Design science

Creating knowledge via the design of innovative artefacts

Andy Weeger

Neu-Ulm University of Applied Sciences

August 30, 2024

Opening remarks

Overview

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 (A. Hevner and Chatterjee 2010).

A focus on artefacts: design, application & evaluation

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 and Chatterjee 2010, 22:11):

  • 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).

Examples

Many technological advances are the result of innovative, creative design science process.

E.g., modelling languages, intelligent agents, the internet, and process mining

The artefact as knowledge

Objective of Design Science Research (A. Hevner and Chatterjee 2010)

 

 

Process

Overview

A three cycle view of design science research (A. R. Hevner 2007)

 

 

 

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:

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

Phases

Phases of design science research (Peffers et al. 2007)

 

 

 

 

 

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, Majchrzak, and Gasser (2002)
Table 2: Examples for DSR in IS (Recker 2021)

Q&A

References

Hevner, Alan R. 2007. “A Three Cycle View of Design Science Research.” Scandinavian Journal of Information Systems 19 (2): 4.
Hevner, Alan, and Samir Chatterjee. 2010. Design Research in Information Systems: Theory and Practice. Vol. 22. Springer Science & Business Media.
Ketter, Wolfgang, Markus Peters, John Collins, and Alok Gupta. 2016. “A Multiagent Competitive Gaming Platform to Address Societal Challenges.” Mis Quarterly 40 (2): 447–60.
Markus, M Lynne, Ann Majchrzak, and Les Gasser. 2002. “A Design Theory for Systems That Support Emergent Knowledge Processes.” MIS Quarterly, 179–212.
Peffers, Ken, Tuure Tuunanen, Marcus A Rothenberger, and Samir Chatterjee. 2007. “A Design Science Research Methodology for Information Systems Research.” Journal of Management Information Systems 24 (3): 45–77.
Recker, Jan. 2021. “The Research Process.” Scientific Research in Information Systems: A Beginner’s Guide.
Seidel, Stefan, Leona Chandra Kruse, Nadine Székely, Michael Gau, and Daniel Stieger. 2018. “Design Principles for Sensemaking Support Systems in Environmental Sustainability Transformations.” European Journal of Information Systems 27 (2): 221–47.