KENNESAW, Ga. | May 2, 2023

KENNESAW, Ga. (Mar 14, 2023) — There has been an ongoing debate about the disconnect between theory and practice. While purely theoretical research is certainly valuable, we also need to keep in mind that our work should have broader impact to society and go beyond just the journal publications. Theory and practice are not mutually exclusive and, as academics, it is our responsibility to come up with innovative ideas and bridge the gap between the two.

One successful approach to address this issue is design science research (DSR). DSR is a relatively new research paradigm developed more formally in the early 2000s by Hevner et al. (2004) within the information Systems (IS) discipline. It emerged as a response to the calls for more practical research that managers can utilize in their work (November, 2004; Zmud, 1997). Over the years, DSR has evolved and established as a reliable and validated approach to conducting high quality research, especially in the field of information systems (IS).

In the center of DSR is the idea that theory can be leveraged to address societal needs. The initial IS research framework consisted of the following components (see Fig 1. for more details):

EnvironmentPeople
Processes
Organizations
Knowledge baseFoundations
Methodologies
IS ResearchDevelop/build
Justify/Evaluate.
 

The main idea behind DSR is that the researchers would develop some kind of an artifact (theory, framework, construct, model, algorithm, system, instantiation, measure, validation criteria, etc.) in response to the needs of the specific environment. Unlike purely theoretical research, here scholars can work closely with the target group and even solicit their feedback, similar to agile methods in software development. The benefit of such interactions is the increased relevance of the artifact and the study as a whole. Researchers utilize their theoretical knowledge to build a solid foundation and to demonstrate the rigor of their work. The growing number of failed projects (Denicol, 2020) suggests that often practitioners alone cannot develop successful business practices. Trial and error could be very costly for organizations (Sosna, 2010), which presents an opportunity for researchers to intervene by following a validated DSR methodology.

In order for DSR artifacts to be useful and relevant to the stakeholders, they undergo rigorous design and evaluation, often including multiple rounds of feedback and improvements. While this process could be laborious and time consuming, it can potentially lead to long-term cost savings by doing things right the first time (Wieringa, 2009). Over the years, as DSR has matured, scholars proposed new strategies for improving the evaluation process to include aspects such as effectiveness, efficacy, efficiency, ethics, and elegance (Checkland, 1999; Venable, 2016;) to measure the impact of the proposed artifacts more accurately. Scholars have also proposed new approaches to more clearly articulate DSR contributions in the context of IS research (Baskerville et al., 2016) and to improve its impact on innovation (Gregor & Hevner, 2014). Overall, as DSR is still an evolving field, we can expect new developments in the types of artifacts being created and evaluations being performed.   

Over the past 20 years DSR has established itself as a viable and innovative research paradigm due to its ability to successfully utilize academic theories to solve relevant practical issues. Some examples of DSR artifacts include a taxonomy for risk assessment of critical infrastructures (Plachkinova & Vo, 2023), a data-driven dashboard for criminal sentencing decisions (Vo & Plachkinova, 2023), and a framework for implementing least privilege across people, process, and technology within organizations (Plachkinova & Knapp, 2022). While all of these studies required a significant amount of time and resources, it is rewarding to see how academic work can benefit society in different areas. As researchers, our goal is not just to publish papers, but to make an impact, and DSR is one way that we can put our knowledge to practice.

If you are interested in learning more about DSR and its potential applications to your specific discipline, please contact Dr. Mia Plachkinova at mplachki@kennesaw.edu.

 

References:

Baskerville, R., Baiyere, A., Gregor, S., Hevner, A., & Rossi, M. (2018). Design science research contributions: Finding a balance between artifact and theory. Journal of the Association for Information Systems, 19(5), 3.

Checkland, P. (1999). Systems thinking. Rethinking management information systems, 45-56.

Denicol, J., Davies, A., & Krystallis, I. (2020). What are the causes and cures of poor megaproject performance? A systematic literature review and research agenda. Project Management Journal, 51(3), 328-345.

Gregor, S., & Hevner, A. R. (2014). The Knowledge Innovation Matrix (KIM): A clarifying lens for innovation. Informing Science: the International Journal of an Emerging Transdiscipline, 17, 217-239.

Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design Science in Information Systems Research 1. Design Science in IS Research MIS Quarterly, 28 (1), 75–105.

November, P. (2004). Seven reasons why marketing practitioners should ignore marketing academic research. Australasian Marketing Journal, 12(2), 39-50.

Plachkinova, M. (2023). A Taxonomy for Risk Assessment of Cyberattacks on Critical Infrastructure (TRACI). Communications of the Association for Information Systems, 52(1), 1.

Plachkinova, M., & Knapp, K. (2022). Least Privilege across People, Process, and Technology: Endpoint Security Framework. Journal of Computer Information Systems, 1-13.

Sosna, M., Trevinyo-Rodríguez, R. N., & Velamuri, S. R. (2010). Business model innovation through trial-and-error learning: The Naturhouse case. Long range planning, 43(2-3), 383-407.

Venable, J., Pries-Heje, J., & Baskerville, R. (2016). FEDS: a framework for evaluation in design science research. European Journal of Information Systems, 25(1), 77-89.

Vo, A., & Plachkinova, M. (2023). Showcase: A Data-Driven Dashboard for Federal Criminal Sentencing. Forthcoming in Journal of the Association for Information Systems.

Wieringa, R. (2009, May). Design science as nested problem solving. In Proceedings of the 4th international conference on design science research in information systems and technology (pp. 1-12).

Zmud, B. (1997). Editor's Comments. Management Information Systems Quarterly, 21(3), 1.