.
This unique set of technologies represents the development of a strong and innovative R&d portfolio.
The third level of analysis consisted of a technology-to-technology relationship assessment within a single IA.
The result of the visualization pointed to specific technological efforts within an IA that could be combined to create a larger effort that could in turn attract future funding outside of the LDRD program.
compare, and leverage objective technological strengths to attract new external customers. 4. 3. Landscape mapping of DOE LDRD A map of the DOE LDRD data set was created using the same technique described previously
or ideas outside the technology clusters within the map. We note that a more global map
which future opportunities to fund. 4. 4. Link analysis of DOE LDRD The Sandia-specific link analysis assisted in the understanding of the technologies within,
and the relationships among, the technologies from different IAS. The next step was to take the localized knowledge extracted from the IA analysis and compare the strengths and weakness with the rest of the DOE complex.
only the strongest links between technologies and laboratories were extracted and visualized. Fig. 8 identifies the relationships between laboratories and technology,
and thus laboratories with common technology competencies. For example, laboratory B has an area of common technical focus with laboratory A through lithography, laboratory C through fuel cells and biological systems,
and laboratory D through biological systems and semiconductors. The identification of these common points directs us to btechnology categoriesq that can be analyzed further to identify the portfolio of technology that characterizes the capabilities of each laboratory.
For example, when clicking on the fuel cells node in Fig. 8 when using the Clearforest link analysis tool interactively,
and technologies that have weaker links than in the original visualization. Drilling down into a technology is a powerful analysis technique,
and provides greater detail for the laboratory and IAS. The value of this analysis lies in its ability to identify the technological capabilities of each laboratory,
in addition to determining whether duplication or collaborative opportunities exist. The second analysis consisted of linking each individual IA to other laboratories in the DOE complex through common technologies.
The analysis was conducted by selecting each IA in turn and exposing all laboratory and technology relationships associated with it.
The result was a visualization that placed the IA in the middle of the link map with a minimum of 50 nodes identifying direct and indirect Fig. 8. Clearforest link analysis map of specific technology linkages between different laboratories within the U s
. DOE complex. Thicker lines indicate stronger relationships. K. W. Boyack, N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1134 relationships.
The indirect relationships were explored to identify complimentary technology outside of Sandia, and thus to assist in the identification of new
aid the IA leaders in forecasting the direction of technology development. Although this is not equivalent to more traditional and long-term forecasting methods such as Delphi studies or scenarios,
it is nonetheless an effective means of guiding the science and technology enterprise in the shorter term.
or consolidate efforts to create a more focused and effective technology development program. In the near future, we plan to expand our scope to include not only the LDRD information from DOE laboratories,
This will allow us to broaden the technology intelligence that forms the context of our maps
and forecast technology paradigm shifts, which in turn will allow us to take a stronger role in accelerating the development of cutting-edge technology.
This is a research question and possible future that is worthy of exploration. Acknowledgements The authors gratefully acknowledge the support of the LDRD Program, Sandia National Laboratories,
Vis. 2001 (2001) 23 30.5 K. W. Boyack, B. N. Wylie, G. S. Davidson, Domain visualization using Vxinsight for science and technology management, J. Am.
Nabeel holds a Master's degree in Management of Technology (MOT) from the Anderson Schools of Management at the University of New mexico.
His current areas of interest include the integration of information visualization technology with business intelligence. K. W. Boyack, N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1136
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constructing organisational capacities in roadmapping projects at VTT Technical research Centre of Finland, Technology analysis & Strategic management, 24:8, 821-841, DOI:
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research and technology organisation (RTO; anticipatory; agency; culture; roadmapping; strategy process Introduction The geographical scales of innovation systems are interlinked currently more than ever.
In the article, we focus especially on research and technology organisations (RTOS. As argued by Arnold, Clark,
whose predominant activities are to proviid research and development, technology, and innovation services to enterprises, governments, and other clients.
especially by increasing the innovation activities in industry through technology platforms, stretching technoloogica capabilities of companies, and connecting research-based theoretical knowledge with practical knowledge through applications.
We use a notion of process-based roadmapping that widens the horizons of traditional technology roadmapping in such directions as visionary strategic management, network building and development,
and the use of technology. In this article, we open a view towards the systemic capacities, based on a perspective of an organisation as a complex system that is mobile in space time.
and long term) and roadmap layers (such as drivers, markets, and enabling technologies). In the systemic context, roadmapping refers to a continuous and transparent process, not a single exercise,
and communication technology (ICT), differs vastly from the long term of a highly inert field, such as transportation infrastructure.
First is the culture of technology roadmapping, in which the roadmapping is approached as a normative instrument to identify relevant emerging technologies
and to align these technologies with explicit product plans and related action steps (see e g.
Phaal, Farrukh, and Probert 2001. In this culture, the roadmappiin process is a tool to endorse product development.
Process-based strategy roadmapping is methodologically more flexible and exploratory than traditiiona technology roadmapping. The roadmaps are approached not as hermetic plans to achieve definite goals (e g. new products),
When there is a need to link technological and societal trajectories, roadmapping is an apt instrument.
and technologies in a certain time frame. The fifth way is to identify single targets in the roadmap structure.
to identify logical temporal sequences in a specific roadmap layer, such as enabling technology. Downloaded by University of Bucharest at 05:05 03 december 2014 Systemic transformation, anticipatory culture,
The first knowledge space is the technology space, which basically covers the domain of techniica knowledge,
emphasising technology as an object, that is, as a technological solution and a gadget, cutting through three temporal scales.
The second is the social/actor space, which covers all the issues that are primarily dependent on relations between different social actors inside and outside the organisation.
Knowledge Key systemic capacities space Description associated with the space Forms of project knowledge Technology Covers a certain domain of technical knowledge, e g. different technologies, gadgets,
and development, cutting through the three temporal scales Capacities for the renewal of the technological basis:
Building a technological vision Scoping new enabling technologies or new products Identifying temporal sequences Identifying singular elements,
such as separate technologies, applications, and solutions Social/actor Covers issues that are primarily dependent on relations between different actors inside
and market drivers Strategy Strategic and holistic view of the research objects Strategic capacity of the organisation and/or entity Holistic roadmaps to be used in long-term strategic planning Technology space
It should be noted that the differences between the technology space and the social/actor space are mainly heuristic,
In the context of an RTO with an emphasis on technology development, this separation is, in our view,
useful because it enables the organisation to set specific targets both for technologies as solutions and organisational actors as realisers of these solutions.
at one end of the continuum there is technology as a mere object (a solution), and at the other end there is technology as socio-technical constellation combining the technological object,
related subject positions (e g. developer, user, non-user, early adopter, latecomer, and experimenter), and the wider social settings (e g. geographical, organisational, political, economic, and ethical).
In this space, the technology Downloaded by University of Bucharest at 05:05 03 december 2014 Systemic transformation, anticipatory culture,
Our model starts with a presupposition that in the technology and social/actor spaces the exploration of the more radical futures is restricted usually by the overaal need to identify certain actions in the present.
The first roadmap scope is R&d I, with a perspective of a single technology or object.
This is quite a traditional technology roadmap that aims to build a future perspective for a single technology. The aim of the roadmap is to identify specific action steps towards the future.
This scope is parallel to the technology space. The second roadmap scope is R&d II
but instead of a technology domain, the focus is on the organisational Table 2. Ideal scopes of roadmaps.
R&d perspective on a single technology or object Roadmapping single technologies from a certain perspective Enhancing organisational capacities in a certain technology field Building vision
and associated steps mainly in the technological space Drafting action steps to advance the implementatiio of the technology in question R&d II:
technology and social/actor space, R&d I scope Our first example is a roadmapping process that is aimed to renew a line of organisational compettenc that is already rather well established at VTT.
The first workshop was about drivers and technologies. The second workshop considered the future markeets business potential,
I type of technology roadmap that is aimed to contribute to the technology space and the social/actor space.
The project roadmapped a single type of technology sector and thus endorsed the organisational capacities in this domain.
The knowledge spaces of the project are summarised in Table 3. The building services roadmap operated, first, in the technology space.
It was aimed to build capacities for the renewal of VTT's technological basis by stressing the development of a more service-oriented approach.
The project knowledge in the technology space was constructed by building explicit technology visions, such as a novel way to characterise building services,
and identifying novel technological concepts, such as a virtual power plant and the‘black box'of a building. The project also operated in the social/actor space.
Knowledge Key systemic capacities space Description associated with the space Forms of project knowledge Technology Exercise covered the field of building services with an explicit focus on the future possibilities,
especially through ICT applications Capacities for the renewal of technological basis internally at VTT Technology visions were built,
e g. in a novel way to characterise building services Catalysing a new bedrock for building services in Finland by stating the VTT state-of-the-art in research New enabling technologies were identified,
e g. advanced materials Several novel single technology elements were embedded in the roadmaps Social/actor Exercise covered social/actor space from the selected perspectives Capacities for linking of knowledge internally, e g. construction and ICT
market-based visions were built on the basis of current technology trajectories and also by tracking disruptive alternatives Markets for adoption of novel solutions, e g. integrated ICT Endorsed a view of VTT as a key player in the renewal of building services markets,
e g. spread the vision of technological possibilities for rather conservative markets in building and construction Novel market features and actors were identified,
The construction machinery roadmap was aimed to develop new service capacities for a network of technology-oriented companies,
Visionary ideas about technology-enabled services could also be one way to stimulate Downloaded by University of Bucharest at 05:05 03 december 2014 Systemic transformation, anticipatory culture,
and technology-enabled services in the field Constructing a horizontal anticipatory agency, especially through novel technology and services concepts Endorsing education
and international influences in the field Making visionary timelines for the adoption of new solutions Envisioning development projects based on the results this aspired culture.
In addition, the field of construction machinery should actively endorse a kind of horizontal anticipatory agency, for example, through novel technology and services concepts,
from a readiness to adopt new technological solutions, to the construction of novel knowledge linkages in an organisation,
The key systemic capacities in these cases emphasised especially the structural openness towards new technological impulses
which was about building an explicit service-oriented R&d trajectory in an engineering-oriented RTO,
On the basis of the cases, it can be assessed that roadmapping is most applicable to processes aimed either at the technology space, the social/actor space,
simulation, modellling technology mining, or cognitive mapping could provide useful data for the identification of potential‘boundary'competencies.
His current research focusses on socio-spatial transformations induced by science, technology, and innovation policies. He has published widely in the field of foresight, on topics such as roadmapping, emerging technologies and infrastructures,
and socio-technical change. Minna Halonen is a research scientist at VTT. Her research focusses on foresight and socio-technical change
Tech Degree from Helsinki University of Technology. Sirkku Kivisaari works as a senior scientist at VTT.
Tech.)) is a research scientist atvtt. He has been working in different roadmap processes as a senior facilitator.
(i) organisational development enabled by new communicational methods and (ii) marketing issues of environmental technologies. Nina Wessberg is a senior scientist in Foresight and Socio-Technical change team at VTT.
She holds a Dr. Degree in environmental policy and M. Sc. degree in environmental technology. References Aaltonen, M. 2007.
A study of social and economic impacts of research and technology organisations. A report to EARTO.
Technology road mapping: Linking technology resources into business planning. International Journal of Technology management 26, no. 1: 12 9. Geels, F. W. 2004.
From sectoral systems of innovation to socio-technical systems: Insights about dynamics and change from sociology and institutional theory.
Research policy 33, nos. 6 7: 897 920. Halonen, M.,K. Kallio, and E. Saari. 2010.
Disruptive technology roadmaps. Technological forecasting & Social Change 71, no. 2: 141 59. Kostoff, R. N, . and R. R. Schaller. 2001.
Technological Analysis & Strategic management 21, no 3: 381 405. Lee, S.,andy. Park. 2005. Customization of technology roadmaps according to roadmapping purposes:
Technologies and markets. VTT Tiedotteita 2379. Edita, Helsinki In Finnish. Petrick, I. J, . and A e. Echols. 2004.
Technology roadmapping in review: A tool for making sustainable new product development decisions. Technological forecasting & Social Change 71, no. 1 2: 81 100.
Technology roadmapping: Linking technology resources to business objectives. Cambridge: University of Cambridge. http://www. ifm. eng. cam. ac. uk/ctm/publications/tplan/trm white paper. pdf (accessed August 18, 2009.
Phaal, R c. J. P. Farrukh, and D. R. Probert. 2004. Technology roadmapping a planning framework for evolution and revolution.
Technological forecasting & Social Change 71:5 26. Phaal, R c. J. P. Farrukh, and D. R. Probert. 2006.
Technology management tools: Concept, development and application. Technovation 26, no. 3: 336 44. Phaal, R, . and G. Muller. 2009.
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Huang a a School of management and Economics, Beijing Institute of technology, Beijing, China b Technology policy and Assessment Center, Georgia Institute of technology, Atlanta, GA,
Ying Guo, Tingting Ma, Alan L. Porter & Lu Huang (2012) Text mining of information resources to inform Forecasting Innovation Pathways, Technology analysis & Strategic management, 24:8, 843-861, DOI:
Terms & Conditions of access and use can be found at http://www. tandfonline. com/page/termsanndconditions Technology analysis & Strategic management Vol. 24, No.
btechnology Policy and Assessment Center, Georgia Institute of technology, Atlanta, GA, USA Highly uncertain dynamics of New and Emerging science and Technologies pose special challenges to traditional forecasting tools.
Once a set of multi-database, emerging technology search results has been obtained, we devise a means to help extract intelligence on key technology components and functions, major stakeholders,
and potential applications. We present results pertaining to the development of dye-sensitised solar cells. Keywords:
Tech Mining; nanotechnology; dye-sensitised solar cells, technology intelligence 1. Introduction New and Emerging science and Technologies(‘NESTS')are studied increasingly because of their potentially important‘emerging applications'.
'However, the highly uncertain dynamics of NESTS pose special challenges to traditional forecasting tools. Capturing and exploring multiple potential innovation pathways show considerable promise as a way of informing technology management and research policy.
We have devised a 4-stage, 10-step approach to Forecast Innovation Pathways(‘FIP'.'This process integrates (a) heavily empirical‘Tech Mining'with (b) heavily expert-based multipath mapping.
It combines a range of Future-oriented technology analysis(‘FTA'TOOLS. These include innovation system modelling, text mining of Science, Technology & Innovation(‘ST&I')information resources, trend analyses, actor analyses,
and forecasting workshops. This paper explores the systematisation of the FIP analytical approach through the application of Tech Mining.
It presents results pertaining to the development of dye-sensitised solar cells (DSSCS. We treat DSSC abstract records through 2010 based on searches in four databases.
We employ a set of multi-database NEST search results, sharing progress on our efforts to devise algorithms to help extract key technology components, significant actors, and potential applications.*
*Corresponding author. Email: huanglu628@163. com ISSN 0953-7325 print/ISSN 1465-3990 online 2012 Taylor & francis http://dx. doi. org/10.1080/09537325.2012.715491 http
FTA TOOLS have expanded from technology forecasting of incrementally advancing technologies (e g. consider Moore's law describing some six decades of continual advances in semiconductor capabilities)( Roper et al. 2011).
Such technology opportunitiie analysis (Porter et al. 1994) for NESTS poses notable challenges. FTA increasingly includes science-based technologies
with less orderly developmental trajectories (cf. Technology Futures analysis Methodsworking Group 2004; Cagnin et al. 2008). ) The analytical components that we address should be considered in the context of performing FTA (Porter 2010)
and applying it to serve technology policy or management ends (Scapolo, Porter, and Rader 2008). Recently, Robinson (Robinson et al. 2011) has introduced the approach of‘FIP'.
'That paper provides conceptual background for the endeavour of combining‘Tech Mining'(Porter and Cunninngha 2005) and‘multipath mapping'(Robinson and Propp 2008).
It explores the promise of this approach through its application to two illustrative innovation situations:
This paper illustrates application of the FIP approach for a further case, that of nanotechnology-enhanced solar cells(‘NESCS'.
and national policy-makers as they formulate infrastructures to encourage innovation. 2. Background 2. 1. Tech Mining and FTAS Bibliometrics counting activity levels and identifying patterns in R&d bibliographic records,
plus patent analyses has contributed to science and technology studies for decades (cf. Van Raan 1988. With the expansion of databases that compile abstract records
Tech Mining (Porter and Cunningham 2005) is our shorthand for such activities.‘‘Research profiling'(Porter, Kongthon,
and Lu 2002) examines a technology of interest by search and retrieval of abstract records on the topic.
Classical technology forecasting methods were devised to address incrementally advancing technological systems. These methods keyed on technical system parameters, somewhat more than on socioeconnomi system aspects.
Today's NESTS are more apt for incorporating science-based advances (e g. biotechnologies and nanotechnologies),
which emerging technologies contribute to commercial innovation. To facilitate the analysis of technological change, Hekkert et al. 2007) articulate‘functions of innovation systems'.
'Some researchers look into what kind of innovation transfer is most effective (e g. Liu, Tang, and Zhu 2008;
as well as undesirable while the course of technology development remains more malleable (Collingridge 1980; van Merkerk and van Lente 2005.
We note several innovation system conceptual modelling efforts pertaining particularly to energy technology, given our case focus on solar cells.
Several scholars seek to understand the driving forces and the blocking mechanisms that influence the development and diffusion of sustainable technologies (Jacobsson and Johnson 2000;
the technology delivery system(‘TDS')has demonstrated enduring value by capturing and representing (1) key enterprise (to‘deliver'an innovation) and (2) contextual factors (impinging on such delivery).
and Rossini (1985) developed a TDS for microcomputer technology in developing countries, spotlighting the importance of language barriers.
quite willing to revisit the earlier steps as one learns more about the emerging technology and distinguishes vital issues affecting potential commercial or other applications.
It seeks to gain basic understanding of the technology how it works and what functions it can accomplish (Step A). In addition,
and applying this technology (Step B). As discussed earlier, we favour TDS modelling to do this compactly and informatively.
We seek innovation indicators (i e. empirical measures to gauge technological maturation and prospects for successful applications.
We also seek to determine how technological characteristics link to functional advantages, applications, and potential users (Step E). Figure 1. Framework for forecasting NEST innovation pathways.
At Georggi Tech, we drew upon two faculty members to orient our work. Most importantly, we found a willing Phd student (Chen Xu) to collaborate in our analyses.
Work in this stage should take into account competing technologies that may hold advantages over the target NEST under study.
3. 2. The case of DSSCS Nanotechnology entails engineering matter at molecular scale, seeking novel applications of new materials and devices.
Within the context of ongoing empirical analyses of nanotechnology(‘nano')R&d, we focus here on how nanomaterials are being used to enhance the performance of solar cells,
an important renewable energy technology form (also known as photovoltaics). DSSCS, one type of nano-enabled solar cells with special promise,
and are less equipment intensive than other solar cell technologies. 3. 3. Data We chose a modular,
Technology assessment (Step H) has not been carried out yet. Synthesis and reporting (Step I) to explicit target users have not been emphasised in this scholarly exercise.
with a secondary interest in the DSSC characterisation. 4. 1. Compose TDS (Step B) The TDS approach is akin to other technology innovation system approaches,
In terms of the enterprises to accomplish commercial innovation based on DSSC technology, we sketch three loose groups of companies.
Tech Mining the various publication and patent abstract records can track the emergence of key terms over time to spotlight new (appearing only in the most recent time period) and hot subtechnoologie (i e. those appearing
The rapid growth of DWPI and Factiva data suggests that DSSC technology is becoming more mature
the Chinese Academy of Sciences (CAS), the National Institute of Advanced Industrial Science & Technology (AIST Japan), Uppsala University (Sweden),
& Technology with 1013 cites; and three others have 1330 to 1717 cites (to their many publications.
9. 5 Korean Institute of Science & Technology 1. 9 5. 1 6. 3 8. 2 Korea University 2. 3 10.3 6
2 1 Konarka Technologies Inc. 7*11 11*9*Dong Jin Semichem Co Ltd 0 1 16*8*Sony Corp
and among organisations. 4. 4. Determine potential applications (Step E) We introduced a new technique called‘cross-charting'to explore the links from technological attributes (e g. particular nanomaterials or nanostructures and particular technical advances) to functional
that is, cross-charting can suggest ways that particular technologies might link to potential applications. The content of a given cross-chart will vary depending on one's interests.
and advantageous nanomaterials. The idea is that this helps focus monitoring efforts to seek out advances that could facilitate our desired application.
Conversely, we would direct less attention to other nanomaterials and solar cell types that offer less potential gain for our target application.
This could help us to identify potential partners with complementary interests at different places along this technology development progression, thereby serving‘Open innovation'purposes (Chesbrough 2006.
In this paper and in companion analyses of nanobiosensors, we found value in subdividing the technical elements (e g. distinguishing among various nanostructured materials;
tracking materials to technology to functions to applications. engaging with those knowledgeable about the technology through the process of further specifying the set of important, and distinctive, functions;
We also contacted Georgia Tech and Emory University colleagues with a background in solar cells. One professor invited us to meet him.
In addition, a faculty member with expertise in innovation processes and nanotechnology joined the three of us (Guo, Huang,
key promising technologies can be identified and positioned in a time frame; and obstacles and opportunities that will facilitate
4. 7. Calls to perform technology assessment (Step H) Much of the FIP process serves to promote the first type of technology assessment evaluation of competing technologies.
A first step is to broaden the technology assessment beyond the technology alone, to expand selection criteria beyond technical functionality to consider cost, infrastructures, and compatibility.
This leads us to the second type of technology assessment impact assessment. We especially would like to identify potential hazards
DSSCS reflect a variety of component technologies, with R&d emphases distributed among them. In one stream of exploration, we consider the intersection of advanced dye formulations (to enhance light energy capture)
The potential draws on the practical combination of empirical and expert knowledge to capture key technology and contextual attributes, affecting the prospects for effective applicatioons Drawing attention to innovation pathways (e g.
which in turn can trigger consideration about research, technology management, and policy options. Laying out alternative pathways also raises impact assessment needs.
Her current specialty is technology management and assessment, particularly focusing on how to forecast the likely innovation pathways for emerging nano-related technologies and applications.
Tingting Ma is a Phd candidate in Management Science and Engineering, Beijing Institute of technology of China.
Now, she is also a visiting scholar in the School of Public policy at Georgia Institute of technology.
Her specialty is science and technology management particularly the study of technology forecasting and assessment. She is focusing on research on emerging science and technology topics.
Alan L. Porter is Director of R&d for Search Technology, Inc.,Norcross, GA. He is also Professor Emeritus of Industrial & Systems Engineering,
and of Public policy, at Georgia Tech, where he continues as the co-director of the Technology policy and Assessment Center.
He is the author of some 220 articles and books, including Tech Mining (Wiley, 2005).
He and the co-authors are preparing a Second Edition of Forecasting and Management of Technology (Wiley.
He is pursuing ways to exploit science and technology information to generate and visualise intelligence on emerging technologies.
Lu Huang is a faculty member in the School of management and Economics, Beijing Institute of technology. Her specialty is science and technology management, particularly the study of technology forecasting and assessment.
She is focusing on research on emerging science and technology topics. References Cagnin, C.,M. Keenan, R. Honston, F. Scapolo,
and R. Barré, eds. 2008. Future-oriented technology analysis: Strategic intelligence for an innovation economy. Berlin, Heidelberg: Springer-verlag. Chesbrough, H. W. 2006.
Open innovation: A new paradigm for understanding industrial innovation. In Open innovatiion Researching a new paradigm, ed. H. W. Chesbrough, W. Vanhaverbeke,
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Lexington: Lexington Books. Appendix 1. WOS DSSC search terms No. Records Term Annotation#1 4000 TS=((dye-sensiti*)or (dye*same sensiti*)or (pigment-sensiti*)or (pigment same sensiti*)or (dye*same sense)) same
some of them will come to market soon Copper indium diselenide (CIS) Low temperature fabrication Cadmium telluride (Cdte) To improve efficiency (by nanotechnology) Enlarged the effective optical path for absorption...
and holes needed to travel Third generation Cadmium sulphide (Cds) To improve efficiency (mainly by Quantum Dots nanotechnology) Utilisation of materials
Totally new principle (by nanotechnology) Enlarged the effective optical path for absorption DSSCS Is coming to market Organic materials Shorten the path that electrons
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