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18th International Conference on Wirtschaftsinformatik,
September 2023, Paderborn, Germany
Designing Digital Infrastructures
for Industrial Data Ecosystems – A Literature Review
Research Paper
Anna Maria Schleimer1,2 and Estelle Duparc2
1 Fraunhofer Institute for Software and Systems Engineering ISST,
Data Business, Dortmund, Germany
anna.schleimer@isst.fraunhofer.de
2 TU Dortmund, Chair of Industrial Information Management, Dortmund, Germany
{anna.schleimer, estelle.duparc}@tu-dortmund.de
Abstract. Data ecosystems are driven by political and market demands but face
strong barriers that require their own socio-technical infrastructure for their
realization. In particular, the Industrial Internet of Things (IIoT) has distinct
characteristics and requirements for software-based IIoT data ecosystem
infrastructures to facilitate inter-organizational, trusted data sharing and value
creation. However, the design and purposeful management of infrastructures are
challenging, as they emerge organically. To identify opportunities and
foundations for design knowledge contributions, this study assesses in a
structured literature review a total of 415 papers about digital infrastructures for
their applicability to data ecosystem settings considering data as main asset, data
sovereignty, trust, and common value propositions. The findings suggest that
industrial data ecosystems and digital infrastructure research are so far not
connected, despite some implicit overlaps exist. Therefore, a research agenda is
presented as well as a concept matrix with the main findings from existing
infrastructural research.
Keywords: Data Ecosystem, Digital Infrastructures, Data Infrastructure, IIoT,
Industrial Data
1 Introduction
How to “work collaboratively and deal with industrial exposure, competitive
advantages and confidentiality issues?” stated the World Economic Forum (2020, 8)
when outlining the main challenges to accelerate the impact of the Industrial Internet
of Things (IIoT), especially for small and medium enterprises. The IIoT concept has
traditionally a strong focus on modern sensor technology and hardware data collecting
devices since IIoT builds upon the Internet of Things (IoT), “where common objects
are regarded as things connected to an information internet” (Zhang et al. 2020, 1).
Despite the traditionally strong emphasis on hardware, software and data processing
aspects are key factors for IIoT success nowadays. The importance of software is
reflected by the observation that “key revenue activities associated with IoT deal with
platforms, applications and services”, whereas the hardware segment decreased to only
30% (CBI June 2022; Jovanovic 2023). The following example outlines the benefits:
Highly-connected IIoT devices enable companies in the logistic sector to “easily track
their drivers’ activities, vehicles’ locations, and all goods’ delivery status through a
real-time logistics management system supported by heterogeneous networks and
wide-deployed IIoT devices” (Huang et al. 2021, 1416–1417). This example illustrates
that most scenarios cross enterprise boundaries and connect large system landscapes.
Even complete data assets are not isolated, but may belong to multiple organizations
and systems embedded in larger scenarios (Baars et al. 2021, 1). Hence, the
implementation of the “new era [of] industrial system[s] requests a continuous chain of
data sharing […]” with data flowing among multiple parties (Zheng and Cai 2020, 968).
But particularly the issues of data protection and security are impeding the realization
of many promising application scenarios (Huang et al. 2021; Zheng and Cai 2020).
Further, most existing ecosystems are predominantly created around a focal company
(Baars et al. 2021, 2), raising strategic and business political concerns. To avoid
dependencies and centralistic power structures, fair and balanced ecosystem approaches
without an implicit “winner-takes-all” mentality gain increasingly attention (Wang
2021, 421).
Data ecosystems tackle these challenges by providing “infrastructures for the secure
and reliable data exchange among organizations” (Capiello et al. 2020, 66). Within data
ecosystems, data sovereignty is a key aspect, especially when it comes to sensitive
industrial data (Jarke 2020; e.g., Gelhaar and Otto 2020). Recently, multiple consortia
and initiatives emerged to build industrial data ecosystems (e.g., Catena-X 2023;
Manufacturing-X 2022). The ecosystem viewpoint allows a peculiarly perspective on
IIoT “in the light of joint value creation” (Adner and Kapoor 2010, 329). Thus,
especially in the industrial sector, where IIoT technologies are being leveraged, the
proliferation of data ecosystems across an organization requires a dedicated software
infrastructure. Infrastructures are a new type of Information Systems (IS) artifact, and
their scope extends far beyond individual systems. A traditional single-system view is
“no longer sufficient to describe the challenges managers and chief information officers
(CIOs) face today” (Fürstenau et al. 2019, 1319; Tilson et al. 2011). Thus, the 21st
Century declares the “age of the digital infrastructure” (Elaluf-Calderwood et al. 2011,
1; Tilson et al. 2011). Infrastructures remain mostly invisible and become evident when
they fail (Star and Ruhleder 1996) - or are lacking as in the case of data ecosystems for
IIoT. Therefore, a strong need exists for knowledge about digital infrastructures for
IIoT data ecosystems. In order to gather the existing knowledge from generic research
on digital infrastructures and pave a way for future contributions, we pose the following
research question:
What are white spots in existing research about industrial digital infrastructure
for data ecosystems?
This paper is structured as follows: After introducing the topic (1), the background
section (2) provides fundamentals of industrial data ecosystems (2.1) and digital
infrastructure (2.2). The following section (3) describes the method and process steps
of the structured literature review. The resulting findings are presented in form of a
concept matrix (4) and discussed afterwards (5). An agenda for future research presents
the conclusions of the white spot assessment (6.1) followed by the summary including
the contributions and limitations (6.2).
2 Background
2.1 Industrial data ecosystems
Generally speaking, ecosystems describe “a group of interacting firms that depend on
each other’s activities” (Jacobides et al. 2018, 2256). More concrete and from a
strategic perspective, ecosystems are a structure of a “multilateral set of partners that
need to interact in order for a focal value proposition to materialize” (Adner 2017, 42).
While ecosystems are often understood as loose affiliations to a focal organization, the
structural view focuses on “activities and actors over which [a] focal organization may
have no control, and with whom they have no direct contact” (Adner 2017, 44). Data
ecosystems are a special form of ecosystems, where the particular challenges and
concerns about data in inter-organizational settings are addressed. While software and
hardware infrastructure remain important aspects as well, data ecosystems emphasize
data as standalone-asset and “data-based resource[s], such as datasets and data sources”
(Oliveira and Lóscio 2018, 7). Data ecosystems are defined as “a set of networks
composed by autonomous actors that directly or indirectly consume, produce or provide
data and other related resources” where each “actor performs one or more roles and is
connected to other actors through relationships, in such a way that actors collaboration
and competition promotes Data Ecosystem self-regulation” (Oliveira and Lóscio 2018,
4). The connection between autonomous actors is fundamentally based on their own
interests or business models (Oliveira and Lóscio 2018, 7). These actors are usually
understood as individual organizations who are competing yet cooperating and have a
so-called co-opetition relationship with each other (Tiwana 2014). Within an IIoT
context, actors can also describe autonomous devices that interact to exchange
information. For example, a group of sensors that share data depending on their location
and applicable legislation, or different policies defined by their different owners.
Industrial data have characteristics, that challenge the technical and organizational
design of data ecosystems. They are of high value and, thus, highly protected private
data, as well as often heterogeneous types, may include time-sensitive streaming data,
as well as occur in large volumes (Dai et al. 2020). This is in contrast to, for example,
static geographical map data, or publicly available open data.
2.2 Digital Infrastructures
While the term infrastructure is often used to just describe one particular hardware
technology, such as broadband services, digital infrastructures are a dedicated concept
to emphasize socio-technical perspectives on ”a new stage in the evolution of IT” and
emphasize how “IT has become deeply socially embedded, and is coordinated across
diverse socio-technical worlds” (Tilson et al. 2011, 26). The six characteristics of
[information] infrastructures are being enabling, shared, open, socio-technical,
heterogeneous, and building upon an installed base (Hanseth and Monteiro 1998, 10).
Infrastructures are not sharply defined “through a distinct set of functions” nor have
“strict boundaries” like applications (Tilson et al. 2011, 26). Instead, they are system
collectives enabling generativity. This way, infrastructures support or enable a wide
range of functionalities in contrast to being tied to single, strictly defined application
scenarios (Hanseth and Monteiro 1998, 10).
Different research streams emerged to deal with the genesis of digital infrastructures
and have different assumptions about their emergence: In the network view, a designer
or policy maker “would seek to facilitate translation of interests and technology
inscriptions” (Koutsikouri et al. 2017, 4714) following actor-network thinking (Callon
1984; Latour 1987). Second, the complexity perspective emphasizes the “adaptation
processes of heterogeneous actors” (e.g., Braa et al. 2007). Third, the relational view
focuses on a “community of practice” and sense-making (Koutsikouri et al. 2017, 4714;
Vaast and Walsham 2009). In summary, active design as a form of intervention in an
evolution is treated with skepticism because infrastructures are understood as emergent,
evolutionary, and therefore not fully controllable or manageable phenomena. In 2010,
Hanseth and Lyytinen (2010) formulated a design theory in the form of design
principles for information infrastructures to address especially early growth and
bootstrap problems
1
(Koutsikouri et al. 2017, 4714).
With regard to a dedicated infrastructure for data as main asset, a commonly used
definition for data infrastructure does not exist. Swanson (2021, 3) states “that data
becomes infrastructure when its provision is consolidated in a network open to a
community of use”. Others define the derm data infrastructure according to Bharadwaj
(2000) in a resource-based-view perspective as a “technological resource consisting of
hardware and software components that are necessary for collecting, storing,
processing, analyzing, and sharing data and information across the organization”
(Madhala et al. 2022, 4). Following this preliminary work, as a working definition, we
define the term data infrastructures in this article from a data ecosystem perspective as
a socio-technical structure necessary for collecting, storing, processing, analyzing, and
in particular sharing data and information across organizations. Despite some research
streams follow the perspective that an infrastructure, including those for data, can not
be actively designed and shaped, an infrastructure for data is “an effortful
accomplishment” (Swanson 2021, 5). It requires purposeful action and investment.
Thus, an active design is necessary to progress and satisfy user and market demands.
1
According to (Koutsikouri et al. 2017, 4714), the design principles can be summarized as
“design initially for usefulness; build upon existing installed bases; expand installed base by
persuasive tactics to gain momentum; make the design of IT capability as simple as possible;
and modularize the digital infrastructure” (Hanseth and Lyytinen 2010).
3 Structured Literature Review
To analyze existing knowledge on digital infrastructure design, this study performs a
structured literature review (Webster and Watson 2002). This research method creates
replicable findings in a systematic approach and requires a process documentation of
each step during the data collection and analysis. A five-step approach following the
one of vom Brocke et al. (2009) structures the process: The definition of the review
scope (I), the conceptualization of the topic (II), literature search (III), literature
analysis and synthesis (IV), and future research agenda (V).
To clearly define the review scope (I), we employ the taxonomy after Cooper (1988).
The study focusses on research outcomes, (design) theories, and applications (such as
managerial knowledge) regarding the design of data ecosystems. The goal is to apply
existing knowledge of digital infrastructures to data ecosystems design. To this end, we
conduct an exhaustive, selective, and structured literature review from a neutral
perspective. The study addresses specialized scholars of IS and engineering, but also
practitioners in the field of data sharing, data management, and data ecosystems who
are concerned with socio-technial aspects fo their implementation.
Second, the outline of the most fundamental concepts, as well as challenges and
terms provides an overview of the topic (II). Recent journal papers and conference
proceedings that deal with the topic of digital infrastructures and data ecosystems
provide such insights. Additionally, a backward-search of most relevant articles led to
an overview of key issues and concepts.
In the third step (III), we searched for the keywords “data infrastructure” and
“software infrastructure” in the title, abstract, and keywords of A- and B-ranked
journals as well as relevant IS conferences listed in the scopus or AISEL databases.
Since these results are not comprehensive, the search was extended to the more general
search string “digital infrastructure”. The terms mentioned were searched for
individually, because there were none to very limited search results returned when
combined directly with the term "data ecosystem".
Afterwards (IV), the literature analysis and synthesis started with a screening of
abstracts. The selected articles provide insights into the design of digital infrastructures
or their particular characteristics in mechanisms like evolution, creation, emergence, or
implementation. Papers that did not focus on the standalone concepts of “digital
infrastructure”, “data infrastructure”, or “software infrastructure” were dismissed.
Table 1 Search result metrics
Database
Search String
in abstract and title
Results
Refined
Selected
AISEL +
Scopus
“data infrastructure”
20
7
3
“software infrastructure”
4
2
2
“digital infrastructure”
391
108
36
Total
415
117
41
Additionally, also papers without inter-organizational settings or large enterprises
were discarded. For example, those papers that solely focused on single technologies
such as broadband, or isolated applications. Also, articles that do not contain research
results but were still identified through the search mechanisms, such as tables of
contents or planned research projects, are not helpful in answering the research question
and are therefore discarded. The colloquial and interdisciplinary use of the term "digital
infrastructure" leads to many non-target articles. Therefore, step four is repeated to
exclude further irrelevant articles based on a detailed analysis of the content. The result
is the detailed analysis presented as concept matrix (Webster and Watson 2002),
described in the following section 4. The structured literature review concludes with a
research agenda (V), presented in section 6.1. The research agenda provides the basis
for enhancing or developing the identified whitespots.
4 Concept Matrix
A coding process was used to assess the identified literature in a concept matrix. In the
matrix, contributions containing direct characteristics or application scenarios of data
ecosystems are marked with an "x". As well, implicit references to data ecosystems
frequently occurred, which are marked with a bracketed "(x)". Next to the direct
occurrence of the data ecosystem concept, further related concepts were identified:
Value proposition as a pre-requisite, active infrastructure design activities, passive
evolution or emergence activities, data assets, data sovereignty, and trust: The term
“value proposition” refers to a joint value proposition that is addressed by the
infrastructure according to the structural understanding by Adner (2017). Then, the
keywords about active and passive design outlines whether the assumption of an
organic, rather passive evolving infrastructural emergence and evolution is assumed, or
if more active design and influence activities exist (as summarized, e.g., by Koutsikouri
et al. 2017). Further, the focus on data as standalone-asset and accompanying concepts
relevant for data ecosystems, namely data sovereignty and trust, are assessed (Capiello
et al. 2020; Oliveira and Lóscio 2018). To further understand the context of the findings,
also the domain of focus is noted. The complete result table can be requested online
2
.
Table 2 presents an shortened version due to space restrictions. It includes all entries
with at least three relevant data ecosystem related findings.
5 Discussion
The results show that none of the studies on digital infrastructure deal in depth and
explicitly with data ecosystems, and few address IIoT scenarios. However, indirectly
the characteristics or challenges of data ecosystems become. For example, some papers
refer to a “digital jungle” (Zimmer et al. 2019; Niemimaa and Zimmer 2020), which
which accounts for the heterogeneity, openness and loose coupling of ecosystem
members. The following section provides a comprehensive, non-exhaustive outline of
2
https://bit.ly/WI23-conceptmatrix-DI
the main findings that are relevant in IIoT data ecosystem context. Next to adressing
management and design issues, which make the most of all findings, also some findings
relate to software and technological emergence issues.
Table 2 Shortened Concept Matrix (continued on following page)
Author
value proposition
[active] infrastructure
design & innovation
[passive]infrastructure
evolution or emergence
data asset focus
data ecosystem
data sovereignty
trust
domain
Andersen and
Bogusz (2017)
(x)
(x)
(x)
finance,
blockchain
Andersen and
Bogusz (2019)
(x)
(x)
(x)
finance,
blockchain
Augustsson et
al. (2019)
(x)
x
x
IT service
industry
Blaschke et al.
(2016)
x
(x)
x
-
Bygstad and
Hanseth (2018)
(x)
x
(x)
healthcare
Bygstad and
Øvrelid (2020)
(x)
x
(x)
healthcare
Bygstad et al.
(2017)
(x)
x
(x)
healthcare
Drechsler et al.
(2022b)
(x)
x
x
(x)
IIoT,
manufacturing
Drechsler et al.
(2022a)
x
x
(x)
IIoT,
manufacturing
Elaluf-
Calderwood et
al. (2011)
x
x
(x)
(x)
(x)
mobile
telecommuni-
cation
Hanseth and
Modol (2021)
(x)
x
(x)
(x)
healthcare
Knol et al.
(2014)
x
x
(x)
x
logistics,
sea cargo
Koutsikouri et
al. (2017)
(x)
(x)
x
(x)
public
transportation
Author
value proposition
[active] infrastructure
design & innovation
[passive]infrastructure
evolution or emergence
data asset focus
data ecosystem
data sovereignty
trust
domain
Madhala et al.
(2022)
x
(x)
x
(x)
cross-industry
Miller et al.
(2011)
(x)
x
x
-
Osmundsen and
Bygstad (2022)
(x)
x
x
energy
Slavova and
Constantinides
(2017)
x
x
(x)
(x)
energy
Spagnoletti et
al. (2022)
(x)
(x)
(x)
online black
markets
Swanson (2021)
x
x
x
x
(x)
-
Tilson et al.
(2011)
x
(x)
(x)
mobile
operating
systems
Ure et al. (2009)
x
(x)
x
(x)
healthcare
Zorina and
Dutton (2021)
(x)
(x)
(x)
residential
Internet
(Table 2 continued)
5.1 Design and management findings
With regard to the ability to control and manage infrastructures in an innovation setting,
Bygstad et al. (2017, 11) find that solutions were not simply implemented in a top-down
manner, rather they resulted from continuous innovation and negotiation. Similarly,
Osmundsen and Bygstad (2022, 158) identify how infrastructures are continuously
developed and how interactions between different organizational roles are designed to
make or give sense. To further analyze the negotiations, Elaluf-Calderwood et al.
(2011) address large-scale networks of different organizations through a lense of tussles
and control points to study collaboration, conflict, and control in digital infrastructures
(Elaluf-Calderwood et al. 2011, 3–4). They note that “Cyberlaw scholars [are]
concerned with the legal regulation of the Internet against abuse [and are going to]
provide a complementary view of infrastructure development” through modalities such
as laws, social norms, markets, and architecture or code (Elaluf-Calderwood et al. 2011,
6; Lessig 2000). These observations are also reflected in the data ecosystem field, as
many development activities are pursued in associations’ working groups and consortia
dealing with legal and architectural topics (e.g., IDSA 2023).
Specific growth tactics are suggested by Koutsikouri et al. (2018, 1014) in the form
of adding services, inventing processes, opening identifiers, and providing interfaces.
Augustsson et al. (2019) focus on managerial challenges in the form of the effects of
control efforts and drifts. Their findings highlight that “infrastructure evolution cannot
be separated from changes related to customers and resources” (Augustsson et al. 2019,
71). For a deeper understanding of the role of contextual factors, Koutsikouri et al.
(2017, 4718) consider contextual triggers as important. They identify three contextual
triggers for infrastructure evolution, namely “adding service value”, “creating design
attractors”, and “lowering infrastructure barriers” (Koutsikouri et al. 2017, 4722).
These findings are relevant for the data ecosystems field, as many activities are started
with specific economic motivations that depend on a concrete market situation, for
example, the Gaia-X association that responds to a market “dominated by a few players
offering a hyper-verticalised model” (Gaia-X AISBL 2023). From a more technical
perspective, Henfridsson and Bygstad (2013) identify configurations of the contextual
conditions “loosely coupled architecture” and “decentralized control” that lead to
mechanisms of scaling, adoption, and innovation. Fürstenau et al. (2019, 1335) tackle
the issue of embeddedness and find three processes that embed systems in digital
infrastructures. They identify a parallel process, a competitive process, and a spanning
process of different systems in relation to their digital infrastructure. Other studies
emphasize how architectural and governance choices evolve during a project’s
trajectory. They imply a need to “simultaneously align policy goals, partners’
expectations, market availability, and resources” (Kempton et al. 2020). The focus on
architectural configuration and implication of architectural design are apparent in data
ecosystem studies as well, as the concept brings along concrete distributed architecture
concepts with organizational implications (e.g., Curry 2020).
5.2 Software-related findings
In addition to the management-oriented findings, some articles also observe software
and technology-oriented aspects. Fink et al. (2020, 252) address software libraries, that
emerge as digital infrastructure. The standardized code packages become boundary
resources when they are offered outside of an organization. Andersen and Bogusz
(2019, 16) emphasize how development practices, especially code forking in open-
source projects, lead to new organizing practices. This is in particular interesting for
data infrastructures, as they mainly base on software and code with a strong emphasis
on open-source software. To focus on the particular characteristics of data, Swanson
(2021, 8) suggests a conceptual framework for data infrastructures divided between two
main pillars of usage and provision. In direct relation to the ecosystem lens, Blaschke
et al. (2016, 2) mention the changing situation towards service ecosystems and argue
that digital infrastructures have to be more closely examined in this context. The study
provides six theoretical foundations of digital infrastructure on the theoretical basis of
service-dominant logic and complexity theory. The identified theoretical bases are
integrity, elasticity, and ambidexterity (Blaschke et al. 2016, 8,9). Also, Zorina and
Dutton (2021, 159) suggest four types of symbiotic and parasitic interactions to
elaborate on the distinctive development paths in ecologies of multiple actions that can
be transferred to IIoT data ecosystems. Other studies draw conclusions from studying
the infrastructure of online black markets, which “operate in absence of formal rules,
legal protection, social legitimacy and despite conflicting goals among actors”
(Spagnoletti et al. 2022, 1811). With their unique setting of aversive forces “and a
negative global impact of its social outcomes” (Spagnoletti et al. 2022, 1812), they are
very specific cases. Yet, in their technology and governance structures, they show some
features of data ecosystems, which are automated networks for data exchange in a zero-
trust setting as well. One identified mechanism is the commoditization of widely
diffused services that attract new buyers and vendors, as well as ensuring the security
of transactions through decentralized systems due to the absence of a central actor, who
is trusted by all parties (Spagnoletti et al. 2022, 1820).
However, few articles refer explicitly to the value derived by an infrastructure.
Madhala et al. (2022, 5) suggest data, data-related human resources, and data
infrastructure as precedent to data analytics capabilities and, thus, business
performance. Bygstad et al. (2017, 11) found that digital infrastructure can support
process innovation, while at the same time process innovation also aids the digital
infrastructure yet seeing them as conflicting forces.
6 Conclusions
6.1 Research agenda
The findings show that none of the articles mention data ecosystems as a dedicated goal
for their designed infrastructure. While some indirect descriptions of data ecosystems
concepts are evident, such as, for example, data sharing and inter-organizational
settings, the knowledge mostly targets a focal company instead of a complete network.
Also, IIoT settings are rare. Especially, aspects of data sovereignty and trust, that are
related to policy management and interoperability, as well as privacy and security, are
missing so far. Even if studies refer to settings around organizations, the involved
parties are implicitly different departments or affiliated users. In an IIoT context, the
different actors can also be agents in the form of devices or services owned by
competing parties, that have to be organized with an appropriate data infrastructure.
The analysis of existing literature leads to five categories of topics that research about
IIoT data ecosystems should address. The identified concepts can be a starting point for
further studies of infrastructures from a data ecosystem perspective.
Active data ecosystem design towards data infrastructures
• Purposeful design knowledge is required to build and shape data
infrastructures, despite they are not in full managerial control.
• The unit of analysis and design needs to be the shared value proposition and
ecosystem without having a single focal company as (implicit) main actor.
• The particular characteristics of data assets and required tools for managing,
sharing, and ensuring trust in zero-trust settings need to be elaborated.
• The high needs for automatization, scalability, and reliability should be
examined in design knowledge.
• The foundational concept “data infrastructure” needs to be carefully defined
and delineated from generic infrastructure perspectives.
• Discussion on suitable organizational forms and process models to pursue
data infrastructure design activities have to be assessed.
Trust and data sovereignty
• The aspects of trust and informed decisions in data ecosystems need to be
addressed not only from social policy or political perspectives but also from
a technology and IS design perspective.
• The socio-technical processes of providing information that enables trustful,
autonomous, and decentral decisions have to be elaborated.
• A connection between legal and technology-oriented fields is required to
elaborate on how regulations or incentives can fuel infrastructural dynamics.
Software infrastructure
• The software development and standardization process for data infrastructure
purposes needs to be better understood as infrastructures are different from
closed applications or single functionalities. For example, to satisfy
longevity or standardization demands.
• The characteristics of inter-organizational, infrastructural software
development have to be better understood, such as the role of open-source
projects or different, heterogeneous implementations of the same
infrastructural components.
• The difference between software infrastructure in contrast to hardware-
dominated infrastructure needs to be understood to enable purposeful
research on their design challenges. For example, they might have different
financing demands as their re-use demands low marginal costs.
Value orientation
• For a purposeful design of infrastructures for data ecosystems, the common
value proposition should be clearly formulated and measurable.
• Dangerous and costly deadlock situations in design processes need to be
understood and avoided. Mechanisms to align shared interests are required,
in the face of conflicting or contrary demands.
Domain-specific knowledge
• The particular characteristics of the industrial domain, especially IIoT
devices as participants in ecosystems, need to be understood.
• The domain-relevant legislation (e.g., data privacy) and its implications have
to be incorporated in socio-technical infrastructure designs.
• For different domains, different infrastructure needs might exist.
6.2 Summary and Limitations
In sum, the findings show that the design issues of infrastructures are a relevant topic
in IS research, especially since the last two decades. However, in light of the design
needs for data ecosystems in the industrial domain using IIoT technologies, the findings
often remain too general or are not addressing key targets. This presents a wide path
for future research, to further elaborate on the particularities of industrial data in data
ecosystems and the design of software-dominated infrastructures. In terms of scientific
contributions, our research addresses the conceptual blurriness around “digital
infrastructure”, “software infrastructure”, and “data infrastructure” by explaining and
connecting them. Thus, the structured literature review provides a synthesis of the
findings of multiple studies from the infrastructure research stream. The outlined
findings can aid practitioners and researchers, especially from the industrial data
ecosystem community, in design processes by pointing to a selection of relevant
studies. Furthermore, the research agenda forms a basis for future research addressing
the design of industrial data ecosystems and sheds light on the existing whitespots.
Future researchers are pointed towards the remaining knowledge gaps. In terms of
managerial contributions, the comprehensive summary provides purposeful insights
into findings from the field of digital infrastructure that can be applied to industrial data
ecosystem projects. The results illustrate the challenges and approaches towards inter-
company, data-driven value creation. Thus, the results support companies in creating
new possibilities of value co-creation based on a distinct infrastructure for IIoT data
assets based on shared interests and values.
The limitations of this study are on the one hand due to the often imprecise use of the
word infrastructure, which challenges to find dedicated articles according to our
definition. Especially, when no further definition or description of the infrastructure
understanding was given. Additionally, the search process depends on the exact
wording, so that deviations in terms or other phrases, such as cyberinfrastructure,
infrastructuring, or similar terms might not be included. Additionally, despite pursuing
a neutral and systematic literature review, due to human errors the analysis might face
some bias and limitations. Future studies could apply automated approaches to solve
this.
7 Acknowledgements
This work has been supported by the German Federal Ministry for Economic Affairs
and Climate Action in context of the Gaia-X 4 KI project (no. 19A21011E).
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