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Creating a Taxonomy of Business Models for Data Marketplaces


Abstract and Figures

Data marketplaces can fulfil a key role in realizing the data economy by enabling the commercial trading of data between organizations. Although data marketplace research is a quickly evolving domain, there is a lack of understanding about data marketplace business models. As data marketplaces are vastly different, a taxonomy of data marketplace business models is developed in this study. A standard taxonomy development method is followed to develop the taxonomy. The final taxonomy comprises of 4 meta-dimensions, 17 business model dimensions and 59 business model characteristics. The taxonomy can be used to classify data marketplace business models and sheds light on how data marketplaces are a unique type of digital platforms. The results of this research provide a basis for theorizing in this rapidly evolving domain that is quickly becoming important.
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Creating a Taxonomy of Business Models for Data Marketplaces
Conference Paper · June 2021
DOI: 10.18690/978-961-286-385-9.23
4 authors, including:
Some of the authors of this publication are also working on these related projects:
Business Models for Data Platforms View project
Montijn van de Ven
Eindhoven University of Technology
Antragama Ewa Abbas
Delft University of Technology
Zenlin Kwee
Delft University of Technology
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ISBN 978-961-286-385-9
1 Eindhoven University of Technology, Department of Industrial Engineering and
Innovation Sciences, The Netherlands; e-mail:
2 Delft University of Technology, Faculty of Technology, Policy and Management, The
Netherlands; e-mail:,,
Abstract Data marketplaces can fulfil a key role in realizing the
data economy by enabling the commercial trading of data
between organizations. Although data marketplace research is a
quickly evolving domain, there is a lack of understanding about
data marketplace business models. As data marketplaces are
vastly different, a taxonomy of data marketplace business models
is developed in this study. A standard taxonomy development
is followed to develop the taxonomy. The final
taxonomy comprises of 4 meta-dimensions, 17 business model
dimensions and 59 business model characteristics. The
taxonomy can be used to classify data marketplace business
models and sheds light on how data marketplaces are a unique
type of digital platforms. The results of this research provide a
basis for theorizing in this rapidly evolving domain that is quickly
becoming important.
1 Introduction
As an organization may not always possess the required data to carry out or improve
their processes and services, they may wish to purchase these data from other
organizations. A data marketplace can enable data purchase by providing a digital
platform through which individuals and organizations can exchange data (Stahl et
al., 2016; Schomm et al., 2013). In contrast to most other platforms, where data is
utilized to improve services or manage customer relationships, on data marketplaces
data is actually the product itself (Spiekermann et al., 2018).
Despite the potential benefits of data marketplaces, in practice, very little data is
shared or traded via these platforms (Koutroumpis et al., 2020). In general, little
research has been conducted on data marketplaces (Thomas & Leiponen, 2016) and
data marketplace business models in particular (Fruhwirth et al., 2020; Spiekermann,
2019). As a foundation for research on a novel and diverse phenomenon, a first step
is developing a taxonomy, because it can be used to classify data marketplace
business models (Lambert, 2015). Two taxonomies of data marketplace business
models are currently available in the literature, i.e. those proposed by Fruhwirth et
al. (2020) and Spiekermann (2019) respectively.
The existing taxonomies (Fruhwirth et al., 2020; Spiekermann, 2019), however,
overlook two main areas which this study aims to address. Firstly, the two studies
mostly focus on the classification of multilateral data marketplaces, while in practice
data trading often happens via bilaterally negotiated contracts (Koutroumpis et al.,
2017). Secondly, the studies view data marketplace business models from a single
firm perspective. However, data marketplaces take part in a network of stakeholders
involving data analysts, application vendors, algorithm developers, data providers,
consultants, licensing entities, and platform providers (Muschalle et al., 2012;
Thomas & Leiponen, 2016).
To address the above two under-researched areas, this study develops a taxonomy
from a multi-stakeholder perspective on business models. We define a business
model as the way a network of stakeholders creates and captures value (Bouwman
et al., 2008). This multi-stakeholder perspective allows us to understand the business
model for the data ecosystem as a whole. Moreover, we define a data marketplace
as a digital system where data is traded as an economic good, that connects data
M. van de Ven, A. E. Abbas, Z. Kwee & M. de Reuver:
Creating a Taxonomy of Business Models for Data Marketplaces
buyers and data sellers, and facilitates data exchange and financial transactions
(Koutroumpis et al., 2020; Stahl et al., 2016). In this way, the term data marketplace
is broadly interpreted, to go beyond the already studied multilateral data
The remainder of this paper is structured as follows: in Section 2, the taxonomy
development process is described. Subsequently, Section 3 presents the developed
taxonomy on the basis of the identified business model dimensions. Lastly, Section
4 provides a conclusion of the research and discusses the scientific contribution,
practical relevance and limitations of this study.
2 Taxonomy development process
To develop the taxonomy, we follow the taxonomy development method by
Nickerson et al (2013). Meta-characteristics of the taxonomy are defined first. Next,
the thirteen ending conditions suggested by Nickerson et al. (2013) were employed.
After that, multiple iterations are conducted to refine the taxonomy.
2.1 Meta-characteristics
Meta-characteristics function as overarching characteristics of the object of interest
(Nickerson et al., 2013). We use the four business model domains of the STOF
ontology (i.e. Service, Technology, Organization and Finance domains as in
Bouwman et al. (2008)) as the meta-characteristics of the taxonomy, as the STOF
approach takes service as a unit of analysis and employs a multi-stakeholder
perspective on business models (Bouwman et al., 2008). This perspective is well-
suited for data marketplaces because a network of business actors are involved in
and around data marketplaces (Muschalle et al., 2012; Thomas & Leiponen, 2016).
2.2 Literature search
We collected dimensions and characteristics from existing literature. A literature
search was conducted to discover existing knowledge about the object of interest
(Webster & Watson, 2002). Google Scholar was consulted to find relevant academic
sources, using the search string “Data marketplaces” AND (“Business models” OR
“Digital platform” OR “Digital marketplace” OR “Data trading” OR “Data
economy”). This string resulted in a total of 359 articles. The articles were scanned
based on their title, abstract and relevance, which resulted in a preliminary selection
of 17 articles. After making this pre-selection of articles, the full text of the articles
was read, which resulted in the exclusion of seven articles that did not explicitly
discuss dimensions or characteristics of data marketplace business models. Based on
cross-reference of the selected articles, we added four additional articles that
presented topic-relevant business model taxonomies to the list. The literature review
resulted in a final set of 14 articles as presented in Table 1.
Table 1: Overview of classifications relevant to data marketplace business models
Author(s) (Year) Type
Schomm et al. (2013)
Dimensions of data providers and data
Stahl et al. (2014a)
Stahl et al. (2014b) 16
Stahl et al. (2017)
Stahl et al. (2016)
Classification of electronic marketplaces
Koutroumpis et al. (2017)
Market designs for data marketplaces
Muschalle et al. (2012)
Pricing models for data marketplaces
Fricker and Maksimov
(2017) 8
Spiekermann (2019)
Taxonomy of data marketplace business models
Fruhwirth et al. (2020)
Bock and Wiener (2017)
Taxonomy of digital business models
Täuscher (2016)
Taxonomy of marketplace business models
Täuscher and Laudien (2018)
Hartmann et al. (2014)
Taxonomy of data-driven business models
2.3 Selection of empirical cases
To account for the practical relevance of the taxonomy, we conducted desk research
between May and July 2020 to build a database of empirical cases of data
marketplaces. Sixty-five websites of data marketplaces that were mentioned in
existing studies of data marketplaces were included in the database (Carnelley et al.,
M. van de Ven, A. E. Abbas, Z. Kwee & M. de Reuver:
Creating a Taxonomy of Business Models for Data Marketplaces
2016; Koutroumpis et al., 2020, 2017; Prlja, 2019; Spiekermann, 2019; Stahl et al.,
2016). The data discovery platform, a website that provides an overview
of over 1,800 data providers and 200 data platforms, was consulted. In total, the
search in the repository of resulted in the discovery of an additional set
of 187 data marketplaces. To complement the database with cases that were not
considered in the existing studies or part of the database, we used the
search engine Google to further conduct a desk research. The keywords “data
marketplace”, “data market” and “data trading platform” were applied during the
search. From this search, fifteen data marketplaces were added to the database.
Four criteria were applied to the companies that resulted from the desk research to
ensure the relevance of the empirical cases. Firstly, data marketplaces that turned out
to be shut down, after inspecting the website, were excluded from the database.
Secondly, the companies that did not fit this study's definition of a data marketplace
were excluded. This implied that data marketplaces that only provided open data,
such as governmental organizations and NGOs, were excluded as these platforms
adopt non-commercial business models (Carnelley et al., 2016). Thirdly, data
marketplaces that did not have an English version of their website were excluded.
Lastly, data marketplaces that were still in the construction phase were excluded.
The application of these four criteria to the cases led to the exclusion of 89 cases.
Therefore, the final database consisted of 178 cases of data marketplaces.
To analyse the business models of existing data marketplaces, a sample was taken
from the database of cases. The empiricist philosophy of classification prescribes to
build a taxonomy based on the consideration of many characteristics (Lambert,
2015). Therefore, the cases of data marketplaces in the database were first segmented
into groups based on the similarity of their characteristics. The website of
categorized data marketplaces based on the type of data traded on the platform. This
variable was selected as the leading sampling variable to explore the variation
between cases in the database. Based on the available information on and
an inspection of the case's website, 138 cases could be labelled by type of data traded
on the platform. The remaining 40 cases in the database were labelled based on the
classification of the cases in the existing studies (Fruhwirth et al., 2020; Spiekermann,
2019) and through the manual inspection of the companies’ website.
The segmentation of data marketplaces by type of data traded on the platform
reveals that some segments of data marketplaces in the database were
overrepresented. This was especially the case for audience data marketplaces, that
constituted over 60% of the cases (N=112). Audience data is combined data about
a certain target group of customers, which is much sought after by marketeers. To
compensate for the overrepresentation, instead of random sampling, a
disproportionate stratified sample of N=40 cases was taken from the database
(Daniels, 2011). The final sample of 40 data marketplaces consisted of ten data
marketplaces on which any type of data is traded (25% of the sample), four financial
and alternative data marketplaces (10%), nine audience data marketplaces (22.5%),
six sensor and mobility data marketplaces (15%), four geo data marketplaces (10%)
and seven health and personal data marketplaces (17.5%) (available here:
2.4 Design iterations
Our design phase started with a conceptual-to-empirical approach (Nickerson et al.,
2013). In these design iterations, the concepts derived from the literature were
compared to the sample of empirical cases. Information on the business models of
the cases was collected from publicly available sources such as company websites
and news articles. The discovered information fragments were coded using the
dimensions and characteristics from the literature review as a guideline (See Table
2). After each case, newly identified characteristics were added to the dimensions of
the taxonomy. After two conceptual-to-empirical design iterations, two empirical-
to-conceptual iterations were conducted, which resulted in the addition of two
dimensions to the taxonomy: enterprise data marketplace and data processing and analytics
tools. After every design iteration, the ending conditions were checked. After two
conceptual-to-empirical iterations and two empirical-to-conceptual iterations, both
the objective and subjective ending conditions were met. Finally, to test the
usefulness of the taxonomy, three mini-case studies were conducted on empirical
cases of data marketplaces that were not part of the sample, i.e. Wibson, QueXopa
and Advaneo respectively. The taxonomy was found to be useful, as the business
models of the cases could be classified based on public information about the cases.
M. van de Ven, A. E. Abbas, Z. Kwee & M. de Reuver:
Creating a Taxonomy of Business Models for Data Marketplaces
Table 2: Coding examples for the value proposition dimension
Easy data
access and/or
“FactSet creates data and technology solutions for investment
professionals around the world, providing instant access to
financial data and analytics that investors use to make crucial
Knoema is a cloud-based data technology platform that makes
data accessible and delivers intelligent data tools to enable data
access and discovery.“
Secure data
“With Dawex Global Data Marketplace providers can
highlight the value of their data while retaining full control over
the distribution and configuration of usage rights.”
“Unlike other data marketplaces, Snowflake Data Marketplace
leverages Snowflake's Secure Data Sharing technology, which
means no data transfer and no need to squeeze data through
APIs or use cloud storage.”
High quality
and unique
“Use exclusive Amazon audiences to reach your ideal audience
on and off Amazon.”
“Quality business data for better sales leads Any campaign is
only as good as the data it’s built on so make sure yours is the
3 Taxonomy of Data Marketplace Business Models
The final taxonomy consists of 4 meta-dimensions, 17 dimensions and 59
characteristics and is presented in Table 3. In the following sections, the data
marketplace business model dimensions are discussed per meta-dimension (STOF).
Table 3: Taxonomy of data marketplace business models
Service domain
Easy data
access and/or
Secure data
High quality and
unique data
All services
in a single
Enterprise data
Yes No
processing and
analytics tools
Yes No
B2B C2B Any
Financial &
Health &
Sensor &
Global Regional Local
Time frame
(Near) real-time
Technology domain
Centralized Decentralized
Data access API Download Specialized software
Data source
provided data
Acquired data
n domain
One-to-one One-to-many Many-to-one
Private Consortium Independent
Finance domain
Commissions Subscriptions Usage fees Asset sales
Pricing model Freemium Pay-per-use Flat fee
Price discovery Set by buyers Negotiation Set by marketplace
Set by
Smart contract
Fiat money Cryptocurrency
M. van de Ven, A. E. Abbas, Z. Kwee & M. de Reuver:
Creating a Taxonomy of Business Models for Data Marketplaces
3.1 Service domain
The value proposition is a statement that indicates the proposed value that an
enterprise intends to deliver to the customer (Bouwman et al., 2008). It often
describes how customers can benefit from using the service and how the enterprise
aims to set itself apart from the competition. Some data marketplaces offer an
enterprise data marketplace as an additional service. An enterprise data
marketplace functions as a private data marketplace that enables organizations to
share data within the company or with external partners, such as suppliers and
customers, that are invited by the focal organization. The data processing and
analytics tools characteristic indicates whether a data marketplace offers additional
tooling on top of the data, to perform analytics activities on proprietary data or data
bought via the platform. The marketplace participants dimension describes the
type of participants that are allowed to register and exchange data on the
marketplace. While most data marketplaces allow the exchange of any type of data
on their marketplace, some data marketplaces focus their data offering towards a
specific industry domain. The geographic scope describes the regions in which
the data marketplace is operating and available to users (Täuscher & Laudien, 2018;
Täuscher, 2016). The time frame dimension describes whether or not the data needs
frequent updates to maintain the relevancy of the data (Schomm et al., 2013).
3.2 Technology domain
Data marketplaces may adopt two types of platform architectures: centralized or
decentralized (Koutroumpis et al., 2017). In the centralized approach, data providers
offer their data products via a predefined centralized location on the platform, such
as a cloud repository. In decentralized platforms, the data products remain at the
data provider and the data is traded using distributed ledger technologies such as
blockchain. Platform providers may provide access to the data in a number of
different ways (Schomm et al, 2013). The data source dimension describes the
origin where the data was gathered or collected by the data marketplace platform
(Hartmann et al., 2014).
3.3 Organization domain
The matching mechanism determines the number of parties on each side of the
platform (Koutroumpis et al., 2017). Besides multilateral data marketplaces, three
more types of data marketplaces exist: bilateral data marketplaces (one-to-one
matching), dispersal data marketplaces (one-to-many matching), and harvest data
marketplaces (many-to-one matching). The platform sponsor can be a private
individual or a group, a consortium of buyers or sellers, or an individual or a group
that is independent of other market players (Stahl et al., 2017, 2016).
3.4 Finance domain
The revenue dimension describes the main source of revenue for a data marketplace
(Spiekermann, 2019; Täuscher & Laudien, 2018; Täuscher, 2016). The pricing
model specifies how the final price for the data good or service is composed
(Fruhwirth et al., 2020; Schomm et al., 2013; Spiekermann, 2019; Täuscher &
Laudien, 2018; Täuscher, 2016). A price discovery function allows buyers and
sellers on the marketplace to determine a transaction price which they both agree on
(Bakos, 1998). Data marketplaces may implement smart contracts to enhance
transparency and to enforce trust among marketplace participants (Fruhwirth et al.,
2020). The payment currency dimension explicates which currencies are accepted
for the payments made by marketplace participants (Fruhwirth et al., 2020).
4 Discussion and Conclusion
The developed taxonomy of data marketplace business model has two key scientific
contributions. First, the results of the study contribute to the scarce knowledge
about data marketplaces and their respective business models (Thomas & Leiponen,
2016). This study adopts a multi-stakeholder perspective on data marketplace
business models by emphasizing the roles in the data ecosystem. The taxonomy
provides an overview of contemporary knowledge about data marketplace business
models and exposes new business model alterations that have emerged in practice.
A second contribution made by this study is related to the interpretation of a data
marketplace. Existing taxonomies (Fruhwirth et al., 2020; Spiekermann, 2019) focus
on studying one type of data marketplaces: multilateral data marketplaces
(Koutroumpis et al., 2017). In our study, data marketplaces are more broadly
M. van de Ven, A. E. Abbas, Z. Kwee & M. de Reuver:
Creating a Taxonomy of Business Models for Data Marketplaces
interpreted as digital systems for trading data as an economic good, that connect
buyers and sellers, and facilitate data exchange and financial transactions. This allows
us to identify additional business model dimensions, which are not part of existing
taxonomies: enterprise data marketplace, data processing and analytics tools,
geographic scope, matching mechanism and platform sponsor. By eliciting how data
marketplace business models differ, we provide a basis for fine-grained theory
development, which is often lacking in platform studies (De Reuver et al., 2018).
The developed taxonomy can guide decision-makers who are exploring the options
of setting up a data marketplace or to join an existing data marketplace. An improved
understanding about data marketplace business models may help to achieve
commercialization, that will make data more accessible and exploitable to
individuals, businesses and authorities.
Although we took a systematic approach, subjectivity in assessing the cases may pose
a limitation. We dealt with this by conducting multiple iterations and re-
interpretations of the data. Further, not all data marketplace companies disclose
sufficient information about all of their business model characteristics. Therefore,
not all empirical cases could be classified into all of the conceptually derived
dimensions. This was especially the case for financially related dimensions such as
revenue partners and cost categories (Täuscher & Laudien, 2018). Lastly, as in any
taxonomy development study, our study is limited to the current set of phenomena
that exist in practice. Hence, future research may update our taxonomy in light of
fundamentally new data marketplace types.
Data marketplaces pose a foundation for the data economy: they enable firms to
access external data to drive their business and to profit from selling their own data.
The EU is investing heavily in data marketplaces in the years to come (European
Commission, 2020). At the same time, ambiguity pertains over what constitutes a
viable data marketplace business model. Our taxonomy takes a broad and multi-
stakeholder perspective to data marketplaces, going beyond the single-firm
multilateral perspective of extant taxonomies. We argue that such a broad conceptual
basis is needed to advance scholarly understanding of ecosystems in the data
economy and to unlock the potential of trading data for a functioning data economy.
The research leading to these results has received funding from the European Union’s
Horizon 2020 Program, under grant agreement 871481 Trusted Secure Data Sharing Space
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... From an academic perspective, recent trends in the European Union policy-making agendas have led to increased studies on business data sharing via data marketplaces, resulting in a constantly expanding yet fragmented body of literature. Recent research provides an understanding of the state-of-the-art in practice via business model studies (e.g., Fruhwirth, Rachinger and Prlja [2], van de Ven et al. [7]), but it does not provide a comprehensive overview of data marketplace research in academia. Consequently, knowledge gaps in data marketplace research remain unclear. ...
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Policymakers and analysts are heavily promoting data marketplaces to foster data trading between companies. Existing business model literature covers individually owned, multilateral data marketplaces. However, these particular types of data marketplaces hardly reach commercial exploitation. This paper develops business model archetypes for the full array of data marketplace types, ranging from private to independent ownership and from a hierarchical to a market orientation. Through exploratory interviews and case analyses, we create a business model taxonomy. Patterns in our taxonomy reveal four business model archetypes. We find that privately-owned data marketplaces with hierarchical orientation apply the aggregating data marketplace archetype. Consortium-owned data marketplaces apply the archetypes of aggregating data marketplace with additional brokering service and consulting data marketplace. Independently owned data marketplaces with market orientation apply the facilitating data marketplace archetype. Our results provide a basis for configurational theory that explains the performance of data marketplace business models. Our results also provide a basis for specifying boundary conditions for theory on data marketplace business models, as, for instance, the importance of network effects differs strongly between the archetypes.
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The modern economy relies heavily on data as a resource for advancement and growth. Data marketplaces have gained an increasing amount of attention, since they provide possibilities to exchange, trade and access data across organizations. Due to the rapid development of the field, the research on business models of data marketplaces is fragmented. We aimed to address this issue in this article by identifying the dimensions and characteristics of data marketplaces from a business model perspective. Following a rigorous process for taxonomy building, we propose a business model taxonomy for data marketplaces. Using evidence collected from a final sample of twenty data marketplaces, we analyze the frequency of specific characteristics of data marketplaces. In addition, we identify four data marketplace business model archetypes. The findings reveal the impact of the structure of data marketplaces as well as the relevance of anonymity and encryption for identified data marketplace archetypes.
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Digital transformation implies the development of data-driven business models and thus the management of data goods. While marketplaces for data are being established and platforms for the exchange of data are being created, companies have to adapt their data management to the increasing requirements. One central question can be deduced: How can data goods be described in a standardized way? This paper describes the development of the metadata model for data goods M4DG. The M4DG, based on an analysis of existing data marketplaces and metadata models of related topics, makes it possible to describe data sources with defined properties. This creates a unified understanding of the properties of data goods to facilitate selection and trading. We are convinced that the M4DG will contribute to the practical design of data management.
Conference Paper
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The business model (BM) concept has gained increasing attention and popularity in both practice and research. In this context, the term "digital BM" emerged as a real buzzword, even though there still seems to be little consensus on what this term actually means and how it can be conceptualized. For example, most existing conceptualizations do not take into account the specific features of digital BMs or present domain-specific taxonomies of such BMs. To address this shortcoming, our study reviews prior literature and follows established guidelines to develop a taxonomy of digital BMs as they relate to both born-online and born-offline companies. To illustrate the usefulness of our taxonomy, we present four mini-cases and classify their digital BMs along the taxonomy dimensions. Our study contributes to the literature by furthering our understanding of the digital BM concept as well as by strengthening the conceptual basis for future research in the area.
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The survey presented in this work investigates emerging markets for data and is the third of its kind, providing a deeper understanding of this emerging type of market. The findings indicate that data providers focus on limited business models and that data remains individualized and differentiated. Nevertheless, a trend towards commoditization for certain types of data can be foreseen, which allows an outlook to further developments in this area.
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Mobile computing and the Internet of Things promises massive amounts of data for big data analytic and machine learning. A data sharing economy is needed to make that data available for companies that wish to develop smart systems and services. While digital markets for trading data are emerging, there is no consolidated understanding of how to price data products and thus offer data vendors incentives for sharing data. This paper uses a combined keyword search and snowballing approach to systematically review the literature on the pricing of data products that are to be offered on marketplaces. The results give insights into the maturity and character of data pricing. They enable practitioners to select a pricing approach suitable for their situation and researchers to extend and mature data pricing as a topic.
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Recent technological advances have enabled the emergence of novel platform business models based on digital marketplaces. Marketplaces like Airbnb or Uber offer platforms to connect previously unmatched demand-side and supply-side participants through innovative forms of value creation, delivery and capture. While countless firms claim to offer the next 'Airbnb for X' or 'Uber for Y', we lack knowledge about the defining characteristics of these business models. To close the gap, this paper provides a conceptually and empirically grounded taxonomy of marketplace business models. Applying a mixed methods approach, we first develop an integrative framework that integrates the value creation, delivery, and capture choices. Guided by the framework, the research systematically analyzes 100 randomly selected marketplaces with content analysis and binary coding. The gathered data is analyzed with cluster analysis techniques to develop a taxonomy for digital marketplace business models. The clustering process reveals six clearly distinguishable types of digital marketplace business models and thus shows that there exists no one-size-fits-all approach to creating, delivering, and capturing value with digital marketplaces. We characterize these distinctive business model types by integrating the qualitative and quantitative insights to advance the understanding of platform business models.
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As digital platforms are transforming almost every industry today, they are slowly finding their way into the mainstream information systems (ISs) literature. Digital platforms are a challenging research object because of their distributed nature and intertwinement with institutions, markets and technologies. New research challenges arise as a result of the exponentially growing scale of platform innovation, the increasing complexity of platform architectures and the spread of digital platforms to many different industries. This paper develops a research agenda for digital platforms research in IS. We recommend researchers seek to (1) advance conceptual clarity by providing clear definitions that specify the unit of analysis, degree of digitality and the sociotechnical nature of digital platforms; (2) define the proper scoping of digital platform concepts by studying platforms on different architectural levels and in different industry settings; and (3) advance methodological rigour by employing embedded case studies, longitudinal studies, design research, data-driven modelling and visualisation techniques. Considering current developments in the business domain, we suggest six questions for further research: (1) Are platforms here to stay? (2) How should platforms be designed? (3) How do digital platforms transform industries? (4) How can data-driven approaches inform digital platforms research? (5) How should researchers develop theory for digital platforms? and (6) How do digital platforms affect everyday life?
Despite the large number of academic contributions, there is no uniform definition of data marketplaces.However, different data marketplaces may vary from eachother in terms of their underlying business model, type ofdata offered, functionality, market mechanisms, etc.
2016, © Emerald Group Publishing Limited. $\textbf{Purpose:}$ The purpose of this paper is to derive a taxonomy of business models used by start-up firms that rely on data as a key resource for business, namely data-driven business models (DDBMs). By providing a framework to systematically analyse DDBMs, the study provides an introduction to DDBM as a field of study. $\textbf{Design/methodology/approach:}$ To develop the taxonomy of DDBMs, business model descriptions of 100 randomly chosen start-up firms were coded using a DDBM framework derived from literature, comprising six dimensions with 35 features. Subsequent application of clustering algorithms produced six different types of DDBM, validated by case studies from the study’s sample. $\textbf{Findings:}$ The taxonomy derived from the research consists of six different types of DDBM among start-ups. These types are characterised by a subset of six of nine clustering variables from the DDBM framework. $\textbf{Practical implications:}$ A major contribution of the paper is the designed framework, which stimulates thinking about the nature and future of DDBMs. The proposed taxonomy will help organisations to position their activities in the current DDBM landscape. Moreover, framework and taxonomy may lead to a DDBM design toolbox. $\textbf{Originality/value:}$ This paper develops a basis for understanding how start-ups build business models capture value from data as a key resource, adding a business perspective to the discussion of big data. By offering the scientific community a specific framework of business model features and a subsequent taxonomy, the paper provides reference points and serves as a foundation for future studies of DDBMs.