Source: https://es.scribd.com/document/249404019/Msc-Desigh-Science
Timestamp: 2019-04-21 12:21:06+00:00

Document:
effectiveness and efficiency of that organization.
technology for managerial and organizational purposes” (Zmud 1997).
management, and use of information systems.
researcher (Markus et al. 2002; Walls et al. 1992).
IS literature (Glass 1999; Winograd 1996, 1998).
solving and organizational capabilities by providing intellectual as well as computational tools.
will follow their development and use.
methods (algorithms and practices), and instantiations (implemented and prototype systems).
the class of problems addressed.
and Elam 1998; Sinha and Vessey 1999).
in the management science literature.
It describes the world as acted upon (processes) and the world as sensed (artifacts). These are interdependent design activities that are central to the IS discipline. methods. and purposefully designed. and organizational infrastructure design are outside the scope of this paper. The artifacts are constructs. deals with design—the purposeful organization of resources to accomplish a goal. During this creative process. we use the proposed guidelines to assess recent exemplar papers published in the IS literature in order to illustrate how authors. This Platonic view of design supports a problemsolving paradigm that continuously shifts perspective between design processes and designed artifacts for the same complex problem. Issues of strategy. and work practices as designed artifacts (Boland 2002).e. 28 No. Models use constructs to represent a real world situation—the design problem and its solution space (Simon 1996). Orlikowski 2000). and work systems (Alter 2003. Available and emerging IT capabilities are a significant factor in determining the strategies that guide an organization. The design process is a sequence of expert activities that produces an innovative product (i. Cutting-edge information systems allow 78 MIS Quarterly Vol. 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. technologies. and instantiations. IT strategy. They are composed of people. This build-and-evaluate loop is typically iterated a number of times before the final design artifact is generated (Markus et al. 2002). 1/March 2004 organizations to engage new forms and new structures—to change the ways they “do business” (Drucker 1988.. the design-science researcher must be cognizant of evolving both the design process and the design artifact as part of the research. The two processes are build and evaluate. artificial. alignment./Design Science in IS Research tion of organizations. reviewers. To achieve a true understanding of and appreciation for design science as an IS research paradigm. 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. 1992). Purposeful artifacts are built to address heretofore unsolved problems. policies. organizational infrastructure. Bunge 1985. Hence. March and Smith (1995) identify two design processes and four design artifacts produced by design-science research in IS. We conclude 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. and IS infrastructure. Orlikowski and Barley 2001). and managers in general (Boland 2002). models. IS research must address the interplay among business strategy. structures. Models aid problem and solution understanding and frequently represent the connection . Following Klein and Myers (1999) treatise on the conduct and evaluation of interpretive research in IS. Our subsequent discussion of design science will be limited to the activities of building the IS infrastructure within the business organization.Hevner et al. A Framework for IS Research Information systems and the organizations they support are complex. and editors can apply them consistently. This interplay is becoming more crucial as information technologies are seen as enablers of business strategy and organizational infrastructure (Kalakota and Robinson 2001. They are evaluated with respect to the utility provided in solving those problems. 1991. Design is both a process (set of activities) and a product (artifact)—a verb and a noun (Walls et al. Much of the work performed by IS practitioners. the design artifact). Simon 1996). Figure 1 illustrates the essential alignments between business and information technology strategies and between organizational and information systems infrastructures (Henderson and Venkatraman 1993). Constructs provide the language in which problems and solutions are defined and communicated (Schön 1983). an important dichotomy must be faced.
or some combination. textual descriptions of “best practice” approaches. “Strategic Alignment: Leveraging Information Technology for Transforming Organizations. 1993. mathematical algorithms that explicitly define the search process to informal. and development capabilities. Instantiations show that constructs./Design Science in IS Research Business Strategy Strategy Alignment Information Systems Design Activities Organizational Design Activities Organizational Infrastructure Information Technology Strategy Infrastructure Alignment Information Systems Infrastructure Figure 1. IS research is conducted in two complementary phases. and how users appropriate it.” IBM Systems Journal (32:1). Figure 2 presents our conceptual framework for understanding. models. how the artifact affects it. culture. tasks. They provide guidance on how to solve problems. They also enable researchers to learn about the real world. that is. Venkatraman. or methods can be implemented in a working system. We use this framework to position and compare these paradigms. enabling concrete assessment of an artifact’s suitability to its intended purpose. In it are the goals. Methods define processes.) between problem and solution components enabling exploration of the effects of design decisions and changes in the real world. 1995). and characteristics of people within the organization. Business needs are assessed and evaluated within the context of organizational strategies. applications. 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 iden- MIS Quarterly Vol. capabilities. structure. and existing business processes. They are positioned relative to existing technology infrastructure. (business) organizations. They demonstrate feasibility. 1/March 2004 79 . Given such an articulated business need. 28 No. executing. These can range from formal. Framing research activities to address business needs assures research relevance. it is composed of people. The environment defines the problem space (Simon 1996) in which reside the phenomena of interest. Organizational Design and Information Systems Design Activities (Adapted from J. Henderson and N. and evaluating IS research combining behavioral-science and design-science paradigms. For IS research. and their existing or planned technologies (Silver et al. communication architectures. Together these define the business need or “problem” as perceived by the researcher. Such perceptions are shaped by the roles. how to search the solution space. problems.Hevner et al. and opportunities that define business needs as they are perceived by people within the organization.
The knowledge base is composed of foundations and methodologies. In both cases. 80 MIS Quarterly Vol. Prior IS research and results from reference disciplines provide foundational theories. and instantiations used in the develop/build phase of a research study. Methodologies provide guidelines used in the justify/evaluate phase. models. In design science. 1/March 2004 artifact and the need to refine and reassess. The goal of behavioralscience research is truth. our position is that truth and utility are inseparable. computational and mathematical methods are . We recognize that such theories are approximations and are subject to numerous assumptions and conditions. A theory may yet to be developed to the point where its truth can be incorporated into design. methodologies are typically rooted in data collection and empirical analysis techniques. constructs. However.Hevner et al. Rigor is achieved by appropriately applying existing foundations and methodologies. The refinement and reassessment process is typically described in future research directions. In behavioral science. 28 No. As argued above. The knowledge base provides the raw materials from and through which IS research is accomplished. frameworks. 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. Information Systems Research Framework tified business need.2 The goal of designscience research is utility. research assessment via the justify/evaluate activities can result in the identification of weaknesses in the theory or 2 Theories posed in behavioral science are principled explanations of phenomena. instruments./Design Science in IS Research Environment Relevance IS Research Rigor People •Roles •Capabilities •Characteristics Organizations •Strategies •Structure & Culture •Processes Technology •Infrastructure •Applications •Communications Architecture •Development Foundations Develop/Build •Theories •Artifacts Applicable Knowledge Business Needs Assess Knowledge Base Refine •Theories •Frameworks •Instruments •Constructs •Models •Methods •Instantiations Methodologies Justify/Evaluate •Data Analysis •Analytical •Case Study •Experimental •Field Study •Simulation Techniques •Formalisms •Measures •Validation Criteria Capabilities Application in the Appropriate Environment Additions to the Knowledge Base Figure 2. Truth informs design and utility informs theory. An artifact may have utility because of some as yet undiscovered truth. methods.
often the requisite knowledge is nonexistent (Markus et al. of necessity. however. System building is then the routine application of the knowledge base to known problems./Design Science in IS Research primarily used to evaluate the quality and effectiveness of artifacts. designed. and become normal or routine.g. however. high-level languages. each new artifact created for that discipline or environment is “an experiment” that “poses a question to nature” (Newell and Simon 1976. Rittel and Webber 1984). 1/March 2004 81 . Existing knowledge is used where appropriate. 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.. implemented. such as constructing a financial or marketing information system using best practice artifacts (constructs.e. 28 No. 1996. formalized. On the other hand. personal computers. models. Innovations. Consequently the guidelines we present below are. Design activities are endemic in many professions. intelligent agents. of necessity. we agree with Simon (1996) that a theory of design in information systems. Routine design is the application of existing knowledge to organizational problems. MIS Quarterly Vol. and managed. Reliance on creativity and trial-and-error search are characteristic of such research efforts. and the World Wide Web. the Internet.. many design activities have been extensively studied. As design-science research results are codified in the knowledge base.g. teamwork) to produce effective solutions As a result. empirical techniques may also be employed. p 114). In particular. object technology. Technological advances are the result of innovative. Petroski 1996). If not capricious. 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. creative design science processes. In the early stages of a discipline or with significant changes in the environment. adaptive and process-oriented. have had dramatic and at times unintended impacts on the way in which information systems are conceived. One issue that must be addressed in designscience research is differentiating routine design or system building from design research. The difference is in the nature of the problems and solutions. Within the IS discipline.Hevner et al. such as database management systems. design-science research addresses important unsolved problems in unique or innovative ways or solved problems in more effective or efficient ways. malleable processes and artifacts) • a critical dependence upon human cognitive abilities (e. software components. creativity) to produce effective solutions • a critical dependence upon human social abilities (e. 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. methods. they become best practice. is in a constant state of scientific revolution (Kuhn 1996). That is. Pahl and Beitz 1996. and instantiations) existing in the knowledge base. they are at least arbitrary (Brooks 1987) with respect to business needs and existing knowledge.. Design-science research in IS addresses what are considered to be wicked problems (Brooks 1987. 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. the engineering profession has produced a considerable literature on design (Dym 1994. 2002).
reviewers. 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 . Guideline 1: Design as an Artifact The result of design-science research in IS is. 121). 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). Such theories must explain how artifacts are created and adapted to their changing environments and underlying technologies. However. Each is discussed in detail below. they emphasize the importance of “those bundles of cultural properties packaged in some socially recognizable form such as hardware and software” (p. It is broader in the sense that we include not only instantiations in our definition of the IT artifact but also the constructs. many of which include components of the organization and people involved in the use of a computer-based artifact. In this way. Weber (1987) argues that theories of long-lived artifacts (instantiations) and their representations (Weber 2003) are fundamental to the IS discipline. we advise against mandatory or rote use of the guidelines. it must yield utility for the specified problem. thorough evaluation of the artifact is crucial (Guideline 3). 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). design-science research requires the creation of an innovative. design-science research is differentiated from the practice of design. That is. 1/March 2004 contend that each of these guidelines should be addressed in some manner for design-science research to be complete. Researchers. Hence. formally represented. purposeful artifact (Guideline 1) for a specified problem domain (Guideline 2). In the following section. Table 1 summarizes the seven guidelines. solving a heretofore unsolved problem or solving a known problem in a more effective or efficient manner (Guideline 4). a purposeful IT artifact created to address an important organizational problem. 28 No. Finally. and how to apply each of the guidelines in a specific research project.Hevner et al. the IT artifact as an instantiation. However. Our purpose for establishing these seven guidelines is to assist researchers. editors. 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. design science is inherently a problem solving process. Novelty is similarly crucial since the artifact must be innovative. they are applied to specific exemplar research efforts.. The artifact itself must be rigorously defined. where. i. and editors must use their creative skills and judgment to determine when.e. by definition. models. editors. and readers to understand the requirements for effective design-science research. Orlikowski and Iacono (2001) call the IT artifact the “core subject matter” of the IS field. reviewers. Following Klein and Myers (1999). Because the artifact is purposeful. and methods applied in the development and use of information systems. The process by which it is created. Although they articulate multiple definitions of the term IT artifact. and internally consistent (Guideline 5). Our definition of IT artifacts is both broader and narrower then those articulated above. It must be described effectively./Design Science in IS Research Guidelines for Design Science in Information Systems Research As discussed above. enabling its implementation and application in an appropriate domain. we 82 MIS Quarterly Vol. and often the artifact itself. coherent. and readers to determine. How well the research satisfies the intent of each of the guidelines is then a matter for the reviewers.
Tsichritzis 1998). We acknowledge that perceptions and fit with an organization are crucial to the successful development and implementation of an information system. Guideline 7: Communication of Research Design-science research must be presented effectively both to technology-oriented as well as management-oriented audiences. and use of information systems can be effectively and efficiently accomplished (Denning 1997. Instead. zero. (2002) where the theory addresses both the process of design and the designed product. Guideline 3: Design Evaluation The utility. We argue. with the constructs defined by Arabic numbers. design foundations. design. and instantiations are equally crucial and that design-science research efforts are necessary for their creation. 28 No. that the capabilities of the constructs. 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. Schön 1983). “solving a problem simply means representing it so as to make the solution transparent. Guideline 5: Research Rigor Design-science research relies upon the application of rigorous methods in both the construction and evaluation of the design artifact. The field of mathematics was revolutionized. constructs provide the vocabulary and symbols used to define problems and solutions. Furthermore. quality. This definition of the artifact is consistent with the concept of IS design theory as used by Walls et al. for example. and products through which the analysis. Representation has a profound impact on design work. a model. 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.Hevner et al.” MIS Quarterly Vol. over time. Guideline 2: Problem Relevance The objective of design-science research is to develop technology-based solutions to important and relevant business problems. and/or design methodologies. 1/March 2004 83 . They enable the construction of models or representations of the problem domain. They have a significant impact on the way in which tasks and problems are conceived (Boland 2002. models. Guideline 4: Research Contributions Effective design-science research must provide clear and verifiable contributions in the areas of the design artifact. technical capabilities. however. More precisely./Design Science in IS Research Table 1. and place notation. artifacts are innovations that define the ideas. and efficacy of a design artifact must be rigorously demonstrated via well-executed evaluation methods. p. methods. (1992) and Markus et al. Simon (1996. implementation. 132) states. or an instantiation. a method. 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. The search for an effective problem representation is crucial to finding an effective design solution (Weber 2003). practices. artifacts constructed in designscience research are rarely full-grown information systems that are used in practice.
). but we argue that it is a necessary one. 187). there is uncertainty about its ability to perform appropriately.Hevner et al. technical capabilities. 180). 1997). and workflow analysis (Basu and Blanning 2000). interfaces. for example. It provides “proof by construction” (Nunamaker 1991a). social systems.) are necessary to address such issues. Methods for building such models have also been the subject of considerable research (Halpin 2001./Design Science in IS Research The entity-relationship model (Chen 1976). Bhargava et al. Once feasibility was demonstrated by constructing an expert system in a selected domain. consensus building. Similar examples exist in requirements determination (Bell 1993. TOP Modeler (Markus et al. We argue that a combination of technology-based artifacts (e.. practices.. Storey et al. 28 No. it was not clear if such a system could be constructed. 1991. Basu and Blanning 1994). structures. p. 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. Behavioral science approaches this goal through the development and justification of theories explaining or predicting phenomena that occur. compensation.g. Dey et al. 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. database design and integration (Dey et al. Furthermore.g. etc. For example.g. the technology acceptance model provides a theory that explains and predicts the acceptance of information technologies within organizations (Venkatesh 2000). Constructing a system instantiation that automates a process demonstrates that the process can. Design-science research in IT often addresses problems related to some aspect of the design of an information system. Design science approaches this goal through the construction of innovative artifacts aimed at changing the phenomena that occur. and people-based artifacts (e. As an exemplar of design-science research (see below). Artifact instantiation demonstrates feasibility both of the design process and of the designed product. 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. It was not clear how to describe or represent it. to name a few important areas of IS design-science research. this research resulted in a commercial product that “has been used in over two dozen ‘real use’ situations” (p. Storey et al. McCarthy 1982. individual and group decision support systems (Aiken et al. 2002). 2003). and the automated task is important to the IS community. Such a result is significant IS research only if there is a serious question about the ability to construct such an artifact. etc. . prior to the construction of the first expert system (instantiation). Hence. 1/March 2004 To illustrate further. or how well it would perform. 1998).. Each must inform and challenge the other. is a set of constructs for representing the semantics of data. the instantiations produced may be in the form of intellectual or software tools aimed at improving the process of information system development.” Construction of such a prototype artifact in a research setting or in a single organizational setting is only a first step toward its deployment. Trice and Davis 1993). is a tool that instantiates methods for the development of information systems that support “emergent knowledge processes. reporting relationships. system conceptualizations and representations. 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. 1999. etc. organizationbased artifacts (e. 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. 2002.). 1998. 1997). 84 MIS Quarterly Vol. be automated. training. This theory challenges design-science researchers to create artifacts that enable organizations to overcome the acceptance problems predicted. Parsons and Wand 2000. for example. in fact.
only too often to conclude that those dollars were wasted (Keil 1995.Hevner et al. usability. and efficacy of a design artifact must be rigorously demonstrated via wellexecuted evaluation methods. demonstrated that network latency is a major component in the response-time performance of distributed databases. Keil and Robey 1999)./Design Science in IS Research Formally. operate. Guideline 3: Design Evaluation The utility. This community would welcome effective artifacts that enable such problems to be addressed—constructs by which to think about them. Problem solving can be defined as a search process (see Guideline 6) using actions to reduce or eliminate the differences (Simon 1996). The business environment establishes the requirements upon which the evaluation of the artifact is based. quality. Hence. performance. Prior research in distributed database design ignored latency because it assumed a low-bandwidth network where latency is negligible. Keil et al. implement. and information technology. 1/March 2004 85 . These definitions imply an environment that imposes goal criteria as well as constraints upon a system. Thus. When analytical metrics are appropriate. For IS researchers. models by which to represent and explore them. operate. design. 28 No. IT artifacts can be evaluated in terms of functionality. To be relevant to this community. business problems and opportunities often relate to increasing revenue or decreasing cost through the design of effective business processes. methods by which to analyze or optimize them. manage. As two examples. a problem can be defined as the differences between a goal state and the current state of a system. evaluation of a designed IT artifact requires the definition of appropriate metrics and possibly the gathering and analysis of appropriate data. and instantiations that demonstrate how to affect them. 1998. As available technology or organizational environments change. for example. reliability. however. implement. and other relevant quality attributes. The relevance of any design-science research effort is with respect to a constituent community. 2003) and search algorithms can be evaluated using information retrieval metrics such as precision and recall (Salton 1988). and evaluate the technologies that enable their development and implementation. completeness. Because design is inherently an iterative and incremental activity. Economic theory often portrays the goals of business organizations as being related to profit (utility) maximization. assumptions made in prior research may become invalid. that constituent community is the practitioners who plan. accuracy. A design artifact is complete and effective when it satisfies the requirements and constraints of the problem it was meant to solve. Evaluation is a crucial component of the research process. evaluation includes the integration of the artifact within the technical infrastructure of the business environment. manage. fit with the organization. and evaluate information systems and those who plan. Organizations spend billions of dollars annually on IT. designed artifacts may be mathematically evaluated. Business organizations are goal-oriented entities existing in an economic and social setting. The design of organizational and interorganizational information systems plays a major role in enabling effective business processes to achieve these goals. design. Johansson (2000). As in the justification of a behavioral science theory. latency can account for over 90 MIS Quarterly Vol. This environment includes the technical infrastructure which itself is incrementally built by the implementation of new IT artifacts. Design-science research efforts may begin with simplified conceptualizations and representations of problems. organizations. the evaluation phase provides essential feedback to the construction phase as to the quality of the design process and the design product under development. consistency. 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. research must address the problems faced and the opportunities afforded by the interaction of people. In a high-bandwidth network.
Thus. we know good style when we see it. landscaping. Johansson et al.. 1998. (2003) extended prior distributed database design research by developing a model that includes network latency and the effects of parallel processing on response time. performance) 3. He describes it as a marriage between simplicity and .Hevner et al. 28 No.. Analytical Static Analysis: Examine structure of artifact for static qualities (e.g. Experimental Controlled Experiment: Study artifact in controlled environment for qualities (e. usability) 4. In other words. 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 Dynamic Analysis: Study artifact in use for dynamic qualities (e. has style. architecture. art. 86 MIS Quarterly Vol. The measurement of style lies in the realm of human perception and taste.. Given the problem and solution requirements. 1/March 2004 Design. relevant research) to build a convincing argument for the artifact’s utility Scenarios: Construct detailed scenarios around the artifact to demonstrate its utility percent of the response time. Good designers bring an element of style to their work (Norman 1988). For example. Winograd 1996)... music). we posit that design evaluation should include an assessment of the artifact’s style.g. descriptive methods of evaluation should only be used for especially innovative artifacts for which other forms of evaluation may not be feasible. The selection of evaluation methods must be matched appropriately with the designed artifact and the selected evaluation metrics. Design Evaluation Methods 1. The goodness and efficacy of an artifact can be rigorously demonstrated via well-selected evaluation methods (Basili 1996. Kleindorfer et al./Design Science in IS Research Table 2.g. Gelernter (1998) terms the essence of style in IS design machine beauty. Observational Case Study: Study artifact in depth in business environment Field Study: Monitor use of artifact in multiple projects 2.g. Descriptive Informed Argument: Use information from the knowledge base (e. While difficult to define. style in IS design is widely recognized and appreciated (Kernighan and Plauger 1978. execution paths) in the artifact implementation 5. These are summarized in Table 2.g. in all of its realizations (e.. 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. Zelkowitz and Wallace 1998). Testing Functional (Black Box) Testing: Execute artifact interfaces to discover failures and identify defects Simulation – Execute artifact with artificial data Structural (White Box) Testing: Perform coverage testing of some metric (e.g. The evaluation of designed artifacts typically uses methodologies available in the knowledge base.
however. observational. Weber 1997). TAM. 1997). for example. The ultimate assessment for any research is. Simon (1996) also notes the importance of style in the design process..g. Measures and evaluation metrics in particular are crucial components of design-science research. design tools. analytical. previously unsolved problem. 1995. the creative development and use of evaluation methods (e. It may extend the knowledge base (see below) or apply existing knowledge in new and innovative ways. and prototype systems (e. methods. 2002. Although TAM is posed as a behavioral theory. Beyond these. 1992) are examples of such artifacts. exercising the artifact in the environment produces significant value to the constituent IS community. design algorithms (Storey et al. information systems themselves being models of the business. One or more of these contributions must be found in a given research project. ontologies (Wand and Weber 1993. methodologies). challenges and adds value to designers who participate in the process. expert systems) are examples of such artifacts.. experimental. Guideline 4: Research Contributions Effective design-science research must provide clear contributions in the areas of the design artifact. models. The artifact must enable the solution of heretofore unsolved problems.e. The Design Artifact.e.. These artifacts must be “implementable. GDSS. The right-facing arrow at the bottom of the figure from IS Research to the Knowledge Base in Figure 2 also indicates these contributions. “What are the new and interesting contributions?” Design-science research holds the potential for three types of research contributions based on the novelty. Most often. and/or design evaluation knowledge (i. 28 No. The right-facing arrow at the bottom of the figure from IS Research to the Knowledge Base in Figure 2 indicates these contributions. within the limits of satisfactory constraints. Finally. appropriately evaluated constructs. design construction knowledge (i. The ability to creatively vary the design process. 1. Foundations. and descriptive) and new evaluation metrics provide design-science research contributions. generality./Design Science in IS Research formalisms. As shown in Figure 2 by the left-facing arrow at the bottom of the figure from IS Research to the Environment. Modeling Methodologies. 3. power that drives innovation in science and technology.” hence the importance of instantiating design science artifacts.g. it also provides metrics by which a designed information system or implementation process can be evaluated. Criteria for assessing contribution focus on representational fidelity and implementability. In behavioral-science research. presents a framework for predicting and explaining why a particular information system will or will not be accepted in a given organizational setting (Venkatesh 2000).Hevner et al. 1/March 2004 87 . Markus et al. The creative development of novel. testing. 2. the research must demonstrate a clear contribution to the business environment. and significance of the designed artifact. foundations). Guideline 5: Research Rigor Rigor addresses the way in which research is conducted. rigor is MIS Quarterly Vol. problem and solution representations. or instantiations that extend and improve the existing foundations in the design-science knowledge base are also important contributions.. Walls et al. Its implications for design itself are as yet unexplored. the contribution of design-science research is the artifact itself. System development methodologies. Artifacts must accurately represent the business and technology environments used in the research. Design-science research requires the application of rigorous methods in both the construction and evaluation of the designed artifact. and innovative information systems (Aiken 1991. solving an important.
subject selection.. the environments in which IT artifacts must perform and the artifacts themselves may defy excessive formalism. Even formal mathematical proofs rely on evaluation criteria against which the performance of an artifact can be measured. ends. Guideline 6: Design as a Search Process Design science is inherently iterative. an overemphasis on rigor can lessen relevance. In both design-science and behavioral-science research. Design is essentially a search process to discover an effective solution to a problem. models. Claims about artifacts are typically dependent upon performance metrics. 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. Overemphasis on rigor in behavioral IS research has often resulted in a corresponding lowering of relevance (Lee 1999). the principal aim is to determine how well an artifact works. Problem solving can be viewed as utilizing available means to reach desired ends while satisfying laws existing in the environment (Simon 1996). Abstraction and representation of appropriate means. The search for the best.” In particular. Furthermore.. not to theorize about or prove anything about why the artifact works. However. Design-science research often simplifies a problem by explicitly representing only a subset of the . Effective design requires knowledge of both the application domain (e./Design Science in IS Research often assessed by adherence to appropriate data collection and analysis techniques. Ends represent goals and constraints on the solution. along with behavioral IS researchers (Applegate 1999). However. Means are the set of actions and resources available to construct a solution. designed artifacts are often components of a human-machine problem-solving system. 28 No. and tasks. training. good designs that can be implemented in the business environment.Hevner et al. rigor is derived from the effective use of the knowledge base—theoretical foundations and research methodologies. with respect to the construction activity. Constructs. or optimal.g. that it is possible and necessary for all IS research paradigms to be both rigorous and relevant. 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. important parts of the problem may be abstracted or “assumed away.g. Methods for this type of evaluation are not unlike those for justifying or testing behavioral theories. in an attempt to be mathematically rigorous. and laws are crucial components of design-science research. Laws are uncontrollable forces in the environment. requirements and constraints) and the solution domain (e. We argue. Simon (1996) describes the nature of the design process as a Generate/Test Cycle (Figure 3). methods. and instantiations must be exercised within appropriate environments. 1/March 2004 comparability. Design-science research often relies on mathematical formalism to describe the specified and constructed artifact. 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. Appropriate subject groups must be obtained for such studies. design is often intractable for realistic information systems problems. These factors are problem and environment dependent and invariably involve creativity and innovation. For such artifacts. knowledge of behavioral theories and empirical work are necessary to construct and evaluate such artifacts. Issues that are addressed include 88 MIS Quarterly Vol. This is where design-science and behavioral-science researchers must complement one another. time. Again. Heuristic search strategies produce feasible. technical and organizational). Because design-science artifacts are often the “machine” part of the humanmachine system constituting an information system. Or. rigor must be assessed with respect to the applicability and generalizability of the artifact.
the sheer size and complexity of the solution space will often render the problem computationally infeasible. Laws are represented by the values of constants used in the utility function and constraints. secure.” one of the key issues faced by IS managers (Brancheau et al. it may not be possible to determine. 1/March 2004 89 . ends. standard operations research techniques can be used to determine an optimal solution for the specified end conditions. and laws are refined and made more realistic. As means. The design task involves the creation. ends.. without explicitly specifying all possible solutions. Given the wicked nature of many information system design problems. utilization. Although its construction is based on prior theory and existing design knowledge. satisficing (Simon 1996). and laws or by decomposing a problem into simpler subproblems. 1996). and laws for IS design problems can often be represented using the tools of mathematics and operations research. ends. let alone explicitly describe. 28 No.Hevner et al. Means are represented by decision variables whose values constitute an implementable design solution. Such simplifications and decompositions may not be realistic enough to have a significant impact on practice but may represent a starting point. i. this does not mean that design-science research is inappropriate for such a problem. constructing an artifact that “works” well for the specified class of problems. ends. Ends are represented using a utility function and constraints that can be expressed in terms of decision variables and constants. Progress is made iteratively as the scope of the design problem is expanded. The set of possible design solutions for any problem is specified as all possible means that satisfy all end conditions consistent with identified laws. However.e. The Generate/Test Cycle relevant means. the search is for satisfactory solutions. The means. 311)./Design Science in IS Research Generate Design Alternatives Test Alternatives Against Requirements/Constraints Figure 3. p. While it is important MIS Quarterly Vol. it simply qualifies as “credentialed knowledge” (Meehl 1986. it may or may not be entirely clear why it works or the extent of its generalizability. determine their utility and constraints (ends). For example. and specify all cost and benefit constants (laws). That is. When these can be formulated appropriately and posed mathematically. the design artifact becomes more relevant and valuable. the relevant means. Clearly such an approach is infeasible. Even when it is possible to do so. to build a “reliable. however. and responsive information systems infrastructure. a designer would need to represent all possible infrastructures (means). and assessment of heuristic search strategies. In such situations. or laws (Vessey and Glass 1998).
well-organized appendices. Markus et al. This establishes repeatability of the research project and builds the knowledge base for further research extensions by design-science researchers in IS. 28 No. While we agree with this statement. one from Information Systems Research. 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. 1/March 2004 should be committed to constructing (or purchasing) and using the artifact within their specific organizational context. but on the knowledge required to effectively apply the artifact “within specific contexts for individual or organizational gain” (p. 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. 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. which develops techniques for implementing anonymity in Group Decision Support Systems (GDSS) environments • Aalst and Kumar (2003). one from Decision Support Systems. ix). (2002). describe their search process in terms of iteratively identifying deficiencies in constructed prototype software systems and creatively developing solutions to address them.Hevner et al. as advised by Zmud. but rather to illuminate the designscience guidelines. Management-oriented audiences need sufficient detail to determine if the organizational resources 90 MIS Quarterly Vol. The articles are • Gavish and Gerdes (1998). Application of the Design Science Research Guidelines To illustrate the application of the design-science guidelines to IS research. It is also important for such audiences to understand the processes by which the artifact was constructed and evaluated. we have selected three exemplar articles for analysis from three different IS journals. is an appropriate communication mechanism for such an audience. 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. 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. That is. 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./Design Science in IS Research to understand why an artifact works. and one from MIS Quarterly. the emphasis must be on the importance of the problem and the novelty and effectiveness of the solution approach realized in the artifact. which proposes a design for an eXchangeable Routing Language (XRL) to support electronic commerce workflows among trading partners . Another is to compare produced solutions with those constructed by expert human designers for the same problem situation. for example. even if we cannot completely explain why it works. 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. Presenting that detail in concise. The use of heuristics to find “good” design solutions opens the question of how goodness is measured. Technology-oriented audiences need sufficient detail to enable the described artifact to be constructed (implemented) and used within an appropriate organizational context. Guideline 7: Communication of Research Design-science research must be presented both to technology-oriented as well as managementoriented audiences.
If utility is not demonstrated (evaluation). research is called for in the areas of process structures and support and task structures and support. or designscience. Nunamaker et al. decision-making methods. 1996). 1988. it has no utility. states the need for both developmental and empirical research agendas (Dennis et al. Several such GDSS papers have appeared in MIS Quarterly (e. Nunamaker et al. there has been a noticeable lack of research on the design of techniques for implementing anonymity in GDSS environments. software development). Recent GDSS literature surveys demonstrate the large numbers of GDSS research papers published in the IS field and. and its utility are not presented in a manner such that the implications for research and practice are clear. However. if the problem.g. Process structure and support technologies and methods are generic to all GDSS environments and tasks. then there is no basis upon which to accept the claims that it provides any contribution (contribution). 1988. for example. Task structure and support are specific to the problem domain under consideration by the group (e. group memory. 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. the artifact. That is the essence of design science. While the pros and cons of anonymity in GDSS are much researched.. However. more importantly. implementability). 28 No. “What utility does the new artifact provide?” and “What demonstrates that utility?” Evidence must be presented to address these two questions. and Gasser (2002). Furthermore. The University of Arizona Electronic Meeting System group. Sengupta and Te’eni 1993). If the artifact does not solve the problem (search. Jarvenpaa et al. 1993. Contribution arises from utility. 1988. Behavioral research studies have shown both positive and negative impacts on group interactions. The Design and Implementation of Anonymity in GDSS: Gavish and Gerdes 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. 1991b). The central role of design science in GDSS is clearly recognized in the early foundation papers of the field. Most assume the introduction of a new information technology or process in the GDSS environment and then study the individual. which proposes a design theory for the development of information systems built to support emergent knowledge processes The fundamental questions for design-science research are. 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). group. task-related decisions. MIS Quarterly Vol. GDSS participants can express their views freely without fear of embarrassment or reprisal. Dennis and Wixom 2001. Gavish and Gerdes (1998) address this issue by designing five basic mechanisms to provide GDSS procedural anonymity. Technologies and methods for distributed communications. Dickson et al.g. and anonymity are a few of the critical design issues for GDSS process support needed in any task domain. 1/March 2004 91 . it cannot provide utility. On the positive side. only a small number of GDSS papers can be considered to make true design-science research contributions./Design Science in IS Research • Markus. anonymity can encourage freeriding and antisocial behaviors. or organizational implications using a behavioral-science research paradigm. The issue of anonymity has been studied extensively in GDSS environments.. Gallupe et al.Hevner et al. medical decision making. the wide variety of research paradigms applied to GDSS research (e. Fjermestad and Hiltz 1998.g. If existing artifacts are adequate.. Majchrzak. Developmental. Task support includes the design of new technologies and methods for managing and analyzing task-related information and using that information to make specific. then publication in the IS literature is not appropriate (communication).
After a definition and brief discussion of each type. Field studies and surveys clearly indicate that participants rank anonymity as a highly desired attribute in the GDSS system. the authors recommend that a . each mechanism is proved to correctly provide the claimed anonymity benefits. The design-science process employed by the authors is to state the desired procedural anonymity attributes of the GDSS system and then to 92 MIS Quarterly Vol. Proposed designs are presented and anonymity claims are proved to be correct.Hevner et al. GDSS systems must modify standard communication protocols and include additional transmission procedures to ensure required levels of anonymity. It is shown that the operational costs of supporting the proposed anonymity mechanisms can be quite significant. in Appendix A. Design Evaluation The evaluation consists of two reported activities. and procedural. This is a very difficult requirement because standard network protocols typically attach source information in headers to support reliable transmission protocols. Thus. Chaum 1981. Section 4 presents a thorough costbenefit analysis. Design as an Artifact The authors design a GDSS system architecture that provides a rigorous level of procedural anonymity. 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). 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 Design as a Search Process The authors motivate their design science research by identifying three basic types of anonymity in a GDSS system: environmental. rigorous results that have been applied to the design of many practical security and privacy mechanisms. they focus on the design of mechanisms for procedural anonymity. the ability of the GDSS system to hide the source of any message. Formal proof methods are used to validate the effectiveness of the designed mechanisms. Research Rigor Gavish and Gerdes base their GDSS anonymity designs on past research in the fields of cryptography and secure network communication protocols (e. 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. Second../Design Science in IS Research Problem Relevance The amount of interest and research on anonymity issues in GDSS testifies to its relevance. content. design mechanisms to satisfy the system requirements for anonymity. Schneier 1996).g. In addition. These research areas have a long history of formal. Thus. 1/March 2004 The procedures and communication protocols that implement these mechanisms in a GDSS system are the artifacts of this research. 28 No. First. the communication protocols to implement the mechanisms add considerable complexity to the system. A thorough discussion of the costs and benefits of the proposed anonymity mechanisms is provided in Section 4 of the paper.
. A Workflow Language for Interorganizational Processes: Aalst and Kumar Workflow models are an effective means for describing. The authors do not claim to have implemented the proposed anonymity mechanisms in a prototype or actual GDSS system.e. While XML has been widely accepted as a protocol for exchanging business data. open environments. This is the very relevant problem addressed by this research. While the authors provide a thorough discussion of cost-benefit tradeoffs toward the end of the paper. Problem Relevance Interorganizational electronic commerce is growing rapidly and is projected to soon exceed one trillion dollars annually (eMarketer 2002). The design of XRL is based upon Petri nets. UDDI. 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. useful information for a managerial audience. Managers should have a good understanding of the implications of anonymity in GDSS meetings. RosettaNet) to enable businesses to execute transactions in standardized. MIS Quarterly Vol. While workflow models have been used for many years to manage intraorganizational business processes. A multitude of electronic commerce solutions are being proposed (e. there is now a great demand for effective tools to model interorganization processes across heterogeneous and distributed environments. workflow models). an instantiation of the designed artifact remains to be evaluated in an operational GDSS environment.. Thus. the paper also contains important. analyzing. as well as supporting the extensibility of the language. Research Communication Although the presentation of this research is aimed at an audience familiar with network system concepts such as encryption and communication protocols. 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. implementing. 28 No. such as those found in electronic commerce and complex supply chains (Kumar and Zhao 2002).. ebXML. Workflow management systems are becoming integral components of many commercial enterprise-wide information systems (Leymann and Roller 2000). workflow languages) and workflow architectures are promulgated by the Workflow Management Coalition (WfMC 2000).g. Standards for workflow semantics and syntax (i. there is still no clear standard for exchanging business process information (e. The authors develop a workflow management architecture and a prototype implementation to evaluate XRL in a proof of concept.Hevner et al.g. 1/March 2004 93 . This understanding must include an appreciation of the costs of providing desired levels of participant anonymity. and managing business processes. Aalst and Kumar (2003) investigate the problem of exchanging business process information across multiple organizations in an automated manner. These contributions advance our understanding of how best to provide participant anonymity in GDSS meetings./Design Science in IS Research cost-benefit justification be performed before determining the level of anonymity to implement for a GDSS meeting. 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 interorganizational business process. which provide a formal basis for analyzing the correctness and performance of the workflows.
g. Thus. Thus. Choice. Condition. 1/March 2004 The second research artifact is the XRL/flower workflow management architecture in which XRLdescribed processes are executed. the fundamental control flow structures of sequence. This verification tool is described more completely in a different paper (Aalst 1999). XRL is based on Petrinet formalisms and described in XML syntax. the Interoperability Wf- . they are careful not to claim that XRL is complete in the formal sense that all possible business processes can be modeled in XRL./Design Science in IS Research Research Rigor Design as an Artifact Research on workflow modeling has long been based on rigorous mathematical techniques such as Markov chains. iteration. Such formalisms also enable the development of automated tools to manipulate and analyze complex workflow designs. and there are no dead tasks in the workflow. and concurrency are supported in XRL. In this paper. Parallel_part_sync. The XRL routing scheme is parsed by an XML parser and stored as an XML data structure. 28 No. Another artifact of this research is a workflow verification tool named Wolfan that verifies the soundness of business process workflows. The paper presents a prototype implementation of the XRL/flower architecture as a proof of concept (Aalst and Kumar 2003). There are two clearly identifiable artifacts produced in this research. and Terminate.e. Results of each task are sent back to the engine which then executes the next step in the process until completion.. Sequence. They show the Petri-net representation of each construct. heterogeneous environment. The majority of these languages are proprietary and difficult to adapt to ad hoc business process design. Wait_any. The language is extensible in that adding a new construct simply requires defining its Petri-net representation and adding its syntax to the XRL.Hevner et al.. this research draws from a clearly defined and tested base of modeling literature and knowledge. Stop. the workflow language XRL is designed. correctness. While_do. 94 MIS Quarterly Vol. no Petri-net tokens are left behind upon termination. Parallel_sync. and Petri nets (Aalst and Hee 2002). The search for a complete set of XRL constructs is left for future research. Each language construct in XRL has an equivalent Petri-net representation presented in the paper. 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. Petri nets provide the underlying semantics for XRL. Using the terminology from the paper. This structure is read into a Petri-net engine which determines the next step of the business process and informs the next task provider via an e-mail message. Interorganizational business processes are specified via XRL for execution in a distributed. queueing networks. These formal semantics allow for powerful analysis techniques (e. • The fit of XRL with proposed standards is studied. In particular. The basic routing constructs of XRL define the specific control flow of the business process. workflows traverse routes through available tasks (i. Wait_all. However. Soundness of a workflow requires that the workflow terminates. 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. performance) to be applied to the designed workflow models. business services) in the electronic business environment. Any_sequence. The authors demonstrate the capabilities of XRL in several examples. First. Parallel_no_ sync. The authors build 13 basic constructs into XRL: Task. decision.
g. control flow information) to support interorganizational electronic commerce. They are crucial to many manufacturing organizations. 179). new product development.. Majchrzak. These include “basic research. and distributed expert knowledge” (p. the authors do not make a formal claim for the representational completeness of XRL. Research Contributions The clear contributions of this research are the design artifacts—XRL (a workflow language). and Gasser Despite decades of research and development efforts. They apply this framework to the design of vigilant executive information systems. However. • The Petri-net foundation of XRL allows the authors to claim the XRL workflows can be verified for correctness and performance. 183). Early approaches used a “waterfall” approach where requirements were defined and validated prior to initiating design efforts which. motivates a managerial audience with a strong introduction on risks and benefits of applying interorganizational workflows to electronic commerce applications. The screens demonstrate a mail-order routing schema case study. The presentation. 182) and where processes are characterized by “highly unpredictable user types and work contexts” (p. Another interesting contribution is the extension of XML in its ability to describe and transmit routing schemas (e. Prototyping approaches emerged next. Walls et al. rapid application development. strategic business planning. 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). Problem Relevance The relevance and importance of the problem are well demonstrated.. (2002) extend this framework to the development of information systems to support emergent knowledge processes (EKPs)—processes in which structure is “neither possible nor desirable” (p. unpredictable user types and use contexts. particularly those in high-tech MIS Quarterly Vol. 1/March 2004 95 . as discussed above. and extreme programming (Kruchten 2000).g. were completed prior to implementation (Royce 1998). is not suitable for interorganizational workflows. while primarily technical with XML coding and Petri-net diagrams throughout. effective methods for developing information systems that meet the information requirements of upper management remain elusive. 28 No. 36). (1992) propose a framework for a prescriptive information system design theory aimed at enabling designers to construct “more effective information systems” (p. XRL is extensible since new constructs can be added to the language based on their translation to underlying Petri-net representations. in turn. thus. followed by numerous proposals including CASE toolbased approaches. RosettaNet) provide some level of control flow specification for predefined business activities. Electronic commerce standards (e.Hevner et al. XRL/flower (a workflow architecture and its implemented prototype system). describe a class of management activities that they term emergent knowledge processes (EKPs)./Design Science in IS Research XML Binding standard (WfMC 2000) does not at this time include the specification of control flow and. Research Communication This paper provides clear information to both technical and managerial audiences. and organization design” (p. Markus et al. 186). Markus et al. and Wolfan (a Petri-net verification engine). Information Systems Design for Emergent Knowledge Processes: Markus. • A research prototype of XRL/flower has been implemented and several of the user interface screens are presented. They are characterized by “process emergence. but do not readily allow the ad hoc specification of business processes.
the artifact created in this research effort. They recognize the potential for significant performance improvements offered by such integration. Such organizations recognize the need to integrate organizational design and information system design with manufacturing operations. TOP Modeler was commercialized and used in a number of different organizational redesign situations. noting that existing information system development methodologies focus on structured or semi-structured decision processes and are inadequate for the development of systems to support EKPs. 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. Markus et al. this work effectively used theoretical foundations from IS and organizational theory. defined and applied appropriate performance measures. and tested the artifact within an appropriate context. (1992) and poses a prescription for designing information systems to support EKPs. Design as an Artifact The TOP Modeler is an implemented software system (instantiation). 186). 1/March 2004 in over two dozen ‘real use’ situations. It also includes tools to support the design and construction of these knowledge bases. 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. Not surprisingly. applied appropriate research methods in developing the artifact. implementation and iteration are central to this research. It was evaluated theoretically using standard metrics from the expert systems literature and empirically using data gathered from numerous electronics manufacturing companies in the United States. General Motors. TOP Modeler. Design as a Search Process As discussed above. The extensive experience. Yet few have realized that potential. problems identified. executive information systems. argue that this is due to a lack of an adequate design theory and lack of scientifically based tools. creativity. and Texas Instruments” (p. an object-oriented query generator. Research Rigor The presented work has theoretical foundations in both IS design theory and organizational design theory. the artifact was commercialized and “used 96 MIS Quarterly Vol. . The knowledge-base constructed within TOP Modeler was formed from a synthesis of socio-technical systems theory and the empirical literature on organizational design knowledge. solutions posed and implemented. and the cycle was then repeated. intuition. The authors study prototypes that instantiate posed or newly learned design prescriptions. its development attracted the attention and active participation of several large. It uses the basic notions of IS design theory presented in Walls et al. 187). In summary. It is composed of an object-oriented user interface. Their use and impacts were observed. Finally./Design Science in IS Research industries. and an analysis module built on top of a relational meta-knowledge base that enables access to “pluggable” knowledge bases representing different domains. Digital Equipment Corporation. 28 No.Hevner et al. Prior research in developing decision support systems. high-tech manufacturing organizations including “Hewlett-Packard. As a result. 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. squarely addresses this problem. and expert systems serves as a foundation for this work and deficiencies of these approaches for the examined problem type serve as motivation. As mentioned above. The TOP Modeler supports a development process incorporating the six principles for developing systems to support EKPs.” (p. These interventions occurred over a period of 18 months within the aforementioned companies as they dealt with organizational design tasks.
MIS Quarterly Vol. making it difficult for a technical researcher or practitioner to replicate their work. An agenda for addressing such issues is presented. Research Communication This work presents two types of artifacts. This focuses on validation. changes are induced in the organization that cannot be controlled. Furthermore. 28 No. “Only the accumulated weight of empirical evidence will establish the validity” of such claims. Since it is the first such artifact. The identification of deficiencies in the resultant artifact provides evidence that these artifacts are ill-suited to the task at hand. TOP Modeler exemplifies “proof by construction”—it is feasible to construct an information system to support EKPs. Their presentation addresses each of the design guidelines posed above. given that methodologies for developing information systems to support semi-structured management activities are the closest available artifacts. No formal evaluation was attempted in the sense of comparison with other artifacts. Again. nor is it a criticism of this work. this was accomplished by using principles from these methodologies to inform the initial design of TOP Modeler. it is appropriate to use them as a comparative measure. Markus et al. evaluation. and is based on observation during the development and deployment of a single artifact. 187) but additional study is required to assess the comparative effectiveness of other possible approaches in this or other contexts. This is not surprising. trades off rigor for relevancy. its construction is itself a contribution to design science. The uniqueness of the artifacts and the innovation inherent in them are presented so that managerial researchers and IT managers are aware of the new capabilities. 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. 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. identify an opportunity to apply information technology in a new and innovative way. 1/March 2004 97 . this is not a criticism of this work. Technical details of TOP Modeler are not presented. formal. this approach is extremely effective. However. the evidence suggests that TOP Modeler was successful in supporting organizational design” (p. Research Contributions The design-science contributions of this research are the TOP Modeler software and the design principles. In effect./Design Science in IS Research Design Evaluation Evaluation is in the context of organizational design in manufacturing organizations. Because TOP Modeler is the first artifact to address this task. TOP Modeler demonstrates the feasibility of developing an artifact to support organizational design and EKPs within high-tech manufacturing organizations. because the authors are able to articulate the design principles upon which its construction was based. TOP Modeler. these serve as hypotheses to be tested by future empirical work. “In short. Recognizing that existing system development methods and instantiations are aimed at structured or semistructured activities.Hevner et al. As mentioned above. In the initial stages of a discipline. Their applicability to the development of other types of information systems can also be tested. and the challenges of improvement inherent in the evaluation process. TOP Modeler (an instantiation) and a set of design principles (method) that address a heretofore unsolved problem dealing with the design of an information system to support EKPs. its evaluation using formal methods is deferred until future research. There simply are no existing artifacts that address the same problem. TOP Modeler demonstrates the feasibility of using the design principles to develop an artifact to support EKPs. the authors have taken a creative and innovative approach that. of necessity. As the authors point out. rigorous evaluation and comparison with alternative approaches in a variety of contexts become crucial to enable claims of generalizability.
(2000) to align IS design-science research with real-world production experience. relevance. It focuses on creating and evaluating innovative IT artifacts that enable organizations to address important information-related tasks. The latter gap can be reduced considerably by developing and framing the industrial solutions based on our proposed guidelines. We argue that both designscience and behavioral-science paradigms are needed to ensure the relevance and effectiveness of IS research. construction. It is both an organizational and a technical discipline that is concerned with the analysis. the underlying assumption being that of the natural sciences. evolution. and computer science. implemen- 98 MIS Quarterly Vol. Robey 1996. 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. Within this setting. Thus. Publication of these results will help accelerate the development of domain independent and scalable solutions to large-scale information systems problems within organizations. and codified. Hence. Orlikowski and Barley 2001). The behavioralscience research paradigm is reactive with respect to technology in the sense that it takes technology as “given. use. deployment. Results from such industrial experience can be framed in the context of our seven guidelines. interpretivism) have been the focus of much recent attention (Klein and Myers 1999.. the design-science research paradigm is proactive with respect to technology. we reiterate the call made earlier by March et al. management sciences. evaluation. 1/March 2004 tation. we encourage collaborative industrial/academic research projects and publications based on such experience. explicated. positivism vs. Markus et al. and technology (Lee 2000). Information systems research lies at the intersection of people.” It focuses on developing and justifying theories that explain and predict phenomena related to the acquisition.g. Weber 2003)./Design Science in IS Research Discussion and Conclusions Philosophical debates on how to conduct IS research (e. Given the artificial nature of organizations and the information systems that support them. potentially resulting in theories and principles addressing outdated or ineffective technologies. We recognize that a lag exists between academic research and its adoption in industry. Guidelines addressing evaluation. and rigor are especially important in providing this . organizational theory. and use of such technologies.Hevner et al. We argue strongly that IS research must be both proactive and reactive with respect to technology. That is. The behavioral-science paradigm seeks to find “what is true. The dangers of a behavioral-science research paradigm are overemphasis on contextual theories and failure to adequately identify and anticipate technological capabilities. the design-science paradigm can play a significant role in resolving the fundamental dilemmas that have plagued IS research: rigor. 28 No. discipline boundaries.” While it can be argued that utility relies on truth. 1995). management. The major emphasis of such debates lies in the epistemologies of research. contributions. the design-science paradigm seeks to create “what is effective. potentially resulting in well-designed artifacts that are useless in real organizational settings. We also recognize the possible ad hoc nature of technology-oriented solutions developed in industry. behavior. organizations. and technology (Silver et al. 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. somewhere some truth exists and somehow that truth can be extracted. and management of information system artifacts in organizational settings (Madnick 1992. The dangers of a design-science research paradigm are an overemphasis on the technological artifacts and a failure to maintain an adequate theory base. It relies on and contributes to cognitive science. It is also important to distinguish between “system building” efforts and design-science research. the discovery of truth may lag the application of its utility.” In contrast. (2002) is an excellent example of such collaboration.
Two examples are the promises made by the artificial intelligence community in the 1980s (Feigenbaum and McCorduck 1983) and the more recent research on object-oriented databases (Chaudhri and Loomis 1998). We believe that design science will play an increasingly important role in the IS profession. The field is still very young lacking the cumulative theory development found in other engineering and social-science disciplines. and solution processes that clarify the knowledge produced by the research effort. just as importantly to managers. The challenge for design-science researchers in IS is to inform managers of the capabilities and impacts of new IT artifacts. report on the benefits and difficulties encountered when a technology is implemented within an organization. Just as important to IS researchers. and trial-anderror methods. design results can be overtaken by technology before they even appear in the research literature. Insufficient sets of constructs. the use of a design artifact on a single project may not generalize to different environments (Markus et al. methods. As we move forward. The existing knowledge base is often insufficient for design purposes and designers must rely on intuition. models. many informal. we learn about the nature of the problem. 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. • Design-science research is perishable. Its focus is on describing the implications of technology—its impact on individuals. In its execution. before adequate payback can be achieved by committing organizational resources to implementing those results./Design Science in IS Research distinction.g. analytical mathematical models) are criticized as having no relationship to “real-world” environments. the artifact itself represents an experiment. there exist a number of exciting challenges facing the design-science research community in IS. For example. 28 No. IS managers in particular are actively engaged in design activities—the creation. finding representational techniques with an acceptable balance between the two is very difficult. It is passive with respect to technology. solutions. In new and emerging applications of technology. or discuss how managers might facilitate the use of a technology. evaluation. A few are summarized here. It regularly includes studies that examine how people employ a technology. the importance of developing and implementing prototype artifacts (Newell and Simon 1976). and improvement of purposeful IT artifacts that enable organizations to achieve their goals. descriptive IS models lack an underlying theory base. Much of the research published in MIS Quarterly employs the behavioral-science paradigm.. experience. the environment. deployment. and the possible solutions— hence. and organizations. The trade-offs between relevance and rigor are clearly problematic. It is important to demonstrate the feasibility and utility of such a theoretical base to a managerial audience that must make technologyadoption decisions that can have far-reaching impacts on the organization. often ignoring or “under-theorizing” the artifact itself (Orlikowski and Iacono 2001). The underlying formalism required by these guidelines helps researchers to develop representations of IS problems. Orman (2002) argues that many of the equivocal results MIS Quarterly Vol.Hevner et al. Zelkowitz and Wallace 1998). groups. • • • There is an inadequate theoretical base upon which to build an engineering discipline of information systems design (Basili 1996). On the other hand. 2002). Highly abstract representations (e. A constructed artifact embodies the designer’s knowledge of the problem and solution. and tools exist for accurately representing the business/technology environment. Rapid advances in technology can invalidate design-science research results before they are implemented effectively in the business environment or. 1/March 2004 99 .
Cambridge. NY. 23-46. A. its formal specification. and Bostrom. pp. 1991. Ron Weber. 28 No. and Hee. pp. R. D. M. Batra. the design of an artifact. such as MIS Quarterly. December 1994. K. This work has also benefited from seminars and discussions at Arizona State University./Design Science in IS Research in IS behavioral-science studies can be explained by a failure to differentiate the capabilities and purposes of the studied technology. Social Theory.. pp. Philosophy. Basu. The comments provided by several anonymous editors and reviewers greatly enhanced the content and presentation of the paper. and the Thought of George Herbert Mead (SUNY Series in Philosophy of the Social Sciences). and Vogel. Methods. 345-357. (ed..” Information Systems Research (14:1). W. Acknowledgements We would like to thank Allen Lee. solutions.” Communications of the AIS (12). March 1999... “Metagraphs: A Tool for Modeling Decision Support Systems. “18 Reasons Why IT-Reliant Work Systems Should Replace ‘The IT Artifact’ as the Core Subject Matter of the IS Field. Georgia State University.” ACM Transactions on Information Systems (9:1).” Systems Analysis-Modeling-Simulation (34:3). and Kumar. often by comparison with competing artifacts. 2002. Berlin. W.). and Future. 365-394. are integral to design-science research. S... October 2003. R.” in Proceedings of the 18thInternational Conference on Software Engineering. 126-139. will be an important step toward integrating the design-science and behavioralscience communities in IS. and The University of Utah. “Wolfan: A Petri-Net-Based Workflow Analyzer. pp.” Management Science (40:12).. February 1990. L.Hevner et al. Hoffer. pp. Sheng. W. As stated earlier. 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. M. 75-95. and Blanning. pp. Basili. M. The effective presentation of designscience research in major IS journals. J. Zelkowitz (eds. “XML-Based Schema Definition for Support of Interorganizational Workflow. March 2000. A. A. 17-36. and an assessment of its utility. MA.” Communications of the ACM (33:2). pp. Applegate. R. “Integrating Expert Systems with Group Decision Support Systems. R. and Blanning.” Information Systems Research (11:1). The MIT Press. 442-449.” MIS Quarterly (23:1). . O. Maibaum and M. 100 MIS Quarterly Vol. Michigan State University. Aboulafia. pp.1996. Albany. Germany. “A Comparison of User Performance between the Relational and the Extended Entity Relationship Models in the Discovery Phase of Database Design. State University of New York Press. Current. P. and Systems. Florida International University. 1579-1600. T. “The Role of Experimentation in Software Engineering: Past.). Design science is active with respect to technology. 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. engaging in the creation of technological artifacts that impact people and organizations. W.. Aiken. W. and evaluation approaches adequate to servicing the IS research and practitioner communities. D. These must be combined with behavioral and organizational theories to develop an understanding of business problems. R. A. Aalst. We would particularly like to thank Brian Pentland and Steve Alter for feedback and suggestions they provided on an earlier version of this paper. March 25-29. Workflow Management: Models. “Rigor and Relevance in MIS Research—Introduction. Basu. contexts. 1999. Notre Dame University. 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. “A Formal Approach to Workflow Analysis. W. pp. March 2003. 1/March 2004 References Aalst. 1-2. V. L. Alter. Aalst. January 1991.
. Social Science and Technology.. February 1997. D. McGraw-Hill. F. C. Chen. Boland. and Vogel. B. L. K. “Information Technology to Support Electronic Meetings..). and Piela. 173-194. 61-68.” Journal of Management Information Systems (18:3). pp. D. 1379-1395. and De. Prentice-Hall. W. C. J..” MIS Quarterly (20:2). Weatherhead School of Management. “The Computer Scientist as Toolsmith II. Storey. R.Hevner et al. and McLean. F.” Communications of the ACM (40:2). Brooks. R. D.. “A Probabilistic Decision Model for Entity Matching in Heterogeneous Databases. 225-242. Dey. G. 453-486. Spring 1998. “Untraceable Electronic Mail. B. Dennis.” MIS Quarterly (23:1). Jr. pp.” Journal of Management Information Systems (19:4). P. and Barron. J. “From Data Properties to Evidence. “The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. 189-208. pp.” MIS Quarterly (17:2). Object Databases in Practice.” Harvard Business Review (66:1). Spring 2003. H. Frontiers of Management Workshop. Patel. S. Jr. Drucker. Boland (ed. J. Janz.. R.. P. “On Formal Semantics and Analysis of Typed Modeling Languages: An Analysis of Ascend. and Wetherbe. Engineering Design. and Digital Pseudonyms. George. Dickson. M. 1/March 2004 101 . “Should the Optional Property Construct be Used in Conceptual Modeling? A Theory and Three Empirical Tests. and Wixom. V. pp. Denning. 84-87.. New York. Treatise on Basic Philosophy: Volume 7—Epistemology & Methodology III: Philosophy of Science and Technology—Part II: Life Science. T. D. P. G.. R. December 2001. pp. June 14-15.” ACM Transactions on Database Systems (24:4). Structure and Development (2nd ed. P. J. Dennis. 3-16. MIS Quarterly Vol. “The Coming of the New Organization. Return Addresses. pp 10-19. Cambridge University Press. December 1993. R.” IEEE Computer (20:4). W. Bunge. pp.. E. 28 No. A.” Harvard Business Review (69:6).” Management Science (44:10)../Design Science in IS Research Bell. and McLean. 132-134. Boston. P. F. pp. pp.. “Exploring Modes of Facilitative Support for GDSS Technology. and Weber. Davis. Chaum.. June 1993. Dey.. L. New York. Krishnan. A.” Information Systems Research (12:4). 45-53. 235-257. pp.. A. “Investigating the Moderators of the Group Support Systems Use with Meta-Analysis.. 1976. M..” Communications of the ACM (39:3). Reidel Publishing Company.. P. Partridge. December 1988. 60-95. pp 384405. L.” Information Systems Research (3:1). March 1996. pp. December 1999.. R. pp. pp. Sim. E. pp. January-February 1988. P. D.” Communications of the ACM (24:2). J. I.. March 1999. B. W. “A New Social Contract for Research. “Key Issues in Information Systems Management: 1994-95 SIM Delphi Results. J. DeLone. Winter 2001-02..edu). 1985. J. pp. 9-30. “The New Productivity Challenge. Brooks. H. R. and Loomis. October 1998. F. Benbasat. Drucker. Brancheau. “Improving Database Design through the Analysis of Relationships. Bodart.). “The Entity-Relationship Model: Toward a Unified View. Dym. 965969. M. cwru.. A. M. Upper Saddle River. NJ. Bhargava. March 1992. 45-53. 2002 (available online at http://design. pp. NovemberDecember 1991. and Robinson. Chaudhri.” IEEE Transactions on Knowledge and Data Engineering (5:6).” ACM Transactions on Database Systems (1:1). P. February 1981. June 1996. 591-624. 9-36. Sarkar. “Design in the Punctuation of Management Action” in Managing as Designing: Creating a Vocabulary for Management Education and Research. pp.. A. pp. 1994. Jessup.. Management Information Systems: Conceptual Foundations. S. MA. 1998.. April 1987. H. A. H. and Olson. “Information Systems Success: The Quest for the Dependent Variable. 1985. Nunamaker. “No Silver Bullet: Essence and Accidents of Software Engineering. M.” MIS Quarterly (12:4). and Zmud. “Empirical Research in Information Systems: The Practice of Relevance. DeLone. F. P.” INFORMS Journal on Computing (10:2). D.
“Pulling the Plug: Software Project Management and the Problem of Project Escalation. Keil. AddisonWesley. pp. New York. “Strategic Alignment: Leveraging Information Technology for Transforming Organizations..emarketer. D. Y. V.. “A Set of Principles for Conducting and Evaluating Interpretive Field Studies in Information Systems. 2000. “A Framework for Identifying Software Project Risks. and Ganeshan. K... 67-94. P. and Robinson.” Decision Support Systems (23:4). and Dickson. MA. New York. G.. Jarvenpaa. 103-104.” The International Journal of Accounting Information Systems (3:1).. E-Commerce Trade and B2B Exchanges. and Naumann. M. Halpin. Gavish. Geerts. “Systems Thinking. Fjermestad. pp. Lee. Inc.Hevner et al.” MIS Quarterly (12:4). pp. W. E. A. August 1998. J. March 2002 (available online at http://www. M. DeSanctis. Feigenbaum. Kernighan. H. Johansson.” Decision Support Systems (32:3). T. IL. T. Addison-Wesley. 1978. 645-666. R.0: Roadmap for Success. M. March 1999. 28 No.” Decision Sciences Journal (34:4). 2001. (12:2). “Modeling Network Latency and Parallel Processing in Distributed Database Design. “Inaugural Editor’s Comments. Boston. “Comparing Data Modeling Formalisms. 183-194. C. 1087-1099. E./Design Science in IS Research eMarketer. J. “Computer Support for Meetings of Groups Working on Unstructured Problems: A Field Experiment. “On Design. The Rational Unified Process: An Introduction (2nd ed.). Glass. R. 265-278.” IBM Systems Journal (32:1). 2001. and McCarthy. pp. October 1998.” MIS Quarterly (23:1)... R. and March.. “Turning Around Troubled Software Projects: An Exploratory Study of the Deescalation of Commitment to Failing Courses of Action. pp. G. and Hiltz. N. B. pp. Basic Books. S.. 76-83. pp. The Structure of Scientific Revolutions (3rd ed.. pp. Klein. 1998. R. Addison-Wesley Pearson Education. 63-87. pp. 2000. D.” Journal of Management Information Systems (15:3). 1983. 1993. January 2002.). and McCorduck. and Zhao. and Huber.” Information Technology Management (1). March/April 1999. ISR. E. O’Neill. Keil. Lyytinen. Reading.p hp?ecommerce_trade). and Schmidt.. G. Keil. Kleindorfer.. R. Lee. Gelernter. Chicago. Kalakota. E-Business 2. 103-115.” Communications of the ACM (41:11).” Journal of Management Information Systems. K. 102 MIS Quarterly Vol... M. G. pp. M. J. S. pp. R. T. “Computer-Based Support for Group Problem-Finding: An Experimental Investigation. Gallupe. and Gerdes. S. v-xi. Winter 1998-99. “An Ontological Analysis of the Primitives of the ExtendedREA Enterprise Information Architecture.. D.” MIS Quarterly.. pp. G. P. Information Modeling and Relational Databases. “Workflow Support for Electronic Commerce Applications. A. L. J. University of Chicago Press.” Communications of the ACM (38:6). M. pp. December 2002. November 1998. MA. 1996. “Anonymous Mechanisms in Group Decision Support Systems Communication. “On the Impact of Network Latency on Distributed Systems Design. 421-447. pp. (15:4) December 1999. December 1988. The Fifth Generation: Artificial Intelligence and Japan’s Computer Challenge to the World. Inc. and Robey. S. and Venkatraman. A.. Kuhn. A. 7-149. 1-16. Henderson.” MIS Quarterly (23:1). June 1995.” MIS Quarterly (19:4) December 1995.. P. McGraw-Hill. P. Fall 2003. and Paradigms: Heeding Three Lessons from the Past to Resolve Three Dilemmas in the . Design Science. 1/March 2004 Johansson. Reading.com/products/report. pp..).” Management Science (44:8). 297-328. Machine Beauty: Elegance and the Heart of Technology. J. J. Rao. B. June 1988. 277298. Editorial Statement and Policy.” IEEE Software (16:2). March. Kim.. “An Assessment of Group Support Systems Experimental Research: Methodology and Results. D.. J. S. 2002. G. New York... J. The Elements of Programming Style (2nd ed. MA. Cule. Kumar. T. “Validation in Simulation: Various Positions in the Philosophy of Science. M. and Myers. Information Systems Research (13:4). and Plauger. Morgan Kaufmann Publishers. Kruchten. March 1999.
edu/~aslee/ICIM-keynote-2000). Parsons. J. L.). pp. July 1991b. Dallas. Nunamaker. December 12-13.” MIS Quarterly (26:3). Hubs. New York. V. M. J. and Wand. S. G. P. London. D. Vogel. S. March 1976.. 1-9. pp. T. Currency Doubleday. 1986. Cambridge. and Beitz. A. September. (13:3). The Design of Everyday Things. and Elam. 251-266. Winter 1991a. W. and Roller.. 1/March 2004 103 .. “Electronic Meeting Systems to Support Group Work. and Gasser. “The REA Accounting Model: A Generalized Framework for Accounting Systems in a Shared Data Environment.. 2002. M. Vogel.. 228-268.. Prentice-Hall. (34:7). H. pp. L. Norman. W. Engineering Design: A Systematic Approach. pp. IL.. pp.. V.” ACM Transactions on Database Systems (25:2). 40-61. MIS Quarterly Vol. 89-106./Design Science in IS Research Present to Direct a Trajectory for Future Research in the Information Systems Field. and Simon. and Iacono. Markus. P. December 1995. Hevner. Pahl.” Keynote Address. 207-222. J. University of Chicago Press. Dennis. 2000.” Communications of the ACM (19:3). June 2001. McCarthy.. June 2001.” Information Systems Research (9:1). 163-207. 554578. Leymann. H. people. Majchrzak. “Using Technology and Constituting Structures: A Practice Lens for Studying Technology in Organizations. J.. “Semantic Structuring in Analyst Acquisition and Representation of Facts in Requirements Analysis. and Purdin. D. Whinston (eds. “Computer Science as Empirical Inquiry: Symbols and Search.” Information Systems Research (11:4). and Barley. pp. Taiwan. December 2000. pp.” Communications of the ACM. 1982. Y. A. Harvard University Press. R. A. “A Design Theory for Systems that Support Emergent Knowledge Processes.” Journal of Information Systems Frontiers (4:2). J. W. A.. Storey and A. J. 1988. Production Workflow: Concepts and Techniques. S.).. M. Upper Saddle River. E. “Technology and Institutions: What Can Research on Information Technology and Research on Organizations Learn From Each Other?.. W. “Design and Natural Science Research on Information Technology. June 2000.” in Metatheory in Social Science.. Mittleman. Orman. J. D. S. A. TX. W. F. 179-212.. D. Invention by Design: How Engineers Get from Thought to Thing. Petroski. L.Hevner et al. Springer-Verlag. J. 1996. E. Orlikowski. D. “Emancipating Instances from the Tyranny of Classes in Information Modeling. Fiske and R. D. S. E. MA. “Electronic Markets. T. “The Challenge: To Be Part of the Solution Instead of Being Part of the Problem. G. pp. pp.. March. J. 404-428. Nunamaker.. Valacich. NJ. Nunamaker.” Decision Support Systems (15:4). May 2000 (available online at http://www. W. “Lessons from a Dozen Years of Group Support Systems Research: A Discussion of Lab and Field Findings.. pp. J. and Intermediaries.. G. March 1998. “Systems Development in Information Systems Research. pp. 37-63. Orlikowski. Winter 1996-97. March. D. 315-338. Hierarchies. Meehl. pp. Shweder (eds.” Journal of Management Information Systems.” in Proceedings of the Second Annual Workshop on Information Technology and Systems. 113-126. pp.. S. M. Marakas. C. 28 No. 1992. Newell.” Organization Science (11:4).” The Accounting Review (58:3). Chicago. Orlikowski. 2002. J. 327-341. and Ram. Chen. R. and Balthazard. “What Social Scientists Don’t Understand.” Information Systems Research (12:2). Eleventh International Conference on Information Management..” Journal of Management Information Systems (7:3). Briggs. and Smith. December 2000. pp 145-165. 1996..” MIS Quarterly (25:2). 121-134. pp. “Research Commentary: An Agenda for Information Technology Research in Heterogeneous and Distributed Environment. “Research Commentary: Desperately Seeking the ‘IT’ in IT Research—A Call to Theorizing the IT Artifact. pp.vcu. and George. Madnick. T.
pp. pp.. 1/March 2004 Storey. pp. Wand. Vessey.). DeGross (eds.. 1998. and Weber. R. March 1992. Walls. Trice. pp. MIT Press. “On the Ontological Expressiveness of Information Systems Design Analysis and Design Grammars.. M. “Determinants pf Perceived Ease of Use: Integrating Control... M.” Information Systems Research (14:3).” Information Systems Research (8:3). Schön. Markus. 28 No. R. K. L. Software Project Management: A Unified Framework. De and J. Denning and R. and Weber.). 1988. and Han. pp. The Sciences of the Artificial (3rd ed. 400-408. “Building an Information System Design Theory for Vigilant EIS. W. “Automated Construction of Knowledge-Bases from Examples. W. K. Australia. R. 1983.). A..” Journal of Information Systems (3:3). Tsichritzis../Design Science in IS Research Purao. iii-xi. J. December 2000. pp. Widmeyer. Reading. B. 203-233.. Inc. M. Charlotte. NC. . Opportunity. Simon. L. May 1998. MA. New York. R. Inc. 99-102. V.. Y. Weber. pp. D. pp. 269-290. A. Algorithms. Weber. New York. Addison-Wesley. A. T. Automatic Text Processing: The Transformation. I. pp. G. 229-244. 342-365. N. B. January 1996. MA. Tam. and Beath. R. C. “A Respecification and Extension of the DeLone and McLean Model of IS Success. Metcalfe (eds. Y.” in Beyond Calculation: The Next Fifty Years of Computing. pp. H. 1995. Rittel. “Research Commentary: Diversity in Information Systems Research: Threat. pp. 1984. “Should Computer Scientists Experiment More?” IEEE Computer (31:5). New York. New York.. “Cognitive Feedback in GDSS: Improving Control and Convergence. 262-288.. and Responsibility. 1993. M. 361-390.. V. 217-237.. December 13-15. September 1997. D. and Webber. Cambridge. 1998.” Information Systems Research (11:4). “On the Deep Structure of Information Systems. V. “Heuristics for Reconciling Independent Knowledge Bases. pp. “Planning Problems Are Wicked Problems. MA. M.). Intrinsic Motivation. O. 1996. Sengupta.” Communications of the ACM (41:4). Y. 144167. “Editor’s Comments: Still Desperately Seeking the IT Artifact. M. C..” Information Systems Research (4:3). “Strong Vs. Ontological Foundations of Information Systems. September 1993.. Tichy. “The Information Technology Interaction Model: A Foundation for the MBA Core Course.” Information Systems Research (3:1). pp. S.” Information Systems Research (1:2). P. J.” in Developments in Design Methodology. 1997. Applied Cryptography: Protocols. Basic Books. June 2003. A. John Wiley and Sons. S. Brisbane.” MIS Quarterly (27:2). 36-59.). Reading. Venkatesh. Wand. and Davis. Copernicus Books. Analysis. “An Empirical Investigation of Entity-Based and Object-Oriented Data Modeling: A Development Life Cycle Approach. Weak Approaches to Systems Development. Dey. September 2003.. and Source Code in C (2nd ed. April 1998. March 1993. 471-512.” MIS Quarterly (19:3). “The Dynamics of Innovation. Schneier. “Database Design with Common Sense Business Reasoning and Learning. Sinha. Addison-Wesley. R. S. and Te’eni. September 1995.” Information Systems Journal (5). 104 MIS Quarterly Vol. 240-253. Chiang.” Information Systems Research (7:4). Silver. 1996. The Reflective Practitioner: How Professionals Think in Action. and Vessey.” MIS Quarterly (17:1). D. Goldstein.” in Proceedings of the Twentieth International Conference on Information Systems.” ACM Transactions on Database Systems (22:4). R. June 1990. R. 259-265. Coopers & Lybrand. and Glass. I. G. D.. 1999. Robey. C. pp. December 1997. and Emotion into the Technology Acceptance Model. Seddon. D. A. and Sundaresan.. Storey. R. D. H. John Wiley & Sons. pp. pp. G. J. and El Sawy. and Retrieval of Information by Computer.. Royce. pp. P. I.Hevner et al. H. “Improving Reuse-Based System Design with Learning. P. 32-40. C. 87-113. Cross (ed. P. Salton.
1/March 2004 105 . Dr.D.S. Denning and R. P. Hevner is a member of ACM. Calcutta in 1981 and a Ph. Hevner is an Eminent Scholar and Professor in the College of Business Administration at the University of South Florida. Information Systems Research. in Computer Science from Purdue University. in Industrial Engineering and M. from the University of Illinois at Urbana-Champaign. Jinsoo Park is an assistant professor of information systems in the College of Business Administration at Korea University. His research interests are in the areas of semantic interoperability and metadata management in interorganizational information systems./Design Science in IS Research Weber. and intelligent agents for data management. “Editor’s Comments. He has held faculty positions at the University of Maryland at College Park and the University of Minnesota. Bringing Design to Software. Salvatore T. and healthcare information systems. His research has appeared in journals such as Communications of the ACM. T. He currently serves on the editorial board of Journal of Database Management. and Management Science. MA. Inc. Metcalfe (eds. Vanderbilt University. semantic modeling. He has published numerous research papers on these topics and has consulted for several Fortune 500 companies.. Zelkowitz. Addison-Wesley. New York. He holds a Ph. March is the David K. Winograd. His research interests are in information system development. May 1998. AIS. D. IEEE. in Science from the University of Madras in 1979. MIS Quarterly Vol. 23-31. IEEE Transactions on Knowledge and Data Engineering. Zmud.Hevner et al. R. distributed data- base design. and electronic commerce.” in Beyond Calculation: The Next 50 Years of Computing. Ram has published articles in such journals as Communications of the ACM. xxi-xxii. IEEE Transactions on Knowledge and Data Engineering. “Toward a Theory of Artifacts: A Paradigmatic Base for Information Systems Research. and Information Systems Research. and Wallace. “Experimental Models for Validating Technology. Her research has been funded by IBM. IEEE Transactions on Knowledge and Data Engineering. “The Design of Interaction. and Information Systems Frontiers.D.” Journal of Information Systems (1:2). heterogeneous information resource management and integration. He holds the Salomon Brothers/Hidden River Corporate Park Chair of Distributed Technology. and INFORMS. and data modeling. IEEE. Her research deals with interoperability in heterogeneous databases. 2000. 3-19.” Document Number WFMC-TC1023. He is currently a senior editor for Information Systems Research and an associate editor for Decision Sciences Journal.D. Copernicus Books. distributed database systems. National Institute of Standards and Technology (NIST). and the Office of Research and Development of the Central Intelligence Agency (CIA). T. in 1985. and INFORMS. degrees in Operations Research from Cornell University. Dr.D. Reading. “Workflow Standard—Interoperability WfXML Binding. Wilson Professor of Management at the Owen Graduate School of Management. Spring 1987. Version 1. 28 No. R. Workflow Management Coalition. software engineering. data allocation. WfMC. pp. National Aeronautics and Space Administration (NASA). AIS. Sudha Ram is the Eller Professor of MIS at the University of Arizona. National Science Foundation (NSF). pp.” MIS Quarterly (21:2). M. pp. He was formerly on the faculty of the Carlson School of Management at the University of Minnesota. PGDM from the Indian Institute of Management. 1996. and Ph.). pp. 149-162. Hevner received a Ph.S. June 1997. in MIS from the University of Arizona. Winograd.0. About the Authors Alan R. 1998.” IEEE Computer (31:5). knowledge sharing and coordination. He received a B. He is a member of ACM. He served as the Editorin-Chief of ACM Computing Surveys and as an associate editor for MIS Quarterly. She received a B. Dr. His published research articles appear in IEEE Computer. His areas of research interest include information systems development..S.
1/March 2004 .106 MIS Quarterly Vol. 28 No.
Introduction Difinitions of Research Assignment (Ime Anyin).

References: art. 86
 V. 
 V. 
 V. 
 V. 
 V.