Design Science in Information Sys...
Design Science in Information Systems Research Alan R. Hevner Information Systems and Decision Sciences College of Business Administration University of South Florida 4202 E. Fowler Avenue, CIS1040 Tampa, FL 33620 Phone: (813) 974-6753 Fax: (813) 974-6749 Email: email@example.com Salvatore T. March Owen Graduate School of Management Vanderbilt University Nashville, TN 37203 Phone: (615) 322-7043 Fax: (615) 3437177 Email: Sal.March@owen.vanderbilt.edu Jinsoo Park College of Business Administration Korea University Seoul, 136-701 Korea Phone: +82-2-3290-1943 Fax: +82-2-922-7220 E-mail: firstname.lastname@example.org Sudha Ram Management Information Systems Department Eller College of Business and Public Administration The University of Arizona Tucson, AZ 85721 Phone: (520) 621-2748 Fax: (520) 621-2433 Email: email@example.com Accepted for Publication in MIS Quarterly
Design Science in Information Systems Research Acknowledgements: We would like to thank Allen Lee, Ron Weber, and Gordon Davis who in different ways each contributed to our thinking about design science in the Information Systems profession and encouraged us to pursue this line of research. We would also like to acknowledge the efforts of Rosann Collins who provided insightful comments and perspectives on the nature of the relationship between behavioral- science and design-science research. This work has also benefited from seminars and discussions at Arizona State University, Florida International University, Georgia State University, Michigan State University, Notre Dame University, and The University of Utah. We would particularly like to thank Brian Pentland and Steve Alter for feedback and suggestions they provided on an earlier version of this paper. The comments provided by several anonymous editors and reviewers greatly enhanced the content and presentation of the paper. Author Biographies: Alan R. Hevner: Alan R. Hevner is an Eminent Scholar and Professor in the College of Business Administration at the University of South Florida. He holds the Salomon Brothers/Hidden River Corporate Park Chair of Distributed Technology. His areas of research interest include information systems development, software engineering, distributed database systems, and healthcare information systems. He has published numerous research papers on these topics and has consulted for several Fortune 500 companies. Dr. Hevner received a Ph.D. in Computer Science from Purdue University. He has held faculty positions at the University of Maryland at College Park and the University of Minnesota. Dr. Hevner is a member of ACM, IEEE, AIS, and INFORMS. Salvatore T. March: Salvatore T. March is the David K. Wilson Professor of Management at the Owen Graduate School of Management, Vanderbilt University. He received a B.S. in Industrial Engineering and M.S. and Ph.D. degrees in Operations Research from Cornell University. His research interests are in information system development, distributed database design, and electronic commerce. His research has appeared in journals such as Communications of the ACM, IEEE Transactions on Knowledge and Data Engineering, and Information Systems Research. He served as the Editor-in-Chief of ACM Computing Surveys and as an Associate Editor for MIS Quarterly. He is currently a Senior Editor for Information Systems Research and an associate editor for Decision Sciences Journal. Jinsoo Park: Jinsoo Park is an assistant professor of information systems in the College of Business Administration at Korea University. He was formerly on the faculty of the Carlson School of Management at the University of Minnesota. He holds a Ph.D. in MIS from the University of Arizona. His research interests are in the areas of semantic interoperability and metadata management in inter-organizational information systems, heterogeneous information resource management and integration, knowledge sharing and coordination, and data modeling. His published research articles appear in IEEE Computer, IEEE Transactions on Knowledge and Data Engineering, and
Information Systems Frontiers. He currently serves on the editorial board of Journal of Database Management. He is a member of ACM, IEEE, AIS, and INFORMS. Sudha Ram: Sudha Ram is the Eller Professor of MIS at the University of Arizona. She received a B.S. in Science from the University of Madras in 1979, PGDM from the Indian Institute of Management, Calcutta in 1981 and a Ph.D. from the University of Illinois at Urbana-Champaign, in 1985. Dr. Ram has published articles in such journals as Communications of the ACM, IEEE TKDE, ISR, and Management Science. Her research deals with interoperability in heterogeneous databases, semantic modeling, data allocation, and intelligent agents for data management. Her research has been funded by IBM, NIST, NSF, NASA, and ORD (CIA).
Design Science in Information Systems Research ABSTRACT Two paradigms characterize much of the research in the Information Systems discipline: behavioral science and design science. The behavioral-science paradigm seeks to develop and verify theories that explain or predict human or organizational behavior. The design-science paradigm seeks to extend the boundaries of human and organizational capabilities by creating new and innovative artifacts. Both paradigms are foundational to the IS discipline, positioned as it is at the confluence of people, organizations, and technology. Our objective is to describe the performance of design-science research in Information Systems via a concise conceptual framework and clear guidelines for understanding, executing, and evaluating the research. In the design- science paradigm knowledge and understanding of a problem domain and its solution are achieved in the building and application of the designed artifact. Three recent exemplars in the research literature are used to demonstrate the application of these guidelines. We conclude with an analysis of the challenges of performing high-quality design-science research in the context of the broader IS community. Keywords: Information Systems research methodologies, design science, design artifact, business environment, technology infrastructure, search strategies, experimental methods, creativity ISRL Categories: AI01, AI02, AI03, AC03, FB04, IB01, IB02
1 Design Science in Information Systems Research 1. INTRODUCTION Information systems are implemented within an organization for the purpose of improving the effectiveness and efficiency of that organization. Capabilities of the information system and characteristics of the organization, its work systems, its people, and its development and implementation methodologies together determine the extent to which that purpose is achieved (Silver et al. 1995). It is incumbent upon researchers in the Information Systems (IS) discipline to "further knowledge that aids in the productive application of information technology to human organizations and their management" (ISR 2002, inside front cover) and to develop and communicate "knowledge concerning both the management of information technology and the use of information technology for managerial and organizational purposes" (Zmud 1997). We argue that acquiring such knowledge involves two complementary but distinct paradigms, behavioral science and design science (March and Smith 1995). The behavioral-science paradigm has its roots in natural science research methods. It seeks to develop and justify theories (i.e., principles and laws) that explain or predict organizational and human phenomena surrounding the analysis, design, implementation, management, and use of information systems. Such theories ultimately inform researchers and practitioners of the interactions among people, technology, and organizations that must be managed if an information system is to achieve its stated purpose, namely improving the effectiveness and efficiency of an organization. These theories impact and are impacted by design decisions made with respect to the system development methodology used and the functional capabilities, information contents, and human interfaces implemented within the information system. The design-science paradigm has its roots in engineering and the sciences of the artificial (Simon 1996). It is fundamentally a problem-solving paradigm. It seeks to create innovations that define the ideas, practices, technical capabilities, and products through which the analysis, design, implementation, and use of information systems can be effectively and efficiently accomplished (Tsichritzis 1997 Denning 1997). Such artifacts are not exempt from natural laws or behavioral theories. To the contrary, their
2 creation relies on existing "kernel theories" that are applied, tested, modified, and extended through the experience, creativity, intuition, and problem solving capabilities of the researcher (Walls et al. 1992 Markus et al. 2002). The importance of design is well recognized in the IS literature (Glass 1999 Winograd 1996 Winograd 1997). Benbasat and Zmud (1999, p. 5) argue that the relevance of IS research is directly related to its applicability in design, stating that the implications of empirical IS research should be "implementable, ��� synthesize an existing body of research, ��� [or] stimulate critical thinking" among IS practitioners. However, designing useful artifacts is complex due to the need for creative advances in domain areas in which existing theory is often insufficient. "As technical knowledge grows, IT is applied to new application areas that were not previously believed to be amenable to IT support" (Markus et al. 2002, p. 180). The resultant IT artifacts extend the boundaries of human problem solving and organizational capabilities by providing intellectual as well as computational tools. Theories regarding their application and impact will follow their development and use. Here, we argue, is an opportunity for IS research to make significant contributions by engaging the complementary research cycle between design-science and behavioral-science to address fundamental problems faced in the productive application of information technology. Technology and behavior are not dichotomous in an information system. They are inseparable (Lee 2000). They are similarly inseparable in IS research. Philosophically these arguments draw from the pragmatists (Aboulafia 1991) who argue that truth (justified theory) and utility (artifacts that are effective) are two sides of the same coin and that scientific research should be evaluated in light of its practical implications. The realm of IS research is at the confluence of people, organizations, and technology (Lee 1999 Davis and Olson 1985). IT artifacts are broadly defined as constructs (vocabulary and symbols), models (abstractions and representations), methods (algorithms and practices), and instantiations (implemented and prototype systems). These are concrete prescriptions that enable IT researchers and practitioners to understand and address the problems inherent in developing and successfully implementing information systems within organizations (March and Smith
3 1995 Nunamaker et al. 1991a). As illustrations, Walls et al. (1992) and Markus et al. (2002) present design-science research aimed at developing executive information systems (EISs) and systems to support emerging knowledge processes (EKPs), respectively, within the context of "IS design theories." Such "theories" prescribe "effective development practices" (methods) and "a type of system solution" (instantiation) for "a particular class of user requirements" (models) (Markus et al. 2002, p 180). Such prescriptive theories must be evaluated with respect to the utility provided for the class of problems addressed. An IT artifact, implemented in an organizational context, is often the object of study in IS behavioral-science research. Theories seek to predict or explain phenomena that occur with respect to the artifact's use (intention to use), perceived usefulness, and impact on individuals and organizations (net benefits) depending on system, service, and information quality (DeLone and McLean 1992 Seddon 1997 DeLone and McLean 2003). Much of this behavioral research has focused on one class of artifact, the instantiation (system), although other research efforts have also focused on the evaluation of constructs (e.g., Batra et al. 1990 Kim and March 1995 Bodart et al. 2001 Geerts and McCarthy 2002) and methods (e.g., Marakas and Elam 1998 Sinha and Vessey 1999). Relatively little behavioral research has focused on evaluating models, a major focus of research in the management science literature. Design science, as the other side of the IS research cycle, creates and evaluates IT artifacts intended to solve identified organizational problems. Such artifacts are represented in a structured form that may vary from software, formal logic and rigorous mathematics to informal natural language descriptions. A mathematical basis for design allows many types of quantitative evaluations of an IT artifact, including optimization proofs, analytical simulation, and quantitative comparisons with alternative designs. The further evaluation of a new artifact in a given organizational context affords the opportunity to apply empirical and qualitative methods. The rich phenomena that emerge from the interaction of people, organizations, and technology may need to be qualitatively assessed to yield an understanding of the phenomena adequate for theory development or problem solving (Klein and Meyers 1999). As field studies enable behavioral-science researchers to understand organizational phenomena in context, the
4 process of constructing and exercising innovative IT artifacts enable design-science researchers to understand the problem addressed by the artifact and the feasibility of their approach to its solution (Nunamaker et al. 1991a). The primary goal of this paper is to inform the community of IS researchers and practitioners of how to conduct, evaluate, and present design-science research. We do so by describing the boundaries of design science within the IS discipline via a conceptual framework for understanding information systems research (Section 2) and by developing a set of guidelines for conducting and evaluating good design-science research (Section 3). We focus primarily on technology-based design although we note with interest the current exploration of organizations, policies, and work practices as designed artifacts (Boland 2002). Following Klein and Myers (1999) treatise on the conduct and evaluation of interpretive research in IS , we use the proposed guidelines to assess recent exemplar papers published in the IS literature in order to illustrate how authors, reviewers, and editors can apply them consistently (Section 4). We conclude (Section 5) with an analysis of the challenges of performing high-quality design-science research and a call for synergistic efforts between behavioral-science and design- science researchers. 2. A FRAMEWORK FOR IS RESEARCH Information systems and the organizations they support are complex, artificial, and purposefully designed. They are composed of people, structures, technologies, and work systems (Bunge 1985 Simon 1996 Alter, 2003). Much of the work performed by IS practitioners, and managers in general (Boland 2002), deals with design ��� the purposeful organization of resources to accomplish a goal. Figure 1 illustrates the essential alignments between business and information technology strategies and between organizational and information systems infrastructures (Henderson and Venkatraman 1993). The effective transition of strategy into infrastructure requires extensive design activity on both sides of the figure ��� organizational design to create an effective organizational infrastructure and information systems design to create an effective information system infrastructure.
5 These are interdependent design activities that are central to the IS discipline. Hence, IS research must address the interplay among: business strategy, IT strategy, organizational infrastructure, and IS infrastructure. This interplay is becoming more crucial as information technologies are seen as enablers of business strategy and organizational infrastructure (Kalakota and Robinson 2001 Orlikowski and Barley 2001). Available and emerging IT capabilities are a significant factor in determining the strategies that guide an organization. Cutting-edge information systems allow organizations to engage new forms and new structures ��� to change the ways they "do business" (Drucker 1988 Drucker 1991 Orlikowski 2000). Our subsequent discussion of design science will be limited to the activities of building the IS infrastructure within the business organization. Issues of strategy, alignment, and organizational infrastructure design are outside the scope of this paper. Business Strategy Information Technology Strategy Organizational Infrastructure Information Systems Infrastructure Strategy Alignment Infrastructure Alignment Organizational Design Activities Information Systems Design Activities Figure 1: Organizational Design and Information Systems Design Activities (Henderson and Venkatraman 1993)
6 To achieve a true understanding of and appreciation for design science as an IS research paradigm, an important dichotomy must be faced. Design is both a process (set of activities) and a product (artifact) ��� a verb and a noun (Walls et al. 1992). It describes the world as acted upon (processes) and the world as sensed (artifacts). This Platonic view of design supports a problem-solving paradigm that continuously shifts perspective between design processes and designed artifacts for the same complex problem. The design process is a sequence of expert activities that produces an innovative product (i.e., the design artifact). The evaluation of the artifact then provides feedback information and a better understanding of the problem in order to improve both the quality of the product and the design process. This build-and-evaluate loop is typically iterated a number of times before the final design artifact is generated (Markus et al. 2002). During this creative process, the design-science researcher must be cognizant of evolving both the design process and the design artifact as part of the research. March and Smith (1995) identify two design processes and four design artifacts produced by design-science research in IS . The two processes are build and evaluate. The artifacts are constructs, models, methods, and instantiations. Purposeful artifacts are built to address heretofore unsolved problems. They are evaluated with respect to the utility provided in solving those problems. Constructs provide the language in which problems and solutions are defined and communicated (Schon 1993). Models use constructs to represent a real world situation ��� the design problem and its solution space (Simon 1996). Models aid problem and solution understanding and frequently represent the connection between problem and solution components enabling exploration of the effects of design decisions and changes in the real world. Methods define processes. They provide guidance on how to solve problems, that is, how to search the solution space. These can range from formal, mathematical algorithms that explicitly define the search process to informal, textual descriptions of "best practice" approaches, or some combination. Instantiations show that constructs, models or methods can be implemented in a working system. They demonstrate feasibility, enabling concrete assessment of an artifact's suitability to its intended purpose. They
7 also enable researchers to learn about the real world, how the artifact affects it, and how users appropriate it. Figure 2 presents our conceptual framework for understanding, executing, and evaluating IS research combining behavioral-science and design-science paradigms. We use this framework to position and compare these paradigms. The environment defines the problem space (Simon 1996) in which reside the phenomena of interest. For IS research, it is composed of people, (business) organizations, and their existing or planned technologies (Silver et al. 1995). In it are the goals, tasks, problems, and opportunities that define business needs as they are perceived by people within the organization. Such perceptions are shaped by the roles, capabilities, and characteristics of people within the organization. Business needs are assessed and evaluated within the context of organizational strategies, structure, culture, and existing business processes. They are positioned relative to existing technology infrastructure, applications, communication architectures, and development capabilities. Together these define the business need or "problem" as perceived by the researcher. Framing research activities to address business needs assures research relevance. Given such an articulated business need, IS research is conducted in two complementary phases. Behavioral science addresses research through the development and justification of theories that explain or predict phenomena related to the identified business need. Design science addresses research through the building and evaluation of artifacts designed to meet the identified business need. The goal of behavioral-science research is truth1. The goal of design-science research is utility. As argued above, our position is that truth and utility are inseparable. Truth informs design and utility informs theory. An artifact may have utility because of some yet undiscovered truth. A theory may yet to be developed to the point where its truth can be incorporated into design. In both cases, research assessment via the justify/evaluate activities can result in the identification of weaknesses in the theory or 1 Theories posed in behavioral-science are principled explanations of phenomena. We recognize that such theories are approximations and are subject to numerous assumptions and conditions. However, they are evaluated against the norms of truth or explanatory power and are valued only as the claims they make are borne out in reality.
8 artifact and the need to refine and reassess. The refinement and reassessment process is typically described in future research directions. The knowledge base provides the raw materials from and through which IS research is accomplished. The knowledge base is composed of Foundations and Methodologies. Prior IS research and results from reference disciplines provide foundational theories, frameworks, instruments, constructs, models, methods, and instantiations used in the develop/build phase of a research study. Methodologies provide guidelines used in the justify/evaluate phase. Rigor is achieved by appropriately applying existing foundations and methodologies. In behavioral science, methodologies are typically rooted in data collection and empirical analysis techniques. In design science, computational and mathematical methods are primarily used to evaluate the quality and effectiveness of artifacts however, empirical techniques may also be employed. The contributions of behavioral-science and design-science in IS research are assessed as they are applied to the business need in an appropriate environment and as they add to the content of the knowledge base for further research and practice. A justified theory that is not useful for the environment contributes as little to the IS literature as an artifact that solves a nonexistent problem. One issue that must be addressed in design-science research is differentiating routine design or system building from design research. The difference is in the nature of the problems and solutions. Routine design is the application of existing knowledge to organizational problems, such as constructing a financial or marketing information system using "best practice" artifacts (constructs, models, methods, and instantiations) existing in the knowledge base. On the other hand, design-science research addresses important unsolved problems in unique or innovative ways or solved problems in more effective or efficient ways. The key differentiator between routine design and design research is the clear identification of a contribution to the archival knowledge base of foundations and methodologies.
9 Figure 2: Information Systems Research Framework Additions to the Knowledge Base Environment IS Research Knowledge Base People ��� Roles ��� Capabilities ��� Characteristics Organizations ��� Strategies ��� Structure & Culture ��� Processes Technology ��� Infrastructure ��� Applications ��� Communications Architecture ��� Development Capabilities Foundations ��� Theories ��� Frameworks ��� Instruments ��� Constructs ��� Models ��� Methods ��� Instantiations Methodologies ��� Data Analysis Techniques ��� Formalisms ��� Measures ��� Validation Criteria Develop / Build ��� Theories ��� Artifacts Justify / Evaluate ��� Analytical ��� Case Study ��� Experimental ��� Field Study ��� Simulation Assess Refine Business Needs Applicable Knowledge Application in the Appropriate Environment Relevance Rigor
10 In the early stages of a discipline or with significant changes in the environment, each new artifact created for that discipline or environment is "an experiment" that "poses a question to nature" (Newell and Simon 1976, p 114). Existing knowledge is used where appropriate however, often the requisite knowledge is nonexistent (Markus et al. 2002). Reliance on creativity and trial and error search are characteristic of such research efforts. As design-science research results are codified in the knowledge base, they become "best practice." System building is then the routine application of the knowledge base to known problems. Design activities are endemic in many professions. In particular, the engineering profession has produced a considerable literature on design (Dym 1994 Pahl and Beitz 1996 Petroski 1996). Within the IS discipline, many design activities have been extensively studied, formalized, and have become normal or routine. Design-science research in IS addresses what are considered to be wicked problems (Rittel and Webber 1984 Brooks 1987 Brooks 1996). That is, those problems characterized by: ��� Unstable requirements and constraints based upon ill-defined environmental contexts, ��� Complex interactions among subcomponents of the problem and its solution, ��� Inherent flexibility to change design processes as well as design artifacts (i.e., malleable processes and artifacts), ��� A critical dependence upon human cognitive abilities (e.g., creativity) to produce effective solutions, and ��� A critical dependence upon human social abilities (e.g., teamwork) to produce effective solutions. As a result, we agree with Simon (1996) that a theory of design in information systems, of necessity, is in a constant state of scientific revolution (Kuhn 1996). Technological advances are the result of innovative, creative design science processes. If not "capricious," they are at least "arbitrary" (Brooks 1987) with respect to business needs and existing knowledge. Innovations, such as database management systems, high-level languages, personal computers, software components, intelligent agents, object technology, the Internet, and the World Wide Web, have had dramatic and at times unintended impacts on the way in which information systems are conceived,
11 designed, implemented, and managed. Consequently the guidelines we present below are, of necessity, adaptive and process-oriented. 3. GUIDELINES FOR DESIGN-SCIENCE IN INFORMATION SYSTEMS RESEARCH As discussed above, design science is inherently a problem solving process. The fundamental principle of design-science research from which our seven guidelines are derived is that knowledge and understanding of a design problem and its solution are acquired in the building and application of an artifact. That is, design-science research requires the creation of an innovative, purposeful artifact (Guideline 1) for a specified problem domain (Guideline 2). Because the artifact is "purposeful," it must yield utility for the specified problem. Hence, thorough evaluation of the artifact is crucial (Guideline 3). Novelty is similarly crucial since the artifact must be "innovative," solving a heretofore unsolved problem or solving a known problem in a more effective or efficient manner (Guideline 4). In this way, design-science research is differentiated from the practice of design. The artifact itself must be rigorously defined, formally represented, coherent, and internally consistent (Guideline 5). The process by which it is created, and often the artifact itself, incorporates or enables a search process whereby a problem space is constructed and a mechanism posed or enacted to find an effective solution (Guideline 6). Finally, the results of the design-science research must be communicated effectively (Guideline 7) both to a technical audience (researchers who will extend them and practitioners who will implement them) and to a managerial audience (researchers who will study them in context and practitioners who will decide if they should be implemented within their organizations). Our purpose for establishing these seven guidelines is to assist researchers, reviewers, editors, and readers to understand the requirements for effective design- science research. Following Klein and Myers (1999), we advise against mandatory or rote use of the guidelines. Researchers, reviewers, and editors must use their creative skills and judgment to determine when, where, and how to apply each of the guidelines in a specific research project. However, we contend that each of these guidelines should be addressed in some manner for design-science research to be complete.