Location Data – A Trade-off between Control and Value
Business Model Implications
Jonas Breuer, Heritiana Ranaivoson, Uschi Buchinger and Pieter Ballon
iMinds-SMIT, Vrije Universiteit Brussel, Brussels, Belgium
Keywords: Location based Services, Personal Data, Multi Sided Markets, Control & Value, Business Models.
Abstract: The evolution of mobile technologies, and the enormous increase of users have also consolidated location
related services as an inherent part of the mobile service landscape. The possibilities to detect one's
whereabouts and relate them to any kind of networked information offer benefits for users and various kinds
of businesses. However, LBS also present issues, harmful particularly to users’ privacy. This paper assesses
mutually beneficial interaction in multi-sided markets (value is collective and actors are interdependent) and
the gatekeeper role of user ownership (i.e. control over the user and data). It adopts a business modelling
perspective to: a) define the value network around LBS as two-sided markets, where the LBS provider
intermediates between end-users and Third Parties; and b) extract potential revenue models. It focuses on
trade-offs between who has control in the ecosystem and how value is created. Finally, the paper assesses
how current developments around LBS contribute to changes in the position of the user within the
The combination of location-related services with
mobile devices has opened up huge business
opportunities and, beyond, a “new local-mobile
paradigm” (BI-Intelligence, 2013). A survey
conducted by the Pew Research Center in 2013 in
the US confirms this paradigm, stating that “local is
a bigger part of the broader social media landscape,
and the rise of local services is strongly tied to the
increase in smartphone ownership” (Zickuhr, 2013,
p. 2). It states that 74% of adult smartphone owners
in the US use their phone for directions or other
information based on their current location. The
emergence of this local-mobile paradigm is certainly
linked to the evolution of technology: according to
BI-Intelligence’s research, there are over 770 million
GPS-enabled smartphones. Consequently, location-
data is increasingly present in the entire mobile
space (BI-Intelligence, 2013).
The local-mobile paradigm also promises pivotal
potential in regards of what has come to be called
“bricks & clicks”, integrating both offline and online
retail presences in a business model (BM). This
combination of virtual and physical stores, of
ordering, delivery, and pick-up seems crucial for
traditional retailers’ survival nowadays (see e.g. The
Economist, 2012).
The paradigm concerns physical proximity to
stores. But it is especially about providing the right
information at the right time and place, “relevant to
the specific environment and [with] a sense of
immediacy that responds to the unique moment the
consumer is in.” (ScreenMediaDaily, 2014, p. 3) As
Bob Liodice
states, “what I love about digital place-
based media is that it’s so targeted. The ability to
zero-in on your particular audience is a phenomenal
advancement.” (in ScreenMediaDaily, 2014, p. 4)
Advertisers can indeed be excited about such novel
possibilities for profiling and targeting, and do not
hesitate to promote the potential utility for
In fact, users can benefit of such precise and
content-rich communication. However, there are
also obvious downsides, notably in terms of privacy.
Essentially, this is about a crucial trade-off for the
user; between derived value on the hand, and control
over data on the other. According to Acquisti et al,
“individuals want to protect the security of their data
and avoid the misuse of information they pass to
other entities. However, they also benefit from
sharing with peers and third parties information that
makes mutually satisfactory interactions possible.”
(2010, p. 3) Problematically, even privacy settings -
President and CEO, Association of National Advertisers
Breuer J., Ranaivoson H., Buchinger U. and Ballon P..
Location Data – A Trade-off between Control and Value - Business Model Implications.
DOI: 10.5220/0005061001770188
In Proceedings of the 11th International Conference on e-Business (ICE-B-2014), pages 177-188
ISBN: 978-989-758-043-7
2014 SCITEPRESS (Science and Technology Publications, Lda.)
the control tool for users over data use – are too
often user-unfriendly (Pollach, 2007). Consequently,
the work at hand assesses such trade-offs in the
context of location data, emphasising the position of
the user.
The purpose of this paper is to a) define the value
network around LBS as two-sided markets, where
the LBS provider intermediates between end-users
and Third Parties; and b) extract potential revenue
models. Thus doing, the paper focuses on trade-offs
between who has control in the ecosystem and how
value is created. Finally, the paper assesses how
current developments around LBS contribute to
changes in the position of the user within the
To do so, the paper tackles the issue from a
business modelling perspective.
It combines a
review of the literature on location-based services
(LBS) and location data arising thereof, with actual
examples of LBS providers. It thus complements
and contributes to existing literature, which is either
focused on technical aspects (see e.g. Schiller and
Voisard, 2004; Choudhury et al., 2009); or on user
implications of LBS, notably in terms of privacy
(see e.g. Tang et al., 2010). Within the latter
category, a growing literature focuses on costs and
benefits of protecting (i.e. control) or giving in
privacy (i.e. potential value) for users (see notably
Acquisti, 2010).
The remainder of the paper is organised as
follows. In Chapter 2, LBS are defined more
precisely, and market figures are provided to
illustrate their growing economic importance.
Chapter 3 describes the conceptual framework.
Chapter 4 applies the framework to LBS in order to
better analyse the issues raised. At the
microeconomic level, the paper describes options in
terms of value network and revenue model. The
implications on the industry level are then assessed,
particularly regarding the relationships between the
actors around the LBS providers.
2.1 What Are Location-based Services?
Within the telecommunication industry, operators
are widely deploying their mobile networks and
looking for new areas of future growth. Besides
providing the traditional options, which telephony
affords, data services have become another pillar,
and many of these services will be location
enhanced (Schiller and Voisard, 2004; Tang et al.,
2010). The following paragraphs assess various
meanings and business applications of LBS.
Schiller et al., define LBS as a “concept that
denotes applications integrating geographic location
(i.e. spatial coordinates) with the general notion of
services” (2004, p. 1). Spiekermann describes
location services as “services that integrate a mobile
device’s location or position with other information
so as to provide added value to a user” (2004, p. 10).
This manifests in the reduction of confusion,
improvement of consumption experiences, and the
delivery of high-quality service options that derive
from the implementation of location-based services.
Added value is also created through the merging
with existing information and customer databases.
“New services can emerge at the interface of the
customer and other Third Parties wishing to deliver
location-based services” (Rao and Minakakis, 2003,
p. 63). These incremental enhancements can then be
2.1.1 Types of LBS
The concept behind LBS is rather straightforward,
but applications are manifold, and so are respective
classifications in literature. At the most basic level, a
distinction is suggested between “location aware”
services (see e.g. Levijoki, 2001), such as e.g.
Google Maps, and those, which facilitate “location
sharing”. Latter is more popular with users, because
the sharing of locations with others is socially driven
and linked to social networks (Tang et al., 2010).
Some applications focus on location sharing with
one or a few persons, such as Glympse, which
enables to share your location for a user-defined
amount of time. Other applications, such as
Foursquare or Facebook Check-in, support location
sharing with a large group of people (ibid, 2010).
Most studies further categorise LBS according to
the type of service delivered to the user. Chen and
Lin (2011) conclude five categories, namely
entertainment, information, navigation, commerce
and security/tracking. Each category has its attached
services, e.g. entertainment corresponds to either
gaming or community/friend-finder. Other
classifications of LBS are based on users’
motivations (Tang et al., 2010), the targeted
audience (Vrček et al., 2008), the application sector
(Levijoki, 2001), type of delivery mode (Schiller and
Voisard, 2004), or the recipient group size (see
chapter 2.1.2).
For the topic at hand, another basic
categorisation based on the type of delivery mode
can be significant. LBS services can be delivered
following two modes: push and pull (Schiller et al.,
2004; Xu et al., 2009). While the former supplies
location-sensitive content to users based on their
location without them requesting it, the latter needs
users to request the information or services. The first
mode is less popular; the user has less to no control
about inbound communication, and fears privacy
invasion or potential costs that might emerge
(Spiekerman, 2004). Also the second situation
presents some downsides, in particular because the
pull mode is more cumbersome to handle, requiring
more effort on the side of the user.
Although such categorisations can be convenient
for assessment, in reality most LBS combine
different aspects, such as location awareness and
location sharing; or entertainment and information.
In fact, most success stories in the field originate
from convenient and/or exciting combinations:
Runtastic for instance, a company providing popular
sports-related applications, combines traditional
fitness with location-aware mobile applications,
relevant information (e.g. fitness plans), and options
for location sharing in groups and communities
(Runtastic GmbH, 2013).
2.1.2 Data-Sharing Modes
As mentioned, LBS allow different data sharing
modes between the users. They differ in terms of
whom the user (if given this control) targets as
receiver of provided data to other users. Figure 1
depicts five layers for sharing data. The innermost
circle represents the user as data provider him-
/herself, because the gathered data can be stored and
valuable for him-/herself. The second circle presents
the situation of data being valuable when shared
with one other person, or – the third circle – a
selected group of people. This is usually in the hands
of the user to share this data with a friend or a group
of friends. Beyond the control of the individual are
the last (broadest) two circles, when the service
provider shares data of an individual with the
community (i.e. the entire user group of that service
or application) or even with all (e.g. publicly
displayed, without the need to register to the
particular service).
2.1.3 Collecting Data for Marketing
These categorisations take into account factors
mainly from the perspective of the user. Currently,
Figure 1: Five layers of data sharing.
many LBS provide the canvasses and maps for the
user to facilitate positioning and data logging. They
then collect information about them and use it for
advertising purposes. This then adheres to two-sided
market logics: by letting users benefit from using a
service and generating data through the service, the
LBS provider holds a valuable asset, also for Third
Parties, which can access this data (and thus profit)
under agreed terms and conditions.
LBS particularly foster possibilities of Location-
based marketing (LBM). LBM presents enormous
potential for marketers and advertisers. It has been
found out that the simple fact of being physically
close to a business raises the click-through-rates
(CTR) of its advertisements: location-based ads
generate significantly higher CTR (BI-Intelligence,
2013). In comparison, the effectiveness of traditional
online advertising has been low ever since the first
banner ad was introduced, to an industry average of
only 0.4%; with the use of ad-blocking software ever
expanding (ScreenMediaDaily, 2014, p. 3).
Consequently, mobile location-based advertising is
expected to grow 150% by 2020, thus constituting
almost 65% of total mobile advertising revenue in
2018 (ibid, 2014, p. 5).
An important aspect is that, in general, the
whereabouts of people are revealing information
beyond mere location: they are key elements of the
type and nature of users’ activities. With this
information, inferences about needs and the
selection of specific products and services can be
drawn. If a business “knows the end user’s exact
location, and is able to target useful (and billable)
information at that point in time, the benefits can be
mutual” (Rao and Minakakis, 2003, p. 63). Actually
services can be adjusted to the user’s context,
namely his/her location, but also any other relevant
information (personal preferences; time; location
type; time available; needs; immediate physical
neighbourhood, etc.). This is also referred to as
contextual offering (Lee, 2005): providing the right
information at the right time and place, “relevant to
the specific environment and [with] a sense of
immediacy that responds to the unique moment the
consumer is in.” (ScreenMediaDaily, 2014, p. 3)
Hence, a business profits by delivering relevant,
timely, and engaging content.
2.2 The Market for LBS
LBS’ overall economic potential is enormous. In
Europe, the market for LBS is only emerging.
According to the Location Based Marketing
Association EMEA Survey 2011 interpreted by
Verhoef (2011), LBS users were checking-in not
more than once per day. Also companies were found
more reluctant with planning LBS campaigns. Still,
although “businesses in Europe have not been
picking up on location-based promotions […] it’s all
the more promising that over half of the respondents
indicated they check-in more than 3 times per week”
(ibid, 2011). The survey focussed on location-based
deal services (Foursquare is the most popular
example) including especially check-ins and the
conduction of deals. Today, LBS applications are
much more multi-faceted and encompass multiple
purposes and features. They differentiate also in the
aspects of one’s whereabouts they emphasise, and
the associated context they provide.
Hence, despite the reluctance concerning mobile
LBS in this 2011 study, a 2013 forecasts revenues to
grow from EUR 325 million in 2012 at a compound
annual growth rate of 20.5% to reach EUR 825
million in 2017 (Berg Insight AB, 2013). Berg
Insight’s report finds that local search, social
networking and navigation services are the top
application categories in terms of active users. It
further states that also mobile workforce
management services aiming to improve operational
efficiency are gaining in popularity (ibid, 2011).
3.1 Business Modelling: Control &
Value in LBS
The perspective presented here is based on the
business modelling framework provided by Ballon
(2007), which is simplified in the Business Model
Matrix below (see Table 1). The origin of this
approach to business modelling is arguably the
internet-based economy (see e.g. Al-Debei and
Avison, 2010; Hawkins, 2001), where innovative
business models, i.e. novel ways of interacting with
customers and within networks, have become a
source for success. It has been used in various
analyses, in particular applied to the media and
telecom industries. While there are many business
model frameworks proposed in the literature,
notably Osterwalder (Osterwalder, 2004) and
Chesbrough (Chesbrough, 2006), these are usually
more suited for aiding individual firms and less
appropriate for guiding collective innovation
processes. It is therefore necessary to consider a
stream of research that attempts to provide a more
coherent treatment of the most relevant business
model parameters while at the same time focusing
mainly on the relationships between the stakeholders
involved. Thus, the business model matrix is
particularly applicable for the work at hand, even
more so as it enables taking into account the
interdependency and trade-offs between control and
The business model framework consists of four
abstract layers (see Table 1): value network,
technology design, financial model, and value
proposition. We categorise the former two as
impacting mainly control-related aspects. The latter
two, on the other hand, affect mostly value-related
issues. Each layer is built on certain integral
Table 1: Business model configuration matrix.
Control Parameters
Value Parameters
This paper adheres roughly to those layers and
parameters, as they provide convenient means of
orientation and structuring. Nonetheless, not all are
of equal relevance for the work at hand. Based on
the framework, we have focussed on certain key
parameters, which determine the control-value trade-
off in LBS.
The Value Network layer is regarded as most
significant for evaluating the interplay of actors
(here focused on location data). Its parameters
revolve around the architecture of actors (physical
persons or corporations mobilizing tangible or
intangible resources), roles (business processes
fulfilled by one or more actors with according
capabilities) and relationships (contractual
exchanges of products, services for financial or other
resources). In particular the User Ownership
parameter constitutes a pillar for the following
analysis. In general, it relates to the relationship with
the customer, examining, amongst others, the access
to key information on the customer, the type of
contact (direct or intermediated), the level of
intensity and proximity to the customer (Ballon,
2007, p. 11). Therefore, in the context of LBS, it
also relates to data handling and sharing, i.e. how a
LBS treats its users’ data regarding data mining.
Moreover it takes into consideration data sharing
between community members and/or Third Parties
and how that affects a business model. User
Ownership then is about how users (the voluntary or
unknowing providers of location data) and their
personal information are treated.
The Financial layer is also highly relevant. In
particular the Revenue Model is taken into account,
i.e. how revenue is generated. Measures through
which money actually streams into the company are
depending on decisions such as how the LBS
provider addresses Users and Third Parties, or
whether the LBS provider relies on hybrid models
(e.g. Freemium).
Whereas Technology Design of LBS certainly
determines their functioning, it is here mainly taken
as a given parameter. Also the Value Proposition is
considered as subordinated to other parameters,
although User Involvement (referring to the role of
users in the creation of value) is particularly
important for LBS, as they at least tacitly need to
accept to provide their location data.
3.2 Two-sided Markets and Platforms
Technically, an ICT platform may refer to a
hardware configuration, an operating system, a
software framework or any other common entity on
which a number of associated components or
services run. Economically, platforms and their
providers mediate and coordinate between various
stakeholders (Cortade, 2006; Ballon, 2009). There
are actually externalities between these stakeholders,
which platforms internalise (Armstrong, 2006).
Two- (or multi-) sided markets are two markets,
which the platform connects, and where the utility
that any user A derives from the use of the platform
is correlated to the number of users B (and
In the mobile environment, different stakeholders
try to position themselves as mediator and
coordinator of various stakeholder groups (Ballon,
2009). In such constellations, gatekeeper roles are
often what promises most control over the value
network, and thereby most profit. Gatekeepers are
the entities that control bottlenecks in the network
(as derived from media and communication studies),
selecting and processing ideas and information (ibid,
2009, p. 10). In the current analysis, LBS provider
constitute these gatekeeper roles. In the context of
this work, gatekeeper roles are those, which promise
user ownership.
This section analyses LBS’ value network as a two-
sided market with the LBS provider being the hub
between users and Third Parties. It thus
conceptualizes and evaluates the structure of the
value network as the first business model parameter.
In a second step it analyses the financial flow in the
value network revealing different strategies for
creating revenue. Finally, several trends are named
with potential to impact these parameters opening a
discussion for challenges, and issues concerning the
use of location data.
4.1 Microeconomic Level
4.1.1 Value Network
The LBS as Platform
LBS providers offer and operate LBS. They collect,
gather and edit location and other data about users.
They are responsible towards them regarding the use
of such data, in particular to whom they make this
data available or accessible. In the early years of
conceptualising LBS, it was expected that the
mobile network operator would constitute the
bottleneck of the system by occupying this role and
gathering the data (see e.g. Rao and Minakakis,
2003). Instead, specialized location-based service
providers (also referred to as applications) have
emerged that cover these aspects. LBS business
models often revolve around deal services, i.e. view
retailers or businesses on the virtual map and receive
promotions or special offers when shopping at the
actual location. LBS are by far not restricted to those
aspects. Also through “check-ins” or location
sharing functionalities the location is collected. The
integration of LBS in other applications, such as
social network applications, can equally boost the
generation of location data by users.
Applications – or the platforms that host them –
deploy or provide the technology and interfaces for
locating users (Cusumano, 2010; Gohring, 2013;
Schechner, 2013). They rely on mobile devices that
are continuously connected to the Internet and on the
users’ interest in sharing information with friends
and acquaintances (Schapsis, n.d.). By downloading
such applications, users agree to the terms and
conditions that either let the application track their
location (push strategy) or are asked to input their
location actively (pull strategy), or are facing a
combination of push and pull.
Figure 2: Roles of LBS platform.
The Third Party
Third Parties demand data about – or access to – the
users/customers and are willing to pay money to the
middlemen who offer/grant such access. Third
Parties are manifold, with advertising networks and
marketing companies on the forefront, working on
behalf of their clients, namely industries selling
consumer goods and services. The benefits of having
information about customers are obvious for
business operations. Third Parties can also be
merchants, other service providers, etc.
While market-level information or modelled data
was dominant for decades, i.e. generalized
characteristics of consumer groups and market
segments, ICT enables identification of customers
up to the point of individual profiling (Electronic
Privacy Information Center, n.d.), including data
related to the users’ location. Such individual-
specific information often also includes sensitive
data. The benefits for businesses (and other
organisations) are clear: better connection and
adjustment of activities to customers or user
segments, due to better decision making processes,
fewer risk taking, higher profits and generally better
marketing (Couts, 2013b).
The User
The use of LBS can be two-fold for the customer: on
the one hand it can help reduce confusion, advance
the consumption experience and
Figure 3: Roles of third party.
provide high-quality service options. It can lead to
better customer segmentation and targeted
communication from the industry as well as handier
processes and less effort. This might actually be in
the interest of the end customer. On the other hand,
LBS raise multiple concerns, above all on privacy
issues. Using LBS may result in unwanted actions
such as intrusive marketing activities, discriminating
treatment, public exposure, misuse of data, fraud and
harm (Dailey, 2013; Phelps et al., 2000).
Customers are thus put in a difficult situation,
not least because privacy settings, that claim to
protect the information of individuals when they get
in contact with businesses, are vague, misleading,
lacking transparency or are displayed in a user-
unfriendly way (Pollach, 2007). In this context, the
so-called privacy paradoxon relates to the
discrepancy between a person’s intent not to disclose
personal information and his/her actual behaviour
(Norberg et al., 2007). The phenomena describes
that even aware or concerned people willingly
disclose personal information for certain benefits or
in fear of missing some information and
opportunities by non-disclosure. This trade-off is
probably even more substantial with LBS than with
other services, as users can benefit directly, in terms
of convenience, efficiency, special deals, or, more
indirectly, social capital.
Figure 4: Roles of user.
LBS Platform
Customer Data
Third Party
Revenue Source
Data Consumption
Service Usage/
Inter-actor Relationships
In the interplay of the three actors, the LBS provider
positions itself as the intermediate between users and
Third Parties, thus creating a picture of a two-sided
market (Figure 6). In this position it facilitates the
exchange and interplay whilst determining rules,
terms and conditions.
Figure 5: Actors, roles & relationships in the value
4.1.2 Revenue Models
As a platform serving two different but
interdependent market sides, the LBS provider is
also in a position to decide whether to monetize the
access to the customer base, and for how much. LBS
providers follow different strategies for collecting
and sharing user data, and for charging Third
Third-Party based
This revenue model, with the incorporation of Third
Parties, is probably the most prominent strategy.
Here, Third Parties pay for being visible on the
virtual map and promote their information, deals or
offers. By processing data over customers, the
platform provider has a valuable asset in return for
Third Parties’ money. And since many of the
applications that utilise LBS are free for users, the
platforms are dependent on broaching such
additional revenue streams. In their role as
intermediates between different actors, they
welcome the opportunity to cooperate with Third
Parties (Rochet and Tirole, 2002). Foursquare is a
good example of how providers execute this
engagement (Foursquare, n.d.). Revenues are
dependent on the information pushed from Third
Parties to users, e.g. proportional to the amount of
people who have been in contact with a given ad.
Transaction based
Beyond providing data, LBS can also allow
transactions to take place. In this case, one source of
revenue is transaction fees. Carambla (Carambla,
n.d.), for instance, is a mobile application serving as
a platform that connects private providers of parking
spots with people in need for a parking facility.
Every time such a connection takes place (i.e. a car
driver uses a parking spot), Carambla gets 25% of
the payment by the car driver to the provider of
parking spots. Part of the money certainly goes to
the payment service provider.
Direct User Revenue based
Also revenue models exist that do not require the
incorporation of Third Parties. Direct revenue
models (where the customers would pay, per act or
on a subscription basis) are such an option, notably
in the form of freemium revenue models. Freemium
models are hybrid models that rely on cross-
subsidization. Free access is given to a version with
limited LBS features. These free LBS offers are
financially supported by the subscription of some
customers to a premium (see e.g. Anderson, 2009).
Versioning (a form of price discrimination) is at the
core of such freemium strategies. It consists in
different versions of a given content being provided
for different prices (Varian, 1989) (thus in the case
of freemium, free vs. paying versions). The
consumer chooses which version they are going to
use, based on preferences (i.e. their willingness-to-
pay for the product). Runtastic provides mobile apps
that use GPS to map and record routes in terms of
distance, time, pace, calorie consumption, and offer
individual training plans. Its apps and website-
membership are available for free, but it offers up-
selling possibilities to upgrade and include
additional information, statistics, services, etc.
(Gschwandtner, 2013).
4.2 On the Industry Level: Who Owns
the LBS-User?
The processes of collecting data happen on one side
of a market, the benefits that users gain (such as
LBS Platform
Customer Data
Third Party
Service Usage/
social capital, deals, specials etc.) constitute the
other side. In order for such markets to function,
various actors are involved in respective business
models. Users are at the core of LBS, not only as
providers of location data, but as being potential
buyers. Paradoxically, although they are the actors
that generate the key resource, they are usually not
treated as a self-determined entity. User ownership,
i.e. control over the user and his/her data, is the
parameter that applies in this regard. The following
section reminds that the user faces a trade-off in
terms of benefits and harms of ceding parts of their
privacy. On the micro-level, the user can truly and
tangibly benefit and therefore agree to such a trade-
off. Here, however, the issues go beyond individual
trade-offs: user location data is a commodity, an
“economic asset class” (vide infra).
4.2.1 A New Economic Asset
Personal data in general and location data in
particular have been asserted as valuable economic
assets (see Schwab et al., 2011). The World
Economic Forum has established personal data as a
new economic “asset class” (2011). It further
distinguishes between three types of personal data: i)
volunteered data is such that is “explicitly shared”
by a user, e.g. in a social network, ii) observed data
or “captured by recording the actions of
individuals”, and iii) inferred data, which means it is
“based on analysis of volunteered or observed
information” (ibid 2011, p. 7). In this context,
location data can be regarded and treated as one
category of personal data. It can often be classified
as observed data, but it is also more or less
“explicitly shared” and voluntarily provided in many
cases. The more data, enriched through all kinds of
related information and situated in the right contexts,
the more patterns and information can be extracted
as inferred data; this is obviously valuable for
commercial players. Such precise information about
consumers is highly sought after (Hildebrandt et al.,
As a consequence, the trend (and arguably a
requirement for commercial success) is to combine
location-based services with other types of mobile
and online services. Location-based features are
often not the primary purpose of an app, but function
as additional incentives for use, for instance as
creative location-based notifications. Facebook,
Google, Yelp, Instragram and Groupon are
applicable examples in this regard.
From a user perspective, an important purpose of
using location-sharing applications is actually often
the socially-driven intention to make one’s
whereabouts public via a social network (Tang et al.,
2010). In other words, users voluntarily provide
information to their network. This combination of
the local-mobile paradigm with social networking
aspects is prominent among users, but it makes LBS
also especially interesting for businesses: the
precision of the situation surrounding a user dictates
the relevance of corresponding information.
Information, for instance shared via a social
network, can thus be correlated and used for
targeting and relevant communication.
Practices of collecting user location data, of
tracking and targeting have however raised certain
concerns. This is the case especially because data
generated by the user might be processed further by
the service provider, without the knowledge and/or
against the interest of the user: he/she does not know
how his/her information is processed, with whom it
is shared (and if he/she knows, may not approve it).
In fact, business models around personal data are
often based at least to some degree on a lack of
transparency and privacy.
4.2.2 Data Brokers
In the multi-sided market around location data, on
the industry level, new profitable roles have evolved.
Alongside the service provider, data brokers have
consolidated a position in the ecosystem, building
business models on trading, combining and
recombining data and datasets In the US alone,
business with personal data generates millions of
revenue for companies such as Acxiom, Experian,
and Epsilon (Tanner, 2013).
The brokers’ interference and trading activity is
mostly intransparent, adding confusion and
uncertainty to the market (Couts, 2013a). Data
brokers work with data from public records and
information provided by users. Former is the data
that the state and public authorities gather (e.g.,
name, gender, age, ethnicity, education level, social
security number, driver’s license number or voter
registration to name just a few). Latter is the data
that people provide or generate e.g. in social
networks, via sweepstake or warranty cards, mail
rebate forms, forum posts, Web browser cookies,
loyalty reward cards, mobile applications and more.
The data types can be divergent and situated e.g. in
the context of work and education (employment
history) personal life (sexual preferences, religion,
relationship status, etc.) and much more.
Despite several contradicting examples, one of
the reasons why data aggregators have not yet
changed their data collection methods and
implemented privacy-friendly technologies in a
large-scale is due to the commercial value of
customer datasets (Schwab et al., 2011). The
aggregating actor (i.e. LBS provider) might use the
data itself, or sell it to other entities interested in
information about (potential) customers. Such
reselling of data is an option for businesses to open
new, profitable revenue sources. Data aggregation
and processing patterns are often not transparent,
communicated in a reader-unfriendly way, difficult
to understand for the user, and based on the
presumption that the provision of a privacy-policy
alone already eliminates users’ concerns (Milne and
Culnan, 2004; Pollach, 2007). Consequently, the
user can exert little control about who can access
and process his/her data, commercially or for other
Even though there may be potential mutual
benefits for all actors involved in the value network,
existing asymmetries of knowledge, however, “make
the functioning of such a market inefficient”
(Schwartz in Hildebrandt et al., 2013, p. 15).
Kashmir Hill reported for instance that the company
MEDbase200, selling medical industry related
information, offered lists including rape victims,
alcoholism sufferers and AIDS/HIV sufferers (Hill,
2013). The secrecy and non-transparency of these
actors in the value network evoked attention in the
public. Practices of MEDbase200 were exposed at a
hearing in the US congressional hearing, aimed to
“examine the data broker industry and how industry
practices may impact consumers” (U.S. Senate
Committee, 2013).
4.2.3 User Ownership
Even though the user is a significant actor in the
value network, generating data and thereby value,
he/she is often treated as non-autonomous. Location
data is being commoditised, and little control is
given to users. These issues have evoked a debate
about whether and how a person can retain some
degree of control over personal data, ultimately
about the question of who owns users’ location data.
Upon this, new approaches arose that aim to
reinvest control in the user, often termed Personal
Data Management (PDM). PDM is about inverting
that relationship, about establishing the user as a
self-determined actor. Data is not being used and/or
shared without a person’s consent, or at least without
being transparent about how it is used and with
whom it is shared (Hildebrandt et al., 2013, p. 6).
Some LBS already put at the core of their service the
fact that only a restricted group of clearly identified
persons have access to the location data. Glympse,
for instance, is an application that enables to share
location, prioritising that “[w]ith Glympse, you are
in complete control – you choose WHO you want to
see your location, WHEN they can see it and for
HOW LONG the recipient is allowed to see it.”
(Glympse Inc, 2012).
Other LBS providers choose different
approaches. Trip Advisor then illustrates that users
can be willing to provide some personal data when
they are aware and get useful or desired information
or other benefits in return. The service shares user-
generated content by default (ratings, reviews of
locations where their users have been) with anyone
using an Internet browser. In this case, the user
knows he/she makes content available, i.e. rates
locations, writes reviews about places, etc. Also
special deals and discounts could be a sufficient
incentive for a user to provide location data, as well
as being visible in a social community, or simply
being able to get meaningful information in return.
The strong social component certainly plays a role in
this regard: “The more one’s friends (as well as
other consumers) get comfortable with disclosing
data online, the higher is the opportunity costs for
those individuals who do not join a service in order
to protect their data” (Acquisti, 2010, p. 11).
For the user, the trade-off between what they are
asked to disclose and what benefits they get in return
can be significant. Thus, the weighting of privacy
concerns and abandoning of data ownership can shift
on the side of the user. Nonetheless, intransparent
practices and unaccountable actors, such as data
brokers, infringe the set-up of the two-sided market
model (i.e. the balance) and thus impact the
constitution of the value network, eventually causing
redistribution of the user ownership. Treating
location information as an economic asset directly
affects the revenue model, and most actors are
interested in selling location data they have
collected. However, by doing so, they might
ultimately even undermine the users’ willingness to
use the services and thus to share such data. This
means that the weighting of control and value affects
not only the user. Also the generation of revenue for
LBS providers, data-demanding Third Parties, and
data brokers depends on this trade-off.
The paper has analysed location-based services from
a business point of view, with a focus on their value
network and financial model. It has set out with a
definition and background on location-based
services and the market on which such services are
operating. While several typologies of LBS exist in
the literature, we have a proposed a typology of the
data that are at the core of LBS. This typology
depends on whom the user targets as receiver of
his/her location data. Value can emerge when data is
used/shared with i) the user him-/herself; ii) one
other receiver; iii) a group of receivers; iv) the
community of the LBS provider; v) open to
browsing traffic on the Internet. Which model/s
is/are facilitated falls under the control of the LBS
For the LBS provider, location data of the user is
mainly relevant as a means to facilitate location-
based marketing (LBM) for Third parties. While
promising enormous economic potential, the market
in Europe for LBS (and in consequence LBM) has
only just started to. Location-based deal services are
thereby at the forefront. Third Parties such as
merchants or retailers are addressing the customer
via the LBS provider with deals, offers and
promotions at the point of sale.
The paper has addressed LBS providers as
entities with a certain configuration of business
model parameters. It has shown the value network of
LBS, where the provider acts as a platform on multi-
sided markets that connects data generating users
and data demanding parties. The platform balances
interests of its stakeholders: Third Parties wish to
reduce marketing spending by individual, pointed
targeting Users fear misuse and harm related to the
sharing of their location data. Beyond this balancing
act, the platform as a business entity needs to create
revenues in this market, so far it has done so by
charging Third Parties while including end-users for
free. However, new emerging trends in this field
have the potential to change the strategies and
configurations of these variables.
Data Brokers and other entities, entering the
network as new intermediary actors, can cause
reconfiguration of the value network and the
gatekeeper position and consequently customer
ownership. A new classification of data as an
economic asset can lead to new assessment of the
value of data and thus impact the financial streams
in the network.
The configuration of these parameters are
directly linked to decisions of granting control
and/or creating value in the network. In this context,
the paper has discussed the fact that the user is not
integrated as a self-determined actor in the value-
creating ecosystem. Although user-generated data is
the most valuable asset in the value network, other
interrelated actors do not treat him/her as on equal
From a business modelling perspective, an
interesting question is how a trade-off between all
actors’ interests can be facilitated, which is actually
profitable for all actors involved. After all, the LBS
provider can make choices concerning which entity
is granted information control, and to what extent.
Today, user-centric data management is debated,
where “the user has the full control over his/her
identity and consistent user experience during all
transaction when accessing his/her services”
(Bhaskar and Kapoor, 2013, p. 462). This is about
reinvesting control over data into the user. From a
commercial perspective, the idea of user-centric
personal data management could be interesting; at
the least appropriate infrastructure is required. Since
such management needs to be usable and simple,
also new services and business roles could arise,
supporting the user in handling the data. Indeed, it
has been argued before that the entity asserting user
ownership is in a strong position in the multi-sided
market around location-based services.
More research is however needed to assess how
user-centric data management is or can be
implemented, and the real impact on users. Also the
question of how business models can be created in
this context remains to be answered. Despite the
limited scope of this paper, its aim of establishing
the trade-off between control and value as an
essential element of corresponding economic
activity functions as a stepping stone: for future
research and for determining innovative and
successful strategies, which can lead to sustainable
business, and perhaps even a consolidation of the
user’s position.
SoLoMIDEM is an R&D project co-funded by IWT
(Agentschap voor Innovatie door Wetenschap en
Technologie), the government agency for innovation
by science and technology founded by the Flemish
Government. Companies and organizations involved
in the project are VUB/SMIT, K.U.Leuven/COSIC,
UGent/MICT, K.U.Leuven/ICRI, iMinds/iLab.o,
CityLive – MobileVikings, NGData nv, Lin.K nv,
Cultuurnet Vlaanderen, iRail
Acquisti, A., 2010. The economics of personal data and
the economics of privacy. OECD. Retrieved from
Al-Debei, M. M., Avison, D., 2010. Developing a unified
framework of the business model concept. European
Journal of Information Systems, 19(3), 359–376.
Anderson, C., 2009. Free: The Future of a Radical Price.
Armstrong, M., 2006. Competition in two-sided markets.
The RAND Journal of Economics, 37(3), 668–691.
Ballon, P., 2007. Business modelling revisited: the
configuration of control and value. Info, 9(5), 6–19.
Ballon, P., 2009. Platform Types and Gatekeeper Roles:
the Case of the Mobile Communications Industry.
Presented at the Summer Conference on CBS-
Copenhagen Business School, Denmark.
Berg Insight AB., 2013. Mobile Location-Based Services
– 7th Edition.
BI-Intelligence, 2013. Location Data Is Transforming
Mobile - Business Insider. Retrieved March 31, 2014,
from http://www.businessinsider.com/location-data-is-
Carambla. (n.d.). Carambla, Park Smartly. Retrieved April
23, 2014, from http://carambla.com/
Chen, P.-T., Lin, Y.-S., 2011. An Analysis on Mobile
Location-based Services. Retrieved from
Chesbrough, H., Rosenbloom, R., 2002. The role of the
business model in capturing value from innovation:
evidence from Xerox Corporation’s technology spin-
off companies. Industrial and Corporate Change,
11(3), 529–555. doi:10.1093/icc/11.3.529
Choudhury, T., Quigley, A., Strang, T., Suginuma, K.
(Eds.)., 2009. Location and Context Awareness (Vol.
5561). Berlin, Heidelberg: Springer Berlin Heidelberg.
Retrieved from http://www.springerlink.com/index/10.
Cortade, T., 2006. A Strategic Guide on Two-Sided
Markets Applied to the ISP Market. MPRA Paper.
Retrieved from http://ideas.repec.org/p/pra/mprapa/
Couts, A., 2013a. How data brokers profit off you without
your (or the law’s) knowledge | Digital Trends.
Retrieved April 23, 2014, from http://www.digital
Couts, A., 2013b. Forget “privacy,” we need a new term
for control of our online lives | Digital Trends.
Retrieved April 23, 2014, from http://www.digital
Cusumano, M. A., 2010. Staying Power: Six Enduring
Principles for Managing Strategy and Innovation in an
Uncertain World (Lessons from Microsoft, Apple,
Intel, Google, Toyota and More). Oxford University
Dailey, K., 2013. Should online jokes be criminal? BBC
News Magzin Online. Retrieved from http://www.bbc.
Electronic Privacy Information Center. (n.d.). Electronic
Privacy Information Center. Privacy and Consumer
Foursquare. (n.d.). Foursquare for Business. Retrieved
April 23, 2014, from http://business.foursquare.com/
Glympse Inc., 2012. Glympse. Company web profile.
Retrieved August 29, 2013, from www.glympse.com
Gohring, N., 2013. Google picks up team that built
Android data-collection tool. CITEworld. Retrieved
June 27, 2013, from http://www.citeworld.com/mobile
Gschwandtner, F., 2013. Interview with Gschandtner,
Runtastic CEO. Research2Guidance. Retrieved
October 21, 2013, from http://www.research2guidanc
Hawkins, R., 2001. The Business Model as a Research
Problem in Electronic Commerce, Socio-economic
Trends Assessment for the digital Revolution (STAR)
IST Project (Issue Report No. 4). Brighton.
Hildebrandt, M., O’Hara, K., Waidner, M., 2013.
Introduction to The Value of Personal Data - Was ist
Aufklärung in the Age of Personal Data Monetisation.
In Digital Enlightenment Yearbook 2013. IOS Press.
Hill, K., 2013. Data Broker Was Selling Lists Of Rape
Victims, Alcoholics, and “Erectile Dysfunction
Sufferers” - Forbes. Retrieved April 7, 2014, from
Lee, T., 2005. The impact of perceptions of interactivity
on customer trust and transaction intentions in mobile
commerce. Journal of Electronic Commerce Research,
6(3), 165–180.
Levijoki, S., 2001. Privacy vs Location Awareness.
Department of Computer Science, Helsinki University
of Technology. Retrieved from http://www.tml.hut.fi/
Milne, G. R., Culnan, M. J., 2004. Strategies for reducing
online privacy risks: Why consumers read (or don’t
read) online privacy notices. Journal of Interactive
Marketing, 18(3), 15–29. doi:10.1002/dir.20009
Norberg, P. A., Horne, D. R., Horne, D. A., 2007. The
Privacy Paradox: Personal Information Disclosure
Intentions versus Behaviors. Journal of Consumer
Affairs, 41(1), 100–126. doi:10.1111/j.1745-
Phelps, J., Nowak, G., Ferrell, E., 2000. Privacy concerns
and consumer willingness to provide personal
information. Journal of Public Policy & Marketing,
Pollach, I., 2007. What’s Wrong with Online Privacy
Policies? Communications of the ACM, 50(9), 103–
108. doi:http://doi.acm.org/10.1145/ 1284621.1284627
Rao, B., Minakakis, L., 2003. Evolution of Mobile
Location-based Services.pdf. Communications of the
ACM, 46(12), 61–65.
Rochet, J.-C., Tirole, J., 2002. Cooperation among
Competitors: Some Economics of Payment Card
Associations. The RAND Journal of Economics,
33(4), 549–570. doi:10.2307/3087474
Runtastic GmbH., 2013. Runtastic - makes sports
funtastic. Retrieved October 21, 2013, from
Schapsis, C. (n.d.). Location Based Social Networks,
Location Based Social apps and games - Links.
BDNooZ LBS Strategies. Retrieved July 6, 2013, from
Schechner, S., 2013. Google Privacy Comes Under Fire
From European Watchdogs. Wall Street Journal.
Retrieved from http://online.wsj.com/article/SB10001
Schiller, J., Voisard, A., 2004. Location-Based Services.
Schiller, J., Voisard, A., 2004. Location-Based Services.
Schwab, K., Marcus, A., Oyola, J. R., Hoffman, W., Luzi,
M., 2011. Personal Data-The Emergence of a New
Asset Class. World Economic Forum.
ScreenMediaDaily, 2014. Why Location Is the New
Currency of Marketing.
Spiekermann, S., 2004. General Aspects of Location-
Based Services. In J. Schiller & A. Voisard (Eds.),
Location-Based Services (pp. 9–27). San Francisco,
CA: Elsevier.
Tang, K. P., Lin, J., Hong, J. I., Siewiorek, D. P., Sadeh,
N., 2010. Rethinking location sharing: exploring the
implications of social-driven vs. purpose-driven
location sharing. In Proceedings of the 12th ACM
international conference on Ubiquitous computing (pp.
85–94). Copenhagen, Denmark. Retrieved from
Tanner, A., 2013. Senate Report Blasts Data Brokers For
Continued Secrecy - Forbes.Forbes.com. Retrieved
April 4, 2014, from http://www.forbes.com/sites/
The Economist, 2012. Retailers and the internet: Clicks
and bricks | The Economist. Retrieved April 1, 2014,
from http://www.economist.com/node/21548241
U.S. Senate Committee, 2013. Hearings - What
Information Do Data Brokers Have on Consumers,
and How Do They Use It? U.S. Senate Committee on
Commerce, Science, & Transportation. Retrieved
April 7, 2014, from
Varian, H., 1989. Price Discrimination. In R. Schmalensee
& R. D. Willig (Eds.), Handbook of Industrial
Organization (Vol. I, p. 56). Elsevier Science
Verhoef, P., 2011. Location Based Marketing Association
EMEA Survey 2011: The Infographic. clarion
consulting. Retrieved from http://clarionconsulting.
Vrček, N., Bubaš, G., Bosilj, N., 2008. User Acceptance
of Location-based Services. Proceedings of World
Academy of Science: Engineering & Technology, 43.
Xu, H., Gupta, S., Shi, P., 2009. Balancing User Privacy
Concerns in the Adoption of Location-Based Services:
An Empirical Analysis across Pull-Based and Push-
Based Applications. Presented at the iConference.
Retrieved from http://www.ideals.illinois.edu/handle/
Zickuhr, K., 2013. Location Based Services (No.
202.419.4500) (p. 25). Pew Research Center’s Internet
& American Life Project. Retrieved from