Salvatore T. March

Design science in information systems research

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.

Design and natural science research on information technology

Research in IT must address the design tasks faced by practitioners. Real problems must be properly conceptualized and represented, appropriate techniques for their solution must be constructed, and solutions must be implemented and evaluated using appropriate criteria. If significant progress is to be made, IT research must also develop an understanding of how and why IT systems work or do not work. Such an understanding must tie together natural laws governing IT systems with natural laws governing the environments in which they operate. This paper presents a two dimensional framework for research in information technology. The first dimension is based on broad types of design and natural science research activities: build, evaluate, theorize, and justify. The second dimension is based on broad types of outputs produced by design research: representational constructs, models, methods, and instantiations. We argue that both design science and natural science activities are needed to insure that IT research is both relevant and effective.

Integrated decision support systems: A data warehousing perspective

Successfully supporting managerial decision-making is critically dependent upon the availability of integrated, high quality information organized and presented in a timely and easily understood manner. Data warehouses have emerged to meet this need. They serve as an integrated repository for internal and external data-intelligence critical to understanding and evaluating the business within its environmental context. With the addition of models, analytic tools, and user interfaces, they have the potential to provide actionable information resources-business intelligence that supports effective problem and opportunity identification, critical decision-making, and strategy formulation, implementation, and evaluation. Four themes frame our analysis: integration, implementation, intelligence, and innovation.

User evaluations of IS as surrogates for objective performance

User evaluations of information systems are frequently used as measures of MIS success, since it is extremely difficult to get objective measures of system performance. However, user evaluations have been appropriately criticized as lacking a clearly articulated theoretical basis for linking them to systems effectiveness, and almost no research has been found that explicitly tests the link between user evaluations of systems and objectively measured performance. In this paper, we focus on user evaluations of task-technology fit for mandatory use systems and develop theoretical arguments for the link to individual performance. This is then empirically tested in a controlled experiment with objective performance measures and carefully validated user evaluations. Statistically significant support for the link is found for one measure of performance but not for a second. These findings are consistent with others which found that users are not necessarily accurate reporters of key constructs related to use of IS, specifically that self reporting is a poor measure of actual utilization. The possibility that user evaluations have a stronger link to performance when users receive feedback on their performance is proposed. Implications are discussed.

Research Commentary: An Agenda for Information Technology Research in Heterogeneous and Distributed Environments

Application-driven, technology-intensive research is critically needed to meet the challenges of globalization, interactivity, high productivity, and rapid adaptation faced by business organizations. Information systems researchers are uniquely positioned to conduct such research, combining computer science, mathematical modeling, systems thinking, management science, cognitive science, and knowledge of organizations and their functions. We present an agenda for addressing these challenges as they affect organizations in heterogeneous and distributed environments. We focus on three major capabilities enabled by such environments: Mobile Computing, Intelligent Agents, and Net-Centric Computing. We identify and define important unresolved problems in each of these areas and propose research strategies to address them.

Comparing data modeling formalisms

Accurate specification and validation of information requirements is critical to the development of organizational information systems. Semantic data models were developed to provide a precise and unambiguous representation of organizational information requirements [9, 17]. They serve as a communication vehicle between analysts and users. After analyzing 11 semantic data models, Biller and Neuhold [3] conclude that there are essentially only two types of data modeling formalisms: entity-attribute-relationship (EAR) models and object-relationship (OR) models. Proponents of each claim their model yields “better” representations [7] than the other. There is, however, little empirical evidence to substantiate these claims.

Allocating data and operations to nodes in distributed database design

The allocation of data and operations to nodes in a computer communications network is a critical issue in distributed database design. An efficient distributed database design must trade off performance and cost among retrieval and update activities at the various nodes. It must consider the concurrency control mechanism used as well as capacity constraints at nodes and on links in the network. It must determine where data will be allocated, the degree of data replication, which copy of the data will be used for each retrieval activity, and where operations such as select, project, join, and union will be performed. We develop a comprehensive mathematical modeling approach for this problem. The approach first generates units of data (file fragments) to be allocated from a logical data model representation and a characterization of retrieval and update activities. Retrieval and update activities are then decomposed into relational operations on these fragments. Both fragments and operations on them are then allocated to nodes using a mathematical modeling approach. The mathematical model considers network communication, local processing, and data storage costs. A genetic algorithm is developed to solve this mathematical formulation. >

The information systems research cycle

What distinguishes information systems from closely aligned disciplines such as computer science, organizational science, management science, economics, or systems engineering? How does IS research balance the demands of relevance and rigor to make effective contributions to both theory and practice? As senior researchers in IS, the authors have engaged in many debates on these questions and have come to some conclusions about what makes this burgeoning field unique and how to properly plan, execute, and evaluate IS research as well as transition it into practice.

Building and implementing an information architecture

An information architecture is a personnel, organization, and technology independent profile of the major information categories used within an organization. It provides a way to map information needs, relate them to specific business functions, and document their interrelationships. It is used to guide applications development and facilitate integration and sharing of data. This paper describes an approach for developing a corporate/global information architecture. It also describes how the information architecture can be used to guide new systems development efforts. Experience at Pillsbury U. S. Foods is used to illustrate the concepts involved. While the process used at Pillsbury necessarily reflects the unique combination of circumstances present within that organization, it also reflects the basic requirements for developing and implementing an information architecture in any large organization.

Techniques for Structuring Database Records

Structuring database records by considering data item usage can yield substantial efficiencies in the operating cost of database systems. However, since the number of possible physical record structures for database of practical significance is enormous, and their evaluation is extremely complex, determining efficient record structures by full enumeration is generally infeasible. This paper discusses the techniques of mathematical clustering, iterative grouping refinement, mathematical programming, and hierarchic aggregation, which can be used to quickly determine efficient record structures for large, shared databases.