including data before its transformation and its integration, via learning, appreciation and anticipation, into a systemic outcome.
For example, the location and nature of measuring instruments can have important implications for the data reported.
objective data), while subjectivism (deriving explanation from interpretation and artificial reconstruction of reality) lies at the artificial pole.
and external validity (e g. surveys provide reliable data distributions but their validity in actually measuring constructs is suspect).
more data Downloaded by University of Bucharest at 05:02 03 december 2014 776 H. van Lente and more developments (Konrad 2006.
Methods and data The research design is based on an inductive and multiple-case study of a group of selected firms.
Data were collected through the combination of various sources and through an iterative proceess First, we collected publicly available data on the industry
and the selected firms, including historical annual reports, financial analysts'reports, conference presentations by top managers,
and technical papers supplemented publicly available data. Third, we interviewed a sample of senior and mid-level managers
Macro forces and their likely evolution are described in BASF‘Global economy Scenarios',where econometric models elaborate basic data in both qualitative and quantitative terms,
A model for uncertainty and strategic foresight In the prior sections, we sketched the strategic foresight approaches that emerged from our data through
Data collection and data analyses were designed in order to improve the construct and internna validity of our conceptual framework.
heavily dependent on the flow of ideas, data and information into a business and its network decision-making in its place in society.
and allow data-sharing stimulus to support decisions-Information based; IT used to build applications centred on processes rather than on functions,
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 given the availability of relevant data, we have embarked on a program to map our LDRD IAS.
This paper describes the project plan, detailed processes, data sources, tool sets, and sample analyses and validation activities associated with the mapping of Sandia's LDRD IAS. 2. Project plan The original plan associated with this assessment activity consisted of several steps,
Meetings with the Table 1 Data used to produce Sandia-specific and DOE LDRD maps
and mapping between fields from different data sources Calls (RFP) New proposals Continuation proposals Project reports Publications U s. DOE LDRD data ID number ID
a second set of visualizations was created to include data on all U s. Department of energy (DOE)- funded R&d activities related to the IAS.
Copies of the data, visuals, and navigation tools were provided also to IA leaders to allow them to explore the data independently. 3. Process,
data, and tools Two different types of visualizations, each designed to provide different types of information,
were created for this activity. The first can be described as a landscape map, which is suited particularly to looking for patterns and trends in large data sets.
The second type is a link analysis map, which is valuable for identifying specific topic-based relationships within large data sets.
The landscape maps were created using a process consistent with commonly accepted methods of mapping knowledge domains 1 (see Fig. 1:!
N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1124 3. 1. Data collection Two different sets of data were compiled from multiple sources
The data for the Sandia-specific visualizations consisted of 1209 records from the five IAS
These data are proprietary to Sandia, and are not generally available externally. To create the DOE LDRD visualizations,
FY2003 data were not yet available. Of these 180 duplicated existing Sandia-specific records and another 200 had no titles or descriptive text,
and were removed thus from the data set. A total of 990 of the new records had both titles and descriptive text,
With the Sandia-specific and additional DOE data, this set consisted of 5112 records. 3. 2. Similarity calculation LSA is a technique based on the vector space model that has found recent application in information retrieval.
We also used an optimized stopword list prior to construction of the initial term Fig. 1. Process of putting data into a Vxinsight map. 3 FY=fiscal year,
the data set is loaded into Vxinsight for exploration and analysis. Vxinsight is a tool that allows visualization and navigation of an abstract information space,
or restricting the data displayed to a certain time span and sliding through sequences of years with a slider.
Relationships among the individual data records may be displayed as arrows between documents and understood at many levels of detail.
Details about any data record are also available upon demand. Effective use of the labels, zooming,
Fig. 4 shows the same data in a scatterplot view, where different symbols are used for the different IAS.
The Vxinsight views are meant more for active navigation of data than for presentation of results.
and data sets on his computer so that he could explore the data independently and draw his own conclusions related to both assessment
and potential future directions. 4. 2. Link analysis of IAS The analyses of the visualizations in Section 4. 1 tend to strongly convey the patterns
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
and is shown in Fig. 6. The purpose of this map was primarily to identify additional opportunities by comparison of Sandia IA data with work of national interest that is being funded at other DOE laboratories.
The roughly 3800 records added to the Sandia IA data add significant context and content that provide fodder for new ideas.
However, such a map would take much more data and time to construct. Fig. 6 shows that significant areas of the graph, especially at the top and right, are covered not at all by any of the Sandia IAS.
and circles within the dashed region of Fig. 6. All of the non-Sandia records have been marked as black dots in Fig. 7. Examination shows several small clusters of data in areas that are very related to our computational
The data used for this analysis consisted of LDRD calls, proposals, and projects for the IAS,
but also much data from industry and academia. This will allow us to broaden the technology intelligence that forms the context of our maps
or cognitive mapping could provide useful data for the identification of potential‘boundary'competencies. Third, research should pay more attention to the systemic and temporal relativity of the organisations, that is, to how the interplay of past, present,
and work to relate the content of the data searches to particular innovation process trajectories. Downloaded by University of Bucharest at 05:05 03 december 2014 Text mining of information resources 845 2. 2. Analysing NESTS NESTS comprise a loose category (Foxon et al. 2005;
3. Framework and data 3. 1. Framework The FIP framework includes four stages, broken down into 10 steps (Figure 1). We label Steps A j,
and profile that activity and the associated actors from these data (Steps C and D). Many analytical tools can serve to profile R&d,
and are less equipment intensive than other solar cell technologies. 3. 3. Data We chose a modular,
We created search algoritthm somewhat tailored for each of the four databases (details in Appendix 1). Data-cleaning in Vantagepoint software (Porter et al. 2007) refined the data downloaded from the four databases.
The data from both DWPI and Factiva show a small peak in 2005 and suddenly decrease in 2006.
Actually, the data from Compendex also grow slower in 2006. We Are downloaded by University of Bucharest at 05:05 03 december 2014 850 Y. Guo et al.
although the data in 2009 and 2010 were collected not completely by Thomson Reuters at the time of the downloading.
The rapid growth of DWPI and Factiva data suggests that DSSC technology is becoming more mature
Based on the SCI data set, we identified the top 11 research publishing institutions (Table 1)
without doing extensive data-cleaning); US National Renewable Energy Lab (NREL) is second with 4780,
. Leading DSSC companies'prevalence in various data sources. SCI EI DWPI Factiva Samsung SDI Co. Ltd 52*38 65*4 Sharp Co. Ltd 27*24 17*4 Nippon oil
identifying the leading organisations active in each of the different data sources. Table 2 compares selected organisations in this way. 5 Note the variation in prominence across these data sets.
For instance, Samsung is the leading patentee and publisher (in this compilation) on DSSCS but has not been mentioned frequently in conjunction with business actions (Factiva database.
and then zooming into these through augmented expert engagement exercisses The richness of the data is unquestionable,
The amount of available data time horizons for innovation, and scope of study all reinforce the need to adapt these 10 steps to one's priorities.
*)or (systemic sclerosis) or (diffuse scleroderma) or (Deep space Station Controller) or (Data Storage Systems Center) or (decompressive stress strain curve or (double-sidebandsupprresse carrier) or (Flexible AC Transmission Systems
and exclude (2) noisy data#3 330 TS=((dye-Photosensiti*)or (dye same Photosensiti*)or (pigment-Photosensiti*)or (pigment same Photosensiti*))same((solar or Photovoltaic or photoelectr
*or cancer) to exclude noisy data#4 188 TS=((dye adj (sensiti*or photosensiti*))and (conduct*or semiconduct*))same electrode*)and electrolyte*)not (wastewater or wastewater or degradation)) Search term
or wastewater or degradation) to exclude noisy data Total 4104#1 or#2 or#3 or#4 Combined search terms Downloaded by University of Bucharest at 05:05 03 december 2014 Text mining of information resources 861 Appendix 2. Different generations of solar cells Material
Correspondence analysis is used a widely method to grasp the relations between two different categories of data.
supported by data and literature. The fiches draw out potential disruptive factors that will constrain or accelerate the phenomenon described.
natural and anthropogenic disasters and their consequences based on monitoring data and advanced understanding of their origins and development 57.1 1. 71 Techniques for prospecting natural resources,
Table IV The influence of the foresight studies on policy decision-making Influence on policy-making Evaluation of influence on policy-making FS1 The foresight data were used as an information source for many political purposes:
There is long history in economics of the use of patent data to understand the process of invention and innovation (Griliches, 1990;
and confirmed further by other complementary data or sources. Note 1. Nodexl is an online free tool for social network analysis,
We define data as quantitative when consisting of numerical information and a methodology as quantitative when applying statistical/mathematical tools.
In contrast, we define data as qualitative when consisting of non-numerical information (such as text, images,
on the grounds that quantitative extrapolation from past data is not sufficient to address the uncertainties of the future
Tuomi 14 suggests that the ontological unpredictability4 of innovative process cannot be removed by more accurate data or incremental improvements in existing predictive models.
and Cahill 1 for further details on the origin and definition of the acronym FTA. 2 Quantitative participatory methods could for instance relate to the online sharing of big amounts of data,
who see FTA EXERCISES as attempts to collect knowledge about‘posits'or possible futures, their plausibility and limits, their internal consistency and conformity with models and data, their consistency with expert judgement,
Järvanpää et al. 34 analyse the use of bibliometric data for distinguishing between science-based and conventional technologies,
Visualisation of quantitative data 36 can be a useful way of bringing these data to a workshop or another qualitative process.
Examples of current and upcoming FTA practices Internet-based tools allowing for integration of data of various sorts Online sharing of perspectives on different data types:
new technologies such as web 2. 0 can be used by FTA to streamline operations by increasing interactive participation of stakeholders, speeding-up the provision of information and feedbacks and integrating data of different sorts (pictures
, documents, numerical data, free text, videos. 6 The Risk assessment and Horizon scanning Initiative (Singapore) developed a Service Oriented Based Horizon scanning Architecture (SOSA) allowing sharing perspectives on data sets
in order to amplify data outliers and help users avoid getting blind-sided through premature convergence 40.
It consists of an intranet based network of people, tools and data (from unstructured text from internet to reports uploaded by experts),
and the sharing of perspectives across the network is supported by a set of perspective visualisation tools.
Online analysis of data and creation of knowledge repositories: Cooke and Buckley 7 believe that web 2. 0 tools can be used to make data of all sorts accessible to respondents and researchers:
Respondents no longer merely respond to signals: they generate the data, they edit it, via their communal participation, revising it in response to others,
irrespective of whether the others are researchers, clients or respondents (p. 289). However, to date no concrete examples of this approach could be identified,
/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:
When used in combination with foresight data collected online, network analysis can be used to enable robust analysis of foresight data,
which are often complex to present and codify. Nugroho and Saritas 42 propose a framework for this, building on online foresight survey data,
and by pointing at benefits in the various phases of a foresight process: it reveals the structural features of the data
and can inform the foresight process on emerging links or relationships, groups or clusters. The implication for foresight methods is that network analysis can introduce a‘systemic'perspective emphasising relationships between actors,
even in projects combining quantitative and qualitative methods, data 7 At first sight, this method is more suitable for FTA for businesses.
or qualitative, depending on the type of data they rely on. They may generate as an output, informed estimates about the future.
the data (advantages and limitations) that have been used, and the alternatives (or lack thereof) amongst which the analyst had to choose.
perceptions on the expectations of different audiences, methodological preferences of the (mixed methods) researcher, structure of the research project, different timelines for different method types, skill specialisms, the nature of the data, ontological differences,
In this way it is possible to make more intelligible how data are collected processed and analysed in the process.
methods research and data analyses, J. Mixed Methods Res. 4 (4)( 2010) 342 360.60 R. B. Johnson, A j. Onwuegbuzie, Mixed methods research:
, fitting a curve to the historical data under the assumption that whatever forces are collectively driving the trend will continue into the future unabated.
and data source The most fundamental and challenging task is to select suitable indicators and data sources.
Thirteen indicators are selected for TLC assessment (Table 2). All the data of the indicators are extracted by priority year (the first filing date year for a patent application
In this research, we choose the Derwent Innovation Index (DII) as the data source and Vantagepoint (VP) for data cleaning and extraction.
But the patent information in the early years is unavailable (patent data in DII covers 1963 to the present.
and analyse indicator data. 2. 4. Data process First, we develop a map for 13 indicators of each training technology.
It is common to process multidimensional data by matrix. The original data are extracted by Vantagepoint
and imported into MS Excel 13 rows of indicators, 30 columns (years) for TFT-LCD (from 1978 to 2007), 36 columns (years) for CRT (from 1972 to 2008),
/Technological forecasting & Social Change 80 (2013) 398 407 We propose a normalisation method with two steps to pre-process the original data.
The first step is data smoothing by calculating three-year moving averages. The original data are defined as A A1;
A2: ð1þ Here A1, A2 represent the original data of TFT-LCD and CRT respectively.
Then the smoothed data of TFT-LCD and CRT are defined as A A1; A2 h i ð2þ 0 500 1000 1500 2000 2500 3000 3500 1978 1979 1980 1981 1982 1983 1984 1985 1986
1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
A2 represent the smoothed data of TFT-LCD and CRT respectively. The next step is to divide the smoothed data by their maximums.
The normalised data are defined as A A1; A2 h i ð5þ A1 i; j ð Þ A1 i;
j ð Þ maxj A1 i; j ð Þ; i 1; 13; j 1; 30 ð6þ A2 i;
A2 represent the normalised data of TFT-LCD and CRT respectively. We then apply the same normalisation steps to the NBS data.
The smoothed data and the final normalised data of NBS are defined as B b respectively, B i;
k ð Þ B i; k þ 1 ð Þþb i; k ð Þþb i;
ð9þ Then the nearest neighbour (NN) classifier is applied to the normalised data to measure the stage status of NBS.
In the paper, we employ it to process the multidimensional (13-D) data. The normalised data of TFT-LCD and CRT form the training set O (O R13),
and the normalised data of NBS are considered as a test set (R13). There are 30 training points in the TFT-LCD training set,
36 training points in the CRT training set, and 24 test points in the NBS test set.
Technology managers might informtheir NBS R&d investments by analysing patent application data from 1997 to the present to identify hot research topics or technological gaps.
to process the 13-D data by calculating the nearest distance among the test point
since data of the all indicators can be downloaded from most patent databases. Certainly, our study possesses limitations.
and obtain more data to validate the method. Second, we did not consider the technology type.
European Management Forum, Davos, 1981.10 H. Ernst, The use of patent data for technological forecasting: the diffusion of CNC-technology in the machine tool industry, Small Bus. Econ. 9 (4)( 1997) 361 381.11 T. H. Lee, N. Nakicenovic, Life cycle of technology
use of patent data, IEEE in Beijing, 2008.22 M. Meyer, Does science push technology? Patents citing scientific literature, Res.
Res. 41 (1993) 435 449.39 J. H. Friedman, N. I. Fisher, Bump Hunting in high-dimensional data, Stat.
1998) 769 805.58 A. Ben-Tal, A. Nemirovski, Robust solutions of linear programming problems contaminated with uncertain data, Math.
EMA is first and foremost an alternative way of using the available models, knowledge, data, and information.
Another example is the case where there is ample data available but also disagreement or uncertainty about which data to use.
EMA can be used to identify the extent to which the choice of data influences the model outcomes.
Instead of debating the choice of the right data, the debate can then shift to the development of policies or plans that produce satisfying results across the alternative sets of data.
Other possible uses of EMA include the identification of extreme cases, both positive and negative,
in order to get insight into the bandwidth of expected outcomes, and the identification of conditions under which significant shifts in performance can be expected.
Therefore, there is a need for data reduction techniques. One way of analyzing the results is to identify runs that share the same dynamic behavior over time.
There is an emerging field that studies the clustering of time series data. A wide variety of methods and techniques are being explored 34.
and different sources and types of information and data. EMA offers practitioners a model-based method for handling such situations.
Chang. 17 (2007) 73 85.33 J. H. Friedman, N. I. Fisher, Bump hunting in high-dimensional data, Stat.
Clustering of time series data a survey, Pattern Recog. 38 (2005) 1857 1874.35 J. H. Kwakkel, W. E. Walker, V. A w. J. Marchau
it is not sensible to extrapolate the future from data and relationships of the past.
unstructured data that implicate potential discontinuities 72. In addition, including perspectives from the different stakeholders can reveal new areas for innovation 73.4.2.3.
not only deal with the collection of data and models; they also involve the interaction of the stakeholders, their ideas, values and capacities for social change.
Amplification Today data on the behaviour of people is collected already constantly and used for individdua marketing based on user behaviour.
However, data on this dimension are only available for a much smaller number of countries,
and no data are available for Denmark. Two dimensions are of special interest for this paper:
This fact challenges Keenan and Popper's factors for explaining variations and similarities in regional foresight data.
a discussion paper was prepared that contained the government's overall objectives for the theme and key data and prerequisites.
and data sources We compared experiences at the local level with experiences at the national level.
and whether there was sufficient willingness to cooperate with the study and access to civil servants for interviews and other data sources.
One of the authors conducted both national level inquiries that were used as data sources for this article.
In the first, exploratory phase, both primary and secondary data sources were used to scan the TV industry
and‘fleshed'based on the secondary data that were gathered. Personas are‘fictitious, specific, concrete representations of target users'that are used for conveying information about a (future) user population in product design and innovation processes 27.
Personas are usually based on empirical data and real-world observations. In most cases however, researchers need to fall back on secondary sources 28
which were explored further in the cultural probing. 3. 2. 2. 2. Phase 2. Fig. 2 provides a schematic overview of the different personas that were developed in phase 2, based on the gathered data on current
Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at http://dx. doi. org/10.1016/j. futures. 2014.01.009.
and the regularity by which related and relevant data is collected 32.5. Strategic management of initiatives: these refer to the actions selected in each of the four BSC perspectives to achieve the defined strategic targets (step 1 above.
To collect data for the EICT and EIT ICT Labs case studies a participant-observer approach was utilized. 2 In both cases
data collection instruments included access to key documents, such as reports, internal documents, presentations and meeting minutes and observations through active participation within the organizations and, to some extent, in the build up Phase in the WINN
Also, a stand-alone and self-sustaining foresight process run by EICT could draw on the broad data basis available through the involvement of all partners.
It should be noted that this article is based on data from three cases. Although these give important impulses for research addressing foresight
threats) and scorecard analyses (Sripaipan, 2006), analytical hierarchy process, data envelopment analysis, multicriteria decision analyses Combinations Scenario-simulation (gaming),
and data of three governmental horizon Table 2. FTA scores for modelling and horizon scanning FTA score for modelling FTA score for horizon scanning Characteristic Score Comment Characteristic
The analysis leads to specific process recommendatiion for national horizon scannings related to how data are gathered, analysed, synthesised and used.
It concludes with a proposal to build a European netwoor for using joint scan data
not only during the collecctio of data, but also to guide the interpretation and synthesis of data and to create support for the implementation of results.
Who engages in horizon scanning? Horizon scans are initiated and used by different privaat and public organisations, mainly for strategic reasons.
compare basic data (lists of issues and issue descripptions from the horizon scans of the UK, The netherlands and Denmark;
develop a model for continuous data sharing and comparison; compare working methods and methodologies used by the different horizon scans
and the ways in which the scan data were used. Joint horizon scanning Science and Public policy February 2010 10 Joining up the data To compare the data of the different scans
and create a common corpus for further analysis a joint database was developed on basis of the Sigma Scan of the UK Foresight HSC.
This database was adapted to incorporrat the data from the Danish and Netherlands horiizo scans. Comparison of the scan data The comparison of data was based on the data of the UK HSC Sigma Scan11 and Delta Scan12 as publisshe on the internet
and the data in the report on Denmark (OECD, 2007) and The netherlands'Horizzo Scan Report 2007 (In't Veld et al.
2008). ) To facilitate the comparison, some relabelling of the categories that were used was necessary (see Table 1). From these categories13 we derived the followwin set of main categories:
Analysing data Data were compared on the subcategory level. An attempt was made also to select some issue clusters with estimated high impact to investigate the usefulnees of joint horizon scanning as preparation for more in-depth foresight to design common policies
The possible use of the horizon scan data at the European commission (EC) level was discussed in interviews with representattive of different directorates within the EC.
-and decision-makers (by supplying systematically gathered and analyyse data on opportunities, challenges and optioons to provide the basis for resilient
Development of the national horizon scans Data collection All three scans were developed in phases. In the first phase
The gathering of data for the UK Sigma Scan was facilitated by Outsights-Ipsos MORI, while the Delta Scan of the S&t developments was carried out by the Institute for the Future.
The primary data for the Danish scan were deliverre by the OECD International Futures Programme Unit with support from DASTI,
and Public policy February 2010 12 discussions with representatives from different ministrries The primary data for The netherlands scan were collected by the COS Horizon scanning team
After completion, the data in the OECD DASTI scan were published on the OECD website. Principal use of the scan data The UK horizon scan (see Figure 1) has tended to be used as part of a client-oriented project approach
where the starting point is a client (for example, a government departmeent reflecting on its strategic direction or policy.
Part of the HSC engagement with the client will be an analysis of scan data (and data from other speciaalis sources) relevant to the client's policy domaain Depending on the issues encountered in this analysis,
workshops may be organised with different stakeholders, providing a broad range of inputs to the policy and creating relevant new networks that cross not only policy domains
In Denmark, the scan issues were used as input for the selection of prioritised research themes in a four-year cycle of research funding (see Figure 3). The scan data were used alongside the outcome of a public internet‘hearing'process that delivered an additional input
of 366 proposals from the general Sigma Scan development Activity Engagements Output Scan the scans Categorise data Create the E-database Society
The launch of the scan data was covered well by the UK and some international press.
The distribution of the issues over the different categories in each scan is shown in Table 3. Analysis of the joint data
the data can quite easily be compared at the revised subcategory level. This comparison led to the conclusion that the scans contained many similar issues that were closely relaate that were taken up in all three scans (or at least in two.
if data can be incorporated from scans developed by countries on the other side of the world, at different stages of economic development or with contrasting political (and geopolitical) systems.
or link all scan data in one central database that will be used to develop proposals for joint foresiigh on common themes (through EC
but also that the shared scan data provide a common basis for further joint foresight to develop joint research programs and even policies.
Cooperation The use of joint scan data at the European level could offer a useful way of addressing the complex challenges the world
and which will focus on the use of scan data to address particular challenges that were indicated in the EC's World 2025 exercise (Fauroult, 2009).
and countries and organisations that contribute relevant data and experttise This network would then be available to poliic groups within the EC (and other international groups),
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