Opening remarks

Overview

Design science research is “a research paradigm in which a designer answers questions relevant to human problems via the creation of innovative artefacts, thereby contributing new knowledge to the body of scientific evidence. The designed artefacts are both useful and fundamental in understanding that problem” (A. Hevner & Chatterjee, 2010, p. 5).

Knowledge and understanding about a problem and its solution are gained by the creation and application of an artefacts
Predominant research methodology used in engineering and computer science, partially also in IS research as well as other disciplines

Nature of the problem

Design science research (DSR) in IS addresses what are considered to be wicked problems, that are problems that are difficult to define and solve due to their interconnected and evolving nature (A. Hevner & Chatterjee, 2010, p. 11):

The problem itself is not well-defined and can change over time, also because of ill-defined environmental contexts.
The problem is complex and there are many interrelated subcomponents/factors that make it difficult to isolate.
Solutions are neither right nor wrong, but better or worse. Each solution can lead to new problems, which is why we need to rely on our human cognitive abilities (e.g. creativity).
The involvement of multiple stakeholders with disparate perspectives and interests renders consensus a challenging objective. The production of effective solutions necessitates the deployment of collective human social abilities (e.g., teamnwork).

DSR requires inherent flexibility to change design processes as well as design artefacts (i.e., malleable processes and artefacts).

The artefact as knowledge

Figure 1: Objective of Design Science Research (A. Hevner & Chatterjee, 2010)

In design science, the research interest is on creating or changing human-created, artificial objects (i.e., artefacts) with the aim of improving on existing solutions to problems or perhaps providing a first solution to a problem (Recker, 2021).

Different types of artefacts exist (A. Hevner & Chatterjee, 2010, p. 6):

Constructs (vocabulary and symbols)
Models (abstractions and representations)
Methods (algorithms and practices)
Instantiations (implemented and prototype systems)
Design theories (improved models of design or design processes)

Process

Overview

Figure 2: A three cycle view of design science research (A. R. Hevner, 2007)

The context in which specific phenomena of interest occur is called the environment. In the realm of information systems research, this environment encompasses elements like people, organizational structures, and existing digital technologies. For any given project, the environment needs to justify why a particular artefact, which is the central focus of design science research, holds significance for stakeholders in that specific field.

The knowledge base serves as the foundation for conducting design science research. It’s made up of previous research, findings from related disciplines, and a range of tools, models, methods, and theories that are available for use during the design phase. The knowledge base plays a crucial role in ensuring that design science maintains rigor.

Connecting the research project’s context and the design science activities is the relevance cycle; the rigor cycle links design science activities with the foundational scientific principles, experience, and expertise found in the knowledge base. The core design cycle continually loops between constructing and assessing the design artefact, closely interacting with research processes.

A. R. Hevner (2007) emphasizes that these three cycles—the relevance cycle, the rigor cycle, and the central design cycle—need to be clearly recognizable in any design science research project.

Evaluation criteria

A primary criterion for evaluating design science is the demonstrated utility of the design artefact, which also presents a significant challenge.

Utility refers to an improvement that goes beyond the current level of usefulness. This definition also implies three essential criteria that must be fulfilled:

The demonstrated utility of the artefact should be innovative and original.
The artefact’s should bring about a positive difference when compared to existing solutions.
The artefact’s superiority in utility should be convincingly proven through a thorough evaluation.

The interpretation of utility can vary. It might be expressed as a performance measure, indicating the degree of enhancement a new artefact offers over an existing solution. Alternatively, it could be gauged through end users’ perspectives, considering factors like efficacy, efficiency, effectiveness, or other relevant criteria. In some cases, utility could even be understood in terms of humanistic aspects like aesthetics or the sense of pleasure it provides.

Phases

Figure 3: Design science research process model (Peffers et al., 2007)

The DSRP model proposed by Peffers et al. (2007) provides a structured approach for conducting design science research, emphasizing the creation of practical solutions to real-world problems while ensuring rigor through evaluation and communication of results.

Problem identification: This phase involves identifying a relevant and significant problem in the context of information systems. Researchers explore the problem’s practical implications and motivations, ensuring that it aligns with the needs of practitioners or organizations.
Definition of objectives: Researchers define clear objectives for designing a solution to the identified problem. These objectives outline what the designed artefact aims to achieve and the benefits it should provide.
Design and development: In this phase, researchers design and develop the innovative artefact as a solution to the problem. This involves creating a detailed design, building the artefact, and ensuring it aligns with the defined objectives.
Demonstration: The developed artefact is demonstrated to stakeholders, practitioners, or experts to showcase its functionality and potential benefits. Feedback is gathered to assess the artefact’s initial usability and feasibility.
Evaluation: The artefact’s effectiveness, efficiency, and utility are rigorously evaluated using appropriate evaluation methods. This phase aims to determine the artefact’s impact on solving the identified problem and whether it meets the defined objectives.
Communication: The final phase involves communicating the results of the design science research. Researchers document their findings, insights, and lessons learned. This documentation contributes to the body of knowledge in the field and helps practitioners understand and apply the developed artefact.

Guidlelines

Guideline	Description
Design as an artefact	DSR must produce a viable artefact in the form of a construct, a model, a method, or an instantiation
Problem relevance	The objective of DSR is to develop technology-based solutions to important and relevant business problems
Design evaluation	The utility, quality, and efficacy of a design artefact must be rigorously demonstrated via well-executed evaluation methods
Research contributions	Effective DSR must provide clear and verifiable contributions in the areas of the design artefact, design foundations, and/or design methodologies
Research rigor	Application of rigorous methods the construction and evaluation of the artefact
Design as a search process	The search for an effective artefact requires utilising available means to reach desired ends while satisfying laws in the problem environment

Table 1: Guidelines for DSR (Peffers et al., 2007)

Examples

Level of contribution	Suitable artefact	Example
Situated implementation	Instantiations (software products or implemented processes)	Ketter et al. (2016)
Design principles as emerging knowledge	Constructs, methods, models, design principles, technological rules	Seidel et al. (2018)
Well-developed design theory	Design theories about embedded phenomena (mid-range and grand theories)	Markus et al. (2002)

Table 2: Examples for DSR in IS (Recker, 2021)

Great example of a rigorously executed DSR: Yang et al. (2023) (check it out!)

References

Hevner, A. R. (2007). A three cycle view of design science research. Scandinavian Journal of Information Systems, 19(2), 4.

Hevner, A., & Chatterjee, S. (2010). Design research in information systems: Theory and practice (Vol. 22). Springer Science & Business Media.

Ketter, W., Peters, M., Collins, J., & Gupta, A. (2016). A multiagent competitive gaming platform to address societal challenges. Mis Quarterly, 40(2), 447–460.

Markus, M. L., Majchrzak, A., & Gasser, L. (2002). A design theory for systems that support emergent knowledge processes. MIS Quarterly, 179–212.

Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), 45–77.

Recker, J. (2021). The research process. Scientific Research in Information Systems: A Beginner’s Guide.

Seidel, S., Chandra Kruse, L., Székely, N., Gau, M., & Stieger, D. (2018). Design principles for sensemaking support systems in environmental sustainability transformations. European Journal of Information Systems, 27(2), 221–247.

Yang, Y., Qin, Y., Fan, Y., & Zhang, Z. (2023). Unlocking the power of voice for financial risk prediction: A theory-driven deep learning design approach. Mis Quarterly, 47(1).