Design Science Research according to Peffers et al

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Introduction

"Two paradigms characterize much of the research in the Information Systems discipline: behavioral science and design science. The behavioral science paradigm seeks to develop and verify theories that explain or predict human or organizational behavior. The design-science paradigm seeks to extend the boundaries of human and organizational capabilities by creating new and innovative artifacts. Both paradigms are foundational to the IS discipline, positioned as it is at the confluence of people, organizations, and technology." (Hevner et. al.)[1]

In many publications, essays, editorials or books describing the Design Science Research (DSR) method the authors aim to support researchers to conduct DSR projects for example by providing guidelines[1][2] or how to extend on existing design knowledge[3] and many more. Gregor and Hevner provide foundational guidance by describing their understanding and application of DSR concepts and providing guidance for researches on how to:

  1. Appreciate the levels of artifact abstractions that may be DSR contributions
  2. Identify appropriate ways of consuming and producing knowledge when you are preparing journal articles or other scholarly works
  3. Understand and position the knowledge contributions of your research projects
  4. Structure a DSR article so that it emphasizes significant contributions to the knowledge base

Their focal contribution is the DSR knowledge contribution framework with two dimensions based on the existing state of knowledge in both the problem and solution domains for the research opportunity under study. In addition, they propose a DSR communication schema with similarities to more conventional publication patterns, but which substitutes the description of the DSR artifact in place of a traditional results section. [4]

Other researchers propose a set of core dimensions of a Design Science Research project that facilitates effective capture of the most relevant aspects of a DSR project to efficiently plan and communicate key considerations and conceptualizations of a DSR project. In this work, the authors represent six dimensions in the form of a DSR grid, a one-page visualization of the DSR project that is adjustable to the specific purpose for using the concept.[5]

Another important role in DSR is how to specify design theory so that it can be communicated, justified, and developed cumulatively. In the essay of Gregor and Jones they focus on the structural components or anatomy of design theories in IS[6]. Baskerville and Pries-Heje focus on how design theories are explanatory. In their work, they demonstrate that design theories deliver functional explanations with a simple and elegant structure explaining generalized solution components by the related generalized requirements[7].

In the book, Design Science Research Cases[8] several DSR cases are presented by experienced researchers in the field. It offers readers access to real-world DSR studies, together with the authors’ reflections on their research processes. The description of the cases supports researchers in addition to existing introductions to DSR methods and processes.

Process description

DSR Process
The author's designed a Design Science Research process model that would meet three objectives:
  1. It would be consistent with prior literature
  2. It would provide a nominal process model for doing DS research
  3. It would provide a mental model for presenting and appreciating DS research in IS

The process includes six steps:

  1. Problem identification and motivation
  2. Objectives for a solution
  3. Design and development
  4. Demonstrate
  5. Evaluation
  6. Communication

Problem identification and motivation

Description

Define the specific research problem and justify the value of a solution. Since the problem definition will be used to develop an effective artifactual solution, it may be useful to atomize the problem conceptually so that the solution can capture the problem’s complexity. Justifying the value of a solution accomplishes two things: it motivates the researcher and the audience of the research to pursue the solution and to accept the results and it helps to understand the reasoning associated with the researcher’s understanding of the problem.


Resources required: Knowledge of the state of the problem and the importance of its solution.[9]

Examples

Literature Review (Literature Review according to vom Brocke et al. or Literature Review according to Webster and Watson)

Further Readings

Peffers, K., Tuunanen, T., Rothenberger, M. A., and Chatterjee, S. 2007. “A Design Science Research Methodology for Information Systems Research,” Journal of Management Information Systems (24:3), Taylor & Francis Ltd, pp. 45–77. (https://doi.org/10.2753/MIS0742-1222240302).

Objectives of a solution

Description

Infer the objectives of a solution from the problem definition. The objectives can be quantitative, e.g., terms in which a desirable solution would be better than current ones, or qualitative, e.g., where a new artifact is expected to support solutions to problems not hitherto addressed. The objectives should be inferred rationally from the problem specification.


Resources required: Knowledge of the state of problems and current solutions and their efficacy if any.[9]

Further Readings

Peffers, K., Tuunanen, T., Rothenberger, M. A., and Chatterjee, S. 2007. “A Design Science Research Methodology for Information Systems Research,” Journal of Management Information Systems (24:3), Taylor & Francis Ltd, pp. 45–77. (https://doi.org/10.2753/MIS0742-1222240302).

Design and development

Description

Create the artifactual solution. These artifacts are theoretical structures, templates, processes, or instantiations with each narrowly defined. This task involves deciding the desired functionality of the artifact and its design of the artifact.
Tools needed to shift from goals to design and production include theoretical expertise that can be brought to bear as a solution.[9]

Further Readings

Peffers, K., Tuunanen, T., Rothenberger, M. A., and Chatterjee, S. 2007. “A Design Science Research Methodology for Information Systems Research,” Journal of Management Information Systems (24:3), Taylor & Francis Ltd, pp. 45–77. (https://doi.org/10.2753/MIS0742-1222240302). Gregor, S., Chandra Kruse, L., Seidel, S., 2020. The Anatomy of a Design Principle. Journal of the Association for Information Systems Forthcoming.

Demonstration

Description

Demonstrate the artifact's effectiveness in solving the problem. It may include its use in experimentation, simulation, case study, proof, or other related activity.

Resources required: Good understanding of how the tool can be used to solve the problem.

Further Readings

Peffers, K., Tuunanen, T., Rothenberger, M. A., and Chatterjee, S. 2007. “A Design Science Research Methodology for Information Systems Research,” Journal of Management Information Systems (24:3), Taylor & Francis Ltd, pp. 45–77. (https://doi.org/10.2753/MIS0742-1222240302).

Evaluation

Description

Observe and measure how well the artifact supports a solution to the problem. This activity involves comparing the objectives of a solution to actual observed results from the use of the artifact in the demonstration. It requires knowledge of relevant metrics and analysis techniques.

Depending on the nature of the problem venue and the artifact, evaluation could include such items as a comparison of the artifact's functionality with the solution objectives from activity 2 above, objective quantitative performance measures, such as budgets or items produced satisfaction surveys, client feedback, or simulations. At the end of this activity, the researchers can decide whether to iterate back to step 3 to try to improve the effectiveness of the artifact or to continue on to communication and leave further improvement to subsequent projects. The nature of the research venue may dictate whether such iteration is feasible or not.[9]

Further Readings

Peffers, K., Tuunanen, T., Rothenberger, M. A., and Chatterjee, S. 2007. “A Design Science Research Methodology for Information Systems Research,” Journal of Management Information Systems (24:3), Taylor & Francis Ltd, pp. 45–77. (https://doi.org/10.2753/MIS0742-1222240302).

Venable, J., Pries-Heje, J., and Baskerville, R. 2016. “FEDS: A Framework for Evaluation in Design Science Research,” European Journal of Information Systems (25:1), Taylor & Francis, pp. 77–89. (https://doi.org/10.1057/ejis.2014.36).
Hevner AR, March ST, Park J and Ram S (2004) Design Science in Information Systems Research. MIS Quarterly 28(1), 75-105.

Communication

Description

Communicate the problem and its importance, the artifact, its utility and novelty, the rigor of its design, and its effectiveness to researchers and other relevant audiences, such as practicing professionals, when appropriate. In scholarly research publications, researchers might use the structure of this process to structure the paper, just as the nominal structure of an empirical research process (problem definition, literature review, hypothesis development, data collection, analysis, results, discussion, and conclusion) is a common structure for empirical research papers. Communication requires knowledge of the disciplinary culture.[9]

Examples

Further Readings

Peffers, K., Tuunanen, T., Rothenberger, M. A., and Chatterjee, S. 2007. “A Design Science Research Methodology for Information Systems Research,” Journal of Management Information Systems (24:3), Taylor & Francis Ltd, pp. 45–77. (https://doi.org/10.2753/MIS0742-1222240302).

References

  1. ↑ 1.0 1.1 Hevner AR, March ST, Park J and Ram S (2004) Design Science in Information Systems Research. MIS Quarterly 28(1), 75-105.
  2. ↑ van der Merwe A., Gerber A., Smuts H. (2020) Guidelines for Conducting Design Science Research in Information Systems. In: Tait B., Kroeze J., Gruner S. (eds) ICT Education. SACLA 2019. Communications in Computer and Information Science, vol 1136. Springer, Cham. https://doi.org/10.1007/978-3-030-35629-3_11
  3. ↑ vom Brocke, J., Winter, R., Hevner, A., Maedche, A. (2020), Accumulation and Evolution of Design Knowledge in Design Science Research – A Journey Through Time and Space, in: Journals of the Association for Information Systems (JAIS), 2020, forthcoming (ABDC_2016: A*; ABS: 4; ISI: 3.487; ISI: 2.109; VHB_3: A).
  4. ↑ Gregor S and Hevner AR (2013) Positioning and Presenting Design Science Research for Maximum Impact. MIS Quarterly 37(2), 337-55.
  5. ↑ vom Brocke, J., Maedche, A. (2019), The DSR Grid: Six Core Dimensions for Effectively Planning and Communicating Design Science Research Projects, in: Electronic Markets, Volume 29, Issue 3, pp 379–385 (ABDC: A; ABS: 2; ISI: 2.121; VHB: B).
  6. ↑ Gregor S and Jones D (2007) The Anatomy of a Design Theory. Journal Of The Association For Information Systems 8(5), 312-335.
  7. ↑ Baskerville, R.; Pries-Heje, J.: Explanatory Design Theory, in: Business & Information Systems Engineering, 2, 5, 2010, pp. 271-282.
  8. ↑ Brocke, J. vom, Hevner, A., and Mädche, A. (eds.). 2020. Design Science Research. Cases, Progress in IS, Springer International Publishing. https://doi.org/10.1007/978-3-030-46781-4.
  9. ↑ 9.0 9.1 9.2 9.3 9.4 Peffers K, Tuunanen T, Rothenberger M and Chatterjee S (2007) A Design Science Research Methodology for Information Systems Research. Journal Of Management Information Systems 24(3), 45-77.
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