Design Science in Information Systems Research


Alan R. Hevner
Information Systems and Decision Sciences
College of Business Administration
University of South Florida
4202 E. Fowler Avenue, CIS1040
Tampa, FL 33620
Phone: (813) 974-6753
Fax: (813) 974-6749
Email: ahevner@coba.usf.edu


Salvatore T. March
Owen Graduate School of Management
Vanderbilt University
Nashville, TN 37203
Phone: (615) 322-7043
Fax: (615) 3437177
Email: Sal.March@owen.vanderbilt.edu


Jinsoo Park
College of Business Administration
Korea University
Seoul, 136-701
Korea
Phone: +82-2-3290-1943
Fax: +82-2-922-7220
E-mail: jinsoo.park@acm.org


Sudha Ram
Management Information Systems Department
Eller College of Business and Public Administration
The University of Arizona
Tucson, AZ 85721
Phone: (520) 621-2748
Fax: (520) 621-2433
Email: ram@bpa.arizona.edu


Accepted for Publication in MIS Quarterly

Design Science in Information Systems Research


Acknowledgements: We would like to thank Allen Lee, Ron Weber, and Gordon Davis
who in different ways each contributed to our thinking about design science in the
Information Systems profession and encouraged us to pursue this line of research. We
would also like to acknowledge the efforts of Rosann Collins who provided insightful
comments and perspectives on the nature of the relationship between behavioral-
science and design-science research. This work has also benefited from seminars and
discussions at Arizona State University, Florida International University, Georgia State
University, Michigan State University, Notre Dame University, and The University of
Utah. We would particularly like to thank Brian Pentland and Steve Alter for feedback
and suggestions they provided on an earlier version of this paper. The comments
provided by several anonymous editors and reviewers greatly enhanced the content
and presentation of the paper.

Author Biographies:
Alan R. Hevner: Alan R. Hevner is an Eminent Scholar and Professor in the College of
Business Administration at the University of South Florida. He holds the Salomon
Brothers/Hidden River Corporate Park Chair of Distributed Technology. His areas of
research interest include information systems development, software engineering,
distributed database systems, and healthcare information systems. He has published
numerous research papers on these topics and has consulted for several Fortune 500
companies. Dr. Hevner received a Ph.D. in Computer Science from Purdue University.
He has held faculty positions at the University of Maryland at College Park and the
University of Minnesota. Dr. Hevner is a member of ACM, IEEE, AIS, and INFORMS.

Salvatore T. March: Salvatore T. March is the David K. Wilson Professor of
Management at the Owen Graduate School of Management, Vanderbilt University. He
received a B.S. in Industrial Engineering and M.S. and Ph.D. degrees in Operations
Research from Cornell University. His research interests are in information system
development, distributed database design, and electronic commerce. His research has
appeared in journals such as Communications of the ACM, IEEE Transactions on
Knowledge and Data Engineering, and Information Systems Research. He served as
the Editor-in-Chief of ACM Computing Surveys and as an Associate Editor for MIS
Quarterly. He is currently a Senior Editor for Information Systems Research and an
associate editor for Decision Sciences Journal.

Jinsoo Park: Jinsoo Park is an assistant professor of information systems in the
College of Business Administration at Korea University. He was formerly on the faculty
of the Carlson School of Management at the University of Minnesota. He holds a Ph.D.
in MIS from the University of Arizona. His research interests are in the areas of
semantic interoperability and metadata management in inter-organizational information
systems, heterogeneous information resource management and integration, knowledge
sharing and coordination, and data modeling. His published research articles appear in
IEEE Computer, IEEE Transactions on Knowledge and Data Engineering, and

Information Systems Frontiers. He currently serves on the editorial board of Journal of
Database Management. He is a member of ACM, IEEE, AIS, and INFORMS.

Sudha Ram: Sudha Ram is the Eller Professor of MIS at the University of Arizona. She
received a B.S. in Science from the University of Madras in 1979, PGDM from the
Indian Institute of Management, Calcutta in 1981 and a Ph.D. from the University of
Illinois at Urbana-Champaign, in 1985. Dr. Ram has published articles in such journals
as Communications of the ACM, IEEE TKDE, ISR, and Management Science. Her
research deals with interoperability in heterogeneous databases, semantic modeling,
data allocation, and intelligent agents for data management. Her research has been
funded by IBM, NIST, NSF, NASA, and ORD (CIA).

Design Science in Information Systems Research


ABSTRACT

Two paradigms characterize much of the research in the Information Systems
discipline: behavioral science and design science. The behavioral-science
paradigm seeks to develop and verify theories that explain or predict human or
organizational behavior. The design-science paradigm seeks to extend the
boundaries of human and organizational capabilities by creating new and
innovative artifacts. Both paradigms are foundational to the IS discipline,
positioned as it is at the confluence of people, organizations, and technology.
Our objective is to describe the performance of design-science research in
Information Systems via a concise conceptual framework and clear guidelines
for understanding, executing, and evaluating the research. In the design-
science paradigm knowledge and understanding of a problem domain and its
solution are achieved in the building and application of the designed artifact.
Three recent exemplars in the research literature are used to demonstrate the
application of these guidelines. We conclude with an analysis o f the challenges
of performing high-quality design-science research in the context of the broader
IS community.



Keywords: Information Systems research methodologies, design science, design
artifact, business environment, technology infrastructure, search strategies,
experimental methods, creativity

ISRL Categories: AI01, AI02, AI03, AC03, FB04, IB01, IB02

1
Design Science in Information Systems Research

1. INTRODUCTION
Information systems are implemented within an organization for the purpose of
improving the effectiveness and efficiency of that organization. Capabilities of the
information system and characteristics of the organization, its work systems, its people,
and its development and implementation methodologies together determine the extent
to which that purpose is achieved (Silver et al. 1995). It is incumbent upon researchers
in the Information Systems (IS) discipline to "further knowledge that aids in the
productive application of information technology to human organizations and their
management" (ISR 2002, inside front cover) and to develop and communicate
"knowledge concerning both the management of information technology and the use of
information technology for managerial and organizational purposes" (Zmud 1997).
We argue that acquiring such knowledge involves two complementary but distinct
paradigms, behavioral science and design science (March and Smith 1995). The
behavioral-science paradigm has its roots in natural science research methods. It
seeks to develop and justify theories (i.e., principles and laws) that explain or predict
organizational and human phenomena surrounding the analysis, design,
implementation, management, and use of information systems. Such theories
ultimately inform researchers and practitioners of the interactions among people,
technology, and organizations that must be managed if an information system is to
achieve its stated purpose, namely improving the effectiveness and efficiency of an
organization. These theories impact and are impacted by design decisions made with
respect to the system development methodology used and the functional capabilities,
information contents, and human interfaces implemented within the information system.
The design-science paradigm has its roots in engineering and the sciences of the
artificial (Simon 1996). It is fundamentally a problem-solving paradigm. It seeks to
create innovations that define the ideas, practices, technical capabilities, and products
through which the analysis, design, implementation, and use of information systems can
be effectively and efficiently accomplished (Tsichritzis 1997; Denning 1997). Such
artifacts are not exempt from natural laws or behavioral theories. To the contrary, their

2
creation relies on existing "kernel theories" that are applied, tested, modified, and
extended through the experience, creativity, intuition, and problem solving capabilities of
the researcher (Walls et al. 1992; Markus et al. 2002).
The importance of design is well recognized in the IS literature (Glass 1999;
Winograd 1996; Winograd 1997). Benbasat and Zmud (1999, p. 5) argue that the
relevance of IS research is directly related to its applicability in design, stating that the
implications of empirical IS research should be "implementable, … synthesize an
existing body of research, … [or] stimulate critical thinking" among IS practitioners.
However, designing useful artifacts is complex due to the need for creative advances in
domain areas in which existing theory is often insufficient. "As technical knowledge
grows, IT is applied to new application areas that were not previously believed to be
amenable to IT support" (Markus et al. 2002, p. 180). The resultant IT artifacts extend
the boundaries of human problem solving and organizational capabilities by providing
intellectual as well as computational tools. Theories regarding their application and
impact will follow their development and use.
Here, we argue, is an opportunity for IS research to make significant contributions
by engaging the complementary research cycle between design-science and
behavioral-science to address fundamental problems faced in the productive application
of information technology. Technology and behavior are not dichotomous in an
information system. They are inseparable (Lee 2000). They are similarly inseparable in
IS research. Philosophically these arguments draw from the pragmatists (Aboulafia
1991) who argue that truth (justified theory) and utility (artifacts that are effective) are
two sides of the same coin and that scientific research should be evaluated in light of its
practical implications.
The realm of IS research is at the confluence of people, organizations, and
technology (Lee 1999; Davis and Olson 1985). IT artifacts are broadly defined as
constructs (vocabulary and symbols), models (abstractions and representations),
methods (algorithms and practices), and instantiations (implemented and prototype
systems). These are concrete prescriptions that enable IT researchers and
practitioners to understand and address the problems inherent in developing and
successfully implementing information systems within organizations (March and Smith

3
1995; Nunamaker et al. 1991a). As illustrations, Walls et al. (1992) and Markus et al.
(2002) present design-science research aimed at developing executive information
systems (EISs) and systems to support emerging knowledge processes (EKPs),
respectively, within the context of "IS design theories." Such "theories" prescribe
"effective development practices" (methods) and "a type of system solution"
(instantiation) for "a particular class of user requirements" (models) (Markus et al. 2002,
p 180). Such prescriptive theories must be evaluated with respect to the utility provided
for the class of problems addressed.
An IT artifact, implemented in an organizational context, is often the object of study
in IS behavioral-science research. Theories seek to predict or explain phenomena that
occur with respect to the artifact's use (intention to use), perceived usefulness, and
impact on individuals and organizations (net benefits) depending on system, service,
and information quality (DeLone and McLean 1992; Seddon 1997; DeLone and McLean
2003). Much of this behavioral research has focused on one class of artifact, the
instantiation (system), although other research efforts have also focused on the
evaluation of constructs (e.g., Batra et al. 1990; Kim and March 1995; Bodart et al.
2001; Geerts and McCarthy 2002) and methods (e.g., Marakas and Elam 1998; Sinha
and Vessey 1999). Relatively little behavioral research has focused on evaluating
models, a major focus of research in the management science literature.
Design science, as the other side of the IS research cycle, creates and evaluates IT
artifacts intended to solve identified organizational problems. Such artifacts are
represented in a structured form that may vary from software, formal logic and rigorous
mathematics to informal natural language descriptions . A mathematical basis for design
allows many types of quantitative evaluations of an IT artifact, including optimization
proofs, analytical simulation, and quantitative comparisons with alternative designs.
The further evaluation of a new artifact in a given organizational context affords the
opportunity to apply empirical and qualitative methods. The rich phenomena that
emerge from the interaction of people, organizations, and technology may need to be
qualitatively assessed to yield an understanding of the phenomena adequate for theory
development or problem solving (Klein and Meyers 1999). As field studies enable
behavioral-science researchers to understand organizational phenomena in context, the

4
process of constructing and exercising innovative IT artifacts enable design-science
researchers to understand the problem addressed by the artifact and the feasibility of
their approach to its solution (Nunamaker et al. 1991a).
The primary goal of this paper is to inform the community of IS researchers and
practitioners of how to conduct, evaluate, and present design-science research. We do
so by describing the boundaries of design science within the IS discipline via a
conceptual framework for understanding information systems research (Section 2) and
by developing a set of guidelines for conducting and evaluating good design-science
research (Section 3). We focus primarily on technology-based design although we note
with interest the current exploration of organizations, policies, and work practices as
designed artifacts (Boland 2002). Following Klein and Myers (1999) treatise on the
conduct and evaluation of interpretive research in IS , we use the proposed guidelines to
assess recent exemplar papers published in the IS literature in order to illustrate how
authors, reviewers, and editors can apply them consistently (Section 4). We conclude
(Section 5) with an analysis of the challenges of performing high-quality design-science
research and a call for synergistic efforts between behavioral-science and design-
science researchers.

2. A FRAMEWORK FOR IS RESEARCH
Information systems and the organizations they support are complex, artificial, and
purposefully designed. They are composed of people, structures, technologies, and work
systems (Bunge 1985; Simon 1996; Alter, 2003). Much of the work performed by IS
practitioners, and managers in general (Boland 2002), deals with design – the purposeful
organization of resources to accomplish a goal. Figure 1 illustrates the essential
alignments between business and information technology strategies and between
organizational and information systems infrastructures (Henderson and Venkatraman
1993). The effective transition of strategy into infrastructure requires extensive design
activity on both sides of the figure – organizational design to create an effective
organizational infrastructure and information systems design to create an effective
information system infrastructure.

5
These are interdependent design activities that are central to the IS discipline .
Hence, IS research must address the interplay among: business strategy, IT strategy,
organizational infrastructure, and IS infrastructure. This interplay is becoming more
crucial as information technologies are seen as enablers of business strategy and
organizational infrastructure (Kalakota and Robinson 2001; Orlikowski and Barley
2001). Available and emerging IT capabilities are a significant factor in determining the
strategies that guide an organization. Cutting-edge information systems allow
organizations to engage new forms and new structures – to change the ways they "do
business" (Drucker 1988; Drucker 1991; Orlikowski 2000). Our subsequent discussion
of design science will be limited to the activities of building the IS infrastructure within
the business organization. Issues of strategy, alignment, and organizational
infrastructure design are outside the scope of this paper.
Business
Strategy
Information
Technology
Strategy
Organizational
Infrastructure
Information
Systems
Infrastructure
Strategy
Alignment
Infrastructure
Alignment
Organizational
Design
Activities
Information
Systems
Design
Activities

Figure 1: Organizational Design and Information Systems Design Activities
(Henderson and Venkatraman 1993)

6
To achieve a true understanding of and appreciation for design science as an IS
research paradigm, an important dichotomy must be faced. Design is both a process
(set of activities) and a product (artifact) – a verb and a noun (Walls et al. 1992). It
describes the world as acted upon (processes) and the world as sensed (artifacts). This
Platonic view of design supports a problem-solving paradigm that continuously shifts
perspective between design processes and designed artifacts for the same complex
problem. The design process is a sequence of expert activities that produces an
innovative product (i.e., the design artifact). The evaluation of the artifact then provides
feedback information and a better understanding of the problem in order to improve
both the quality of the product and the design process. This build-and-evaluate loop is
typically iterated a number of times before the final design artifact is generated (Markus
et al. 2002). During this creative process, the design-science researcher must be
cognizant of evolving both the design process and the design artifact as part of the
research.
March and Smith (1995) identify two design processes and four design artifacts
produced by design-science research in IS . The two processes are build and evaluate.
The artifacts are constructs, models, methods, and instantiations. Purposeful artifacts
are built to address heretofore unsolved problems. They are evaluated with respect to
the utility provided in solving those problems. Constructs provide the language in which
problems and solutions are defined and communicated (Schon 1993). Models use
constructs to represent a real world situation – the design problem and its solution
space (Simon 1996). Models aid problem and solution understanding and frequently
represent the connection between problem and solution components enabling
exploration of the effects of design decisions and changes in the real world. Methods
define processes. They provide guidance on how to solve problems, that is, how to
search the solution space. These can range from formal, mathematical algorithms that
explicitly define the search process to informal, textual descriptions of "best practice"
approaches, or some combination. Instantiations show that constructs, models or
methods can be implemented in a working system. They demonstrate feasibility,
enabling concrete assessment of an artifact's suitability to its intended purpose. They

7
also enable researchers to learn about the real world, how the artifact affects it, and
how users appropriate it.
Figure 2 presents our conceptual framework for understanding, executing, and
evaluating IS research combining behavioral-science and design-science paradigms.
We use this framework to position and compare these paradigms.
The environment defines the problem space (Simon 1996) in which reside the
phenomena of interest. For IS research, it is composed of people, (business)
organizations, and their existing or planned technologies (Silver et al. 1995). In it are
the goals, tasks, problems, and opportunities that define business needs as they are
perceived by people within the organization. Such perceptions are shaped by the roles,
capabilities, and characteristics of people within the organization. Business needs are
assessed and evaluated within the context of organizational strategies, structure,
culture, and existing business processes. They are positioned relative to existing
technology infrastructure, applications, communication architectures, and development
capabilities. Together these define the business need or "problem" as perceived by the
researcher. Framing research activities to address business needs assures research
relevance.
Given such an articulated business need, IS research is conducted in two
complementary phases. Behavioral science addresses research through the
development and justification of theories that explain or predict phenomena related to
the identified business need. Design science addresses research through the building
and evaluation of artifacts designed to meet the identified business need. The goal of
behavioral-science research is truth1. The goal of design-science research is utility. As
argued above, our position is that truth and utility are inseparable. Truth informs design
and utility informs theory. An artifact may have utility because of some yet
undiscovered truth. A theory may yet to be developed to the point where its truth can
be incorporated into design. In both cases, research assessment via the
justify/evaluate activities can result in the identification of weaknesses in the theory or

1 Theories posed in behavioral-science are principled explanations of phenomena. We recognize that
such theories are approximations and are subject to numerous assumptions and conditions. However,
they are evaluated against the norms of truth or explanatory power and are valued only as the claims they
make are borne out in reality.

8
artifact and the need to refine and reassess. The refinement and reassessment
process is typically described in future research directions.
The knowledge base provides the raw materials from and through which IS
research is accomplished. The knowledge base is composed of Foundations and
Methodologies. Prior IS research and results from reference disciplines provide
foundational theories, frameworks, instruments, constructs, models, methods, and
instantiations used in the develop/build phase of a research study. Methodologies
provide guidelines used in the justify/evaluate phase. Rigor is achieved by
appropriately applying existing foundations and methodologies. In behavioral science,
methodologies are typically rooted in data collection and empirical analysis techniques.
In design science, computational and mathematical methods are primarily used to
evaluate the quality and effectiveness of artifacts; however, empirical techniques may
also be employed.
The contributions of behavioral-science and design-science in IS research are
assessed as they are applied to the business need in an appropriate environment and
as they add to the content of the knowledge base for further research and practice. A
justified theory that is not useful for the environment contributes as little to the IS
literature as an artifact that solves a nonexistent problem.
One issue that must be addressed in design-science research is differentiating
routine design or system building from design research. The difference is in the nature
of the problems and solutions. Routine design is the application of existing knowledge
to organizational problems, such as constructing a financial or marketing information
system using "best practice" artifacts (constructs, models, methods, and instantiations)
existing in the knowledge base. On the other hand, design-science research addresses
important unsolved problems in unique or innovative ways or solved problems in more
effective or efficient ways. The key differentiator between routine design and design
research is the clear identification of a contribution to the archival knowledge base of
foundations and methodologies.

9

Figure 2: Information Systems Research Framework
Additions to the
Knowledge Base
Environment IS Research Knowledge Base
People
• Roles
• Capabilities
• Characteristics

Organizations
• Strategies
• Structure & Culture
• Processes

Technology
• Infrastructure
• Applications
• Communications
Architecture
• Development
Capabilities
Foundations
• Theories
• Frameworks
• Instruments
• Constructs
• Models
• Methods
• Instantiations

Methodologies
• Data Analysis
Techniques
• Formalisms
• Measures
• Validation Criteria
Develop / Build
• Theories
• Artifacts
Justify / Evaluate
• Analytical
• Case Study
• Experimental
• Field Study
• Simulation
Assess Refine
Business
Needs
Applicable
Knowledge
Application in the
Appropriate Environment
Relevance Rigor

10
In the early stages of a discipline or with significant changes in the environment,
each new artifact created for that discipline or environment is "an experiment" that
"poses a question to nature" (Newell and Simon 1976, p 114). Existing knowledge is
used where appropriate; however, often the requisite knowledge is nonexistent (Markus
et al. 2002). Reliance on creativity and trial and error search are characteristic of such
research efforts. As design-science research results are codified in the knowledge
base, they become "best practice." System building is then the routine application of
the knowledge base to known problems.
Design activities are endemic in many professions. In particular, the engineering
profession has produced a considerable literature on design (Dym 1994; Pahl and Beitz
1996; Petroski 1996). Within the IS discipline , many design activities have been
extensively studied, formalized, and have become normal or routine. Design-science
research in IS addresses what are considered to be wicked problems (Rittel and
Webber 1984; Brooks 1987; Brooks 1996). That is, those problems characterized by:
Unstable requirements and constraints based upon ill-defined environmental
contexts,
Complex interactions among subcomponents of the problem and its solution,
Inherent flexibility to change design processes as well as design artifacts (i.e.,
malleable processes and artifacts),
A critical dependence upon human cognitive abilities (e.g., creativity) to produce
effective solutions, and
A critical dependence upon human social abilities (e.g., teamwork) to produce
effective solutions.
As a result, we agree with Simon (1996) that a theory of design in information
systems, of necessity, is in a constant state of scientific revolution (Kuhn 1996).
Technological advances are the result of innovative, creative design science processes.
If not "capricious," they are at least "arbitrary" (Brooks 1987) with respect to business
needs and existing knowledge. Innovations, such as database management systems,
high-level languages, personal computers, software components, intelligent agents,
object technology, the Internet, and the World Wide Web, have had dramatic and at
times unintended impacts on the way in which information systems are conceived,

11
designed, implemented, and managed. Consequently the guidelines we present below
are, of necessity, adaptive and process-oriented.

3. GUIDELINES FOR DESIGN-SCIENCE IN INFORMATION SYSTEMS RESEARCH
As discussed above, design science is inherently a problem solving process. The
fundamental principle of design-science research from which our seven guidelines are
derived is that knowledge and understanding of a design problem and its solution are
acquired in the building and application of an artifact. That is, design-science research
requires the creation of an innovative, purposeful artifact (Guideline 1) for a specified
problem domain (Guideline 2). Because the artifact is "purposeful," it must yield utility
for the specified problem. Hence, thorough evaluation of the artifact is crucial
(Guideline 3). Novelty is similarly crucial since the artifact must be "innovative," solving
a heretofore unsolved problem or solving a known problem in a more effective or
efficient manner (Guideline 4). In this way, design-science research is differentiated
from the practice of design. The artifact itself must be rigorously defined, formally
represented, coherent, and internally consistent (Guideline 5). The process by which it
is created, and often the artifact itself, incorporates or enables a search process
whereby a problem space is constructed and a mechanism posed or enacted to find an
effective solution (Guideline 6). Finally, the results of the design-science research must
be communicated effectively (Guideline 7) both to a technical audience (researchers
who will extend them and practitioners who will implement them) and to a managerial
audience (researchers who will study them in context and practitioners who will decide if
they should be implemented within their organizations).
Our purpose for establishing these seven guidelines is to assist researchers,
reviewers, editors, and readers to understand the requirements for effective design-
science research. Following Klein and Myers (1999), we advise against mandatory or
rote use of the guidelines. Researchers, reviewers, and editors must use their creative
skills and judgment to determine when, where, and how to apply each of the guidelines
in a specific research project. However, we contend that each of these guidelines
should be addressed in some manner for design-science research to be complete.

12
How well the research satisfies the intent of each of the guidelines is then a matter for
the reviewers, editors, and readers to determine.
Table 1 summarizes the seven guidelines. Each is discussed in detail below. In
the following section, they are applied to specific exemplar research efforts.

Table 1: Design-Science Research Guidelines
Guideline Description
Guideline 1: Design as an Artifact Design-science research must produce a
viable artifact in the form of a construct, a
model, a method, or an instantiation.
Guideline 2: Problem Relevance The objective of design-science research is
to develop technology-based solutions to
important and relevant business problems.
Guideline 3: Design Evaluation The utility, quality, and efficacy of a design
artifact must be rigorously demonstrated via
well-executed evaluation methods.
Guideline 4: Research Contributions Effective design-science research must
provide clear and verifiable contributions in
the areas of the design artifact, design
foundations, and/or design methodologies.
Guideline 5: Research Rigor Design-science research relies upon the
application of rigorous methods in both the
construction and evaluation of the design
artifact.
Guideline 6: Design as a Search Process The search for an effective artifact requires
utilizing available means to reach desired
ends while satisfying laws in the problem
environment.
Guideline 7: Communication of Research Design-science research must be presented
effectively both to technology-oriented as
well as management-oriented audiences.

3.1 Guideline 1: Design as an Artifact
The result of design-science research in IS is, by definition, a purposeful IT artifact
created to address an important organizational problem. It must be described
effectively, enabling its implementation and application in an appropriate domain.
Orlikowski and Iacono (2001) call the IT artifact the "core subject matter" of the IS
field. Although they articulate multiple definitions of the term "IT artifact," many of which
include components of the organization and people involved in the use of a computer-

13
based artifact, they emphasize the importance of "those bundles of cultural properties
packaged in some socially recognizable form such as hardware and software" (p. 121),
i.e., the IT artifact as an instantiation. Weber (1987) argues that theories of "long-lived"
artifacts (instantiations) and their representations (Weber 2003) are fundamental to the
IS discipline. Such theories must explain how artifacts are created and adapted to their
changing environments and underlying technologies.
Our definition of IT artifacts is both broader and narrower then those articulated
above. It is broader in the sense that we include not only instantiations in our definition
of the IT artifact but also the constructs, models, and methods applied in the
development and use of information systems. However, it is narrower in the sense that
we do not include people or elements of organizations in our definition nor do we
explicitly include the process by which such artifacts evolve over time. We conceive of
IT artifacts not as independent of people or the organizational and social contexts in
which they are used but as interdependent and coequal with them in meeting business
needs. We acknowledge that perceptions and fit with an organization are crucial to the
successful development and implementation of an information system. We argue,
however, that the capabilities of the constructs, models, methods, and instantiations are
equally crucial and that design-science research efforts are necessary for their creation.
Furthermore, artifacts constructed in design-science research are rarely full-grown
information systems that are used in practice. Instead, artifacts are innovations that
define the ideas, practices, technical capabilities, and products through which the
analysis, design, implementation, and use of information systems can be effectively and
efficiently accomplished (Tsichritzis 1997; Denning 1997). This definition of the artifact
is consistent with the concept of IS "design theory" as used by Walls et al. (1992) and
Markus et al. (2002) where the theory addresses both the process of design and the
designed product.
More precisely, constructs provide the vocabulary and symbols used to define
problems and solutions. They have a significant impact on the way in which tasks and
problems are conceived (Schon 1993; Boland 2002). They enable the construction of
models or representations of the problem domain. Representation has a profound
impact on design work. The field of mathematics was revolutionized, for example, with

14
the constructs defined by Arabic numbers, zero, and place notation. The search for an
effective problem representation is crucial to finding an effective design solution (Weber
2003). Simon (1996, p. 132) states, "solving a problem simply means representing it so
as to make the solution transparent."
The Entity-Relationship model (Chen 1976), for example, is a set of constructs for
representing the semantics of data. It has had a profound impact on the way in which
systems analysis and database design are executed and the way in which information
systems are represented and developed. Furthermore these constructs have been
used to build models of specific business situations that have been generalized into
patterns for application in similar domains (Purao et al. 2003). Methods for building
such models have also been the subject of considerable research (Storey et al. 1997;
Halpin 2001; McCarthy 1982; Parsons and Wand 2000).
Artifact instantiation demonstrates feasibility both of the design process and of the
designed product. Design-science research in IT often addresses problems related to
some aspect of the design of an information system. Hence the instantiations produced
may be in the form of intellectual or software tools aimed at improving the process of
information system development. Constructing a system instantiation that automates a
process demonstrates that the process can, in fact, be automated. It provides "proof by
construction" (Nunamaker 1991a). The critical nature of design-science research in IS
lies in the identification of as yet undeveloped capabilities needed to expand IS into new
realms "not previously believed amenable to IT support" (Markus et al. 2002, p. 180).
Such a result is significant IS research only if there is a serious question about the
ability to construct such an artifact, there is uncertainty about its ability to perform
appropriately, and the automated task is important to the IS community. TOP Modeler
(Markus et al. 2002), for example, is a tool that instantiates methods for the
development of information systems that support "emergent knowledge processes."
Construction of such a prototype artifact in a research setting or in a single
organizational setting is only a first step toward its deployment, but we argue that it is a
necessary one. As an exemplar of design-science research (see below), this research
resulted in a commercial product that "has been used in over two dozen 'real use'
situations" (p. 187).

15
To illustrate further, prior to the construction of the first expert system (instantiation),
it was not clear if such a system could be constructed. It was not clear how to describe
or represent it, or how well it would perform. Once feasibility was demonstrated by
constructing an expert system in a selected domain, constructs and models were
developed and subsequent research in expert systems focused on demonstrating
significant improvements in the product or process (methods) of construction (Tam
1990; Trice and Davis 1993). Similar examples exist in requirements determination
(Bell 1993; Bhargava et al. 1998), individual and group decision support systems (Aiken
et al. 1991; Basu and Blanning 1994), database design and integration (Dey et al. 1998;
Dey et al. 1999; Storey et al. 1997), and workflow analysis (Basu and Blanning 2000),
to name a few important areas of IS design-science research.

3.2 Guideline 2: Problem Relevance
The objective of research in information systems is to acquire knowledge and
understanding that enable the development and implementation of technology-based
solutions to heretofore unsolved and important business problems. Behavioral science
approaches this goal through the development and justification of theories explaining or
predicting phenomena that occur. Design science approaches this goal through the
construction of innovative artifacts aimed at changing the phenomena that occur. Each
must inform and challenge the other. For example, the Technology Acceptance Model
(Venkatesh 2000) provides a theory that explains and predicts the acceptance of
information technologies within organizations. This theory challenges design-science
researchers to create artifacts that enable organizations to overcome the acceptance
problems predicted. We argue that a combination of technology-based artifacts (e.g.,
system conceptualizations and representations, practices, technical capabilities,
interfaces, etc.), organization-based artifacts (e.g., structures, compensation, reporting
relationships, social systems, etc.), and people-based artifacts (e.g., training,
consensus building, etc.) are necessary to address such issues.
Formally, a problem can be defined as the differences between a goal state and the
current state of a system. Problem solving can be defined as a search process (see
Guideline 6) using actions to reduce or eliminate the differences (Simon 1996). These

16
definitions imply an environment that imposes goal criteria as well as constraints upon a
system. Business organizations are goal-oriented entities existing in an economic and
social setting. Economic theory often portrays the goals of business organizations as
being related to profit (utility) maximization. Hence, business problems and
opportunities often relate to increasing revenue or decreasing cost through the design
of effective business processes. The design of organizational and inter-organizational
information systems plays a major role in enabling effective business processes to
achieve these goals.
The relevance of any design-science research effort is with respect to a constituent
community. For IS researchers that constituent community is the practitioners who
plan, manage, design, implement, operate, and evaluate information systems and those
who plan, manage, design, implement, operate, and evaluate the technologies that
enable their development and implementation. To be relevant to this community,
research must address the problems faced and the opportunities afforded by the
interaction of people, organizations, and information technology. Organizations spend
billions of dollars annually on IT, only too often to conclude that those dollars were
wasted (Keil 1995; Keil et al. 1998; Keil and Robey 1999). This community would
welcome effective artifacts that enable such problems to be addressed – constructs by
which to think about them, models by which to represent and explore them, methods by
which to analyze or optimize them, and instantiations that demonstrate how to affect
them.

3.3 Guideline 3: Design Evaluation
The utility, quality, and efficacy of a design artifact must be rigorously demonstrated
via well-executed evaluation methods. Evaluation is a crucial component of the
research process. The business environment establishes the requirements upon which
the evaluation of the artifact is based. This environment includes the technical
infrastructure which itself is incrementally built by the implementation of new IT artifacts.
Thus, evaluation includes the integration of the artifact within the technical infrastructure
of the business environment.

17
As in the justification of a behavioral science theory, evaluation of a designed IT
artifact requires the definition of appropriate metrics and possibly the gathering and
analysis of appropriate data. IT artifacts can be evaluated in terms of functionality,
completeness, consistency, accuracy, performance, reliability, usability, fit with the
organization, and other relevant quality attributes. When analytical metrics are
appropriate, designed artifacts may be mathematically evaluated. As two examples,
distributed database design algorithms can be evaluated using expected operating cost
or average response time for a given characterization of information processing
requirements (Johansson et al. 2003) and search algorithms can be evaluated using
information retrieval metrics such as precision and recall (Salton 1988).
Because design is inherently an iterative and incremental activity, the evaluation
phase provides essential feedback to the construction phase as to the quality of the
design process and the design product under development. A design artifact is
complete and effective when it satisfies the requirements and constraints of the problem
it was meant to solve. Design-science research efforts may begin with simplified
conceptualizations and representations of problems. As available technology or
organizational environments change, assumptions made in prior research may become
invalid. Johansson (2000), for example, demonstrated that network latency is a major
component in the response-time performance of distributed databases. Prior research
in distributed database design ignored latency because it assumed a low-bandwidth
network where latency is negligible. In a high-bandwidth network, however, latency can
account for over 90 percent of the response time. Johansson et al. (2003) extended
prior distributed database design research by developing a model that includes network
latency and the effects of parallel processing on response time.
The evaluation of designed artifacts typically uses methodologies available in the
knowledge base. These are summarized in Table 2. The selection of evaluation
methods must be matched appropriately with the designed artifact and the selected
evaluation metrics. For example, descriptive methods of evaluation should only be
used for especially innovative artifacts for which other forms of evaluation may not be
feasible. The goodness and efficacy of an artifact can be rigorously demonstrated via

18
well-selected evaluation methods (Basili 1996; Kleindorfer et al. 1998; Zelkowitz and
Wallace 1998).

Table 2: Design Evaluation Methods
Case Study – Study artifact in depth in business environment 1. Observational
Field Study – Monitor use of artifact in multiple projects
Static Analysis – Examine structure of artifact for static qualities
(e.g., complexity)
Architecture Analysis – Study fit of artifact into technical IS
architecture
Optimization – Demonstrate inherent optimal properties of
artifact or provide optimality bounds on artifact behavior
2. Analytical
Dynamic Analysis – Study artifact in use for dynamic qualities
(e.g., performance)
Controlled Experiment – Study artifact in controlled environment
for qualities (e.g., usability)
3. Experimental
Simulation – Execute artifact with artificial data
Functional (Black Box) Testing – Execute artifact interfaces to
discover failures and identify defects
4. Testing
Structural (White Box) Testing – Perform coverage testing of
some metric (e.g., execution paths) in the artifact implementation
Informed Argument – Use information from the knowledge base
(e.g., relevant research) to build a convincing argument for the
artifact’s utility
5. Descriptive
Scenarios – Construct detailed scenarios around the artifact to
demonstrate its utility

Design, in all of its realizations (e.g., architecture, landscaping, art, music), has
style. Given the problem and solution requirements, sufficient degrees of freedom
remain to express a variety of forms and functions in the artifact that are aesthetically
pleasing to both the designer and the user. Good designers bring an element of style to
their work (Norman 1988). Thus, we posit that design evaluation should include an
assessment of the artifact’s style.
The measurement of style lies in the realm of human perception and taste. In other
words, we know good style when we see it. While difficult to define, style in IS design is

19
widely recognized and appreciated (Kernighan and Plauger 1978; Winograd 1996).
Gelernter (1998) terms the essence of style in IS design "machine beauty." He
describes it as a marriage between simplicity and power that drives innovation in
science and technology. Simon (1996) also notes the importance of style in the design
process. The ability to creatively vary the design process, within the limits of
satisfactory constraints, challenges and adds value to designers who participate in the
process.

3.4 Guideline 4: Research Contributions
Effective design-science research must provide clear contributions in the areas of
the design artifact, design construction knowledge (i.e., foundations), and/or design
evaluation knowledge (i.e., methodologies). The ultimate assessment for any research
is “What are the new and interesting contributions?” Design-science research holds the
potential for three types of research contributions based on the novelty, generality, and
significance of the designed artifact. One or more of these contributions must be found
in a given research project.
1. The Design Artifact - Most often, the contribution of design-science research is the
artifact itself. The artifact must enable the solution of heretofore unsolved
problems. It may extend the knowledge base (see below) or apply existing
knowledge in new and innovative ways. As shown in Figure 2 by the left-facing
arrow at the bottom of the figure from Design Science Research to the
Environment, exercising the artifact in the environment produces significant value to
the constituent IS community. System development methodologies, design tools ,
and prototype systems (e.g., GDSS, expert systems) are examples of such
artifacts.
2. Foundations - The creative development of novel, appropriately evaluated
constructs, models, methods, or instantiations that extend and improve the existing
foundations in the design-science knowledge base are also important contributions.
The right-facing arrow at the bottom of the figure from Design Science Research to
the Knowledge Base in Figure 2 indicates these contributions. Modeling
formalisms, ontologies (Wand and Weber 1993; Wand and Weber 1995; Weber

20
1997), problem and solution representations , design algorithms (Storey et al. 1997),
and innovative information systems (Walls et al. 1992; Markus et al. 2002; Aiken
1991) are examples of such artifacts.
3. Methodologies - Finally, the creative development and use of evaluation methods
(e.g., experimental, analytical, observational, testing, and descriptive) and new
evaluation metrics provide design-science research contributions. Measures and
evaluation metrics in particular are crucial components of design-science research.
The right-facing arrow at the bottom of the figure from Design Science Research to
the Knowledge Base in Figure 2 also indicates these contributions. TAM
(Venkatesh 2000), for example, presents a framework for predicting and explaining
why a particular information system will or will not be accepted in a given
organizational setting. Although TAM is posed as a behavioral theory, it also
provides metrics by which a designed information system or implementation
process can be evaluated. Its implications for design itself are as yet unexplored.
Criteria for assessing contribution focus on representational fidelity and
implementability. Artifacts must accurately represent the business and technology
environments used in the research, information systems themselves being models of
the business. These artifacts must be "implementable," hence the importance of
instantiating design science artifacts. Beyond these, however, the research must
demonstrate a clear contribution to the business environment, solving an important,
previously unsolved problem.

3.5 Guideline 5: Research Rigor
Rigor addresses the way in which research is conducted. Design-science research
requires the application of rigorous methods in both the construction and evaluation of
the designed artifact. In behavioral-science research rigor is often assessed by
adherence to appropriate data collection and analysis techniques. Overemphasis on
rigor in behavioral IS research has often resulted in a corresponding lowering of
relevance (Lee 1999).
Design-science research often relies on mathematical formalism to describe the
specified and constructed artifact. However, the environments in which IT artifacts must

21
perform and the artifacts themselves may defy excessive formalism. Or, in an attempt
to be "mathematically rigorous," important parts of the problem may be abstracted or
"assumed away." In particular, with respect to the construction activity, rigor must be
assessed with respect to the applicability and generalizability of the artifact. Again, an
overemphasis on rigor can lessen relevance. We argue, along with behavioral IS
researchers (Applegate 1999), that it is possible and necessary for all IS research
paradigms to be both rigorous and relevant.
In both design-science and behavioral-science research, rigor is derived from the
effective use of the knowledge base – theoretical foundations and research
methodologies. Success is predicated on the researcher’s skilled selection of
appropriate techniques to develop or construct a theory or artifact and the selection of
appropriate means to justify the theory or evaluate the artifact.
Claims about artifacts are typically dependent upon performance metrics. Even
formal mathematical proofs rely on evaluation criteria against which the performance of
an artifact can be measured. Design-science researchers must constantly assess the
appropriateness of their metrics and the construction of effective metrics is an important
part of design-science research.
Furthermore, designed artifacts are often components of a human-machine
problem-solving system. For such artifacts, knowledge of behavioral theories and
empirical work are necessary to construct and evaluate such artifacts. Constructs,
models, methods, and instantiations must be exercised within appropriate
environments. Appropriate subject groups must be obtained for such studies. Issues
that are addressed include comparability, subject selection, training, time, and tasks.
Methods for this type of evaluation are not unlike those for justifying or testing
behavioral theories. However, the principal aim is to determine how well an artifact
works, not to theorize about or prove anything about why the artifact works. This is
where design-science and behavioral-science researchers must complement one
another. Because design-science artifacts are often the "machine" part of the human-
machine system constituting an information system, it is imperative to understand why
an artifact works or does not work to enable new artifacts to be constructed that exploit
the former and avoid the latter.

23
sub-problems. Such simplifications and decompositions may not be realistic enough to
have a significant impact on practice but may represent a starting point. Progress is
made iteratively as the scope of the design problem is expanded. As means, ends, and
laws are refined and made more realistic the design artifact becomes more relevant and
valuable. The means, ends, and laws for IS design problems can often be represented
using the tools of mathematics and operations research. Means are represented by
decision variables whose values constitute an implementable design solution. Ends are
represented using a utility function and constraints that can be expressed in terms of
decision variables and constants. Laws are represented by the values of constants
used in the utility function and constraints.
The set of possible design solutions for any problem is specified as all possible
means that satisfy all end conditions consistent with identified laws. When these can be
formulated appropriately and posed mathematically, standard operations research
techniques can be used to determine an optimal solution for the specified end
conditions. Given the wicked nature of many information system design problems,
however, it may not be possible to determine, let alone explicitly describe the relevant
means, ends, or laws (Vessey and Glass 1998). Even when it is possible to do so, the
sheer size and complexity of the solution space will often render the problem
computationally infeasible. For example, to build a "reliable, secure, and responsive
information systems infrastructure," one of the key issues faced by IS managers
(Brancheau et al. 1996), a designer would need to represent all possible infrastructures
(means), determine their utility and constraints (ends), and specify all cost and benefit
constants (laws). Clearly such an approach is infeasible. However, this does not mean
that design-science research is inappropriate for such a problem.
In such situations, the search is for satisfactory solutions , i.e., satisficing (Simon
1996), without explicitly specifying all possible solutions. The design task involves the
creation, utilization, and assessment of heuristic search strategies. That is, constructing
an artifact that "works" well for the specified class of problems. Although its
construction is based on prior theory and existing design knowledge it may or may not
be entirely clear why it works or the extent of its generalizability; it simply qualifies as
"credentialed knowledge" (Meehl 1986, p. 311). While it is important to understand why

24
an artifact works, the critical nature of design in IS makes it important to first establish
that it does work and to characterize the environments in which it works, even if we
cannot completely explain why it works. This enables IS practitioners to take advantage
of the artifact to improve practice and provides a context for additional research aimed
at more fully explicating the resultant phenomena. Markus et al. (2002), for example,
describe their search process in terms of iteratively identifying deficiencies in
constructed prototype software systems and creatively developing solutions to address
them.
The use of heuristics to find "good" design solutions opens the question of how
goodness is measured. Different problem representations may provide varying
techniques for measuring how good a solution is. One approach is to prove or
demonstrate that a heuristic design solution is always within close proximity of an
"optimal" solution. Another is to compare produced solutions with those constructed by
expert human designers for the same problem situation.

3.7 Guideline 7: Communication of Research
Design-science research must be presented both to technology-oriented as well as
management-oriented audiences. Technology-oriented audiences need sufficient detail
to enable the described artifact to be constructed (implemented) and used within an
appropriate organizational context. This enables practitioners to take advantage of the
benefits offered by the artifact and it enables researchers to build a cumulative
knowledge base for further extension and evaluation. It is also important for such
audiences to understand the processes by which the artifact was constructed and
evaluated. This establishes repeatability of the research project and builds the
knowledge base for further research extensions by design-science researchers in IS.
Management-oriented audiences need sufficient detail to determine if the
organizational resources should be committed to constructing (or purchasing) and using
the artifact within their specific organizational context. Zmud (1997) suggests that
presentation of design-science research for a managerial audience requires an
emphasis not on the inherent nature of the artifact itself, but on the knowledge required
to effectively apply the artifact "within specific contexts for individual or organizational

25
gain" (p. ix). That is, the emphasis must be on the importance of the problem and the
novelty and effectiveness of the solution approach realized in the artifact. While we
agree with that assessment, we note that it may be necessary to describe the artifact in
some detail to enable managers to appreciate its nature and understand its application.
Presenting that detail in concise, well-organized appendices, as advised by Zmud, is an
appropriate communication mechanism for such an audience.

4. APPLICATION OF THE DESIGN SCIENCE RESEARCH GUIDELINES
To illustrate the application of the design-science guidelines to IS research, we
have selected three exemplar articles for analysis from three different IS journals, one
from Decision Support Systems, one from Information Systems Research, and one from
MIS Quarterly. Each has strengths and weaknesses when viewed through the lens of
the above guidelines. Our goal is not to perform a critical evaluation of the quality of the
research contributions, but rather to illuminate the design-science guidelines. The
articles are:
Gavish and Gerdes (1998) develop techniques for implementing anonymity in Group
Decision Support Systems (GDSS) environments.
Aalst and Kumar (2003) propose a design for an eXchangeable Routing Language
(XRL) to support electronic commerce workflows among trading partners.
Markus, Majchrzak, and Gasser (2002) propose a design theory for the development
of information systems built to support emergent knowledge processes.
The fundamental questions for design-science research are, "What utility does the
new artifact provide?" and "What demonstrates that utility?" Evidence must be
presented to address these two questions. That is the essence of design science.
Contribution arises from utility. If existing artifacts are adequate then design-science
research that creates a new artifact is unnecessary (it is irrelevant). If the new artifact
does not map adequately to the real world (rigor) it cannot provide utility. If the artifact
does not solve the problem (search, implementability) it has no utility. If utility is not
demonstrated (evaluation) then there is no basis upon which to accept the claims that it
provides any contribution (contribution). Furthermore, if the problem, the artifact, and its

26
utility are not presented in a manner such that the implications for research and practice
are clear, then publication in the IS literature is not appropriate (communication).

4.1 The Design and Implementation of Anonymity in GDSS - Gavish and
Gerdes (1998)
The study of group decision support systems (GDSS) has been and remains one of
the most visible and successful research streams in the IS field. The use of information
technology to effectively support meetings of groups of different sizes over time and
space is a real problem that challenges all business organizations. Recent GDSS
literature surveys demonstrate the large numbers of GDSS research papers published
in the IS field and, more importantly, the wide variety o f research paradigms applied to
GDSS research (e.g., Nunamaker et al. 1996; Fjermestad and Hiltz 1998; Dennis and
Wixom 2001). However, only a small number of GDSS papers can be considered to
make true design-science research contributions. Most assume the introduction of a
new information technology or process in the GDSS environment and then study the
individual, group, or organizational implications using a behavioral-science research
paradigm. Several such GDSS papers have appeared in MIS Quarterly, e.g.
(Jarvenpaa et al. 1988; Dickson et al. 1993; Sengupta and Te’eni 1993; Gallupe et al.
1988).
The central role of design science in GDSS is clearly recognized in the early
foundation papers of the field. The University of Arizona Electronic Meeting System
group, for example, states the need for both developmental and empirical research
agendas (Dennis et al. 1988; Nunamaker et al. 1991b). Developmental, or design-
science, research is called for in the areas of process structures and support and task
structures and support. Process structure and support technologies and methods are
generic to all GDSS environments and tasks. Technologies and methods for distributed
communications, group memory, decision-making methods, and anonymity are a few of
the critical design issues for GDSS process support needed in any task domain. Task
structure and support are specific to the problem domain under consideration by the
group (e.g., medical decision making, software development). Task support includes

27
the design of new technologies and methods for managing and analyzing task-related
information and using that information to make specific, task-related decisions.
The issue of anonymity has been studied extensively in GDSS environments.
Behavioral research studies have shown both positive and negative impacts on group
interactions. On the positive side, GDSS participants can express their views freely
without fear of embarrassment or reprisal. However, anonymity can encourage free-
riding and antisocial behaviors. While the pros and cons of anonymity in GDSS are
much researched, there has been a noticeable lack of research on the design of
techniques for implementing anonymity in GDSS environments. Gavish and Gerdes
(1998) address this issue by designing five basic mechanisms to provide GDSS
procedural anonymity.

Problem Relevance
The amount of interest and research on anonymity issues in GDSS testifies to its
relevance. Field studies and surveys clearly indicate that participants rank anonymity
as a highly desired attribute in the GDSS system. Many individuals state that they
would refuse to participate in or trust the results of a GDSS meeting without a
satisfactory level of assured anonymity (Fjermestad and Hiltz 1998).

Research Rigor
Gavish and Gerdes base their GDSS anonymity designs on past research in the
fields of cryptography and secure network communication protocols (e.g., Chaum 1981;
Schneier 1996). These research areas have a long history of formal, rigorous results
that have been applied to the design of many practical security and privacy
mechanisms. Appendix A of the exemplar paper provides a set of formal proofs that the
claims made by the authors for the anonymity designs are correct and draw their validity
from the knowledge base of this past research.

Design as a Search Process
The authors motivate their design science research by identifying three basic types
of anonymity in a GDSS system – environmental, content, and procedural. After a

28
definition and brief discussion of each type, they focus on the design of mechanisms for
procedural anonymity; the ability of the GDSS system to hide the source of any
message. This is a very difficult requirement because standard network protocols
typically attach source information in headers to support reliable transmission protocols.
Thus, GDSS systems must modify standard communication protocols and include
additional transmission procedures to ensure required levels of anonymity.
The design-science process employed by the authors is to state the desired
procedural anonymity attributes of the GDSS system and then to design mechanisms to
satisfy the system requirements for anonymity. Proposed designs are presented and
anonymity claims are proved to be correct. A thorough discussion of the costs and
benefits of the proposed anonymity mechanisms is provided in Section 4 of the paper.

Design as an Artifact
The authors design a GDSS system architecture that provides a rigorous level of
procedural anonymity. Five mechanisms are employed to ensure participant
anonymity:
All messages are encrypted with a unique session key.
The sender’s header information is removed from all messages.
All messages are re-encrypted upon retransmission from any GDSS server.
Transmission order of messages is randomized.
Artificial messages are introduced to thwart traffic analysis.
The procedures and communication protocols that implement these mechanisms in a
GDSS system are the artifacts of this research.

Design Evaluation
The evaluation consists of two reported activities. First, in Appendix A, each
mechanism is proved to correctly provide the claimed anonymity benefits. Formal proof
methods are used to validate the effectiveness of the designed mechanisms. Second,
Section 4 presents a thorough cost-benefit analysis. It is shown that the operational
costs of supporting the proposed anonymity mechanisms can be quite significant. In
addition, the communication protocols to implement the mechanisms add considerable

29
complexity to the system. Thus, the authors recommend that a cost-benefit justification
be performed before determining the level of anonymity to implement for a GDSS
meeting.
The authors do not claim to have implemented the proposed anonymity
mechanisms in a prototype or actual GDSS system. Thus, an instantiation of the
designed artifact remains to be evaluated in an operational GDSS environment.

Research Contributions
The design-science contributions of this research are the proposed anonymity
mechanisms as the design artifacts and the evaluation results in the form of formal
proofs and cost-benefit analyses. These contributions advance our understanding of
how best to provide participant anonymity in GDSS meetings.

Research Communication
Although the presentation of this research is aimed at an audience familiar with
network system concepts such as encryption and communication protocols, the paper
also contains important, useful information for a managerial audience. Managers
should have a good understanding of the implications of anonymity in GDSS meetings.
This understanding must include an appreciation of the costs of providing desired levels
of participant anonymity. While the authors provide a thorough discussion of cost-
benefit tradeoffs toward the end of the paper, the paper would be more accessible to a
managerial audience if it included a stronger motivation up front on the important
implications of anonymity in GDSS system development and operations.

4.2 A Workflow Language for Inter-Organizational Processes - Aalst and
Kumar (2003)
Workflow models are an effective means for describing, analyzing, implementing,
and managing business processes. Workflow management systems are becoming
integral components of many commercial enterprise-wide information systems
(Leymann and Roller 2000). Standards for workflow semantics and syntax (i.e.,
workflow languages) and workflow architectures are promulgated by the Workflow

30
Management Coalition (WfMC 2000). While workflow models have been used for many
years to manage intra-organizational business processes, there is now a great demand
for effective tools to model inter-organization processes across heterogeneous and
distributed environments, such as those found in electronic commerce and complex
supply chains (Kumar and Zhao 2002).
Aalst and Kumar (2003) investigate the problem of exchanging business process
information across multiple organizations in an automated manner. They design an
eXchangable Routing Language (XRL) to capture workflow models that are then
embedded in eXtensible Markup Language (XML) for electronic transmission to all
participants in an inter-organizational business process. The design of XRL is based
upon Petri-nets which provide a formal basis for analyzing the correctness and
performance of the workflows, as well as supporting the extensibility of the language.
The authors develop a workflow management architecture and a prototype
implementation to evaluate XRL in a proof of concept.

Problem Relevance
Inter-organizational electronic commerce is growing rapidly and is projected to soon
exceed one trillion dollars annually (eMarketer 2002). A multitude of electronic
commerce solutions are being proposed (e.g., ebXML, UDDI, RosettaNet) to enable
businesses to execute transactions in standardized, open environments. While XML
has been widely accepted as a protocol for exchanging business data, there is still no
clear standard for exchanging business process information (e.g., workflow models).
This is the very relevant problem addressed by this research.

Research Rigor
Research on workflow modeling has long been based on rigorous mathematical
techniques such as Markov chains, queueing networks, and Petri-nets (Aalst and Hee
2002). In this paper, Petri-nets provide the underlying semantics for XRL. These
formal semantics allow for powerful analysis techniques (e.g., correctness,
performance) to be applied to the designed workflow models. Such formalisms also
enable the development of automated tools to manipulate and analyze complex

31
workflow designs. Each language construct in XRL has an equivalent Petri-net
representation presented in the paper. The language is extensible in that adding a new
construct simply requires defining its Petri-net representation and adding its syntax to
the XRL. Thus, this research draws from a clearly defined and tested base of modeling
literature and knowledge.

Design as a Search Process
XRL is designed in the paper by performing a thorough analysis of business
process requirements and identifying features provided by leading commercial workflow
management systems. Using the terminology from the paper, workflows traverse
routes through available tasks (i.e., business services) in the electronic business
environment. The basic routing constructs of XRL define the specific control flow of the
business process. The authors build 13 basic constructs into XRL – Task, Sequence,
Any_sequence, Choice, Condition, Parallel_sync, Parallel_no_sync, Parallel_part_sync,
Wait_all, Wait_any, While_do, Stop, and Terminate. They show the Petri-net
representation of each construct. Thus, the fundamental control flow structures of
sequence, decision, iteration, and concurrency are supported in XRL.
The authors demonstrate the capabilities of XRL in several examples. However,
they are careful not to claim that XRL is complete in the formal sense that all possible
business processes can be modeled in XRL. The search for a complete set of XRL
constructs is left for future research.

Design as an Artifact
There are two clearly identifiable artifacts produced in this research. First, the
workflow language XRL is designed. XRL is based on Petri-net formalisms and
described in XML syntax. Inter-organizational business processes are specified via
XRL for execution in a distributed, heterogeneous environment.
The second research artifact is the XRL/Flower workflow management architecture
in which XRL-described processes are executed. The XRL routing scheme is parsed
by an XML parser and stored as an XML data structure. This structure is read into a
Petri-net engine which determines the next step of the business process and informs

32
the next task provider via an email message. Results of each task are sent back to the
engine which then executes the next step in the process until completion. The paper
presents a prototype implementation of the XRL/Flower architecture as a proof of
concept (Aalst and Kumar 2003).
Another artifact of this research is a workflow verification tool named Wolfan that
verifies the soundness of business process workflows. Soundness of a workflow
requires that the workflow terminates, no Petri-net tokens are left behind upon
termination, and there are no dead tasks in the workflow. This verification tool is
described more completely in a different paper (Aalst 1999).

Design Evaluation
The authors evaluate the XRL and XRL/Flower designs in several important ways:
XRL is compared and contrasted with languages in existing commercial workflow
systems and research prototypes. The majority of these languages are proprietary
and difficult to adapt to ad-hoc business process design.
The fit of XRL with proposed standards is studied. In particular, the Interoperability
Wf-XML Binding standard (WfMC 2000) does not at this time include the
specification of control flow and, thus, is not suitable for inter-organizational
workflows. Electronic commerce standards (e.g., RosettaNet) provide some level of
control flow specification for predefined business activities, but do not readily allow
the ad-hoc specification of business processes.
A research prototype of XRL/Flower has been implemented and several of the user
interface screens are presented. The screens demonstrate a mail-order routing
schema case study.
The Petri-Net foundation of XRL allows the authors to claim the XRL workflows can
be verified for correctness and performance. XRL is extensible since new
constructs can be added to the language based on their translation to underlying
Petri-Net representations. However, as discussed above, the authors do not make a
formal claim for the representational completeness of XRL.

33
Research Contributions
The clear contributions of this research are the design artifacts – XRL (a workflow
language), XRL/Flower (a workflow architecture and its implemented prototype system),
and Wolfan (a Petri-Net verification engine). Another interesting contribution is the
extension of XML in its ability to describe and transmit routing schemas (e.g., control
flow information) to support inter-organizational electronic commerce.

Research Communication
This paper provides clear information to both technical and managerial audiences.
The presentation, while primarily technical with XML coding and Petri-Net diagrams
throughout, motivates a managerial audience with a strong introduction on risks and
benefits of applying inter-organizational workflows to electronic commerce applications.

4.3 Information Systems Design for Emergent Knowledge Processes - Markus,
Majchrzak, and Gasser (2002)
Despite decades of research and development efforts, effective methods for
developing information systems that meet the information requirements of upper
management remain elusive. Early approaches used a "waterfall" approach where
requirements were defined and validated prior to initiating design efforts which, in turn,
were completed prior to implementation (Royce 1998). Prototyping approaches
emerged next followed by numerous proposals including CASE tool-based approaches,
rapid application development, and extreme programming (Kruchten 2000). Walls et al.
(1992) propose a framework for a prescriptive information system design theory aimed
at enabling designers to construct "more effective information systems" (p. 36). They
apply this framework to the design of vigilant executive information systems. The
framework establishes a class of user requirements (model of design problems) that are
most effectively addressed using a particular type of system solution (instantiation)
designed using a prescribed set of development practices (methods). Markus et al.
(2002) extend this framework to the development of information systems to support
emergent knowledge processes (EKPs) – processes in which structure is "neither

35
socio-technical systems theory and the empirical literature on organizational design
knowledge. It was evaluated theoretically using standard metrics from the expert
systems literature and empirically using data gathered from numerous electronics
manufacturing companies in the U.S. Development of TOP Modeler used an "action
research paradigm" starting with a "kernel theory" based on prior development methods
and theoretical results and iteratively posing and testing artifacts (prototypes) to assess
progress toward the desired result. Finally, the artifact was commercialized and "used
in over two dozen 'real use' situations." (p. 187). In summary, this work effectively used
theoretical foundations from IS and organizational theory, applied appropriate research
methods in developing the artifact, defined and applied appropriate performance
measures, and tested the artifact within an appropriate context .

Design as a Search Process
As discussed above, implementation and iteration are central to this research. The
authors study prototypes that instantiate posed or newly learned design prescriptions.
Their use and impacts were observed, problems identified, solutions posed and
implemented, and the cycle was then repeated. These interventions occurred over a
period of 18 months within the aforementioned companies as they dealt with
organizational design tasks. As a result not only was the TOP Modeler developed and
deployed but prescriptions (methods) in the form of six principles for developing
systems to support EKPs were also devised. The extensive experience, creativity,
intuition, and problem solving capabilities of the researchers were involved in assessing
problems and interpreting the results of deploying various TOP modeler iterations and
in constructing improvements to address shortcomings identified.

Design as an Artifact
The TOP Modeler is an implemented software system (instantiation). It is
composed of an object-oriented user interface, an object-oriented query generator, and
an analysis module built on top of a relational meta -knowledge base that enables
access to "pluggable" knowledge bases representing different domains. It also includes
tools to support the design and construction of these knowledge bases. The TOP

36
Modeler supports a development process incorporating the six principles for developing
systems to support EKPs. As mentioned above TOP Modeler was commercialized and
used in a number of different organizational redesign situations.

Design Evaluation
Evaluation is in the context of organizational design in manufacturing organizations,
and is based on observation during the development and deployment of a single
artifact, TOP Modeler. No formal evaluation was attempted in the sense of comparison
with other artifacts. This is not surprising, nor is it a criticism of this work. There simply
are no existing artifacts that address the same problem. However, given that
methodologies for developing information systems to support semi-structured
management activities are the closest available artifacts it is appropriate to use them as
a comparative measure. In effect this was accomplished by using principles from these
methodologies to inform the initial design of TOP Modeler. The identification of
deficiencies in the resultant artifact provides evidence that these artifacts are ill-suited to
the task at hand.
Iterative development and deployment within the context of organizational design in
manufacturing organizations provide opportunities to observe improvement but do not
enable formal evaluation -- at each iteration changes are induced in the organization
that cannot be controlled. As mentioned above, the authors have taken a creative and
innovative approach that, of necessity, trades-off rigor for relevancy. In the initial stages
of a discipline this approach is extremely effective. TOP Modeler demonstrates the
feasibility of developing an artifact to support organizational design and EKPs within
high-tech manufacturing organizations . "In short, the evidence suggests that TOP
Modeler was successful in supporting o rganizational design" (p. 187) but additional
study is required to assess the comparative effectiveness of other possible approaches
in this or other contexts. Again this is not a criticism of this work; rather it is a call for
further research in the general class of problems dealing with emergent knowledge
processes. As additional research builds on this foundation formal, rigorous evaluation
and comparison with alternative approaches in a variety of contexts become crucial to

38
5. DISCUSSION AND CONCLUSIONS
Philosophical debates on how to conduct IS research (e.g., positivism vs.
interpretivism) have been the focus of much recent attention (Klein and Myers 1999;
Robey 1996; Weber 2003). The major emphasis of such debates lies in the
epistemologies of research, the underlying assumption being that of the natural
sciences. That is, somewhere some truth exists and somehow that truth can be
extracted, explicated, and codified. The behavioral-science paradigm seeks to find
"what is true." In contrast, the design-science paradigm seeks to create "what is
effective." While it can be argued that utility relies on truth, the discovery of truth may
lag the application of its utility. We argue that both design-science and behavioral-
science paradigms are needed to ensure the relevance and effectiveness of IS
research. Given the artificial nature of organizations and the information systems that
support them, the design-science paradigm can play a significant role in resolving the
fundamental dilemmas that have plagued IS research: rigor, relevance, discipline
boundaries, behavior, and technology (Lee 2000).
Information systems research lies at the intersection of people, organizations and
technology (Silver et al. 1995). It relies on and contributes to cognitive science,
organizational theory, management sciences, and computer science. It is both an
organizational and a technical discipline that is concerned with the analysis,
construction, deployment, use, evaluation, evolution, and management of information
system artifacts in organizational settings (Madnick 1992; Orlikowski and Barley 2001).
Within this setting, the design-science research paradigm is proactive with respect
to technology. It focuses on creating and evaluating innovative IT artifacts that enable
organizations to address important information-related tasks. The behavioral-science
research paradigm is reactive with respect to technology in the sense that it takes
technology as "given." It focuses on developing and justifying theories that explain and
predict phenomena related to the acquisition, implementation, management, and use of
such technologies. The dangers of a design-science research paradigm are an
overemphasis on the technological artifacts and a failure to maintain an adequate
theory base, potentially resulting in "well-designed" artifacts that are useless in real
organizational settings. The dangers of a behavioral-science research paradigm are

39
overemphasis on contextual theories and failure to adequately identify and anticipate
technological capabilities, potentially resulting in theories and principles addressing
outdated or ineffective technologies. We argue strongly that IS research must be both
proactive and reactive with respect to technology. It needs a complete research cycle
where design science creates artifacts for specific information problems based on
relevant behavioral science theory and behavioral science anticipates and engages the
created technology artifacts.
Hence we reiterate the call made earlier by March et al. (2000) to align IS design-
science research with real-world production experience. Results from such industrial
experience can be framed in the context of our seven guidelines. These must be
assessed not only by IS design-science researchers but also by IS behavioral-science
researchers who can validate the organizational problems as well as study and
anticipate the impacts of created artifacts. Thus, we encourage collaborative
industrial/academic research projects and publications based on such experience.
Markus et al. (2002) is an excellent example of such collaboration. Publication of these
results will help accelerate the development of domain independent and scalable
solutions to large-scale information systems problems within organizations. We
recognize that a lag exists between academic research and its adoption in industry. We
also recognize the possible ad hoc nature of technology-oriented solutions developed in
industry. The latter gap can be reduced considerably by developing and framing the
industrial solutions based on our proposed guidelines.
It is also important to distinguish between "system building" efforts and design-
science research. Guidelines addressing evaluation, contributions, and rigor are
especially important in providing this distinction. The underlying formalism required by
these guidelines helps researchers to develop representations of IS problems,
solutions, and solution processes that clarify the knowledge produced by the research
effort.
As we move forward, there exist a number of exciting challenges facing the design-
science research community in IS. A few are summarized here.
There is an inadequate theoretical base upon which to build an engineering
discipline of information systems design (Basili 1996). The field is still very young

40
lacking the cumulative theory development found in other engineering and social-
science disciplines. It is important to demonstrate the feasibility and utility of such a
theoretical base to a managerial audience that must make technology-adoption
decisions that can have far-reaching impacts on the organization.
Insufficient sets of constructs, models, methods, and tools exist for accurately
representing the business/technology environment. Highly abstract representations
(e.g., analytical mathematical models) are criticized as having no relationship to
"real-world" environments. On the other hand, many informal, descriptive IS models
lack an underlying theory base. The trade-offs between relevance and rigor are
clearly problematic; finding representational techniques with an acceptable balance
between the two is very difficult.
The existing knowledge base is often insufficient for design purposes and designers
must rely on intuition, experience, and trial-and-error methods. A constructed
artifact embodies the designer's knowledge of the problem and solution. In new and
emerging applications of technology the artifact itself represents an experiment. In
its execution, we learn about the nature of the problem, the environment, and the
possible solutions – hence the importance of developing and implementing
prototype artifacts (Newell and Simon 1976).
Design-science research is perishable. Rapid advances in technology can
invalidate design-science research results before they are implemented effectively in
the business environment or, just as importantly to managers, before adequate
payback can be achieved by committing organizational resources to implementing
those results. Two examples are the promises made by the artificial intelligence
community in the 1980’s (Feigenbaum and McCorduck 1983) and the more recent
research on object-oriented databases (Chaudhri and Loomis 1998). Just as
important to IS researchers, design results can be overtaken by technology before
they even appear in the research literature. How much research was published on
the Year 2000 problem before it became a non-event?
Rigorous evaluation methods are extremely difficult to apply in design-science
research (Tichy 1998; Zelkowitz and Wallace 1998). For example, the use of a

41
design artifact on a single project may not generalize to different environments
(Markus et al. 2002).
We believe that design science will play an increasingly important role in the IS
profession. IS managers in particular are actively engaged in design activities – the
creation, deployment, evaluation, and improvement of purposeful IT artifacts that enable
organizations to achieve their goals. The challenge for design-science researchers in
IS is to inform managers of the capabilities and impacts of new IT artifacts.
Much of the research published in MIS Quarterly employs the behavioral-science
paradigm. It is passive with respect to technology, often ignoring or "under-theorizing"
the artifact itself (Orlikowski and Iacono 2001). Its focus is on describing the
implications of "technology" – its impact on individuals, groups, and organizations. It
regularly includes studies that examine how people employ a technology, report on the
benefits and difficulties encountered when a technology is implemented within an
organization, or discuss how managers might facilitate the use of a technology. Orman
(2002) argues that many of the equivocal results in IS behavioral-science studies can
be explained by a failure to differentiate the capabilities and purposes of the studied
technology.
Design science is active with respect to technology, engaging in the creation of
technological artifacts that impact people and organizations. Its focus is on problem
solving but often takes a simplistic view of the people and the organizational contexts in
which designed artifacts must function. As stated earlier, the design of an artifact, its
formal specification, and an assessment of its utility, often by comparison with
competing artifacts, are integral to design-science research. These must be combined
with behavioral and organizational theories to develop an understanding of business
problems, contexts, solutions, and evaluation approaches adequate to servicing the IS
research and practitioner communities. The effective presentation of design-science
research in major IS journals, such as MIS Quarterly, will be an important step toward
integrating the design-science and behavioral-science communities in IS.

42
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