The Changing Structure of DSS Research:An Empirical Investigation through Author Cocitaion Mapping

The 2004 IFIP International Conference on Decision Support Systems (DSS2004)Decision Support in an Uncertain World

Introduction

DSSs are a relatively young field of study. As a field of study continues to grow and becomes a coherent field, study of the intellectual development of the field is important (Culnan (1986).

Researchers can benefit by understanding this process and its outcomes because it reveals the vitality and the evolution of thought in a discipline gives a sense of its future identifies the basic commitments that will serve as the

foundations of the field It atrophies if it cuts itself off from curiosity, diversity, and reflection"

For DSS to become a coherent and substantive field, a continuing line of research must be built on the foundation of previous work. Without it, there may be good individual fragments rather than a cumulative tradition (Keen, 1980).

This research assesses on-going changes in the intellectual development and structure of DSS research.

Emphasis on contrasting the structural changes over the period of:- 1969 through 1990 and - 1991 through 1999

DATA

A total of about 1600 citing articles in the DSS area 632 -- 1969 through 1990 (28.7 articles/year) 984 -- 1990 through 1999 (98.4 articles/year)

A total of about 25,000 cited references taken from the

citing articles

Research methodology

Author cocitation analysis (ACA) "a set of data gathering,, analytical, and graphical display

techniques that can be used to produce empirical maps of prominent authors in various areas of scholarship"

The tools used in ACA

factor analysismultidimensional scalingcluster analysis.

Research methodology

The Assumptions in Author cocitation analysis (ACA) “cocitation is a measure of the perceived similarity, conceptual linkage, or cognitive relationship between two cocited items (documents or authors).” “Cocitation studies of specialties and fields yield valid representations of intellectual structure.”

Steps in ACA 1. Selection of Authors

2. Retrieval/compilation of Cocitation Frequencies 3. Multivariate Analysis 4. Interpretation

When does the cocitation occur?

When a citing paper cites any work of authors in reference lists

Ref. of Paper #1 Ref. of Paper #2 Ref. of Paper #3 Ackoff Ackoff Ackoff Bonczek Ackoff Ackoff Bonczek Applegate Blanning

Blanning Applegate Blanning Whinston Blanning Whinston

Sample Cocitation Matrix Ackoff Applegate Bonczek Blanning

Whinston

Ackoff Applegate Bonczek Blanning Whinston

Ackoff Applegate Bonczek Blanning Whinston Ackoff Applegate 1 Bonczek 1 0 Blanning 2 0 2 Whinston 2 1 1 2

Author Cocitation Analysis

"a set of data gathering, analytical, and graphical display techniques that can be used to produce empirical maps of prominent authors in various areas of scholarship" (McCain 1990)

Can you see the trunk, branches, and the roots of the tree?

ACA: A Tool for Digging Up the Roots, Trunks, Branches

Results

Factor analysis of the data (1990-1999) extracted 11 factors

Six major areas of DSS research 1. group support systems 2. design 3. model management 4. implementation/user interfaces 5. Evaluation 6. multiple criteria DSS

Five contributing disciplines 1. cognitive science 2. computer supported

cooperative works 3. organizational science 4. social psychology 5. MCDM

Systems Science

Organization Science

Cognitive Science

Implementation

User InterfaceFoundations

ModelManagement

Artificial Intelligence

MCDSS

MCDM GDSS

Artificial Intelligence

Communication

Organization Science

Psychology

Theory , Applications, and Contributing Disciplines of DSS

Interfaces(Dialogue)

Model

J Contributing disciplines

J1.Artificial Intelligence J2.Cognitive Science

J3.Communication Theory

J4. MCDM

J5.Organization Science

J6.Systems Science

J7.Psychology

Decision maker(s)

Interface

Data Model

Design Implementation Evaluation

Marketing DSS

POMDSS

Financial DSS

Decisionmaker(s)

SDSS

GDSS

ODSS

KBDSS

MCDSS

data

Organizational goals

Improving effectiveness of the DM’s problem-solving process

A specific DSS

C D E

F G H I

B Functional app.

What has happened/is happening in DSS research since 1991?

the DSS area has undergone profound structural changes in the intellectual structure over the past 10 years (1990-1999).

-- steady, strengthening, emerging, dying, and slowly growing areas.

The steady areas model management

organizational science multiple criteria decision making Artificial Intelligence

What has happened in DSS research?

The Strengthening AreasGSS The Emerging Areas Design Implementation Cognitive science The Dying Areas Foundation and Individual Differences (appeared to be no longer active)

Group DSS

The result of this study clearly shows that GDSS has become a central part of DSS research area over the past five years.

Some of the important recent developments

(1) There have been continuing developments and enhancements of GDSS tools to support and augment the existing group DSS and electronic meeting systems such as the following: -- An idea consolidator -- An optimization-based group decision tool for

combining subjective estimates and extracting the underlying knowledge of group members

Group DSS

-- A group software for modeling and analyzing business process re-engineering;

-- An interactive videodisk-based GDSS for directing the pattern, timing, and contents in group decision making;

-- A prototype GDSS for multicultural and multilingual communication to translate among several foreign languages such as English, German, Korean and Spanish;

Group DSS

(2) A wide range of GDSS/electronic meeting systems/decision conferencing system applications has been reported to support/facilitate the following areas: -- Strategic management meetings -- Quality improvement process -- Knowledge acquisition for multiple experts -- Distributed decision making involving fairly

large numbers of participants (tens to hundreds);

Group DSS

-- Developing a cognitive map of users of object-oriented techniques for understanding individual and group perceptions [48];

-- Developing national economic policy [49]; -- Expediting the requirements specification in

the system development process -- Facilitating the United States Army’s group

decision making in geographically distributed environments ; and

Group DSS

(3) GDSS is being integrated with other technologies such as ES and case-based reasoning, etc.

A prototype system that embedded ES into GDSS is developed to make a GDSS a more user-friendly and powerful tool

The distributed artificial intelligence approach for designing and developing group problem solving systems is being investigated to coordinate organizational activities in a distributed environment

GDSS Empirical Research Model (U. of AZ)

Group

Task

Context

EMS

Process Outcome

Model management

Some of notable approaches include the following:(1) Development of Graph-based modeling:

Jones presented a prototype system of graph-based modeling, NETWORKS, which allows the user to represent a wide variety of decision problems in a graphical form such as bar chart, decision tree, decision network, etc.. Further, the users manipulate the models (e.g.., deleting/adding subtrees for decision trees) using a graph-grammar by applying a set of operations (or productions).

Model management

(2) Object-oriented approach:

Using the object-oriented framework, Muhanna [37] designed and implemented a prototype model management system to build, store, retrieve composite models and to maintain the integrity of model bases through providing the functional capability of model sharing, integration, and reusability. e.g., a DSS formulates and integrates optimization and simulation modeling and heuristic reasoning for non-expert users through an object-oriented domain-specific knowledge base.

Model management

(3) Modeling by analogy (Analogical modeling) is suggested as potentially fruitful avenues for increasing the productivity of model formulation. This approach is using a process by which model X is constructed based on a known model for problem X and the similarity between problems X and Y [31].

(4) In addition to a knowledge base which stores facts and rules, case- based reasoning systems maintain a case base which is a repository of all previous cases solved. To find a solution for a new problem, the system identifies the most similar case from its case base to be applied.

Model management

(4) Active modeling systems are expert systems embedded modeling systems which provide intelligent support to the modelers. e.g. a knowledge-based linear programming (LP) model construction system

Model management

(5) Integrating model management and inductive machine learning in an adaptive decision support system.

by incorporating machine learning capabilities for model management .

the system adapts itself to the environment through continuously updating and refining the knowledge-base .

(6) Model integration using metagraph: the process of model integration can be significantly expedited by utilizing certain connectivity properties in metagraph.

User Interfaces

Intelligent agents (a.k.a. intelligent interfaces, adaptive interfaces) research is an emerging interdisciplinary research area involving researchers from such fields as expert systems, decision support systems, cognitive science, psychology, databases, etc.

The primary purpose of agent research is to "develop software systems which engage and help all types of end users" in order to reduce work and information overload, teach, learn, and perform tasks for the user.”

A NEW GENERATION OF A NEW GENERATION OF ACTIVE/INTELLIGENT DSSACTIVE/INTELLIGENT DSS

WHAT A GREAT IDEA!

A SYSTEM THAT BRINGS TOGETHER THE ANALYTICAL STYLE OF DSS AND THE JUDGMENTAL STYLE OF ES/AI

CONCLUSION AND IMPLICATIONS FOR FUTURE DSS RESEARCH

This study identified a dynamic dimension of DSS research areas to account for the ongoing changes in its "disciplinary matrix" -- the four emerging areas (Implementation, Design, and Cognitive science); continuously growing areas (GDSS, Model management, MCDM, and Organization science); and dying areas (Individual difference and Foundations).

Focus of DSS research appears to be shifting from the study of DSS components (data, model, individual differences of decision makers) during the periods of 1970 through 1990 to the design, implementation, and user-interface management (which have not been shown to be substantive DSS research subspecialties in the previous research), to provide useful guiding principles for practitioners in the integrated processes of design, implementation, and evaluation of decision support systems.

CONCLUSION AND IMPLICATIONS FOR FUTURE DSS RESEARCH

World Wide Web-based DSS is another emerging topic in the DSS area. The World Wide Web is increasingly being used as the client/server platform of many business organizations due to its network and platform-independence and very low software/ installation/maintenance costs. The web-based solutions are low cost vehicles for easily accessing, analyzing, and distributing timely business information from corporate databases through OLAP.

The Internet and corporate intranets opened a wide possibility of

building global DSS/interorganizational DSS to deal with problems of global natures. As we enter the age of the global village where geographical and temporal boundaries are shrinking rapidly, global DSS/Interorganizational DSS support systems are emerging as the new frontiers in management information systems area.

Single

Multiple

Number ofOperatingCountries

Group DSS

GroupSupportSystems

1970's 1980's 1990's 2000's

Web-based DSS

GlobalDSS

Enterprise/Organizational

DSS

Inter-OrganizationalDSS

single-userDSS

Single Decision Maker,Single Organization

Extended enterprisesdecision support

Cross-functional decisionSupport for an

organization

group decision makers,single organization

Number ofOrganizations