Design science (methodology)

Last updated

Design science research (DSR) is a research paradigm focusing on the development and validation of prescriptive knowledge in information science. Herbert Simon distinguished the natural sciences, concerned with explaining how things are, from design sciences which are concerned with how things ought to be, [1] that is, with devising artifacts to attain goals. [2] [ further explanation needed ] Design science research methodology (DSRM) refers to the research methodologies associated with this paradigm. It spans the methodologies of several research disciplines, for example information technology, which offers specific guidelines for evaluation and iteration within research projects.

Contents

DSR focuses on the development and performance of (designed) artifacts with the explicit intention of improving the functional performance of the artifact. DSRM is typically applied to categories of artifacts including algorithms, human/computer interfaces, design methodologies (including process models) and languages. Its application is most notable in the Engineering and Computer Science disciplines, though is not restricted to these and can be found in many disciplines and fields. [3] [4] DSR, or constructive research, [5] in contrast to explanatory science research, has academic research objectives generally of a more pragmatic nature. Research in these disciplines can be seen as a quest for understanding and improving human performance. [6] Such renowned research institutions as the MIT Media Lab, Stanford University's Center for Design Research, Carnegie Mellon University's Software Engineering Institute, Xerox’s PARC, and Brunel University London’s Organisation and System Design Centre, use the DSR approach. [3]

Design science is a valid research methodology to develop solutions for practical engineering problems. [7] Design science is particularly suitable for wicked problems. [8]

Objectives

The main goal of DSR is to develop knowledge that professionals of the discipline in question can use to design solutions for their field problems. Design sciences focus on the process of making choices on what is possible and useful for the creation of possible futures, rather than on what is currently existing. [9] This mission can be compared to the one of the ‘explanatory sciences’, like the natural sciences and sociology, which is to develop knowledge to describe, explain and predict. [6] Hevner states that the main purpose of DSR is achieving knowledge and understanding of a problem domain by building and application of a designed artifact. [10] [11]

Evolution and applications

Since the first days of computer science, computer scientists have been doing DSR without naming it. They have developed new architectures for computers, new programming languages, new compilers, new algorithms, new data and file structures, new data models, new database management systems, and so on. Much of the early research was focused on systems development approaches and methods. The dominant research philosophy in many disciplines has focused on developing cumulative, theory-based research results in order to make prescriptions. It seems that this ‘theory-with-practical-implications’ research strategy has not delivered on this aim, which led to search for practical research methods such as DSR. [12]

Characteristics

The design process is a sequence of expert activities that produces an innovative product. [13] The artifact enables the researcher to get a better grasp of the problem; the re-evaluation of the problem improves the quality of the design process and so on. This build-and-evaluate loop is typically iterated a number of times before the final design artifact is generated. [14] In DSR, the focus is on the so-called field-tested and grounded technological rule as a possible product of Mode 2 research with the potential to improve the relevance of academic research in management. Mode 1 knowledge production is purely academic and mono-disciplinary, while Mode 2 is multidisciplinary and aims at solving complex and relevant field problems. [6]

Guidelines in information systems research

Hevner et al. have presented a set of guidelines for DSR within the discipline of Information Systems (IS). [10] DSR requires the creation of an innovative, purposeful artifact for a special problem domain. The artifact must be evaluated in order to ensure its utility for the specified problem. In order to form a novel research contribution, the artifact must either solve a problem that has not yet been solved, or provide a more effective solution. Both the construction and evaluation of the artifact must be done rigorously, and the results of the research presented effectively both to technology-oriented and management-oriented audiences.

Hevner counts 7 guidelines for a DSR: [10]

  1. Design as an artifact: Design-science research must produce a viable artifact in the form of a construct, a model, a method, or an instantiation.
  2. Problem relevance: The objective of design-science research is to develop technology-based solutions to important and relevant business problems.
  3. Design evaluation: The utility, quality, and efficacy of a design artifact must be rigorously demonstrated via well-executed evaluation methods.
  4. Research contributions: Effective design-science research must provide clear and verifiable contributions in the areas of the design artifact, design foundations, and/or design methodologies.
  5. Research rigor: Design-science research relies upon the application of rigorous methods in both the construction and evaluation of the design artifact.
  6. Design as a search process: The search for an effective artifact requires utilizing available means to reach desired ends while satisfying laws in the problem environment.
  7. Communication of research: Design-science research must be presented effectively both to technology-oriented as well as management-oriented audiences.

Transparency in DSR is becoming an emerging concern. DSR strives to be practical and relevant. Yet few researchers have examined the extent to which practitioners can meaningfully utilize theoretical knowledge produced by DSR in solving concrete real-world problems. There is a potential gulf between theoretical propositions and concrete issues faced in practice—a challenge known as design theory indeterminacy. Guidelines for addressing this challenges are provided in Lukyanenko et al. 2020. [15]

The engineering cycle and the design cycle

The engineering cycle is a framework used in Design Science for Information Systems and Software Engineering, proposed by Roel Wieringa. [16]

Artifacts

Artifacts within DSR are perceived to be knowledge containing. This knowledge ranges from the design logic, construction methods and tool to assumptions about the context in which the artifact is intended to function (Gregor, 2002).

The creation and evaluation of artifacts thus forms an important part in the DSR process which was described by Hevner et al., (2004) and supported by March and Storey (2008) as revolving around “build and evaluate”.

DSR artifacts can broadly include: models, methods, constructs, instantiations and design theories (March & Smith, 1995; Gregor 2002; March & Storey, 2008, Gregor and Hevner 2013), social innovations, new or previously unknown properties of technical/social/informational resources (March, Storey, 2008), new explanatory theories, new design and developments models and implementation processes or methods (Ellis & Levy 2010).

A three-cycle view

DSR can be seen as an embodiment of three closely related cycles of activities. [17] The relevance cycle initiates DSR with an application context that not only provides the requirements for the research as inputs but also defines acceptance criteria for the ultimate evaluation of the research results. The rigor cycle provides past knowledge to the research project to ensure its innovation. It is incumbent upon the researchers to thoroughly research and reference the knowledge base in order to guarantee that the designs produced are research contributions and not routine designs based upon the application of well-known processes. The central design cycle iterates between the core activities of building and evaluating the design artifacts and processes of the research.

Ethical issues

DSR in itself implies an ethical change from describing and explaining of the existing world to shaping it. One can question the values of information system research, i.e., whose values and what values dominate it, emphasizing that research may openly or latently serve the interests of particular dominant groups. The interests served may be those of the host organization as perceived by its top management, those of information system users, those of information system professionals or potentially those of other stakeholder groups in society. [12]

Academic Examples of Design Science Research

There are limited references to examples of DSR, but Adams has completed two PhD research topics using Peffers et al.'s DSRP (both associated with digital forensics but from different perspectives):

2013: The Advanced Data Acquisition Model (ADAM): A process model for digital forensic practice [18]

2024: The Advanced Framework for Evaluating Remote Agents (AFERA): A Framework for Digital Forensic Practitioners [19]

See also

Related Research Articles

<span class="mw-page-title-main">Design</span> Plan for the construction of an object or system

A design is a concept of or proposal for an object, a process, or a system. Design refers to something that is or has been intentionally created by a thinking agent, though it is sometimes used to refer to the nature of something – its design. The verb to design expresses the process of developing a design. In some cases, the direct construction of an object without an explicit prior plan may also be considered to be a design. A design is expected to have a purpose within a certain context, usually has to satisfy certain goals and constraints, and to take into account aesthetic, functional, economic, environmental or socio-political considerations. Typical examples of designs include architectural and engineering drawings, circuit diagrams, sewing patterns and less tangible artefacts such as business process models.

<span class="mw-page-title-main">Research</span> Systematic study undertaken to increase knowledge

Research is "creative and systematic work undertaken to increase the stock of knowledge". It involves the collection, organization, and analysis of evidence to increase understanding of a topic, characterized by a particular attentiveness to controlling sources of bias and error. These activities are characterized by accounting and controlling for biases. A research project may be an expansion of past work in the field. To test the validity of instruments, procedures, or experiments, research may replicate elements of prior projects or the project as a whole.

Social science is one of the branches of science, devoted to the study of societies and the relationships among individuals within those societies. The term was formerly used to refer to the field of sociology, the original "science of society", established in the 18th century. In addition to sociology, it now encompasses a wide array of academic disciplines, including anthropology, archaeology, economics, human geography, linguistics, management science, communication science, psychology and political science.

<span class="mw-page-title-main">Systems engineering</span> Interdisciplinary field of engineering

Systems engineering is an interdisciplinary field of engineering and engineering management that focuses on how to design, integrate, and manage complex systems over their life cycles. At its core, systems engineering utilizes systems thinking principles to organize this body of knowledge. The individual outcome of such efforts, an engineered system, can be defined as a combination of components that work in synergy to collectively perform a useful function.

An information system (IS) is a formal, sociotechnical, organizational system designed to collect, process, store, and distribute information. From a sociotechnical perspective, information systems are composed by four components: task, people, structure, and technology. Information systems can be defined as an integration of components for collection, storage and processing of data of which the data is used to provide information, contribute to knowledge as well as digital products that facilitate decision making.

The following outline is provided as an overview of and topical guide to software engineering:

<span class="mw-page-title-main">Systems development life cycle</span> Systems engineering terms

In systems engineering, information systems and software engineering, the systems development life cycle (SDLC), also referred to as the application development life cycle, is a process for planning, creating, testing, and deploying an information system. The SDLC concept applies to a range of hardware and software configurations, as a system can be composed of hardware only, software only, or a combination of both. There are usually six stages in this cycle: requirement analysis, design, development and testing, implementation, documentation, and evaluation.

<span class="mw-page-title-main">Systems science</span> Study of the nature of systems

Systems Science, also referred to as systems research, or, simply, systems, is a transdisciplinary field that is concerned with understanding simple and complex systems in nature and society, which leads to the advancements of formal, natural, social, and applied attributions throughout engineering, technology and science, itself.

System of systems is a collection of task-oriented or dedicated systems that pool their resources and capabilities together to create a new, more complex system which offers more functionality and performance than simply the sum of the constituent systems. Currently, systems of systems is a critical research discipline for which frames of reference, thought processes, quantitative analysis, tools, and design methods are incomplete. The methodology for defining, abstracting, modeling, and analyzing system of systems problems is typically referred to as system of systems engineering.

A learning cycle is a concept of how people learn from experience. A learning cycle will have a number of stages or phases, the last of which can be followed by the first.

The term conceptual model refers to any model that is formed after a conceptualization or generalization process. Conceptual models are often abstractions of things in the real world, whether physical or social. Semantic studies are relevant to various stages of concept formation. Semantics is fundamentally a study of concepts, the meaning that thinking beings give to various elements of their experience.

Design science refers to a scientific, i.e. rational and systematic, approach to designing. An early concept of design science was introduced in 1957 by R. Buckminster Fuller who defined it as a systematic form of designing which he applied especially in innovative engineering design. The concept has been more broadly defined by the Design Science journal as “quantitative and qualitative research in the creation of artifacts and systems, and their embedding in our physical, virtual, psychological, economic, and social environment”.

<span class="mw-page-title-main">Enterprise modelling</span>

Enterprise modelling is the abstract representation, description and definition of the structure, processes, information and resources of an identifiable business, government body, or other large organization.

<span class="mw-page-title-main">Packaging engineering</span> Broad topic ranging from design conceptualization to product placement

Packaging engineering, also package engineering, packaging technology and packaging science, is a broad topic ranging from design conceptualization to product placement. All steps along the manufacturing process, and more, must be taken into account in the design of the package for any given product. Package engineering is an interdisciplinary field integrating science, engineering, technology and management to protect and identify products for distribution, storage, sale, and use. It encompasses the process of design, evaluation, and production of packages. It is a system integral to the value chain that impacts product quality, user satisfaction, distribution efficiencies, and safety. Package engineering includes industry-specific aspects of industrial engineering, marketing, materials science, industrial design and logistics. Packaging engineers must interact with research and development, manufacturing, marketing, graphic design, regulatory, purchasing, planning and so on. The package must sell and protect the product, while maintaining an efficient, cost-effective process cycle.

The following outline is provided as a topical overview of science; the discipline of science is defined as both the systematic effort of acquiring knowledge through observation, experimentation and reasoning, and the body of knowledge thus acquired, the word "science" derives from the Latin word scientia meaning knowledge. A practitioner of science is called a "scientist". Modern science respects objective logical reasoning, and follows a set of core procedures or rules to determine the nature and underlying natural laws of all things, with a scope encompassing the entire universe. These procedures, or rules, are known as the scientific method.

<span class="mw-page-title-main">Design rationale</span>

A design rationale is an explicit documentation of the reasons behind decisions made when designing a system or artifact. As initially developed by W.R. Kunz and Horst Rittel, design rationale seeks to provide argumentation-based structure to the political, collaborative process of addressing wicked problems.

Work system has been used loosely in many areas. This article concerns its use in understanding IT-reliant systems in organizations. A notable use of the term occurred in 1977 in the first volume of MIS Quarterly in two articles by Bostrom and Heinen (1977). Later Sumner and Ryan (1994) used it to explain problems in the adoption of CASE. A number of socio-technical systems researchers such as Trist and Mumford also used the term occasionally, but seemed not to define it in detail. In contrast, the work system approach defines work system carefully and uses it as a basic analytical concept.

Enterprise engineering is the body of knowledge, principles, and practices used to design all or part of an enterprise. An enterprise is a complex socio-technical system that comprises people, information, and technology that interact with each other and their environment in support of a common mission. One definition is: "an enterprise life-cycle oriented discipline for the identification, design, and implementation of enterprises and their continuous evolution", supported by enterprise modelling. The discipline examines each aspect of the enterprise, including business processes, information flows, material flows, and organizational structure. Enterprise engineering may focus on the design of the enterprise as a whole, or on the design and integration of certain business components.

Roelf Johannes (Roel) Wieringa is a Dutch computer scientist who was a professor of Information Systems at the University of Twente, specialized in the "integration of formal and informal specification and design techniques".

<span class="mw-page-title-main">Vijay Vaishnavi</span> Georgian computer scientist (born 1948)

Vijay Kumar Vaishnavi is a noted researcher and scholar in the computer information systems field with contributions mainly in the areas of design science, software engineering, and data structures & algorithms, authoring over 150 publications including seven books in these and related areas, and co-owning a patent. He is currently Professor Emeritus at the Department of Computer Information Systems, Georgia State University. He is Senior Editor Emeritus of MIS Quarterly and is on the editorial boards of a number of other major journals. His research has been funded by the National Science Foundation (NSF) as well as by the industry.

References

  1. ↑ Kessler, EH (2013). Encyclopedia of management theory. Thousand Oaks, Calif.: SAGE. p. 2. ISBN   9781412997829.
  2. ↑ Simon, Herbert A. (1988). "The Science of Design: Creating the Artificial". Design Issues. 4 (1/2): 67–82 [69]. doi:10.2307/1511391. ISSN   0747-9360. JSTOR   1511391.
  3. 1 2 Vaishnavi, V., Kuechler, W. (2004/21). “Design Science Research in Information Systems” January 20, 2004 (updated in 2017 and 2019 by Vaishnavi, V. and Stacey, P.; last updated November 24, 2021. URL: http://desrist.org/design-research-in-information-systems
  4. ↑ Kuechler B, Vaishnavi V (2008). "On theory development in design science research: Anatomy of a research project". European Journal of Information Systems. 17 (5): 489–504. doi:10.1057/ejis.2008.40. S2CID   16297257.
  5. ↑ Dresch, Aline; Lacerda, Daniel Pacheco; Valle, José Antônio Jr. Antunes (2015). Design Science Research: A Method for Science and Technology Advancement. Cham: Springer. pp. i. doi:10.1007/978-3-319-07374-3. ISBN   978-3-319-07373-6.
  6. 1 2 3 Van Aken JE (2005). "Management research as a design science: Articulating the research products of mode 2 knowledge production in management". British Journal of Management. 16 (1): 19–36. doi:10.1111/j.1467-8551.2005.00437.x.
  7. ↑ Peffers, Ken; Tuunanen, Tuure; Rothenberger, Marcus A.; Chatterjee, Samir (2007-12-01). "A Design Science Research Methodology for Information Systems Research". Journal of Management Information Systems. 24 (3): 45–77. doi:10.2753/MIS0742-1222240302. ISSN   0742-1222. S2CID   17511997.
  8. ↑ Hevner; March; Park; Ram (2004). "Design Science in Information Systems Research". MIS Quarterly. 28 (1): 75. doi:10.2307/25148625. JSTOR   25148625.
  9. ↑ Simon, H.A. (1969). The sciences of the artificial. MIT Press.
  10. 1 2 3 Hevner, A. R.; March, S. T.; Park, J. & Ram, S. Design Science in Information Systems Research. MIS Quarterly, 2004, 28, 75-106. URL: https://citeseerx.ist.psu.edu/pdf/7d02dc5c8c0b316e592244c441796e6ad31d8bff
  11. ↑ Aparicio, J.T.; Aparicio, M.; Costa, C.J. (2023). "Design Science in Information Systems and Computing". In Anwar, S.; Ullah, A.; Rocha, Á.; Sousa, M.J. (eds.). Proceedings of the International Conference on Information Technology and Applications. Lecture Notes in Networks and Systems. Vol. 614. Springer, Singapore. doi:10.1007/978-981-19-9331-2_35.
  12. 1 2 Iivari J (2007). "A paradigmatic analysis of information systems as a design science". Scandinavian Journal of Information Systems. 19 (2): 39.
  13. ↑ Watts S; Shankaranarayanan G & Even A (2009). "Data quality assessment in context: A cognitive perspective". Decis Support Syst. 48 (1): 202–211. doi:10.1016/j.dss.2009.07.012.
  14. ↑ Markus ML; Majchrzak A & Gasser L. "A design theory for systems that support emergent knowledge processes". MIS Quarterly. 2002: 179–212.
  15. ↑ HEC Montréal, Canada; Lukyanenko, Roman; Parsons, Jeffrey; Memorial University of Newfoundland, Canada (2020-09-01). "Research Perspectives: Design Theory Indeterminacy: What Is it, How Can it Be Reduced, and Why Did the Polar Bear Drown?". Journal of the Association for Information Systems. 21 (5): 1343–1369. doi:10.17705/1jais.00639. S2CID   222094969.
  16. ↑ Wieringa, Roel (2014). Design science methodology for information systems and software engineering. Heidelberg. ISBN   978-3-662-43839-8. OCLC   899248827.{{cite book}}: CS1 maint: location missing publisher (link)
  17. ↑ Hevner AR (2007). "The three cycle view of design science research". Scandinavian Journal of Information Systems. 19 (2): 87.
  18. ↑ https://www.researchgate.net/publication/258224615_The_Advanced_Data_Acquisition_Model_ADAM_A_process_model_for_digital_forensic_practice
  19. ↑ https://espace.curtin.edu.au/bitstream/handle/20.500.11937/93974/Adams%20RB%202023%20Public.pdf?sequence=1&isAllowed=y

Research examples

Further reading

This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.