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Design science (methodology)
Design science (methodology)
Design science (methodology)
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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.

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

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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 that 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

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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

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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

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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

[edit]
Main article: Engineering cycle

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

Artifacts

[edit]
See also: Human factors and ergonomics and Human reliability

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

[edit]

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

[edit]

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

[edit]

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

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References

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Research examples

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Further reading

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[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia

Design science (methodology)

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Design science (DSR), also known as design science methodology, is a that focuses on the creation, development, and evaluation of purposeful artifacts—such as constructs, models, methods, or instantiations—to solve identified real-world problems and advance scientific knowledge in applied disciplines. This approach emphasizes an iterative process of building innovative solutions and rigorously assessing their utility, effectiveness, and theoretical contributions, distinguishing it from purely behavioral or . Originating from and the sciences of the artificial, DSR has become particularly prominent in information systems (IS) research since the early , where it addresses the need to bridge and practice by producing both descriptive about design processes and prescriptive for artifact implementation. Key guidelines for conducting DSR, as outlined in seminal work, include treating the environment as an exogenously given constraint, focusing on artifact relevance and rigor, designing and evaluating artifacts to meet objectives, and ensuring clear communication of contributions. A widely adopted process model for DSR involves six steps: identifying and representing the problem, defining objectives for a solution, ing and developing the artifact, demonstrating its use, evaluating its performance, and communicating the results to stakeholders. In broader applications beyond IS, DSR extends to fields like , human-computer interaction, and organizational management, where it supports the generation of novel tools, frameworks, or systems that tackle complex, ill-structured problems. in DSR can employ various methods, including empirical testing, analytical simulations, assessments, or case studies, to validate artifact utility while contributing to theoretical advancements. Despite its strengths in fostering , DSR requires careful attention to rigor to avoid superficial designs, ensuring that artifacts are generalizable and grounded in relevant knowledge bases. Overall, this plays a crucial role in application-oriented sciences by integrating problem-solving with knowledge creation, enabling researchers to produce tangible impacts on practice.

Fundamentals

Definition

Design science research (DSR) is a research paradigm that develops and validates prescriptive knowledge through the construction and evaluation of innovative artifacts designed to solve identified organizational or practical problems, setting it apart from descriptive sciences that focus on explaining natural phenomena. In this context, "design" refers to the intentional creation of purposeful artifacts—such as constructs, models, methods, or instantiations—that address specific needs, while "science" emphasizes the rigorous generation of generalizable knowledge through empirical validation and iterative improvement of these artifacts. The intellectual roots of DSR trace back to Herbert Simon's 1969 concept of the "sciences of the artificial," which posits that designed objects, or artifacts, are central to understanding and improving human-made systems, as opposed to naturally occurring ones. Simon argued that artificial sciences deal with entities shaped by human goals and constraints, enabling the study of how systems can be engineered to achieve desired outcomes. DSR distinguishes itself from other paradigms by prioritizing the question of "what works" in practical applications over explanatory "why" inquiries; unlike sciences, which seek universal truths about , or behavioral sciences in social domains, which predict human actions, DSR focuses on building and assessing viable solutions to enhance performance in complex environments.

Objectives

Design science research (DSR) primarily aims to solve real-world problems by designing and developing viable artifacts that address identified organizational or technical challenges. These artifacts, such as constructs, models, methods, or instantiations, are intended to extend the boundaries of human and organizational capabilities through innovative solutions. By focusing on the creation of technology-based interventions, DSR ensures that research outputs are directly applicable to practical contexts, such as improving systems efficiency or supporting processes. A core objective of DSR is to contribute to the by generating generalizable design principles and prescriptive theories that guide future artifact development. These prescriptive theories emphasize how specific problems can be effectively addressed, moving beyond descriptive explanations to actionable propositions. This role in knowledge creation involves bridging theory and practice through the of artifact utility and efficacy, thereby balancing —ensuring practical utility—and rigor—upholding scientific validity. Such contributions enable the accumulation of design knowledge that informs subsequent research and application in diverse domains. Expected outcomes from DSR include artifacts that demonstrate improved performance in targeted problem areas, such as enhanced systems or optimized operational processes. These outcomes are measured against objectives of (practical applicability), (effectiveness in solving the problem), and generalizability (applicability beyond the specific context). For instance, success is gauged by whether an artifact achieves measurable improvements in system efficiency or user performance without compromising broader applicability.

Historical Development

Origins

The origins of design science methodology can be traced to 19th- and early 20th-century practices, which emphasized systematic and scientific approaches to design amid the industrial revolution's demands for efficient production and innovation. These roots lie in the application of rational methods to complex industrial problems, influenced by advancements in and the integration of scientific principles into curricula. By the mid-20th century, World War II-era operational research further solidified this foundation, promoting analytical techniques for optimizing systems and artifacts in resource-constrained environments. Philosophical underpinnings emerged concurrently, with thinkers distinguishing design's constructive nature from the descriptive focus of natural sciences. In the , advanced these ideas through his concept of "," formalized as Comprehensive Anticipatory Design Science (CADS), aimed at solving global challenges like resource scarcity via technology. Fuller's approach stressed discovering generalized principles of nature—such as , or achieving more with less—and applying them comprehensively to anticipate future needs and enhance human well-being through integrated artifact design. Early computational and systems-oriented influences bolstered this development, particularly as articulated by in his 1948 book Cybernetics: Or Control and Communication in the Animal and the Machine. Wiener's framework highlighted feedback loops as mechanisms for artifacts to interact dynamically with their environments, fostering in both biological and mechanical systems and laying groundwork for systems theory's role in purposeful design. and , formalized in the 1950s from wartime optimization efforts, provided additional pre-DSR context by emphasizing artifact creation through mathematical modeling and decision support in uncertain settings, bridging rigor with managerial inquiry. Herbert Simon played a pivotal role in synthesizing these strands with his 1969 book The Sciences of the Artificial, which positioned design as a rigorous science of situated artifacts engineered to adapt to specific environments. Simon introduced to explain design decisions under cognitive and informational constraints, contrasting design's normative focus on "how things ought to be" with the explanatory aims of natural sciences. This conceptualization elevated artifact-oriented inquiry from practical to a foundational methodology.

Evolution and Key Milestones

The formalization of design science methodology began in the 1970s with foundational work viewing design as an empirical and experimental process in . In 1976, Allen Newell and described computer science as an empirical inquiry, emphasizing the creation and testing of artifacts as a way to probe natural phenomena, laying early groundwork for design-oriented research paradigms. During the 1980s, this approach transitioned into information systems through early design science efforts in and , exemplified by Peter Chen's 1976 Entity-Relationship model, which demonstrated artifact construction for solving practical problems. These developments highlighted design science's role in technology management, with applications in expert systems that proved the feasibility of building innovative solutions to address complex problems. In the 1990s, design science gained further traction in information systems research, with key contributions formalizing its theoretical underpinnings. Salvatore T. March and Gerald F. Smith (1995) proposed a framework distinguishing (focused on building and evaluating artifacts) from (focused on explanation), providing a structured basis for research. Concurrently, John Walls, George Widmeyer, and Omar El Sawy (1992) advanced design theories for specific IS applications, such as vigilant executive information systems, emphasizing generalizable principles for artifact development. This period also saw growing recognition within the IS community, evidenced by increasing discussions and presentations at conferences like the International Conference on Information Systems (ICIS), which helped integrate design science into mainstream IS scholarship. A pivotal milestone occurred in 2004 with the publication of "Design Science in Information Systems Research" by Alan R. Hevner, Salvatore T. March, Jinsoo Park, and Sudha Ram in MIS Quarterly, which synthesized prior work into a rigorous framework, including seven guidelines for conducting and evaluating research, thereby elevating its status as a core in IS. This paper addressed criticisms of methodological looseness and provided a clear path for artifact-centric studies to contribute to knowledge. The 2010s brought refinements to design science methodology, particularly in evaluation practices and methodological flexibility. John Venable, Jan Pries-Heje, and Richard Baskerville (2012) introduced a comprehensive framework for evaluation in design science research (FEDS), offering strategies and methods to ensure rigorous assessment of artifacts while considering goals, risks, and stakeholder needs. Additionally, integrations with agile development and emerged, enabling iterative, user-involved processes; for instance, adaptations of design science incorporated agile principles to support and stakeholder collaboration in software projects. By the 2020s, methodology expanded beyond information systems into broader disciplines, addressing contemporary challenges like in sociotechnical systems. In 2024, Tuure Tuunanen, Michael D. Myers, and Matti Rossi published "Dealing with Complexity in Design Science Research: A Methodology Using Design Echelons" in MIS Quarterly, proposing an echeloned approach to manage hierarchical in artifact and . Recent applications continue to leverage for creating knowledge-intensive artifacts in areas such as AI and , emphasizing iterative validation and domain-specific utility. This evolution underscores design science's adaptability, with ongoing conferences like DESRIST highlighting its global relevance; the 2025 edition, held June 2-4 in , , focused on "Contextual Design Science Research: Local Solutions for Global Challenges" and attracted around 101 participants.

Core Principles

Characteristics

Design science methodology is characterized by its constructive orientation, which emphasizes the creation and improvement of artifacts—such as constructs, models, methods, and instantiations—intended to solve practical problems in domains like information systems, rather than merely observing or explaining existing phenomena as in natural or behavioral sciences. This approach treats the development of innovative solutions as a core research activity, where artifacts serve as the primary outputs to enhance utility and effectiveness in real-world applications. A defining feature is its iterative process, involving repeated cycles of designing, building, and evaluating artifacts to refine them progressively and ensure they meet intended objectives. This build-and-evaluate loop allows researchers to incorporate feedback from initial implementations, adapting solutions based on insights and emerging requirements, thereby fostering continuous over linear investigative methods. The methodology maintains a balance between relevance and rigor, ensuring that artifacts address pressing organizational or practical challenges while being firmly grounded in established scientific knowledge and methodological soundness. is achieved by aligning designs with real-world needs, such as improving processes, whereas rigor is upheld through systematic against theoretical foundations and empirical validation to avoid unsubstantiated claims. Design science integrates multiple methods for artifact validation, combining qualitative approaches like case studies and simulations with quantitative techniques such as experiments and analytical modeling to provide comprehensive assessment. This multi-method strategy enables robust testing across different dimensions, from utility in specific contexts to broader theoretical contributions, enhancing the credibility and applicability of the resulting artifacts. Finally, the methodology prioritizes generalizability through the derivation of design principles that extend beyond individual artifacts or instances, offering meta-requirements and guidelines applicable to classes of problems. These principles contribute to a cumulative , allowing future researchers and practitioners to adapt and reuse solutions in varied settings, thus promoting and long-term impact.

Guidelines for Research

Design science research in information systems (IS) is guided by a set of operational principles that ensure the creation and validation of useful artifacts while maintaining scientific rigor. These guidelines translate the abstract characteristics of design science—such as its focus on problem-solving through innovative constructs—into practical directives for researchers. A foundational set of seven guidelines was formalized by Hevner et al. in 2004, specifically tailored for IS contexts. The first guideline emphasizes design as an artifact, requiring researchers to produce viable artifacts such as constructs, models, methods, or instantiations that address organizational problems and are described for effective implementation. The second, problem relevance, mandates that artifacts solve important and unsolved wicked problems relevant to the IS community, ensuring alignment with real-world needs. Third, design evaluation calls for rigorous demonstration of the artifact's utility, quality, and efficacy through well-executed methods like case studies or controlled experiments. Fourth, research contributions requires clear articulation of novel additions to the knowledge base, whether through the artifact itself, its design knowledge, or methodological improvements. Fifth, research rigor insists on applying disciplined methods from the knowledge base in both artifact construction and evaluation to support generalizability. Sixth, design as a search process views artifact development as an iterative exploration of solution spaces, constrained by environmental factors and objectives. Finally, communication of research demands effective dissemination to technical and non-technical audiences, balancing detail on the artifact with its broader applicability. Building on these, Peffers et al. (2007) proposed an expanded process model with six sequential activities to operationalize design science research in IS. These include problem identification and motivation, where the research problem is defined and its significance justified; definition of objectives for a solution, specifying artifact requirements; design and development, where the artifact is built; demonstration, showing its application in context; evaluation, measuring performance against objectives; and communication, sharing findings. This model provides a structured yet flexible framework, allowing entry at any step based on context. In IS applications, these guidelines are tailored to software and system design, where artifact utility is often assessed via metrics such as benchmarks (e.g., response time or throughput in database systems) and scores. For instance, in IS emphasizes empirical testing to quantify improvements in system efficiency or user adoption rates. DSR principles have roots and applications in non-IS fields, such as , , and . In expert systems development using DSR, knowledge elicitation is a core element of the design process, often involving structured interviews and protocol analysis to capture domain expertise for rule-based artifacts.

Methodological Frameworks

Engineering and Design Cycles

The engineering cycle in design science research, as proposed by Roel Wieringa (2014), represents a structured approach derived from practices, emphasizing systematic problem-solving within defined constraints. It typically encompasses three key phases: problem investigation to identify issues, their causes, and requirements; treatment design to synthesize solutions; and treatment validation to implement, test, and verify the artifact against requirements. This cycle prioritizes optimization and reliability, ensuring that artifacts perform effectively in known contexts, as articulated in foundational methodologies for information systems and . In contrast, the design cycle adopts a more fluid, iterative process focused on and novelty, diverging from strict to foster . It involves phases such as empirical investigation to ground ideas, designing general treatments, and validation through empirical means, allowing for exploration of uncharted solutions without rigid adherence to predefined requirements for a specific context. This approach highlights breakthrough thinking and adaptation, often incorporating user involvement to evolve artifacts organically. Design science research integrates these cycles to balance rigor and creativity in artifact development, leveraging the engineering cycle's structure for feasibility while employing the design cycle's iteration for innovation and generalization. For instance, in projects, initial engineering analysis defines core requirements, followed by design-cycle prototyping and user testing to refine features iteratively until optimization is achieved. This hybrid enables comprehensive artifact creation that is both practical and inventive. Key differences lie in focus: optimizes within constraints via investigation-design-validation, whereas drives innovation through empirical grounding, theorizing, and . The three-cycle view extends this binary framework by incorporating a relevance cycle for broader applicability.

The Three-Cycle View

The three-cycle view of design science research (DSR), proposed by Hevner in , provides a structured framework that integrates practical problem-solving with theoretical advancement through three interconnected cycles: the relevance cycle, the design cycle, and the rigor cycle. This model emphasizes the iterative nature of DSR, ensuring that research artifacts are developed to address real-world needs while contributing to foundational knowledge. The relevance cycle links the application environment—encompassing organizational problems, opportunities, and user needs—to the derivation of specific requirements and design objectives for artifacts. It operates by identifying gaps in the environment and specifying solutions that must be contextually viable, thereby grounding the research in practical utility. The design cycle focuses on the core artifact-building process, involving iterative activities of construction, evaluation, and refinement to produce viable constructs, models, methods, or instantiations. Evaluations within this cycle assess both internal validity (e.g., through simulations or prototypes) and external applicability (e.g., via case studies). The rigor cycle connects the design process to an established knowledge base, drawing on existing theories, methods, and results to inform artifact development, while also contributing new validated knowledge back to the base through rigorous assessments. These cycles interconnect dynamically to form a holistic process: the relevance cycle feeds requirements into the cycle, which in turn generates artifacts that are validated against the rigor cycle's standards; feedback loops allow refinements, such as updated environmental insights informing rigor additions or iterations enhancing theoretical contributions. This interplay ensures that DSR artifacts are not only technically sound but also theoretically justified, bridging the gap between applicability and generalizability. In application, the model can be visualized as a triangular flow: the application environment at the base supplies inputs to the relevance cycle (arrow to requirements), which directs the design cycle (central loop of build-evaluate-refine), whose outputs loop to the rigor cycle (arrow to ) and return artifacts to the environment for deployment; bidirectional arrows between cycles denote continuous feedback. This structure promotes artifacts that are both practically useful—solving domain-specific issues—and theoretically grounded—advancing the —differentiating DSR from purely behavioral or descriptive paradigms. Recent advancements extend this model to handle in sociotechnical systems, such as those involving multiple stakeholders or emergent behaviors, by incorporating adaptive feedback mechanisms through a of "design echelons." Introduced in 2024, design echelons decompose the cycles into hierarchical, self-contained phases (e.g., problem analysis, objectives definition, artifact development, demonstration, and evaluation), enabling nonlinear iterations and concurrent validations that adapt to evolving . This extension builds on the foundational three-cycle view by adding organizing logic for scalability in complex environments, such as enterprise architectures or initiatives.

Artifact Development

In design science research, artifact development constitutes the core activity of constructing innovative solutions to address identified problems within a domain. These artifacts serve as the tangible outputs that extend the boundaries of human and organizational capabilities by providing new constructs, models, methods, or implementations tailored to practical needs. The process emphasizes iterative building grounded in existing knowledge, ensuring that artifacts are not only functional but also contribute to theoretical advancement. The of artifacts, as originally proposed by March and Smith, delineates four fundamental types that encapsulate the spectrum of outputs in information systems and related fields:
  • Constructs: These form the foundational and concepts defining the problem and solution space, such as symbols, units of measure, or essential primitives that enable communication and reasoning within the domain.
  • Models: Representing abstractions of reality, models depict relationships among constructs, often through diagrams, mathematical formulations, or simulations that approximate system behavior for analysis and prediction.
  • Methods: These encompass algorithms, processes, or techniques for achieving specific objectives, including step-by-step procedures or optimization strategies that guide action or computation.
  • Instantiations: Physical or software implementations that operationalize constructs, models, and methods, such as prototypes, tools, or deployed systems demonstrating feasibility in real-world contexts.
This classification ensures comprehensive coverage of design contributions, from theoretical to practical applications. The development process for artifacts typically proceeds iteratively from defining requirements to prototyping and validation. It begins with specifying objectives derived from problem , informing the of the artifact at an appropriate level of detail—such as conceptual sketches for models or for instantiations—drawing on established theories and feasibility assessments. Prototyping follows, involving the creation of initial versions to explore solutions, often refined through cycles of feedback and adjustment. Validation occurs via rigorous testing to confirm the artifact meets its intended purpose, with success evaluated against key criteria: (practical value in solving the problem), completeness (thorough coverage of requirements without gaps), and (effectiveness in achieving desired outcomes under specified conditions). Evaluation of developed artifacts employs a range of methods to demonstrate their quality and applicability, categorized as follows:
  • Observational methods, such as case studies, involve deploying the artifact in natural settings to observe its performance and impact on stakeholders.
  • Analytical methods, including simulations, use mathematical modeling or logical analysis to assess artifact behavior under varying conditions without real-world implementation.
  • Experimental methods, like controlled tests, manipulate variables in or field experiments to measure and isolate effects attributable to the artifact.
  • Descriptive methods, such as analytical frameworks, rely on expert argumentation or scenario-based analysis to argue the artifact's alignment with domain requirements.
These approaches are selected based on the artifact type and context, ensuring multifaceted validation. To maintain rigor throughout development, artifacts must embody relevant design principles—such as generality, elegance, and solvability—while ensuring to the underlying of empirical and analytical foundations. This involves documenting how kernel theories or prior inform each design decision, preventing construction and facilitating and extension by subsequent scholars. Such reinforces the artifact's contribution to the cumulative in the field.

Applications

In Information Systems

Design science research (DSR) in information systems (IS) primarily involves the creation and evaluation of artifacts such as decision support systems, ontologies, and enterprise architectures to address practical problems in organizational contexts. These artifacts are designed to improve information processing, , and within businesses and institutions. For instance, decision support systems built through DSR enable analysis for managers, while ontologies provide structured representations of to facilitate in IS environments. DSR has significantly shaped IS theory by generating design principles that guide the development of secure systems, such as principles for implementing access controls in cloud-based IS that balance and protection against data breaches. These principles, derived from artifact s, have influenced theoretical models in IS, emphasizing the interplay between technical design and socio-technical factors. For example, Venable et al.'s framework has been widely adopted to assess artifact rigor, leading to over 500 citations in IS literature for its role in validating design outcomes. The methodological fit of DSR in IS addresses key challenges like and user adoption through rigorous methods, including quantitative assessments of performance metrics and qualitative feedback from stakeholders. User adoption is enhanced via cycles, where prototypes are refined based on empirical from field trials, ensuring alignment with organizational workflows.

In Other Disciplines

Design science methodology (DSM), originally rooted in information systems, has been adapted to address complex challenges in sciences, where it facilitates the development of artifacts for organizational problem-solving. A comprehensive 2025 outlines DSM's application in this domain, emphasizing a five-phase cycle—, synthesis, creation, , and —to tackle field problems within and across organizations, such as strategy formulation and entrepreneurial innovation. In , a of sciences, DSM supports process models for ; for instance, a 2024 synthesizes how approaches create artifacts like optimization algorithms and decision-support systems to enhance efficiency in networks amid . In and , DSM enables the creation of digital artifacts tailored to industry-specific needs, particularly in (BIM). A 2025 systematic review of 112 studies highlights DSM's role in developing BIM-based tools for project simulation, risk assessment, and lifecycle , demonstrating its utility in bridging theoretical constructs with practical artifacts like parametric models and frameworks. This adaptation underscores DSM's versatility in handling the dynamic, technology-driven environment of , where artifacts must integrate empirical data with rigor to improve outcomes in infrastructure development. Project management has increasingly incorporated DSM for the co-creation of knowledge artifacts that address practical gaps in methodologies and tools. A 2024 study in the International Journal of Project Management proposes DSM as a rigorous approach for designing artifacts, such as frameworks for and performance metrics, emphasizing with practitioners to ensure relevance and generalizability across diverse project contexts. In emerging fields like social sciences and AI ethics, DSM integrates with qualitative methods to produce hybrid artifacts that combine interpretive insights with structured design. A 2025 study merges qualitative design techniques, such as and participatory , with DSM's cycles to develop social intervention tools, enhancing the methodology's applicability in understanding human-centered phenomena like community dynamics. Similarly, in AI ethics, DSM has been used to craft expert systems and guidelines; for example, a 2025 framework applies DSM to address ethical challenges in AI-driven , resulting in artifacts like decision-support protocols that incorporate mitigation and transparency evaluations. Adaptations of DSM guidelines often involve modifying evaluation criteria and artifact scopes to align with domain-specific imperatives, such as in . For artifacts, DSM principles are adjusted to emphasize indirect environmental impacts, incorporating lifecycle assessments and stakeholder phronesis (practical wisdom) into the design cycle; a foundational framework illustrates how artifacts like eco-efficient prototypes balance utility with long-term ecological effects, ensuring relevance in fields like and . These modifications highlight DSM's flexibility, prioritizing context-aware rigor over rigid adherence to original information systems guidelines.

Ethical Considerations

Key Ethical Issues

In design science research (DSR), ethical issues arise primarily from the creation and deployment of artifacts intended to solve practical problems, particularly in information systems where these artifacts can amplify societal harms if not carefully considered. Key concerns include the potential for artifacts to embed and perpetuate biases, compromise and , impose environmental costs, and obscure for outcomes. These issues are exacerbated by the methodology's emphasis on and , which can sometimes prioritize functionality over broader societal implications. Bias in design represents a core ethical challenge, as DSR artifacts may perpetuate inequalities when requirements gathering overlooks diverse stakeholders, leading to solutions that disadvantage marginalized groups. For instance, in AI systems developed through DSR, homogeneous design teams can introduce "hegemonic design bias," resulting in algorithms that perform poorly for underrepresented populations. This perpetuation of bias stems from insufficient inclusion of varied perspectives during artifact specification and evaluation phases, potentially reinforcing systemic inequities in areas like hiring or lending platforms. Privacy and security risks are prominent in DSR artifacts that handle sensitive , such as systems for healthcare or , where like breaches or pervasive monitoring can erode user trust and . Artifacts designed without robust safeguards may inadvertently enable capitalism, as seen in location-tracking applications that collect beyond stated purposes, violating principles of and minimization. These issues are particularly acute in technical domains where the complexity of artifacts obscures potential vulnerabilities, making it difficult to anticipate harms during the build-and-evaluate cycles. Sustainability emerges as an ethical concern due to the environmental impacts of DSR artifacts, including resource-intensive production, operation, and disposal that contribute to carbon emissions and e-waste. For example, energy-hungry data centers supporting cloud-based artifacts can undermine the methodology's goals if direct effects like high electricity consumption are not assessed, while indirect effects—such as enabling unsustainable business processes—further exacerbate ecological degradation. This tension highlights how DSR's focus on immediate utility can conflict with long-term , especially in green IT initiatives where artifacts aim to promote but often overlook lifecycle costs. Accountability poses challenges in determining responsibility for artifact failures or harms, as the collaborative and iterative nature of DSR blurs lines between researchers, developers, and deployers, often lacking mechanisms for transparent documentation. Principles for ethical design emphasize the need for clear in artifact development to assign liability, yet many projects fail to articulate who bears responsibility for unintended consequences, such as discriminatory outcomes in deployed systems. This opacity can shield stakeholders from repercussions, complicating redress for affected parties and undermining in DSR outputs. As of 2025, emerging issues in DSR for expert systems, particularly in ethical AI design, include heightened scrutiny of how artifacts integrate principles like fairness and transparency to address biases in processes. Recent studies highlight the ethical imperative to mitigate environmental impacts in AI-driven expert systems, such as through reduced computational demands to align with , underscoring the evolving need for DSR to proactively embed these considerations in artifact utility assessments. Additionally, regulations like the EU AI Act, effective from 2024 with key provisions applying in 2025, require conformity assessments, transparency, and bias mitigation for high-risk AI artifacts developed in DSR, influencing ethical practices across the EU and globally.

Integrating Ethics into Practice

To integrate ethics into design science research (DSR), scholars have proposed extending foundational guidelines, such as those outlined by Hevner et al. (2004), by incorporating principles from value-sensitive design (VSD). VSD emphasizes the proactive identification and balancing of human values, such as , , and inclusivity, throughout the artifact development process. This adaptation particularly enhances the relevance cycle of DSR—where environmental understanding and stakeholder needs are assessed—by mandating comprehensive to uncover diverse ethical values and potential conflicts early on. For instance, in VSD-integrated DSR projects, researchers conduct empirical investigations, like surveys and workshops, to map stakeholder values onto design requirements, ensuring artifacts address societal impacts beyond technical utility. Process integration further embeds by incorporating systematic checks across DSR's core cycles: the rigor cycle (knowledge grounding), design cycle (artifact building), and relevance cycle (environmental application). Ethical impact assessments, which evaluate potential societal harms, biases, and , are particularly recommended during artifact evaluation phases to validate not only functionality but also moral alignment. These assessments draw from frameworks like anticipatory technology ethics, involving iterative reviews at each cycle iteration to refine designs responsively. In practice, this means pausing artifact prototyping to assess risks, such as data violations in information systems, and adjusting based on empirical feedback. Practical tools and frameworks support this integration, including ethical matrices for structured artifact review and participatory design methods to amplify marginalized voices. An ethical matrix organizes evaluation by listing stakeholders, relevant values, and potential impacts in a tabular format, facilitating systematic scrutiny during design iterations; for example, it can highlight how an AI artifact might disproportionately affect vulnerable groups. , often fused with VSD, involves end-users and underrepresented stakeholders in co-creation workshops, ensuring ethical reflexivity and democratic input that traditional DSR might overlook. These approaches promote inclusive artifact outcomes, as seen in projects developing assistive technologies where user feedback directly shapes ethical safeguards. DSR can also produce normative artifacts explicitly focused on , such as compliance models or value-aligned frameworks, treating ethical principles as objectives rather than afterthoughts. These artifacts operationalize norms like or equity into evaluable constructs, enabling their deployment in complex systems like healthcare IT. For example, a normative artifact might include built-in auditing mechanisms to enforce transparency, contributing to both practical solutions and advancing ethical in DSR. Emerging hybrid approaches in DSR, such as combining technical and qualitative methods, support ethical integration by enabling multimethod evaluations that address in AI-driven systems. These methods, informed by principles, enhance accountability in evolving technological landscapes.

Challenges and Future Directions

Current Challenges

Design science research (DSR) encounters significant challenges in managing , particularly when addressing wicked problems characterized by ill-defined goals, multiple stakeholders, and evolving requirements. These problems often involve non-linear interactions among sociotechnical elements, which strain sequential DSR methodologies like the Design Science Research Methodology (DSRM) that assume linear progression through phases such as problem identification, , demonstration, , and communication. For instance, in complex environments like enterprise , emergent issues such as improper data flows may only surface during demonstration, necessitating repeated iterations that disrupt planned processes. Evaluation rigor remains a persistent hurdle in DSR, as generalizing artifact results beyond controlled prototypes to broader contexts proves difficult due to contextual dependencies and limited testing. Empirical validation often suffers from trade-offs between artificial evaluations, which provide high control but low realism, and naturalistic ones, which offer yet introduce confounding variables that undermine precision. The Framework for in Design Science (FEDS) highlights how inadequate method selection can lead to Type I or Type II errors, with summative assessments rarely demonstrating utility across diverse settings. Interdisciplinary barriers further complicate DSR, as integrating knowledge from diverse fields—such as combining DSR artifacts with behavioral theories—requires reconciling differing epistemological assumptions and terminologies. Efforts to leverage synergies between design and behavioral sciences often falter due to siloed expertise, making it challenging to co-develop artifacts that address both technical efficacy and human-centered impacts. Resource constraints pose practical obstacles to DSR's iterative cycles, particularly in settings with limited time, budget, or expertise, which can prevent thorough demonstrations and evaluations. Real-world costs, including travel and stakeholder engagement, frequently restrict researchers from conducting the multiple instantiations needed for robust artifact refinement. Quantifying contributions in DSR becomes especially problematic in agile contexts, where rapidly evolving artifacts defy traditional metrics focused on static outcomes, complicating assessments of advancement or practical impact. In such environments, measuring theorizing paths—whether natural, applied, or improvement-oriented—requires adaptive frameworks to capture incremental value amid frequent iterations. In recent years, the integration of (AI) and machine learning (ML) into design science research (DSR) has emerged as a prominent trend, particularly in 2025, where DSR methodologies are increasingly applied to design AI artifacts such as models that incorporate ethical considerations from the outset. For instance, frameworks leveraging generative AI to support DSR processes enable the creation of artifacts that address uncertainty management, explainability, and fairness in AI systems, ensuring responsible deployment in domains like healthcare. This shift is driven by the need to align AI development with DSR's artifact-centric approach, as evidenced by exploratory models that use AI to enhance problem identification and cycles in DSR projects. A growing emphasis on within DSR reflects heightened awareness following recent climate reports, such as the World Meteorological Organization's State of the Global Climate 2024, which underscore the urgency of eco-effective designs. DSR is now frequently employed to develop artifacts for green design and the , including tools that facilitate resource-efficient systems like platforms and decision support systems for building lifecycle assessments. These applications prioritize socio-technical-ecological integrations, aligning DSR outputs with (SDGs) through AI-driven solutions for digital . Hybrid methodologies in DSR are gaining traction in 2025, blending traditional DSR with qualitative methods and agile practices to address complex, challenges. Recent papers highlight integrations like with agile Scrum frameworks, which enhance inclusivity and adaptability in artifact development for educational and software contexts. This merging allows for richer stakeholder involvement and , as seen in echeloned DSR projects that combine conceptual mapping with analytic hierarchy processes for strategic artifacts. Such approaches mitigate rigidity in conventional DSR while preserving its focus on generalizable . Global and collaborative DSR is advancing through open-source platforms that promote artifact development and enhance generalizability across diverse contexts. Platforms like facilitate shared DSR projects, enabling citizen and stakeholders to co-design systems for problem exploration, particularly in inclusive and marginalized communities. This trend fosters transparency and , as DSR inherently supports academic-practitioner collaborations to scale innovative solutions worldwide. By 2025, these platforms have become integral to tracks in major DSR conferences, emphasizing for robust, replicable artifacts. Looking ahead, DSR holds potential for applications in by 2030, building on 2025 advancements in expert systems like digital twins and generative AI. These developments, highlighted in DSR methodologies for AI-augmented systems, suggest frameworks that integrate ethical and scalable artifact design. Such outlooks position DSR to tackle frontier challenges in computational paradigms.

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