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Foresight support systems: The future role of ICT for foresight
Heiko A. von der Gracht
a,
⁎,VictorA.Bañuls
b,1
,MurrayTuroff
c,2
,
Andrzej M.J. Skulimowski
d,f,3
,TedJ.Gordon
e,4
a
Friedrich-Alexander-University (FAU) Erlangen-Nuremberg, Lange Gasse 20, 90403 Nuremberg, Germany
b
Pablo de Olavide University, MIS, Ctra. de Utrera km. 1, 41013 Sevilla, Spain
c
Information Systems Department, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, U.S.
d
AGH University of Science and Technology, Department of Automatic Control and Biomedical Engineering, Decision Sciences Laboratory, al. Mickiewicza30,30-050 Kraków,
Poland Kraków, Poland
e
The Millennium Project, 1 Smilax Dr, Old Lyme, CT, 06371 USA
f
International Centre for Decision Sciences and Forecasting, Progress & Business Foundation, ul. J. Lea 12B, 30-048 Kraków, Poland
article info abstract
Article history:
Received 22 July 2014
Accepted 10 August 2014
Available online 28 August 2014
The articlerepresents an introduction to the emerging field of foresight supportsystems. There is a
growing interest in the design, accessibility, application and development of ICT-based systems
for foresight processes by foresight practitioners and researchers alike. The foresight discipline
applies more than 30 different techniques to obtain valid and profound conclusions about future
developments and scenarios. Until now, only a few of these techniques havebeen transferred into
reliable software applications. However, this situation is changing quickly and many prototypes
and ICT applications arebeing developed throughout the world.Moreover, there hasbeen a recent
trend to develop even more complex software suites that embed and interlink multiple
techniques. In order to respond to the dynamic progress in the field, we introduce this Special
Issue on the future role of ICT in foresight. We selected nine informative articles that present the
most recent research on foresight support systems, which form the basis for ongoing scientific
debate in this new research stream.
© 2014 Elsevier Inc. All rights reserved.
Keywords:
Foresight
Data
Information
System
Decision
Technolo gical Forecasting & Social Change 97 (2015) 1–6
⁎Corresponding author. Tel.: +49 911 5302 444; fax: + 49 911 5302
460.
E-mail addresses: heikovdg@gmx.de (H.A. von der Gracht),
vabansil@upo.es (V.A. Bañuls), murray.turoff@gmail.com (M. Turoff),
ams@agh.edu.pl (A.M.J. Skulimowski), tedjgordon@gmail.com (T.J. Gordon).
1
Tel.: +34 954 97 7926; fax: +34 954 34 9339.
2
Tel.: +1 973 596 3366; fax: +1 973 596 5777.
3
Tel.: +48 12 6360100; fax: +48 12 6368787.
4
Tel.:+18604348608.
1. Motivation
Today, more and more foresight activities are supported by
information and communication technology (ICT). Consulting
firms offer trend databases and have increasingly their own
scenario software packages in place; prediction markets have
made great strides into business management (Ho and Chen,
2007). Moreover, the advantages of the Internet have been
increasingly utilized for foresight methods, such as the Delphi
technique (e.g. Gordon and Pease, 2006; Gnatzy et al., 2011;
Gheorghiu et al., 2009; Linstone and Turoff, 2011). Obtaining
data relevant for the future from web-mining and data-mining
has recently gained attention in literature, too (e.g. Chan and
Franklin, 2011; Olson et al., 2012). Methods for using ICT in
creative group decision-making are being developed (Comes
et al., 2011; Dalal et al., 2011). Furthermore, a general tendency
to analyze big data sets in different analytic activities is driven by
the rapid IT development that allowed analysts to solve many
hitherto non-tractable computational problems. This trend
applies to foresight as well, amplified by different techniques
to fuse qualitative and quantitative data (Skulimowski, 2012a).
Especially web 2.0 tools are considered to bring the two worlds
of quantitative and qualitative techniques in foresight closer to
each other, while promoting cross-disciplinary learning
(Haegeman et al., 2013).
Contents lists available at ScienceDirect
Technological Forecasting & Social Change
http://dx.doi.org/10.1016/j.techfore.2014.08.010
0040-1625/© 2014 Elsevier Inc. All rights reserved.
Overall, ICT-based applications are an important enabler of
foresight capabilities and will gain in importance in coming
years (Gordon et al., 2005; Rohrbeck, 2010). Just recently,
a global Delphi study on the future role of ICT in foresight
revealed that it is quite likely that ICT will revolutionize the
practice of foresight until 2020 (probability of occurrence: 63%)
(Keller and von der Gracht, 2014). The expert panel of
177 foresight experts from 38 different countries expects a
fundamental shift from the collection of foresight data to
the wise interpretation of information and its transfer into
strategies and actions.
However, the field is emerging, and as of yet –as might be
expected –very incoherently. Decision support systems have
proven to be a valuable component in managerial decision
making by providing reliable and objective assessments of
operational issues. This concept is now being transferred to
decisions with a longer and more strategic time horizon. Since
strategic foresight is often credited as a crucial antecedent
for long-term success in companies, and up to now foresight
processes have largely been project-based and individually
implemented, it seems particularly promising to integrate
methods of foresight in decision support systems. The phrase
“foresight support systems”first emerged in 2000 (Walden
et al., 2000), but has since then only sporadically been used
among others in conferences (see e.g. Skulimowski, 2012b)–
until recently. The research of Banuls and Salmeron (2011)
in 2011 marks the beginning of the systematic study of the
methodology of foresight support systems as an emerging
separate field of research. Such ICT systems allow experts and
stakeholders to collaborate over an entire foresight process
and thereby support reaching decisions oriented towards the
future.
We define foresight support systems as collaborative
computer-based systems aimed at supporting (1) communica-
tion, (2) statistical and qualitative data analysis, including
expert assessments (3) decision modeling (4) and rules of
order in foresight processes. They should provide a modular,
multi-methodological platform for information exchange and
creation, collaboration, analyses and assessments. Foresight
support systems should enable a general but also solution-
oriented foresight process to examine short and long-term
developments and scenarios. For classification of ICT-related
foresight we propose a framework of Schaltzeit GmbH, which
was presented during a workshop on foresight support systems
hosted by the German Network Futures Research (“Netzwerk
Zukunftsforschung”
5
) in January 2014. (See Fig. 1).
As demonstrated by this Special Issue many different
research projects are currently paving the way for this new
research stream. The research papers included cover three
main components that form a research framework for
this Special Issue: A) conceptual, B) methodological and
C) technological dimension. Moreover, three comprehensive
studies illustrate current developments in the defense,
health and telecommunication industries.
The remainder of the article is structured in the following
way. First, we will summarize the selected articles of the
Special Issue in Section 2, while highlighting the essential
results of each piece and following the logic of the noted
research framework. Second, we provide a discussion on future
trends and expected changes in this new research stream.
We specifically focus on research gaps and future topics for
consideration.
2. Current state of research on foresight support systems
2.1. Conceptual research
Attending the conceptual dimension, we present two
articles that cover the early attempts of foresight support
systems and establish the premises for their successful
implementation.
Glenn (2015-in this issue) contributes to the Special Issue
with an article that establishes the research background and
attempts of foresight support systems through the revision and
discussion of the term collective intelligence. The author largely
reflects on The Millennium Project that defines collective
intelligence as an emergent property from synergies among
three elements: 1) data/info/knowledge; 2) software/hardware;
as well as 3) experts and other individuals with insight. The
5
www.netzwerk-zukunftsforschung.eu.
Specialization
Automation
Efficiency
Validity / relevance
ICT-based
foresight
ICT-supported
foresight
Integration
vs.
vs.
vs.
Cost
Benefit
Complexity
Ease-of-use
Standard software Purpose-built-software
Software suite Single application
Fig. 1. Classification criteria of ICT-related foresight.
2H.A. von der Gracht et al. / Technological Forecasting & Social Change 97 (2015) 1–6
system continually learns from feedback to produce just-in-time
knowledge for better decisions than if any of these elements
would act alone. Foresight support systems would be included in
this classification, as well as other collective intelligence systems
refereed by the author such as Turoff's Electronic Information
Exchange System (see e.g. Turoff and Hiltz, 1978), several efforts
developed by the Millennium Project and the MIT or even
Wikipedia. Moreover, the paper illustrates an application by The
Millennium Project for the Egyptian Academy of Scientific
Research and Technology.
Keller et al. (2015-in this issue) propose five basic premises
that should be accomplished by foresight support systems:
information platform, collaboration, incentivization, systemic
perspective and support. Through these premises the authors
underline the necessity of supporting real-time communica-
tion and collaboration, and do not narrow or limit the
foresight process to specific methods, input or output
variables. They present the conceptualization of an indus-
trial foresight support system, which is designed to imple-
ment a continuous and embedded foresight process among
partners of a regional cluster. Through this implementation
the authors argue that, in an industrial context, engaging in
foresight (1) enables clusters and organizations to face
discontinuous change, (2) avoids lock-in of business clusters
by networking and knowledge exploitation and that (3) col-
laborative foresight can support such networking.
2.2. Methodological research
Regarding the methodological dimension, we have two
papers that cover trends in some of the most appropriated
methodologies for foresight support systems support:
Scenarios, Delphi Method and Prediction Markets.
Comes et al. (2015-in this issue) address the complexity of
strategic decisions and the resulting multitude of scenarios in
foresight contexts. They take a decision- and action-oriented
stance by proposing an approach to support making the trade-
offs between accuracy and resources spent by prioritizing
scenarios based on their significance for a specific decision even
on the basis of incomplete information. They use an emergency
management use-case to illustrate how the resulting reduced
set of scenarios helps reducing the time for the scenario
construction while leading to the same ranking of decision
alternatives as a more exhaustive set of scenarios.
Prokesch et al. (2015-in this issue) introduce a novel
combination of a prediction market and Delphi methodology
into a foresight support system. This electronic combination of
the two foresight techniques represents a relatively recent
innovation. The authors' proposal provides not only a market
forecast, but also delivers an entire forecast distribution. The
authors illustratetheir foresight support systemin practice by a
web-based forecasting study on macroeconomic indicators
with a financial expert group in a market environment.
2.3. Technological research
According to our definition, ICT is a main premise of a
foresightsupport system.It is for that reasonthat technological
issues should be carefully addressed. We present two papers
that explore the potential of Web 2.0 and data-text mining by
use of a foresight support system architecture.
Raford (2015-in this issue) explores the role that Web 2.0
approaches may play in qualitative scenario planning, using data
from five empirical case studies. Two categories of measures are
used to compare results between cases; participation charac-
teristics, such as the number and type of participants involved,
and interaction characteristics, such as the number of variables
and opinions incorporated, the mechanisms of analysis, etc. The
systems examined were found to have substantial positive
impact on the early stages of the scenario process. The results
are discussed in the context of emerging issues and opportuni-
ties for scenario planning, and particularly for public scenario
projects. Readers get insights how such tools and platforms
might change scenario practice over time.
Woo et al. (2015-in this issue) propose the use of Web-, text-,
and data-mining techniques for supporting corporate foresight
activities. They claim that organizations could utilize the rich,
objective decision-making data contained within Web forums
through which participants, who have common interests,
disseminate and receive information and form self-contained
communities. They apply their approach to the medical industry
in order to identify the major needs of Alzheimer disease
patients and their families through the analysis of online forums.
2.4. Comprehensive Studies
Durst et al. (2015-in this issue) illustrate a case of a foresight
support system tailored to the specific needs of the German
Federal Armed Forces. This research arises from the isolation of
specialized IT systemsfor supporting various strategic foresight
methods and foresight activities and the need of providing a
holistic solution. The article contributes to the field of foresight
support systems by documenting and demonstrating how to
combine different foresight methods and integrate expert
opinions in order to transform the strategic foresight process
into a powerful but major undertaking.
Kolominsky-Rabas et al. (2015-in this issue) present a
review of foresight methods in health care context and
introduce research on foresight support system development
in the project ProHTA (Prospective Health Technology Assess-
ment). ProHTA aims to develop a platform targeting health care
manufacturers and decision makers. It facilitates the assess-
ment of innovative health technologies prior to their launch.
The ProHTA approach focuses on interdisciplinary work related
to forecasting with hybrid simulation consisting of system
dynamics models for macro-simulation and agent-based models
for micro-simulation. The simulation has been run in its first case
for Mobile Stroke Units (MSUs).
Rohrbeck et al. (2015-in this issue) present a system of IT
tools that the Deutsche Telekom Innovation Laboratories have
built over the past 8 years. These tools are designed to support
their corporate foresight activities and interface directly with the
innovation management pipeline. The IT tools are used to fa-
cilitate the discussion between scouts and innovation manage-
ment and support innovation management workflow from the
discovery of change, interpretation, and triggering of managerial
responses. The overall system consists of a tool for scanning of
weak signals on change, a tool for collecting ides for innovations,
and one for triggering organizational responses. On the basis
of the case, the authors identified a positive impact from the
IT tools on productivity of scouts, foresight practitioners,
and the interface towards internal stakeholders. In addition,
3H.A. von der Gracht et al. / Technological Forecasting & Social Change 97 (2015) 1–6
they report that IT tools can contribute to lowering the
barriers for implementing foresight systems and that they
can positively influence the value creation of corporate
foresight.
3. Future trends in the field of foresight support systems
Our Introduction and selected articles in this Special Issue
elaborate on the current and future role of ICT for foresight. ICT
capacity for supporting foresight exercises is growing expo-
nentially. Similarly, the number of platforms and pioneering
projects is increasing. Besides the selected ones in this issue,
further platforms include iknow (see e.g. European Commis-
sion, 2011), Pivot (Noack et al., 2013), Shaping Tomorrow
(see e.g. Ramos et al., 2012; Calof et al., 2012), SCETIST
(Skulimowski, 2012a), EIDOS (Cserny et al., 2009)andthe
Emergency Preparedness Plattforms of NJIT (Turoff et al.,
2008). However, during the last 30 years, most of the efforts
in the ICT industry have been focused on empowering commu-
nications, ubiquity and participation capabilities among citi-
zens. People around the world have accepted the new internet-
based environment as ordinary and are used to applying
technologies to share knowledge both in private and profes-
sional contexts. In this sense, Linstone and Turoff (2011) claim
that the Internet has advanced us into the “age of participation”
and with the novel web-based use of classical techniques, such
as Delphi or Scenarios, we involve knowledgeable people
working on a common task at any time and in any place.
Following their argument, the Internet needs to evolve to
create the “age of collaboration”. For this purpose, the design of
foresight support systems is crucial since it involves several
aspects (communication, statistical and qualitative data anal-
ysis including expert assessments, decision modeling, rules
of order). Previous and current solutions in foresight that
are mostly focused on the communication level need to be
surpassed.
Therefore, we require flexible, open and powerful foresight
methodologies and technologies that support collective intel-
ligence systems. In this sense, merging developments from
other fields, such as the semantic web, artificial intelligence,
text and data mining, ontologies, the psychology of decision
making, simulation, pattern recognition and decision support
technologies, is crucial to generate accessible global knowl-
edge. However, it is likely that future systems will have a
modular architecture that allows for implementation of the
most popular functionalities in an inter-operational context.
We can expect common data repositories that are tailored for
predictive analytics to be widely used in foresight support
systems. In addition to general project-related dimensions
(cost, security, flexibility, quality, etc.) the following specific
dimensions will play a key role in future system design: the
danger of tool fatigue, the consideration of personal value and
learning experience, as well as the accountability of value con-
tribution by a single person. The latter will especially be impor-
tant in a corporate context where employees might question to
what extent the success of one's work can be attributed to
individual work contribution.
Nevertheless, the Internet phenomena are global but
asymmetric and there are two –or even more –velocities
that spread the use and acceptance of ICT technologies. In
developing global foresight support systems, the perspectives
and needs of those who are still not connected to the Internet
should not be overlooked (Skulimowski, 2013). Presently,
approximately one-third of the world's population uses the
Internet; the UN's goal is for 60 percent of the world to have
access to the Internet (50 percent in developing countries
and 15 percent in the least developed countries) by 2015
(Broadband Commission, 2012). When considering the general
technological background of foresight, we should be aware of
the existence of potential users and stakeholders, who are not
fully proficient in modern IT. This circumstance may lead, in
turn, to designing foresight support systems that meet the
requirements of such groups still suffering from the digital
divide, e.g. in developing countries or people in isolated areas
that have only recently received access to the Web. In this
sense, social factors concerning collaboration, privacy, security,
and openness should also be considered.
Moreover, there are large differences in the way people
collaborate in different parts of the world, even in one
country. Although sharing a common methodological back-
ground, foresight support systems are likely to be ap-
plications tailored to specific needs and circumstances. A
universal foresight support system could be too complicated
for some types of application and most of its modules and
functionalities would then be useless to the user. At the
German Network Futures Research workshop referred to in
Section 1, the difficulties concerning the scope of a systemic
solution were discussed. It was questioned whether a com-
plex suite of all available foresight techniques able to
address any potential foresight issue is truly the “holy
grail”the community should strive for. Such a solution
might be too complex to become the dominant application
in the market; instead, small, simple, easy-to- use apps might
be the appropriate way to revolutionize foresight practice.
During the panel discussions, it also became apparent that
even the best software suite cannot replace training and
assistance in foresight projects. The progress in the field of
foresight support systems will drive the need for support. It
is likely that new professional competence profiles will
emerge in the foresight discipline.
Moreover, it is important to underline the role of
creativity in foresight support systems. We should be
innovative and challenging in the design of foresight support
systems as well as in their use. If we ask obvious things, we
will receive obvious (and non-meaningful) answers. If we
work with experts, we probably will not obtain meaningful
feedback if the foresight support system does not meet
their expectations. Therefore, a design team should include
psychologists, sociologists, economists, mathematicians and
technologists, and graphic artists in order to address
complex challenges from a variety of perspectives. The
integration of these sources of knowledge will be a research
challenge for both the design and the data analysis stages of
the foresight exercises.
Finally, although we stipulate that foresight support
systems must have a direct link to ICT, including the
foresight professional as part of a system would provide
analytical depth. The term “foresight support system”not
only comprises direct reference to ICT, but also relates to the
wider context of a “system”. Thus, future research might also
elaborate on the systemic and philosophical aspect of the
field.
4H.A. von der Gracht et al. / Technological Forecasting & Social Change 97 (2015) 1–6
Acknowledgements
We would like to thank all authors who submitted their
work and thereby contributed to the Special Issue. Further-
more, we would like to thank all colleagues who supported the
review process with critical but constructive feedback.
References
Banuls, V.A., Salmeron, J.L., 2011. Scope and design issues in foresight support
systems. Int. J. Foresight Innov. Policy 7, 338–351.
Broadband Commission, 2012. The State of broadband 2012: Achieving digital
inclusion for all. Broadband Commission, New York, (September).
Calof, J., Miller, R., Jackson, M., 2012. Towards imp actful fore sight: view points
from foresight consultants and academics. Foresight 14, 82–97.
Chan, S.W.,Franklin, J., 2011.A text-based decisionsupport system for financial
sequence prediction. Decis. Support. Syst. 52, 189–198.
Comes, T., Hiete, M., Wijngaards, N., Schultmann, F., 2011. Decision maps: A
framework for multi-criteria decision support under severe uncertainty.
Decis. Support. Syst.52, 108–118.
Comes, T.,Wijngaards, N., Vande Walle, B., 2015. Exploring the fut ure: Runtime
scenario selection for complex and time-bound decisions. Technol.
Forecast. Soc. Chang. 97, 29–46 (in this issue).
Cserny, A., Utasi, A., Domokos, E., 2009. Using a Decision Support Software in
the Course of the Planning of a Waste Ma nagement System in Hungary.
In: Simeonov, L.I., Hassanien, M.A. (Eds.), Exposure and Risk Assess-
ment of Chemical Pollution—Contemporary Methodology. Springer,
pp. 473–486.
Dalal, S., Khodyakov, D., Srinivasan, R., Straus, S., Adams, J., 2011. ExpertLens:
A system for eliciting opinions from a large pool of non-collocated
experts with diverse knowledge. Technol. Forecast. Soc. Chang. 78,
1426–1444.
Durst, C., Durst, M., Kolonko, T., Neef, A., Greif, F., 2015. A holistic approach to
strategic foresight: A foresight support system for the German Federal
Armed Forces. Technol.Forecast. Soc. Chang. 97, 91–104 (in this issue).
European Commission, 2011. iKnoW Policy Alerts. EC, Brussels.
Gheorghiu, R., Curaj, A., Paunica, M., Holeab, C., 2009. Web 2.0 and the
emergence of future oriented communities. Econ. Comput. Econ. Cybern.
Stud. Res. 43, 117–127.
Glenn, J.C., 2015. Collective intelligence systems and an application by The
Millennium Project for the Egyptian Academy of Scientific Research and
Technology. Technol. Forecast. Soc. Chang. 97, 7–14 (in this issue).
Gnatzy, T., Warth, J., von der Gracht, H., Darkow, I.-L., 2011. Validating an
Innovative Real-Time Delphi Approach-A methodological comparison
between real-time and conventional Delphi studies. Technol. Forecast.
Soc. Chang. 78, 1681–1694.
Gordon,T., Pease, A., 2006.RT Delphi: an efficient, “round-less”almost real time
Delphi method. Technol. Forecast. Soc. Chang. 73, 321–333.
Gordon, T.J., Glenn, J.C., Jakil, A., 2005. Frontiers of futures research: what's
next? Technol. Forecast. Soc. Chang. 72, 1064–1069.
Haegeman, K., Marinelli, E., Scapolo, F., Ricci, A., Sokolov, A., 2013. Quantitative
and qualitative approaches in Future-oriented Technology Analysis (FTA):
From combination to integration? Technol. Forecast. Soc. Chang. 80,
386–397.
Ho, T.-H., Chen, K.-Y., 2007. Discovering and managing new product
blockbusters: The magic and science of prediction markets. Calif. Manag.
Rev. 50, 144–158.
Keller, J., von der Gracht, H.A., 2014. The influence of information and
communication technology (ICT) on future foresight processes—Results
from a Delphi survey. Technol. Forecast. Soc. Chang. 85, 81–92.
Keller,J., Markmann, C.,von der Gracht, H.A.,2015. Foresightsupport systemsto
facilitate regional innovations: A conceptualization case for a German
logistics cluster. Technol. Forecast. Soc. Chang. 97, 15–28 (in this issue).
Kolominsky-Rabas, P.L., Djanatliev, A., Wahlster, P., Gantner-Bär, M., Hofmann,
B., German, R., Sedlmayr, M., Reinhardt, E., Schüttler, J., Kriza, C., 2015.
Technology foresight for medical device development through hybrid
simulation: The ProHTA Project.Technol. Forecast. Soc. Chang. 97, 105–114
(in this issue).
Linstone, H.A., Turoff, M., 2011. Delphi: a brief look backward and forward.
Technol. Forecast. Soc. Chang. 78, 1712–1719.
Noack, P., Gaya-Piqué, L., Haralabus, G., Auer, M., Jain, A., Grenard, P., 2013.
Technology Foresight and nuclear test verification: a structured and
participatory approach. EGU General Assembly Conference Abstracts, p.
10434.
Olson, D.L., Delen, D., Meng, Y., 2012. Comparative analysis of data mining
methods for bankruptcyprediction. Decis. Support. Syst. 52, 464–473.
Prokesch, T., von der Gracht, H.A., Wohlenberg,H., 2015. Integrating prediction
market and Delphi methodology into a foresight suppor t system—
Insights from an online game. Technol. Forecast. Soc. Chang. 97, 47–64
(in this issue).
Raford, N., 2015. Online foresight platforms: Evidence for the impact on
scenario planning & strategic foresight. Technol. Forecast. Soc. Chang.
97, 65–76 (in this issue).
Ramos, J., Mansfield, T., Priday, G., 2012. Foresight in a network era: peer-
producing alternative futures. J. Futur. Stud. 17.
Rohrbeck, R., 2010. Corporate Foresight: Towards a Maturity Model for the
Future Orientation of a Firm. Springer, Berlin, Heidelberg.
Rohrbeck, R., Thom, N., Arnold, H., 2015. IT tools for foresight: The integrated
insight andresponse system of DeutscheTelekom Innovation Laboratories.
Technol. Forecast. Soc. Chang. 97, 115–126 (in this issue).
Skulimowski, A.M.J., 2012a. A foresight support system to manage knowledge
on information society evolution.In: Aberer, K., Flache, A., Jager, W., Liu, L.,
Tang, J., Guéret, C. (Eds.), Social Informatics: 4th international conference,
SocInfo 2012 LausanneLecture Notes in Computer Science. vol. 7710.
Springer, Berlin, Heidelberg, pp. 246–259.
Skulimowski, A.M.J., 2012b. Discovering complex system dynamics with
intelligent data retrieval tools. In: Zhang, Y., Zhou, Z.-H., Zhang, C., Li, Y.
(Eds.), Intelligent Science and Intelligent Data EngineeringLecture
Notes in Computer Science. vol. 7710. Springer, Berlin, Heidelberg,
pp. 614–626.
Skulimowski, A.M.J., 2013. Universal Intelligence, Creativity, and Trust in
Emerging Global Expert Systems. In: Rutkowski, L., Korytkowski, M.,
Scherer, R., Zadeh, L.A., Zurada, J.M. (Eds.), Artificial Intelligence and Soft
Computing,12th International Conference, ICAISC 2013, ZakopaneLecture
Notes in Artificial Intelligence. vol. 7895. Springer, Berlin, Heidelberg, pp.
582–592.
Turoff,M., Hiltz, S.R., 1978.User behavior patternsin the Electronic Information
Exchange System. Proceedings of the 1978 annual conference vol. 2. ACM,
pp. 659–665.
Turoff, M.,White, C., Plotnick,L., Hiltz, S.R., 2008. Dynamic emergency response
management for large scale decision making in extreme events. In:
Fiedrich, F., Van de Walle, B. (Eds.), Proceedings of the 5th International
ISCRAM Conference. ISCRAM, Washington, DC, USA, pp. 462–470.
Walden, P., Carlsson, C., Liu, S., 2000. Industry foresight with intelligent agents.
Hum. Syst. Manag. 19, 169–180.
Woo, J., Lee, M.J., Ku, Y., Chen, H., 2015. Modeling the dynamics of medical
information through web forums in medical industry. Technol. Forecast.
Soc. Chang. 97, 77–90 (in this issue).
Dr.Heiko A.von der Gracht is a Post-doctoral Researcher at Friedrich-
Alexander-University Erlangen-Nuremberg, Chair of Supply Chain Management,
Germany, and Head of Think Tank for Futures Management at the Institute of
Corporate Education e. V. (incore), whichissupportedandsponsoredbyKPMGin
Germany. His research interests encompass corporate foresight, the Delphi and
scenario techniques, foresight skills and education, and quality standards in
futures research. His works have been published in several books and peer-
reviewed journals, including Technological Forecasting & Social Change, Futures,
Foresight, and the European Journal of Futures Research.
Dr.VictorA.Bañuls is Associate Professorof Management Information Systems
at the Universidad Pablo de Olavide (UPO) at Seville (Spain). He research has
been published in journalssuch as Technological Forecasting and SocialChange,
Technovation, IEEESMC and Futures (amongothers). Moreoverhe has been co-
chair of the track “Foresight, Planning and Risk Analysis in Emergency
Management”at the ISCRAM (Information Systems for Crisis Response and
Management) conference since 2010 being member of the board of this
organization since 2014. His present research efforts are focused on foresight
methodologies, information systems and emergency management.
MurrayTuroff is a DistinguishedProfessor Emeritusat the New Jersey Institute
of Technology. He is a coeditor of a recent book on Emergency Management
Information Systems(M.E. Sharpe 2010). Besideshis early and continuing work
with the Delphi Method, he spent most of his academic research career in the
designand evaluation of Computer Mediated Communicationsystems. His well
cited 1975book (Google scholar) with Harold Linstone on the DelphiMethod is
available free on his website (http://is.njit.edu/turoff). After 9/11 he turned his
attention back to his early work in Emergency Management and in 2004, he
was a cofounder of the international organization ISCRAM (Information
Systems for Crisis Response and Management). Together with Starr Roxanne
Hiltz he was a coauthor of a prizewinning book in 1978that predicted the Web
as we know it today: The Network Nation: Human Communication Via
Computer. MIT Press reprinted this book in 1993. He is a co-editor of a recent
special issue of TFSC on Emergency Management Planning and Foresight (80,
2013).
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Andrzej M.J.Skulimowski is a Professor at the Department of Automatic
Control and Biomedical Engineering, AGH University of Science and Technol-
ogy, Kraków, Poland,and the Directorof the Decision Sciences Laboratory. Since
1995, he has been the President of the International Progress and Business
Foundation, Kraków,where he directed over 80 research, consulting and policy
supportprojects within the EU Framework Programs, ESTO,Interreg, LLP, ERDF,
GTD, and other programs. His main field of expertise is decision-support
systems, foresight,multicriteria decision analysis, R&D and innovation policies.
He is the author and editor of 10 books, including a recent monograph on
“Selected methods, applications, and challenges of multicriteria optimization”
(AGH Publishers, 2014) and over 150 scholarly papers.
Theodore Jay Gordon is a specialist in forecasting methodology, planning, and
policy analysis. He is co-founder andBoard member of The Millennium Project,
a global think tank. The Millennium Project was selected among the top ten
think tanksin the world for new ideas by the2013 University of Pennsylvania's
GoTo Think Tank Index. He also serves as emeritus director of the Institute for
Global Ethics. He is the recipient of the Ed Cornish “Futurist of the Year”award
and is a recipient of the Shaping Tomorrow Lifetime Achievement Award.
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