RMF4DSR:A Risk Management Framework for Design Science Research

Design science research (DSR) is a complex form of research that combines very heterogeneous activities requiring different skills. Information systems and technologies are complex entities that have more elaborate areas of risk to manage. Research to invent and evaluate new IT artefacts introduces new areas of risk As yet, there is little experience with managing risk in DSR or even identification of types of risks to be managed. This paper analyses DSR research activities and elaborates known principles and practices of risk management and applies them to DSR to develop a new framework (RMF4DSR) for identifying, assessing, prioritizing, and treating potential risks inherent to DSR. Potential users of the framework include experienced and novice DSR researchers. The framework classifies six potential risk areas and enumerates specific key risks within each area. It includes risk assessment and treatment models. The paper applies the framework to a recent DSR case study to provide initial evidence of its value and feasibility and further evaluates the RMF4DSR framework by asking researchers to apply it in a workshop and surveying the participants’ opinions about the utility of the framework

Dealing with emergent design science research projects in IS

Multiple models, methods and frameworks have been proposed to guide Design Science Research (DSR) application to address relevant classes of problems in Information Systems (IS) discipline. While much of the ambiguity around the research paradigm has been removed, only the surface has been scratched on DSR efforts where researcher takes an active role in organizational and industrial engagement to solve a specific problem and generalize the solution to a class of problems. Such DSR projects can have a significant impact on practice, link theories to real contexts and extend the scope of DSR. Considering these multiform settings, the implications to theorizing nor the crucial role of researcher in the interplay of DSR and IS projects have not been properly addressed. The emergent nature of such projects needs to be further investigated to reach such contributions for both theory and practice. This paper raises multiple theoretical, organizational and managerial considerations for a meta-level monitoring model for emergent DSR projects

Designing TRiDS: Treatments for Risks in Design Science

Design Science Research (DSR) has many risks. Researchers inexperienced in DSR, especially early career researchers (ECRs) and research students (e.g. PhD students) risk inefficient projects (with delays, rework, etc.) at best and research project failure at worst if they do not manage and treat DSR risks in a proactive manner. The DSR literature, such as the Risk Management Framework for Design Science Research (RMF4DSR), provides advice for identifying risks, but provides few suggestions for specific treatments for the kinds of risks that potentially plague DSR. This paper describes the development of a new purposeful artefact (TRiDS: Treatments for Risks in Design Science) to address this lack of suggestions for treatment of DSR risks. The paper describes how the purposeful artefact was developed (following a DSR methodology), what literature it draws upon to inspire its various components, the functional requirements identified for TRiDS, and how TRiDS is structured and why. The paper also documents the TRiDS purposeful artefact in detail, including four main components: (1) an extended set of risk checklists (extended from RMF4DSR), (2) a set of 47 specific suggestions for treating known risks in DSR, (3) a classification of the treatments identified into 14 different categories, and (4) a look-up table for identifying candidate treatments based on a risk in the extended risk checklists. The treatment suggestions and guidance in TRiDS serve as a supplement to RMF4DSR by helping DSR researchers to identify treatments appropriate for a particular DSR project (or program) and thereby to improve DSR project efficiency and the probability of DSR project success

A MULTI-PERSPECTIVE FRAMEWORK FOR THE INVESTIGATION OF TOOL SUPPORT FOR DESIGN SCIENCE RESEARCH

Tool support for design science research (DSR) is increasingly recognized by the DSR community as an important but neglected area of research. Extrapolating from the achievements of tool support for more established research approaches, tool support for DSR promises to lower the barriers for the delivery of more rigorous, comparable, and, thus, relevant DSR. However, to this date, little research has looked at the challenge of providing tool support for DSR systematically. As a first step to close this gap, prior research in the form of a workshop by the DESRIST community focused on identifying an initial understanding as well as set of requirements for tool support in DSR. This paper extends and complements this prior research with a qualitative analysis of in-depth interviews (n=12) about the topic of tool support in DSR with a broad variety of design science researchers. The major contribu- tion of this work is that it goes beyond the mere collection of requirements and uses qualitative data analysis to dive deeper into the understanding of tool support in DSR as well as associated opportuni- ties and challenges. Based on this analysis, we developed a multi-perspective framework for the inves- tigation of tool support for DSR, which we position as a promising foundation for future research on the emerging topic of tool support for DSR

A Proficiency Model for Design Science Research Education

Design science research (DSR) produces knowledge via the design and evaluation of innovative solutions to real-world problems. DSR provides an improved understanding of how and why the solutions work. While DSR is being widely embraced in many research disciplines, its educational pedagogy so far remains immature with little guidance on how best to inform and train various audiences on relevant and rigorous DSR skillsets. Grounded on the authors’ wide experience in designing and delivering DSR courses over the past decades, we develop a “DSR Proficiency Model” to highlight key skills required to succeed in planning, applying, and communicating DSR. We recognize the different educational environments and student backgrounds that DSR courses must accommodate and provide actionable guidance for mapping the proficiency model to academic, training, and executive audiences. Informative examples demonstrate how we have structured DSR curricula for different academic and executive education programs