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Abstract

There has been a surge of interest in the design science research (DSR) paradigm as central to information systems (IS) studies in the past 20 years. The goal of a DSR research project is to extend the boundaries of human and organizational capabilities by designing new and innovative artifacts represented by constructs, models, methods, and instantiations (Hevner et al. 2004; Peffers et al. 2007; Gregor and Hevner 2013). Broadly speaking, DSR aims to add to knowledge of how things can and should be constructed or arranged (i.e., designed), usually by human agency, to achieve a desired set of goals. For example, design knowledge in the IS discipline includes knowledge of how to structure and construct a database system, how to model business processes, how to align IS with organizational strategy, and how to deliver data analytics for effective decision making (e.g., Becker et al. 2015). DSR results in IS have been shown to create significant economic and societal impact (vom Brocke et al. 2013). Beyond the IS field, DSR is a central research paradigm in many other domains including engineering, architecture, business, economics, and other information technologyrelated disciplines for the creation of novel solutions to relevant design problems. With its focus on the design and deployment of innovative artifacts, DSR is ideally positioned to make both research and practice contributions to the field of digital innovation (DI). Digital innovation is the appropriation of digital technologies in the process of and as the result of innovation. Digital innovation emphasizes combinations of digital and physical components to produce novel products (Yoo et al. 2010). In contrast to process innovation research, digital innovation research focuses on product and service innovation using digital technologies. Digital innovation is rapidly becoming a dominant topic and research focus in the fields of innovation, entrepreneurship, strategic management, organizational design, and information systems. The phenomena of digital innovation encompasses new digital technologies, information digitization, digitally-enabled generativity, and innovation management with a greater range and reach of innovation across organizational boundaries (Yoo et al. 2010; Fichman et al. 2014). Surveys show that organizations across a wide range of disciplines view digital innovation to be of vital importance (Fielt and Gregor 2016). However, we observe that there is little understanding and scarce literature on the relationships and synergies between DSR and DI. With this special issue, we present a set of DSR studies that show how DI artifacts are both produced by and used in DSR projects. In this editorial, we study the various roles that DI may play in DSR projects. We propose that the DSR paradigm provides formal and effective paths for demonstrating measurable impacts of DI in practice as well as contributing new DI knowledge to the appropriate discipline knowledge bases. The resulting framework provides a starting point for understanding the interactions between DI and DSR to the mutual benefit of both fields of study. Finally, we apply the new framework to the papers selected for the special issue highlighting the DI roles in each of the reported DSR projects.
EDITORIAL
Roles of Digital Innovation in Design Science Research
Alan Hevner Jan vom Brocke Alexander Maedche
Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2018
1 Introduction
There has been a surge of interest in the design science
research (DSR) paradigm as central to information systems
(IS) studies in the past 20 years. The goal of a DSR
research project is to extend the boundaries of human and
organizational capabilities by designing new and innova-
tive artifacts represented by constructs, models, methods,
and instantiations (Hevner et al. 2004; Peffers et al. 2007;
Gregor and Hevner 2013). Broadly speaking, DSR aims to
add to knowledge of how things can and should be con-
structed or arranged (i.e., designed), usually by human
agency, to achieve a desired set of goals. For example,
design knowledge in the IS discipline includes knowledge
of how to structure and construct a database system, how to
model business processes, how to align IS with organiza-
tional strategy, and how to deliver data analytics for
effective decision making (e.g., Becker et al. 2015). DSR
results in IS have been shown to create significant eco-
nomic and societal impact (vom Brocke et al. 2013).
Beyond the IS field, DSR is a central research paradigm in
many other domains including engineering, architecture,
business, economics, and other information technology-
related disciplines for the creation of novel solutions to
relevant design problems.
With its focus on the design and deployment of inno-
vative artifacts, DSR is ideally positioned to make both
research and practice contributions to the field of digital
innovation (DI). Digital innovation is the appropriation of
digital technologies in the process of and as the result of
innovation. Digital innovation emphasizes combinations of
digital and physical components to produce novel products
(Yoo et al. 2010). In contrast to process innovation
research, digital innovation research focuses on product
and service innovation using digital technologies. Digital
innovation is rapidly becoming a dominant topic and
research focus in the fields of innovation, entrepreneurship,
strategic management, organizational design, and infor-
mation systems. The phenomena of digital innovation
encompasses new digital technologies, information digiti-
zation, digitally-enabled generativity, and innovation
management with a greater range and reach of innovation
across organizational boundaries (Yoo et al. 2010; Fichman
et al. 2014). Surveys show that organizations across a wide
range of disciplines view digital innovation to be of vital
importance (Fielt and Gregor 2016).
However, we observe that there is little understanding
and scarce literature on the relationships and synergies
between DSR and DI. With this special issue, we present a
set of DSR studies that show how DI artifacts are both
produced by and used in DSR projects. In this editorial, we
study the various roles that DI may play in DSR projects.
We propose that the DSR paradigm provides formal and
effective paths for demonstrating measurable impacts of DI
in practice as well as contributing new DI knowledge to the
appropriate discipline knowledge bases. The resulting
A. Hevner
Information Systems and Decision Sciences Department,
University of South Florida, Tampa, FL, USA
e-mail: ahevner@usf.edu
J. vom Brocke
Institute of Information Systems, University of Liechtenstein,
Vaduz, Liechtenstein
e-mail: jan.vom.brocke@uni.li
A. Maedche (&)
Institute of Information Systems and Marketing (IISM),
Karlsruhe Service Research Institute (KSRI), Karlsruhe Institute
of Technology, Karlsruhe, Germany
e-mail: alexander.maedche@kit.edu
123
Bus Inf Syst Eng
https://doi.org/10.1007/s12599-018-0571-z
framework provides a starting point for understanding the
interactions between DI and DSR to the mutual benefit of
both fields of study. Finally, we apply the new framework
to the papers selected for the special issue highlighting the
DI roles in each of the reported DSR projects.
2 DSR Knowledge Consumption and Production Model
Information systems research consumes and produces two
basic types of knowledge: (1) behavioral science-oriented
research activities primarily grow propositional knowledge
or X-knowledge (comprising descriptive and explanatory
knowledge), and, (2) DSR-oriented research activities pri-
marily grow applicable (or prescriptive) knowledge or k-
knowledge (Gregor and Hevner 2013). Contributions to the
kknowledge base typically comprise knowledge about
technological (i.e., digital) innovations that directly affect
individuals, organizations, or society while also enabling
the development of future innovations (Winter and Albani
2013). Contributions to the Xknowledge base enhance our
understanding of the world and the phenomena our tech-
nologies harness (or cause). Research projects may com-
bine both paradigms of inquiry and contribute to both
knowledge bases.
The relationships of design knowledge produced and
consumed in DSR projects and the (design) knowledge
bases are shown in Fig. 1. This figure is adapted and
simplified from (Drechsler and Hevner 2018) and clearly
illustrates paired modes of consuming and producing
knowledge between the DSR project and the Xand k
knowledge bases. The k-knowledge is further divided into
two sub-categories. The Solution Design Entities collect
the prescriptive knowledge as represented in the tangible
artifacts, systems, and processes designed and applied in
the problem solution space. The growth of design theories
around these solutions is captured in the Solution Design
Theories knowledge base (Gregor and Hevner 2013).
Knowledge can be projected from the specific application
solutions into nascent theories around solution technolo-
gies, actions, systems, and design processes based on the
new and interesting knowledge produced in a DSR project.
Thus, we can describe the interactions of a specific DSR
project with the extant knowledge bases in the following
consuming and producing modes:
Descriptive (X) Knowledge: X-knowledge (or kernel
knowledge) informs the understanding of a problem, its
context, and the underlying design of a solution entity
(Arrow 1). As results of the research project, the design
and real-world application of solution entities or design
knowledge enhances our descriptive understanding of
how the world works via the testing and building of
new X-knowledge (Arrow 2).
Prescriptive (k) Solution Design Entities: Existing
solution entities, design processes, or design systems
are re-used to inform novel designs of new entities,
processes, or systems (Arrow 5) (vom Brocke and
Buddendick 2006). Within a DSR project, effective
solution entities, design processes, or design systems
are produced and contributed to new k-knowledge
(Arrow 6).
Prescriptive (k) Solution Design Theories: Solution
design knowledge, in the form of growing design
theories, informs the design of a solution entity, a
design process or a design system (Arrow 3). Within a
DSR project, effective principles, features, actions, or
effects of a solution entity or a design process or system
are generalized and codified in solution design knowl-
edge (e.g., design theories or technological rules)
(Arrow 4).
Fig. 1 DSR projects and modes
of producing and consuming
design knowledge (adapted
from Drechsler and Hevner
2018)
123
A. Hevner et al.: Roles of Digital Innovation, Bus Inf Syst Eng
3 Digital Innovation Roles in DSR
Drawing from our experiences working on DSR projects
involving innovative artifacts and from our knowledge of
the extant DSR literature, we observe multiple roles that
digital innovations may play in DSR projects. However,
nowhere have we found a comprehensive framework that
organizes and describes prevalent DI roles in DSR. In the
following, we propose an initial framework with the
description of six potential DI roles in DSR projects.
A DSR project may include one or more of these DI roles.
We further map the different roles to the DSR paradigm as
shown in Fig. 2.
3.1 Role 0: Understanding of the DI Problem Space
A starting point of all DSR projects is to gain a full
understanding of the relevant problem space and the per-
tinent descriptive theories that may inform the design of
digital innovations in that space. We label this as Role 0
due to its fundamental nature in a DSR project. The
research team draws from the current knowledge base of
‘kernel theories’ that provide the underpinning justificatory
knowledge that informs problem understanding and solu-
tion artifact construction (Walls et al. 1992; Gregor and
Jones 2007). Such descriptive theories are likely based on
the study of previous digital innovations and their appli-
cations in interesting application environments. Thus, the
use of descriptive theory around current DI to inform new
DI supports a Role 0 use to understand the project problem
space.
3.2 Role 1: Design of a DI Technical Artifact
The formative goal of a DSR project is the design of a
novel technical artifact. In the field of information systems,
that artifact will most likely be delivered in the form of
digital innovation. A DI artifact grows through multiple
cycles of build and evaluation activities (Sonnenberg and
vom Brocke 2012) until it is ready to be introduced into the
problem space and its new knowledge added to the k
knowledge base as a technical solution artifact (see Fig. 2).
Depending on the maturity of the problem and solution
environments, the DI contribution to the knowledge base
can be totally new knowledge in the form of an invention,
an advance on existing solution knowledge, or the exap-
tation of known solution knowledge to a new problem
(Gregor and Hevner 2013,2014). The DI artifact contri-
bution in this role corresponds to Simon’s (1996) inner (or,
interior) environment of the artifact. The inner design of
the artifact builds the technical core (e.g., hardware, soft-
ware) that provides the intended behaviors to the world via
well-defined interfaces. Role 1 supports the design and
implementation of a well-defined technical DI artifact and
its introduction into the knowledge base of solution design
entities.
3.3 Role 2: Design of an Artifact for Deployment
and Use of a DI Artifact
Most DSR projects not only produce a novel DI artifact but
also the required processes and procedures for the
deployment and use of that artifact in the problem context.
These processes are novel ‘use artifacts’ that describe
effective methods to make the best use of the DI artifact for
problem solutions (see Fig. 2). Thus, the role of the DI
artifact is as a pre-defined entity with the DSR goal being
one of designing methods of its application to important
problems. This role corresponds to Simon’s (1996) outer
(or, exterior) environment of the artifact. The DI artifact
interfaces provide the exigent opportunities for its appli-
cation. We note that a single DSR project may iterate
between the DI artifact in its Role 1 (internal technical
design) and Role 2 (external use design) as it attempts to
Fig. 2 Roles of DI in DSR
123
A. Hevner et al.: Roles of Digital Innovation, Bus Inf Syst Eng
satisfy project goals. The new processes and procedures for
use of the DI artifact are added to the knowledge base of
solution design entities.
3.4 Role 3: Design of a Socio-Technical DI System
Role 3 of a DI artifact is in its composition (perhaps with
other DI artifacts) into a larger, more complex socio-
technical system (Demetis and Lee 2016). The DSR project
builds and evaluates the integration of the multiple DI
artifacts into a new system to address different and, likely,
more challenging problem contexts. The integrated system
itself becomes a novel DI artifact that is added to the k
knowledge base as a solution design entity (see Fig. 2).
Research issues of artifact compatibilities, interfaces, and
integration become paramount in this type of DSR project.
Thus, the DI artifact in its Role 3 plays a component role
for the design of a more complex, integrated Role 1 DI
artifact.
3.5 Role 4: Development of Design Theories
Surrounding a DI Artifact
An essential research activity in a DSR project is the
reflection, learning, and design theorizing around the
development of DI artifacts. The project captures pre-
scriptive design knowledge from the DSR design cycles
and from the introduction of the DI artifact into the prob-
lem space of the project. These solution design theories can
be represented in multiple formats as knowledge for tech-
nology, action, integration, or process (see Fig. 2)
(Drechsler and Hevner 2018). The goal of design theories
in the form of nascent or mid-range theories is to grow
understanding of the DI artifact for generalizing its tacit
knowledge for future improvements and adaptations to new
problem contexts (Gregor and Hevner 2013). Thus, Role 4
of a DI artifact in DSR provides a rigorous base for cap-
turing solution knowledge in the form of solution design
theories as a prescriptive research contribution. We would
expect the use of a DI artifact in Role 4 to be combined in a
DSR project with its use in preceding Roles 1, 2, or 3.
3.6 Role 5: Use of a DI Artifact as a Creativity Tool
in the DSR Solution Process
Innovative artifacts are often used as creativity tools in a
DSR project to provide new ways of thinking and doing in
the design of novel artifacts. For example, the ideation
activity in a DSR project can be supported with novel
visualization tools (e.g., mind mapping tools) as digital
innovations. Furthermore, specialized tools such as
MyDesignProcess have been suggested recently to support
entire DSR processes executed by (distributed) design
teams (Morana et al. 2018). This DI role is termed an
operant innovation resource by Nambisan (2013). In Role
5, a DI artifact acts on other resources to build and evaluate
new designs. The DSR project would retrieve this existing
DI artifact from the prescriptive knowledge base and use it
in the DSR process of creating a new DI artifact (see
Fig. 2).
4 Special Issue Papers
The five DSR projects presented in this special issue pro-
vide an interesting variety of how DI artifacts participate in
different research roles. Here we briefly describe each of
the following papers and highlight the roles of DI artifacts
in each of the DSR projects.
1. Service-Dominant Business Model Design for Digital
Innovation in Smart Mobility
The authors claim that many digital innovations that
exhibit smart mobility features require business-modeling
support in order to achieve broader impacts in large-scale
systems, such as smart city applications (e.g., Role 3). To
this end, the Service-Dominant Business Model Radar
(SDBM-R) is designed as a business-engineering frame-
work. The goal is to better support the design of new
business models for the incorporation of digital innovations
for collaborative transport of people and goods. Thus, the
design of SDBM-R itself is a Role 1 application of DI in
the project, while the use of SDBM-R in the design of new
business models is a Role 5 application.
2. Scenario-based Design Theorizing – The Case of a
Digital Idea Screening Cockpit
This paper studies the ideation process within organi-
zations. A thorough survey of the literature on DI idea
generation (Role 0), provides a set of criteria for idea
screening. The authors design a Digital Idea Screening
Cockpit (DISC) to organize and track ideas in organiza-
tions (Role 1). In addition, a new ideation process of
generating, assessing, selecting, and tracking of how ideas
become digital innovations is built around the use of the
DISC tool (Role 2). Finally, the authors perform design
theorizing around the DISC artifact to support its adapta-
tion to the specific needs of an organization (Role 4).
3. Towards Digital Transformation in Fashion Retailing:
A Study of Automated Checkout Systems
This paper presents a cyber-physical system enabling
automated checkout in fashion retail stores. Automated
checkout systems have the potential to reduce costs and in
parallel improve customer experience. Building on differ-
ent innovative digital technologies the paper follows Role 1
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A. Hevner et al.: Roles of Digital Innovation, Bus Inf Syst Eng
and designs an IT artifact that leverages hardware and
software components to automatically detect purchases.
The design of the artifact takes various constraints such as
privacy and reliability under consideration. The evaluation
of the artifact in a laboratory environment shows that
shopping baskets can be detected in an efficient and reli-
able way.
4. Engineering of Augmented Reality-Based Information
Systems: Design and Implementation for Intralogistics
Services
This article focusses on digital innovation facilitated by
augmented reality glasses, and, following Role 1, it pro-
vides several artifacts to support the development of aug-
mented reality-based systems. The authors first take a
domain specific lens investigating 36 use cases for aug-
mented reality in the logistics service domain, so that their
work also incorporates Role 0 providing a detailed DI
problem understanding in a specific domain, which they
operationalize by means of meta-requirements. They derive
design principles as well as an architecture for augmented
reality-based system design in the logistics services
domain, which outline principles of form and function and,
thus, according to Role 4, mark important contributions
towards the development of DI design theories. Applying
concurrent evaluation, the authors develop a number of
prototypes and, based on their observations from these
projects, derive a more general framework for the aug-
mented reality glasses implementations, which following
Role 1 serves as a technical DI artifact. They discuss their
findings and outline future research opportunities.
5. Design Principles for Systematic Search Systems:
Synthesis of a Rigorous Multi-Cycle DSR Journey
This article investigates how to design innovative arti-
facts to facilitate systematic searches on research literature.
Existing systematic literature search systems (SLSS) are
studied to build a set of meta-requirements for an improved
artifact (Role 0). A prototype SLSS, LitSonar, is built and
evaluated via several controlled experiments (Role 1).
Finally, a set of design principles for effective SLSS is
proposed to guide the development of innovative search
systems in different application contexts (Role 4).
5 Conclusions
In this editorial, we have discussed the many roles DI plays
in DSR, and we have shown that, in turn, DSR is ideally
positioned to make both research and practice contributions
to the field of digital innovation. Based on the DSR
knowledge consumption and production model, we have
derived six specific roles of DI in DSR: Understanding of
the DI Problem Space (Role 0), Design of a DI Technical
Artifact (Role 1), Design of an Artifact for Deployment and
Use of a DI Artifact (Role 2), Design of a Socio-Technical
DI System (Role 3), Development of Design Theories
Surrounding a DI Artifact (Role 4), Use of a DI Artifact as
a Creativity Tool in the DSR Solution Process (Role 5). We
have used these roles to characterize the five articles on DI
in DSR presented in this paper, and the papers, in turn,
illustrate the six roles we have introduced.
We very much invite fellow researchers to contribute to
the exciting research fields in the intersection between DI
and DSR, and we very much hope that this special issue
will give many examples of the manifold research
opportunities.
Acknowledgements We would like to cordially thank all colleagues
who have supported this special issue in roles of reviewers and edi-
tors. We highly appreciate the strong support of our community in
making this special issue happen.
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Road freight operators (RFOs) optimize their fleet management processes using fleet telematics systems (FTSs). Therefore, the selection of FTSs by RFOs is driven by transport specifications from the customer side leading to substantial search costs. However, FTSs vary significantly in their design requirements to assist road freight operations. Hence, we analyze 74 web pages from FTSs of existing telematics vendors to elicit 31 design requirements (DRs) which we aggregated into nine requirement sets (RSs). Subsequently, 42 practitioners from five digital road freight service enterprises experienced in using FTSs validate the DRs and evaluate their importance with RSs following the Analytical Hierarchy Process (AHP) method. The results reveal that DRs and RSs promoting driver monitoring and IT integration are perceived more important than items promoting fleet and logistics support. Our contribution sheds light on an emerging topic in logistics and establishes a knowledge base that guides the design of future FTSs.
... Through semi-structured interviews, we identified how organizations understand the constructs related to their application of blockchain. This effort highlights the kernel knowledge boundaries that provide understanding of the problem and the organizational context (Hevner, Brocke, & Maedche, 2019). We expect to contribute on how the market position can influence the understanding of blockchain in the RE system, so we selected two organizations with different market positions and backgrounds to explore similarities and differences, relying on theoretical sampling (Eisenhardt & Graebner, 2007). ...
Thesis
Constant evolution in the global manufacturing resulting from various forces like innovation, changing demands, competition, and regulations is forcing manufacturing enterprises towards more digital and smarter operations to stay competitive in their respective markets. This evolution of manufacturing towards digitalisation led to a new paradigm in the last decade, which has been called the “fourth industrial revolution” or “Industry 4.0”. Yet, it is not a trivial matter for manufacturing organisations to deal with the accelerating frequency of radical changes by means of new strategies, methods, and technologies. Evolving dynamic forces have immense impacts on the digital transformation of manufacturing operations as well as the priorities of scholarly works. Nowadays, it is more apparent and easier to comprehend the relation between evolving market dynamics and their reciprocal consequences on products, processes, and manufacturing systems. Accordingly, manufacturing organisations must handle the initiation of change as well as its propagation, which triggers a multitude of unpredictable and complex modifications in production. This challenge is characterised as the concurrent/coordinated evolution of products, processes, and systems, in other words, “co-evolution” in scholarly works. The Virtual Factory (VF), as “an immersive virtual environment wherein digital twins of all factory entities can be created, related, simulated, manipulated, and communicate with each other in an intelligent way”, enables data integration across the manufacturing value chain as well as the integrated use of technologies and methodologies. Therefore, VF is recognised by scholars as a useful and effective solution to deal with the co-evolution paradigm. However, there are still significant gaps in the knowledge domain as well as empirical challenges in the application domain in terms of designing, developing, and utilising the VF concept. Therefore, the purpose of VF research work is to address such gaps and challenges by designing and developing artefacts and frameworks together with empirical evaluations of designed artefacts in the industrial cases. The VF research work presented in this thesis is the final outcome of a three-year- long PhD study conducted as part of a comprehensive research collaboration project named Smart Factories. The thesis on hand is the final effort to frame the three-year-long research aiming to establish a systemic design and development approach for DT-based VF, employing a collaborative virtual reality capability that can integrate product, process, and system models to support the manufacturing enterprises for handling co-evolution problems during their adaptation to evolving environments. Thus, with this final effort, this thesis is aiming to: Establish comprehensive and methodical foundations for the empirical, conceptual, and philosophical discussions supporting the previously discovered and disseminated knowledge on DT-based VF employing a collaborative virtual reality capability that can integrate product, process, and system models.
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During a search, phrase-terms expressed in queries are presented to an information retrieval system (IRS) to find documents relevant to a topic. The IRS makes relevance judgements by attempting to match vocabulary in queries to documents. If there is a mismatch, the problem of vocabulary mismatch occurs. The aim is to examine ways of searching for documents more effectively, in order to minimise mismatches. A further aim is to understand the mechanisms of, and the differences between, human and machine-assisted, retrieval. The objective of this study was to determine whether IRS-H (an IRS using the hybrid indexing method) and human participants agree or disagree on relevancy judgments, and whether the problem of mismatching vocabulary can be solved. A collection of eighty research documents and sixty-five phrase-terms were presented to (i) IRS-H and four participants in Test 1, and (ii) IRS-H and one participant (aided by search software) in Test 2. Statistical analysis was performed using the Kappa coefficient. IRS-H and the four participants' judgements disagreed. IRS-H and the participant aided by search software judgments did agree. IRS-H solves the problem of mismatching vocabulary between a query and a document.
Chapter
The article deals with the flow processes rational organization issues and their service support in the of economy digitalization conditions. Special attention is paid to customer segmenting to provide them with an optimal package of transport and logistics services among the alternatives based on economic and mathematical modeling methods, as well as feedback organizing in order to determine the service satisfaction degree provided to them. To form a transport service rational system in the supply chains, a step-by-step action algorithm was developed and an service quality integral indicator was calculated. The authors state modeling methods usage has become possible allowing consumers to choose a rational set of services from the “price-quality” ratio point of view in the digital innovation era.
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The information systems (IS) field contains a rich body of knowledge on approaches, methods, and frameworks that supports researchers in conducting design science research (DSR). It also contains some consensus about the key elements of DSR projects—such as problem identification, design, implementation, evaluation, and abstraction of design knowledge. Still, we lack any commonly accepted tools that address the needs of DSR scholars who seek to structure, manage, and present their projects. Indeed, DSR endeavors, which are often complex and multi-faceted in nature and involve various stakeholders (e.g., researchers, developers, practitioners, and others), require the support that such tools provide. Thus, to investigate the tools that DSR scholars actually need to effectively and efficiently perform their work, we conducted an open workshop with DSR scholars at the 2017 DESRIST conference in Karlsruhe, Germany, to debate 1) the general requirement categories of DSR tool support and 2) the more specific requirements. This paper reports on the results from this workshop. Specifically, we identify nine categories of requirements that fall into the three broad phases (pre-design, design, and post design) and that contribute to a software ecosystem for supporting DSR endeavors.
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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 paper reports on the results of a Delphi study with 143 information systems (IS) academics that was designed to explore what IS academics perceive to be the grand challenges of the IS discipline. The results provide evidence that the scholarly IS discipline is still much concerned with itself, for instance, in terms of its identity, relevance, foundational theory, or methodological pluralism – suggesting that the old debate on IS identity is not yet overcome. It thus cannot be claimed that the study identifies the grand challenges of the discipline – still it becomes noticeable that the academic community sees potentials for the IS discipline to have societal impact. A total of 21 challenges are identified, of which six challenges are categorized as ''meta challenges for further developing the IS discipline'' and the remaining 15 challenges are categorized as ''IS research challenges'' pertaining to socio-technical systems, IS infrastructures, society and ecology, as well as social well-being and affectivity. We provide a ranking of all challenges according to their relevance, potential impact, and possible time frame of realization. The results have some important implications for IS as a discipline as well as its prospective future societal role. It is hoped that through our study we can contribute to the important debate on the challenges of the academic IS discipline.
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Design science research (DSR) has staked its rightful ground as an important and legitimate Information Systems (IS) research paradigm. We contend that DSR has yet to attain its full potential impact on the development and use of information systems due to gaps in the understanding and application of DSR concepts and methods. This essay aims to help researchers (1) appreciate the levels of artifact abstractions that may be DSR contributions, (2) identify appropriate ways of consuming and producing knowledge when they are preparing journal articles or other scholarly works, (3) understand and position the knowledge contributions of their research projects, and (4) structure a DSR article so that it emphasizes significant contributions to the knowledge base. Our focal contribution is the DSR knowledge contribution framework with two dimensions based on the existing state of knowledge in both the problem and solution domains for the research opportunity under study. In addition, we propose a DSR communication schema with similarities to more conventional publication patterns, but which substitutes the description of the DSR artifact in place of a traditional results section. We evaluate the DSR contribution framework and the DSR communication schema via examinations of DSR exemplar publications.
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The 50-year march of Moore’s Law has led to the creation of a relatively cheap and increasingly easy-to-use world-wide digital infrastructure of computers, mobile devices, broadband network connections, and advanced application platforms. This digital infrastructure has, in turn, accelerated the emergence of new technologies—social media, cloud computing, analytics and “big data,” 3D printing, and intelligent autonomous systems. These types of new technologies enable transformations in how we live and work, how companies organize, and the structure of entire industries. As a result, it has become increasingly important for all business students (MBAs and undergraduates alike) to have a strong and appropriate grounding in IT in general and digital innovation in particular—in order to manage, lead and transform organizations that can benefit from digital innovation. Yet, at many schools students do not get such grounding, either because the required IS core class is stuck in the past, or because the required business core excludes IS altogether. We present a vision for a redesigned IS core class that adopts digital innovation as a fundamental and powerful concept (FPC). A good FPC serves as both a foundational concept and an organizing principle for a course. We espouse a particularly broad conceptualization of digital innovation that allows for a variety of teaching styles and topical emphases for the IS core class. This conceptualization includes three types of innovation (i.e., process, product, and business model innovation), and four stages for the overall innovation process (i.e., discovery, development, diffusion, and impact). Based on this conceptualization, we examine the implications of adopting digital innovation as an FPC. We also briefly discuss broader implications relating to: (1) the IS curriculum beyond the core class, (2) the research agenda for the IS field, and (3) the identity and legitimacy of IS in business schools.
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The contribution of this paper is twofold. We revisit the three-cycle procedural view of Design Science Research (DSR) as introduced by Hevner, and we propose a structuring mechanism for the DSR Knowledge Base (KB) and theproblem/context specification that supports our revisited DSR procedure. Regarding the first contribution, we argue that each design cycle has rigor related as well as relevance related aspects. We therefore introduce a one-cycle view of DSR, comprised of alternating core activities design and evaluation. With every iteration, the understanding of the environment and the problem to be solved are enhanced. Additionally, every design iteration allows for revisiting the Knowledge Base in order to improve the problem solution. Concerning the second contribution, we propose to structure the DSR KB by means of two two-dimensional maps to support an efficient search for existing, reusable solution artifacts. One map concerns the application scope; the other one concerns the artifact character. Solution artifacts are then organized with regard to their type, generality, application domain and coverage. We demonstrate the applicability of the proposed DSR KB structure using the domain of change projects in organizations as an exemplar.
Chapter
We distinguish several design knowledge types in IS research and examine different modes of utilizing and contributing design knowledge that can take place during design science research (DSR) projects. DSR projects produce project design knowledge, which is project-specific, possibly untested, conjectural, and temporary; thus, distinct from the more stable contributions to the propositional and prescriptive human knowledge bases. We also identify solution design knowledge as distinct from solution design entities in the prescriptive knowledge base. Each of the six modes of utilizing or contributing knowledge (i.e. design theorizing modes) we examine draws on different knowledge types in a different way to inform the production of project design knowledge (including artifact design) in a DSR project or to grow the human knowledge bases in return. Design science researchers can draw on our design theorizing modes and design knowledge perspectives to utilize the different extant knowledge types more consciously and explicitly to inform their build and evaluation activities, and to better identify and explicate their research’s contribution potential to the human knowledge bases.
Article
Just as Lee, Briggs, and Dennis (2014) showed that a rigorous conception of “explanation” leads to requirements for a positivist theory to satisfy, and just as Lee and Hovorka (2015) showed that a rigorous conception of “interpretation” leads to requirements for an interpretive theory to satisfy, we show that a rigorous conception of “systems” leads to certain requirements for a systems theory to satisfy. We apply basics of systems science in general, as well as basics of Luhmann's (Luhmann, 1995; Moeller, 2006) systems perspective in particular. We illustrate these basics with empirical material from a case about the role of information technology in anti-money laundering. The example demonstrates that research in information systems, which has been informed by positivism, interpretivism, and design, can be additionally and beneficially informed by systems science — which, ironically, has been largely absent in information “systems” research.
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In this paper, I follow up on my previous article about information systems as a reference discipline for new product development (Nambisan, 2003) and assess the extant research on this topic. To facilitate the assessment, I develop a framework that considers information technology's (IT's) dual roles as operand resource and as operant resource and its impact on innovation process and on innovation outcome. My analysis reveals the advance that has been made in understanding IT's role as operand resource in innovation and the considerable opportunity that exists to explore IT's emerging role as operant resource in innovation. I also comment on the need for IS scholars working in this area to make careful choices regarding their research topic and theoretical perspectives to enhance the potential impact on and contribution to the product/service innovation literature.