Towards the Formalisation of the Decision
Support Systems Domain –
Step 1: Investigating the Decision Support
Systems Curriculum
A report prepared by the Task force on DSS Curriculum for the
Working Group 8.3 of the International Federation for Information
Processing (IFIP).
Date: June 2008
Version 1.8
© IFIP WG 8.3 - DSS Curriculum Task Force
Do not reproduce without explicit permission from the Chairs of the task force (see
section 1 of the report)
Table of Contents1.
1. The DSS curriculum Task Force - IFIP WG8.3
2. Audience Expected.
3. Summary of Key points.
4. Task Force Report.
4.1 The Discipline of DSS.
4.2 The Disciplines of Reference for a DSS Curriculum
4.2.1. Intellectual Relationship between DSS and Business
Disciplines
4.2.2 Intellectual Relationship between DSS and NonBusiness Disciplines
4.2.3 Conclusion
4.2.4 References
4.3 The DSS curriculum Grid and how to use it
4.3.1 The Grid (see the other document)
4.3.2 Relevance for a DSS career path
4.3.3 Course Prerequisites.
4.3.4 Multi-level DSS Curricula.
4.3.5 General Description of Courses (methods of delivery,
additional components, such as case study and other
support material).
4.3.6 General considerations on what a DSS course should
look like
5. Conclusion and Future work.
6. Appendices
Appendix 1: The DSS Curricula Grid
Appendix 2: coding the threads in the DSS curriculum
Appendix 3: List of useful References (Books and Journal Papers).
1
The template for this report was derived from that initially proposed in November 2006 by Manuel
Mora based on the MSIS2006 Curriculum (see for instance: MSIS 2006 Curriculum Preview by J.T.
Gorgone, P. Gray, E.A. Stohr, J.S. Valacich, and R.T. Wigand, Communications of the Association for
Information Systems (Volume 15, 2005) 534-554).
1.- The Task Force IFIP WG8.3.
The task force on DSS curriculum was created at the Bi-annual conference of
the working group in London in July 2006. Initially, the notion that this work
should be undertaken was suggested by Peter Gelleri in discussions with
Frederic Adam, Csaba Csaki, Patrick Brezillon, Patrick Humphreys and Zita
Paprika beginning in March 2006. The task force was formally announced at
the business meeting at the end of the London event. Following this, nineteen
participants registered their interest in contributing to the work of the task force
and began working on creating this report. Along the way, a number of
additional participants joined the group effort or were asked to join and add
specific contributions to the work, bringing the total number of participants to
twenty-one (see table 1 below, participants listed in alphabetical order of the
last name).
Table 1: List of task force members
People
Frédéric Adam*2
Marko Bohanec
Patrick Brezillon
Sven Carlsson
João Clímaco
Csáki Csaba
Sean Eom
Péter Gelléri
George Huber
Patrick Humphreys
Garrick Jones
Peter Keenan
Laszlo Mero
Manuel Mora
Karen Neville
Peter O'Donnell
Dan O'Leary
Zita Paprika
David Paradice*2
Daniel Power
Pascale Zaraté
Institution
University College Cork, Ireland
Institut Jozef Stefan, Ljubljana,
Slovenia
Universite Paris 6, France
Lund University, Sweden
Universidade de Coimbra, Portugal
University College Cork, Ireland
Southeast Missouri State
University, USA
Corvinus University, Budapest,
Hungary
University of Texas, USA
London School of Economics, UK
London School of Economics, UK
University College Dublin, Ireland
Eötvös Loránd University,
Budapest, Hungary
Autonomous University of
Aguascalientes, Mexico
University College Cork, Ireland
Monash University, Australia
University of Southern California,
USA
Corvinus University, Budapest,
Hungary
Florida State University, USA
University of Northern Iowa, USA
IRIT – INP Toulouse, France
email
FAdam@afis.ucc.ie
marko.bohanec@ijs.si
brezil@poleia.lip6.fr
Sven_Carlsson@hermes.ics.lu.se
jclimaco@inescc.pt
csakics@itm.bme.hu
sbeom@semo.edu
peter.gelleri@winsdom.com
George.Huber@mccombs.utexas.edu
P.Humphreys@lse.ac.uk
g.a.jones1@lse.ac.uk
peter.keenan@ucd.ie
mero@phone2play.com
mmora@securenym.net
KNeville@afis.ucc.ie
podonnell@gmail.com
DOLeary@marshall.usc.edu
zita.paprika@uni-corvinus.hu
dparadice@cob.fsu.edu
power@dssresources.com
Pascale.Zarate@irit.fr
During late 2006, 2007 and the first half of 2008, using an email distribution list
for communication and dissemination of the work, the task force prepared this
report, with varying levels of contribution from its different members.
*
Chairs of the task force on DSS curriculum.
2. Audience Expected.
The audience for this preliminary report is the working group, but there is a
wider opportunity to broaden this audience to external stakeholders both inside
and outside the academic community. Initially, our work is aimed at promoting
the development and implementation of a DSS curriculum that would be
universally accepted. However, in the second instance, it will be required to
disseminate this work within the corporate environment where it can be used for
re-writing job descriptions and hiring suitable candidates.
Section 5 of this report considers some of the logical future work that could be
undertaken, probably by another task force, towards a broader objective, such
as for instance the professionalization of the DSS domain. The extent and
scope of this future work are beyond the scope of this report: they will be
proposed as items for discussions at the business meeting of the working group
in Toulouse in July 2008.
3. Summary of key points.
This report puts forward some important points for discussion towards the
formalization of a multi-level DSS curriculum.
It discusses the intellectual foundation of the field and its reference disciplines.
It proposes two artifacts (presented in Appendices 1 and 2) which can be used
as a basis for listing in a coherent fashion all the topics and sub-topics that
should be included in the curriculum at the different levels of study on the one
hand (App 1) and to code the make up in terms of topics of the different
underlying themes that run through the DSS field in its wide sense.
In addition, it considers the student audience which should be the target of DSS
programmes at various levels and explains the relevance of this discussion to
the concept of “DSS career”.
The report also discusses what a DSS course should look like and presents
some examples of courses currently being offered in a sample of institutions.
Finally, the report suggests a reflection on the future of DSS, in terms of its use
of leading edge technologies and in terms of the professionalization of the DSS
career.
4. Task Force Report.
4.1 The Discipline of DSS3.
The field of Decision Support Systems is one of the most enduring in the
Information Systems domain, having emerged in the 1960’s, at the beginning of
the history of Information Systems as an area of research, from the pioneering
work of Simon, Keen and Scott Morton amongst many others. Through five
decades, this field has continued to attract considerable and increasing
attention from researchers and practitioners, under a variety of names and
banners.
Back in 1991, Teng and Galletta (1991) conducted a survey of IS researchers
which showed 32% of respondents listed DSS as their area of research, with a
further 22% listing artificial intelligence and 21% database management
systems. These three areas, which totaled 281 mentions out of 845 valid
responses, were actually the top 3 IS research areas listed by Teng and
Galletta. In more recent times, Arnott and Pervan’s (2006) comprehensive
review of the DSS field yielded 1093 research papers published from 1990 to
2004 in 14 major journals, the key journals in a broadly defined IS research
area. These papers represented 15.2% of all papers published in the 14
journals.
These statistics confirm the status of DSS as a core area of the IS domain and
one with a dynamism well illustrated by the strength of such international
groupings as the AIS Special Interest Group on Decision Support, Knowledge
and Data Management Systems (SIG DSS), the Working Group 8.3 of the
International federation for Information processing (IFIP) on DSS and the EURO
Working Group on DSS (EWG DSS). The varied membership of these grouping
and the range of backgrounds and the multiple expertise of their members
illustrate in vivid fashion the multitude of areas of research which DSS can now
claim to have successfully integrated, ranging from Operations Research, to
Management and to Social Psychology. This accumulation of knowledge has
resulted in a field in constant evolution with excellent, insightful and sometimes
even fascinating research results.
The research presented in IFIP 8.3 and other groupings indicates that thinking
of organisations as decision making entities and the systems to support them as
decision support system is a perspective that has served our discipline very well
and will continue to do so in the future.
However, the success of the DSS field is also one of its weaknesses: as the
area has grown and knowledge from an increasing number of domains is
brought to bear on the problems that must be studied, it becomes increasingly
difficult to maintain a complete vision of DSS and all its latest developments.
Already, it is arguably the case that a researcher would have to struggle to keep
a handle on all requisite knowledge to be able to understand or review all the
DSSs papers produce in a year. For instance, when both qualitative, human
3
This section borrows from the preface of Adam, F and Humphreys, P (2008) Encyclopaedia of
Decision Making and Decision Support Technologies, Idea Publishing Group, Hershey, PS.
oriented and quantitative, mathematical approaches are used, it becomes
difficult to keep pace with developments in Multi-Criteria Decision Making or in
the modeling of uncertainty on the one hand, as well as with our increased
understanding of the role of emotions in human decision making on the other
hand. Furthermore, for students of DSS, young researchers and practitioners
who want to train themselves to be worthy of the DSS specialists label, it is
increasingly difficult to know what to learn and what skills to practice.
The aim of the DSS curriculum taskforce must be understood in this context: the
aim is to stop and think for a while so as to formalize our vision of the DSS
domain in its broad sense and present an organized and understandable stateof-the-art picture of its topics and applications.
In doing so, there are two different ways to try to provide a good picture of the
identity of the DSS area: These two different approaches to identify the core of
the DSS field are: the normative approach and the descriptive approach. The
former seeks to define what research subspecialties should be included based
on established rules and boundaries for a field. The latter defines the core of
The DSS field in terms of what DSS researchers do and everything that the
DSS community studies (Alter, 2003).
ACA analysis of bibliographic database of the DSS literature (1991-2004)
revealed the core research subspecialties of DSS field (Eom, 2007). The
uncovered research subspecialties are (1) group support systems, (2) model
management, (3) foundations, (4) evaluation, (5) user interface, and (6) multiple
criteria decision support systems/negotiation support systems.
Section 4.2 elaborates further on our efforts to understand the reference
disciplines of DSS and their contributions.
Alter, S., "The IS Core - XI: Sorting Out the Issues About the Core, Scope, and
Identity of the IS Field," Communications of the Association for Information
Systems, Volume 12, Number Article 41, 2003, pp. 607-628.
Arnott, D., and Pervan, G. (2006), Eight Key Issues for the Decision Support
Systems Discipline. In Adam, Brezillon, Carlsson, and Humphreys (eds)
Creativity and Innovation in Decision Making and Decision Support,
Decision Support Press, London, UK.
Eom, S. B., The Development of Decision Support Systems Research: A
Bibliometrical Approach, The Edwin Mellen Press, Lewiston, NY, 2007.
Teng, J. and Galletta, D. (1991) MIS research directions: a survey of
researchers' views, Database, Winter/Spring 1991, 53-62.
4.2 The Disciplines of Reference for a DSS Curriculum4.
The area of decision support systems has made meaningful progress over the
past two decades and is in the process of solidifying its domain and
demarcating its reference disciplines. Studying reference disciplines enriches
DSS research as investigators adopt their theories, methodologies,
4
This section was largely contributed by Sean Eom based on his on-going work on the structure of DSS.
philosophical bases and assumptions as well as assess what these theories
imply for DSS research.
Applying author co-citation analysis to the extensive database of DSS literature
(1970-2005) lets us identify the most prominent DSS reference disciplines.
Author co-citation analysis (ACA) is the principal bibliometric tool to establish
relationships among authors in an academic field, and thus can identify
subspecialties of a field and indicate how closely each these subgroups is
related to each of the other subgroups. This section is based on (Eom, 1997;
Eom, 2007; Eom, 2008).
The result of ACA produced a dendogram illustrating hierarchical clustering of
several DSS research subspecialties and multiple reference disciplines
including systems science, organizational science, cognitive science, artificial
intelligence, multiple criteria decision making, communication science, and
psychology.
4.2.1. Intellectual Relationship between DSS and Business Disciplines
Organization Science Contributions to DSS
DSS Design and Organization Science: Detailed understanding of individual,
group, and organizational decision processes is a prerequisite for effective DSS
design. DSS researchers have developed several development methodologies
such as a decision centered approach, an organizational change process
approach, the ROMC (Representation, Operations, Memory Aids, and Control
Mechanisms) approach, and a systems-oriented approach. The organizational
change process approach necessitates the understanding of both the normative
decision process that the system is intended to generate and the actual
decision process that exists. Organizational decision making models such as
the rational model, the organizational process model, and the satisficing model
have contributed to the development of DSS design methodologies. The
contributions of organization science are further detailed in (Eom and Farris,
1996).
GSS and Organization Science: Many techniques developed by organization
scientists such as silent and independent idea generation, presenting each idea
in a round-robin procedure, silent independent voting, and so forth have been
successfully utilized in the development of group decision support systems.
Multiple Criteria Decision Making (MCDM) Contributions to DSS
By their nature, MCDM problems usually have numerous non-dominated
solutions. To single out a decision alternative, An array of diverse MCDM
techniques provides decision makers with more flexibility in solving ill-structured
problems through direct interaction with analytical models. The MCDM
algorithms/techniques include ordinal comparisons, pairwise alternative
comparisons, implicit utility functions, goal programming and analytical
hierarchical process, and others.
Other Business Discipline Contributions to DSS
Accounting: To most decision makers, including accountants, maintaining
consistency of judgment is critically important. Accounting research
demonstrates how behavioral decision theory developed by cognitive scientists
enriches the understanding of accounting problems with an ultimate goal of
decision improvement through the improvement of the consistency of judgment.
This line of research focuses on the examinations of the effects of heuristics on
the accuracy of judgment using statistical decision theory, such as Bayes'
theorem, as a criterion for evaluating intuitive or probabilistic judgments. These
approaches provided a theoretical foundation for developing DSSs (including
expert systems) to estimate probability of bankruptcy, predict fraud, evaluate
sample evidence and make sample-size choice in audit settings, rank
importance of materiality factors, and make many other judgments of
probability.
Economics: DSS researchers have referenced economic theory of teams to
explain various issues in designing and implementing group decision support
systems. Especially notable, the theory of games is concerned with providing
strategies for the games, both zero-sum and non-zero-sum, played by two or
more persons with different interests and constrained by different rules of the
game. On the other hand, the economic theory of teams is concerned with the
case of several persons who have common interests in making decisions.
Management Science: Management science models have been essential
elements of DSSs. Forecasting and statistical models, simulation, integer
programming, linear programming, and network models have been powerful
tools that have been increasingly embedded in DSSs. Advances in algorithms
such as large-scale primal transshipment algorithm developed by management
scientists
make it possible for unsophisticated users to obtain readily
understandable outputs. Advanced implementations of algorithms such as
simplex methods, the new interior point, branch-and-bound algorithms, and so
forth have been incorporated in commercially available software tools for DSS
development (e.g., Excel).
Strategic Management: Porter's work on techniques for analyzing industries
and competitors and creating/sustaining superior performance have provided an
impetus and theoretical basis for developing DSSs that analyze an
organization’s external environment, its industry’s trends, mergers and
acquisitions, product/market position, facilitate strategic planning at various
levels (corporate, division, department) and with various functions.
4.2.2 Intellectual Relationship between DSS and Non-Business Disciplines
Systems Science Contributions to DSS
Systems Science and DSS Design: Churchman developed the theory of
designing inquiring systems, which defined a set of necessary conditions for
conceiving a system. The set of conditions provides the system designer with a
set of precepts for building an integral system. The theory of design integrity is
applied to designing effective DSS, which explicitly consider a common set of
DSS elements simultaneously including DSS environment, task characteristics,
access pattern, DSS roles and function, and DSS components, strongly
reflecting Churchman's view that "all systems are design nonseparable."
Cognitive Science Contributions to DSS
The central component of cognitive science is the study of the human adult's
normal, typical cognitive activities such as language understanding, thinking,
visual cognition, and action by drawing on a number of disciplines such as
linguistics,
artificial
intelligence,
philosophy,
cognitive
psychology,
neuroscience, and cognitive anthropology. Cognitive psychology has been
especially influential in the development of individual differences/user interface,
implementation, and foundation subspecialties of the DSS area. Cognitive
psychology deals with the study of visual information processing; neuroscience
and neural networks; cognitive skills in problem solving; reasoning including
reasoning about probability; judgment and choice; recognizing pattern, speech
sounds, words, and shapes; representing descriptive and procedural
knowledge; learning and memory; and structure and meaning of languages
including morphology and phonology.
Cognitive psychology has made important contributions toward a better
understanding of the relationship between the effectiveness of problem
structuring and an individual's general thinking skills. A user's ability to create
and use visual images is positively related to better problem-solving and
problem-structuring performance.
Cognitive Science and Implementation: The theory of Newell and Simon
(1972) has been applied to understand relationships between problem
presentation to decision makers and successful implementation of DSSs. The
organization of the problem representation significantly influences the structure
of the problem space and the problem-solving processes decision makers use.
Therefore, when their problem-solving processes are adapted to the problem
representation, decision makers make effective decisions, and this leads to
successful implementations of DSSs.
Artificial Intelligence Contributions to DSS Model Management
Since 1975, model management has developed as an important DSS research
specialty that encompasses several topics including model construction, model
base structure and representation, and model base processing. Artificial
intelligence (AI) has strongly influenced the development of the model
management subspecialty. The concept of knowledge-based model
management systems was introduced to support tasks of formulating a new
decision model and/or choosing an existing model from the model base,
analyzing the model, and interpreting the model's result. Other researchers
present the use of artificial techniques for determining how models and data
should be integrated and for representing models and developing mechanical
methods for automatic selection, synthesis, and sequencing of models in
response to a user query. Research of intelligent agents (known also
knowbots, softbots, or adaptive systems) is an emerging interdisciplinary area
involving investigators from such directions as expert systems, decision support
systems, cognitive science, psychology, databases, and so on.
Knowledge Management Contributions
Much of the early work in knowledge management was tied closely to artificial
intelligence. However, over time knowledge management has gradually
become its own area, and one that is probably closer to DSS than any one
other discipline. Knowledge management even has similar goals as DSS, such
as providing the users with knowledge to facilitate their decision making. For
example, O’Leary (1998, p. 54) defined enterprise knowledge management as
formally managing knowledge resources in order to facilitate access and reuse
of knowledge, typically by using advanced information technology. Knowledge
management is less aimed at providing information, and more concerned with
knowledge or knowledge from information. As a result, it is less concerned with
numeric data and more concerned with symbolic representations that are likely
to be more directly usable. Knowledge management also makes heavy use of
intelligent agents and capabilities, such as ontologies to categorize and facilitate
knowledge use and reuse.
Psychology Contributions to Group DSSs
Psychology appears to be one of the major disciplines that has greatly
influenced the development of DSSs intended to support the multiple
participants engaged in making a group decision. Psychology is a diverse field
with many branches such as cognitive psychology, industrial and organizational
psychology, and social and behavioral psychology.
An important issue in the study of a group decision support system (GDSS) is
how to minimize the dysfunctions of group interaction processes such as
evaluation apprehension, cognitive inertia, domination by a few individuals, and
so on. In devising GDSSs to minimize the dysfunctions, researchers have
sought to build on/extend the research results of group dynamics, which seeks
the answer to the following question: How is behavior influenced by others in a
group? In the area of group dynamics, Shaw (1981) and McGrath (1984)
provide an integrative conceptual framework for synthesizing the voluminous
body of group research and present approaches to the study of groups.
A series of experiments by psychologists such as Diehl and Stroebe (1987)
conclude that "individuals brainstorming alone and later pooling produce more
ideas, of a quality at least as high, as do the same number of people
brainstorming in a group" due to several possible reasons such as evaluation
apprehension, free riding, and production blocking. A significant finding of Diehl
and Strobe's experiments is their recognition of the magnitude of production
blocking impacts on productivity loss of brainstorming groups. By manipulating
blocking directly, Diehl and Strobe (1987) were able to determine that
production blocking accounts for most of the productivity loss of real
brainstorming groups. Therefore, their findings suggest that it might be more
effective to ask group members first to develop their ideas in individual
sessions; then these ideas could be discussed and evaluated in a group
session.
Siegel and others (1986) investigate the behavioral and social implications of
computer-mediated communications, seeking to answer the question "Do
computer-mediated communications change group decision making?" Results
of their experiments suggest that simultaneous computer-mediated
communication significantly affects efficiency, member participation,
interpersonal behavior, and group choice, when compared to the face-to-face
meetings.
Janis and Mann (1977) analyze psychological processes involved in conflict,
choices, commitment, and consequential outcomes and advance a descriptive
conflict theory. Their theory is concerned with when, how, and why
psychological stress generated by decisional conflict impinges on the rationality
of a person's decisions and how people actually cope with the stresses of
decisional conflicts. Based on the theoretical assumptions derived from
extensive research on the psychology of stress, Janis and Mann provide a
general theoretical framework for integrating diverse findings from
psychological/behavioral science research and reviewed the main body of
psychological/behavioral science research concerning the determinants of
decisional conflicts.
Communication Theory Contributions to GDSSs
The study of human communication is an interdisciplinary field investigating
communication processes of symbolic interaction. The field of human
communication is broadly divided into interpersonal, group, organizational, and
mass communication. Communication theorists have addressed questions of
group decision making such as: How do groups affect individuals? What factors
contribute to task output?
Stemming from the human communication school of thought, coordination
theory has been proposed as a guiding set of principles for developing and
evaluating GDSSs. Coordination theory analyzes various kinds of dependencies
among activities and investigates the identification and management of
coordination processes.
Contributions to the DSS Field from Other Disciplines
Computer Science: Relational database management theories, from the
discipline of computer science, have substantially influenced decision support
system foundations, architectures, and implementations since the early days of
the DSS field.. Ongoing innovations in database management, such as multidimensional data models, data warehousing, data marts, high level query
languages, and distributed databases continue to be important to DSS
progress.
Database management has also impacted the DSS specialty area of model
management. The structured modeling approach is an extension of the entity-
relationship data model and advocates a set of model manipulation operators.
In addition to data base management, computer scientists have influenced the
development of research in the subspecialty of DSS user interface design and
evaluation.
4.2.3 Infrastructure
DSS takes place in a context within an organization that has a particular
technology infrastructure. That infrastructure has a number of levels that
influence what can be done from a DSS perspective, and different organizations
have different capabilities. Some basic infrastructure environment
considerations include the following. First, what is the computing environment,
e.g., is the DSS embedded in a personal computing, mobile computing, etc.
environment. Second, what is the networking environment? Is the system part
of a broader system integrated into a peer computing environment, is the
system linked to the Internet, etc. Third, what is the information environment?
For example, is there a large transaction processing capability, such as an
enterprise resource planning system (e.g., O’Leary 2000) shoving information
into the system or is information captured specifically for the particular system,
and to what extent is the DSS integrated with the conventional systems?
Fourth, what kind of security is in place? Is there sharing of information?
4.2.4 Conclusion
Decision making, and attendant issues, is a subject of research in many
disciplines. To contribute to a full understanding of DSS as a field of study, it is
necessary to examine, in a historical context, the intellectual connections of
decision support systems research to a variety of reference disciplines. DSS
research has benefited from the investigations business disciplines such as
organization science, management science (including MCDM), accounting, and
strategic management. It has also benefited from investigations of many
disciplines outside the business arena such as artificial intelligence, systems
science, psychology, cognitive science, computer science, and communication
theory.
Through a thorough examination of the intellectual relationships between DSS
research subspecialties and contributing disciplines, patterns of positive,
constructive interactions identified. First, ideas, concepts, and terms (e.g.,
electronic meeting, groupware, teleconferencing) are coined by the researchers
in diverse academic disciplines. Second, research findings in reference
disciplines such as AI and MCDM have been applied to forge new DSS
research subspecialties such as artificially intelligent decision support systems
and multiple criteria decision support systems. Third, reference disciplines such
as database management have been applied and extended to build a theory of
models as a guide to the management of models in DSSs. Research based on
the well-established reference disciplines with abundant theories is most likely
to lead to the development of new theories.
4.2.4 References
Alter, S., "The IS Core - XI: Sorting Out the Issues About the Core, Scope, and
Identity of the IS Field," Communications of the Association for Information
Systems, Volume 12, Number Article 41, 2003, pp. 607-628.
Diehl, M., and Stroebe, W., "Productivity Loss in Brainstorming Groups:
Towards The Solution of a Riddle," Journal of Personality and Social
Psychology, Volume 53, Number 3, 1987, pp. 497-509.
Eom, S. B., "Assessing The current State of Intellectual Relationships between
The Decision Support Systems Area and Academic Disciplines," In Proceedings
of The Eighteenth International Conference on Information Systems, K. Kumar
and J. I. DeGross (Ed.), International Conference on Information Systems,
Atlanta, GA, 1997, pp. 167-182.
Eom, S. B., The Development of Decision Support Systems Research: A
Bibliometrical Approach, The Edwin Mellen Press, Lewiston, NY, 2007.
Eom, S. B., "Reference disciplines of decision support systems," In Handbook
on Decision Support Systems 1: Basic Themes, F. Burstein and C. W.
Holsapple (Ed.), 1, Springer-Verlag, Berlin, Heiderberg, 2008, pp. 141-159.
Eom, S. B. , and Farris, R., "The Contributions of Organizational Science to The
Development of Decision Support Systems Research Subspecialties," Journal
of the American Society for Information Science, Volume 47, Number 12, 1996,
pp. 941-952.
Janis, I. L., and Mann, L., Decision Making: A Psychological Analysis of
Conflict, Choice, and Commitment, Free Press, A Division of Macmillan
Publishing Co. Inc., New York, 1977.
McGrath, J. E., Groups: Interaction and Performance, Prentice Hall, Englewood
Cliffs, NJ, 1984.
Newell, A., and Simon, H. A., Human Problem Solving, Prentice Hall,
Englewood Cliffs, NJ, 1972.
Shaw, M. E., Group Dynamics: The Psychology of Small Group Behaviour,
McGraw-Hill, New York, 1981.
Siegel, J.; Dubrovsky, V. J.; Kiesler, S.; and McGuire, T. W., "Group Processes
in Computer-Mediated Communication," Organizational Behaviour and Human
Decision Processes, Volume 37, Number 2, 1986, pp. 157-187.
4.3 The DSS curriculum Grid and how to use it
4.3.1 The Grid (see Appendices 1 and 2)
The DSS curriculum Grid, which is presented in full in Appendix one of this
report, is the core contribution of this report. It represents our perception of the
building blocks of the DSS curriculum, expressed in a multi-level way, to take
into account the topics that need to be included respectively, in a general
awareness undergraduate DSS course, in a specialized undergraduate DSS
course, in a post graduate DSS course and in a Doctoral / professional practice
DSS course.
It has taken several iterations to arrive at the presentation of the Grid, which is
summarized in table 2 below. The table is split horizontally in four key domains,
basic disciplines, basic DSS concepts, DSS concepts and DSS and
organisations. Overall, it is hoped that these four broad categories can capture
much of the essentials of the DSS discipline. Within these four domains,
thirteen sub-categories have been proposed to enable the proper classification
of the specific areas of knowledge that must be represented in the DSS
curriculum. Again, these sub-areas have been unpacked by members of the
task force such that the complete tables now identify one hundred and twenty
nine distinct topics that make up the multi-level DSS curricula.
Evidently, table 2 deserves a few comments.
First of all, Appendix 1 in its entirety is intended as a work in progress. Because
the task force has to begin at the very early stage by developing work methods
and tools for accumulating the contributions of its members, the two years
elapsed between the London and Toulouse events was not sufficient to produce
a definitive document. In any case, the small membership of the task force with
respect to the breadth and share number of topics, means that this report needs
to be reviewed by a broader group – i.e., the full membership of the working
group, before it can be finalized. However, the shape and format of the Grid
lends itself very well to reworking existing topics and appending new topics and
the work can continue on Appendix 1 beyond the Toulouse event.
Secondly, the coding of the proposed topics with the levels of education is not
set in stone. It is likely that different national groups for instance, may have
different perceptions of the relative importance of some of the topics. Thus,
some institutions may regard the more quantitative elements of the curriculum
as requisite knowledge, pretty much irrespective of the level of education. Other
institutions may prefer to cover more managerial topics at lower levels, such
that limited attention leads to making different choices of priority at the different
levels. Again, the shape and format of Appendix 1 lends itself very well to
variations in coding which could be colour-coded and explicated by way of foot
notes added to the table.
Thirdly, the DSS Curriculum Grid only represents a first step in building a DSS
profession. Nonetheless, this is a crucial building block which can be used by
many stakeholders towards aligning their course offerings with what we
perceive to be best practice in teaching DSS going forward.
Table 2: Synthetic presentation of the DSS Grid (see Appendix 1 for the full version)
All
Local
Areas / Topics
Levels of education
UG DSS
awareness
course
UG specialty
course / DSS
Practitioner
PG course /
Advanced DSS
Practitioner
Comments
Doctoral
course / DSS
Researcher
Basic Disciplines
A
Mathematical Foundations
B
Psychology
C
Economics
D
Law & Ethics
E
Accounting
Basics of Decision Support (DSS core concepts)
F
Theories behind decision support
G
Decision models & modeling
H
Technology behind DS
DSS
I
Software Tools & Engineering
J
Types of DSS
Decision Support and Organizations
K
Analyzing decision situations –
managerial decision making
L
Decision consulting and facilitation
M
Knowledge engineering
N
Domains of Application
Note: UG denotes Undergraduate and PG denotes Post-graduate
Finally, the grid cannot be complete and useful without a final observation: the
topics as presented in Appendix 1 do not exist in isolation; they are connected
to each other within higher level themes that run across the categories of the
framework. These, themes, e.g.: uncertainty, modeling the real world, making
decisions etc., are critically important in building up an effective understanding
of the problems faced by DSS researchers and practitioners. Yet, they do not
lend themselves to forming the basis of a systematic classification of the topics
in the DSS domain and therefore are not explicitly listed in the DSS Grid.
Therefore, there is an additional articulation in the conceptual presentation of
DSS which is missing in Appendix 1. Further discussion within the group has
led to a proposed method to best represent these themes using the term
“threads5” (a term emerging in technology education). Appendix 2 shows the
proposed coding of three sample threads that seem to be unambiguously
needed in the context of our discipline: Modeling, Uncertainty and DSS software
development. Whilst not DSS topics per se, because they are too broad and far
reaching, more at the level of a full course than a single topic, these are critical
5
Since fall 2006, the Bachelor of Science in Computer Science Curriculum at the Georgia Institute of
Technology (United States) has been built on their concept of Threads™. Developed by the Georgia
Institute of Technology College of Computing faculty, Threads™ allow students to create an individual
Computing curriculum within a specified perspective of the computing field. See:
http://www.cc.gatech.edu/education/undergrad/bscs/threads-white-paper/view
issues within the discipline of DSS and Appendix 2 shows the road map
towards acquiring the requisite skills for Modeling, for understanding the role
and impact of uncertainty in decision making and how to deal with it and finally,
how to develop effective DSS applications.
It is proposed that Appendix 2 be used in an interactive session at the
Toulouse meeting in order to seek broad consensus on the threads that need to
be represented and their proposed components.
4.3.2 Relevance for a DSS career path
Decisions are integral to all work, but the degrees to which explicit decision
support issues are relevant vary. The columns in the grid distinguish between
awareness of decision support issues on one end of a continuum of knowledge
to in-depth focus on these issues at the other end.
The first column anchors the “awareness” end of the DSS knowledge
continuum. It indicates the fundamental topics for which required knowledge is
needed in any work domain for a worker to be cognizant of basic factors that
impact decision making.
Certainly, a worker who carries the designation of DSS “practitioner” needs
more than DSS “awareness.” Such a worker will likely be a “knowledge worker,”
who is well acquainted with decision processes in a specific work domain. The
second column in the grid indicates the topics in which a DSS practitioner
should be well versed.
The third column in the grid designates the knowledge areas one should
reasonably expect an advanced DSS practitioner to command. It may be
unreasonable for some advanced DSS practitioners to command expertise in
every topic area indicated in the third column of the grid. In this case, one would
expect the advanced DSS practitioner to have deep knowledge in one or more
of the major subtopic areas. Regardless of the expertise of the advanced DSS
practitioner, s/he should have command of all of the topics indicated in the first
and second columns of the grid.
The fourth column of the grid indicates the topic knowledge one can expect in a
DSS researcher. As is the case with the advanced DSS practitioner, the number
of topics indicated is very large and some specialization may be required.
An ability to integrate the knowledge areas is of utmost importance as one
progresses from DSS awareness to the depth of knowledge required for
advanced practice or DSS research. The advanced practitioner and DSS
researcher should be able to synthesize these knowledge areas into a coherent
model of decision making. The advanced DSS practitioner is likely to use such
a model to better understand and improve decision making in one or a few
specific application domains. The DSS researcher will likely use the model to
better understand and improve one or a few specific areas of the decision
making process, perhaps generically or perhaps in a specific application
domain.
4.3.3 Course Prerequisites.
In considering the issue of pre-requisites, one may be tempted to say that there
are simply no pre-requisite to the DSS curriculum as anyone may be taught
about DSS from any level. In reality however, this initial impression must be
corrected. The DSS area is not really a true beginner’s area as mathematics or
history may be because it is closely flanked on two sides by two important
blocks of very important exposure: (1) general IS knowledge in terms of
computing technology and data capture and manipulation – even basic
computing skills and (2) exposure to the organizational environment, to the
extent that one might understand the role and limits of the different functional
areas of the firm. Considering the task of teaching DSS to complete beginners
with no previous exposure to either of these blocks would seem very difficult
and initially, would probably amount to teaching about other things than DSS.
For instance, some understanding of databases and how they store information
– e.g.: notions about data modeling – are clear pre-requisite for any serious
DSS course. In practice, we have noted that students take so long to internalize
the practice of data modeling that, when this is taught within the context of a
DSS course, it ends up becoming quite an obstacle to pursuing a DSS specific
agenda in a timely manner6.
Thus, it is a solid starting point to regard constituencies such as computer
science (who already understand the technologies and / or development tools)
and business graduates (who understand some aspects of the problem
domains) as the most likely audience of DSS courses. Practitioners who have a
need for DSS applications in their job and need to step up on the End-UserComputing ladder or have been asked to partake in DSS development without
having the required specific expertise in DSS are also two logical audiences for
DSS courses. Other than these four groups, it can be the case that other
audiences may benefit from limited exposure to DSS concepts on a small scale,
but this may not amount to a DSS course.
Within the group of business graduates, some specialists have a greater need
for DSS knowledge such as graduates destined to work in the Financial
services for instance and finance courses will probably increasingly feature DSS
specific modules, beyond the generic computing skill modules. At MBA level, it
is also very likely that all programmes will include a very complete DSS module,
geared towards do-it-yourself approaches to DSS development.
In total, the audience for DSS courses is perhaps not as widespread as the
audience for more general IS courses and this may act as a constraint for the
long term future of DSS. Nevertheless, this will not be a problem as long as the
technology and conceptual sides of the DSS domains continue to develop and
that the DSS expertise is in high demand in industry.
6
Discussions with Csaba Csaki and Dave Sammon, both teachers of introductory DSS courses at PostGraduate level.
4.3.4 Multi-level DSS Curricula.
As discussed in an earlier section, decision processes permeate all areas of life,
but the degrees to which explicit decision support issues are relevant vary. The
columns in the grid distinguish between educational awareness of decision
support issues on one end of a continuum of knowledge to a doctoral level
focus on these issues at the other end.
All graduates of any academic major will benefit from DSS awareness. The first
column of the grid indicates topics that provide a foundation for DSS
awareness. A single course that exposes the student to these topics is
recommended.
A program that explicitly includes DSS education should expose the student to
greater knowledge and skills in the DSS domain than are found in the DSS
awareness level of expertise. The second column of the grid indicates a range
of topics that should be included in an undergraduate course that focuses on
DSS.
The third column in the grid is applicable when a student requires greater study
of decision processes. In this case, one or more additional courses that expose
the student to greater depth in DSS will be needed and this column of the grid
provides a guide to the topics that should be considered. As the extent that a
student’s academic focus is on decision making processes increases, a greater
number of these topics should be included in the student’s education.
Significant knowledge breadth and depth is expected at the doctoral level of
study. The fourth column in the grid indicates a broad range of topics that
provide a foundation for doctoral study. Doctoral study often becomes the basis
for research, hence the comments regarding the appropriate preparation for the
DSS researcher apply here.
In cases where the doctoral study focus is on decision processes within a
specific domain, the doctoral student will need to be able to synthesize the
topics in the fourth column within that domain context. Where the doctoral study
is on decision making process per se, the student must be able to synthesize
the topics in the fourth column in a manner appropriate for the advanced study
of a particular aspect of the process.
4.3.5 General Description of Courses (methods of delivery, additional
components, such as case study and other support material).
This section presents a sample of three courses from different geographical
areas which have been proposed by members of the task force.
A sample course from Jožef Stefan Institute (Ljubljana
Slovenia):
SYSTEMS AND TECHNIQUES OF DECISION SUPPORT
AIMS
The aim of this course is to familiarize students with methods, techniques and systems for
supporting complex real-life decision-making tasks. Special emphasis is on the methods of
decision analysis and multi-attribute modeling, and their practical applications.
SYLLABUS
Decision-making; Problems of real-life decision-making; Decision support systems; Relation
with information systems, databases, expert systems and data mining systems; Data
warehouses and OLAP (On-Line Analytical Processing); Decision analysis; Methods and
techniques of decision analysis: decision trees, influence diagrams, multi-attribute models;
Analytic hierarchical process (AHP); Qualitative multi-attribute modeling; Group support
systems; Computer-based tools for decision support and modeling: DATA, Analytica, HiView,
DEXi.
LITERATURE
Mladenić, D., Lavrač, N., Bohanec, M., Moyle, S (eds.). Data Mining and Decision Support:
Integration and Collaboration. Kluwer Academic Publishers, 2003.
R.T. Clemen: Making Hard Decisions: An Introduction to Decision Analysis. Duxbury, 1997.
E. Jereb, M. Bohanec, V. Rajkovič: DEXi: Računalniški program za večparametrsko odločanje.
Moderna organizacija, 2003.
E.G.Mallach: Decision Support and Data Warehouse Systems. McGraw-Hill, 2000.
E.Turban, J.E.Aronson: Decision Support Systems & Intelligent Systems. Prentice Hall, 2000.
A list of selected readings is also provided to the students.
WEB PAGES
M. Bohanec: Systems and Techniques of Decision Support.
http://www-ai.ijs.si/MarkoBohanec/STDM.html
M. Bohanec: IJS Decision Support Resources. http://www-ai.ijs.si/MarkoBohanec/dss.html
ASSESSMENT
Each student is assigned a decision-making project and requested to present the results in a
written report and public presentation. The presentation is combined with an oral exam.
Requirements and Procedure
1. Each student is required to make their Practical Assignment and write a report
2. The written report should be delivered to the professor (preferably sent by e-mail)
3. Examinations: Each student should give a 10-15 minutes oral presentation of their work
(preferably accompanied by PowerPoint slides).
4. Discussion and oral examination will take place immediately after each student's
presentation.
Topics
1. Introduction to Decision Making and Decision Support
2. Decision Analysis Part 1: Decision Analysis; Decision Tables and Decision Criteria
3. Decision Analysis Part 2: Decision Trees
4. Decision Analysis Part 3: Influence Diagrams
5. ID's Continued: Examples; Demo: Analytica, DPL; Decision Analysis Part 4: MultiAttribute Models (intro)
6. Decision Analysis Part 4: Multi-Attribute Models (continued); MADM Applications (intro)
7. DEXi: Methodology and Software
8. AHP: Methodology and Software
9. Combining Data Mining and Decision Support
10. Introduction to Data Warehouses and OLAP
Practical Assignment
1.
2.
3.
4.
Define your own decision-making problem.
Create a multi-attribute model containing about 10-20 basic attributes.
Define about 4-6 alternatives (possibly real-life ones)
Implement the model in:
o DEXi, and
o Quantitative modeling software of your choice (Web-HIPRE, WinPre,
spreadsheet, MATLAB; see also Lecture 6 for some links)
5. Evaluate and analyse the alternatives.
6. Write a report and hand it to your professor.
A completed practical assignment is required to enter the final oral examination, and will
account for 50% of the final evaluation mark.
The report should consist of:
1.
2.
3.
Title page: Project title and Author info
Main Part: About 2 pages containing:
1.
Introduction:

Description of the decision problem

Aims and goals of your decision
2.
Description of attributes and their structure
3.
Description of alternatives
4.
Utilisation of the model:

Evaluation of alternatives

Analysis: Sensitivity and/or what-if analysis
5.
Conclusion:

Which alternative to choose and why (its good and bad points)

Brief comparison with other alternatives
Appendices:
1.
Printout of your DEXi model
2.
Printout (or some other kind of documentation) of the quantitative model
A sample graduate course from Autonomous University of
Aguascalientes (Mexico):
Advanced Information Systems (DSS & EIS)
Master in Informatics and Computer-Based Technologies.
COURSE OBJECTIVES
To understand the theoretical foundations about the Decision Making Process made in business
organizations.
To know the main characteristics of the several Decision-Making Support Systems (DMSS)
available at present for individual and group decision-makers.
To acquire the know-how about system development approaches for DMSS.
To understand the real problems and research-based recommendations associated with the
implementation process of DMSS.
To acquire a vision of the market trends and emergent approaches for DMSS.
COURSE DESCRIPTION
This course reviews theoretical models of how organizations and individuals make business
decisions and practical studies about the present and emergent Computer-Based Systems
oriented to support this managerial activity (we called them Decision-Making Support Systems
(DMSS)) so that IT practitioners gain theoretical and practical knowledge to implement them
efficiently and effectively in business organizations.
We will begin with a discussion of core concepts from Decision-Making and Organizations
including: Organizational Systems, Organizational Decisions, Managerial Roles of Executives,
Decision-Making Models, Cognitive styles of Decision-Makers and Macro & Micro Processes of
Decision-Making. Next we will review an overview of the Computer-Based Systems proposed to
support the Decision-Making Process.
The review of these topics will be useful for students to acquire the theoretical foundations of
Decision-Making in business organizations. Then, we will concentrate on practical issues
related with the development of DMSS including: Executive Information Systems (EIS), Decision
Support Systems (DSS0), Ruled-Based Expert Systems (RBES) and Integrated DMSS. With
the previous topics it is expected that students have acquired the practical knowledge to
develop their term project.
Finally, we will discuss the challenges and trends in DMSS in order to get efficient and effective
implementations in business organizations.
READINGS. It is expected that students read the assigned readings before attending the
lecture in order to participate in class discussion.
LECTURES. Lectures will be presented in verbal expositions from the lecturer with the support
of notes slides and demos. At present, there is not a video available for this course.
CLASS DISCUSSION. Brief case studies will be discussed in class.
STUDENT'S PARTICIPATION. A set of special topics issues of the course will be assigned to
teams composed of five students, which they will present to the group.
TEAM PROJECT. Each team is required to build a Decision Making Support System in a
domain problem of your own choice. The final product will be shown to the group in the last
session. The estimated exposition time is 60 minutes per team.
COURSE GRADING POLICY.
Readings by team
Research Assignment by team
Final Exam
Term Project
Software Design Documentation
Beta Software Product
15%
15%
20%
50%
15%
35%
TEXTBOOK.
Turban, E. and Aronson, J. "Decision Support Systems and Intelligent Systems," Prentice-Hall,
5th. ed., 1998.
REFERENCE BOOKS.
Holsapple, C. W. and Whinston, A. B. "Decision Support Systems: A Knowledge-based
Approach", West Publishing Company, 1996.
Sauter, V. "Decision Support Systems", John Wiley & Sons, 1997.
Marakas, G., "Decision Support Systems in the 21st Century", Prentice-Hall, 1999.
Sprague, R. H. and Watson, Hugh J., "Decision Support for Management", Prentice-Hall,
1995.
COURSE OUTLINE AND COURSE SCHEDULE..
1. Foundations of Organizational Decisions (OD).
1.1 Organizations (a Systems View).
1.2 Managerial Roles of Executives.
1.3 Organizational Decisions.
Exercise 1. Team-based reading and group discussion of the Case of "Royal Dutch/Shell",
published in the book "Strategic Management", from C. W.L. Hill and G. R. Jones, Houghton
Mifflin Co., 1992.
Exercise 2. Team-based classification and group discussion of cases of Business Decisions
based on "Strategic Decision Making and Intelligent Executive Support Systems", V. Savolein &
S. Liu (1995).
1.4 The Decision-Making Process (DMP).
1.5 Theoretical Models of DMP.
1.6 Conclusions about OD.
2. Decision-Making Support Systems. (DMSS).
2.1 Classification of DMSS.
2.2 Analysis of DSS, EIS, ES/KBS.
2.3 Integrated DMSS.
2.4 Benefits of Decision-Making Support Systems.
2.5 Conclusions about DMSS.
Exercise 3. Team-based reading and group discussion of the classic paper "The
Decision-Making Paradigm of Organizational Design", G. Huber & R. McDaniel (1986).
Exercise 4. Presentation of demos of DSS, EIS, ES/KBS and team-based and group
discussion about the characteristics.
Exercise 5. Team-based development of a draft version of the IDMSS project proposal.
Exercise 6. Locate by team a real case with demo of a DSS, EIS and ES/KBS, analyze
it and build a web-page of the results.
3. DMSS Development Methodologies.
3.1 DSS Development Methodologies.
3.2 ES/KBS Development Methodologies.
3.3 EIS Development Methodologies.
3.4 Integrated DMSS Development Methodologies.
3.5 Conclusions about DMSS Development Methodologies.
Exercise 7. Design of a small DSS.
Exercise 8. Design of a small Rule-Based ES.
Exercise 9. Design of a small EIS.
Exercise 10. Review of the IDMSS project proposal by team.
Exercise 11a. Each team will research commercial issues of DMSS that will be presented to the
group next session in Saturday.
4. Implementation of DMSS.
4.1 Concept, challenges and issues of DMSS Implementation.
4.2 Factors of Influence in Successful Implementations of DSS, EIS and
ES/KBS.
4.3 Theoretical Factors of Influence in Successful Implementations of
Integrated DMSS.
4.4 Conclusions of DMSS Implementation issues.
No assignments.
5. Emergent Approaches and Market Trends for DMSS.
5.1 Datawarehouses and DataMarts.
5.2 Business Intelligence Tools (OLAP, ROLAP, MOLAP).
5.3 Data Mining Systems.
5.4 Intelligent Systems (ANN, FLS,GAS).
5.5 Groupware and Collaborative Work Systems for Decision-Making
(GDSS)
5.6 Enterprise Information Portals.
5.7 Conclusions about Emergent Approaches and Market Trends for
DMSS.
Exercise 11b. Team-based presentation of the DMSS research topic assigned.
A sample course from Florida State University (United States):
ISM 4117 Decision Support and Expert Systems Management (3 hours). Prerequisite:
Database Management. The design, development, implementation, and management of
decision support and expert systems; includes concepts of data management, modeling
decision support systems, and decision making. For MIS majors only.
This is a capstone class that should be taken during a student’s final semester.
Overall objectives:
 Develop an understanding of business strategy concepts.
 Develop an understanding of how information technology can be integrated into
organizations to create and sustain competitive advantage.
 Develop an understanding of the role of computers in direct support of managerial decisionmaking.
 Integrate and reinforce knowledge gained from prerequisite classes in systems design,
database, and programming.
 Apply this understanding to the design of typical systems for managerial decision support.
Specific skills and knowledge that students will be able to demonstrate upon successful
completion of the course:







Integrate investments in information technologies with the principles of strategic
management
Demonstrate an understanding of the concepts of decision support systems structure and
the principles of their design.
Analyze typical decision situations to determine whether it is practical to support them with
computer technology, and if so, how.
Design and implement a decision support system.
Evaluate emerging technologies and issues in the context of decision support systems as
well as the management of information technology.
Link IT to strategic decision making and competitive advantage in organizations.
Gain an appreciation of working on systems development projects in a team environment
and obtain experience with project management.
Project:
A comprehensive project requiring the design and implementation of a DSS, including the
database design and a functional interface, is required. The project requires effort distributed
over a 10-12 week period in 4-person teams.
4.3.6 General considerations on what a DSS course should look like
The important issue when it comes to designing courses, whether on DSS or
anything else can be simplified in terms of: (1) what level do students have
coming into the programme of study, (2) what level are they expected to have
when they leave the programme of study and (3) the analysis of the gap that
therefore needs to be filled.
Leveraging the discussion presented in section 4.3.3, a number of typical types
of programmes can be proposed, based on the basic diagram shown in figure 1
below.
The basic dimensions of Figure 1 are: (1) the extent of exposure to the practice
of DSS and (2) the extent of exposure to the theory of DSS. Both dimensions
can be interpreted as a continuum between no exposure at all and extensive
exposure, but in Figure 1, exposure to theory is divided up between Beginner,
Intermediate and Expert for the sake of argument.
Extensive exposure to Practical experience
Intermediate
Expert
Exposure to Theory
No Exposure to Theory
Beginner
No exposure to Practical experience
In considering how to use Figure 1, one can first consider that there is a general
progression between the bottom left corner and the upper right corner, with the
red line (the full line in black and white) representing a practitioner’s long term
learning with DSS. However, depending on the experience followed by
individuals, it is possible to conceive of courses that would be designed to meet
the needs of practitioners with already some practical experience of DSS but no
exposure to DSS theory as represented by the dashed line (though one would
assume that this is less and less common at the stage given the emergence of
many DSS courses at undergraduate level and the increasing number of
graduates with at least some exposure to DSS theory). Furthermore, it is likely
that exposure to the general IS curriculum may mean that students with the
basics of DSS theory covered, come in at an intermediate level without any
practical experience (see dotted line).
The interesting aspect about Figure 1 is that it should allow the representation
of any DSS course, and maybe the chaining of different courses. It may also be
used to represent the audience to whom the courses are destined.
Going beyond this initial discussion, one would then consider the means applied
to serving the objectives of the course. Prima facia, class room-based teaching
methods are probably best suited for the theory side of the course objectives,
but an increasing number of options are open for the practical side. At this
stage, it is likely that any educator would be convinced that a DSS course at
whatever level, but especially at an expert level should essentially be a hybrid
course with the proper mix of theoretical and practical elements. This not only
serves a learning-by-doing objective, but also accounts for the primarily
practical orientation of the DSS area, except for certain types of research
projects which are essentially theoretical. This means that it is essential to
consider which methods are best used to promote a hybrid form of learning and
the latest environments, mostly the virtual environments lend themselves very
well for such courses. Qudrat-Ullah and Karakul (20077) present a review of
experiments to use Virtual Learning Environments (VLEs) for improving
decision making, but recent experimentation in Second Life® lead us to
speculate that this virtual world presents one of the most compelling
opportunities to introduce radical changes in the teaching of practically oriented
domains.
During the IFIP 8.3 conference in Toulouse in July 2008, a panel has been
proposed to discuss these issues and the outcomes of this panel will be
inserted in this report after the event.
5. Conclusion and Future work.
This report has presented on-going work by members of the task force on the
DSS curricula (having established that its multi-level nature means that there is
more than one DSS curriculum) to develop a comprehensive and coherent
picture of the domain of DSS from the point of view of the skills required to be
recognized as a DSS practitioner and DSS expert. Appendices 1 and 2 present
two vision of the curriculum which combine to provide the first stage in a
formalization of the DSS domain which can be used to define our work, our
research and our teaching.
5.1 Future Developments
In addition, to the material covered here, there is an emerging set of technology
developments ranging from Web 2.0 (Wikis, Blogs etc.) to Web X.0 to even
developments such as the Wii. We need to make sure that we don’t ignore
emerging future developments as we look back on what has been done. As an
example, Wikis and blogs offer the opportunity for users to contribute insights in
an environment that has received little attention to-date, yet offers much
7
Qudrat-Ullah and Karakul (2007) Decision Making in Interactive Learning Environments,
Journal of Decision Systems, 16(1), 79-100.
academic opportunity. Further, future computing may be more Wii-like than
current personal computing capabilities, yet many corporate and academic IS
“experts” have not even used a Wii. As a result, it can be important to make
sure to experience and anticipate how contemporary developments will
influence future systems.
5.2 Professionalization
Although it has been argued that business schools have lost their way (e.g.,
Bennis and O’Toole 2005) in an apparent “physics envy,” that DSS maintain
their real world connections and provide appropriate links to the professional
community.
It is worth noting that, in parallel to our work, the IFIP organisation at its highest
level has undertaken a similar project of professionalizing the IT. Selected
extracts from the FIRST REPORT OF THE IFIP PROFESSIONAL PRACTICE
TASK
FORCE,
as
published
on
the
IFIP
web
site
(see
http://www.ifip.or.at/projects/ITProf_Report.pdf) are presented below as a way
to demonstrate the congruence of the aims of the report and the aim of the IFIP
task force on professionalization. Interestingly, the two groups have started their
work independently.
The report states that:
“The Task Force resolved unanimously to recommend to IFIP Council that it should
proceed with a programme to create and promote an international IT profession on the
basis of the following objectives:


To initiate a vigorous programme of activity to promote professionalism worldwide
To achieve the following outcomes:
1. By increasing professionalism, to improve the ability of business and the
wider community to exploit the potential of information technology
effectively and consistently;
2. To build professionalism in IT to the level at which it exists in other areas of
professional activity;
3. To develop a profession that is respected and valued for the contribution it
makes to the exploitation and application of IT for the benefit of all –
government, business leaders, IT employers, IT users and customers;



To establish an international grouping to speak globally about issues relating to the
IT profession
To ensure that the voice of the IT practitioner is clearly and powerfully expressed
alongside other competing groups.
To provide an opportunity for IFIP to raise substantially its global profile”
Furthermore, the report lists these potential benefits and opportunities:
“The Task Force recommendation reflects a strongly held view that there is now a very real
opportunity to build and implement successfully an international IT profession based on
globally recognised standards. IT is now a global industry which needs a global profession
to provide:

a common language within which to describe professional skills and competences


a standard means of measurement for professional skills and competences
a mechanism for the independent assurance of quality of those professional skills
and competences.
These are seen as powerful advantages that could deliver significant benefits to all those
involved – to commercial organisations seeking to sell professional skills, to those seeking
to buy professional skills, to those interested in regulating the trade (for example, in terms
of establishing effective immigration controls) and not least to the practitioners themselves.”
Finally, the report lists a number of building blocks that would be required in order to achieve a
respected profession status for IT.
“An IT profession is needed which:
•Is
defined in terms of its ability to play a full part in all stages of IT exploitation
Is seen as – and sees itself as – an integral part of the business
Has appropriate non-technical skills, including management, business and leadership skills,
as core competences.
Is about both Information and Technology
Lays greater emphasis on the accreditation of current capability and competence
Demands greater personal responsibility on the part the practitioner.
Is attractive to a wider group of entrants than at present – including those groups alienated
by the current image of the profession”
This general initiative provides an adequate backdrop for us to work in parallel
within IFIP 8.3 on the development of a formalized DSS profession. This report
constitute progress towards a first stage to the creation of such a
professionalization of the DSS domain, which would be aimed at promoting the
establishment of a professional discipline of DSS, with recognized DSS
specialists, a clearly identifiable DSS career path(s) and maybe even a DSS
accreditation scheme for our undergraduate and postgraduate programmes that
include the requisite DSS topics.
In the detail of the work which the 8.3 working group may decide to undertake
would be the following building blocks:
(1) DSS Curriculum – multi-level so as to provide a blueprint for accreditation –
this is the purpose of this report.
(2) An accreditation body made up for instance of members of IFIP 8.3, AIS SIG
on DSS and the EURO group. These could be elected at the same time as the
officers of the respective groups and each group would have a quota in the
accreditation body. Members would be elected for a fixed term of 3 years and
an overlap of the different terms could be implemented so that the whole body
is only replaced in portions rather than all at once.
(3) A Chairperson – one of the six members.
(4) A web presence with information on the accreditation body and its missions.
(5) An accreditation process – composed of an application form for accreditation
which would be submitted by course / programme directors and a review by the
Accreditation body – followed by feed back and judgement on each case.
Then, the issue of membership of an Association of DSS Specialists that would
be free, but subject to judging would-be members’ credentials, could be
considered.
These are issues for consideration by the working group as a whole and which,
if it is decided to pursue them, could be worked upon by a new task force which
would take this report to the next stage and formalize the above ideas by
working on a blueprint to develop a proper structure to support the future
professionalization of DSS.
6. Appendices
Appendix 1: The DSS Curricula Grid
Appendix 2: coding the threads in the DSS curriculum
Appendix 3: List of useful References (Books and Journal Papers).
Appendix 1: IFIP WG 8.3 - Task force on DSS Curriculum – The DSS curriculum Grid
# all
# local
Areas / Topics
Comments
Levels of education
Undergraduate
awareness course
/ DSS awareness
Undergraduate
specialty course /
DSS Practitioner
Post-graduate
Doctoral course /
course / Advanced DSS Researcher
DSS Practitioner
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Optional knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Optional knowledge
Optional knowledge
Required knowledge
Optional knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Basic Disciplines
A
Mathematical Foundations
1.
2.
3.
4.
5.
A-1.
A-2.
A-3.
A-4.
A-5.
6.
7.
A-6.
A-7.
8.
9.
10.
11.
A-8.
A-9.
A-10.
A-11.
12.
A-12.
13.
14.
15.
A-13.
A-14.
A-15.
introductory mathematical statistics
Bayesian theory / Bayesian networks
risk theory, risk taking
decision algorithms
Mathematical theories of multiple criteria
decision making
utility theory
multi-attribute utility theory / (or multicriteria) models
decision trees
Influence diagrams
Game theory
Fuzzy Systems (Fuzzy Decision Trees,
Fuzzy Inference Systems, Fuzzy Linear
Programming)
outranking methods (PROMETHEE,
ELECTRE, ORESTE, AHP)
Rough Set Theory
Markov models
Neural networks
B
Psychology
B-1.
B-2.
B-3.
intelligence, creativity
creative decision making
Conscious and unconscious thinking
16.
17.
18.
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
19.
20.
21.
22.
23.
24.
B-4.
B-5.
B-6.
B-7.
B-8.
B-9.
25.
B-10.
26.
B-11.
rationality, intuition
the role of rational and intuitive thoughts
motivations, emotions
social perception, values
basics of social psychology
cultural differences, ways of thinking
(frames of mind)
behavior and performance of groups and
organizations
the psychology of decision making
C
Economics
C-1.
C-2.
C-3.
C-4.
basics of microeconomics
basics of business economics
Finance
equilibrium models
D
Law & Ethics
D-1.
D-2.
D-3.
D-4.
D-5.
different legal systems
basic principles of civil law
contract law
the consulting contract
ethical issues
27.
28.
29.
30.
31.
32.
33.
34.
35.
E
Accounting
36.
37.
38.
39.
E-1.
E-2.
E-3.
E-4.
40.
41.
42.
43.
44.
45.
46.
E-5.
E-6.
E-7.
E-8.
E-9.
E-10.
E-11.
Where data comes from?
Basic business processes
Ontology
Semantic modeling of accounting
phenomena
Continuous monitoring and auditing
XML/XBRL/ebXML
IS/IT risks and controls
Governance
Regulations (e.g., Sarbanes / Oxley in US)
Business intelligence
Data mining
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
47.
E-12.
48.
E-13.
49.
E-14.
50.
E-15.
Revenue transaction processing cycle
Purchases transaction processing cycle
Conversion (Production) transaction
processing cycle
Payroll/Expenditures transaction
processing cycle
Financial Reporting cycle
Breakeven analyses
Process costing
Job costing
Activity based costing
Theory of constraints (TOC)
Financial ratio analysis
ERP – system in general and in terms of
database model to know where data is
located
ERP – Financial module operations
ERP – Controlling module operations
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Basics of Decision Support (DSS core concepts)
F
Theories behind decision support
51.
F-1.
52.
53.
54.
F-2.
F-3.
F-4.
55.
56.
57.
F-5.
F-6.
F-7.
58.
F-8.
basic concepts, trends in decision theory
(normative, descriptive, OR, Decision
Analysis, ...)
the theory of bounded rationality
Decision process (stages, typical tasks)
Context in Decision Making,
Contextualization for decision making
Collaborative decision making
Risk and uncertainty in decision making
Uncertainty Quantification and
Propagation
Verbal Decision Analysis
G
Decision models & modeling
G-1.
G-2.
the process of modeling
models of artificial intelligence
59.
60.
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
61.
62.
63.
G-3.
G-4.
G-5.
64.
65.
66.
67.
68.
G-6.
G-7.
G-8.
G-9.
G-10.
69.
G-11.
70.
G-12.
problem structuring, problem modeling
Influence diagrams
Goal Programming,
Goal Seeking
Modeling real life problems and situations
conflict theory
cooperation, coalitions
finding out preconditions, relevance check
case typology, matching real cases to
models
Levels of Representation of Decision
Problems
Modeling the context of decision making:
role of the context, contextual elements
and contextual graphs
H
Technology behind DS
71.
H-1.
72.
73.
74.
75.
76.
H-2.
H-3.
H-4.
H-5.
H-6.
77.
H-7.
78.
H-8.
Group decision making – methods,
techniques
Distributed decision making
Heuristics for Decision Support
Visualization in DSS
Simulation
SOFTWARE AGENTS
(Multi-Agent Simulation,
Multi-Agent Simulation and Modeling,
Intelligent Agents)
Video and internet technologies based
decision techniques
Images of the future: prognoses, scenarios,
scripts, ontologies, decisional KMS
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
DSS
I
Software Tools & Engineering
79.
I-1.
80.
I-2.
basic principles of decision support
software
common architectures
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
81.
I-3.
82.
I-4.
83.
I-5.
84.
I-6.
Types of decision systems (data-oriented,
model-oriented, knowledge-oriented, etc)
End-User’s decision support tools (MCDM
packages)
Developing
decision Support technologies, tools
Decision support systems engineering
(SDLCs, Methodologies)
J
Types of DSS
85.
86.
87.
88.
89.
J-1.
J-2.
J-3.
J-4.
J-5.
90.
91.
92.
93.
94.
95.
96.
97.
98.
99.
J-6.
J-7.
J-8.
J-9.
J-10.
J-11.
J-12.
J-13.
J-14.
J-15.
100.
101.
102.
103.
J-16.
J-17.
J-18.
J-19.
104.
105.
J-20.
J-21.
EIS
MIS
DMSS
iDMSS
Group Support Systems,
Group Work Systems
Mobile Decision Support
Data cubes
Business Intelligence
Data Warehousing, OLTP, OLAP, etc.
DSS relation to other IT systems
Communication-driven
Data-driven / Database-oriented
Document-driven / Text-oriented
Knowledge-driven / Rule-oriented
Model-driven / Solver-oriented /
Spreadsheet-oriented
Desktop
Enterprise-wide
Compound (two or more architectures)
Domain focused (medical, transportation,
etc.)
AI-based
Agent-based
Decision Support and Organizations
K
Analyzing decision situations –
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
managerial decision making
106.
107.
108.
109.
110.
111.
112.
K-1.
K-2.
K-3.
K-4.
K-5.
K-6.
K-7.
Business processes
Strategic Decision Making
Attention-Based View
Debiasing Decision Making
Decision Makers
Decisional Episodes
Diagramming for Business Modeling
L
Decision
facilitation
113.
114.
L-1.
L-2.
115.
L-3.
116.
117.
118.
119.
L-4.
L-5.
L-6.
L-7.
problems of legitimization
marketing techniques of decision
consulting
success criteria for consulting evaluating and measuring performance
Communicating decisions
the consulting process
Decision conferencing
The Facilitator and the Chauffeur
M
Knowledge engineering
M-1.
M-2.
M-3.
M-4.
M-5.
Role of information in decision making
knowledge acquisition
knowledge representation
knowledge utilization
knowledge management and decision
making
N
Domains of Application
125.
126.
N-1.
N-2.
127.
128.
N-3.
N-4.
Cases in the engineering domain
Cases in the business management
domain
Cases in the governmental domain
Challenges in decision-making support
systems (the complex worldwide
decision problems)
120.
121.
122.
123.
124.
consulting
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
and
Required knowledge
Required knowledge
Required knowledge
Required knowledge
129.
130.
131.
132.
133.
134.
135.
N-5.
N-6.
O-1
O-2
O-3
O-4
O-5
Forecasting (DSS for)
O
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Required knowledge
Optional knowledge
Optional knowledge
Optional knowledge
Optional knowledge
Optional knowledge
Infrastructure Issues
Hardware Infrastructure
Network Infrastructure
Software Integration (e.g., EAI)
Information Integration
Security
© IFIP WG 8.3 - DSS Curriculum Task Force
Do not reproduce without explicit permission from the Chairs of the task force (see section 1 of the report)
Appendix 2: coding the threads in the DSS curriculum
#
all
#
local
Areas / Topics
Modeling
Uncertainty
X
X
X
X
X
DSS Software
Development
Basic Disciplines
A
Mathematical Foundations
136.
137.
138.
139.
140.
A-16.
A-17.
A-18.
A-19.
A-20.
141. A-21.
142. A-22.
143.
144.
145.
146.
A-23.
A-24.
A-25.
A-26.
147. A-27.
148. A-28.
149. A-29.
150.
151.
152.
153.
154.
introductory mathematical statistics
Bayesian theory / Bayesian networks
risk theory, risk taking
decision algorithms
Mathematical theories of multiple
criteria decision making (OR)
utility theory
multi-attribute utility theory / (or
multi-criteria) models
decision trees
Influence diagrams
Game theory
Fuzzy Systems (Fuzzy Decision
Trees, Fuzzy Inference Systems,
Fuzzy Linear Programming)
outranking methods (PROMETHEE,
ELECTRE, ORESTE, AHP)
Rough Set Theory
Markov models
B
Psychology
B-12.
B-13.
B-14.
B-15.
B-16.
intelligence, creativity
creative decision making
Conscious and unconscious thinking
rationality, intuition
the role of rational and intuitive
thoughts
Comments
Threads
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
More threads
155.
156.
157.
158.
motivations, emotions
social perception, values
basics of social psychology
cultural differences, ways of thinking
(frames of mind)
159. B-21. behavior and performance of groups
and organizations
160. B-22. the psychology of decision making
161.
162.
163.
164.
165.
166.
167.
168.
169.
B-17.
B-18.
B-19.
B-20.
C
Economics
C-5.
C-6.
C-7.
C-8.
basics of microeconomics
basics of business economics
Finance
equilibrium models
D
Law & Ethics
D-6.
D-7.
D-8.
D-9.
D-10.
different legal systems
basic principles of civil law
contract law
the consulting contract
ethical issues
E
Accounting
170.
171.
172.
173.
E-16.
E-17.
E-18.
E-19.
174.
175.
176.
177.
178.
E-20.
E-21.
E-22.
E-23.
E-24.
Where data comes from?
Basic business processes
Ontology
Semantic modeling of accounting
phenomena
Continuous monitoring and auditing
XML/XBRL/ebXML
IS/IT risks and controls
Governance
Regulations (e.g., Sarbanes / Oxley
in US)
Business intelligence
Data mining
Revenue transaction processing cycle
Purchases transaction processing
179. E-25.
180. E-26.
181. E-27.
X
X
X
X
X
X
X
X
X
X
X
X
X
182. E-28.
183. E-29.
184. E-30.
cycle
Conversion (Production) transaction
processing cycle
Payroll/Expenditures transaction
processing cycle
Financial Reporting cycle
Breakeven analyses
Process costing
Job costing
Activity based costing
Theory of constraints (TOC)
Financial ratio analysis
ERP – system in general and in terms
of database model to know where
data is located
ERP – Financial module operations
ERP – Controlling module operations
X
X
X
Basics of Decision Support (DSS core
concepts)
F
Theories behind decision
support
185. F-9.
186. F-10.
187. F-11.
188. F-12.
189. F-13.
190. F-14.
191. F-15.
basic concepts, trends in decision
theory (normative, descriptive, OR,
Decision Analysis, ...)
the theory of bounded rationality
Decision process (stages, typical
tasks)
Context in Decision Making,
Contextualization for decision
making
Collaborative decision making
Risk and uncertainty in decision
making
Uncertainty Quantification and
Propagation
X
X
X
X
X
X
X
X
X
X
X
X
192. F-16.
G
Verbal Decision Analysis
Decision models & modeling
193. G-13. the process of modeling
194. G-14. models of artificial intelligence
195. G-15. problem structuring, problem
modeling
196. G-16. Influence diagrams
197. G-17. Goal Programming,
Goal Seeking
G-18.
Modeling real life problems and
198.
situations
199. G-19. conflict theory
200. G-20. cooperation, coalitions
201. G-21. finding out preconditions, relevance
check
202. G-22. case typology, matching real cases to
models
203. G-23. Levels of Representation of Decision
Problems
204. G-24. Modeling the context of decision
making: role of the context,
contextual elements and contextual
graphs
H
205. H-9.
206.
207.
208.
209.
210.
H-10.
H-11.
H-12.
H-13.
H-14.
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Technology behind DS
Group decision making – methods,
techniques
Distributed decision making
Heuristics for Decision Support
Visualization in DSS
Simulation
SOFTWARE AGENTS
(Multi-Agent Simulation,
Multi-Agent Simulation and
Modeling,
Intelligent Agents)
X
X
X
X
X
X
X
X
211. H-15. Video and internet technologies
based decision techniques
212. H-16. Images of the future: prognoses,
scenarios, scripts, ontologies,
decisional KMS
X
X
X
X
DSS
I
213. I-7.
214. I-8.
215. I-9.
216. I-10.
217. I-11.
218. I-12.
Software Tools &
Engineering
basic principles of decision support
software
common architectures
Types of decision systems (dataoriented, model-oriented, knowledgeoriented, etc)
End-User’s decision support tools
(MCDM packages)
Developing
decision Support technologies, tools
Decision support systems
engineering (SDLCs, Methodologies)
J
Types of DSS
219.
220.
221.
222.
223.
J-22.
J-23.
J-24.
J-25.
J-26.
224.
225.
226.
227.
J-27.
J-28.
J-29.
J-30.
EIS
MIS
DMSS
iDMSS
Group Support Systems,
Group Work Systems
Mobile Decision Support
Data cubes
Business Intelligence
Data Warehousing, OLTP, OLAP,
etc.
DSS relation to other IT systems
Communication-driven
Data-driven / Database-oriented
228. J-31.
229. J-32.
230. J-33.
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
231. J-34.
232. J-35.
233. J-36.
234. J-37.
235. J-38.
236. J-39.
237. J-40.
238. J-41.
239. J-42.
Document-driven / Text-oriented
Knowledge-driven / Rule-oriented
Model-driven / Solver-oriented /
Spreadsheet-oriented
Desktop
Enterprise-wide
Compound (two or more
architectures)
Domain focused (medical,
transportation, etc.)
AI-based
Agent-based
X
X
X
X
X
X
X
X
X
X
Decision Support and Organizations
K
Analyzing decision situations
– managerial decision
making
240.
241.
242.
243.
244.
245.
246.
K-8.
K-9.
K-10.
K-11.
K-12.
K-13.
K-14.
Business processes
Strategic Decision Making
Attention-Based View
Debiasing Decision Making
Decision Makers
Decisional Episodes
Diagramming for Business Modeling
L
Decision consulting and
facilitation
247. L-8.
248. L-9.
249. L-10.
250. L-11.
251. L-12.
252. L-13.
problems of legitimization
marketing techniques of decision
consulting
success criteria for consulting evaluating and measuring
performance
Communicating decisions
the consulting process
Decision conferencing
X
X
253. L-14.
M
The Facilitator and the Chauffeur
Knowledge engineering
254. M-6.
255.
256.
257.
258.
Role of information in decision
making
M-7. knowledge acquisition
M-8. knowledge representation
M-9. knowledge utilization
M-10. knowledge management and decision
making
N
X
X
X
X
X
X
Domains of Application
259. N-7.
260. N-8.
Cases in the engineering domain
Cases in the business management
domain
261. N-9. Cases in the governmental domain
262. N-10. Challenges in decision-making
support systems (the complex
worldwide decision problems)
263. N-11. Forecasting (DSS for)
264. O
Infrastructure Issues
Hardware Infrasture
265. O-1
Network Infrastructure
266. O-2
Software Integration (e.g., EAI)
267. O-3
Information Integration
268. O-4
Security
269. O-5
© IFIP WG 8.3 - DSS Curriculum Task Force
Do not reproduce without explicit permission from the Chairs of the task force (see section 1 of the report)
Appendix 3: List of useful References (Books and Journal Papers).
Ackoff, R. L. (1967) Management MISinformation systems, Management Science,
14(4), 147-156.
Alter, S. (1992) Why persist with DSS when the real issue is improving decision
making?, in Decision Support Systems: Experiences and Expectations, Jelassi (Ed.),
North Holland.
Anthony, R. N. (1965), Planning and Control Systems: a Framework for Analysis,
Harvard University Press, Boston.
Argyris, C. and Schon, D. A. (1978), Organisational Learning, Addison-Wesley,
Reading, Mass.
Bellman, R., Zadeh, L. “Decision-Making in a Fuzzy Environment”, Management
Science, 17(4), 1970, p. 141-164 .
Bennis, W. and O’Toole, J., “How Business Schools Lost their Way,” Harvard Business
Review, May 2005, pp. 1-9.
Bourgeois L. and Eisenhardt K. (1988) Strategic decision processes in high velocity
environments: four cases in the microcomputer industry, Management Science,
34(7), 816-835.
Brunsson, N. (1989) The Organisation of Hypocrisy, John Wiley & Sons, England.
Buede, D. (1981). Structuring Value Attributes. Interfaces, 16(2), 52-62.
Checkland, P. (1981), Systems Thinking - Systems Practice, Wiley Publications,
Chichester.
Cohen, D., March, J.G. and Olsen, J.P. (1972) A garbage can model of organisational
choice, Administrative Science Quarterly, 17, 1-25.
Courtney, J. F. (2001) Decision Making and Knowledge Management in Inquiring
Organizations: Toward a New Decision-Making Paradigm for DSS, Decision
Support Systems, 31(1), 17-38.
Cyert, R. M. and March, J. G. (1963), A Behavioural Theory of the Firm, Prentice-Hall,
Englewood Cliffs, New Jersey.
Daft R. L. and Lengel R. H. (1986) Organisational information requirements media
richness and structural design, Management Science, 32(5), 554-571.
Daft, R. and Weick, K. E. (1984) Toward a Model of Organisations as Interpretations
Systems, Academy of Management Review, 9, 284-295.
Drucker, P.F. (1967) The effective decision, Harvard Business Review, January /
February, 71-77.
Drucker, P.F. (1970), Technology, Management and Society, Heinemann, London.
Earl, M.J. and Hopwood, A.G. (1980) From management information to information
management, In Lucas, Land, Lincoln and Supper (Eds) The Information Systems
Environment, North-Holland, IFIP, 1980, 133-143.
Eisenhardt K. M. (1990) Speed and strategic choice: how managers accelerate decision
making, California Management Review, 31, 39-54.
Eisenhardt K. M. (1989) Making fast decisions in high velocity environments, Academy
of Management Journal, 32(3), 543-576.
Elam, J., Konsynski, B. (1987) “Using Artificial Intelligence Techniques to Enhance
the Capabilities of Model Management Systems”, Decision Sciences, 18, 487-501.
El Sawy, O.A. (1985) Personal information systems for strategic scanning in turbulent
environments: Can the CEO go on-line?, MIS Quarterly, 9, 53-60.
Feldman M. and March J. (1981) Information in organisations as signal and symbol,
Administrative Science Quarterly, 26, 171-186.
Forgionne, G.A. “Decision Technology Systems: a vehicle to consolidate decision
making support”, Information Processing and Management, 27(6), 1991, 679-697.
Forgionne, G. and R. Kohli.(1995). Integrated MSS effects: an empirical health care
investigation. Information Processing and Management,. 31(6), pp. 879-896.
Forgionne G. An AHP model of DSS effectiveness. European Journal of Information
Systems 1999; 95-106.
Forgionne, G., Mora, M., Gupta, J., Gelman, O. “Decision-Making Support Systems”,
in: Encyclopedia of Information Science and Technology, Hershey, PA, Idea
Group, 2005, p. 759-765.
Galbraith J. (1977), Organisational Design, Addison-Wesley, Mass..
Galbraith J. (1974) Organisation design: an information processing view, Interfaces,
4(3), 28-37.
Galbraith J. (1973) Designing Complex Organisations, Addison-Wesley, Mass..
Gorry, A. and Scott Morton, M. (1989) Retrospective commentary on the Gorry and
Scott Morton framework, Harvard Business Review, Spring, 58-60.
Gorry A. and Scott Morton. M. (1971) A Framework for Management Information
Systems, Sloan Management Review, Fall, 55-70.
Goul, M., Henderson, J., Tonge, F. “The Emergence of Artificial Intelligence as a
Reference Discipline for Decision Support Systems Research”, Decision Sciences,
23, 1992, p. 1263-1276.
Gupta, J.N.D., Forgionne, G., & Mora, M. (2006). Intelligent Decision-making Support
Systems: foundations, achievements and challenges, London, Springer, 2006.
Holsapple, C., Whinston, A.
Decision Support Systems: A Knowledge-based
Approach, West Publishing Company, 1996.
Howard, R. “Decision Analysis: Practice and Promise”, Management Science, 34(6),
1988, p. 679-695.
Howard, R. & Matheson, J. (2005). Influence Diagram. Decision Analysis, 2(3), 127-143.
Howard, R. & Matheson, J. (2005). Influence Diagram Retrospective. Decision
Analysis, 2(3), 144–147.
Huber, G.P. A Theory of the Effects of Advanced Information Technologies on
Organizational Design, Intelligence, and Decision Making. Academy of
Management Review, 15(1), 1990, p. 47-71.
Huber G. (1984) The nature and design of post-industrial organisations, Management
Science, 30(8), 928-951.
Huber, G.P. Managerial Decision Making, Scott-Foresman, 1980.
Huber G. and McDaniel R. (1986) The decision-making paradigm of organisational
design, Management Science, 32(5), 572-589.
Isenberg, D. (1984) How senior managers think, Harvard Business Review,
November/December 1984, 81-90.
Keegan W. (1974) Multinational scanning: a study of the information sources utilised
by headquarters executives in multinational companies, Administrative Science
Quarterly, 19, 411-421.
Keen, P. (1987) Decision Support Systems: the Next Decade, Decision Support
Systems, 3, 253-265.
Keen, P.G. and Scott Morton, M.S. (1978), Decision Support Systems: An
Organisational Perspective, Addison-Wesley, Reading, Mass.
Kotter, J. (1984), What effective managers really do, Harvard Business Review,
November/December, 156-167.
March, J.P. (1987) Ambiguity and Accounting: the elusive link between information
and decision making, Accounting, Organisations and Society, 12(2), 153-168.
March, J.P. (1981) Footnotes to organisational change, Administrative Science
Quarterly, 26, 563-577.
March, J. and Simon, H. (1993), Organisations, (2nd edition), Blackwell Publishers,
Cambridge, Mass.
McCosh, A., Correa-Perez, B. “The Optimization of What ?”, In: Intelligent Decisionmaking Support Systems: foundations, achievements and challenges, London,
Springer, 2006 (in press), p. 487-506.
McGrath, J. E (1984), Groups - Interaction and Performance, (1st Edition), PrenticeHall, Englewood Cliffs, N.J..
Mintzberg H. (1983) Power In and Around Organisations, Englewood Cliffs, N.J. :
Prentice-Hall.
Mintzberg H. (1979) The Structuring of Organisations, Englewood Cliffs, N.J. :
Prentice-Hall.
Mintzberg Henry. (1976) Planning on the left side and managing on the right, Harvard
Business Review, Jul./Aug., 120-130.
Mintzberg H. (1975) The managers job: folklore and fact, Harvard Business Review,
Jul./Aug., 49-61.
Mintzberg H. (1973) The Nature of Managerial Work, Prentice Hall.
Mintzberg, H. and Van der Heyden, L. (1999) Organigraphs: drawing how companies
really work, Harvard Business Review, September-October, pp 87-95.
Mintzberg, H., Ahlstrand, B. and Lampel, J. (1998) Strategy Safari, Prentice Hall
Europe, Hemel Hempstead, Hertfordshire, UK.
Mintzberg, H., Raisinghani, D. and Theoret, A. (1976) The Structure of “Unstructured”
Decision Processes, Administrative Science Quarterly, 21, 246-275.
Mora M., Forgionne G., Cervantes F., Garrido L., Gupta, J.N.D., Gelman O. Toward a
Comprehensive Framework for the Design and Evaluation of Intelligent Decisionmaking Support Systems , Journal of Decision Systems, 2005; 14(3); 321-344.
Morgan, G. (1986), Images of Organisations, Sage Publications, London.
Newell, A. “The Knowledge Level”, AI Magazine, Summer, 1981, p.1-33.
Newell, A. and Simon, H.A. (1972) Human Problem Solving, Prentice-Hall, Englewood
Cliffs, N.J.
Nichols, J., Goul M., “Synergizing the Artificial Intelligence and Decision Support
Research Streams: Over a Decade of Progress with New Challenges on the
Horizon”, In: Intelligent Decision-making Support Systems: foundations,
achievements and challenges, London, Springer, 2006 (in press), p. 47-58.
Nohria N. (1992) Introduction: Is a network perspective a useful way to studying
organisations, in Nohria and Eccles (Eds) Networks and Organisations: Structure
Form and Action, Harvard Business Scholl Press, Boston Mass.
Nolan, R., The Stages Theory, 9-193-141, Harvard Business School, 1993.
O’Leary, D.E., "Enterprise Knowledge Management," IEEE Computer, March 1998,
pp. 54-61.
O’Leary, D. E., Enterprise Resource Planning Systems, Cambridge University Press,
Cambridge, U.K., 2000.
Parlos, P. (Ed) (2000). Multi-Criteria Decision Making Methods: a Comparative Study.
Kluwer: Dordrecht.
Pennings, J. M. (1983), Decision Making: An Organisational Behaviour Approach,
Weiner, New York.
Pomerol, J. & Adam, F. (2004). Practical Decision Making – From the Legacy of
Herbert Simon to Decision Support Systems. In: Decision Support in an Uncertain
and Complex World: The IFIP TC8/WG8.3 International Conference 2004, 1-11.
Porter, M.E. and Millar, V.E. (1985) How information can give you competitive
advantage, Harvard Business Review, July/August, 149-160.
Rockart J. and DeLong D. (1988), Executive Support Systems: The Emergence of Top
Management Computer Use, Business One, Irwin.
Rockart J. and Van Bullen C. (1984), The rise of Management Computing, Dow Jones
Irwin, Homewood, Illinois.
Sage, A. “Behavioral and Organizational Considerations in the Design of Information
Systems and Processes for Planning and Decision Support”, IEEE Trans. in
Systems, Man and Cybernetics, 11(9), 1981, p. 640-678.
Satty, T.L. (1994). How to Make a Decision: The Analytic Hierarchy Process.
Interfaces, 24(6), 19-43
Sauter, V. (1999). Intuitive Decision-Making. Communications of the ACM, 42(6), 109115.
Scott-Morton, M. Management Decision Systems: Computer Based Support for
Decision Makin, Division of Research, Harvard University, 1971.
Silver M. (1991), Systems that Support Decision Makers, Wiley and Sons, Chichester.
Simon, H.A. Administrative Behavior: a study of Decision-Making process in
Administrative Organizations, Free Press, New York, 1997, 4th. Ed. (1st. edition
in 1960)
Simon, H.A. “Two Heads are Better than One: The Collaboration between AI and OR”,
Interfaces, 17(4), 1987, p. 8-15.
Simon H. (1977) The New Science of Management Decisions, Prentice Hall, Englewood
Cliff, New Jersey.
Simon H. (1955) A Behavioural Model of Rational Choice, Quarterly Journal of
Economics, 69, February.
Simon, H.A., Dantzing, G., Hogart, R., Plott, C., Raiffa, H., Schelling, T., Shepsle, K.,
Thaler, R., Tversky, A. and Winter, S. “Decision Making and Problem Solving”,
Interfaces, 17(5), 1987, p. 11-31.
Sprague, R. “A Framework for the Development of Decision Support Systems”, MIS
Quarterly, 4(4), 1980, p. 1-26.
Sprague, R. and Carlson, E.D. (1982), Building Effective Decision Support Systems,
Englewood Cliffs, New Jersey.
Sprague R. and Watson H. (1986) Decision Support Systems: Putting Theory into
Practice, Prentice-Hall International (UK) Limited, London.
Turban, E., Aronson, J. Decision Support Systems and Intelligent Systems, PrenticeHall, Upper Saddle River, 1998, 5th. Ed.
Tversky, A. (1969) Intransitivity of preferences, Psychological Review, 76, 31-48.
Tversky, A. and Kahneman, D. (1974) Judgement under uncertainty: Heuristics and
biases, Sciences, 185, 1124-1131.
Watson, R.T., DeSanctis, G. and Scott Poole, M. (1988) Using a GDSS to facilitate
group consensus: some intended and unintended consequences, Management
Information Systems Quarterly, 12, 201-225.
Weick, K. E. (1979), The Social Psychology of Organisations (2nd edition), AddisonWesley, Reading, MA.
Yoon, P.K., & Hwang, C.L. (1995). Multiple Attribute Decision Making. SAGE
University Paper. Series: Quantitative Applications in the Social Sciences:
Thousands Oaks.
Zachary, W. “A Cognitively Based Functional Taxonomy of Decision Support
Techniques”, Human-Computer Interaction, 2, 1986, p. 25-63.