Major Currents in the Information Systems Field Series Editors Leslie Willcocks and Allen Lee

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1 Major Currents in the Information Systems Field Series Editors Leslie Willcocks and Allen Lee Volume 3: Design Science Theories and Research Practices Volume Editor Alan R. Hevner INTRODUCTION I am pleased and honored to serve as the editor for the Design Science Theories and Research Practices volume of the series of Major Currents in the Information Systems Field. The design science research paradigm is poised to take its rightful place as a synergistic and equal partner along side other research paradigms in the Information Systems (IS) field. In doing so, however, it is vital that the design science research community provide clear and consistent definitions, ontologies, boundaries, guidelines, and deliverables for the design and execution of high quality design science research projects. The goals of this volume in disseminating recent thinking on design science theories and research practices and in providing exemplar design science papers contribute to a greater understanding of design science research in the IS community. Communicating design science theories and research practices is essential not only to support acceptance among IS professionals but also to establish the credibility of IS design science research among the larger body of design science researchers in computer science, engineering fields, architecture, the arts, and other design-oriented communities. Relevance and Rigor in Design Science Design science values the presence of both relevance and rigor as essential ingredients of an exemplary research project. While specific projects may vary in amounts of relevance and rigor, however measured, both must be present in some degree for the research results to be considered valid and useful. Research relevance is a strong point of design science research projects. Consideration of relevance initiates design science research within an application context that not only provides the requirements for the research (e.g., the opportunity/problem to be addressed) but also defines acceptance criteria for the ultimate evaluation of the research results. Does the design artifact improve the application environment and how can this improvement be

2 measured? The outputs from the design science research effort must be returned into the environment for study and evaluation in the application domain. The results of the field testing will determine whether additional iterations of design refinements are needed. The field study of the artifact can be executed by means of appropriate research methods such as action research 1. The new artifact might have deficiencies in functionality or in its inherent qualities (e.g., performance, usability) that may limit its utility in practice. Another result of field testing may be that the requirements input to the design research were incorrect or incomplete with the resulting artifact satisfying the requirements but still inadequate to the opportunity or problem presented. By definition, effective design science research must result in a design artifact that has measured utility in an application environment. Thus, relevance is assured in a successful design research project. Note that this requirement for relevance may not be a prerequisite for other research paradigms. For example, natural science research may study phenomena to understand truth with no apparent relevance to any application environment. Consideration of rigor in design science research is based on the researcher s skilled selection and application of the appropriate theories and methods for constructing and evaluating the artifact. Design science research is grounded on existing ideas drawn from the domain knowledge base. Inspiration for creative design activity can be drawn from many different sources to include rich opportunities/problems from the application environment, existing artifacts, analogies/metaphors, and theories 2. Additions to the knowledge base as results of design science research will include any additions or extensions to the original theories and methods made during the research, the new artifacts (design products and processes), and all experiences gained from performing the research and field testing the artifact in the application environment. It is imperative that a design research project makes a compelling case for its rigorous bases and contributions lest the research be dismissed as a case of routine design. Definitive research contributions to the knowledge base are essential to selling the research to an 1 You will find in-depth discussions on the relationships between Action Research and Design Science in references: P. Jarvinen, Action Research is Similar to Design Science, Quality & Quantity, (41), 2007, pp A. Lee, Action is an Artifact: What Action Research and Design Science Offer to Each Other, in Information Systems Action Research: An Applied View of Emerging Concepts and Methods, N. Koch, Editor, Springer, Inc., 2007, pp See: J. Iivari, A Paradigmatic Analysis of Information Systems as a Design Science, Scandinavian Journal of Information Systems, (19:2), 2007.

3 academic audience just as useful contributions to the environment are the key selling points to a practitioner audience. Thus, it is essential that a design research project embody sufficient levels of relevance and rigor to make a convincing case that 1) the resulting design artifact will have utility in the application environment and 2) the research will make a scientific contribution to the domain knowledge base. Design science research is essentially a pragmatic discipline. Pragmatism is a school of thought that considers practical consequences or real effects to be vital components of both meaning and truth. Design science research is essentially pragmatic in nature due to its emphasis on relevance; making a clear contribution into the application environment. In support of the second requirement of rigor, extending the content of the knowledge base is what separates design research from the practice of routine design. Together, it is the synergy between relevance and rigor and the contributions to both the application domain and the scientific knowledge base that define exemplary design science research. Design Science Papers in this Volume The design science papers included in this volume represent only a small portion of the excellent body of design science work produced in the Information Systems community over the past twenty years. The difficult task of paper selection was admirably performed by a distinguished Advisory Panel of design science researchers in the IS field: Amit Basu, Southern Methodist University Samir Chatterjee, Claremont Graduate School Alan Hevner, University of South Florida Juhani Iivari, University of Oulu Matthias Jarke, RWTH Aachen Ramayya Krishnan, Carnegie-Mellon University Stuart Madnick, Massachusetts Institute of Technology Salvatore March, Vanderbilt University Jay Nunamaker, University of Arizona Jeffrey Parsons, Memorial University of Newfoundland Sudha Ram, University of Arizona

4 Keng Siau, University of Nebraska Veda Storey, Georgia State University Alexander Tuzhilin, New York University The selection process was guided (not dictated) by several general rules. Papers should be considered seminal, published in refereed journals, highly cited, and of topical interest. Authors should come from the IS design science community. Diversities of research topic area, journal outlet, and national origin were sought. In particular, the panel focused on papers whose principal research methodology was design science and whose principal research contribution included an information system artifact. Basically What papers would we recommend to peer researchers, knowledgeable IS practitioners, and students for them to be informed on the theories and practices of design science research? The Advisory Panel participated in three cycles of paper selection. In the first cycle, the panelists individually nominated all the papers that they considered worthy of being exemplars of design science research in IS. Over 120 papers were initially nominated. The nominated list was distributed and in the second cycle, each panelist was asked to select twenty papers for inclusion in the volume. Reasons for selecting each paper were given by the panelists. This cycle produced a list of design science papers ranked by the number of votes received and annotated by all comments. This list was distributed to the panel and the third cycle proceeded via an interactive exchange of messages among the panel to reach consensus on a list of twenty papers. This list was vetted by the Senior Editors and the Volume Editor to produce the final list of sixteen papers contained in this volume. To introduce the reader to the contents of this volume, I will briefly review published research papers as grouped by topical areas of the design artifacts addressed in the research. Within each topic, references will be provided to both the papers included in the volume and to other important design science papers on the topic considered worthy of inclusion by the panel but which could not be included for various reasons such as space. Readers are encouraged to expand their design science readings beyond just the papers in this volume to include the other

5 referenced papers below and the most recently published design science papers which are too new to have been considered for this volume. Design Science Theory and Practice The volume begins with a set of seminal papers that address the foundations and definitions of design science theory and practice in the field of Information Systems. The role of the design science research paradigm in IS has been and continues to be a widely-discussed and debated topic. These papers have established a base set of definitions, theories, practice guidelines, and boundaries for design science research. Future discussions on the role of design science research in IS will draw from and extend the ideas found here. The appearance of the Hevner, March, Park, and Ram [1] paper in MIS Quarterly provided needed visibility of the design science research paradigm to the greater IS community. Drawing significantly from the original design science framework found in March and Smith [2], the MISQ paper posits a set of practice guidelines for performing, reviewing, and understanding effective design science research in IS. Some of the earliest thinking in the IS community on design research is found in Nunamaker, Chen, and Purdin [3]. This paper clearly identifies IS development research as a creative design activity separate from research that evaluates the results of the developed systems in action. The important search for a foundation of design science theory was begun in Walls, Widmeyer, and El Sawy [4] in the context of executive information systems and has been extended by Markus, Majchrzak, and Gasser [5] for emergent knowledge processing systems. Information Systems Development and Systems Modeling The IS community has made many key contributions in the modeling and development of software and information systems. An accompanying volume of this series focuses on Information Systems Development (ISD), so only a few papers illustrating exemplar design science research will be mentioned here and no papers in this area are included in this volume. The modeling of systems dynamics is presented in Abdel-Hamid and Madnick [6]. This very novel approach has led to a better understanding of the dynamics of many IS project

6 management issues. Hevner and Mills [7] present a formal approach for designing information systems that combines key aspects of structured and object-oriented development. A highly influential paper by Chidamber and Kemerer [8] proposes a comprehensive set of metrics for evaluating the quality of an object-oriented system design. Information and Data Modeling The modeling of data and information is an extremely relevant and active topic of research in IS. There are a number of important design science contributions in this area from the IS community. Wand and Weber [9] pioneered the use of ontological analysis and design in IS modeling. This seminal paper uses a formal approach to design and evaluate semantic data models and ontologies. This work is extended in Wand, Storey, and Weber [10] to the analysis and design of relationships in data models. An interesting and different approach to understanding and modeling data is presented in Parsons and Wand [11]. The new approach focuses on the advantages of instance-based data models over class-based data models. Ram and Park [12] address issues of semantic interoperability among heterogeneous data sources. They design and evaluate techniques for detecting and resolving various semantic conflicts among different data schemas. Finally, in this rich field, Wang, Storey, and Firth [13] provide a comprehensive framework for understanding and analyzing the quality of data and information. Database Systems Design The control and management of large datasets in database systems have inspired a number of interesting design science projects. Ram and Narasimhan [14] provide an excellent example of using mathematical modeling to optimize the allocation of databases in a distributed system environment. Their approach incorporates a deep understanding of essential database control requirements and management costs. Query optimization on relational databases is a challenging topic with a long history of design science research contributions. Dey and Sankar [15] describe an interesting extension of the relational model for the management and querying of uncertain (i.e. probabilistic) data.

7 Knowledge and Information Integration The requirement to manage and use data, information, and knowledge across multiple distributed and possibly heterogeneous information systems has led to a very active research stream on how to design solutions for issues such as interoperability and integration. An important early paper by Mylopoulos, Borgida, Jarke, and Koubarakis [16] presents a set of key principles for knowledge representation in information systems. Goh, Bressan, Madnick, and Siegel [17] provide an interesting and effective approach for representing and reasoning about the semantics of information and its integration from multiple sources. The effective retrieval of data across heterogeneous database systems is explored by Krishnan, Li, Steier, and Zhao [18] via formal modeling. A comprehensive approach for IS interoperability is presented by Park and Ram [19]. Semantic mediators are applied to resolve conflicts and to provide query-processing capabilities on multiple heterogeneous databases. Data Warehousing and Mining Effective data warehouse designs allow massive amounts of historical data to be organized, stored, and analyzed effectively. Mining of these data via analytic tools allows the discovery of interesting and useful patterns and trends. Silberschatz and Tuzhilin [20] discuss the key ideas of knowledge discovery in data warehouses, such as what makes data patterns interesting from a business perspective. Datta, VanderMeer, and Ramamritham [21] present several innovative indexing structures and optimization algorithms to improve the performance of query processing in massive data warehouses. An application of data warehousing to community health care decision making is demonstrated by Berndt, Hevner, and Studnicki [22]. Issues of data cleansing and integration of data from varied sources into a common schema are highlighted. Network and Telecommunications Systems The design of network and telecommunications systems is typically performed by technical groups in the computer science and engineering fields. However, an interesting design of session protocols for videoconferencing on desktops is presented in Chatterjee, Abhichandani, Tulu, and Li [23]. The authors present guidelines for deploying and managing services based on this new protocol throughout an enterprise application.

8 Decision Support Systems Research on decision support systems (DSS) has been a mainstay of the IS community. A seminal paper by Nunamaker, Dennis, Valacich, Vogel, and George [24] reports on the design of a electronic meeting system for group decision support. The full complexity of design science research is demonstrated in this paper from the theory of group DSS through the system design process to field testing in organizational settings. Basu and Blanning [25] propose a formal modeling tool, metagraphs, to support decision-making in rich environments. A more recent paper studies recommender systems as an aid to decision support. Adomavicius, Shankaranarayanan, Sen, and Tuzhilin [26] design a multidimensional rating method that combines contextual information with traditional user data to outperform standard methods of recommendation. Workflow Systems Workflow systems capture the critical flows of business transactions in order to analyze and improve their effectiveness and efficiency. Innovative research by Kumar and Zhao [27] provides a general framework for representing dynamic routing and operational control options in complex workflows. Another important contribution is made by Van der Aalst and Kumar [28] by demonstrating how workflow schemas can be represented effectively in XML. This allows efficient automation of transactions in a distributed Internet-based application environment. Electronic Commerce Systems The proliferation and vast success of electronic commerce systems have created a strong interest in design science research in this area. Research by Chen, Houston, Sewell, and Schatz [29] demonstrates that innovative methods for organizing information on the Internet can enhance users abilities to browse and find relevant information. Internet auctions are highly visible activities in electronic commerce. Bapna, Goes, and Gupta [30] design an effective heuristic bidding strategy in multiunit, business-to-consumer online auctions.

9 Acknowledgements I want to thank the members of the Design Science Advisory Panel for their dedicated efforts to evaluate and select the papers for this volume. My efforts as editor have been partially supported by the U.S. National Science Foundation (NSF) while I have worked on assignment at NSF. However, any opinion, finding, and conclusion expressed in this introduction are mine and do not necessarily reflect the views of the NSF. Design Science References: (* indicates inclusion in current volume) Design Science Theory and Practice 1. * A. Hevner, S. March, J. Park, and S. Ram, S., Design Science in Information Systems Research, MIS Quarterly (28:1), 2004, pp S. March and G. Smith, Design and Natural Science Research on Information Technology, Decision Support Systems (15:4), 1995, pp * J. Nunamaker, M. Chen, and T. Purdin, "Systems Development in Information Systems Research," Journal of Management Information Systems (7:3), Winter 1991, pp * J. Walls, G. Widmeyer, and O. El Sawy, Building an Information System Design Theory for Vigilant EIS, Information Systems Research (3:1), 1992, pp * M. Markus, A. Majchrzak, L. Gasser, "A Design Theory for Systems that Support Emergent Knowledge Processes," MIS Quarterly (26:3), September, 2002, pp Information Systems Development and Systems Modeling 6. T. Abdel-Hamid and S. Madnick, "Lessons Learned from Modeling the Dynamics of Software Development," Communications of the ACM, (32:12), December 1989, pp , A. Hevner and H. Mills, "Box Structured Methods for Systems Development with Objects," IBM Systems Journal, (32:2), 1993, pp S. Chidamber and C. Kemerer, A Metrics Suite for Object Oriented Design, IEEE Transactions on Software Engineering, (20:6), June 1994, Information and Data Modeling 9. Y. Wand and R. Weber, An Ontological Model of an Information System, IEEE Transactions on Software Engineering, (16:11), November 1990,

10 10. Y. Wand, V. Storey, and R. Weber, An Ontological Analysis of the Relationship Construct in Conceptual Modeling, ACM Transactions on Database Systems (24:4), December 1999, pp J. Parsons and Y. Wand, Emancipating Instances from the Tyranny of Classes in Information Modeling, ACM Transactions on Database Systems, (25:2), June 2000, pp * S. Ram and J. Park, Semantic Conflict Resolution Ontology (SCROL): An Ontology for Detecting and Resolving Data and Schema-Level Semantic Conflicts, IEEE Transactions on Knowledge and Data Engineering, (16:2), February 2004, pp * R. Wang, V. Storey, and C. Firth, A Framework for Analysis of Data Quality Research, IEEE Transactions on Knowledge and Data Engineering, (7:4), August 1995, pp Database Systems Design 14. * S. Ram and S. Narasimhan, "Database Allocation in A Distributed Environment: Incorporating A Concurrency Control Mechanism and Queueing Costs," Management Science, (40:8), August 1994, pp D. Dey and S. Sarkar, A Probabilistic Relational Model and Algebra, ACM Transactions on Database Systems, (21:3) September1996, pp Knowledge and Information Integration 16. * J. Mylopoulos, A. Borgida, M. Jarke, and M. Koubarakis, Telos: Representing Knowledge about Information Systems, ACM Transactions on Information Systems, (8:4), October 1990, pp * C. Goh, S. Bressan, S. Madnick, and M. Siegel, Context Interchange: New Features and Formalisms for the Intelligent Integration of Information, ACM Transactions on Information Systems, (17:3), July 1999, pp * R. Krishnan, X. Li, D. Steier, and J. Zhao, "On Heterogeneous Database Retrieval: A Cognitively-Guided Approach", Information Systems Research, (12:3), September 2001, pp J. Park and S. Ram Information Systems Interoperability: What Lies Beneath? ACM Transactions on Information Systems, (22:4), October 2004, pp Data Warehousing and Mining 20. * A Silberschatz and A. Tuzhilin, What Makes Patterns Interesting in Knowledge Discovery Systems, IEEE Transactions on Knowledge and Data Engineering, (8:6),

11 December 1996, pp A. Datta, D. VanderMeer, and K. Ramamritham, Parallel Star Join + DataIndexes: Efficient Query Processing in Data Warehouses and OLAP, IEEE Transactions on Knowledge and Data Engineering, (14:6), December 2002, pp D. Berndt, A. Hevner, and J. Studnicki, The CATCH Data Warehouse: Support for Community Health Care Decision Making, Decision Support Systems, (35), June 2003, pp Network and Telecommunications Systems 23. * S. Chatterjee, T. Abhichandani, B. Tulu, and H. Li, "SIP-based Enterprise Converged Network for Voice/Video over IP: Implementation and Evaluation of Components", IEEE Journal on Selected Areas in Communications, (23:10), October 2005, pp Decision Support Systems 24. J. Nunamaker, A. Dennis, J. Valacich, D. Vogel and J. George, "Electronic Meeting Systems," Communications of the ACM, (34:7), July 1991, pp * A. Basu and R. Blanning, Metagraphs: A Tool for Modeling Decision Support Systems, Management Science, (40:12), December 1994, pp G. Adomavicius, R. Shankaranarayanan, S. Sen, and A. Tuzhilin, Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach ACM Transactions on Information Systems, (23:1), January 2005, pp Workflow Systems 27. * A. Kumar and J. Zhao, "Dynamic Routing and Operational Controls in Workflow Management Systems", Management Science, (45:2), February 1999, pp * W. van der Aalst and A. Kumar, XML-Based Schema Definition for Support of Interorganizational Workflow, Information Systems Research, (14:1), March 2003, pp Electronic Commerce Systems 29. H. Chen, A. Houston, R. Sewell, and B. Schatz, Internet Browsing and Searching: User Evaluations of Category Map and Concept Space Techniques, Journal of the American Society for Information Science, (49:7), July 1998, pp * R. Bapna, P. Goes, and A. Gupta, Analysis and Design of Business-to-Consumer Online Auctions, Management Science, (49:1), January 2003, pp