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Toward Business Models for a Meta-Platform: Exploring Value Creation in
the Case of Data Marketplaces
Antragama Ewa Abbas
Delft University of Technology
a.e.abbas@tudelft.nl
Anneke Zuiderwijk
Delft University of Technology
a.m.g.zuiderwijk-vaneijk@tudelft.nl
Hosea Ofe
Delft University of Technology
h.a.ofe@tudelft.nl
Mark De Reuver
Delft University of Technology
g.a.dereuver@tudelft.nl
Abstract
Investigating meta-platforms has been a
continuing concern within information system
literature due to the increasingly complex
constellations of platforms in ecologies of ecosystems.
A meta-platform is a platform built on top of two or
more platforms, hence connecting their respective
ecosystems. One promising case to benefit from meta-
platforms is data marketplaces: a particular type of
platform that facilitates responsible (personal and
non-personal) data sharing among companies. Given
that business models for meta-platforms are largely
unexplored in this emerging case, how they can create
value for data marketplaces remain speculative. As a
starting point toward business model investigations,
this paper explores value creation of a meta-platform
in the case of data marketplaces. We interviewed
fourteen data-sharing consultants and six meta-
platform experts. We identify three potential value
creation archetypes of a meta-platform. The discovery
aggregator archetype emphasizes searching and
dispatching value, while the brokerage one focuses on
promoting and supporting value. Finally, the one-
stop-shop archetype creates value by standardizing,
regulating, sharing, and experimenting. This study is
among the first that explore value creation archetypes
for a meta-platform, thus identifying core value as a
base for further business model investigations.
Keywords: business models, value creation, meta-
platforms, data marketplaces, data sharing.
1. Introduction
The newest wave of digital platform innovations
has led to increasingly interconnected platforms, often
referred to as the ecology of platforms (Hilbolling et
al., 2020). Mosterd et al. (2021) refer to this
phenomenon as Platform-To-Platform Openness
(PTPO), meaning “the extent to which a platform is
interoperable with other platforms” (p. 1). Therefore,
PTPO is increasingly relevant in a fragmented market
to strengthen the network effect required for digital
platforms to thrive. One type of PTPO is a meta-
platform: an overarching platform that connects two
(or more) platforms, thereby interconnecting their
respective platform ecosystems (Mosterd et al., 2021).
In all, a meta-platform, in turn, also has an ecosystem
composed of multiple sub-ecosystems (e.g., Wang,
2021). Due to the modular nature of a meta-platform’s
technological design, coordination costs are reduced,
and innovation will be simpler to organize (Mosterd et
al., 2021). An example of a meta-platform is Trivago,
which federates digital platforms (e.g., Expedia,
Booking, or Airbnb) in the tourism sector. Such
platforms benefit from Trivago as a first discovery
channel, hence exposing their platforms to larger user
bases (Perelygina et al., 2022).
More recently, meta-platforms have become of
research interest to the Information System (IS)
community. Specifically, a few studies start to
(implicitly) discuss business model topics concerning
meta-platforms. For example, Floetgen, Mitterer, et al.
(2021) explore how a meta-platform can create value
by integrating services and resources among two
mobility platforms. As another example, Veile et al.
(2022) describe cases in which meta-platforms create
value by providing standardized infrastructure.
At the same time, recent developments in the Data
Economy have resulted in a proliferation of data
marketplace literature (Abbas et al., 2021). Data
marketplace “are platforms that provide the necessary
infrastructure and services to facilitate the exchange of
data products between data providers and data
consumers from different environments” (Driessen et
al., 2022, p. 1). Given the fragmented nature of data
marketplaces, data providers and consumers suffer
from difficult data discovery processes and expensive
vendor lock-in (European Commission, 2020). In all,
fragmentation hinders data marketplaces from
reaching sufficient network effects.
The existing fragmented nature of data
marketplaces opens an opportunity to study meta-
platforms in the data marketplace context. Meta-
platforms may allow data marketplaces to enhance
their value creation. In fact, many meta-platform
projects have started recently, such as TRUSTS
1
and
i-3 Market
2
. Although meta-platforms have existed for
years (e.g., Trivago), they are now emerging in the
new context of data marketplaces. Data marketplaces
are fundamentally different from typical digital
platforms (e.g., due to the nature of data as an
experience good, its non-rival characteristic, and weak
appropriate regime) (Koutroumpis et al., 2020). These
differences may challenge our understanding of what
we know about meta-platform business models. We
take a business model lens to develop a holistic
understanding of the inner workings of this new
phenomenon.
Nevertheless, to the best of our knowledge, no
previous study has investigated the business model
aspects of this specific type of meta-platforms.
Consequently, how a meta-platform can create value
for data marketplaces remains speculative. Hence, this
study explores business models for a meta-platform
for the specific case of data marketplaces. This paper
focuses on value creation because this component is
often the first issue to tackle in business model
endeavors. Thus, we ask the following question: How
can a meta-platform potentially create value in the
case of data marketplaces?
2. Theoretical background
This section provides background information
concerning: a) business models for digital platforms,
b) data marketplaces, and c) meta-platforms.
2.1. Digital platform business models
Although business models can be observed via
various lenses, we follow recent reviews (e.g.,
Böttcher et al., 2022) that categorize business models
for digital platforms from the well-established
components by Teece (2010): value creation, delivery,
and capture. These three components are interrelated
and often employed to draw business model logic (of
digital platforms). Teece (2010) describes value
creation as the process of making products or services
that bring benefits to intended customers. Value
delivery refers to relevant activities and resources to
1
https://www.trusts-data.eu/ accessed on June 09, 2022
distribute the products (or services) to consumers;
value capture describes necessary monetary activities,
such as defining revenue stream and cost structures, to
sustain a business in the long run.
In this exploratory research, we focus on the first
component of business models: value creation. As we
want to categorize the identified value creation
according to its core focus, we group them into value
creation archetypes. Identifying value creation is often
the first step toward business model development
because it explores the desirability aspects of a
platform: will it create value for customers, and to
what extent do they want this platform? (Osterwalder
et al., 2015). Without clear value creation, meta-
platforms for data marketplaces may not be
commercially viable in the near future. Moreover,
focusing on one aspect allows us to explore the exact
value creation (mechanisms).
2.2. Data marketplaces
The core value of data marketplaces is to facilitate
responsible business data sharing (Driessen et al.,
2022). One use case example is sharing commercial
space data (earth observation) from satellites for
building 3D simulations to model physical phenomena
(Space Data Marketplaces, 2022). Fruhwirth et al.
(2020) reveal that data marketplace value creation
emphasizes privacy protection, data quality guarantee,
time relevancy, and pre-purchase testability. In line
with this finding, van de Ven et al. (2021) also stress
the importance of secure data sharing, high (and
unique) data assets, and easy data tooling. Concerning
security and privacy concerns, for example, data
marketplaces frequently employ emerging
technologies (such as multi-party computation) to
improve trust and reduce risk in data sharing (Agahari
et al., 2022)
Spiekermann (2019) suggests that data
marketplaces should go beyond sharing “raw” data.
Instead, they need to provide analytical functionality.
This assertion is supported by a finding from Bergman
et al. (2022) that stresses the data paradigm as
solutions rather than mere “items.” One concrete way
is to create value by providing aggregated or
standardized services. Koutroumpis et al. (2020)
emphasize the essential value creation of data
marketplaces: 1) enabling data provenance to track the
origin and use of data assets, and 2) exercising data
quality functionalities. This value creation is needed
given the nature of data as experience and non-
rivalrous goods (abstract, intangible, and easily
duplicated).
2
https://www.i3-market.eu/ accessed on June 09, 2022
In summary, the variety in data marketplace
offerings and their fragmentation open an opportunity
to explore the value creation of a meta-platform for
this specific case. For instance, a meta-platform can
recommend users to go to data marketplaces with
specific value creation or monetization schemes. It
also helps to identify and mitigate business model
incompatibilities. For instance, if one data
marketplace offers a dataset for free and the other
charges a price, they cannot be easily federated.
2.3. Meta-platforms
A meta-platform is a platform that coordinates,
integrates, and connects various existing platforms
(Zhang & Williamson, 2021). Referring to our
example in Section 1, Trivago is one example of a
meta-platform, where Expedia, Booking, or Airbnb
have a role as platform participants. Hence, meta-
platforms generally have a core characteristic of the
need for participating platforms and thus cannot exist
in a stand-alone nature (Lagutin et al., 2019). They
must coordinate with multiple platform elements, such
as platform core services or technical infrastructure
(Soursos et al., 2016). In addition, meta-platforms
need to consider other relevant stakeholders (such as
end-users and third-party complementor of platform
participants) to exercise value creation.
By understanding the above characteristic, we can
now discuss value creation of a meta-platform. In a
recent quantitative empirical paper, Ulrich and Alt
(2021) discuss how a meta-platform may help
integrate social networking platforms to close gender
gaps in the IS community. They highlight the
coordination effort to provide seamless integration
services for any participating platforms, ranging from
Software Development Kits (SDKs) to integrated
Application Programming Interfaces (APIs)
consumed by the platform participants.
A core focus of a meta-platform is to standardize
platform components (Mosterd et al., 2021). In their
explanation for IoT data sharing, a meta-platform may
help create a smart home environment consisting of
sensors and devices that are vendor-independent. They
also give an interesting idea of having a filter function
to help a specific mobility platform interoperable to a
specific partner (e.g., road safety authorities).
Meta-platforms also offer potential value creation
to increase network effects. For example, a meta-
platform provides a subscription management service
(Floetgen, Mitterer, et al., 2021). In this example, end-
users of a bank only need to join (and interact with) a
meta-platform to manage online streaming services
from many platform participants. A meta-platform can
also focus on aggregating information. Floetgen,
Strauss, et al. (2021) give another example in the
mobility industry where two platform providers join
their forces to create a meta-platform. The meta-
platform creates inter-modal routing algorithms to find
the most optimum travel route for travelers by
considering social distancing parameters as an input
(given the COVID-19 situation).
Another mentioned value creation of a meta-
platform refers to the “center of gravity,” which can
redirect the strategic direction of its platform
participants (Zhang & Williamson, 2021). This
happens when a meta-platform acts as a keystone
player (e.g., Alipay or WeChat pay). Hence, a meta-
platform has a high degree of influence and is even
responsible for supporting platform participants’
growth and legitimacy. To summarize, meta-platforms
can lead to industry convergence by facilitating
innovation services and networked business models
(Langley et al., 2021).
Taken together, our review of existing literature
on meta-platforms reveals three important points.
First, we do not find an explicit definition of meta-
platforms. Hence, clear boundary conditions of meta-
platforms are lacking. Second, we also do not find a
discussion of success or failure stories of meta-
platforms, meaning studies that theorize business
model configurations and performance are lacking.
Third, to the best of our knowledge, we do not identify
a single study that discusses the business models of
meta-platforms in the data marketplace context.
3. Research approach
We conducted an exploratory study because very
little is currently known about business models of
meta-platforms for data marketplaces. An inductive
qualitative approach is a common approach to
studying a new phenomenon (Sekaran & Bougie,
2016). We need flexibility when conducting this
research because meta-platforms are not yet a well-
defined and widely accepted concept. Hence, we
employed a semi-structured interview approach as a
primary data collection method to enable flexible
follow-up and probing questions (Edwards & Holland,
2013).
We selected a non-probability sampling strategy,
so-called judgment sampling, to select interview
participants we considered experts (Sekaran &
Bougie, 2016). We adopted this strategy since we
investigated a novel phenomenon that only a few
people are familiar with (Etikan et al., 2016). We
aimed to engage with a representative of two primary
groups: 1) meta-platform experts and 2) business data
sharing consultants. The following criteria were used
to identify participants: 1) familiarity with meta-
platforms and data marketplaces (i.e., knowledge of,
experience with, or consideration of), 2) experience in
decision-making processes, especially business
models, and 3) proficiency in English.
Firstly, on October 22, 2020, we conducted an
online workshop as a preparation activity before
conducting our semi-structured interviews. We
conducted this workshop to get an initial and quick
insight into potential value creation of a meta-platform
in the data marketplace context. The participants were
experts working on an EU project to create a meta-
platform for data marketplaces. The participants were
fifteen individuals from different commercial and non-
commercial organizations. We began by discussing
the pain points of data marketplace operators. We later
discussed potential value creation of meta-platforms
that might mitigate the pain points. For example, we
discussed the costly development and upgrading of the
technology infrastructure of data marketplaces. Hence,
one potential value is to provide shared services for
non-differentiating capabilities (e.g., billing
mechanisms). In total, we identified five potential
value creation of meta-platforms in the data
marketplace case (see Subsection 4.1).
Secondly, we interviewed twenty participants [I-
01 to I-20], consisting of fourteen (internal or external)
business data sharing consultants and six meta-
platform experts. These consultants promote and
engage with business data sharing on behalf of their
respective organizations; the meta-platform experts
are currently involved in interoperable data
marketplace innovation projects. The complete
participant overview can be seen in the online
supplementary material (Appendix 1)
3
. Between July
and November 2021, we conducted online interviews
using Microsoft Teams. The interviews lasted 40
minutes on average.
Our main question asked how a meta-platform
can create value in the data marketplace context,
particularly how it could benefit the three primary
stakeholders of a meta-platform (data marketplace
operators, providers, and consumers). Before jumping
into this question, we asked several introductory
questions, such as interviewees’ familiarity with data
marketplaces, to set the stage. We showed one typical
option of a meta-platform conceptualization (i.e., a so-
called one-stop-shop, refer to Subsection 4.3) with a
potential scenario (i.e., data providers joining a meta-
platform directly) to ensure the same conceptual
understanding. We allowed participants to challenge
this conceptualization and scenario, resulting in two
other significant findings in the later stage. The
3
The supplementary material can be accessed here:
https://doi.org/10.4121/21103867
detailed scenarios and interview protocols can be seen
in Appendix 2 and 3, respectively.
We inductively analyzed our interview transcripts
by adopting a two-phase coding: we intuitively
annotated potential value creation into a first-order
category, then grouped them further into a second-
order category. Afterward, we engaged with existing
literature to find inspiration for identifying value
creation archetypes (see Subsection 4.3). Finally, we
assigned the previously identified second-order
category to the most appropriate value creation
archetype.
We describe the code procedures in a data
structure presented in Figure 1. For example, we read
the following excerpt from a participant:
“But when I have several [data market] options in
front of me and have to evaluate, okay, the existence,
the inclusion of a data marketplace in a metadata
market engine, it could be a plus to evaluate, if I have
to make three-four choices, I would make the choice
that has the biggest outlook in the market.” [I-01]
We annotated this excerpt into the finding the data
marketplace with the biggest outlook first-order
category, which further grouped into the searching
second-order category. Finally, we assigned this first-
order category to the discovery aggregator value
creation archetype. To increase the internal validity of
our analysis, we performed an intercoder reliability
assessment to check the consistency of how the code
procedures are applied by the coder (the first author),
which was then reviewed thoroughly by the second
author. Overall, the authors align and agree with the
presented data structures. For a detailed description of
the relation between the interview transcripts and the
codes, please see Appendix 4 in the supplementary
material.
4. Results
We discover three value creation archetypes of a
meta-platform for data marketplaces: discovery
aggregator, brokerage, and one-stop-shop. We
discuss the value creation of each archetype in the
following subsections, including the logic of how we
derived these archetypes.
4.1. Initial exploration of meta-platform
value creation
This subsection summarizes the preliminary value
creation of a meta-platform based on our workshop
outputs. One potential value creation of a meta-
platform is forwarding traffic. Data consumers can
simply search datasets via a meta-search engine. If
they are interested in specific data assets, they will be
redirected to a data marketplace. In doing so, a meta-
platform can help improve the traffic in existing data
marketplaces (that are, unfortunately, lacking at the
moment).
A meta-platform can also create value by
providing shared services for non-differentiating
capabilities (e.g., billing mechanisms). Hence, data
marketplace participants can focus on their core value
proposition instead of spending too much effort in
managing these non-differencing capabilities. Another
discussed pain point of data marketplace operators is
the costly development and upgrading of data
marketplace technology infrastructure, mainly to keep
up with recent regulations such as the General Data
Protection Regulation and the Data Governance Act.
Still aligning with the principle of shared services, a
meta-platform, therefore, can gradually harmonize
technology infrastructure through coordination and
common standards.
The workshop also explored the potential of
membership alignment across data marketplace
participants. This effort creates value for data
providers and consumers by eliminating the need to
register in multiple marketplaces. Finally, a meta-
platform can also provide a central register of data
marketplace users, hence avoiding problematic users
who previously committed unethical data sharing
activities.
4.2. Value creation themes
This subsection further explores value creation
themes for a meta-platform for data marketplaces
(based on the interview findings). In relation to
Section 3, the “theme” here refers to the value creation
codes in the second-order category. In summary, eight
broad themes emerge from the analysis.
The first identified value creation theme for a
meta-platform is searchability. A meta-platform can
aid in finding data marketplaces with the biggest
outlook. In addition, a meta-platform can facilitate the
search of data assets by enabling homogenized search
across multiple marketplaces. One participant, for
instance, said:
“Yeah, searching data between these [data
marketplace participants] should be homogenized.”
[I-19]
Another value creation theme is dispatching,
meaning that data providers can upload and transfer
their meta-data descriptions, which later be feasible
for many data marketplaces. Data providers can also
receive offers from consumers in many data
marketplaces. In all, this provides forwarding traffic
activities from the perspective of data marketplace
operators. A participant illustrated:
“If I understand it correctly, it should be the meta-
data. The metadata that’s interoperable. We only show
the metadata that other data markets provide, but we
do not necessarily have the data sets or data assets.”
“So that [a data marketplace] users can also see the
offers of other data markets.” [I-17]
A meta-platform can create value by performing
promoting tasks for data providers, such as acting as
an advertising agency. One participant commented:
“So if you look, for example, from a meta-platform
point of view, I would rather see them [meta-
platforms] as an advertising agency where you can
help to find datasets.” [I-03]
Moreover, a meta-platform should be able to
analyze transaction data to inform appropriate data
demands for data providers; as one interviewee put it:
“As a provider, you know or have an idea, at least,
where your data is residing or know if there are any
demands of your data on the different platforms…that
you have insights in the usage or potential use. I get so
statistic, let’s say.” [ I-10]
The comment below illustrates how a meta-
platform can also create promotion value by
showcasing a successful use case, hence providing
proof of data sharing value.
“It is a showcase on [a data provider] can do and
when someone wants to do something with [a data
provider]. They will go directly to [a data provider],
or through the marketplace.” [I-08]
A meta-platform can provide support to data
providers and consumers. For example, a meta-
platform may provide data pricing assistance to help
them get the most optimum price. Another potential
support relates to onboarding processes.
“Then, customers [data providers or consumers]
need to enroll with us over the register and enroll to
our rules and get a contract with us, etc. So probably
that is a bit too much of a hassle, so I think that such a
meta-marketplace could be in the boost for
[customers] to further sell this kind of metadata to the
market.” [I-02]
The next identified value theme is standardizing.
A meta-platform, often together with data marketplace
participants, create standardized Application
Programming Interfaces (APIs).
“Yep. So there is more than one is the standardization
of the marketplace, so you got one marketplace to find
everything, and the second one is the standardization
of the let’s call it API’s to eventually get that data” (I-
08)
One participant also raised a concern about
multiple certifications and membership schemas; so a
meta-platform can create value by bridging this gap:
“Now the interesting thing is, of course, when you are
going to set up a relationship with the data
marketplace, you have, let’s say, specific requirements
for data marketplace. So, for example, if some
customers are connected to marketplace A, data
marketplace B, but you want to expose it to as many as
possible, but you have to comply with the difference.
Let’s say technical requirements or certification
requirements per different marketplaces” [I-12]
Aligned with our initial exploration in the
workshop, one participant also highlighted the
potential of shared services, particularly billing
schemas to be included in meta-platform offerings:
“Maybe there can be also some interoperability in
terms of the pricing. Maybe there can be
interoperability in terms of whether you can purchase
access to the data set of one platform and you can
purchase it through another platform.” [I-18]
We also categorize the data marketplace
membership alignment (from the workshop output) to
this theme due to its attempt to standardize a joint
schema for membership endeavors.
Another identified value creation theme is
regulating, including self-regulating endeavors
between a meta-platform and its data marketplace
participants.
“Sometimes we see that as a public opinion coming,
and we can better organize ourselves for fraud
prevention and cyber security. We really are looking
into it ourselves because the criminal activities are
quicker than the legislator can exactly tell what we
should do about it. So we try to find out what to do.”
[I-12]
By having this self-regulation, a meta-platform
can lead the compatibility with updated leading
technologies.
“Right, so as a hub, it has to be, you know, very agile
and compatible with several top technologies in the
markets.” [I-05]
Additional value creation in the regulating theme
can be drawn from the workshop output: a central
register of data marketplace users. This can be
beneficial to know the transaction history of data
providers and consumers, hence avoiding those who
previously committed unethical data sharing activities.
A meta-platform can also facilitate sharing
features between data marketplace, for example,
computational resources. A participant illustrated this
idea:
“Computing resources probably can be exchanged,
things like that. There is someone who has a lot of
computational resources like GPU stuff that they just
put it online and then on [a data market] you use. You
rent this infrastructure; then you rent those datasets.”
[I-17]
Finally, we discover another theme: developing
programming ecosystems (or Sandbox environments)
to experimenting with data assets.
“Programming ecosystem, maybe a development
ecosystem where these kinds of experiments are also
possible. And then also we are in the future machine
learning models can be exchanged.” [I-17]
4.3. Value creation archetypes of a meta-
platform
This subsection describes the archetypal ways in
which meta-platforms create value. Adopting Piccoli
& Pigni’s (2013) elaboration, we refer to value
creation archetypes as a generalized, high-level
blueprint to portray the value creation focus of a meta-
platform. An archetype consists of multiple
interrelated value creation themes. We develop
archetypes because our participants tend to interpret a
meta-platform differently. One interviewee indicated:
“I think that there are different levels of what [a meta-
platform] means. At the moment, we are completely at
the beginning of the journey.” [I-18]
Figure 1 summarizes and connects a meta-platform’s
value creation for data marketplaces according to its
relevant archetype: discovery aggregator, brokerage,
or one-stop-shop.
Figure 1. Value creation archetypes of a meta-
platform for data marketplaces.
Discovery
aggregator
Brokerage
One-stop-
shop
Standardizing
Promoting
Searching
Dispatching
Finding data marketplaces with the
biggest outlook
Searching data assets
Transferring meta-data description
across data marketplaces
Receiving data requests from other
data marketplaces
Forwarding traffic
(Second-order categories)
Value creation theme (Aggregated
themes)
Acting as advertising agency
Knowing data demands
Showcasing data sharing use cases
Supporting
Providing data pricing supports
Supporting onboarding processes
Sharing
Regulating
Creating self-regulation
Complying with updated top
technologies
Aligning technology architecture
Providing central register of data
marketplace users
Creating API standardization
Providing centralized certification and
membership schema
Providing shared-services
Sharing features between marketplaces
Sharing computational resources
Experimenting
Establishing a programming
ecosystem
(First-order categories)
Value creation Archetype
The first possible value creation archetype for a
meta-platform is the discovery aggregator.
According to literature, the aggregator often collects,
analyzes, and offers insight from multiple data sources
(e.g., Bergman et al., 2022; Garbuio & Lin, 2019). The
discovery aggregator type is not focused on the role of
controlling but rather on creating new connections
between ecologies of platforms. Hence, rather than
enforcing some regulations centrally, this archetype
allows platform participants to decide their path and
niche (Mikołajewska-Zając et al., 2021).
Applied to our study’s context, the discovery
aggregator archetype can emphasize searching and
dispatching value. Consequently, this archetype can
focus on providing meta-data interoperability with
(and among) data marketplace participants. After
redirecting data providers and consumers to relevant
data marketplaces, the meta-platform task is finished.
In this regard, data providers (or consumers) must
register with relevant data marketplaces (and perform
transactions) by themselves. Taken together, this
archetype supposes to be the simplest way a meta-
platform can create value. The below comments
illustrate:
“So the minimum feature, I think, is not far. It is quite
close, within reach. And I think it has to do with, yes,
with discovery, definitely.” [I-16]
“I think in the very minimum case, you need to
transfer the meta-data.” [I-08]
The second potential value creation archetype for
a meta-platform is brokerage. Slightly different from
the discovery aggregation, the brokerage archetype
focuses on managing business relationships (Garbuio
& Lin, 2019). With its deep expertise, the brokerage
archetype generally offers consulting services to solve
specific clients’ problems (Palmié et al., 2021). For
example, the brokerage type can simplify transactions
or provide capacity-building activities to improve
skills (Komninos et al., 2021).
In our context, therefore, the brokerage archetype
can focus on promoting and supporting value. This
archetype provides value (e.g., pricing supports) to
optimize business data sharing based on a) transaction
insights (e.g., data demands) and b) meta-platform
expertise (e.g., experience in successful use cases). In
doing so, this archetype also needs meta-data
interoperability with data marketplace participants.
After finding the desirable data marketplaces and
consumers, this archetype can provide onboarding
support before establishing transactions.
The final value creation archetype of a meta-
meta-platform is the one-stop-shop (OSS). The OSS
archetype in digital platforms often provides fully
automatic services. It enables end-users to
independently use a standardized portal (or a website)
to use cross-platform services (Floetgen, Strauss, et
al., 2021). This standardized portal can be achieved by
technical integration in the backend (Scholta et al.,
2019). Floetgen, Strauss, et al. (2021) reveal that this
value creation often results from a joint alliance
between platform participants. An initiator act as a
coordinator to harmonize the technical integration, and
platform participants come together to share their
resources. Moreover, according to Adebesin et al.
(2013), after achieving technical interoperability,
digital platforms, depending on the goals, may want to
achieve a higher level of interoperability, e.g.,
organizational interoperability.
For data marketplaces, the one-stop-shop
archetype is likely to build upon the value creation
themes of standardizing, sharing, regulating, and
experimenting. With a higher level of interoperability,
it is possible to be interoperable beyond the mere
meta-data, such as the actual data assets themselves,
along with payment and contract interoperability. In
the OSS archetype, data providers and consumers do
not have to register to specific markets to conduct
transactions—they can perform the actual transaction
without leaving the meta-platform.
5. Discussion
We find three value creation archetypes of a meta-
platform: discovery aggregator, brokerage, and one-
stop-shop. Although these three archetypes are
inspired by generic digital platform literature, the
contextualization of meta-platforms, especially in the
data marketplace context, makes the value
specification for each archetype unique. For instance,
Garbuio and Lin (2019) describe the aggregator value
creation archetype on the digital platform healthcare.
These digital platforms provide aggregated
information from multiple sources (such as electronic
health records and recent medical research) to assist
clinicians in better decision-making. In our context,
the aggregator focuses more on the discovery process
across multiple data marketplaces to find the most
relevant data assets for data providers and consumers.
Similarly, while the brokerage archetype in the
healthcare context focuses on building intimate
relationships with patients (by taking care of their
specific needs) (Garbuio & Lin, 2019), the brokerage
archetype in our case focuses more on helping data
providers and consumers to find data assets across data
marketplaces which suits their need best, including
helping to prepare transaction endeavors.
Another example can be seen in the one-stop-shop
value creation archetype. This archetype has typical
characteristics of regulating, standardizing, and
sharing value (Floetgen, Strauss, et al., 2021).
Nevertheless, another value can be added to this
archetype due to our unique case, for instance, the
experimenting value. In all, we argue that the generic
idea of the archetype can be found in the digital
platform literature; it undoubtedly has a different
meaning (or contextualization) in the meta-platform
for data marketplaces. Two reasons behind this are
complex constellations of data marketplaces and the
nature of data itself.
A meta-platform needs to consider that well-
performing, operationalized data marketplaces may
keep their platform closed, or what Hodapp and Hanelt
(2022) termed as planned low interoperability due to
strategic motives to avoid direct competition. Data
marketplace pursuing this competitive strategy is
likely not always welcome with the idea of joining a
meta-platform: they want to protect their market share.
Another issue is that not every data marketplace is
commercially viable at the moment (see a review by
Spiekermann, 2019); hence marketplaces may
potentially “piggyback” the network effects without
sufficiently contributing to the development of meta-
platforms.
A meta-platform also needs to consider (and
prepare for) various impacts of increased network
effects. In addition to antitrust regulation (Mosterd et
al., 2021), a concentrated network effect in a single
digital ecology negatively impacts privacy, security,
homogeneity, and reliability (Hodapp & Hanelt,
2022). Considering homogeneity (i.e., innovation
stagnancy), for instance, if a meta-platform becomes
“too big” with massive network effects, new entrances
of data marketplaces (even with the newest
technological superiority) may not be sufficiently
adopted.
Finally, Márton (2021) argues that every digital
ecology has its limit, and platform designers must
respect that limit. For example, standardization can be
helpful to improve compliance but, at the same time,
make the platform participants too dependent on the
focal platform. Consequently, they may lose their
capability and competitive advantage in the long run.
A meta-platform needs to go beyond considering
business performances; it must examine responsibility
aspects for data marketplace participants.
6. Conclusion
This study explores how a meta-platform can
potentially create value in the case of data
marketplaces. Our findings show that a meta-platform
for data marketplaces can have three distinct value
creation depending on its focus: discovery aggregator,
brokerage, and one-stop-shop. The discovery
aggregator archetype emphasizes searching and
dispatching value, while the brokerage one focuses on
promoting and supporting value. Finally, the one-stop-
shop archetype creates value by standardizing,
regulating, sharing, and experimenting.
We consider several research avenues concerning
our research limitations. Many meta-platform
initiatives are still in the development phase.
Therefore, many of our participants engage with
Minimum Viable Products (MVPs). In this regard, the
applicability of our findings may be limited to the
earliest phases of meta-platform investigation. Future
research should investigate meta-platforms based on
their actual implementation, as the value creation
value may alter as adoption increases. For instance, the
TRUSTS and i-3 Market projects are the candidates to
conduct case study research for the discovery
aggregator and one-stop-shop archetype, respectively.
Future research may also distinguish meta-
platform value creation for specific stakeholders, such
as data marketplace operators, providers, consumers,
and third-party complementors. Furthermore, data
may vary in terms of its sensitivity and privacy
concerns. Thus future research could zoom in on the
specific type of data. We suspect that different data
types (e.g., personal/non-personal) and industry focus
(e.g., automobile, health, insurance) may require
different expectations and, thus, variation in value
creation models of the meta-platforms.
Our study is interpretive and exploratory. Hence,
another possible angle for future research is to connect
the meta-platform to relevant theories. One promising
theoretical framework is the recently proposed
information ecology theory (Wang, 2021). This theory
explores the potential value creation in digital
ecological-related concepts that connect the “part-
whole” relationship between the focal actor and the
participants, relevant to our context). Some final
constructs offered in this theory are relevant to our
findings, such as searching, promoting, and
standardizing. Engaging with this theory may reveal
other potential value creation themes of meta-
platforms.
Considering our focus on value creation, we also
call for more exploration of value delivery and capture
components. In value delivery, research on
architecture and technical interface is vital to
operationalized meta-platforms. In value capture, on
the other hand, a discussion about cost structure and
revenue sharing mechanisms is equally crucial for
viability. For example, by considering the previously
information ecology theory, we need to consider
appropriating endeavors: how can revenue sharing
mechanisms between a meta-platform and data
marketplace participants be aligned? How can
intellectual property rights among the shared features
be managed? The archetypes will likely impact how
we manage value delivery and creation, but this
assertion needs to be assessed further.
Future avenues may also explore the specific
issues emerging in meta-platforms because of their
unique interrelation with data marketplaces. For
example, issues such as data sovereignty in data
marketplaces are among the emerging topic in the
literature (e.g., Hummel et al., 2021). The unique
characteristics of meta-platforms may challenge our
understanding of the antecedents (e.g., root causes)
and consequences (e.g., willingness to share data) on
such specific issues.
Another attractive pathway is to examine the
potential hybrid role of a meta-platform. It is quite
conceivable that users connect to meta-platforms
indirectly (via the underlying data marketplaces) and
directly (so directly uploading/consuming their data to
the meta-platform). Hence, to what extent this hybrid
role affects data marketplaces’ willingness to join is
subject to further examination.
Future research may also investigate the
interrelationship between value creation archetypes, as
these relationships are frequently not mutually
exclusive. Despite the emphasis on regulation and
standardization, the one-stop-shop will likely also
provide searching and dispatching value. On the basis
of this assertion, multiple evolutionary paths can be
observed. For example, the discovery aggregator can
be the starting point because of its simple form. Along
the way, a meta-platform can evolve in either the
brokerage or one-stop-shop direction, which depends
on specific variables (e.g., power or influence on data
marketplaces participants). The veracity of this
assertion must therefore be investigated further.
We frame our contribution to the IS digital
platform literature by considering two main issues:
conceptual ambiguity and scoping (see De Reuver et
al., 2018). As the meta-platform is a new type of
platform, we use business models as a tool to do the
exploration. We find three value creation archetypes:
discovery aggregator, brokerage, and one-stop-shop.
Prior studies do not conceptually define meta-
platforms but rather jump in straight to discuss their
offerings. Taken together, we scope the meta-platform
context, which is an essential first step for creating a
contextualized-classifying theory (see Gregor, 2006).
Hence, we reveal a delineated boundary condition to
theorize meta-platforms, which are underexplored in
the literature.
In addition, we contribute scientifically by adding
specifications to the existing value creation archetypes
in the literature; and show how it is substantially
different case-by-case. Finally, this study is among the
first that explore value creation archetypes for a meta-
platform, thus identifying value differentiation as a
base for further business model investigations.
The findings of our research will be of interest to
practitioners who aim to develop a meta-platform for
data marketplaces. Precisely, they can reflect on
identified archetypes to analyze the focus on their
value creation as a stepping stone toward
commercialization.
7. Acknowledgment
The research leading to these results has received
funding from the European Union’s Horizon 2020
Research and Innovation Programme, under Grant
Agreement no 871481 – Trusted Secure Data Sharing
Space (TRUSTS), from the H2020-ICT-2018-
20/H2020-ICT-2019-2 Call.
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