Table of contents
List of Figures
2.1 Overview on design science
2.2 Comparison with action research and evidence-based management
2.3 Methodological/philosophical categorization
3 Executed research and applied design science
4 Further application and future contributions
List of Figures
Figure 1: Comparison of paradigmatic assumptions of action research and design science
Figure 2: Examples of research methods in design science and its categorization
Figure 3: Research process of VMI in construction
Figure 4: Four steps of "revolutionizing" the NHS
Figure 5: CIMO-logic
Figure 6: Framework for Creating Synergy and Collaboration Between Design and Science
Figure 7: Application of design research criteria to action research
Figure 8: Application of action research criteria to design research
Figure 9: Similarities of action research and design science
Figure 10: Combination of action research and design science
Figure 11: Information Systems Research Framework
Management research is constantly criticized in the academic community to have very little impact on managers in practical life. Often leading managers don’t even take notice of journals with cutting-edge management research (Davies, 2007). This problem is highly discussed in academic circles and often referred to as the utilization problem (van Aken, 2004) or the rigor-relevance dilemma/gap (Fincham & Clark, 2009; Avenier, 2010). The problem is that the conducted management research is either scientifically verified, but not relevant for practice or practically relevant but not scientifically verified (van Aken, 2004).
There are various explanations for this problem. Many researchers claim that a lack of sufficient communication presentation of management research is the root of the problem (Davies, 2007). This is in accordance with the so-called “knowledge transfer problem”(Shapiro, Kirkman, & Courtey, 2007). Others blame the little relevance of management research for practitioners (Denyer, Tranfield, & van Aken, 2007), which is reflected in the “knowledge production problem” (Shapiro, Kirkman, & Courtey, 2007). Furthermore, management research is claimed as “too descriptive” (van Aken, 2004), which means that management science is only describing and analyzing but not actually providing solutions to problems. Lastly, some researchers describe management research as too fragmented in terms of research groups and knowledge products (Denyer, Tranfield, & van Aken, 2007). The researchers claim that too little cooperation between researchers restricts knowledge solutions and weakens the position of management research.
In order to increase relevance of management research and to create a clear academic identity (Tranfield & van Aken, 2006) academic community calls for new research approaches, particularly the so-called design science approach (van Aken, 2004; Pandza & Thorpe, 2010; Bate, 2007; Romme & Damen, 2007; Burgoyne & James, 2006). Design science helps to actually design solutions to field problems but still keeping academic relevance in order to fulfill Pettigrew’s idea of accomplishing both, academic and practical relevance (Pettigrew, 1997).
This paper aims to give the reader an overview and a “sense” about design science and its (possible) practical relevance in management research. First of all, the author will present the ideas of design science and its main determinants of major research contributors. Afterwards, a comparison between design science and the often with design science presented action research and evidence-based management approach is made. Subsequently design science is classified based on its research philosophy/methodology. Chapter 3 will then present two examples of executed research and applied design science. At the end, the author will shortly discuss possible future directions of design science and give some concluding remarks.
In the following, the author will present the idea and determinants of design science in management research. After giving an overview about design science in general, a comparison to action research and evidence-based management is made. Paragraph 2.3 will then conduct a categorization of the methodology/philosophy of design science.
2.1 Overview on design science
First of all, it makes sense to define the terms design science and design research. Van Aken, a strong advocator of design science in management research, defines design science as a “(..) body of knowledge of a particular discipline on designs and design methods” (van Aken, 2007, p. 2). McKay and Marshall define it even more specific as “(..) knowledge of the material culture or artificial world” (McKay & Marshall, 2007, p. 6). Design science is characterized by its focus on solutions to actual field problems and not so-called knowledge problems. The difference here is that knowledge problems only arise from a lack of knowledge of reality while field problems are concerned with the understanding of how to “improve” reality (van Aken, 2007). Design research in turn is the concrete action of solving a field problem within a discipline.
Most academic work about design science is built on Herbert Simon’s (1969, 1996) The Sciences of the Artificial. In this book, Simon distinguishes between three kinds of science: Firstly, formal sciences (logic and mathematics) which are empirically void, that means they do not contain empirical evidence. Secondly, explanatory sciences (physics, chemistry, etc.) which describe and explain natural phenomena. Thirdly, design sciences (engineering, architecture, etc.) which are supposed to create (new and artificial) solutions to existing problems (Davies, 2007).
In the past, management research has mainly been based on explanatory sciences. However, explanatory sciences can never give concrete solutions to problems but only explanations and descriptions of problems. Yet, the recent call for evidence-based management (Pfeffer & Sutton, 2006) is rather based on explanatory sciences than creating solutions. Therefore evidence-based management does not necessarily help to move from a hindsight (explanatory) to a more foresighted research perspective. Authors like Romme (2003) also criticize the exclusive position of explanatory sciences in management research and propose an “interface” or synthesis between explanatory and prescriptive sciences. Avenier (2010) is supporting this standpoint by calling for a new design research paradigm in organization research to get away from the “classical” research model of science.
Design science could be a solution to overcome this hindsight, explanatory perspective in order to be more foresighted. As advocators like van Aken, Denyer, Tranfield or Romme point out, the essential part of design science is the creation of a so-called “technological rule” (van Aken, 2004), “design proposition” (Denyer, Tranfield, & van Aken, 2007) or “means-end proposition” (Tanskanen, Holmström, Elfving, & Talvitie, 2008). All those terms basically mean the same: To define an “instruction to perform a finite number of acts in a given order and with a given aim” (Bunge, as cited in van Aken, 2004, p. 228). Some authors, like van Aken, define this technological rule broader in terms of making the instructions not as specific and rather define it as “a chunk of general knowledge” (van Aken, 2004). However, the principle behind is the same. In simple terms one could say that such a technological rule can be described as “if you want to achieve Y in situation Z, then something like X will help” (van Aken, 2004, p. 227). Therefore, the rule consists of three determinants: A clear goal, an analysis of the context and, most importantly, a solution concept to solve the problem (Davies, 2007).
The process of finding the perfect technological rule is a two step approach: Firstly, the definition of a technological rule. Secondly, to test the rule in its intended context and to do necessary optimization adjustments until the desired state of saturation is reached (van Aken, 2004). The testing part is rather straightforward by using alpha tests (done by the researcher) and beta tests (done by third parties) (Romme, 2003).
An interesting question, however, is the creation and definition of technological rules. Van Aken recommends to use the multiple case study approach where the same kind of problems are solved by using the problem solving cycle. Hereby we can distinguish between the extracting (best-practice research) and the developing (collaboration of researchers and practitioners) multiple case study (van Aken, 2004). Another approach would be the so-called “CIMO-logic” (Denyer, Tranfield, & van Aken, 2007). The CIMO-logic is basically taking an explanatory science perspective by using available academic research to design technological rules. The CIMO-logic can be described as “In this context (C), use Intervention (I) to create Mechanism (M) in order to reach the Outcome (O)” (Denyer, Tranfield, & van Aken, 2007) (Figure 5 in the Appendix for a more detail description of CIMO).
A rather practical perspective is taken by Plsek, Bibby and Whitby (2007). The authors have tested methods for extracting design rules in an organizational change project. One approach was to review written documentation of change programs to come up with design rules. This approach is rather similar to the systematic review process (Tranfield, Denyer, & Smart, 2003). Furthermore, the authors conducted interviews with change leaders and used storytelling methods to find out about the change managers’ concrete actions. Another approach was to “play” scenarios with change leaders about how they would approach certain situations and thus creating rules out of it (Plsek, Bibby, & Whitby, 2007).
As one can see here, it is highly difficult to merely rely on design science methods to create technological rules. In most of the rule creation approaches explanatory science elements are influencing technological rules. Hence, Romme (2003) is recommending a synergy between explanatory science and design science. His framework suggests a “role-distribution” between the design mode and the science mode (Figure 6 in the Appendix for more detail). As explanatory science might give input in empirical findings and problem identification, design science is able to cumulate knowledge and formulate effective intervention measures. The result would be a higher interconnectedness between science and practice by creating so-called “actionable knowledge” (Bate, 2007). Romme also recommends including humanities into the framework in order to respect ethical and aesthetic aspects in design science (Romme, 2003).
Pandza and Thorpe (2010) go even one step further and sharply criticize the narrow view of the current design science discussion. They claim that the strict view on prescriptive research might only affect very few organizational issues. The authors state that some organizational phenoma are just explainable and not prescriptively solvable. In fact, Pandza and Thorpe argue that “we will (..) always be more effective in explaining social action than rigorously guiding it” (Pandza & Thorpe, 2010, p. 183). Pandza and Thorpe rather recommend a design process in which designers use evolutionary patterns and resources to create solutions. Thereby they suggest three different types of design in management. The deterministic design (application of cumulative knowledge), the path-dependent design (using evolutionary patterns) and the path-creation design (constructing artefacts) (Pandza & Thorpe, 2010).
Andriessen in turn criticizes the lack of structure and little practical evidence of success of design science (Andriessen, 2006). He recommends recognizing design research rather as a complementary mode to action research and presents a framework of how to combine those two research methodologies. This framework and other research methodologies will be discussed in more detail in the next paragraph.
2.2 Comparison with action research and evidence-based management
In this paragraph, the author will compare design science with two famous research approaches which are often mentioned in combination with design science: action research and evidence-based management.
- Quote paper
- Markus Karmann (Author), 2013, Design science in management research, Munich, GRIN Verlag, https://www.grin.com/document/213085