E DITOR ’ S C OMMENTS Diversity of Design Science Research

Design in the sciences of the artificial is concerned with what might be, with " changing existing situations into preferred ones " (Simon 1996, p. 130). As a multifaceted concept, design has been studied by IS scholars as an object, process, and capability. As a multidisciplinary concept, it has received significant attention across various traditions of research in the IS community. As a multilevel concept, it has been examined to uncover interdependencies across levels of analysis. As a concept that can redefine human experiences, it has led to the development of problem solving and innovation practices that embody design thinking (Buchanan 2015). We have seen significant discussion in the IS literature about the design science paradigm, which is concerned with developing and evaluating new and innovative IT artifacts to solve problems and through the process contribute to knowledge (Hevner et al. 2004). The discussion has been wide-ranging and has encompassed the distinctive characteristics of design science research, the knowledge contributions of design science research, and how design science research can be conducted and presented effectively and evaluated appropriately (e. A look at papers published in MIS Quarterly and other major IS journals reveals important commonalities in the practice of design science research but also reveals important differences. There are observable genres of design science, which differ with respect to the problems that are addressed, the types of artifacts that are designed and evaluated, the search processes that are used to create and refine the IT artifacts to solve problems, and the types of knowledge contributions that are made. For example, consider the types of problems that are addressed and the artifacts that are developed across a few genres: the computational genre is concerned with solving business and societal problems by developing computational models and algorithms, the optimization genre is concerned with solving operational and decisional problems by developing optimization and related models and heu-MIS Quarterly Vol. 41 No. 1 pp. iii-xviii/March 2017 iii