ArticlePDF Available

Abstract and Figures

Data marketplaces are expected to play a crucial role in tomorrow’s data economy, but such marketplaces are seldom commercially viable. Currently, there is no clear understanding of the knowledge gaps in data marketplace research, especially not of neglected research topics that may advance such marketplaces toward commercialization. This study provides an overview of the state-of-the-art of data marketplace research. We employ a Systematic Literature Review (SLR) approach to examine 133 academic articles and structure our analysis using the Service-Technology-Organization-Finance (STOF) model. We find that the extant data marketplace literature is primarily dominated by technical research, such as discussions about computational pricing and architecture. To move past the first stage of the platform’s lifecycle (i.e., platform design) to the second stage (i.e., platform adoption), we call for empirical research in non-technological areas, such as customer expected value and market segmentation.
Content may be subject to copyright.
Review
Business Data Sharing through Data Marketplaces:
A Systematic Literature Review
Antragama Ewa Abbas 1, * , Wirawan Agahari 1, Montijn van de Ven 2, Anneke Zuiderwijk 1
and Mark de Reuver 1


Citation: Abbas, A.E.; Agahari, W.;
van de Ven, M.; Zuiderwijk, A.; de
Reuver, M. Business Data Sharing
through Data Marketplaces: A
Systematic Literature Review. J. Theor.
Appl. Electron. Commer. Res. 2021,16,
3321–3339. https://doi.org/10.3390/
jtaer16070180
Academic Editors: Andreja Pucihar,
Mirjana Kljajic Borstnar,
Helen Cripps, Roger Bons and
Anand Sheombar
Received: 18 October 2021
Accepted: 30 November 2021
Published: 3 December 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1Faculty of Technology, Policy and Management, Delft University of Technology,
2628 BX Delft, The Netherlands; w.agahari@tudelft.nl (W.A.); A.M.G.Zuiderwijk-vanEijk@tudelft.nl (A.Z.);
G.A.deReuver@tudelft.nl (M.d.R.)
2Department of Industrial Engineering & Innovation Sciences, Eindhoven University of Technology,
5612 AE Eindhoven, The Netherlands; m.r.v.d.ven@tue.nl
*Correspondence: a.e.abbas@tudelft.nl
Abstract:
Data marketplaces are expected to play a crucial role in tomorrow’s data economy, but
such marketplaces are seldom commercially viable. Currently, there is no clear understanding of
the knowledge gaps in data marketplace research, especially not of neglected research topics that
may advance such marketplaces toward commercialization. This study provides an overview of
the state-of-the-art of data marketplace research. We employ a Systematic Literature Review (SLR)
approach to examine 133 academic articles and structure our analysis using the Service-Technology-
Organization-Finance (STOF) model. We find that the extant data marketplace literature is primarily
dominated by technical research, such as discussions about computational pricing and architecture.
To move past the first stage of the platform’s lifecycle (i.e., platform design) to the second stage
(i.e., platform adoption), we call for empirical research in non-technological areas, such as customer
expected value and market segmentation.
Keywords:
data markets; data marketplaces; data exchange; business data sharing; research agenda;
systematic literature review; STOF model
1. Introduction
Data marketplaces are expected to play a crucial role in tomorrow’s data economy [
1
].
A data marketplace can be broadly defined as a multi-sided platform that matches data
providers and buyers. It facilitates business data sharing among enterprises. Key actors
providing data marketplace functionalities include owners, operators, and third-party
providers [
2
4
]. Business data sharing via data marketplaces may contribute to overall
economic growth by stimulating data-driven innovation, improving the competitiveness of
small and medium-sized enterprises (SMEs), and opening up job markets [
5
]. Despite their
potential, data marketplaces have only been commercialized in a few cases (such as Dawex,
Data Intelligence Hub, and Advaneo) [
4
]. Commercialization of such marketplaces enables
the creation of new products and services. It is especially beneficial for organizations that
do not have proprietary access to required data [
6
]. Moreover, commercialization can foster
the integration of third-party providers into data marketplaces, enabling them to enhance
marketplace offerings by providing complementary products and services.
This paper considers all data marketplace archetypes revealed by Fruhwirth, Rachinger,
and Prlja [
2
]: centralized, decentralized, and personal data trading. In centralized data
trading, data marketplaces mediate data exchange from diverse domains and origins, in-
corporating different data types and pricing mechanisms. Advanced data marketplaces in
this archetype employ smart contracts to execute transactions. Decentralized data trading,
on the other hand, relies on a decentralized architecture to operate data marketplaces.
J. Theor. Appl. Electron. Commer. Res. 2021,16, 3321–3339. https://doi.org/10.3390/jtaer16070180 https://www.mdpi.com/journal/jtaer
J. Theor. Appl. Electron. Commer. Res. 2021,16 3322
Finally, personal data trading refers to a Customer-to-Business (C2B) relationship where
individuals can sell their personal information to companies.
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, knowl-
edge gaps in data marketplace research remain unclear. Specifically, we lack understanding
of whether research is scarce on topics that would advance data marketplaces toward
commercialization. As it stands, it might well be that academic research is focusing on
topics that do not help resolve the standstill in data marketplace commercialization.
Adopting the Systematic Literature Review (SLR) guideline provided by Okoli [
8
],
this paper provides a systematic review of research on data marketplaces. To cover the
broad range of issues that plays a role in technology commercialization, we also use the
business model construct as a literature review framework (cf., Solaimani et al. [
9
]). To the
best of our knowledge, our study is the first to provide a comprehensive overview of
data marketplace research, which will be beneficial in steering future research toward
commercializing data marketplaces.
We describe our approach in conducting a systematic literature review in
Section 2
,
followed by the article categorization based on the Service-Technology-Organization-
Finance (STOF) model in Section 3. Then, Section 4discusses the domination of technical
research in the data marketplace literature; Section 4also highlights the future research
agendas. Finally, we close this paper by presenting the main conclusions and limitations of
our study in Section 5.
2. Research Approach
This research employs a Systematic Literature Review (SLR) approach [
8
], summarized
in Figure 1. Okoli [
8
] suggests that an SLR study can be divided into four primary steps.
These are (1) planning, (2) selection, (3) extraction, and (4) execution.
JTAER 2021, 16, FOR PEER REVIEW 2
marketplaces. Finally, personal data trading refers to a Customer-to-Business (C2B) rela-
tionship where individuals can sell their personal information to companies.
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. Specifically, we lack un-
derstanding of whether research is scarce on topics that would advance data marketplaces
toward commercialization. As it stands, it might well be that academic research is focus-
ing on topics that do not help resolve the standstill in data marketplace commercialization.
Adopting the Systematic Literature Review (SLR) guideline provided by Okoli [8],
this paper provides a systematic review of research on data marketplaces. To cover the
broad range of issues that plays a role in technology commercialization, we also use the
business model construct as a literature review framework (cf., Solaimani et al. [9]). To the
best of our knowledge, our study is the first to provide a comprehensive overview of data
marketplace research, which will be beneficial in steering future research toward com-
mercializing data marketplaces.
We describe our approach in conducting a systematic literature review in Section 2,
followed by the article categorization based on the Service-Technology-Organization-Fi-
nance (STOF) model in Section 3. Then, Section 4 discusses the domination of technical
research in the data marketplace literature; Section 4 also highlights the future research
agendas. Finally, we close this paper by presenting the main conclusions and limitations
of our study in Section 5.
2. Research Approach
This research employs a Systematic Literature Review (SLR) approach [8], summa-
rized in Figure 1. Okoli [8] suggests that an SLR study can be divided into four primary
steps. These are (1) planning, (2) selection, (3) extraction, and (4) execution.
Figure 1. The research approach adapted from Okoli [8].
The planning step comprises the activities of determining the objective and research
protocol. Whereas the objective is presented in Section 1, the research protocol, including
the guidelines to synthesize the articles, will be discussed in this section. Next, the selec-
tion step is conducted by identifying the screening criteria and conducting a literature
search. We selected articles based on three criteria: articles should be (1) written in Eng-
lish; (2) published in a peer-reviewed journal or conference proceedings; and (3) focused
on data marketplaces. We employed the search terms of (“data marketplace*”) OR (“data
market*”). Our primary database is Scopus, which comprises a comprehensive database
of many scientific research papers, including the area we are examining in this study. The
Planning
(Section 1 & 2)
Selection
(Section 2)
Extraction
(Section 3)
Execution
(Section 4)
Determining the
objective
Creating a
research
protocol
Identifying the
screening
criteria
Conducting a
literature search
(
n = 505
)
Retrieving
articles
Assessing the
quality via a two-
step screening
approach
(
n = 158
)
Synthesizing the
articles
Creating the
review
(
n = 133
)
Figure 1. The research approach adapted from Okoli [8].
The planning step comprises the activities of determining the objective and research
protocol. Whereas the objective is presented in Section 1, the research protocol, including
the guidelines to synthesize the articles, will be discussed in this section. Next, the selection
step is conducted by identifying the screening criteria and conducting a literature search.
We selected articles based on three criteria: articles should be (1) written in English;
(2) published in a peer-reviewed journal or conference proceedings; and (3) focused on data
marketplaces. We employed the search terms of (“data marketplace*”) OR (“data market*”).
Our primary database is Scopus, which comprises a comprehensive database of many
scientific research papers, including the area we are examining in this study. The literature
search was conducted on 6 July 2020 and resulted in 496 articles. We complemented these
articles with nine additional papers that we consider key literature. These nine articles did
J. Theor. Appl. Electron. Commer. Res. 2021,16 3323
not appear in the initial search because, for instance, they do not use the data marketplace
term explicitly, neither in the title nor abstract.
In the extraction step, we retrieved the articles’ meta-data and saved it in an Excel
spreadsheet (File S1, available here: https://doi.org/10.4121/14673813.v2, accessed on
22 November 2021
). Next, we analyzed the quality of the identified articles by employing
a two-step screening approach. First, we looked into the title and abstract of the selected
papers to assess their relevance. We discussed our assessment internally to reach a consen-
sus, resulting in an exclusion of 225 papers. We excluded the articles because the studies
(1) merely focus on data marketplaces as the core of the research, (2) are published in
a workshop or proceeding description—not in a peer-reviewed research paper, (3) not
written in English, and (4) have no abstract.
Second, we used traditional metrics (i.e., citation numbers, journal ranks, and journal
percentiles) by calculating the average number of citations from the existing 280 articles.
We use the resulting average citation number (7.3, rounded down to 7) as a threshold to
quantitatively assess the paper. We included any articles that were cited more than seven
times. We further assessed those below the threshold in terms of the publication outlet. If a
journal or conference proceedings were ranked above the 50th percentile in their respective
domain, we would consider those outlets as high-quality. As a result, we included any
articles that also belong to these criteria. Using both citation numbers and publication rank
ensured the inclusion of the most prominent and relevant articles.
We also considered alternative metrics (i.e., social media, usage, captures, and men-
tions) provided by the Scopus database, namely the PlumX Metrics [
10
], for the remaining
articles that did not meet both criteria. The rationale is that the novelty of data marketplaces
and its growing interest in the non-scientific community might lead to more discussions in
(among others) social media. As a result, the impact of such articles might not be captured
by traditional metrics. Using these alternative metrics would allow the inclusion of articles
that creates an impact beyond the scientific community. Furthermore, attention to such met-
rics is increasingly used for scientific evaluation to complement traditional metrics [
11
]. We
calculated the average numbers of those alternative metrics based on the existing 280 articles,
resulting in the following threshold: social media = 2.1,
usage = 44.8
,
captures = 43.2
, and
mentions = 0.2. We included any remaining articles that have scores above these numbers,
resulting in 158 papers. By combining both traditional and alternative metrics, we ensure
both scientific reliability and relevance to practice.
In the execution step, we synthesized the included papers and wrote the review
(see Section 4). Following Solaimani, Keijzer-Broers, and Bouwman [
9
], we applied the
Service-Technology-Organization-Finance (STOF) model to synthesize the included papers.
The STOF model is a generic framework to reconstruct the logic of a business and its ecosys-
tem [
12
]. Thus, it enables a high-level representation of the service domain (S), technology
domain (T), organization domain (O), and finance domain (F). The service domain describes
the service offering that the business and its ecosystem intend to deliver to create value
for a target group of customers. The technology domain describes the technical architecture
needed by the business ecosystem to deliver the proposed services. The organization domain
describes how the actors in the business ecosystem are organized to deliver the service
offering, to explicate how the ecosystem intends to create value for the customer. Finally,
the finance domain describes how the business and its ecosystem intend to capture value
from the service offering, including how costs, revenues, and risks are divided among the
different actors in the ecosystem.
The STOF model is suitable for our purpose since it is explicitly designed for ICT-
enabled services such as data marketplaces. Unlike frameworks such as The Business Model
Canvas [
13
], the STOF model explicitly captures the role of technology in commercialization.
Moreover, the STOF model helps to understand the dynamics involved in developing
successful business models (i.e., market adoption and sustainable profitability of the
designed services). Due to the lack of commercialized data marketplaces, it is crucial to
understand what we (do not) know about the breadth of the business models of data
J. Theor. Appl. Electron. Commer. Res. 2021,16 3324
marketplaces, ranging from their value to how they deliver and capture value. Hence, the
STOF model is highly appropriate to structure our review and discussion.
We then read the full text of the 158 remaining articles and classified each article into
a STOF model domain
. Furthermore, each article was further classified into
a category
.
To classify an article, we identified its main research objective while paying attention to
the primary unit of analysis of the research. We employed the following guideline to
categorize the articles (see Table 1). The guideline is inspired by the STOF model [
12
].
In addition, we also considered the well-known ACM Computing Classification System
(https://dl.acm.org/ccs, accessed on 9 August 2021)
to identify the suitable keywords for
our categorization.
Table 1. The guideline to categorize the articles.
STOF Model Domain Description Category Examples
(Included but Not Limited To)
Service
Discussing possible services for end-users (data
providers and buyers); services uniqueness and
differentiators compared to competitors’ offered
services; potential customers who will use and pay
for the developed services.
Customer,previous experience,expected value,market
segment,context,effort (ease of use),tariff,bundling,
perceived value,delivered value,intended value,
value proposition.
Technology Discussing technology needs to deliver
the services.
Technical architecture,applications,devices,service
platforms,billing platform,customer data platform,
technical functionality.
Organizational
Discussing actors and resources to run the services.
Use organization domain to categorize “other”
topics, e.g., demographic aspects,
social implications.
Resources and capabilities,strategies and goals,value
activities,value network,actors,organizational
arrangements,relations,interactions,roles.
Finance Discussing financial schemas to run the services.
Investment sources,capital cost sources,costs,revenue
sources,revenues,risk sources,risk performance
indicators,financial arrangement.
For example, Munoz-Arcentales et al. [
14
] propose an architecture for data usage
and access control. Since the discussion emphasizes technology needs, we classified this
paper into the architecture category in the STOF technology domain. Another example
is a study conducted by Virkar, Viale Pereira, and Vignoli [
5
]. The study discusses the
political, economic, societal impacts of data trading via a data marketplace. After carefully
examining the paper, we classified this paper into the social implication category in the STOF
organization domain. Although some articles can have multiple overlapping topics, we
still attempted to assign each article into a single category. We justified this by analyzing
the central theme of the discussion. Various articles were independently categorized
by multiple authors to assess inter-rater reliability. In general, there was a high level
of agreement between the authors. We also further excluded some irrelevant articles,
including those that did not discuss business data sharing via data marketplaces. Our final
sample consisted of 133 articles.
3. Results: STOF Model Categorization
This section describes the results of our STOF model categorization. In total, we
identify 17 categories (refer to Figure 2). The description for each category is provided in
the following sub-section.
J. Theor. Appl. Electron. Commer. Res. 2021,16 3325
JTAER 2021, 16, FOR PEER REVIEW 5
Figure 2. The selected articles categorized using the STOF model (n = 133).
3.1. The Service Domain
We identify three categories within the STOF service domain (see Table 2). The first
one concerns the data-related aspects. This category explores data properties as a unit of
analysis, such as data characteristics as economic goods [15] and approaches to identify
data quality problems [16]. The second category in the service domain is user preferences.
It discusses data providers’ willingness to share data via data marketplaces considering
aspects such as anonymity [17] and data ownership [18]. In addition, the value theory for
personal data is also proposed [19].
Finally, the most dominant category in the service domain is the value proposition. The
studies in this category generally concern identifying value for data marketplace actors.
For example, Perera et al. [20] and Anderson et al. [21] explore the value of trading Internet
of Things platforms (IoT) and healthcare data, respectively. An additional example is the
value exploration of data marketplaces that trade anonymous personal data [22]. Addi-
tionally, Mamoshina et al. [23] discuss the possibility of blockchain and artificial intelli-
gence implementation to solve concerns from regulators and data providers, specifically
related to the issue of control over data. Match-making services in data marketplaces are
also discussed to ease data providers to advertise their data product; to enable data buyers
to request their data demand [24,25]. Finally, another surprising example is the discussion
of services provided by “stolen data markets,” which refer to marketplaces that trade ille-
gal data such as personal and credit card information [26]. To sum up, the discussion in
the service domain primarily focuses on the services provided by data marketplace oper-
ators and third-party providers to fulfill the needs of data marketplace actors.
23
7
21
30
22
67
353
12
7
13
19
Data-related aspects
User preferences
Value proposition
Architecture
Computational pricing
Data-as-a-Service
Data contracts
Information retrieval
Security and privacy
Classification frameworks
Data ecosystems
Demographic aspects
Governance
Social implications
Economic feasibility
Market analysis
Pricing mechanisms
Service (n = 12) Technology (n = 68) Organization (n = 30) Finance (n = 23)
Figure 2. The selected articles categorized using the STOF model (n= 133).
3.1. The Service Domain
We identify three categories within the STOF service domain (see Table 2). The first
one concerns the data-related aspects. This category explores data properties as a unit of
analysis, such as data characteristics as economic goods [
15
] and approaches to identify
data quality problems [
16
]. The second category in the service domain is user preferences.
It discusses data providers’ willingness to share data via data marketplaces considering
aspects such as anonymity [
17
] and data ownership [
18
]. In addition, the value theory for
personal data is also proposed [19].
Table 2. The service domain.
Category Description Article Reference
Data-related aspects Discussing data properties as a unit of analysis. [15,16]
User preferences
Discussing willingness to share data due to certain aspects.
[1719]
Value proposition Identifying value for data marketplace actors. [2026]
Finally, the most dominant category in the service domain is the value proposition.
The studies in this category generally concern identifying value for data marketplace actors.
For example, Perera et al. [
20
] and Anderson et al. [
21
] explore the value of trading Internet
of Things platforms (IoT) and healthcare data, respectively. An additional example is the
value exploration of data marketplaces that trade anonymous personal data [
22
]. Addition-
ally, Mamoshina et al. [
23
] discuss the possibility of blockchain and artificial intelligence
implementation to solve concerns from regulators and data providers, specifically related
to the issue of control over data. Match-making services in data marketplaces are also
discussed to ease data providers to advertise their data product; to enable data buyers to
request their data demand [
24
,
25
]. Finally, another surprising example is the discussion of
services provided by “stolen data markets,” which refer to marketplaces that trade illegal
data such as personal and credit card information [
26
]. To sum up, the discussion in the
J. Theor. Appl. Electron. Commer. Res. 2021,16 3326
service domain primarily focuses on the services provided by data marketplace operators
and third-party providers to fulfill the needs of data marketplace actors.
3.2. The Technical Domain
Most publications fall within the STOF technology domain. This domain is divided
into six categories (refer to Table 3). In our sample, the first identified category is archi-
tecture. Architecture of data marketplaces can be loosely described as building blocks of
technical components. The discussion in the architecture category is primarily dominated
by blockchain-based systems, which relates to the development of peer-to-peer and de-
centralized data marketplaces [
27
,
28
]. Specifically, the blockchain systems are applied to
specific contexts such as the automotive domain [
29
,
30
], private data sharing [
31
], Internet
of Things (IoT) [
32
35
], or smart cities [
36
]. In other cases, blockchain-based systems
are employed for proposing auditing schema [
37
], credit scoring [
38
], data transaction
integrity [
39
], and Proof of Usage (PoU) algorithm [
40
]. Beyond the blockchain-based
systems, the proposed architecture specifically highlights data access and control based
on the International Data Space (IDS) reference architecture [
14
]. Beyond the blockchain-
based architecture,
Matzutt et al. [41]
discuss a conceptual architecture for personal data
marketplaces, focusing on protecting data privacy, while Mišura and Žagar [
42
] focus
on IoT devices. In addition, Sánchez et al. [
43
] propose a data marketplace architecture
to federate multiple-domain IoT; Pillmann et al. [
44
] propose an information model to
provide a single point of access for vehicle data. Finally, Li et al. [
45
] propose a cost-efficient
middleware for data acquisition service; Ren et al. [
46
] introduce infrastructure architecture
for data placement.
The second category, which is the most discussed category in this domain, is com-
putational pricing. It focuses on technical discussions for data pricing. Computational
pricing emphasizes algorithms as price determination mechanisms [
47
], such as machine
learning-based algorithms to price training data or pre-trained models [48,49]. Advanced
techniques are proposed, such as a smart pricing algorithm based on Stackelberg game
theory. This algorithm is applied in blockchain-based data marketplaces [50].
Next, publications in this category primarily propose query-based pricing mechanisms,
referring to the capability to allow “the price of any query to be derived automatically”
([
51
], p. 43). The studies discuss many aspects, for instance, the implementation of query
pricing [
52
] and dynamic pricing considering “reserve price constraint” that helps data
brokers maximize their revenue [
53
]. Another algorithm allows data price to be derived
from the privacy losses [
54
]. Studies in query-based pricing mechanisms consider many
cases such as query interfaces for mobile crowd-sensed data [
55
,
56
], cloud-based data mar-
ketplaces with possibilities to share cloud resources [
57
], spatial data [
58
], aggregated data
from multiple distributed system [
59
], and data acquired from Application Programming
Interfaces (APIs) [
60
]. Moreover, Tang et al. [
61
] introduce query-based data provenance,
while Wang et al. [
62
] create efficient query-based auctions by considering both the value
data and the resource consumption of queries.
Many other articles also propose data quality-based pricing models by considering a
bi-level mathematical programming model [
63
], Fair Knapsack Pricing [
64
,
65
], or optimal
distributing algorithm [
66
]. Other works on data quality-based pricing specifically focus
on XML dataset properties [
67
,
68
]. Moreover, another topic in this category discusses an
iterative auction-based algorithm with an additional focus on data protection throughout
the auction processes [
69
,
70
]. Still concerning auction, Zheng et al. [
71
] introduce an
auction algorithm for data brokers, aiming for profit maximization in mobile crowdsourcing
data marketplaces.
The rest of the pricing topics are relatively diverse, depending on their specific fo-
cus. Zeng and Ohsawa [
72
] propose a new method to price data based on the clustering
technique. Oh et al. [
73
,
74
] develop data trading models that consider privacy valua-
tion. Likewise, another example explores algorithms for dynamic privacy pricing [
75
].
Hu et al. [76]
develop a blockchain-based incentive structure that incorporates privacy and
J. Theor. Appl. Electron. Commer. Res. 2021,16 3327
security aspects. Still on blockchain-based data trading, Liu et al. [
77
] design a debt-credit
system to solve the efficiency issues. Finally, Yang et al. [
78
] develop a pricing algorithm
from a data science perspective to examine the effect of data quality on machine learning.
Next, the category of data-as-a-service primarily explores the topic of Application
Programming Interfaces (APIs) to enable data providers and buyers to use the services of
data marketplaces. Vu et al. [
79
] aim to ease API implementation by providing a structure
description model. In addition, Truong et al. [
80
] develop a RESTful service specifically for
exchanging data agreements. The following category is data contracts, which generally refer
to formal arrangements between data providers and data buyers to specify data usage.
In this category, abstract models for data contracts are proposed to develop various data
contracts that consider different data types. The studies also propose evaluation techniques
to evaluate data contracts [81,82].
The information retrieval category to support data discovery in data marketplaces such
as information schema [
83
], semantic [
84
], and ontologies [
85
,
86
] are also discussed in the
literature. A review of data search techniques in data marketplaces is also conducted [
87
].
Moreover, Rekatsinas et al. [
88
] introduce a data source management system, which allows
users to identify the most useful data sources for their applications. Finally, the security
and privacy category has also gained much attention in the literature. The topics covered
in this category are related to privacy-preserving technology [
89
93
], property rights
enforcement [94], and secure information models [95].
Table 3. The technical domain.
Topic Description Article Reference
Architecture Proposing building blocks of technical components for data marketplaces. [14,2746]
Computational pricing Discussing technical aspects such as algorithm or query techniques to price the data. [4850,5278]
Data-as-a-Service Exploring the topic of Application Programming Interfaces (APIs) to enable data providers
and buyers to use services of data marketplaces. [79,80]
Data contracts Discovering the models to develop formal arrangements between data providers and data
buyers to specify data usage. [81,82]
Information retrieval Discussing data discovery techniques in data marketplaces. [8388]
Security and privacy Proposing technical enforcements to guarantee security and privacy. [8995]
3.3. The Organization Domain
We identify five categories in the STOF organization domain (refer to Table 4). The first
category is the classification frameworks, which describe data marketplace business models
via a taxonomy [
2
,
96
,
97
]. Next, the category of data ecosystems is also discussed. A data
ecosystem is “a set of networks composed by autonomous actors that directly or indirectly
consume, produce or provide data and other related resources (e.g., software, services, and
infrastructure)” [
98
] (p. 4). Data marketplaces are often categorized as an instance of a data
ecosystem [
99
]. Therefore, the topics in this category examine ecosystem structures that
are relevant to data marketplaces. For instance, Hayashi and Ohsawa [
100
] investigate the
structural characteristics (i.e., how data interacts) in networks. Koutroumpis, Leiponen
and Thomas [
3
] examine data sharing using a conceptual market design perspective.
They identify the requirements for data sharing, specifically comparing small markets
with greater control vs. large markets with less control over data. Another topic is the
exploration of stolen data markets that specifically discuss the processes and market
forces that shape the relationship between involved actors and available products [
101
].
Finally,
W. Thomas
and Leiponen [
6
] and Oliveira, Lima and Lóscio [
99
] review data
ecosystems in the literature and propose research agenda. Subsequently, the category of
demographic aspects can be broadly defined as the description distribution of specific actor
properties, such as population. The topic discussed in this category covers the geographical
distribution of victims [
102
], actor populations [
103
], and community networks structures
in stolen data markets [104].
J. Theor. Appl. Electron. Commer. Res. 2021,16 3328
Next, governance, the most-discussed category in this domain, broadly refers to govern-
ing processes by certain actors (e.g., data marketplace operators) via several mechanisms,
such as norms or power [
105
]. Examples of governance topics include discussion about
policies and strategies in data marketplaces [
106
], a reference model for data protection for
policymakers [
107
], and trust-creating mechanisms to enhance perceived market trustwor-
thiness [
108
]. Other topics analyze social structures [
109
] and facilitating factors of data
trading in stolen data markets [
110
]. Subsequently, the intervention and distributing ap-
proaches to crime prevention in stolen data markets are also discussed [
111
]. Furthermore,
more topics like tax instruments [
112
], a manifesto from data providers to retain control
over their data [
113
], and an elaboration on how multi-party computation (MPC) can be
attributed as a control mechanism [
114
] are also studied. The last topics in this category are
governance mechanisms in the data sharing platform design process [
115
], self-regulation
for fairness and transparency for data sharing [116], as well as discussion about legal and
technical measures for dealing with privacy issues [117].
Finally, the category of social implications refers to the exploration of data marketplace
impacts for society, such as the rise of ethical challenges in genomic health data shar-
ing [
118
]. Likewise, Van Dijck and Poell [
119
] critically examine the claim of the benefits
of health data sharing in platforms. This category also discusses the implications of data
trading for social, political, economic, and cultural contexts [
5
]. Finally, many articles
discuss the topic of exploitation of individual data in personal data marketplaces [
120
123
].
Table 4. The organization domain.
Topic Description Article Reference
Classification frameworks Developing a business model taxonomy for data marketplaces. [2,96,97]
Data ecosystems Examining ecosystem structures that are relevant to data marketplaces,
such as structural characteristics (i.e., how data interacts) in networks. [3,6,99101]
Demographic aspects
Describing the distribution of specific actor properties, such as population.
[102104]
Governance Exploring governing processes by certain actors (e.g., data marketplace
operators) via several mechanisms, such as norms or power. [106117]
Social implications Discussing data marketplace impacts for society. [5,118123]
3.4. The Finance Domain
We identify three categories in the STOF finance domain (see Table 5). The first
category is economic feasibility, examining the possibility to implement data marketplaces
using economic perspectives. It explores the competition between actors using Nash
equilibrium characterization [
124
]. Another category is market analysis. In general, it
examines the market size and value. For instance, Holt et al. [
125
] and Shulman [
126
]
analyze the economic value of stolen data markets. In addition, Soley et al. [
127
] develop a
model for calculating and estimating the monetary value of connected car data.
Table 5. The finance domain.
Topic Description Article Reference
Economic feasibility Examining the possibility to implement data marketplaces using economic perspectives. [124]
Market analysis Examining the market size and value of data marketplaces. [125127]
Pricing mechanisms Discussing mathematical or economic approaches in evaluating, valuating, or pricing
datasets (or data services) in data marketplaces. [47,128145]
Articles in the finance domain are not equally distributed across categories because
most discussions are centralized in pricing mechanisms. Unlike the computational pricing
in the STOF technology domain that focuses on technical aspects like query- or machine
learning-based pricing (see Section 3.2), the pricing mechanisms here emphasize more on
mathematical or economic approaches in valuating or pricing data in data marketplaces.
J. Theor. Appl. Electron. Commer. Res. 2021,16 3329
The topics of this category include data trading models that consider contract the-
ory [
128
], information design perspective [
129
], and equilibrium pricing mechanism based
on Stackelberg game approach [
130
]. Moreover, pricing mechanisms specifically for per-
sonal data are also discussed. For instance, Niu et al. [
131
] propose pricing functions for
aggregated personal data; Parra-Arnau [
132
] mathematically examine the tradeoff between
privacy and money in personal data market; Yuncheng et al. [
133
] identify the properties
that contribute to price personal data, such as data cost, value weight, information entropy,
credit rating, and data reference index; Li et al. [
134
,
135
], discuss an economic theory of
pricing personal data.
Empirical research is also conducted in the finance category. Hayashi and Oh-
sawa [
136
] explore the utility value of data using a workshop and behavioral economic
theory. Subsequently, Muschalle et al. [
137
] outline critical inhibitors of data pricing based
on interview results. Beyond empirical research, systematic literature reviews are also
conducted to study data pricing opportunities and challenges in data marketplaces [
138
].
This approach is also employed to explore the different data pricing models in the data
marketplace literature [47,139].
Other topics are auction-based pricing using the Bayesian mathematical
model [140,141]
,
a pricing mechanism negotiation based on a negotiation game theory based in the energy
domain [
142
], and a generic pricing mechanism based on a non-cooperative game the-
ory in Mobile Crowdsensing [
143
]. Finally, Stahl and Vossen [
144
] discuss data quality
criteria (such as accuracy, completeness) that can be used to relatively price data, while
Jang et al. [145]
propose a three-hierarchal model of data trading and create a pricing
function to achieve Nash Equilibrium (NE).
4. Discussion
This paper aims to investigate the current state-of-the-art of data marketplace research.
Specifically, we want to know whether research lacks topics that would advance data
marketplaces toward commercialization. As indicated in the introduction section, data
marketplaces are hardly commercially exploited, even though the concept has existed
for years. Apparently, existing data marketplaces struggle to move from the initial stage
into the second stage of the platform’s lifecycle (i.e., the platform adoption). One possible
reason for the lack of data marketplace commercialization could be that previous studies
have not dealt extensively with non-technical topics (refer to the findings elaborated in
the previous section). Hence, contributions from the academic perspective toward data
marketplace commercialization are still scant. Therefore, this section discusses various
possible explanations for the technical research domination on data marketplace and
connects these explanations to recommendations for future research.
4.1. Domination of Technical Research in the Data Marketplace Literature
As shown in Figure 2, we reveal that data marketplace research is still primarily
dominated by technical literature. Based on this finding, the pattern of evolution of data
marketplace research tends to follow the technology push (i.e., technological advancement
drives innovation). We suggest three explanations for the dominance of technical research
in data marketplaces literature.
First, funding and project availability are intensely focused on the technological
development of data marketplaces—refer to the description of EU-funded projects on data
markets (https://cordis.europa.eu/programme/id/H2020_ICT-13-2018-2019, accessed
on 9 August 2021). The European data strategy [
1
] provides a clear example of this, as it
intends to “invest
2 billion in a European High Impact Project to develop data processing
infrastructures, data sharing tools, [and] architectures.” Second, with recent increases
in funding, many of these projects are still in the initial design phase. As suggested
by Henfridsson and Bygstad [
146
], the goals in this phase tends to typically focus on
foundational work, such as architectural design. This may explain why the debate in
the data marketplace literature focuses on technical rather than non-technical aspects.
J. Theor. Appl. Electron. Commer. Res. 2021,16 3330
Finally, policymakers and other key stakeholders have already defined the overall aim of
EU-funded projects (e.g., trust and sovereignty) as reflected in regulations and standards
like the European data governance act (https://digital-strategy.ec.europa.eu/en/policies/
data-governance-act, accessed on 17 November 2021) and Gaia-X (https://www.gaia-x.
eu/what-is-gaia-x, accessed on 17 November 2021). In this regard, scholars might take
these aims for granted and immediately focus on designing technical components of data
marketplaces to achieve those pre-determined goals.
As a result of the three above-mentioned developments, extant research on data
marketplaces has so far primarily been published in technical conference proceedings and
in more technology-oriented journals, such as the IEEE Access and the IEEE Internet of
Things Journal.
4.2. Service Domain Aspects
The findings indicate that little attention has been paid to the topics categorized in
the service domain (this domain was covered least by our studied papers). Based on
business model knowledge, this domain is essential and should be the starting point for
data marketplaces to be commercially exploited [
12
]. The topics in the service domain
are essential to design services that fulfill customers’ needs. Although a few attempts
have been made to discuss relevant topics such as value proposition, many other topics
such as customer expected value and market segmentation have barely been discussed in the
selected articles.
Regarding the value proposition, we recommend studies that go beyond the mere
value propositions of facilitating data exchange, and that include data analytics, data
products, and advice. Studies can also distinguish value derived from different data types,
such as real-time versus aggregated data, business versus personal data, and sensitive
versus non-sensitive data. Segmentation is especially promising to study given that data
marketplaces are in principle applicable to any business sector and any business type, but
the desired value proposition likely differs drastically between segments of businesses.
For instance, digitally native firms may be looking merely for access to data for running
their own algorithms, whereas firms without data processing capabilities may look for
additional value propositions of analytics features or even data products that are directly
usable in the daily business practice. Empirical methods such as cluster analysis or class
analysis could help to distinguish segments of data marketplace users, although also
methods that combine qualitative and quantitative research, such as Q-methodology, may
help to distinguish different perspectives on the value that data marketplaces offer. Given
the expected proliferation of data marketplaces in heterogeneous business sectors, we also
call for situated research, such as case studies, that considers how contextual characteristics
of business sectors affect the desired value propositions by data traders.
Besides studies on the value proposition per se, we also recommend studies that
interlink technical and pricing model choices with value delivered to user segments. For in-
stance, decentralized technology paradigms such as blockchain-based data marketplaces
may affect the value that users receive. Similarly, data collaboration algorithms such as
multi-party computation affect value proposition too, as these enable deriving and sharing
business-relevant insights rather than disclosing the raw data. These decentralized and
collaborative technologies may also resolve the negative impacts of using data market-
places, as they afford control over data without a trusted third party. We recommend
design science research (DSR) and (controlled) experiments to derive the impact of these
new technology paradigms on value delivery to data marketplace users.
Moreover, data marketplace projects are often conducted in a consortium based on
academia-practitioners collaborations (e.g., the EU-funded projects). Academic publica-
tions may also reflect the work conducted by practitioners, for instance, by investigating
the challenges and success factors of the few data marketplaces that exist in the market so
far. This is important because, besides an imbalance in the current state of data marketplace
research, we might also lack a clear understanding of problems faced by data marketplaces.
J. Theor. Appl. Electron. Commer. Res. 2021,16 3331
As a result, scholars and practitioners may try to solve the wrong problem or even problems
that do not exist. Hence, comparative case studies and quantitative surveys among data
marketplaces could yield meaningful insights to identify problems faced by such platforms
and suggestions for future development. Given that data sharing and trading is a complex
socio-technical process, investigating non-technical aspects may open opportunities to
speed up the platform adoption process in practice.
4.3. Organizational Domain Aspects
Considering the organizational domain, one crucial overlooked aspect in current
literature is value networks (or ecosystems) that describe actors and their interactions. It is
essential to understand the dynamic to align their vision by developing organizational ar-
rangements to achieve the common goal. In the area of data marketplaces, data governance
and data provenance are especially important areas, in order for data sellers to retain a
sense of being in control of their own data. Possible future research directions include
efforts to transfer ideas from data stewardship and data governance to the area of data
marketplaces. Such studies should not only provide technical or legal means to exert
governance over data sales, but also empirically study the impact of such governance
means on the willingness of data owners to sell their data. The issue of organizational
arrangements will likely become even more important as data marketplaces are emerging
in many different industries with fragmentation, thus leading to an ecology of data mar-
ketplaces with incompatible data governance regimes (see Abbas [
147
]). The cross-over
between organizational arrangements and the service domain is a fertile study ground too,
for instance, in choice experiments that contrast data marketplaces operated by big tech
providers with those of a more decentralized ownership structure.
Other topics such as the meaning of openness in data marketplaces are also worth
investigating. Typically, scholars have emphasized data as the object of openness by identi-
fying approaches to incentivize data sharing. However, openness in data marketplaces can
go beyond access to data, such as access to analytics modules (cf. Mucha and Seppala [
148
])
provided by third-party complements. In this regard, literature on digital platforms (e.g.,
De Reuver et al. [
149
]) might explain why openness matters (or not) in the context of data
marketplaces. On the one hand, openness could attract more service complementors [
150
]
and boost third-party innovation by analytics providers [
151
], ultimately attracting more
users [
152
] and attaining critical mass [
153
]. On the other hand, openness could also
lead to increased costs and effort to control complementors [
154
], especially complements
that could harm platform’s integrity [
155
]. Hence, it would be interesting to see if cur-
rent understandings of platform openness could simply be applied to the new context of
data marketplaces.
Considering actors and their interactions, the value on a data marketplace is not only
provided by a single stakeholder but jointly created in an ecosystem setting. Typically, data
marketplace owners rely on third-party providers to realize their value offerings, such
as data suppliers, data aggregators, applications developers, and service providers [
6
,
99
].
To successfully design and commercialize data marketplaces, it is crucial to identify the
different players in data marketplaces and understand the economic value exchanges
between them. Therefore, future research can focus on studying the roles and value
flows of stakeholders in and around data marketplaces. We recommend using existing
value modeling techniques, such as e3-value [
156
], to connect relevant stakeholders to
their respective value flows. In doing so, the partnerships among data marketplaces and
third-party providers to co-create value are likely to emerge.
4.4. Finance Domain Aspects
The finance domain aspect is essential to create viable business models [
12
]. Neverthe-
less, the current literature merely emphasizes data pricing. Future research should cover
other essential topics in the finance domain, such as cost sources and investments because
they are essential to building operating models of data marketplaces. For example, opera-
J. Theor. Appl. Electron. Commer. Res. 2021,16 3332
tors need to hire internal developers to maintain a stable core system of data marketplaces.
Another example is the need for primary and supporting activities (e.g., marketing or
human resources, respectively) to deliver value to end customers [
157
], which required
careful cost calculation. Therefore, future research could identify a framework to identify
cost sources and calculate them appropriately. Cost sources are also inseparably linked
with investments because marketplace owners need to calculate required capital to sustain
marketplaces in the medium- and long-term [
12
]. Thus, future works can also examine
possibilities of funding sources for data marketplaces, including the transition strategies
(or roadmaps) to connect new funding to the creation of additional services or technology
developments (see De Reuver et al. [158]).
4.5. Research Approaches
Our additional impressions after reading and analyzing the articles are as follows.
We only found a few studies, e.g., Schomakers, Lidynia and Ziefle [
17
], Spiekermann
and Korunovska [
19
], that conduct empirical investigations in non-technical literature.
Case studies on data marketplaces that did reach the next phase of platform adoption
would yield valuable insights into what business model choices lead to viability. More-
over, the many technology-focused studies hardly consider the link between practical
problems, theories, and evaluations, such as is common in Design Science Research (DSR)
approaches [
159
,
160
]. DSR is further helpful in examining data marketplace business
model configurations that do not yet exist, which is essential given the absence of highly
successful data marketplaces businesses in practice. Stronger links between technical solu-
tions and value-related problems would help focus data marketplace research on resolving
practical problems.
The literature also hardly discusses solutions to some core non-technical challenges
of data marketplaces, such as: defining data ownership [
3
], assessing data quality [
3
],
lacking legal frameworks [
116
], lacking technical expertise and resources to operate the
ecosystem [
99
], and unclear organizational structure [
99
]. Thus, we generally suggest con-
ducting various empirical research approaches such as case studies and grounded theory
(see Sekaran and Bougie [161]) to understand those challenges in non-technical domains.
5. Conclusions
This study provides an overview of the state-of-the-art of data marketplace research.
Specifically, we want to know whether research is scarce on topics that would advance
data marketplaces toward commercialization. We find that the existing literature on data
marketplaces is dominated by technical research, such as the discussion related to compu-
tational pricing and architecture. We highlight possible explanations about the dominance
of technical research: the recent project financing availability that has pre-determined goals
such as trusts and sovereignty. Moreover, most current works and research are still in
their infancy; therefore, they focus on the technological advancement of data marketplaces.
We also suggest future research agendas in the service, organizational, and finance do-
mains, equipped with potential research approaches to advance marketplaces for data
toward commercialization.
A limitation of this study is that the topic identification process is subject to the
researchers’ knowledge and interpretations about the topic, i.e., different readers may
have different judgments. However, independently categorizing the present papers by
different authors showed overall alignment. Moreover, as indicated in Section 2, some
articles may have many overlapping topics. Because we attempted to classify an article
into a specific category, we analyzed the central theme of the discussion by examining
the research objectives, questions, and methods of articles. The study is also limited by
its scope and the number of publications included in the analysis due to our criteria, e.g.,
a single database, the timeframe selection, and a paper quality check. Nonetheless, we
argue that we have reached a sufficient level of saturation, i.e., analyzing more articles
J. Theor. Appl. Electron. Commer. Res. 2021,16 3333
from the selected sample did not lead to new categories being identified or major shifts in
the distribution of papers among existing categories.
Practitioners involved in data marketplace developments can reflect on our findings.
Because data marketplaces tend to be rarely commercialized, (research) projects on such
marketplaces need specific tasks to explore viable business models. While doing so, they
can consider our list of literature as a starting point to understand what is currently known
about data marketplaces. Practitioners can also reflect on our suggested (research) approach
to explore potential value for stakeholders.
This study contributes to the literature by (a) providing a comprehensive overview of
current data marketplace research and (b) identifying neglected research topics that may
contribute to data marketplaces’ growth toward commercialization. We set out potential
research topics to help data marketplaces shift from the first stage of the platform’s lifecycle,
i.e., the platform design, to the second stage, i.e., the platform adoption. Our research
provides the essential basis for future research toward the commercialization of data
marketplaces. To sum up, we call for (empirical) research in non-technological domains to
complement the current technology-focused data marketplace research.
Supplementary Materials:
The following are available online at https://doi.org/10.4121/14673813.v2,
File S1: Research Data-Business Data Sharing through Data Marketplaces.
Author Contributions:
Each author made a significant contribution to the reported work. Con-
ceptualization, A.E.A. and W.A.; methodology A.E.A. and W.A.; article review, A.E.A., W.A. and
M.v.d.V.; writing—original draft preparation A.E.A., W.A. and M.v.d.V.; writing—review and editing,
supervision, funding acquisition, A.Z. and M.d.R. All authors have read and agreed to the published
version of the manuscript.
Funding:
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) and No 825225–Safe Data-Enabled Economic Development
(Safe-DEED).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The data presented in this study are openly available 4TU. Research-
Data at https://doi.org/10.4121/14673813.v2, accessed on 22 November 2021.
Acknowledgments:
We would like to thank the anonymous reviewers for their advice to improve
the previous version of the manuscript. This review article is an expanded version of the conference
article [162].
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or
in the decision to publish the results.
References
1.
European Commission. A European Strategy for Data. 2020. Available online: https://digital-strategy.ec.europa.eu/en/policies/
strategy-data (accessed on 17 November 2021).
2.
Fruhwirth, M.; Rachinger, M.; Prlja, E. Discovering Business Models of Data Marketplaces. In Proceedings of the 53rd Hawaii
International Conference on System Sciences, Maui, HI, USA, 7–10 January 2020; pp. 5738–5747.
3. Koutroumpis, P.; Leiponen, A.; Thomas, L.D.W. Markets for data. Ind. Corp. Chang. 2020,29, 645–660. [CrossRef]
4. Spiekermann, M. Data Marketplaces: Trends and Monetisation of Data Goods. Intereconomics 2019,54, 208–216. [CrossRef]
5.
Virkar, S.; Viale Pereira, G.; Vignoli, M. Investigating the Social, Political, Economic and Cultural Implications of Data Trading; Springer
International Publishing: Cham, Switzerland, 2019; pp. 215–229.
6. Thomas, L.; Leiponen, A. Big data commercialization. IEEE Eng. Manag. Rev. 2016,44, 74–90. [CrossRef]
7.
Van de Ven, M.; Abbas, A.E.; Kwee, Z.; de Reuver, M. Creating a Taxonomy of Business Models for Data Marketplaces.
In Proceedings of the 34th Bled eConference-Digital Support from Crisis to Progressive Change, Online, 27–30 June 2021;
pp. 313–325.
8.
Okoli, C. A guide to conducting a standalone systematic literature review. Commun. Assoc. Inf. Syst.
2015
,37, 879–910. [CrossRef]
J. Theor. Appl. Electron. Commer. Res. 2021,16 3334
9.
Solaimani, S.; Keijzer-Broers, W.; Bouwman, H. What we do–and don’t–know about the Smart Home: An analysis of the Smart
Home literature. Indoor Built Environ. 2015,24, 370–383. [CrossRef]
10. Champieux, R. PlumX. J. Med. Libr. Assoc. JMLA 2015,103, 63–64. [CrossRef]
11.
Wouters, P.; Zahedi, Z.; Costas, R. Social media metrics for new research evaluation. In Springer Handbook of Science and Technology
Indicators; Springer: Cham, Switzerland, 2019; pp. 687–713.
12.
Bouwman, H.; Faber, E.; Haaker, T.; Kijl, B.; De Reuver, M. Conceptualizing the STOF Model; Springer: Berlin/Heidelberg, Germany,
2008; pp. 31–70.
13.
Osterwalder, A.; Pigneur, Y. Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers;
John Wiley & Sons: Hoboken, NJ, USA, 2010.
14.
Munoz-Arcentales, A.; López-Pernas, S.; Pozo, A.; Alonso, Á.; Salvachúa, J.; Huecas, G. An Architecture for Providing Data
Usage and Access Control in Data Sharing Ecosystems. Procedia Comput. Sci. 2019,160, 590–597. [CrossRef]
15.
Demchenko, Y.; Los, W.; de Laat, C. Data as economic goods: Definitions, properties, challenges, enabling technologies for future
data markets. ITU J. ICT Discov. 2018,1, 1–10.
16. Zhang, R.; Indulska, M.; Sadiq, S. Discovering Data Quality Problems. Bus. Inf. Syst. Eng. 2019,61, 575–593. [CrossRef]
17.
Schomakers, E.-M.; Lidynia, C.; Ziefle, M. All of me? Users’ preferences for privacy-preserving data markets and the importance
of anonymity. Electron. Mark. 2020,30, 649–665. [CrossRef]
18.
Kamleitner, B.; Mitchell, V.-W. Can consumers experience ownership for their personal data? From issues of scope and invisibility
to agents handling our digital blueprints. In Psychological Ownership and Consumer Behavior; Springer: Cham, Switzerland, 2018;
pp. 91–118.
19. Spiekermann, S.; Korunovska, J. Towards a value theory for personal data. J. Inf. Technol. 2017,32, 62–84. [CrossRef]
20.
Perera, C.; Wakenshaw, S.Y.; Baarslag, T.; Haddadi, H.; Bandara, A.K.; Mortier, R.; Crabtree, A.; Ng, I.C.; McAuley, D.; Crowcroft,
J. Valorising the IoT databox: Creating value for everyone. Trans. Emerg. Telecommun. Technol. 2017,28, 1–17. [CrossRef]
21.
Anderson, N.G.; Pollack, J.; Williams, D. The value of healthcare data in ophthalmology. Curr. Opin. Ophthalmol.
2014
,25, 191–194.
[CrossRef] [PubMed]
22.
Robinson, S.C. What’s your anonymity worth? Establishing a marketplace for the valuation and control of individuals’ anonymity
and personal data. Digit. Policy Regul. Gov. 2017,19, 353–366. [CrossRef]
23.
Mamoshina, P.; Ojomoko, L.; Yanovich, Y.; Ostrovski, A.; Botezatu, A.; Prikhodko, P.; Izumchenko, E.; Aliper, A.; Romantsov, K.;
Zhebrak, A.; et al. Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate
biomedical research and healthcare. Oncotarget 2018,9, 5665–5690. [CrossRef] [PubMed]
24.
Attard, J.; Orlandi, F.; Auer, S. Data value networks: Enabling a new data ecosystem. In Proceedings of the 2016 IEEE/WIC/ACM
International Conference on Web Intelligence (WI), Omaha, NE, USA, 13–16 October 2016; pp. 453–456.
25.
Attard, J.; Orlandi, F.; Auer, S. Exploiting the value of data through data value networks. In Proceedings of the 10th International
Conference on Theory and Practice of Electronic Governance, New Delhi, India, 7–9 March 2017; pp. 475–484.
26.
Hutchings, A.; Holt, T.J. A Crime Script Analysis of the Online Stolen Data Market: Table 1. Br. J. Criminol.
2015
,55, 596–614.
[CrossRef]
27.
Chen, Y.; Guo, J.; Li, C.; Ren, W. FaDe: A Blockchain-Based Fair Data Exchange Scheme for Big Data Sharing. Future Internet
2019
,
11, 225. [CrossRef]
28.
Lawrenz, S.; Sharma, P.; Rausch, A. Blockchain Technology as an Approach for Data Marketplaces. In Proceedings of the 2019
International Conference on Blockchain Technology, Honolulu, HI, USA, 15–18 March 2019; pp. 55–59.
29.
Jeong, B.-G.; Youn, T.-Y.; Jho, N.-S.; Shin, S.U. Blockchain-Based Data Sharing and Trading Model for the Connected Car. Sensors
2020,20, 3141. [CrossRef]
30.
López, D.; Farooq, B. A multi-layered blockchain framework for smart mobility data-markets. Transp. Res. Part C Emerg. Technol.
2020,111, 588–615. [CrossRef]
31.
Ha, M.; Kwon, S.; Lee, Y.J.; Shim, Y.; Kim, J. Where WTS meets WTB: A Blockchain-based Marketplace for Digital Me to trade
users’ private data. Pervasive Mob. Comput. 2019,59, 1–15. [CrossRef]
32.
Özyilmaz, K.R.; Do˘gan, M.; Yurdakul, A. IDMoB: IoT data marketplace on blockchain. In Proceedings of the 2018 Crypto Valley
Conference on Blockchain Technology (CVCBT), Zug, Switzerland, 20–22 June 2018; pp. 11–19.
33.
Park, J.-S.; Youn, T.-Y.; Kim, H.-B.; Rhee, K.-H.; Shin, S.-U. Smart Contract-Based Review System for an IoT Data Marketplace.
Sensors 2018,18, 3577. [CrossRef]
34.
De la Vega, F.; Soriano, J.; Jimenez, M.; Lizcano, D. A Peer-to-Peer Architecture for Distributed Data Monetization in Fog
Computing Scenarios. Wirel. Commun. Mob. Comput. 2018,2018, 5758741. [CrossRef]
35.
Wörner, D. Design of a Real-Time Data Market Based on the 21 Bitcoin Computer; Springer International Publishing: Cham, Switzerland,
2016; pp. 228–232.
36.
Ramachandran, G.S.; Radhakrishnan, R.; Krishnamachari, B. Towards a Decentralized Data Marketplace for Smart Cities.
In Proceedings of the 2018 IEEE International Smart Cities Conference (ISC2), Kansas City, MO, USA, 16–19 September 2018.
37.
Huang, K.; Zhang, X.; Mu, Y.; Rezaeibagha, F.; Wang, X.; Li, J.; Xia, Q.; Qin, J. EVA: Efficient Versatile Auditing Scheme for
IoT-Based Datamarket in Jointcloud. IEEE Internet Things J. 2020,7, 882–892. [CrossRef]
J. Theor. Appl. Electron. Commer. Res. 2021,16 3335
38.
Roman, D.; Stefano, G. Towards a Reference Architecture for Trusted Data Marketplaces: The Credit Scoring Perspective.
In Proceedings of the 2016 2nd International Conference on Open and Big Data (OBD), Vienna, Austria, 22–24 August 2016;
pp. 95–101.
39.
Nasonov, D.; Visheratin, A.A.; Boukhanovsky, A. Blockchain-Based Transaction Integrity in Distributed Big Data Marketplace; Springer
International Publishing: Cham, Switzerland, 2018; pp. 569–577.
40.
Masseport, S.; Lartigau, J.; Darties, B.; Giroudeau, R. Proof of usage: User-centric consensus for data provision and exchange.
Ann. Telecommun. 2020,75, 153–162. [CrossRef]
41.
Matzutt, R.; Müllmann, D.; Zeissig, E.-M.; Horst, C.; Kasugai, K.; Lidynia, S.; Wieninger, S.; Ziegeldorf, J.H.; Gudergan, G.; Wehrle,
K. myneData: Towards a trusted and user-controlled ecosystem for sharing personal data. Informatik
2017
,P275, 1073–1084.
[CrossRef]
42.
Mišura, K.; Žagar, M. Data marketplace for Internet of Things. In Proceedings of the 2016 International Conference on Smart
Systems and Technologies (SST), Osijek, Croatia, 12–14 October 2016; pp. 255–260.
43.
Sánchez, L.; Lanza, J.; Santana, J.; Agarwal, R.; Raverdy, P.; Elsaleh, T.; Fathy, Y.; Jeong, S.; Dadoukis, A.; Korakis, T.; et al.
Federation of Internet of Things Testbeds for the Realization of a Semantically-Enabled Multi-Domain Data Marketplace. Sensors
2018,18, 3375. [CrossRef]
44.
Pillmann, J.; Wietfeld, C.; Zarcula, A.; Raugust, T.; Alonso, D.C. Novel common vehicle information model (CVIM) for
future automotive vehicle big data marketplaces. In Proceedings of the 2017 IEEE Intelligent Vehicles Symposium (IV),
Los Angeles, CA, USA, 11–14 June 2017; pp. 1910–1915.
45.
Li, Y.; Sun, H.; Dong, B. Cost-efficient data acquisition on online data marketplaces for correlation analysis. Proc. VLDB Endow
2018,12, 362–375. [CrossRef]
46.
Ren, X.; London, P.; Ziani, J.; Wierman, A. Datum: Managing Data Purchasing and Data Placement in a Geo-Distributed Data
Market. IEEE ACM Trans. Netw. 2018,26, 893–905. [CrossRef]
47.
Fricker, S.A.; Maksimov, Y.V. Pricing of data products in data marketplaces. In Proceedings of the International Conference of
Software Business, Tallinn, Estonia, 11–12 June 2017; pp. 49–66.
48.
Agarwal, A.; Dahleh, M.; Sarkar, T. A marketplace for data: An algorithmic solution. In Proceedings of the 2019 ACM Conference
on Economics and Computation, Phoenix, AZ, USA, 24–28 June 2019; pp. 701–726.
49.
Niyato, D.; Alsheikh, M.A.; Wang, P.; Kim, D.I.; Han, Z. Market model and optimal pricing scheme of big data and Internet of
Things (IoT). In Proceedings of the 2016 IEEE International Conference on Communications (ICC), Kuala Lumpur, Malaysia,
22–27 May 2016; pp. 1–6.
50.
Liu, K.; Qiu, X.; Chen, W.; Chen, X.; Zheng, Z. Optimal Pricing Mechanism for Data Market in Blockchain-Enhanced Internet of
Things. IEEE Internet Things J. 2019,6, 9748–9761. [CrossRef]
51. Koutris, P.; Upadhyaya, P.; Balazinska, M.; Howe, B.; Suciu, D. Query-Based Data Pricing. J. ACM 2015,62, 1–44. [CrossRef]
52.
Koutris, P.; Upadhyaya, P.; Balazinska, M.; Howe, B.; Suciu, D. Toward practical query pricing with QueryMarket. In Proceedings
of the 2013 ACM SIGMOD International Conference on Management of Data, New York, NY, USA, 22–27 June 2013; pp. 613–624.
53.
Niu, C.; Zheng, Z.; Wu, F.; Tang, S.; Chen, G. Online Pricing with Reserve Price Constraint for Personal Data Markets. IEEE Trans.
Knowl. Data Eng. 2020. [CrossRef]
54.
Niu, C.; Zheng, Z.; Tang, S.; Gao, X.; Wu, F. Making Big Money from Small Sensors: Trading Time-Series Data under Puffer-
fish Privacy. In Proceedings of the IEEE INFOCOM 2019—IEEE Conference on Computer Communications, Paris, France,
29 April–2 May 2019.
55.
Zheng, Z.; Peng, Y.; Wu, F.; Tang, S.; Chen, G. ARETE: On Designing Joint Online Pricing and Reward Sharing Mechanisms for
Mobile Data Markets. IEEE Trans. Mob. Comput. 2020,19, 769–787. [CrossRef]
56.
Zheng, Z.; Peng, Y.; Wu, F.; Tang, S.; Chen, G. An online pricing mechanism for mobile crowdsensing data markets. In Proceedings
of the 18th ACM International Symposium on Mobile Ad Hoc Networking and Computing, Chennai, India, 10–14 July 2017;
pp. 1–10.
57.
Liu, Z.; Hacigümüs, H. Online Optimization and Fair Costing for Dynamic Data Sharing in a Cloud Data Market. In Proceedings
of the 2014 ACM SIGMOD International Conference on Management of Data, Snowbird, UT, USA, 22–27 June 2014; pp. 1359–1370.
58. Sakr, M. A data model and algorithms for a spatial data marketplace. Int. J. Geogr. Inf. Sci. 2018,32, 2140–2168. [CrossRef]
59. Wang, X.; Wei, X.; Liu, Y.; Gao, S. On pricing approximate queries. Inf. Sci. 2018,453, 198–215. [CrossRef]
60.
Upadhyaya, P.; Balazinska, M.; Suciu, D. Price-optimal querying with data APIs. Proc. VLDB Endow.
2016
,9, 1695–1706.
[CrossRef]
61. Tang, R.; Wu, H.; Bao, Z.; Bressan, S.; Valduriez, P. The Price Is Right; Springer: Berlin/Heidelberg, Germany, 2013; pp. 380–394.
62.
Wang, X.; Wei, X.; Gao, S.; Liu, Y.; Li, Z. A novel auction-based query pricing schema. Int. J. Parallel Program.
2019
,47, 759–780.
[CrossRef]
63. Yu, H.; Zhang, M. Data pricing strategy based on data quality. Comput. Ind. Eng. 2017,112, 1–10. [CrossRef]
64. Stahl, F.; Vossen, G. Name your own price on data marketplaces. Informatica 2017,28, 155–180. [CrossRef]
65.
Stahl, F.; Vossen, G. Fair Knapsack Pricing for Data Marketplaces; Springer International Publishing: Cham, Switzerland, 2016;
pp. 46–59.
66.
Tang, R.; Shao, D.; Bressan, S.; Valduriez, P. What You Pay for Is What You Get; Springer: Berlin/Heidelberg, Germany, 2013;
pp. 395–409.
J. Theor. Appl. Electron. Commer. Res. 2021,16 3336
67.
Tang, R.; Amarilli, A.; Senellart, P.; Bressan, S. A Framework for Sampling-Based XML Data Pricing; Springer:
Berlin/Heidelberg, Germany
,
2016; pp. 116–138.
68.
Tang, R.; Amarilli, A.; Senellart, P.; Bressan, S. Get a Sample for a Discount; Springer International Publishing: Cham, Switzerland,
2014; pp. 20–34.
69.
Cao, X.; Chen, Y.; Liu, K.R. An iterative auction mechanism for data trading. In Proceedings of the 2017 IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA, 5–9 March 2017; pp. 5850–5854.
70.
Cao, X.; Chen, Y.; Liu, K.J.R. Data Trading With Multiple Owners, Collectors, and Users: An Iterative Auction Mechanism. IEEE
Trans. Signal Inf. Process. Over Netw. 2017,3, 268–281. [CrossRef]
71.
Zheng, Z.; Peng, Y.; Wu, F.; Tang, S.; Chen, G. Trading Data in the Crowd: Profit-Driven Data Acquisition for Mobile Crowdsensing.
IEEE J. Sel. Areas Commun. 2017,35, 486–501. [CrossRef]
72.
Zeng, Y.; Ohsawa, Y. Re-discover Values of Data Using Data Jackets by Combining Cluster with Text Analysis. Procedia Comput.
Sci. 2017,112, 2195–2203. [CrossRef]
73.
Oh, H.; Park, S.; Lee, G.M.; Choi, J.K.; Noh, S. Competitive Data Trading Model With Privacy Valuation for Multiple Stakeholders
in IoT Data Markets. IEEE Internet Things J. 2020,7, 3623–3639. [CrossRef]
74.
Oh, H.; Park, S.; Lee, G.M.; Heo, H.; Choi, J.K. Personal Data Trading Scheme for Data Brokers in IoT Data Marketplaces. IEEE
Access 2019,7, 40120–40132. [CrossRef]
75.
Xu, L.; Jiang, C.; Qian, Y.; Zhao, Y.; Li, J.; Ren, Y. Dynamic Privacy Pricing: A Multi-Armed Bandit Approach With Time-Variant
Rewards. IEEE Trans. Inf. Forensics Secur. 2017,12, 271–285. [CrossRef]
76.
Hu, J.; Yang, K.; Wang, K.; Zhang, K. A Blockchain-Based Reward Mechanism for Mobile Crowdsensing. IEEE Trans. Comput. Soc.
Syst. 2020,7, 178–191. [CrossRef]
77.
Liu, K.; Chen, W.; Zheng, Z.; Li, Z.; Liang, W. A Novel Debt-Credit Mechanism for Blockchain-Based Data-Trading in Internet of
Vehicles. IEEE Internet Things J. 2019,6, 9098–9111. [CrossRef]
78.
Yang, J.; Zhao, C.; Xing, C. Big Data Market Optimization Pricing Model Based on Data Quality. Complexity
2019
,2019, 1–10.
[CrossRef]
79.
Vu, Q.H.; Pham, T.-V.; Truong, H.-L.; Dustdar, S.; Asal, R. Demods: A description model for data-as-a-service. In Proceedings
of the 2012 IEEE 26th International Conference on Advanced Information Networking and Applications, Fukuoka, Japan,
26–29 March 2012; pp. 605–612.
80.
Truong, H.-L.; Dustdar, S.; Gotze, J.; Fleuren, T.; Muller, P.; Tbahriti, S.-E.; Mrissa, M.; Ghedira, C. Exchanging data agreements in
the daas model. In Proceedings of the 2011 IEEE Asia-Pacific Services Computing Conference, Jeju, Korea, 12–15 December 2011;
pp. 153–160.
81.
Truong, H.L.; Comerio, M.; Paoli, F.D.; Gangadharan, G.R.; Dustdar, S. Data contracts for cloud-based data marketplaces. Int. J.
Comput. Sci. Eng. 2012,7, 280. [CrossRef]
82.
Truong, H.-L.; Gangadharan, G.; Comerio, M.; Dustdar, S.; De Paoli, F. On analyzing and developing data contracts in
cloud-based data marketplaces. In Proceedings of the 2011 IEEE Asia-Pacific Services Computing Conference, Jeju, Korea,
12–15 December 2011; pp. 174–181.
83.
Hatanaka, H.; Abe, A. What Type of Information and Scheme does the Data Market Need? Procedia Comput. Sci.
2015
,60,
1309–1317. [CrossRef]
84.
De Virgilio, R.; Orsi, G.; Tanca, L.; Torlone, R. Semantic data markets: A flexible environment for knowledge management. In
Proceedings of the 20th ACM International Conference on Information and Knowledge Management, Glasgow, Scotland, UK,
24–28 October 2011; pp. 1559–1564.
85.
Morrison, N.; Hancock, D.; Hirschman, L.; Dawyndt, P.; Verslyppe, B.; Kyrpides, N.; Kottmann, R.; Yilmaz, P.; Glöckner, F.O.;
Grethe, J.; et al. Data shopping in an open marketplace: Introducing the Ontogrator web application for marking up data using
ontologies and browsing using facets. Stand. Genom. Sci. 2011,4, 286–292. [CrossRef]
86.
Wijnhoven, F.; Van Den Belt, E.; Verbruggen, E.; Van Der Vet, P. Internal data market services: An ontology-based architecture
and its evaluation. Inf. Sci. 2003,6, 259–271.
87.
Chapman, A.; Simperl, E.; Koesten, L.; Konstantinidis, G.; Ibáñez, L.-D.; Kacprzak, E.; Groth, P. Dataset search: A survey. VLDB J.
2020,29, 251–272. [CrossRef]
88.
Rekatsinas, T.; Dong, X.L.; Getoor, L.; Srivastava, D. Finding Quality in Quantity: The Challenge of Discovering Valuable
Sources for Integration. In Proceedings of the 7th Biennial Conference on Innovative Data Systems Research (CIDR’15),
Asilomar, CA, USA, 4–7 January 2015.
89.
Zhao, Y.; Yu, Y.; Li, Y.; Han, G.; Du, X. Machine learning based privacy-preserving fair data trading in big data market. Inf. Sci.
2019,478, 449–460. [CrossRef]
90.
Niu, C.; Zheng, Z.; Wu, F.; Gao, X.; Chen, G. Achieving Data Truthfulness and Privacy Preservation in Data Markets. IEEE Trans.
Knowl. Data Eng. 2019,31, 105–119. [CrossRef]
91.
Niu, C.; Zheng, Z.; Wu, F.; Gao, X.; Chen, G. Trading Data in Good Faith: Integrating Truthfulness and Privacy Preservation in
Data Markets. In Proceedings of the 2017 IEEE 33rd International Conference on Data Engineering (ICDE), San Diego, CA, USA,
19–22 April 2017; pp. 223–226.
92. Perera, C.; Ranjan, R.; Wang, L. End-to-End Privacy for Open Big Data Markets. IEEE Cloud Comput. 2015,2, 44–53. [CrossRef]
J. Theor. Appl. Electron. Commer. Res. 2021,16 3337
93.
Kiayias, A.; Yener, B.; Yung, M. Privacy-Preserving Information Markets for Computing Statistical Data; Springer: Berlin/Heidelberg,
Germany, 2009; pp. 32–50.
94.
Sørlie, J.-T.; Altmann, J. Sensing as a Service Revisited: A Property Rights Enforcement and Pricing Model for IIoT Data
Marketplaces. In Proceedings of the International Conference on the Economics of Grids, Clouds, Systems, and Services,
Leeds, UK, 17–19 September 2019; pp. 127–139.
95.
Shaabany, G.; Grimm, M.; Anderl, R. Secure Information Model for Data Marketplaces Enabling Global Distributed Manufacturing.
Procedia CIRP 2016,50, 360–365. [CrossRef]
96. Schomm, F.; Stahl, F.; Vossen, G. Marketplaces for data: An initial survey. ACM SIGMOD Rec. 2013,42, 15–26. [CrossRef]
97.
Stahl, F.; Schomm, F.; Vossen, G.; Vomfell, L. A classification framework for data marketplaces. Vietnam J. Comput. Sci.
2016
,3,
137–143. [CrossRef]
98.
Oliveira, M.I.S.; Lóscio, B.F. What is a data ecosystem? In Proceedings of the 19th Annual International Conference on Digital
Government Research: Governance in the Data Age, Delft, The Netherlands, 30 May–1 June 2018; pp. 1–9.
99.
Oliveira, M.I.S.; Lima, G.D.F.B.; Lóscio, B.F. Investigations into Data Ecosystems: A systematic mapping study. Knowl. Inf. Syst.
2019,61, 589–630. [CrossRef]
100.
Hayashi, T.; Ohsawa, Y. Understanding the Structural Characteristics of Data Platforms Using Metadata and a Network Approach.
IEEE Access 2020,8, 35469–35481. [CrossRef]
101.
Holt, T.J.; Lampke, E. Exploring stolen data markets online: Products and market forces. Crim. Justice Stud.
2010
,23, 33–50.
[CrossRef]
102.
Smirnova, O.; Holt, T.J. Examining the Geographic Distribution of Victim Nations in Stolen Data Markets. Am. Behav. Sci.
2017
,
61, 1403–1426. [CrossRef]
103.
Macdonald, M.; Frank, R. Shuffle Up and Deal: Use of a Capture–Recapture Method to Estimate the Size of Stolen Data Markets.
Am. Behav. Sci. 2017,61, 1313–1340. [CrossRef]
104.
Macdonald, M.; Frank, R. The network structure of malware development, deployment and distribution. Glob. Crime
2017
,18,
49–69. [CrossRef]
105. Bevir, M. Governance: A Very Short Introduction; Oxford University Press: Oxford, UK, 2012.
106. Tupasela, A.; Snell, K.; Tarkkala, H. The Nordic data imaginary. Big Data Soc. 2020,7, 1–13. [CrossRef]
107.
Yu, X.; Zhao, Y. Dualism in data protection: Balancing the right to personal data and the data property right. Comput. Law Secur.
Rev. 2019,35, 1–11. [CrossRef]
108.
Odaba¸s, M.; Holt, T.J.; Breiger, R.L. Markets as Governance Environments for Organizations at the Edge of Illegality: Insights
From Social Network Analysis. Am. Behav. Sci. 2017,61, 1267–1288. [CrossRef]
109. Holt, T.J. Exploring the social organisation and structure of stolen data markets. Glob. Crime 2013,14, 155–174. [CrossRef]
110.
Yip, M.; Shadbolt, N.; Webber, C. Why forums? An empirical analysis into the facilitating factors of carding forums. In Proceedings
of the 5th Annual ACM Web Science Conference, Paris, France, 2–4 May 2013; pp. 453–462.
111.
Hutchings, A.; Holt, T.J. The online stolen data market: Disruption and intervention approaches. Glob. Crime
2017
,18, 11–30.
[CrossRef]
112. Thimmesch, A.B. Transacting in Data: Tax, Privacy, and the New Economy. SSRN Electron. J. 2016,94, 145–194. [CrossRef]
113.
Henshall, S. The COMsumer Manifesto: Empowering communities of consumers through the Internet. First Monday
2000
,5.
[CrossRef]
114.
Agahari, W. Platformization of data sharing: Multi-party computation (MPC) as control mechanism and its effect on firms’
participation in data sharing via data marketplaces. In Proceedings of the 33rd Bled eConference: Enabling Technology for a
Sustainable Society, Bled, Slovenia, 28 June 2020; pp. 691–704.
115.
Otto, B.; Jarke, M. Designing a multi-sided data platform: Findings from the International Data Spaces case. Electron. Mark.
2019
,
29, 561–580. [CrossRef]
116.
Richter, H.; Slowinski, P.R. The Data Sharing Economy: On the Emergence of New Intermediaries. IIC Int. Rev. Intellect. Prop.
Compet. Law 2019,50, 4–29. [CrossRef]
117.
Spiekermann, S.; Novotny, A. A vision for global privacy bridges: Technical and legal measures for international data markets.
Comput. Law Secur. Rev. 2015,31, 181–200. [CrossRef]
118.
Ahmed, E.; Shabani, M. DNA Data Marketplace: An Analysis of the Ethical Concerns Regarding the Participation of the
Individuals. Front. Genet. 2019,10, 1–6. [CrossRef]
119.
Van Dijck, J.; Poell, T. Understanding the promises and premises of online health platforms. Big Data Soc.
2016
,3, 1–11. [CrossRef]
120.
Ishmaev, G. The Ethical Limits of Blockchain-Enabled Markets for Private IoT Data. Philos. Technol.
2020
,33, 411–432. [CrossRef]
121.
Charitsis, V.; Zwick, D.; Bradshaw, A. Creating Worlds that Create Audiences: Theorising Personal Data Markets in the Age of
Communicative Capitalism. tripleC Commun. Capital. Crit. Open Access J. Glob. Sustain. Inf. Soc. 2018,16, 820–834. [CrossRef]
122. Elvy, S.-A. Paying for privacy and the personal data economy. Columbia Law Rev. 2017,117, 1369–1460.
123.
Spiekermann, S.; Acquisti, A.; Böhme, R.; Hui, K.-L. The challenges of personal data markets and privacy. Electron. Mark.
2015
,
25, 161–167. [CrossRef]
124.
Guijarro, L.; Pla, V.; Vidal, J.R.; Naldi, M. Competition in data-based service provision: Nash equilibrium characterization. Future
Gener. Comput. Syst. 2019,96, 35–50. [CrossRef]
J. Theor. Appl. Electron. Commer. Res. 2021,16 3338
125.
Holt, T.J.; Smirnova, O.; Chua, Y.T. Exploring and Estimating the Revenues and Profits of Participants in Stolen Data Markets.
Deviant Behav. 2016,37, 353–367. [CrossRef]
126. Shulman, A. The underground credentials market. Comput. Fraud Secur. 2010,2010, 5–8. [CrossRef]
127.
Soley, A.M.; Siegel, J.E.; Suo, D.; Sarma, S.E. Value in vehicles: Economic assessment of automotive data. Digit. Policy Regul. Gov.
2018,20, 513–527. [CrossRef]
128.
Tian, L.; Li, J.; Li, W.; Ramesh, B.; Cai, Z. Optimal Contract-Based Mechanisms for Online Data Trading Markets. IEEE Internet
Things J. 2019,6, 7800–7810. [CrossRef]
129.
Mao, W.; Zheng, Z.; Wu, F. Pricing for Revenue Maximization in IoT Data Markets: An Information Design Perspective.
In Proceedings of the IEEE INFOCOM 2019—IEEE Conference on Computer Communications, Paris, France,
29 April–2 May 2019
;
pp. 1837–1845.
130.
Shen, B.; Shen, Y.; Ji, W. Profit optimization in service-oriented data market: A Stackelberg game approach. Future Gener. Comput.
Syst. 2019,95, 17–25. [CrossRef]
131.
Niu, C.; Zheng, Z.; Wu, F.; Tang, S.; Gao, X.; Chen, G. Unlocking the value of privacy: Trading aggregate statistics over private
correlated data. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining,
London, UK, 19–23 August 2018; pp. 2031–2040.
132. Parra-Arnau, J. Optimized, direct sale of privacy in personal data marketplaces. Inf. Sci. 2018,424, 354–384. [CrossRef]
133.
Yuncheng, S.; Bing, G.; Yan, S.; Xuliang, D.; Xiangqian, D.; Hong, Z. A pricing model for Big Personal Data. Tsinghua Sci. Technol.
2016,21, 482–490. [CrossRef]
134.
Li, C.; Li, D.Y.; Miklau, G.; Suciu, D. A theory of pricing private data. ACM Trans. Database Syst. TODS
2014
,39, 1–28. [CrossRef]
135.
Li, C.; Li, D.Y.; Miklau, G.; Suciu, D. A theory of pricing private data. In Proceedings of the 16th International Conference on
Database Theory, Genoa, Italy, 18–22 March 2013; pp. 33–44.
136.
Hayashi, T.; Ohsawa, Y. Preliminary Case Study on Value Determination of Datasets and Cross-disciplinary Data Collaboration
Using Data Jackets. Procedia Comput. Sci. 2017,112, 2175–2184. [CrossRef]
137.
Muschalle, A.; Stahl, F.; Löser, A.; Vossen, G. Pricing approaches for data markets. In Proceedings of the International Workshop
on Business Intelligence for the Real-Time Enterprise, Istanbul, Turkey, 27 August 2012; pp. 129–144.
138.
Balazinska, M.; Howe, B.; Suciu, D. Data markets in the cloud: An opportunity for the database community. Proc. VLDB Endow.
2011,4, 1482–1485. [CrossRef]
139.
Liang, F.; Yu, W.; An, D.; Yang, Q.; Fu, X.; Zhao, W. A Survey on Big Data Market: Pricing, Trading and Protection. IEEE Access
2018,6, 15132–15154. [CrossRef]
140.
Jiao, Y.; Wang, P.; Feng, S.; Niyato, D. Profit Maximization Mechanism and Data Management for Data Analytics Services. IEEE
Internet Things J. 2018,5, 2001–2014. [CrossRef]
141.
Jiao, Y.; Wang, P.; Niyato, D.; Alsheikh, M.A.; Feng, S. Profit maximization auction and data management in big data markets.
In Proceedings of the 2017 IEEE Wireless Communications and Networking Conference (WCNC), San Francisco, CA, USA,
19–22 March 2017; pp. 1–6.
142.
Yassine, A.; Nazari Shirehjini, A.A.; Shirmohammadi, S. Smart Meters Big Data: Game Theoretic Model for Fair Data Sharing in
Deregulated Smart Grids. IEEE Access 2015,3, 2743–2754. [CrossRef]
143.
Jiang, C.; Gao, L.; Duan, L.; Huang, J. Economics of peer-to-peer mobile crowdsensing. In Proceedings of the 2015 IEEE Global
Communications Conference (GLOBECOM), San Diego, CA, USA, 6–10 December 2015; pp. 1–6.
144.
Stahl, F.; Vossen, G. Data Quality Scores for Pricing on Data Marketplaces; Springer: Berlin/Heidelberg, Germany, 2016; pp. 215–224.
145.
Jang, B.; Park, S.; Lee, J.; Hahn, S.-G. Three Hierarchical Levels of Big-Data Market Model Over Multiple Data Sources for Internet
of Things. IEEE Access 2018,6, 31269–31280. [CrossRef]
146.
Henfridsson, O.; Bygstad, B. The generative mechanisms of digital infrastructure evolution. MIS Q.
2013
,37, 907–931. [CrossRef]
147.
Abbas, A.E. Designing Data Governance Mechanisms for Data Marketplace Meta-Platforms. In Proceedings of the 34th Bled
eConference–Digital Support from Crisis to Progressive Change, Online, 27–30 June 2021; pp. 695–707.
148.
Mucha, T.; Seppala, T. Artificial Intelligence Platforms—A New Research Agenda for Digital Platform Economy. SSRN Electron. J.
2020. [CrossRef]
149. De Reuver, M.; Sørensen, C.; Basole, R.C. The digital platform: A research agenda. J. Inf. Technol. 2018,33, 124–135. [CrossRef]
150.
Van Angeren, J.; Alves, C.; Jansen, S. Can we ask you to collaborate? Analyzing app developer relationships in commercial
platform ecosystems. J. Syst. Softw. 2016,113, 430–445. [CrossRef]
151.
Gawer, A. Bridging differing perspectives on technological platforms: Toward an integrative framework. Res. Policy
2014
,43,
1239–1249. [CrossRef]
152.
Gebregiorgis, S.A.; Altmann, J. IT service platforms: Their value creation model and the impact of their level of openness on their
adoption. Procedia Comput. Sci. 2015,68, 173–187. [CrossRef]
153.
Ondrus, J.; Gannamaneni, A.; Lyytinen, K. The impact of openness on the market potential of multi-sided platforms: A case
study of mobile payment platforms. J. Inf. Technol. 2015,30, 260–275. [CrossRef]
154. Wareham, J.; Fox, P.B.; Cano Giner, J.L. Technology Ecosystem Governance. Organ. Sci. 2014,25, 1195–1215. [CrossRef]
155.
Wessel, M.; Thies, F.; Benlian, A. Opening the floodgates: The implications of increasing platform openness in crowdfunding.
J. Inf. Technol. 2017,32, 344–360. [CrossRef]
J. Theor. Appl. Electron. Commer. Res. 2021,16 3339
156.
Gordijn, J.; Akkermans, J. Value-based requirements engineering: Exploring innovative e-commerce ideas. Requir. Eng.
2003
,8,
114–134.
157. Porter, M.E. The value chain and competitive advantage. Underst. Bus. Process. 2001,2, 50–66.
158.
De Reuver, M.; Bouwman, H.; Haaker, T. Business model roadmapping: A practical approach to come from an existing to a
desired business model. Int. J. Innov. Manag. 2013,17, 1–18. [CrossRef]
159.
Hevner, A.; Chatterjee, S. Design science research in information systems. In Design Research in Information Systems; Springer:
Boston, MA, USA, 2010; pp. 9–22.
160. Hevner, A.R. A three cycle view of design science research. Scand. J. Inf. Syst. 2007,19, 1–6.
161. Sekaran, U.; Bougie, R. Research Methods for Business: A Skill Building Approach; John Wiley & Sons: Hoboken, NJ, USA, 2016.
162.
Abbas, A.E.; Agahari, W.; Van de Ven, M.; Zuiderwijk, A.; de Reuver, M. Business Data Sharing through Data Marketplaces:
A Systematic Literature Review. In Proceedings of the 34th Bled eConference-Digital Support from Crisis to Progressive Change,
Online, 27–30 June 2021; pp. 75–84.
... Our research could also benefit intermediary platforms that facilitate data sharing. As pointed out by Abbas et al. (2021), MPC could affect the value proposition of those platforms by (1) enabling sharing and computation of data insights without disclosing the input data; and (2) affording control over data without a trusted third party. Our study provides empirical evidence that MPC could address control, trust, and risk issues in data sharing, which are challenges that data sharing platforms struggle to deal with (M. ...
... However, we observed that interviewees sometimes based their answers on (1) data-sharing platforms that purely focus on facilitating data exchange between partners; or (2) general view of data sharing without considering intermediaries like data marketplaces. This limitation is expected since interviewees are not very familiar with data marketplaces due to the diversity of data marketplaces' business models (Bergman et al., 2022;Fruhwirth et al., 2020;van de Ven et al., 2021). Therefore, we kept an eye on this issue during the interviews and clarified the concepts to the interviewees when this issue arose. ...
Article
Full-text available
Firms are often reluctant to share data because of mistrust, concerns over control, and other risks. Multi-party computation (MPC) is a new technique to compute meaningful insights without having to transfer data. This paper investigates if MPC affects known antecedents for data sharing decisions: control, trust, and risks. Through 23 qualitative interviews in the automotive industry, we find that MPC (1) enables new ways of technology-based control, (2) reduces the need for inter-organizational trust, and (3) prevents losing competitive advantage due to data leakage. However, MPC also creates the need to trust technology and introduces new risks of data misuse. These impacts arise if firms perceive benefits from sharing data, have high organizational readiness, and perceive data as non-sensitive. Our findings show that known antecedents of data sharing should be specified differently with MPC in place. Furthermore, we suggest reframing MPC as a data collaboration technology beyond enhancing privacy.
... A common understanding of a data marketplace could be as a platform through which data could be purchased or sold [9]. A data marketplace is also defined as a multi-sided platform that matches data providers and buyers and facilitates business data sharing among enterprises [20]. In smart cities and communities, data marketplaces make it easy for data buyers to find new and relevant datasets, and for the data sellers, it makes it easy to create value and perhaps make money from their data. ...
... A single place and mechanism to access data can save money and time for the users [9]. Furthermore, literature suggests that the research and development in the area of data marketplaces have concentrated on the technological and technical infrastructure aspects of a data marketplace rather than the service aspects [20]. Thus, the service aspects such as possible business models, the social dimensions and the collective value of data marketplaces to a community or a business ecosystem around data-dependent service remains to be explored. ...
Chapter
Full-text available
In this era of digitization, data is seen as the new oil due to the abundance of data generated from Internet of Things (IoT) sensors, social media and other platforms. Although prior studies have explored the challenges and opportunities that may arise in using these data to provide value added services, few studies explore how data from public, private and commercial data owners in smart cities and communities could enhance data reuse and sharing and collaboration among the different stakeholders. This study employs the system design approach to develop a data marketplace prototype, which provides data to create value-added services that could improve the lives of citizens. The prototype is developed for easy sharing, trading and utilization of available data for innovative services through collaboration. Qualitative data was collected using semi-structured interviews from experts in academia and industry to validate the concept of a data marketplace. Findings from this study reveal that the prototype data marketplace is useful, easy to use, and supports data trading in smart cities.
... Next, we classified different types of data marketplaces in our taxonomy based on their orientation and ownership structure. In doing so, we answer the call from Abbas et al. (2021) to conduct empirical research in business models to convey data marketplaces toward commercialization. ...
Article
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.
... There are three activities of external integration: supplier development, partnerships with suppliers, and closer relationships with customers [56]. Moreover, companies can take advantage of market data from third-party providers to provide manufacturers with valuable customer data in addition to this integration [57]. ...
Article
Full-text available
Today’s customer no longer wants one-size-fits-all products but expects products and services to be as tailored as possible. Mass customization and personalization are becoming a trend in the digitalization strategy of enterprises and manufacturing in Industry 4.0. The purpose of the paper is to develop and validate a conceptual model for leveraging Industry 4.0 and digitalization to support product customization. We explored the implications and impacts of Industry 4.0 and digitalization on product customization processes and determine the importance of variables. We applied structural equation modeling (SEM) to test our hypotheses regarding the antecedents and consequences of digitalization and Industry 4.0. We estimated the process model using the partial least squares (PLS) method, and goodness of fit measures show acceptable values. The proposed model considers relationships between technology readiness, digitalization, internal and external integration, internal value chain, and customization. The results show the importance of digitalization and technology readiness for product customization. The results reveal that the variable of internal integration plays a crucial mediating role in applying new technologies and digitalization for customization. The paper’s main contribution is the conclusion that, for successful implementation of the customization process, models are required to focus on the internal and external factors of the business environment. Our findings are supported by various practical applications of possible product customization.
Conference Paper
Full-text available
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.
Conference Paper
Full-text available
Data marketplaces are expected to play a crucial role in tomorrow's data economy but hardly achieve commercial exploitation. Currently, there is no clear understanding of the knowledge gaps in data marketplace research, especially neglected research topics that may contribute to advancing data marketplaces towards commercialization. This study provides an overview of the state of the art of data marketplace research. We employ a Systematic Literature Review (SLR) approach and structure our analysis using the Service-Technology-Organization-Finance (STOF) model. We find that the extant data marketplace literature is primarily dominated by technical research, such as discussions about computational pricing and architecture. To move past the first stage of the platform's lifecycle (i.e., platform design) to the second stage (i.e., platform adoption), we call for empirical research in non-technological areas, such as customer expected value and market segmentation.
Article
Full-text available
Currently, “connected cars” are being actively designed over smart cars and autonomous cars, to establish a two-way communication network between the vehicle and all infrastructure. Additionally, because vehicle black boxes are becoming more common, specific processes for secure and efficient data sharing and transaction via vehicle networks must be developed. In this paper, we propose a Blockchain-based vehicle data marketplace platform model, along with a data sharing scheme, using Blockchain-based data-owner-based attribute-based encryption (DO-ABE). The proposed model achieves the basic requirements such as data confidentiality, integrity, and privacy. The proposed system securely and effectively handles large-capacity and privacy-sensitive black box video data by storing the metadata on Blockchain (on-chain) and encrypted raw data on off-chain (external) storage, and adopting consortium Blockchain. Furthermore, the data owners of the proposed model can control their own data by applying the Blockchain-based DO-ABE and owner-defined access control lists.
Article
Full-text available
This paper presents a new consensus algorithm, Proof of Usage (PoU), for the blockchain technology. This consensus is introduced for permissioned (or private) blockchains and is designed for a user-centric personal data market. This market is subject to specific regulations with which conventional blockchains fail to comply. Proof of Usage aims to promote a new paradigm dedicated to usage incentivization, valuation, and control of user data in various sectors, such as banking and insurance. Other consensuses such as Proof of Stake or historical Proof of Work do not encourage coin spending and usage (in fact, Proof of Stake promotes the opposite). However, the value of the currency mainly depends on its use. This paper first introduces a contextualization of blockchain technology and decentralized consensus models. The motivation is then discussed for a new model of personal data exchange in a decentralized but supervised environment. The PoU protocol and its process flow are defined in detail. Furthermore, the paper explores two different approaches regarding the reward mechanism and the incentive model. Finally, the paper focuses on security requirements and how PoU meets such requirements in a permissioned-based blockchain system.
Article
Full-text available
The Nordic countries aim to have a unique place within the European and global health data economy. They have extensive nationally maintained and centralized health data records, as well as numerous biobanks where data from individuals can be connected based on personal identification numbers. Much of this phenomenon can be attributed to the emergence and development of the Nordic welfare state, where Nordic countries sought to systematically collect large amounts of population data to guide decision making and improve the health and living conditions of the population. Recently, however, the so-called Nordic gold mine of data is being re-imagined in a wholly other context, where data and its ever-increasing logic of accumulation is seen as a driver for economic growth and private business development. This article explores the development of policies and strategies for health data economy in Denmark and Finland. We ask how nation states try to adjust and benefit from new pressures and opportunities to utilize their data resources in data markets. This raises questions of social sustainability in terms of states being producers, providers, and consumers of data. The data imaginaries related to emerging health data markets also provide insight into how a broad range of different data sources, ranging from hospital records and pharmacy prescriptions to biobank sample data, are brought together to enable “full-scale utilization” of health and welfare data.
Article
Full-text available
With the emergence of global platforms for trading and buying/selling data, data have become a profitable commodity. The growth of such platforms has necessitated the further expansion of the scope of data in digital economies. To this end, understanding the nature of available data and their relationships between them has become an important challenge for expanding their use. Thus, in this study, we assumed data on the platforms as a population and metadata as the samples. Thus, we quantitatively investigated the structural characteristics of data platforms, while avoiding the risk of lost business opportunities and privacy issues by not sharing the data themselves. By observing the characteristics of data and variables, we found that the data network had a structure that was locally dense and globally sparse, which is quite similar to networks of human relationships. Moreover, we found that data play different roles on the platforms when divided into sharing conditions, namely, shareable data and sensitive data. Finally, we discussed the potential tactics for individuals who create/use data platforms based on our findings. The contributions of this study include a new framework for data platform observation and a method that uses metadata and a network approach to analyze structural characteristics of data.
Article
Full-text available
With the widespread of Internet of Things (IoT) environment, a big data concept has emerged to handle a large number of data generated by IoT devices. Moreover, since data-driven approaches now become important for business, IoT data markets have emerged, and IoT big data are exploited by major stakeholders such as data brokers and data service providers. Since many services and applications utilize data analytic methods with collected data from IoT devices, the conflict issues between privacy and data exploitation are raised, and the markets are mainly categorized as privacy protection markets and privacy valuation markets, respectively. Since these kinds of data value chains (which are mainly considered by business stakeholders) are revealed, data providers are interested in proper incentives in exchange for their privacy (i.e., privacy valuation) under their agreement. Therefore, this paper proposes a competitive data trading model that consists of data providers who weigh the value between privacy protection and valuation as well as other business stakeholders. Each data broker considers the willingness-to-sell of data providers, and a single data service provider considers the willingness-to-pay of service consumers. At the same time, multiple data brokers compete to sell their dataset to the data service provider as a non-cooperative game model. Based on the Nash Equilibrium analysis (NE) of the game, the feasibility is shown that the proposed model has the unique NE that maximizes the profits of business stakeholders while satisfying all market participants.
Article
Full-text available
Three out of nine of S&P500 digital platform companies stand out as building own artificial intelligence (AI) platforms. There is overwhelming empirical evidence of AI technologies are being central to running a digital platform business. However, the current research agenda is not directing researchers to study AI technologies in the context of digital platforms. We have divided the proposed AI platforms research agenda as follows: The first set of questions we propose relates to an overall conceptualization of AI platforms. Thereafter, we recognize specific aspects of AI platforms, which need to be investigated in detail to gain understanding that is more complete. The second set of questions we propose relates to understanding the dynamics between AI platforms and the broader socio-economic context. This topic might be particularly relevant to economies of countries without indigenous AI platforms. Our paper builds on the proposition that AI is a general-purpose technology, which by itself carries properties of a digital platform.
Conference Paper
Data Marketplace Meta-platforms (DMMPs) federate the fragmented set of data marketplaces and are expected to become a pivotal instrument to realize a single European Data Market in 2030. However, one critical hindrance to foster the adoption of business data sharing via DMMPs is data providers' risk of losing control over data. Generally, the literature on interorganizational data sharing has highlighted that data governance mechanisms can help data providers to retain control over their data. Nevertheless, data governance mechanisms in the DMMP context are yet to be explored. Therefore, this research aims to design data governance mechanisms for business data sharing in DMMPs by employing the Design Science Research (DSR) approach. This study contributes to the literature by identifying root causes and consequences of losing control over data and defining prescriptive knowledge regarding design requirements, design principles, and a framework for designing data governance mechanisms within the novel setting of metaplatforms.
Article
The society's insatiable appetites for personal data are driving the emergence of data markets, allowing data consumers to launch customized queries over the datasets collected by a data broker from data owners. In this paper, we study how the data broker can maximize its cumulative revenue by posting reasonable prices for sequential queries. We thus propose a contextual dynamic pricing mechanism with the reserve price constraint, which features the properties of ellipsoid for efficient online optimization and can support linear and non-linear market value models with uncertainty. In particular, under low uncertainty, the proposed pricing mechanism attains a worst-case cumulative regret logarithmic in the number of queries. We further extend our approach to support other similar application scenarios, including hospitality service and online advertising, and extensively evaluate all three use cases over MovieLens 20M dataset, Airbnb listings in U.S. major cities, and Avazu mobile ad click dataset, respectively. The analysis and evaluation results reveal that: (1) our pricing mechanism incurs low practical regret, while the latency and memory overhead incurred is low enough for online applications; and (2) the existence of reserve price can mitigate the cold-start problem in a posted price mechanism, thereby reducing the cumulative regret.