Towards the Formalisation of the Decision Support Systems Domain Step 1: Investigating the Decision Support Systems Curriculum

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Towards the
F
ormalisation

of the Decision
Support Systems Domain



Step 1:
Investigating t
he D
ecision

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 Contents
1
.



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 D
SS Curriculum

4.2.1.

Intellectual Relationship between DSS and B
usiness
Disciplines

4.2.2

Intellectual Relationship between DSS and Non
-
Business 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 D
SS career pa
th

4.3.3

Course Prerequisites.

4.3.4

M
ulti
-
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
loo
k 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 Curri
culum (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 for
ce 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 Brezi
llon, 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:
L
ist of task force members

People

Institution

email

Fr
édé
ric
Adam
*
2

University College Cork, Ireland

FAdam@afis.ucc.ie



M
arko Bohanec

Institut Jozef Stefan, Ljubljana,
Slovenia

marko.bohanec@ijs.si


Patrick Brezillon

Universite Paris 6, France

brezil@poleia.lip6.fr


Sven Carlsson

Lun
d University, Sweden

Sven_Carlsson@hermes.ics.lu.se


João Clímaco

Universidade de Coimbra, Portugal

jclimaco@inescc.pt


Csáki Csaba

University College Cork,

Ireland

csakics@itm.bme.hu


Sean Eom

Southeast Missouri State
University
, USA

sbeom@semo.edu


Péter

Gelléri

Corvinus University, Budapest,
Hungary

peter.gelleri@winsdom.com


George Huber

University of Texas, USA

George.Huber@mccombs.utexas.edu


Patrick Humphreys

London School of Economics
, UK

P.Humphreys@lse.ac.uk


Garrick Jones

London School of Economics
, UK

g.a.jones1@lse.ac.uk


Peter Keenan

University College Dublin, Ireland

peter.
keenan@ucd.ie


Laszlo Mero

Eötvös Loránd University,
Budapest, Hungary

mero@phone2play.com



Manuel Mora

Autonomous University of
Aguascalientes, Mexico

mmora@secur
enym.net


Karen Neville

University College Cork, Ireland

KNeville@afis.ucc.ie


Peter O'Donnell

Monash University, Australia

podonnell@gmail.com


Dan
O'Leary

Univer
sity of Southern California,
USA

DOLeary@marshall.usc.edu


Zita Paprika

Corvinus University, Budapest,
Hungary

zita.paprika@uni
-
corvinus.hu

David
Paradi
ce
*
2

Florida State University, USA

dparadice@cob.fsu.edu


Daniel Power

University of Northern Iowa, USA

power@dssresources.com


Pascale Zaraté

IRIT


INP Toulous
e, France

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, wit
h 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, suc
h
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 ar
tifacts (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 u
p 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 discu
ssion 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 DSS
3
.


The field of Decision Support Systems is one of the most enduring in the
Information Systems do
main, 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 consi
derable 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, wi
th 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, Arn
ott 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 t
he 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 M
anagement 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 t
he range of backgrounds

and the multiple
e
xpertise

of their
members
illustrate in vivid fashion

the
multitude of
area
s

of
research which DSS
can now
claim

to have successfully

integrate
d,

ranging from Operations Research, to
Mana
gement and to Social Psychology. This accumulation of knowledge has
r
esulted 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 system
s to support them as
decision support system is a perspective that has s
erved 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 t
o 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 D
SS, 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 t
his 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 state
-
of
-
the
-
art picture of its topics and applications.


In doing so, there are two diffe
rent ways to try to provide a good picture of t
he
identity of the DSS

area: Thes
e two different approaches to ide
ntify the core of
the DSS field are:

the normative approach and the descriptive approach. The
former seeks to define what research subspecialti
es 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 Associat
ion 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 De
cision 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 d
irections: a survey of
researchers' views,
Database
, Winter/Spring 1991, 53
-
62.


4.2 The Disciplines o
f Reference for a DSS Curriculum
4
.


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
re
lationships

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 Rela
tionship 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 a
pproach. 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 d
ecision 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, pairwi
se 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
consistenc
y 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 ma
teriality 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 rea
dily
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 environm
ent, 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 Disci
plines


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 des
igner 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,
acce
ss 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 a
nthropology. 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 p
rocessing; 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 an
d 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 Intelli
gence 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 representa
tion, and model base processin
g
. 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 exi
sting 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 mechan
ical
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 s
uch 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, ove
r 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 decisio
n 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 i
s 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. Knowl
edge 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 g
reatly
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, a
nd
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 framewor
k 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 suc
h 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
b
locking directly, Diehl and Strobe
(1987)

were able to determine that
production blocking accounts for most of the productivity loss o
f 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 other
s
(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 significa
ntly affects efficiency, member participation,
interpersonal behavior, and group choice, when compared to the face
-
to
-
face
meetings.


Janis and Mann
(1977)

analyz
e 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 t
he 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 fr
amework 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. Co
mmunication 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 Ot
her 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 multi
-
dimensional 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 th
e 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 t
he
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 leve
ls 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 embe
dded 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 particul
ar 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
disc
iplines. 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 benefi
ted 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 artif
icial 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 s
uch 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 appli
ed 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 Reference
s


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., "Producti
vity 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 Americ
an 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 Appendi
ces

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
buildi
ng 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 cours
e 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 con
cepts, 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 classi
fication
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 identif
y

one hundred and twenty
nine distinct topics that m
ake 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
diffe
rent 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 c
over 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 col
our
-
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 alig
ning 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)

Note: UG denotes Undergraduate and PG d
enotes Post
-
graduate


Finally, the grid cannot be complete and useful with
out

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 o
f the
framework. These, themes, e
.
g
.
: uncertainty, modeling the r
eal world, making
decisions etc., a
re critically important in building up an effective understanding
of the problems faced by DSS researchers and practitioners. Yet, they do not
lend themselv
es 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 Appe
ndix 1. Further
discussion within the group
has

led to a proposed method
to best represent these

themes using the term

“threads
5
´??
a

term
emerging in technology education
)
. Appendix 2 shows the
proposed coding of three sample threads that seem to be unambi
guously
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 c
oncept of
Thread
s™. Developed by the
Georgia
Institute of Technology
College of Computing faculty,
Thread
s™ 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



All

Local

Areas / Topics

Levels of education



Comments

UG DS
S
awareness
course

UG specialty
course / DSS
Practitioner

PG course /
Advanced DSS
Practitioner

Doctoral
course / DSS
Researcher


Basic Disciplines







A

Mathematical Foundations







B

Psychology







C

Economics







D

Law & Ethics







E

Ac
counting






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






Deci
sion Support and Organizations







K

Analyzing decision situations


managerial decision making







L

Decision consulting and facilitation







M

Knowledge engineering







N

Domains of Application






issue
s 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 D
SS career path


De
cisions are
integral to all work, but the degrees to which explicit decision
support
issues

are relevant vary.
The columns in the grid distinguish between
awarenes
s of decision support issues on one end of a continuum of knowledge
to in
-
depth focus on thes
e 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
impac
t 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
sec
ond 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 so
me
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 knowled
ge 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 bett
er 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 reali
ty 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 knowledg
e 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 dat
abases 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 wit
hin the context of a
DSS course, it ends up becoming quite an obstacle to pursuing a DSS specific
agenda in a timely manner
6
.


Thus, it is a solid starting point to regard constituencies such as computer
science (who already understand the technologies an
d / 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
-
User
-
Computing
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 exp
osure to DSS con
cepts 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 f
inance 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 t
o 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
te
chnology 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 Post
-
Graduate level.



4.3.4


M
ulti
-
level
DSS Curricula.


As discussed in an earlier section, d
ecision

processe
s
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
awarenes
s 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 tha
t 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 th
e 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 t
he 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 task
s. 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, databa
ses, 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: Ra
č
unalniški program za ve
č
parametrsko odlo
č
anje
.
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 s
elected
r
eading
s is also provided to the students.

WE
B 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 projec
t 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 immedi
ately 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: Infl
uence Diagrams

5.

ID's Continued: Examples; Demo: Analytica, DPL; Decision Analysis Part 4: Multi
-
Attribute 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.

Define your own decision
-
making problem.

2.

Create a multi
-
attribute model containing about 10
-
20 basic attributes.

3.

Define about 4
-
6 altern
atives (possibly real
-
life ones)

4.

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.

Title page: Project title and Author info

2.

Main Part: About 2 pa
ges 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 an
d/or what
-
if analysis

5.

Conclusion:



Which alternative to choose and why (its good and bad points)



Brief comparison with other alternatives

3.

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 Decis
ion 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 appr
oaches 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 th
eoretical 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)) s
o 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 Sys
tems, 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 th
e 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 incl
uding: 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 parti
cipate 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 b
e 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 Suppor
t 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


15%


Research Assignment by team


15%


Final
Exam


20%

Term Project


50%

Software Design Documentation



15%


Beta Software Product



35%



TEXTBOOK.


Turban, E. and Aronson, J. "Decision Support Systems and Intelligent Systems," Prentice
-
Hall,
5th. ed., 1998.


REFERENCE BOOKS.


Holsapple, C. W.


a
nd 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 Executiv
es.



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
-
bas
ed 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 characteri
stics.


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, analy
ze

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 sess
ion 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
I
ntegrated 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 In
telligent 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, implemen
tation, and managem
ent 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 objec
tives:




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 di
rect support of managerial decision
-
making.



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.




Speci
fic 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 deci
sion 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 e
merging 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 deve
lopment 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 req
uires 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 dimension
s

of Figure 1 are: (1) the extent of exposure to the practice
of DSS

and
(2) the extent of exposure to the theo
ry of DSS.
Both dimension
s
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
No exposure to Practical experience
Exposure to Theory
No Exposure to Theory
Beginner
Intermediate
Expert

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
l
earning 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 r
epresented 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). Further
more, 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
wh
o
m
the courses are destined.


Going beyond this initial discussion, one would then consider the means appl
ied
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 lik
ely 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 object
ive, 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 for
m of learning and
the latest environments, mostly the virtual environments lend themselves very
well for such courses.
Qudrat
-
Ullah and Karakul (2007
7
) present a review of
experiments to use Virtual Learning
Environments

(VLEs) for improving
decision makin
g, 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 To
ulouse 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 DS
S 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 Developmen
ts


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 developm
ents 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 an
d Karakul (2007) Decision Making in Interactive Learning Environments,
Journal of Decision Systems
, 16(1), 79
-
100.

academic opportunity. Further, future computin
g
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
influ
ence 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 PRA
CTICE
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 repor
t 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 prog
ramme 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 im
prove 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 prof
ession 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 global
ly 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 recog
nised 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 t
hose 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 bloc
ks 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

D
emands 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 ini
tiative provides an adequate bac
kdro
p 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
est
ablishment 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.


I
n 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 accreditatio
n 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 fo
r a

fixed term of 3 years and
a
n overlap of the different terms
could be implemented
so that the whole body
is only replace
d

in portions

rather than all at once
.


(3) A Chairperson


one of the six members.


(4) A web presence with information on the accre
ditation 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
. Ap
pendices


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
-

Tas
k force on DSS Curriculum


The DSS curriculum Grid

# all

# local

Areas / Topics

Levels of education



Comments

Undergraduate
awareness course
/ DSS awareness

Undergraduate
specialty course /
DSS Practitioner

Post
-
graduate
course / Advanced
DSS Practitioner

Doctoral course /
DSS Researcher


Basic Discipli
nes







A

Mathematical Foundations






1.


A
-
1.


introductory mathematical statistics

Required knowledge

Required knowledge

Required knowledge

Required knowledge


2.


A
-
2.


Bayesian theory / Bayesian networks


Required knowledge

Required knowledge

Required knowledg
e


3.


A
-
3.


risk theory, risk taking



Required knowledge

Required knowledge


4.


A
-
4.


decision algorithms


Required knowledge

Required knowledge

Required knowledge


5.


A
-
5.


Mathematical theories of multiple criteria
decision making



Required knowledge

Required knowledge


6.


A
-
6.


utility theory



Required knowledge

Required knowledge


7.


A
-
7.


multi
-
attribute utility theory / (or multi
-
criteria) models



Required knowledge

Required knowledge


8.


A
-
8.


decision trees

Required knowledge

Required knowledge

Required knowledge

Required knowled
ge


9.


A
-
9.


Influence diagrams






10.


A
-
10.


Game theory



Required knowledge

Required knowledge


11.


A
-
11.


Fuzzy Systems (Fuzzy Decision Trees,
Fuzzy Inference Systems, Fuzzy Linear
Programming)



Optional knowledge

Required knowledge


12.


A
-
12.


outranking methods (PROMETHEE,
ELE
CTRE, ORESTE, AHP)



Required knowledge

Required knowledge


13.


A
-
13.


Rough Set Theory




Optional knowledge


14.


A
-
14.


Markov models



Required knowledge

Required knowledge


15.


A
-
15.


Neural networks



Optional knowledge

Optional knowledge



B

Psychology






16.


B
-
1.


intellige
nce, creativity

Required knowledge

Required knowledge

Required knowledge

Required knowledge


17.


B
-
2.


creative decision making


Required knowledge

Required knowledge

Required knowledge


18.


B
-
3.


Conscious and unconscious thinking



Required knowledge

Required knowled
ge


19.


B
-
4.


rationality, intuition



Required knowledge

Required knowledge


20.


B
-
5.


the role of rational and intuitive thoughts



Required knowledge

Required knowledge


21.


B
-
6.


motivations, emotions



Required knowledge

Required knowledge


22.


B
-
7.


social perception, values



Required knowledge

Required knowledge


23.


B
-
8.


basics of social psychology



Required knowledge

Required knowledge


24.


B
-
9.


cultural differences, ways of thinking
(frames of mind)


Required knowledge

Required knowledge

Required knowledge


25.


B
-
10.


behavior and performance

of groups and
organizations


Required knowledge

Required knowledge

Required knowledge


26.


B
-
11.


the psychology of decision making


Required knowledge

Required knowledge

Required knowledge



C

Economics






27.


C
-
1.


basics of microeconomics



Required knowledge

Req
uired knowledge


28.


C
-
2.


basics of business economics



Required knowledge

Required knowledge


29.


C
-
3.


Finance






30.


C
-
4.


equilibrium models




Required knowledge



D

Law & Ethics






31.


D
-
1.


different legal systems



Required knowledge

Required knowledge


32.


D
-
2.


basic princi
ples of civil law



Required knowledge

Required knowledge


33.


D
-
3.


contract law



Required knowledge

Required knowledge


34.


D
-
4.


the consulting contract


Required knowledge

Required knowledge

Required knowledge


35.


D
-
5.


ethical issues

Required knowledge

Required knowledg
e

Required knowledge

Required knowledge



E

Accounting






36.


E
-
1.


Where data comes from?

Required knowledge

Required knowledge

Required knowledge

Required knowledge


37.


E
-
2.


Basic business processes

Required knowledge

Required knowledge

Required knowledge

Requir
ed knowledge


38.


E
-
3.


Ontology


Required knowledge

Required knowledge

Required knowledge


39.


E
-
4.


Semantic modeling of accounting
phenomena


Required knowledge

Required knowledge

Required knowledge


40.


E
-
5.


Continuous monitoring and auditing

Required knowledge

Required k
nowledge

Required knowledge

Required knowledge


41.


E
-
6.


XML/XBRL/ebXML


Required knowledge

Required knowledge

Required knowledge


42.


E
-
7.


IS/IT risks and controls

Required knowledge

Required knowledge

Required knowledge

Required knowledge


43.


E
-
8.


Governance



Required k
nowledge

Required knowledge


44.


E
-
9.


Regulations (e.g., Sarbanes / Oxley in US)


Required knowledge

Required knowledge

Required knowledge


45.


E
-
10.


Business intelligence


Required knowledge

Required knowledge

Required knowledge


46.


E
-
11.


Data mining


Required knowledge

Req
uired knowledge

Required knowledge


47.


E
-
12.


Revenue transaction processing cycle

Purchases transaction processing cycle

Conversion (Production) transaction
processing cycle

Payroll/Expenditures transaction
processing cycle

Financial Reporting cycle

Required kno
wledge

Required knowledge

Required knowledge

Required knowledge


48.


E
-
13.


Breakeven analyses

Process costing

Job costing

Activity based costing

Theory of constraints (TOC)

Financial ratio analysis


Required knowledge

Required knowledge

Required knowledge


49.


E
-
14.


ERP



system in general and in terms of
database model to know where data is
located

Required knowledge

Required knowledge

Required knowledge

Required knowledge


50.


E
-
15.


ERP


Financial module operations

ERP


Controlling module operations



Required knowledge

Re
quired knowledge


Basics of Decision Support (DSS core concepts)







F

Theories behind decision support






51.


F
-
1.


basic concepts, trends in decision theory
(normative, descriptive, OR, Decision
Analysis, ...)

Required knowledge

Required knowledge

Requi
red knowledge

Required knowledge


52.


F
-
2.


the theory of bounded rationality

Required knowledge

Required knowledge

Required knowledge

Required knowledge


53.


F
-
3.


Decision process (stages, typical tasks)

Required knowledge

Required knowledge

Required knowledge

Require
d knowledge


54.


F
-
4.


Context in Decision Making,

Contextualization for decision making



Required knowledge

Required knowledge


55.


F
-
5.


Collaborative decision making


Required knowledge

Required knowledge

Required knowledge


56.


F
-
6.


Risk and uncertainty in decision mak
ing

Required knowledge

Required knowledge

Required knowledge

Required knowledge


57.


F
-
7.


Uncertainty Quantification and
Propagation



Required knowledge

Required knowledge


58.


F
-
8.


Verbal Decision Analysis



Required knowledge

Required knowledge



G

Decision mode
ls & modeling






59.


G
-
1.


the process of modeling

Required knowledge

Required knowledge

Required knowledge

Required knowledge


60.


G
-
2.


models of artificial intelligence



Required knowledge

Required knowledge


61.


G
-
3.


problem structuring, problem modeling

Required knowl
edge

Required knowledge

Required knowledge

Required knowledge


62.


G
-
4.


Influence diagrams


Required knowledge

Required knowledge

Required knowledge


63.


G
-
5.


Goal Programming,

Goal Seeking



Required knowledge

Required knowledge


64.


G
-
6.


Modeling real life problems and s
ituations


Required knowledge

Required knowledge

Required knowledge


65.


G
-
7.


conflict theory



Required knowledge

Required knowledge


66.


G
-
8.


cooperation, coalitions



Required knowledge

Required knowledge


67.


G
-
9.


finding out preconditions, relevance check

Required know
ledge

Required knowledge

Required knowledge

Required knowledge


68.


G
-
10.


case typology, matching real cases to
models


Required knowledge

Required knowledge

Required knowledge


69.


G
-
11.


Levels of Representation of Decision
Problems



Required knowledge

Required knowl
edge


70.


G
-
12.


Modeling the context of decision making:
role of the context, contextual elements
and contextual graphs


Required knowledge

Required knowledge

Required knowledge



H

Technology behind DS






71.


H
-
1.


Group decision making


methods,
techniques


Requi
red knowledge

Required knowledge

Required knowledge


72.


H
-
2.


Distributed decision making



Required knowledge

Required knowledge


73.


H
-
3.


Heuristics for Decision Support


Required knowledge

Required knowledge

Required knowledge


74.


H
-
4.


Visualization in DSS


Required kno
wledge

Required knowledge

Required knowledge


75.


H
-
5.


Simulation


Required knowledge

Required knowledge

Required knowledge


76.


H
-
6.


SOFTWARE AGENTS

(Multi
-
Agent Simulation,

Multi
-
Agent Simulation and Modeling,

Intelligent Agents)



Required knowledge

Required know
ledge


77.


H
-
7.


Video and internet technologies based
decision techniques



Required knowledge

Required knowledge


78.


H
-
8.


I
mages of the future: prog
noses, scenarios,
scripts, ontologies, decisional KMS




Required knowledge


DSS







I

Software Tools & Engineer
ing






79.


I
-
1.


basic principles of decision support
software

Required knowledge

Required knowledge

Required knowledge

Required knowledge


80.


I
-
2.


common architectures


Required knowledge

Required knowledge

Required knowledge


81.


I
-
3.


Types of decision systems (data
-
ori
ented,
model
-
oriented, knowledge
-
oriented, etc)






82.


I
-
4.


End
-
User’s decision support tools (MCDM
packages)

Required knowledge

Required knowledge

Required knowledge

Required knowledge


83.


I
-
5.


Developing

decision Support technologies, tools



Required knowledge

R
equired knowledge


84.


I
-
6.


Decision support systems engineering
(SDLCs, Methodologies)



Required knowledge

Required knowledge



J

Types of DSS






85.


J
-
1.


EIS

Required knowledge

Required knowledge

Required knowledge

Required knowledge


86.


J
-
2.


MIS

Required knowledge

Required knowledge

Required knowledge

Required knowledge


87.


J
-
3.


DMSS

Required knowledge

Required knowledge

Required knowledge

Required knowledge


88.


J
-
4.


iDMSS

Required knowledge

Required knowledge

Required knowledge

Required knowledge


89.


J
-
5.


Group Support Systems,

Group Work Systems

Required knowledge

Required knowledge

Required knowledge

Required knowledge


90.


J
-
6.


Mobile Decision Support


Required knowledge

Required knowledge

Required knowledge


91.


J
-
7.


Data cubes


Required knowledge

Required knowledge

Required knowledge


92.


J
-
8.


Business Intelligence


Required knowledge

Required knowledge

Required knowledge


93.


J
-
9.


Data Warehousing, OLTP, OLAP, etc.


Required knowledge

Required knowledge

Required knowledge


94.


J
-
10.


DSS relation to other IT systems

Required knowledge

Required knowled
ge

Required knowledge

Required knowledge


95.


J
-
11.


Communication
-
driven

Required knowledge

Required knowledge

Required knowledge

Required knowledge


96.


J
-
12.


Data
-
driven / Database
-
oriented

Required knowledge

Required knowledge

Required knowledge

Required knowledge


97.


J
-
13.


Document
-
driven / Text
-
oriented

Required knowledge

Required knowledge

Required knowledge

Required knowledge


98.


J
-
14.


Knowledge
-
driven / Rule
-
oriented

Required knowledge

Required knowledge

Required knowledge

Required knowledge


99.


J
-
15.


Model
-
driven / Solver
-
oriente
d /
Spreadsheet
-
oriented

Required knowledge

Required knowledge

Required knowledge

Required knowledge


100.


J
-
16.


Desktop

Required knowledge

Required knowledge

Required knowledge

Required knowledge


101.


J
-
17.


Enterprise
-
wide

Required knowledge

Required knowledge

Required
knowledge

Required knowledge


102.


J
-
18.


Compound (two or more architectures)


Required knowledge

Required knowledge

Required knowledge


103.


J
-
19.


Domain focused (medical, transportation,
etc.)

Required knowledge

Required knowledge

Required knowledge

Required knowledge


104.


J
-
20.


AI
-
based



Required knowledge

Required knowledge


105.


J
-
21.


Agent
-
based



Required knowledge

Required knowledge


Decision Support and Organizations







K

Analyzing decision situations







managerial decision making

106.


K
-
1.


Business processes

Required knowl
edge

Required knowledge

Required knowledge

Required knowledge


107.


K
-
2.


Strategic Decision Making

Required knowledge

Required knowledge

Required knowledge

Required knowledge


108.


K
-
3.


Attention
-
Based View


Required knowledge

Required knowledge

Required knowledge


109.


K
-
4.


Debiasing Decision Making


Required knowledge

Required knowledge

Required knowledge


110.


K
-
5.


Decision Makers

Required knowledge

Required knowledge

Required knowledge

Required knowledge


111.


K
-
6.


Decisional Episodes


Required knowledge

Required knowledge

Required kn
owledge


112.


K
-
7.


Diagramming for Business Modeling


Required knowledge

Required knowledge

Required knowledge



L

Decision consulting and
facilitation






113.


L
-
1.


problems of legitimization




Required knowledge


114.


L
-
2.


marketing techniques of decision
consulting


Req
uired knowledge

Required knowledge



115.


L
-
3.


success criteria for consulting
-

evaluating and measuring performance


Required knowledge

Required knowledge



116.


L
-
4.


Communicating decisions


Required knowledge

Required knowledge

Required knowledge


117.


L
-
5.


the consulting
process


Required knowledge

Required knowledge



118.


L
-
6.


Decision conferencing



Required knowledge

Required knowledge


119.


L
-
7.


The Facilitator and the Chauffeur



Required knowledge

Required knowledge



M

Knowledge engineering






120.


M
-
1.


Role of information in dec
ision making

Required knowledge

Required knowledge

Required knowledge

Required knowledge


121.


M
-
2.


knowledge acquisition


Required knowledge

Required knowledge

Required knowledge


122.


M
-
3.


knowledge representation


Required knowledge

Required knowledge

Required knowle
dge


123.


M
-
4.


knowledge utilization


Required knowledge

Required knowledge

Required knowledge


124.


M
-
5.


knowledge management and decision
making

Required knowledge

Required knowledge

Required knowledge

Required knowledge



N

Domains of Application






125.


N
-
1.


Cases in th
e engineering domain

Required knowledge

Required knowledge

Required knowledge

Required knowledge


126.


N
-
2.


Cases in the business management
domain

Required knowledge

Required knowledge

Required knowledge

Required knowledge


127.


N
-
3.


Cases in the governmental domain

R
equired knowledge

Required knowledge

Required knowledge

Required knowledge


128.


N
-
4.


Challenges in decision
-
making support
systems (the complex worldwide
decision problems)




Required knowledge




© IFIP WG 8.3
-

DSS Curriculum Task Force

Do not reproduce without

explicit

permission

from the Chairs of the task force (see sect
ion 1 of the report)


129.


N
-
5.


Forecasting (DSS for)



Required knowledge

Required knowledge


130.


N
-
6.

O

Infrastructure

Issues






131.


O
-
1

Hardware Infrastructure



Required knowledge

Optional knowledge


132.


O
-
2

Network Infrastructure



Required knowledge

Optional knowledge


133.


O
-
3

Software Integration (e.g., EAI)



Required knowledge

Optional knowledge


134.


O
-
4

Information Integration



Required knowledge

Optional knowledge


135.


O
-
5

Security



Required knowledge

Optional knowledge


Appendix 2: coding the threads in the DSS curriculum


#
all

#
local

Areas / Topics

Threads



Comments


Modeling


Uncertainty

DSS Software
Development

More threads


Basic Disciplines







A

Mathematical Foundations






136.


A
-
16.


introductory mathematical statistics

X

X

X



137.


A
-
17.


Bayesian theory / Bayesian networks

X

X




138.


A
-
18.


risk theory, risk taking


X




139.


A
-
19.


decision algorithms



X



140.


A
-
20.


Mathematical theories of multiple
criteria decision
making (OR
)



X




141.


A
-
21.


utility theory






142.


A
-
22.


multi
-
attribute utility theory / (or
multi
-
criteria) models

X

X




143.


A
-
23.


decision trees

X

X




144.


A
-
24.


Influence diagrams


X




145.


A
-
25.


Game theory

X

X




146.


A
-
26.


Fuzzy Systems (Fuzzy Decision
Trees, Fuzzy Inference Systems,
Fuzzy Linear Programming)

X

X




147.


A
-
27.


outranking me
thods (PROMETHEE,
ELECTRE, ORESTE
, AHP
)






148.


A
-
28.


Rough Set Theory


X




149.


A
-
29.


Markov models

X

X





B

Psychology






150.


B
-
12.


intelligence, creativity



X



151.


B
-
13.


creative decision making






152.


B
-
14.


Conscious and unconscious thinking






153.


B
-
15.


rationality, intuition


X

X



154.


B
-
16.


the role of rational and intuitive
thoughts






155.


B
-
17.


motivations, emotions






156.


B
-
18.


social perception, values






157.


B
-
19.


basics of social psychology






158.


B
-
20.


cultural differences, ways of thinking
(frames of mind)






159.


B
-
21.


behavior and performance of grou
ps
and organizations






160.


B
-
22.


the psychology of decision making


X





C

Economics






161.


C
-
5.


basics of microeconomics

X





162.


C
-
6.


basics of business economics

X





163.


C
-
7.


Finance






164.


C
-
8.


equilibrium models

X






D

Law & Ethics






165.


D
-
6.


different legal systems






166.


D
-
7.


basic principles of civil law






167.


D
-
8.


contract law






168.


D
-
9.


the consulting contract






169.


D
-
10.


ethical issues







E

Accounting






170.


E
-
16.


Where data comes from?



X



171.


E
-
17.


Basic business processes

X


X



172.


E
-
18.


Ontology






173.


E
-
19.


Semantic modeling of accounting

phenomena

X





174.


E
-
20.


Continuous monitoring and auditing






175.


E
-
21.


XML/XBRL/ebXML

X


X



176.


E
-
22.


IS/IT risks and controls






177.


E
-
23.


Governance






178.


E
-
24.


Regulations (e.g., Sarbanes / Oxley
in US)






179.


E
-
25.


Business intelligence






180.


E
-
26.


Data mining

X





181.


E
-
27.


Revenue transa
ction processing cycle

Purchases transaction processing
X


X



cycle

Conversion (Production) transaction
processing cycle

Payroll/Expenditures transaction
processing cycle

Financial Reporting cycle

182.


E
-
28.


Breakeven analyses

Process costing

Job costing

Activity

based costing

Theory of constraints (TOC)

Financial ratio analysis

X


X



183.


E
-
29.


ERP


system in general and in terms
of database model to know where
data is located



X



184.


E
-
30.


ERP


Financial module operations

ERP


Controlling module operations






Basics
of Decision Support (DSS core
concepts)







F

Theories behind decision
support






185.


F
-
9.


basic concepts, trends in decision
theory (normative, descriptive, OR,
Decision Analysis, ...)



X



186.


F
-
10.


the theory of bounded rationality


X

X



187.


F
-
11.


Decision process

(stages, typical
tasks)

X


X



188.


F
-
12.


Context in Decision Making,

Contextualization for decision
making

X


X



189.


F
-
13.


Collaborative decision making



X



190.


F
-
14.


Risk and uncertainty in decision
making


X

X



191.


F
-
15.


Uncertainty Quantification and
Propagation


X

X



192.


F
-
16.


Verbal Decision Analysis







G

Decision models & modeling






193.


G
-
13.


the process of modeling

X


X



194.


G
-
14.


models of artificial intelligence

X


X



195.


G
-
15.


problem structuring, problem
modeling

X


X



196.


G
-
16.


Influence diagrams

X


X



197.


G
-
17.


Goal Programming,

Goal Seeking

X


X



198.


G
-
18.


Modeling real life problems and
situations

X


X



199.


G
-
19.


conflict theory



X



200.


G
-
20.


cooperation, coalitions



X



201.


G
-
21.


finding out preconditions, relevance
check

X

X

X



202.


G
-
22.


case typology, matching real cases to
models

X


X



203.


G
-
23.


Levels of Representation
of Decision
Problems

X


X



204.


G
-
24.


Modeling the context of decision
making: role of the context,
contextual elements and contextual
graphs

X


X




H

Technology behind DS






205.


H
-
9.


Group decision making


methods,
techniques



X



206.


H
-
10.


Distributed decision makin
g



X



207.


H
-
11.


Heuristics for Decision Support



X



208.


H
-
12.


Visualization in DSS



X



209.


H
-
13.


Simulation

X


X



210.


H
-
14.


SOFTWARE AGENTS

(Multi
-
Agent Simulation,

Multi
-
Agent Simulation and
Modeling,

Intelligent Agents)

X


X



211.


H
-
15.


Video and internet technologies
based decis
ion techniques



X



212.


H
-
16.


I
mages of the future: prog
noses,
scenarios, scripts, ontologies,
decisional KMS

X

X

X



DSS







I

Software Tools &
Engineering






213.


I
-
7.


basic principles of decision support
software

X


X



214.


I
-
8.


common architectures



X



215.


I
-
9.


Types

of decision systems (data
-
oriented, model
-
oriented, knowledge
-
oriented, etc)



X



216.


I
-
10.


End
-
User’s decision support tools
(MCDM packages)



X



217.


I
-
11.


Developing

decision Support technologies, tools



X



218.


I
-
12.


Decision support systems
engineering (SDLCs,

Methodol
ogies)



X




J

Types of DSS






219.


J
-
22.


EIS



X



220.


J
-
23.


MIS



X



221.


J
-
24.


DMSS



X



222.


J
-
25.


iDMSS



X



223.


J
-
26.


Group Support Systems,

Group Work Systems



X



224.


J
-
27.


Mobile Decision Support



X



225.


J
-
28.


Data cubes



X



226.


J
-
29.


Business Intelligence



X



227.


J
-
30.


Data Warehousing, OLTP,

OLAP,
etc.



X



228.


J
-
31.


DSS relation to other IT systems



X



229.


J
-
32.


Communication
-
driven



X



230.


J
-
33.


Data
-
driven / Database
-
oriented


X

X



231.


J
-
34.


Document
-
driven / Text
-
oriented



X



232.


J
-
35.


Knowledge
-
driven / Rule
-
oriented



X



233.


J
-
36.


Model
-
driven / Solver
-
oriented /
Spr
eadsheet
-
oriented

X


X



234.


J
-
37.


Desktop



X



235.


J
-
38.


Enterprise
-
wide



X



236.


J
-
39.


Compound (two or more
architectures)



X



237.


J
-
40.


Domain focused (medical,
transportation, etc.)



X



238.


J
-
41.


AI
-
based



X



239.


J
-
42.


Agent
-
based



X



Decision Support and Organizations







K

An
alyzing decision situations


managerial decision
making






240.


K
-
8.


Business processes



X



241.


K
-
9.


Strategic Decision Making






242.


K
-
10.


Attention
-
Based View






243.


K
-
11.


Debiasing Decision Making






244.


K
-
12.


Decision Makers






245.


K
-
13.


Decisional Episodes






246.


K
-
14.


Diagrammin
g for Business Modeling

X






L

Decision consulting and
facilitation






247.


L
-
8.


problems of legitimization






248.


L
-
9.


marketing techniques of decision
consulting






249.


L
-
10.


success criteria for consulting
-

evaluating and measuring
performance






250.


L
-
11.


Communicat
ing decisions






251.


L
-
12.


the consulting process






252.


L
-
13.


Decision conferencing






253.


L
-
14.


The Facilitator and the Chauffeur







M

Knowledge engineering






254.


M
-
6.


Role of information in decision
making



X



255.


M
-
7.


knowledge acquisition



X



256.


M
-
8.


knowledge represen
tation

X


X



257.


M
-
9.


knowledge utilization



X



258.


M
-
10.


knowledge management and decision
making



X




N

Domains of Application






259.


N
-
7.


Cases in the engineering domain






260.


N
-
8.


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






265.


O
-
1

Hardware Infrasture






266.


O
-
2

Network Infrastructure






267.


O
-
3

Software Integration (e.g.,

EAI)






268.


O
-
4

Information Integration






269.


O
-
5

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
3
:
List of
useful

Referenc
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Bell
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-
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-
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-
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-

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