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Design Science Research (DSR) is now an accepted research paradigm in the Information Systems (IS) field, aiming at developing purposeful IT artifacts and knowledge about the design of IT artifacts. A rich body of knowledge on approaches, methods, and frameworks supports researchers in conducting DSR projects. While methodological guidance is abundant, there is little support and guidance for documenting and effectively managing DSR processes. In this article, we present a set of design principles for tool support for DSR processes along with a prototypical implementation ( We argue that tool support for DSR should enable researchers and teams of researchers to structure, document, maintain, and present DSR, including the resulting design knowledge and artifacts. Such tool support can increase traceability, collaboration, and quality in DSR. We illustrate the use of our prototypical implementation by applying it to published cases, and we suggest guidelines for using tools to effectively manage design-oriented research.
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Tool-Support for Design Science Research
Tool-Support for Design Science Research:
Design Principles and Instantiation
Jan vom Brocke
University of Liechtenstein
Peter Fettke
German Research Center for Artificial Intelligence
Michael Gau
University of Liechtenstein
Constantin Houy
German Research Center for Artificial Intelligence
Alexander Maedche
Karlsruhe Institute of Technology
Stefan Morana
Karlsruhe Institute of Technology
Stefan Seidel
University of Liechtenstein
Abstract: Design Science Research (DSR) is now an accepted research paradigm in the Information Systems
(IS) field, aiming at developing purposeful IT artifacts and knowledge about the design of IT artifacts. A rich body
of knowledge on approaches, methods, and frameworks supports researchers in conducting DSR projects. While
methodological guidance is abundant, there is little support and guidance for documenting and effectively
managing DSR processes. In this article, we present a set of design principles for tool support for DSR processes
along with a prototypical implementation ( We argue that tool support for DSR should
enable researchers and teams of researchers to structure, document, maintain, and present DSR, including the
resulting design knowledge and artifacts. Such tool support can increase traceability, collaboration, and quality in
DSR. We illustrate the use of our prototypical implementation by applying it to published cases, and we suggest
guidelines for using tools to effectively manage design-oriented research.
Keywords: Design Science Research, design process
1 Introduction
Design Science Research (DSR) is now an important and legitimate research approach in the
Information Systems (IS) field (Gregor & Hevner, 2013), and the adoption of DSR has been
accelerated by the availability of various frameworks, approaches, and methods supporting the DSR
process (e.g., Kuechler & Vaishnavi, 2008; Peffers, Tuunanen, Rothenberger, & Chatterjee, 2007;
Venable, 2006). Strategies to position and present IS research to create impact (Gregor & Hevner,
2013) are intended to help researchers publish their results in highly ranked IS journals (Goes, 2014),
and high-quality DSR can now be found in our discipline’s top journals, such as MIS Quarterly and
Information Systems Research.
While there is ample evidence that DSR has matured over the past years, it is somewhat ironic that
the field has not yet developed and widely adopted computer-based tools that help researchers
document and manage the research process—considering the field’s focus on developing purposeful
IT artifacts. While such tool support is available for other areas (e.g., qualitative and quantitative
research), this is not the case for DSR.
Qualitative research, for instance, demands that researchers follow specific procedures and document
the research process in detail; they are required to provide a clear chain of evidence (Walsham 2015),
corroborate their findings (Strauss & Corbin, 1998), and report on criteria such as intercoder-reliability
(Miles, Huberman, & Saldana, 2013). To this end, the use of computer-based analysis tools such as
atlas.ti ( or NVivo ( has become a de-facto
standard. These tools support researchers in conducting their literature analysis, managing data
Electronic copy available at:
Tool-Support for Design Science Research
collection, analyzing data, and reporting from their study, and editors and reviewers indeed might ask
for coding examples or memos written during the analysis.
Similarly, DSR scholars must ensure the application of rigorous methods to construct and evaluate the
design artifact (Hevner, March, Jinsoo, & Ram, 2004), and they need to systematically plan and
structure research projects and resultant publications (Gregor & Hevner, 2013). Typical DSR
endeavors involve a set of activities ultimately leading to the creation of the design artifact, including
the identification of problem, definition of objectives, design & development, demonstration, and
evaluation (Peffers, et al., 2007). During this process, researchers need to keep track of important
decisions and document the activities carried out, the deliverables from those activities, and the
relationships between those deliverables. DSR is typically collaborative, and researchers have to
exchange and discuss (intermediate) results. Finally, the research outcomes must be archived and
made available for other researchers, reviewers, and practitioners.
Importantly, tool support for DSR must not straight-jacket and over-engineer the DSR process, which
has often been described as involving creativity and decision making (Hevner, 2007; Vaishnavi &
Kuechler, 2008). Consequently, tool support for DSR should support the situational nature of DSR as
a problem-solving paradigmthat is, researchers should not be forced into mechanically applying
existing frameworks and rules, but should be supported in their collaborative effort of making informed
design decisions in specific design contexts.
Against this background, the development of advanced tool support to structure, document, maintain,
and present DSR projects and processes
warrants our field’s attention. Such tool support will
ultimately increase collaboration, traceability, and quality in DSR. In this paper, we present a set of
design principles for supporting DSR as well as a prototypical implementation.
is a web-based platform that supports the documentation and management of DSR, based on the use
of established DSR frameworks and methodologies.
We proceed as follows. We first develop a set of broad design principles for DSR support tools
grounded in seminal methodological works on DSR. Next, we describe our prototypical
implementation that demonstrates the feasibility of the design principles. We then use four recent
examples to illustrate its application in practice. We discuss the implications for research and practice
and provide a set of guidelines for using tools to effectively manage DSR. We conclude by presenting
an outlook on planned future developments.
2 Design Principles for DSR Project Support Tools
The primary objective of tools for supporting DSR in Information Systems is to enable researchers to
document their DSR processes in a complete, correct, precise, comprehensible, open, identifiable,
secure, and collaborative way. In this section, we present a number of design principles (DP) for such
tools, grounded in prior literature on DSR. Design principles capture knowledge about instances of a
class of artifacts (Sein, Henfridsson, Purao, Rossi, & Lindgren, 2011). The design principles we
suggest fall into the category of action- or user-oriented design principles (Chandra, Seidel, & Gregor,
2015), that is, they focus on what the tool should allow users to do.
The development of design principles is based on the understanding that DSR is a problem-solving
paradigm that produces knowledge at different levels of abstraction, ranging from concrete
instantiations to design principles and full-blown design theory (Gregor & Hevner, 2013). We further
consider that DSR is a collaborative endeavor (Sein, et al., 2011), and we thus distinguish four key
user roles: (1) the researcher managing and documenting her research; (2) researchers contributing to
the project; (3) interested researchers viewing the documented research process; (4) practitioners
interested in selected research results of high practical relevance and applicability. In light of these
basic assumptions and key roles, we derive seven design principles for DSR support tools.
First, the tool should enable the managing researcher to create a new DSR project documentation. By
using provided templates based on existing DSR approaches, the researcher can follow established
and well-accepted practices, thereby saving time in creating the new project documentation. There are
a number of well-accepted DSR methodologies (e.g., Kuechler & Vaishnavi, 2012; Peffers, et al.,
2007; Sein, et al., 2011), and researchers should be able to choose the method they deem most
DSR projects typically follow a specific research process, involving stages such as identify problem, build solution, evaluate
solution, etc.; we are interested in tool support that helps design science researchers document their DSR process that,
naturally, is part of a project.
Tool-Support for Design Science Research
suitable for their study. Accordingly, tool support for managing DSR projects should allow for the use
of various approaches to DSR. Correspondingly, the first design principle (DP) is:
DP1 (principle of documentation): Provide features that allow users to create a new DSR project
documentation based on existing DSR approaches.
Besides using established DSR approaches, it is now common to adapt these approaches to the
specific needs of a project (e.g., Seidel et al. use a DSR method drawing on the method proposed by
Peffers and associates (2007)). By using templates, researchers can choose the initial structure of
their project, but this structure can be customized to best fit with the specific situation and the team’s
preferences. Correspondingly, our second design principle is:
DP2 (principle of context-sensitivity): Provide features that allow users to customize a chosen
DSR approach and align it with the situation at hand.
DSR processes consist of various stages (Peffers, et al., 2007) and unfold over time, often iteratively
(Hevner, et al., 2004; Peffers, et al., 2007; Sein, et al., 2011). Consequently, once the DSR project
and its initial structure have been created, the tool should support the research team in documenting
design iterations that contain interlinked design phases with various design activities. Correspondingly,
our third design principle is:
DP3 (principle of design as an iterative process): Provide features that allow users to document
a DSR project in terms of design iterations, design phases, and design activities.
Most DSR projects are collaborative (e.g., Lindgren, Henfridsson, & Schultze, 2004). Managing
activities and keeping track of progress is challenging and increases the effort related to managing the
project. Therefore, the tool should provide support to collaborate with others in planning and
documenting DSR. This includes elements such as registering users and maintaining user profiles.
DP4 (principle of collaboration support): Provide features that allow users to collaborate in DSR
As design science research develops knowledge at various levels of abstraction, ranging from
concrete instantiations to design principles and full-blown design theory (Gregor & Hevner, 2013), tool-
support for DSR must not only support the documentation, but also the extraction of knowledge, for
instance, through memos that allow the research team to reflect on the design choices that were
made, and to extract more general knowledge, often referred to as learning (Sein, et al., 2011).
Memosand techniques of memo writingare mostly known from qualitative research (Glaser, 1978;
Miles, et al., 2013; Strauss & Corbin, 1998). Correspondingly:
DP5 (principle of knowledge extraction): Provide features that support users in extracting
knowledge and documenting learning from their design DSR projects.
Communicating research results is an essential step in DSR (Hevner, et al., 2004). Interested
researchers as well as practitioners should be able to retrieve information about the DSR project, and
tools for effectively supporting DSR projects should support publishing selected results from an
(ongoing) DSR project. Correspondingly:
DP6 (principle of communication support): Provide features that allow users to effectively
communicate research results.
Finally, any tool for documenting research must ensure data privacy and security of stored
information. All research findings kept within such tool must comply with established data privacy
regulations of the researchers’ country and of the research community. Moreover, certain contents will
have to be kept confidentially. Correspondingly:
DP7 (principle of data security): Provide features that ensure data privacy and security of stored
3 Instantiation: Architecture, Data Model, and Key Features
In this section, we describe a prototypical instantiation of our design principles for DSR support tools,
in terms of the general architecture of the tool, its underlying data model, and its key features. The
prototypical implementation serves as a demonstration of the design principles developed (Peffers, et
al., 2007).
Tool-Support for Design Science Research
3.1. Architecture
In order to support convenient access and collaboration, is implemented as a
responsive web-based tool.
The architecture follows a three-tier approach (Figure 1). The first tier
(presentation layer) is a web browser; the second tier (application logic) is implemented through an
engine using dynamic Web content technology; the third tier (storage) is implemented through a
MySQL database. The web browser sends requests to the middle tier, which services by making
queries and updates against the database, and which generates a user interface. Responsive user
interfaces are realized using JavaScript and the CSS framework Bootstrap. To decouple the data
interchange layer from the presentation layer, Ajax is used. This allows changing content dynamically
without the need to reload the entire page. The application itself is written in Python on top of the free
and open-source web framework Django, which follows the model-view-template (MVT) architectural
pattern. All DSR project data provided by the user is stored in a MySQL database.
Figure 1: architecture overview
3.2. Data Model
The data model is based on key elements of DSR projects, which are represented as classes. The
first important class is project. A project has several attributes such as title, description, and
participating researchers. For each project, a template of a specific DSR approach can be selected,
which can be adapted to the specific situation and needs. In DSR, projects are usually divided into
design iterations. A design iteration consists of several design phases with multiple design activities.
All of these elements have a title and a description. Moreover, each of these elements can have
attached memos. The data model thus supports effective documentation of DSR projects. Figure 2
visualizes the key elements of the underlying data model.
The tool is available online at
Tool-Support for Design Science Research
Figure 2. Simplified data model
3.3. Key Features to Implement the Design Principles
The tool supports the documentation of DSR projects as well as knowledge sharing processes in DSR
projects; it allows users to create design traces through documenting activities and design decisions
made throughout the process. Design projects can be publicly displayed, made available to a defined
group of stakeholders, or kept privately.
To accomplish data privacy (DP7), all information stored in is kept strictly
confidential. The service adheres to international research governance standards, such the European
Code of Conduct for Research Integrity, the NSF Responsible Conduct of Research (RCR), Swiss
National Research Foundation Scientific Integrity, and the AIS Code of Research Conduct. The owner
of a research project can decide on the parts that are publicly available. It is further possible to keep
all information private, and use the tool solely for structuring and managing the DSR project.
The user interface was designed with the explicit goal of providing an easy-to-use tool that does not
require a long training period. Specifically, the tool comprises three main parts: navigation, workspace,
and toolbox (Figure 3). The navigation tree on the left hand side provides an overview of projects,
iterations, phases, and activities. Elements can be moved within the treefor instance, one activity
might be moved from one phase to another within an ongoing project. The workspace (middle part in
Figure 3) displays the currently selected elementi.e., a project, an iteration, a phase, or a specific
activity (in Figure 3 a project is selected and the iterations that are part of this project are shown). The
toolbox on the right hand side provides commands such as “New iteration” or “Add publication” and
further allows to add and edit memos. Memos can be added to any elementthat is, at the level of
projects, iterations, phases, or activities. It is further possible to add attachmentssuch as PDF
documentsat the level of activities.
Figure 3:
In what follows, key features are described in more detail, with reference to the design principles they
Tool-Support for Design Science Research
Creating a Project Documentation Space
At the outset, the team of researchers or the individual researcher creates a project documentation
(DP1). A project has a title and a description. Besides, a template can be chosen and the number of
(planned) iterations can be defined. At the time of writing this paper, templates are available for the
DSR process proposed by Peffers et al. (2007), the DSR process described in Kuechler & Vaishnavi
(Kuechler & Vaishnavi, 2008; 2012), action design research as described in Sein et al. (2011), and the
design thinking method (Brown, 2008). Figure 4 shows the “Create a new project” form.
Figure 4: Create a new project
The selection of a template results in the immediate creation of specific phases. Figure 5 shows the
result of a new project based on the phases suggested by Peffers and associates (2007). On top of
the screen it is indicated that these phases are part of “Iteration 1.” Importantly, the number of
iterations can be changed at any time during the project and phases can be edited freely. Thus, any
specific adaptation of these approaches as well as any other approach is possible (DP2).
Figure 5: Overview of iteration: Phases created from template
Structuring and Documenting Projects: Iterations, Phases, Activities
Once a project is created, iterations, phases, and activities can be added, removed, and edited to
structure the research process (DP3). provides a number of features for
continuous documentation:
Tool-Support for Design Science Research
Users can provide a description for each iteration, phase, and activity (Figure 6). The
description should capture the essence of the iteration, phase, and activity.
Users can associate publication links with projects, iterations, phases, and activities (see right
hand side of Figure 6). For instance, publications might provide relevant kernel theory or
methodological guidelines that informed an entire project, an iteration, a phase, or a specific
The tool supports the extraction of knowledge (DP5). Memos can be attached at the level of
projects, iterations, phases, and activities. In DSR, memos are an important vehicle to keep
record of design decisions as well as operational decisions. Memos further provide an
important basis to develop more abstract design knowledge based on specific DSR
processes, that is, to abstract away from a specific situation.
In order to provide contextual information (DP2), activities can be tagged. Tags can, for
instance, describe the situation in which the activity was conducted.
Files can be added to activities. These may include information that was collected within the
activity, for instance, in the form of interview transcripts or quantitative data from a simulation.
Figure 6: Edit activity form
Collaboration, Publishing, Import and Export
Users can invite other users to collaborate on a project (DP4). Collaborators are invited through email.
A complete project summary can be created by selecting “make project visible” at the level of a project
(DP6). If a project is set to “visible,” a link is provided where interested stakeholders can see a
complete summary of the project. Whether memos are shown or not is optional. Besides, it is possible
to export the entire project as an XML file (DP6).
4 Illustrative Examples: Retrospective Documentation of
Published Studies
In this section, we illustrate the application of our prototypical instantiation using four DSR studies
published or forthcoming in European Journal of Information Systems, Journal of the Association for
Information Systems, Journal of Information Technology Theory and Application, and Datenbank-
Spektrum. Each of these articles uses a different DSR approach. Each of them was co-authored by at
least one of the authors of this paper, and we thus had access to all data required to retrospectively
apply to these studies. In our retrospective documentation of published DSR
studies we did not aim at completeness, but at illustrating the use of the system. Applying the tool to
published examples is a first step towards evaluating both the implementation and its underlying
design principles. Table 1 provides an overview of the selected cases.
Tool-Support for Design Science Research
Table 1: Illustrative examples
Objectives & Outcomes
Seidel, Chandra Kruse, Székely,
Gau, & Stieger (forthcoming)
Develops design principles and
revises these design principles
through three rounds of
implementing, demonstrating, and
evaluating a prototypical
Mainly informed by Peffers et al.
Meth, Mueller, & Maedche (2015)
Proposes a design theory for
requirement mining systems
(RMSs) based on two design
principles: (1) semi-automatic
requirement mining and (2) usage
of imported and retrieved
Informed by Kuechler & Vaishnavi
Schacht, Morana, & Maedche
Presents a comprehensive action
design research (ADR) project in
the context of managing project
knowledge reuse, specifically the
KMS artifact Just Know. The entire
process from specifying its
requirements to its implementation
is described step by step.
Informed by Sein et al. (2011)
Houy, Niesen, Calvillo; Fettke;
Loos; Krämer; Schmidt;
Herberger; Speiser; Gass;
Schneider & Philippi (2015),
further development of the basic
concept presented in Houy,
Niesen, Fettke, Loos (Houy,
Niesen, Fettke, & Loos, 2013)
Presents a design research project
dealing with software-supported
automatic identification and
classification of argumentation
structures in German court
Informed by the ARIS phase model
by Scheer (1998):
1.) requirements engineering,
2.) development of basic model,
3.) development of technical model
and architecture,
4.) implementation and testing.
4.1. Example 1 (Seidel, et al., forthcoming)
The paper develops design principles for sensemaking support systems in environmental
sustainability transformations, and the research approach was mainly informed by the DSR
methodology proposed by Peffers and associates (2007). Key objectives included to develop design
principles and evaluate these design principles through the implementation, demonstration, and
evaluation of a purposeful IT artifact.
The study first identified objectives of a class of systems (sense making support systems) along with
an initial set of design principles grounded in prior literature and then went through three cycles of (1)
design and development, (2) demonstration and evaluation, and (3) formalization of learning in terms
of revising the design principles. That is, the study was informed by the steps suggested by Peffers et
al. (2007)identify problem & motivate, define objectives of a solution, design & development,
evaluation, and communicationbut did not strictly follow their approach.
Figure 7 shows an overview of how the DSR process was retrospectively documented in For each iteration (called “round”), there is a short description and an
overview of the number of phases and activities within that iteration/round. Notably, the research
approach was informed by Peffers et al. (2007), but their approach was adapted provides the flexibility required to adapt the DSR approach chosen to
the specific needs of the project.
Tool-Support for Design Science Research
Figure 7: Illustrative example 1: Overview of project with three iterations
4.2. Example 2 (Meth, et al., 2015)
The paper proposes a design theory for requirement mining systems (RMSs) based on two design
principles: (1) semi-automatic requirement mining and (2) usage of imported and retrieved knowledge.
As part of an extensive design project, which led to these principles, the research team implemented a
prototype based on this design theory (REMINER), which supports requirements engineers in
identifying and classifying requirements documented in natural language.
The research design pursued in this project is based on the suggestions of Vaishnavi and Kuechler
(2008). The framework was extended by drawing on Peffers et al. (2007) through specifically
distinguishing between demonstration and evaluation. In the demonstration phase, the artifact was
presented to subject-matter experts from the problem domain (i.e., requirement engineers in our case)
to record their feedback. In the evaluation phase, the artifact was evaluated in a suitable context to
measure its effectiveness and efficiency. Figure 8 shows an overview of how the DSR process was
retrospectively documented in Two design cycles were completed.
Furthermore, the above mentioned phase refinement, distinguishing demonstration and evaluation,
was realized in the tool.
Figure 8: Illustrative example 2: Design cycles with phases
4.3. Example 3 (Schacht, et al., 2015)
The paper discusses an action design research (ADR) project on the design of a project knowledge
management system artifact, from specifying its requirements to its implementation. Based on this
Tool-Support for Design Science Research
process, six design principles for a project knowledge management system were derived. The
research project followed the suggestions by Sein et al. (2011), and was divided into two consecutive
ADR cycles.
Figure 9 visualizes the retrospective documentation of the research project in
Given the complexity of the research project (two design cycles, eight phases, and a total of 15
activities) and its duration of more than three years, using a DSR project tool during the actual
research project runtime would have been beneficial. The retrospective documentation shows how can assist researchers in planning their research activities in the context of an
ADR project and support the documentation of various project results. Especially functionalities to
store attachments and create memos for each activity can support researchers in handling the
complexity and amount of valuable knowledge created in an ADR project.
Figure 9: Illustrative example 3: Supporting complex design cycles
4.4. Example 4 (Houy et al., 2015)
The paper presents and discusses a DSR project named ARGUMENTUM, dealing with argumentation
mining techniques in the context of jurisprudence. In jurisprudence, it is an important task to analyze
court decisions containing complex argumentation structures. The design principles and software
prototype developed in this project support the identification and classification of argumentation
structures in the court decision corpus of the German Federal Constitutional Court (in German:
Bundesverfassungsgericht, BVerfG). The project followed a common IS development project design
informed by the ARIS phase model (Scheer, 1998): (1) requirements engineering, (2) development of
basic model, (3) development of technical model and architecture, and (4) implementation and testing.
While there were two major design iterations planned and executed during the project, each phase in
each iteration was processed in an agile manner with several feedback rounds before the next design
phase was started. Figure 10 illustrates the phase structure of the first design iteration in
The project documentation is publicly available on:
Tool-Support for Design Science Research
Figure 10: Illustrative example 4: Supporting iterative flexible design phase models
4.5. Summary
Our retrospective documentation of four examples shows (1) that the research design used in this
study could be documented in, (2) that supports the
adaptation of existent DSR methodologies, and (3) that complements other
research tools.
5 Discussion & Implications
In this paper, we have proposed design principles for DSR support tools. We have further
demonstrated the feasibility of these principles through their implementation, and we have illustrated
the use of the system through a retrospective analysis of published cases. Our retrospective analysis,
a first steps towards evaluating both our implementation and its underlying design principles, has
shown that the proposed tool is suitable to document a variety of different research designs and can
complement other research tools, for instance, for data analysis. It is our contention that
MyDesignProcess.comand DSR project tools in general termsprovide an important component in
the ecosystem in which DSR projects are conductedalong with other tools to analyze qualitative
(e.g.,NVivo, atlas.ti) and quantitative (e.g., SPSS, R) data.
It is expected that providing tool support for DSR projects is of high relevance for both academia and
practice. For researchers and authors such tools afford documentation of their design-oriented
information systems research processes as well as the extraction of more abstract knowledge about
design, thereby ensuring rigor. For readers, such tools afford comprehending the process of
knowledge creation and artifact development. For reviewers, these tools afford assessing the quality
of design-oriented research. For the DSR and broader IS scientific community, they afford knowledge
sharing and accumulative knowledge creation, which is key for scientific progress, especially in
design-oriented research disciplines (Fettke, Houy, & Loos, 2010). Moreover, the accumulation of
reliable design-related knowledge can support the development of new original IS (design) theories
(Bichler et al., 2016). Ultimately, it is our contention that rigorous and relevant DSR will help improve
the human condition and lead to the betterment of our society. is not without limitations. Specifically, our retrospective analysis has shown
that, in most cases, it will complement rather than substitute the use of other tools, most notably for
the analysis of qualitative and quantitative data. Besides, our evaluation is retrospective, and it will be
interesting to see what we learn from the application of DSR support tools in novel projects that use
such tools from the outset.
Tool-Support for Design Science Research
Against this background, we would like to suggest some basic guidelines for using tools for
documenting DSR research projects:
Planning: Plan ahead. In order to ensure the rigor of an DSR research project, clear
methodological steps need to be taken.
Documentation: Carefully document the entire DSR process, including activities, outcomes,
design decisions, and operational decisions.
Memoing: The documentation process should be supported by extensive memo writing.
Memos allow researchers to keep track of important design decisions as well as operational
decisions. As such, memos provide an important basis for the abstraction of design
knowledge, for instance, in terms of design principles, as well as for reporting from the study.
Flexibility: While the rigorous application of DSR requires clear methodological steps, this
does not mean that researchers cannot adapt existent approaches to suit the specific situation
at hand.
Complementarity: DSR projects are typically complex research endeavors that require the
involvement of various roles (e.g., information systems researcher, software developer,
hardware specialist, etc.) as well as various software tools. For instance, evaluation of IT
artifacts will typically involve qualitative and/or quantitative research methods. Research
teams are challenged to set up a suitable software ecosystem to support this process. Tools
such as should be used in combination with tools such as NVivo for
qualitative analysis or SPSS for the analysis of quantitative data.
6 Conclusion
Our design principles and prototypical instantiation are intended to contribute something to the
ongoing maturation of DSR as an important research paradigm within the Information Systems field
and beyond. Documenting and managing research projects, and thus creating a design trace, is
crucial in order to ensure traceability, collaboration, and quality in DSR projects. DSR has shown to be
a problem-solving paradigm that, with its focus on solutions, can meaningfully contribute to both IS
theory and practice. Tools such as add an important component to the
software ecosystems that support researchers in conducting DSR. is an open
solution in that it is intended to support any design science research project and to seamlessly fit into
any research ecosystem.
In the next step, we will continue to evaluate and to reflect and learn from its
application in practice. Through this iterative process both the underlying design principles and their
implementation will be further developed. Specifically, it is intended to collect user feedback and to
conduct focus groups with users who have used the system in real-life DSR projects for a
considerable amount of time. The development of design principles and tool will reach a stable state
once we reach saturationthat is, additional iterations do not lead to notable changes in the design
principles or their implementation.
We are excited to see how the field will adopt this sort of tool support, and we are confident that DSR
will thrive and continue to play an important role in the future development of our field.
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... However, compared to other fields there is less tool support available for DSR [5]. In qualitative research, for instance, researchers follow specific procedures and are required to document clearly the chain of evidence and report on criteria like intercoder reliability [17]. ...
... (MDP) is an existing and publicly available online platform that supports design science researchers managing, documenting, and executing their DSR projects in a complete, correct, comprehensive, open, identifiable, secure, and collaborative way, while also making their research process explicit and transparent for other researchers [5]. However, documenting is both laborious [21] and timeconsuming. ...
... For example, in the field of medicine where comprehensive and precise documentation is essential, studies showed that up to 28% of a working day is spent on the documentation process [1]. In DSR, documentation is not as critical as in the field of medicine, but it does have an important role to play in understanding the design process and supporting reproducibility of artifacts [5,14]. Tool support in DSR should not only enable the documentation of the design processes, but also decrease the efforts of researchers and increase the benefits of providing documentation. ...
Conference Paper
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Design Science Research (DSR) provides a rich body of frameworks, approaches, and methods to support researchers in conducting DSR projects. However, there is little tool support and guidance for effectively documenting DSR processes. In this article, we present a prototypical implementation of a conversational agent called "DSR Buddy" that is integrated into the existing and publicly available platform. DSR Buddy aims to decrease efforts of DSR project documentation and increase benefits for researchers by providing intuitive DSR activity documentation support. We present an initial set of design features that we have implemented in the form of the DSR Buddy. Additionally, we illustrate the potential of our prototypical implementation by applying it to an exemplary DSR project.
... Furthermore, there exists tool support for researchers to document and structure such DSR processes, for example, developed in the collaborative DSR research project [3]. ...
... Such shortcomings are addressed by distinct tools that have been designed for the purpose of documenting the research process, such as the tool [3]. In order to demonstrate the idea of journaling the design process, we used the tool ...
... The performing aspect of the project is expressed through the different activities executed and captured during the project. Such activities can also contain subactivities in order to structure and organize the process of a DSR project [3]. The complete journal of a DSR project, or only parts of it, can be made publicly accessible and communicated to the community. ...
Full-text available
Design Science Research (DSR) is a highly context-dependent and iterative process. Design processes in DSR projects represent the actual strategy and execution of design knowledge inquiry and are typically unique. However, details of the actual design process are often lost as there is a lack of transparency in published DSR projects. In this research in progress paper, we present the idea of “journaling” the DSR process. We introduce the concept, showcase it with a conceptual framework, present practical applications, discuss implications and outline future research.
... The area of digital innovation includes information digitization, innovation management with a greater variety and reach of disruption across organizational limitations, digitally-enabled generation, and new digital technologies. It has been observed that digital innovation is viewed across a wide range of disciplines as essential importance (Vom Brocke et al., 2017). Resultantly, the two aspects of privacy and security systems have become more important to protect and control the activities and services provided by the internet. ...
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The ancient city of Petra is well known for its rock-carved structure and water traction canal system. This study examines the chances of employing the Greenstone system to create a digital tourism library for the historical city of Petra. In this context, the study has derived three objectives such as; briefly explain the stages of building a digital library using the Greenstone system, investigate students' opinions about the characteristics of the Greenstone system, and investigate students' beliefs about the materials and information that should be included in the digital library. The study used an experimental approach by creating a small-sized digital library to determine the software's suitability. A survey questionnaire containing two sections and 34 questions was distributed among 50 participants. Selected participants were students of Al Hussein Bin Talal University. The results showed that the Greenstone system is particularly suitable for building digital libraries when some of the existing problems have been solved, there is great enthusiasm for establishing the Petra Digital Library, and information about what tourists contribute to tourism will significantly help get virtual development. It is expected to link the library with other entities through a network to share metadata between universities. This study encouraged digital library projects and reflections on the Greenstone system to identify its effectiveness in digitally managing libraries. The study concluded, creation of digital library at Jordan's Al Hussein Bin Talal University Library will provide access to publications about Petra City and other information resources that are not available in print.
... Further a checklist outlining what to consider in defining and presenting the DSR project is provided, and guidance on how to communicate and present DSR (Cahenzli et al. 2021). We also provide access to tools students can use to plan and document their design process (vom Brocke et al. 2017) and keep a journal of their DSR experience (vom Brocke et al. 2021). We continuously grow this toolbox of useful artifacts to support students in learning how to plan and conduct DSR to high standards. ...
Conference Paper
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Design science research (DSR) aims to generate knowledge about innovative solutions to real-world problems. A comparably new stream of research, DSR has matured methodically, and is increasingly catching the interest of researchers, specifically for its potential to contribute to problem solving in society and the economy. Since research methodology curricula develop slowly, however, DSR is still underrepresented in most curricula and courses on research design and methods, and we lack guidance on what and how to teach in a DSR course in a way that enables junior academics to conduct DSR according to high standards. We report on teaching DSR methodology both on PhD and Master levels and for both managerially and technically oriented student populations. Our interactive on-site and distance formats have been refined over 14 years. The PDW presents an effective syllabus, teaching material and experience from conducting over 25 courses with students from over 20 countries across all three geographic AIS regions.
... Such a log file can, for instance, be provided in the appendix of an article. Also, appropriate tools have been developed by the DSR community (Morana et al., 2018;vom Brocke, Fettke, et al., 2017) to document and communicate the design process along the way. The tool ...
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Design science research (DSR) is an established research paradigm aiming to create design knowledge on innovative solutions for real-world problems. As such, DSR has the potential to contribute to the solution of real-world problems of great societal value. In this article, we discuss how DSR can maximize such practical impact. Reflecting on our long-standing collaboration with the globally operating Hilti company, we report on a rich empirical case and derive principles in order to increase the practical relevance and societal contribution of DSR projects. We also derive quality criteria through which DSR articles can demonstrate practical relevance and societal value contribution.
... Design science research (DSR) stands as an important part of information systems (IS) research (Baskerville, 2008;Gregor & Hevner, 2013;Iivari, 2015;vom Brocke et al., 2017) and has gained increased attention in recent years (Leukel et al., 2014;Peffers et al., 2018;Stein et al., 2014). DSR projects evolve around iteratively designing and evaluating artifacts as solutions for prevailing problems (Hevner et al., 2004;Iivari, 2015) and developing prescriptive knowledge about the effective design of such artifacts (Baskerville et al., 2018;Gregor, 2006). ...
Full-text available
Design science research (DSR) has been established as an essential part of information systems research. DSR can provide artificial solutions and prescriptive knowledge about how to solve problems relevant to our modern times. However, DSR has been reported to be in a state of "conceptual confusion." Thus, an ongoing and open discourse regarding how to overcome the causes of this confusion has arisen. Several causes and solutions have been proposed, ranging from conceptualizations of contributions, publication schemas, to the formulation of research strategies and genres. Prominently, the persisting confusion frequently leads editors and reviewers to assess the same study's merit substantially differently, depending on the individual editor's and reviewer's understanding of and preferences for DSR. Consequently, publishing DSR studies is challenging. Against this background, we propose DSR focus as a two-dimensional characteristic of a DSR study, comprising the two dimensions "contribution" and "research approach." Furthermore, we present a DSR focus matrix (DSRFM) as a framework and tool to describe the DSR focus of a study and identify relevant seminal work. Following this framework enables a grounded discussion with editors and reviewers, thus preventing diverting understandings and preferences that may skew the assessment of a study. We demonstrate this ability by positioning research strategies, genres, and seminal works within the matrix's quadrants.
... Authors adopt the steps for developing DPs. For example, [25] described [11]'s procedure for the development of DPs. Thereby, they used [11] as an illustrative example for their design instantiation. ...
Full-text available
Accumulating prescriptive design knowledge, such as design principles (DP), is one of the fundamental goals in design science research projects. As previous studies have examined the use of DPs in practice to advance the development and communication of such principles, we argue that this attention also needs to be paid to how and for what researchers (re-)use DPs. Hence, this paper explores DP usage in cumulative (information systems) research based on the analysis and coding of a sample of 114 articles with 226 in-text citations. In doing this, we aim at contributing to the valuable discourse on DP reuse and accumulation by focusing on usage in research, present preliminary types of DP usage extracted from cumulative literature, as well as raise the awareness for guiding user and designer in how to (re-)use and how to allow for reuse of DPs.
... flags" OR detection). The ATLAS.ti software, a qualitative data analysis software that supports literature analysis [64], was used for analyzing the literature. • The first-round literature review yielded five results without duplicates. ...
Full-text available
Fraud is a significant challenge for organizations, as it is a serious problem with far-reaching consequences. It is estimated that every year, 5% of organizational revenue is lost due to fraud. Occupational fraud refers to a deliberate misuse of one's occupation for personal enrichment. Occupational fraud cases investigated in 125 countries in 2018 and 2019 involved funds that exceeded an estimated 3.6 billion U.S. dollars. Process-based fraud (PBF) is a type of occupational fraud that occurs in business processes and involves a deviation from standard operating procedures. Since business processes represent the work's logic, and through such processes, an organization’s strategy is executed, PBF hinders the achievement of organizational goals, increases costs, and damages customer experiences and relationships. Therefore, as it is impossible to prevent PBF entirely, it needs to be detected. However, PBF detection metrics have not been sufficiently addressed in the literature. Existing metrics are incomplete, overlapping, scattered, and not standardized, while fraud continues, likely on a vast scale. This research organizes, simplifies, and extends existing detection metrics for possible fraud in business processes by developing a taxonomy of PBF detection metrics. Taxonomy plays a vital role in research and management, as the classification of objects helps researchers and practitioners understand and explain complex domains. In this research, design science research is applied, as it is well suited to human-made artifact development such as a taxonomy. First, a systematic literature review is performed on fraud detection metrics in business processes to survey the current state of fraud detection by focusing on PBF metrics, while including all relevant conceptual perspectives of PBF detection. Second, an enhanced taxonomy development method is proposed, which offers complete and actionable DSR research steps that novice researchers can easily implement. Third, a taxonomy of PBF detection metrics is developed. The taxonomy developed provides a holistic view and reveals the relevant dimensions, characteristics, and objects of the PBF detection metrics, with their relationships identified. It improves PBF detection in practice, solves the classification problem, and enhances PBF detection understanding. Fourth, the created taxonomy is employed as an underlying theory to extend, organize, and evaluate the PBF detection metrics. The findings reveal four PBF detection dimensions with the following characteristics: (1) process perspective {time, function, data, resource, and location}, (2) presentation layer {the process’s map, stream, model, instance, and activity}, (3) fraud data scheme {anomalous, discrepant, missing, and wrong}, and (4) fraud domain {generic or specific}. Moreover, 41 PBF detection metrics are deduced from the developed taxonomy, and their application is presented. The developed taxonomy offers a useful tool for anyone seeking to classify, develop, and evaluate PBF detection metrics. Additionally, it helps standardize the concepts of PBF detection metrics to ensure consistency among stakeholders. Furthermore, the developed metrics improve the detection of PBF as they provide a complete, validated, and standardized list of PBF detection metrics that includes all of the necessary PBF detection dimensions. It is expected that PBF detection stakeholders will apply the developed metrics in their practice to increase the effectiveness of the PBF detection process.
... In order to support concurrent design and evaluation, it is suggested to plan and document the build and evaluation activities in one. DSR tools have been developed (vom Brocke et al. 2017, Morana et al. 2018 to keep logs of the research process; such logs can complement a high-level list of research activities used to scope the DSR project in the process dimension. The process documented here may also include activities for theorizing about the design. ...
Full-text available
Design Science Research (DSR) is a problem-solving paradigm that seeks to enhance human knowledge via the creation of innovative artifacts. Simply stated, DSR seeks to enhance technology and science knowledge bases via the creation of innovative artifacts that solve problems and improve the environment in which they are instantiated. The results of DSR include both the newly designed artifacts and design knowledge (DK) that provides a fuller understanding via design theories of why the artifacts enhance (or, disrupt) the relevant application contexts. The goal of this introduction chapter is to provide a brief survey of DSR concepts for better understanding of the following chapters that present DSR case studies.
Designing augmented and tangible experiences that intertwine human practices and expectations, interaction spaces and complex digital artifacts is a complex and multifaceted task that relies upon iterative and multidisciplinary ideation processes. Design thinking techniques have been traditionally used in ideation of such digital artifacts. In this paper, we posit that integrating some software engineering practices can improve ideation by providing a structure to the process and helping to build a shared and permanently documented design rationale. It is not a matter of software engineering versus design thinking but a question of developing a holistic understanding of technological development where discipline and creativity, rationality and emotions and quality centered and people centered coexist. Based on this assumption, we conceived a software tool called CoDICE that offers a virtual co-design space where augmented digital experiences are documented and analyzed in a shared and distributed way. The paper discusses how CoDICE contributes to alleviate some problems of co-design events including the need to support multiple co-design spaces, make explicit the co-design process and its goals, support documentation, justify design decisions, explore multiple ideas and generate a shared representation of the outcomes. Two scenarios are used to illustrate the tool utility: short-term co-design workshops in which the tool enabled multidisciplinary teams of novice designers to explore and structure their ideas and a long-term co-design project where the tool facilitated traceability, documentation, the reuse of design components and the shared elaboration of the design rationale and evolution of the deployed technologies.
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This paper reports on the results of a design science research (DSR) study that develops design principles for information systems (IS) that support organisational sensemaking in environmental sustainability transformations. We identify initial design principles based on salient affordances required in organisational sensemaking and revise them through three rounds of developing, demonstrating and evaluating a prototypical implementation. Through our analysis, we learn how IS can support essential sensemaking practices in environmental sustainability transformations, including experiencing disruptive ambiguity through the provision of environmental data, noticing and bracketing, engaging in an open and inclusive communication and presuming potential alternative environmentally responsible actions. We make two key contributions: First, we provide a set of theory-inspired design principles for IS that support sensemaking in sustainability transformations, and revise them empirically using a DSR method. Second, we show how the concept of affordances can be used in DSR to investigate how IS can support organisational practices. While our findings are based on the investigation of the substantive context of environmental sustainability transformation, we suggest that they might be applicable in a broader set of contexts of organisational sensemaking and thus for a broader class of sensemaking support systems.
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Even though the idea of science enjoys an impressive reputation, there seems to be no precise conception of science. On the one hand, there is no unified definition of the extension of activities subsumed under the notion of science. According to the narrow conception that is common in Anglo-Saxon countries, science is restricted to those disciplines that investigate nature and aim at explanation and prediction of natural phenomena. A wider conception that can be found in various European countries includes social sciences, the humanities and engineering. On the other hand and related to the first aspect, there is still no general consensus on the specific characteristics of scientific discoveries and scientific knowledge. This article includes contributions from various scholars. It is intended to not only lead to a summary of different theoretical streams relevant to our research, but it might also influence the discussion about curricula in our field.
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Knowledge is a fuzzy phenomenon and managing it a complex endeavor. In particular, knowledge reuse possesses the possibility to increase project performance since project teams can benefit from knowledge of former projects. Therefore, knowledge reuse is an essential knowledge management (KM) process phase that needs to receive special attention. Studying KM in general requires one to consider both social and technical aspects. On the one hand, KM highly depends on individuals, their interactions with each other, organizational rules, and cultural aspects forming KM's social perspective. On the other hand, contemporary information technologies promise to support organizations, teams, and individuals in managing what they know. Today, the KM research field is tremendous and full of social and technical insights. However, independent of which aspect of KM is studied, most researchers follow either a technology-driven approach for building innovative KM technologies or a behavioral-research approach to observe and understand complex KM phenomena. Few papers report the design of a KM system that integrates the social and technical perspective by expressing and evaluating design principles according the design science research approach. In this paper, we address this challenge and present a comprehensive action design research (ADR) project in the context of managing project knowledge reuse. Thereby, we present our KMS artifact called Just KNow and discuss the entire process from specifying its requirements to its implementation step by step. This paper helps KM researchers and practitioners make informed decisions. We support researchers in deciding whether the ADR approach is appropriate for their particular research project and provide a guideline for how to apply ADR. We support practitioners by helping them make design decisions when creating and implementing an effective KMS.
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The paper motivates, presents, demonstrates in use, and evaluates a methodology for conducting design science (DS) research in information systems (IS). DS is of importance in a discipline oriented to the creation of successful artifacts. Several researchers have pioneered DS research in IS, yet over the past 15 years, little DS research has been done within the discipline. The lack of a methodology to serve as a commonly accepted framework for DS research and of a template for its presentation may have contributed to its slow adoption. The design science research methodology (DSRM) presented here incorporates principles, practices, and procedures required to carry out such research and meets three objectives: it is consistent with prior literature, it provides a nominal process model for doing DS research, and it provides a mental model for presenting and evaluating DS research in IS. The DS process includes six steps: problem identification and motivation, definition of the objectives for a solution, design and development, demonstration, evaluation, and communication. We demonstrate and evaluate the methodology by presenting four case studies in terms of the DSRM, including cases that present the design of a database to support health assessment methods, a software reuse measure, an Internet video telephony application, and an IS planning method. The designed methodology effectively satisfies the three objectives and has the potential to help aid the acceptance of DS research in the IS discipline.
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The success of information systems (IS) development strongly depends on the accuracy of the requirements gathered from users and other stakeholders. When developing a new IS, about 80 percent of these requirements are recorded in informal requirements documents (e.g., interview transcripts or discussion forums) using natural language. However, processing the resultant natural language requirements resources is inherently complex and often error prone due to ambiguity, inconsistency, and incompleteness. Thus, even highly qualified requirements engineers often struggle to process large amounts of natural language requirements resources efficiently and effectively. In this paper, we propose a design theory for requirement mining systems (RMSs) based on two design principles: (1) semi-automatic requirement mining and (2) usage of imported and retrieved knowledge. As part of an extensive design project, which led to these principles, we also implemented a prototype based on this design theory (REMINER). It supports requirements engineers in identifying and classifying requirements documented in natural language and allows us to evaluate the artifact's viability and the conceptual soundness of our design. The results of our evaluation suggest that an RMS based on our proposed design principles can significantly improve recall while maintaining precision levels. © 2015, Association for Information Systems. All rights reserved.
One point of convergence in the many recent discussions on design science research in information systems (DSRIS) has been the desirability of a directive design theory (ISDT) as one of the outputs from a DSRIS project. However, the literature on theory development in DSRIS is very sparse. In this paper, we develop a framework to support theory development in DSRIS and explore its potential from multiple perspectives. The framework positions ISDT in a hierarchy of theories in IS design that includes a type of theory for describing how and why the design functions: Design-relevant explanatory/predictive theory (DREPT). DREPT formally captures the translation of general theory constructs from outside IS to the design realm. We introduce the framework from a knowledge representation perspective and then provide typological and epistemological perspectives. We begin by motivating the desirability of both directive-prescriptive theory (ISDT) and explanatory-predictive theory (DREPT) for IS design science research and practice. Since ISDT and DREPT are both, by definition, midrange theories, we examine the notion of mid-range theory in other fields and then in the specific context of DSRIS. We position both types of theory in Gregor's (2006) taxonomy of IS theory in our typological view of the framework. We then discuss design theory semantics from an epistemological view of the framework, relating it to an idealized design science research cycle. To demonstrate the potential of the framework for DSRIS, we use it to derive ISDT and DREPT from two published examples of DSRIS.
Die Entwicklung überzeugender Argumentation ist - ebenso wie die Analyse gegebener Argumentationsstrukturen - eine wichtige Aufgabe sowohl in der Rechtswissenschaft als auch in der juristischen Praxis. Beide Aufgaben gestalten sich intellektuell anspruchsvoll und sollten sich auf möglichst viele relevante Hintergrundinformationen stützen. Einer ständig wachsenden Anzahl verfügbarer Informationsquellen steht dabei die beschränkte menschliche Informationsverarbeitungskapazität gegenüber. Um diesen Problemen zu begegnen, wird im Rahmen des vom BMBF geförderten Konsortialprojektes ARGUMENTUM ein Software-Werkzeug entwickelt, das eine automatische Identifikation und Analyse von Argumentationsstrukturen in den elektronisch verfügbaren Entscheidungen des Bundesverfassungsgerichts unterstützen soll. Im vorliegenden Beitrag werden Konzept, Architektur und Implementierung des ARGUMENTUM-Werkzeuges präsentiert und Einblicke in mögliche Anwendungen gegeben.
The common understanding of design science research in information systems (DSRIS) continues to evolve. Only in the broadest terms has there been consensus: that DSRIS involves, in some way, learning through the act of building. However, what is to be built – the definition of the DSRIS artifact – and how it is to be built – the methodology of DSRIS – has drawn increasing discussion in recent years. The relationship of DSRIS to theory continues to make up a significant part of the discussion: how theory should inform DSRIS and whether or not DSRIS can or should be instrumental in developing and refining theory. In this paper, we present the exegesis of a DSRIS research project in which creating a (prescriptive) design theory through the process of developing and testing an information systems artifact is inextricably bound to the testing and refinement of its kernel theory.