Recognizing that design is at the core of information systems development has led to a design-science research paradigm where differing kinds of knowledge goals give form to differing kinds of knowledge processes within a single study. This paper analyzes knowledge production in design-science research to explain how an endogenous form of pluralism characterizes such studies, making it problematic to associate any design-science study with a single view of knowledge production. Instead, a design-science research study exhibits up to four different modes of reasoning, called genres of inquiry. These genres are derived from two dualities that contrast differing knowledge goals and differing knowledge scope in the knowledge production process. The first duality arises from the sometimes seemingly contradictory knowledge goals of science versus design. The second duality reflects the contradiction between the scope of the knowledge produced, which may be idiographic or nomothetic. The evolutionary and iterative nature of a design-science study compels different knowledge goals and scope at different moments throughout a project. Because of this momentary nature, a single design-science study can be associated with multiple genres of inquiry. This understanding of the variety in the genres of inquiry advances the discourse on the nature of design-science research and the justification and evaluation of its outcomes. Consequently, a corresponding set of criteria for knowledge justification and evaluation is provided for each genre of inquiry.
Tsang and Williams offer some good and provocative ideas in their critique of our earlier article on generalizing and generalizability. In this essay we will advance some new ideas by building on those collected in both Tsang and Williams and our original article (Lee and Baskerville 2003). Because IS is a pluralist scientific discipline, one in which both qualitative and quantitative (and both interpretive and positivist) research approaches are valued, "generalize" is unlikely to be a viable term or concept if only one IS research paradigm may lay claim to it and excludes others from using it. Both papers agree on this point, but approach the problem differently. Where we originally generalized generalizability by offering new language, Tsang and Williams conceptualize generalizability by framing it more closely to its older, more statistically oriented form. We agree about the importance of induction and about the classification or taxonomy of different types of induction. We build further in this essay, advancing the ethical questions raised by generalization: A formulation of judgment calls that need to be made when generalizing a theory to a new setting. We further demonstrate how the process of generalizing may actually proceed, based on the common ground between Tsang and Williams and our original article.
Building on neo-institutional theory and theories of innovation and diffusion, recent work in the field of management has suggested that management research and practice is characterized by fashions. A management fashion is a relatively transitory belief that a certain management technique leads rational management progress. Using bibliographic research, we apply Abrahamson's management fashion theory to information systems research and practice. Our findings reveal that information systems research and practice, like management research and practice, is indeed characterized by fashions. These "IS fashion waves" are relatively transitory and represent a burst of interest in particular topics by IS researchers and practitioners. However, while our findings show that IS research closely parallels practice, we suggest that a more proactive engagement of IS academics is needed in the IS fashion-setting process.
The article comments on the paper "Whom Are We Informing? Issues and Recommendations for MIS Research from an Informing Sciences Perspective" by G. Gill and A. Bhattacherjee (G&B), which appeared in an earlier issue of "MIS Quarterly." The authors questions G&B's contention that management information systems (MIS) as an academic discipline has a questionable future. In their view the academic status of MIS is sound, albeit susceptible to improvement. They evaluate G&B's arguments and recommendations, and offer their own.
Generalizability is a major concern to those who do, and use, research. Statistical, sampling-based generalizability is well known, but methodologists have long been aware of conceptions of generalizability beyond the statistical. The purpose of this essay is to clarify the concept of generalizability by critically examining its nature, illustrating its use and misuse, and presenting a framework for classifying its different forms. The framework organizes the different forms into four types, which are defined by the distinction between empirical and theoretical kinds of statements. On the one hand, the framework of firms the bounds within which statistical, sampling-based generalizability is legitimate. On the other hand, the framework indicates ways in which researchers in information systems and other fields may properly lay claim to generalizability, and thereby broader relevance, even when their inquiry falls outside the bounds of sampling-based research.
The conventional wisdom amongst information systems (IS) researchers is that information systems is an applied discipline drawing upon other, more fundamental, reference disciplines. These reference disciplines are seen as having foundational value for IS. We believe that it is time to question the conventional wisdom. We agree that many disciplines are relevant for IS researchers, but we suggest a re-think of the idea of "reference disciplines" for IS. In a sense, IS has come of age. Perhaps the time has come for IS to become a reference discipline for others.
This article presents a new approach to the management of evolutionary prototyping projects. The prototyping approach to systems development emphasizes learning and facilitates meaningful communication between systems developers and users. These benefits are important for rapid creation of flexible, usable information resources that are well-tuned to present and future business needs. The main unsolved problem in prototyping is the difficulty in controlling such projects. This problem severely limits the range of practical projects in which prototyping can be used. The new approach suggested in this article uses an explicit risk mitigation model and management process that energizes and enhances the value of prototyping in technology delivery. An action research effort validates this risk analysis approach as one that focuses management attention on consequences and priorities inherent in a prototyping situation. This approach enables appropriate risk resolution strategies to be placed in effect before the prototyping process breaks down. It facilitates consensus building through collaborative decision making and is consistent with a high degree of user involvement.