Tell me where you are, and I’ll tell you who you
are: Using Foursquare check-in data to
understand its appeal by Vincent Bode
1
Table of Contents
Chapter 1 ...................................................................................................................3
Management Summary .............................................................................................................................................3
Introduction ..................................................................................................................................................................3
Research Question ......................................................................................................................................................8
Sub Questions ................................................................................................................................................................. 8
Contribution of this paper ........................................................................................................................................9
Managerial relevance ................................................................................................................................................. 9
Scientific relevance ...................................................................................................................................................... 9
Chapter 2 ................................................................................................................. 10
Literature Review .................................................................................................................................................... 10
Location Based Networks Introductory Research .........................................................................................11
Foursquare mechanics research ..........................................................................................................................12
Foursquare behavior research ..............................................................................................................................13
Factors affecting connection-based rewards ................................................................................................... 18
Factors affecting competition-based rewards ................................................................................................. 19
Hypotheses................................................................................................................................................................. 19
Characteristics of Early Adopters .......................................................................................................................21
Conceptual model .................................................................................................................................................... 24
Chapter 3 ................................................................................................................. 24
Methodology ............................................................................................................................................................. 24
Research type................................................................................................................................................................24
Data collection .............................................................................................................................................................25
Data Processing ..........................................................................................................................................................26
Data Analysis................................................................................................................................................................28
Chapter 4 ................................................................................................................. 54
Conclusion ................................................................................................................................................................. 54
Hypothesis Discussion ..............................................................................................................................................54
Foursquare users lifestyle discussion.................................................................................................................55
Scientific Implications ........................................................................................................................................... 55
Managerial Implications ........................................................................................................................................ 58
Limitations ................................................................................................................................................................. 61
Future research ......................................................................................................................................................... 62
Appendix A – Survey ............................................................................................... 64
Appendix B - References.......................................................................................... 71
2
Chapter 1
Management Summary
In this paper we find that Foursquare users can be split up into two main types of
users that use the application for different reasons. One group, ‘competition based users’
use Foursquare because of its game-aspect and can be categorized as users participating
in purpose-driven sharing. The other group, ‘connection based users’ are interested in the
social aspect of Foursquare and participate in social-driven sharing. We find that the
groups are motivated to use Foursquare for different reasons and have privacy concerns
and a different activity level. It is also found that competition based users are mainly lead
users while connection based users are found to be early adapters. Lastly, we look at how
effective the marketing tools that are included in Foursquare are in reaching both types of
customers.
Introduction
In the last 20 years, different location sharing systems have been developed
because of the increasing diffusion of GPS and Internet-enabled smartphones. Twitter,
Foursquare and Gowalla are some examples of these. In this thesis, we focus on
Foursquare. Since Foursquare was launched in 2009, it has grown massively. With about
15 million users presently and around 3 million check-ins per day, Foursquare is
definitely catching on1. Even though 15 million users does not compare to 483 million
1
"What Is Foursquare?" About Foursquare. 1 Jan. 2012. Web. 9 Mar. 2012.
<https://foursquare.com/about/>.
3
active Facebook users, Foursquare provides a new exciting service that the social
behemoth does not, location based marketing2.
Location-based marketing can be defined as a companies ‘ability to customize
marketing messages based on a prospect's location and preferences’. To use locationbased marketing, marketers can use four different ways to reach customers. These four
different ways are: location-based services such as Foursquare, Near-field
communications (exchanging information between devices at a close range or NFC),
Bluetooth marketing (like NFC but using Bluetooth) and location-based advertising
(which uses GPS to find potential customers and send them relevant messages)3.
Tussyadiah (2012) defined location based services as network applications which “are
capable of generating marketing stimuli from merchant” and create “competition-based
and connection based rewards [which are] resulted from relevance and connectivity”.
These rewards can in turn lead to variety-seeking and loyalty behavior. According to
Tussyadiah, connection-based rewards depend on consumer characteristics, consumption
situations and social network structure. On the other hand, competition-based rewards on
the other hand are made possible by the application itself by introducing game-like
aspects (Tussyadiah, 2012).
But what is Foursquare exactly? When using Foursquare, users ‘check-in’ into a
location using their smartphone. Their check-in can then be shared on Twitter or
Facebook. Locations can be anything ranging from hotels, restaurants, train stations,
2
Tsukayama, Hayley. "Facebook’s Reach: 845 Million and Counting." The Washington Post. 2 Feb. 2012.
Web. 12 Mar. 2012. <http://www.washingtonpost.com/business/technology/facebooks-reach-845-millionand-counting/2012/02/01/gIQAV0gwiQ_story.html>.
3
Hopkins, Jeanne, and Jamie Turner. "How Location-Based Marketing Can Help You Connect with
Customers." Entrepreneur. 11 Apr. 2012. Web. 02 June 2012.
<http://www.entrepreneur.com/article/223304>.
4
someone’s home or even the queue at a club. Users can also leave tips and pictures about
locations they have been to which other Foursquare users can then look at in the future.
What makes Foursquare special however, is its game-like aspect, creating a fun and
interactive experience for the user. First of all, users can compete to become the ‘mayor’
of a location by being the one to check in the most at a certain location for the past two
months. When checking in, users also score points that are used to keep track of who has
checked in the most. This creates a competition between friends and encourages users to
keep checking in. Another game-like aspect included in Foursquare is the ability to earn
“badges”. These badges are earned by completing different achievements. One example
of such a badge is the ‘local’ badge, which is earned by checking in at the same location
three times in one week. These extras, while not giving any additional benefits
(discounts, special service) do however, make Foursquare a fun and addictive experience
for its users.
What makes Foursquare interesting for businesses however, is its ability to retain,
attract and create customer loyalty. The owner of a business has the ability to create
check-in specials at their venue. These ‘specials’ are incentives for users to go to a certain
venue. For example, an hotel may have a ‘loyalty’ special where a user who has checked
in ten times gets a discount on their stay or a complementary breakfast. A new bar
seeking to attract new customers may consider launching a ‘newbie’ special by giving
away a drink to a user checking in for the first time4. One of the early Foursquare
adapters using check-in specials to boost sales was the Starbucks Coffee Company. After
introducing a special where mayors got a discount of one dollar on a Frappuccino an
4
"Creating a Special." Foursquare. Web. 12 Mar. 2012.
<https://foursquare.com/business/merchants/specials>.
5
average check-in increase of 40-50 percent was found. Not only did this initiative
motivate people to try the more expensive type of coffee, it could also be seen as an easy
way for positive promotion using Foursquare specials (Cocoran 2010). Tasti D-lite in
New York gives another example of a business integrating Foursquare with their existing
loyalty program by giving bonus points to customers who checked in through
Foursquare. After using Foursquare the chain saw an increase in sales of 36 percent
(Brandon, 2010). Using Foursquare and social media effectively in this way is now more
than ever important. In recent years the power has increasingly been shifting from
companies to customers. The above examples, however, show how social media such as
Foursquare can be used to adapt to this new situation (Bernoff & Li, 2009).
What really makes specials interesting however, is the ability for a user to
‘explore’ their surroundings. When opening the Foursquare application, a user is able to
see all of the venues within a certain radius with specials or venues which are interesting
to him or her based on their past check-ins. Users are also able to look up certain location
types in their vicinity such as restaurants with tips and reviews from other users.
Businesses using Foursquare are also given the ability to use the ‘merchant dashboard’.
This dashboard displays information ranging from daily check-ins, frequent visitors or
the amount of check-ins shared on Twitter or Facebook, which provides a great customer
analytics tool5.
Foursquare is not only just interesting for businesses with a physical location. In
the past, Foursquare has been known to work with different brands. The History Channel
for example, has a special badge for people checking in historical places in London. Each
5
"Merchant Dashboard." Foursquare. Web. 12 Mar. 2012.
<https://foursquare.com/business/merchants/dashboard>.
6
check in uncovers some interesting historical facts, and by unlocking all the available
History Channel check-ins in London, users can earn the ‘Historian’ badge6. This is of
course a great way to create brand awareness and create a unique experience with
customers. A second brand working with Foursquare is the Bravo TV network who
partnered with Sephora (a cosmetics chain) to create a unique marketing initiative. The
idea behind the partnership was that Foursquare users following the Bravo brand could
win a Sephora gift certificate of 100 dollars by racing to a location announced by the
brand on Twitter. The campaign was highly successful and was one of the first initiatives
where a big reward was given instead of a small discount on Foursquare (Griner, 2010).
These marketing initiatives are great examples of how Foursquare can be used to
personalize a brand. Through such initiatives, brands are able to create a source of
competitive advantage based on personalizing themselves effectively (Howell & Bublik,
2009).
These Foursquare initiatives make clear how the service can be used to stimulate
customers. Specials and check-ins encourage repeat business (Coles, 2010). Furthermore,
Foursquare provides a new way to contact customers personally, especially those who are
no longer motivated by traditional means. By using Foursquare correctly, businesses are
able to create a relationship with their customers by applying personalized marketing
where dialogue between customer and company is possible (Maul, 2010).
What this paper intends to find however, is how Foursquare can be further used
for marketing purposes effectively, going further than just looking at how Foursquare is
being used by both consumers and companies. For this reason, we want to understand
6
"History Channel - Foursquare." Foursquare. Web. 12 Mar. 2012.
<https://foursquare.com/historychannel>.
7
differences in segments of Foursquare adopters and find the best way to approach them
by companies. According to previous literature by Tussydiah (2012), two major
motivations of Foursquare users are identified: connection-based rewards and
competition-based rewards. The focus of this paper is to investigate the difference
between connection-based rewards and competition-based rewards with data collected
from check-ins and surveys and finally indicate how they affect Foursquare users. This
leads us to our research question.
Research Question
The research question is articulated as follows:
"What are the differences in profiles of segments of Foursquare users based on their
motivations to use the service?"
We expect two types of consumers: those motivated by competition-based
rewards and another group motivated by connection based rewards. We will test
differences of usage behavior of Foursquare users (for instance, leaving
recommendations, user activity intensity, friend network, mayorships and badges) among
the extracted segments. Additionally to the research question, we will also intend to
answer the following sub questions.
Sub Questions
A) Are there any differences in motivation to use Foursquare between users being
motivated by connection-based rewards compared to those being motivated by
competition-based rewards?
B) Are certain types of users with different lifestyles more motivated by one type
of reward or by both?
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C) Does one type of reward create a more active Foursquare user than the other?
D) What characterizes users who are more inclined to respond to one type of
reward?
E) Does check in data provide a better or equal segmentation as demographic data
gathered from a survey?
Contribution of this paper
Managerial relevance
Foursquare tends to be used as a social tool, a way to explore new places and a
fun thing to do for consumers. However, Foursquare is also a new tool for companies to
attract and retain customers by providing a new and unique way to interact with
businesses. Foursquare data can be also used as a new tool for customer analytics.
Foursquare data can be used for real time location based advertising purposes, to offer
targeted offers and/or to design better location-based recommendation systems
connecting with social network data. Through the research done in this paper, companies
will be able to use check-in data in a more extensive way than before. Check-in data can
be used as a way to find out how to motivate young, internet savvy users and learn more
about this customer group. This will make it possible to target them more efficiently
through either competition or connection based rewards, or both.
Scientific relevance
Previous research has been generally focused on why Foursquare and other
location services are being used by people and businesses (Brandon 2010, Lindqvist et
al.2011, Tang et al. 2010, Funk 2007, Liu et al. 2011). Previous research about location
based services are summarized in Table 1 and includes research about friendship
9
prediction models, location recommendation algorithms and a general conceptual model
for location based networks research (Li & Chen 2009, Cranshaw et al. 2010, Ye et al.
2010, Tussyadiah 2012). What this paper aims to find out, is how check-in data itself
could be used to find out why people use the service and what exactly motivates them.
This information can then be used to know not only what makes certain applications
successful and others not, but also why and how consumers are being motivated to use
them. Furthermore, by finding out which behavior is incited by a type of reward (in this
specific scenario), a clearer understanding of behavior related to location-based
applications as well as business initiatives effectiveness can be formed. This will be done
by using Foursquare check-in and survey data providing an accurate view of reality. To
the best of our knowledge, none of previous research has focused on this topic yet and
existent studies have only mainly used survey data. The research that has been done in
the topic is discussed in the next chapter of this thesis.
Chapter 2
Literature Review
Social Media and relevance to Marketing
Starting from a global marketing level and serving as a good presentation to social
media are Mangold and Faulds (2009) who introduce the topic of using social media as a
new element of the marketing promotion mix. The authors discuss what role social media
plays in integrated marketing communications (IMC) and compare it to other more
traditional communication channels such as TV, radio and print. What makes social
media, such as Foursquare special, is its customer orientation requiring a special
approach. According to Mangold and Faulds, social media should be used to engage
10
customers directly and its use will only increase because of four trends. These trends are:
perception of social media as a trustworthy source of information, internet as a mass
media vehicle, social media being used more and more for information searches and
purchasing decisions and lastly, consumers turning away from traditional media.
Mangold & Faulds (2009) conclude with some guidelines as to how to use social media
to form the presence of a brand. This paper gives a good introduction into social media,
however, more in depth research has been done on the subject of location based
networks.
Location Based Networks Introductory Research
One of the first papers on the subject of location-based networks was written by
Ziv & Mulloth (2006). This paper introduces the subject starting from a brief introduction
to the Internet and social networks. The authors then discuss a case study on Dodgeball,
the predecessor to Foursquare. Two main ideas are to be taken from this discussion,
which pertain to this research. Firstly, the authors talk about ‘recognizing the power of
users to generate innovative content either collectively or individually’, which ultimately
‘enable firms to directly tap into the fickle youth market better and understand what
products will be successful for this market segment’. This is of course relevant since it is
exactly what this paper intends to find out. The second point made in the paper, looks at
mobile social networks from a global level and how these networks will affect urban
centers to make them more attractive. The notion that technology will eradicate the need
for dense urban centers is challenged since these type of services help people ‘find new
ways of organizing and interacting, and in doing so, will in some way change the nature
of the social order’ (Ziv & Mulloth, 2006). Following this train of thought is Murphy
11
(2010) talking about the use of Foursquare and similar services from a political and
sociological perspective and the influence of the internet upon them. Murphy (2010)
discusses politics and sociology and how the Internet is influencing them. He presents the
idea of “novelty within the range of familiar” and how this has been exemplified by
Foursquare. The author goes on to discuss how location services such as Foursquare
could be used create “an enhanced understanding not just of those individual participants
but of leisure patterns of an emergent urban class”. Lastly, Murphy discusses the
importance of being present somewhere and how this is affected by Foursquare by adding
certain activities such as mayorships and specials to a physical area, increasing one’s
connection to friends and locations (Murphy, 2010). This understanding of the “emergent
urban class” is what this paper intends to quantify. These papers, while not having any
hard research, do however make clear why this topic must be researched. The research to
be discussed next provides some examples of how Foursquare has been used in the past
as a subject for hard scientific research for prediction models.
Foursquare mechanics research
Further research into the topic of Foursquare regards using location characteristics
to predict certain patterns. Cranshaw et al (2010) look at how location can be used to
predict friendships. From the data gathered from 489 participants a model was created
which predict whether or not participants had a relationship. The model went even further
in being able to analyze what the context of a social gathering would be (Cranshaw et al,
2010). This study provides a good idea of how location data can be used to predict certain
behavior, which is useful in the context (connection and competition based rewards) of
this paper. Ye et al (2010) also looks at improving Foursquare mechanics. In this paper
12
the authors generate location recommendations. The paper looked at two techniques for
location recommendations, namely, Friend Based Collaborative Filtering (FCF) and Geo
Measured FCF. These methods used heuristics which where derived from a Foursquare
data set to come up with recommendations. The FCF approach used the friend list of a
user to generate location recommendations while the variant Geo Measured FCF used
heuristics taken from a Foursquare data set on top of the regular variant. These processes
were found to be equally effective as the ones already used by Foursquare while being
less process intensive (Ye et al, 2010). Li & Chen (2009) on the other hand, looked at the
friend recommendation side of things. Using real world data from a service similar to
Foursquare, a three-layered model was created. This model looked at the correlation of
user friendships with social graph properties, mobility patterns and user attributes. The
research looked at user profile information such as age, gender, friend list and check-ins
to predict user friendships and was able to provide better performance than older
methods. This is an interesting starting point for using check-in data to predict and
categorize users (Li & Chen, 2009). While all these papers look at improving the existent
Foursquare system, they do not actually use Foursquare to its full potential as a tool to
analyze user behavior. The next type of research discussed looks at why and how people
use Foursquare currently focusing on user behavior.
Foursquare behavior research
Tang et al (2010) discuss two types of location sharing namely, purpose and
social driven. The difference between both is the reason why someone would want to
share their location. Purpose driven is all about sharing when sharing is “motivated by
scenarios that emphasize a more utilitarian perspective”. These scenarios include for
13
example planning and coordination (Tang et al., 2010, pg 85). Foursquare is all about
social driven location sharing. Social driven sharing is about increasing social capital and
improving the way one showcases themselves to the outer world. Another factor
differencing social and purpose driven sharing is in how and why locations are disclosed.
The focus of the research done by the authors in this paper was to find out when people
are willing to share their location, and what their privacy concerns are. The research
followed a group of people and studied their location sharing habits and was followed up
by a survey. The two main interesting conclusions regarding this paper have to do with
the fact that people have more privacy concerns when regarding social over purpose
driven sharing and the fact that people tend to try to obscure their location information by
using insider information (Tang et al., 2010). These privacy concerns could have a
negative effect on the data to be used in this research, which is why it should be
considered to use only active Foursquare users as respondents who are likely to be more
comfortable in sharing their location. Tang et al (2010) discuss why users use location
services in general. The following research looks at the specific factors that underlie
social and purpose driven sharing.
The research done by Tussyadiah (2012) discusses the stimulus response model
where location based social network (LSN) marketing is explored and conceptualized.
This paper could be considered as a more in depth of view of Tang et al (2010) focusing
mainly on social driven sharing. The stimulus response model reveals that the ‘key to
LSN marketing is combining relevance and playfulness into a persuasive package that
stimulates consumers’ loyalty and variety behavior’. Furthermore, the research found
LSN’s success is driven by connection-based rewards, which are formed through both the
14
social network and context-aware features of such applications. These connection-based
rewards stimulate consumers not only in their selection of going to a certain venue (being
variety or loyalty behavior) but also in the process of direct and indirect social
communication. Lastly, the paper argues LSN consumers’ behavior is dependent on three
things: consumption situations, consumer characteristics and the structure of their social
network (Tussyadiah, 2012). These three things are what motivate social driven sharing.
This information is relevant to the research done in this paper as it not only provides a
good starting model for location-based research, it also provides a starting point in where
to look for when researching LSN consumers’ behavior and more specifically connection
based rewards. What this model misses however is a discussion of how privacy concerns
may affect user behavior.
Lindqvist et al (2011) have a different approach to how and why people use
Foursquare, including privacy concerns. In the paper, interviews with early adopters, a
qualitative survey and a quantitative survey concerning Foursquare usage are discussed.
From the interview it is found that checking in is done for a variety of reasons including,
personal tracking, discovery of new people, running into friends and the gaming aspect.
The qualitative research analyzed usage patterns and privacy concerns for a broader
group of people. From this research, reasons to use the service included, informing
friends what they are doing, the game-like aspect, tips and discounts at locations and
discovering new places and people. Again, the reasons for checking in can be divided
into connection or competition based rewards. In this case discovery of new people,
running into friends and informing friends what they are doing fall under the connection
based rewards category while the game-like aspect, tips and discounts and discovering
15
new places fall under competition based rewards. For this group of people the privacy
concerns were present but where not much of an issue since they understood how
Foursquare exactly works. These users for example knew it is possible to not share a
check-in and that it is wise to only have friends on Foursquare who are real life friends.
This type of user will probably give the most usable data for the purposes of this
research. A good way to find out which users are less privacy sensitive would be to look
at their check-in and other Foursquare activity. The quantitative survey done gave yet
another look at Foursquare usage. Usage was basically found to be separable into three
factors: game mechanics, social interactions and incentive mechanisms (specials,
discounts). This survey also found that restaurants and bars are the most popular places to
check-in and that barely anyone checks in at schools or at the doctor. Checking in at
home or work was dependent on the respondents privacy concerns. Finally, the
conclusion is made that if someone is using Foursquare, they already have less privacy
concerns than most (Lindqvist et al., 2011). They found five factors of motivation of
using Foursquare:
1. Badges and fun
2. Social Connection
3. Place Discovery
4. Keeping track of places
5. Game to play by yourself
These five factors can all be placed in Tussyadiah (2012) stimulus response model
and will be considered as factors playing a role in either connection or competition based
rewards.
16
Lastly, the research by Vasconcelos et al (2012) is similar to what this paper
intends to find out and uses Foursquare data to conduct research. The authors created a
Foursquare data set from the API with information about 1.6 million venues. With it, four
groups of users where identified based on their Foursquare tipping (leaving tips behind
and doing tips) activity. Two of the profiles where regular users who where different in
the amount of tipping they carried out. A third profile consisted of users who posted a
large amount of tips at a large variety of venues. This profile was seen as influential as it
included brands and famous business and brands. The last profile was a group of users
who posted tips with links in a large group of venues. This group, after manual
inspection, was identified as being ‘spammers’ or leaving behind tips, which had nothing
to do with a venue, but instead promoting a product or service. This research is
interesting as it shows using Foursquare data for research is possible. This paper intends
to take a similar approach to not only identify Foursquare user groups (with check-in
instead of venue history data), but to understand users better as well as their motivation
(Vasconcelos et al., 2012).
Location-based services are a new and developing topic, therefore there is a
scarce number of high quality research done in this area. Another reason for scarce
number of published research is the difficulty of finding good location-based service
usage data because of privacy issues. Most of the existing research has descriptive
purposes and discussed only why people use, how they use or how companies can use
these services. The following table sums up the research findings so far about locationbased services/marketing
Table 1. Overview of previous research about locations-based services/marketing
17
Author
Li & Chen 2009
Focus
Main Conclusion
A multi-layered friendship model Real world location based
for Location Based Networks
network data can be used for
predicting friendship effectively
Social and purpose driven
Finding out what the main
Tang et al. 2010
location sharing
reasons are for location sharing as
well as exploring privacy
concerns
Create a model of friendship in
Cranshaw et al. 2010 Predicting Friendship
online social network based on
location features
Location Recommendation
Developing a new algorithm with
Ye et al. 2010
Algorithms
less overhead for
recommendation engines
Foursquare
usage
and
privacy
Foursquare is used for multiple
Lindqvist et al. 2011
concerns
reasons and privacy concerns are
dealt with by users lightly
Conceptual model of Location
Consumer behavior when using
Tussyadiah 2012
Based Marketing
Location Based Social Marketing
is driven by consumption
situations, consumer
characteristics and the structure
of their social network
Foursquare
user
clusters
based
Foursquare venue data can be
Vasconcelos et al.
on tipping activity
used to cluster users into four
2012
different groups, including one
with spammers
Using Foursquare check-in data
Segmentation of Foursquare users
This thesis (Bode,
for market segmentation
and creating effective targeted
2012)
offers
After discussing the literature, a summary of the factors affecting connection and
competition-based rewards can be given. These factors will be the ones taken into
account when carrying out the research for this paper and will serve as the foundation for
the hypotheses.
Factors affecting connection-based rewards
Connection- based rewards are based on social aspects (Tussyadiah, 2012).
People who are motivated by connection-based rewards are activated by social driven
18
sharing. The following attributes, which can be extracted from Foursquare data, will be
taken into account for connection-based rewards:

Amount of friends on Foursquare

Types of locations checked in at

Demographic data

Psychographic data
Factors affecting competition-based rewards
Competition- based rewards are based on game-like aspects (Tussyadiah, 2012).
People who are motivated by competition-based rewards are activated by purpose driven
sharing. The following attributes will be taken into account for connection-based
rewards:

Amount of badges on Foursquare

Amount of mayorships on Foursquare

Types of locations checked in at

Demographic data

Psychographic data
Psychographic data will be obtained through surveys. Foursquare activity itself will
be measured with amount of check-ins, amount of places on to-do list, and tips left
behind.
Hypotheses
This paper aims at understanding the relationship between Foursquare users and
how they are motivated by either connection or competition based rewards or both.
19
Whether or not the data collected from Foursquare can be used for this purpose is what
the first hypothesis is about. Not only will this paper just look at tipping activity as a way
to segment data as in the Vasconcelos (2012) paper, it will also look at other Foursquare
activity as well survey data. Tussyadiah (2012) discusses the idea that usage behavior
(leaving recommendations, user activity intensity) is motivated by the social aspect
(connection-based rewards) and the game-like aspect (competition-based rewards) of the
game. This distinction is also based on the reason identified by Tang et al (2010) for
location sharing. People in the first segment are more likely to be social driven sharing
while the one’s in the second will be more concerned with purpose driven sharing,
something which can be analyzed with survey data.
These two segments will be based upon the five motivations to use Foursquare as
identified by Lindqvist et al (2011). The users in the first segment will be more concerned
with social connection and badges while the second segment users will be using
Foursquare for keeping track of places, place discovery and as a game to play by
themselves. Because of this reason it is expected that subjects in the competition-based
segment will have a higher amount of badges and mayorships and will check-in at a
wider variety of venues (i.e. overall more active). The segment for connection-based
rewards will have a fewer amount of badges and mayorships but a higher amount of
friends and will likely have a higher concentration of check-ins at social venues.
Therefore:
Hypothesis 1
20
Subjects who use Foursquare motivated by competition-based rewards will be
more active foursquare users than subjects who use foursquare motivated by connectionbased rewards.
Hypothesis 2
Competition-based rewards motivated Foursquare users will have a 2a) higher
amount of badges and 2b) mayorships and will 2c) check-in at a wider variety of venues
compared to connection-based rewards motivated Foursquare users.
Hypothesis 3
Connection-based rewards motivated Foursquare users will have 3a) a higher
amount of friends and will have a 3b) higher concentration of check-ins at social venues
compared to competition-based rewards motivated Foursquare users.
Successful segmentation of Foursquare users should be different in terms of
subject characteristics based on their motivation. After segmentation, we describe
segments and test the difference between them. Furthermore, since Foursquare is a very
recent application, the characteristics of early adopters are relevant to early users of
Foursquare.
Characteristics of Early Adopters
To understand the characteristics of early adopters, it is useful to take a look at
what affects product adoption. The two main leading influences at a consumer level for
product adoption could be argued to be marketing communication and interpersonal
communication (Manchada et al. 2008). Wee (2003) looked at the factors that affect
21
product adoption in the consumer electronics industry. The research looked at the time
taken for consumers to be aware of new products, evaluate information sources for
awareness of portable audio products, define the factors that affect product adoption and
their respective importance. The author comes up with seven factors affecting product
adoption, trial ability, compatibility, relative advantage, observe ability, complexity,
image and perceived risk of adoption (Wee, 2003). While not all factors have an equal
importance, they do reveal what could be the differences between normal users and early
adopters. Early adopters could be considered to be less affected by for example perceived
risk of adoption or complexity making them prone to trying new products faster. Privacy
concerns are relevant to perceived risks of adoption in location based application
adoption context as discussed by Wee (2003). We expect that people who are motivated
by connection-based rewards have less privacy concerns than people who are motivated
by competition-based rewards since users who are concerned with the social aspect are
probably less affected by the seven factors discussed by Wee (2003). Users who use
Foursquare for the game-like aspect on the other hand, will probably be more privacy
concerned because social driven sharing is not their main goal. Therefore we develop the
following hypothesis:
Hypothesis 4:
People who are motivated by connection-based rewards have less privacy
concerns (and less perceived risk) than people who are motivated by competition-based
rewards.
Other interesting research stream exists on the topic of adoption of new social
media applications. Some research has been done which discusses mobile phone
22
marketing through social networks. According to the authors there always is a group of
people who are the “opinion leaders” who are key influencers in whether or not a social
network will be successful. Users who participate in more social activities and who are
more in contact with public media are characterized as opinion leaders. They are also
willing to accept new products more easily and show more emotion towards new
products or services while using them more than regular users. The authors conclude by
identifying three factors of opinion leaders: personality, ability and knowledge and social
status (Liu et Al, 2011). Funk (2007) discusses this as well arguing firms should focus on
“lead users” for the successful adoption of mobile shopping applications. The author
carried out one hundred firm interviews, and discovered that the impact lead users have is
immense on the adoption of new mobile shopping applications (Funk, 2007). This is
relevant to this research because through this research behavior of early adoption users
will be analyzed as well. Users who are connection-based are more likely to be
categorized as lead users because they probably share the same characteristics.
Connection-based users, like lead users are probably personable, knowledgeable about
social media and have an elevated social status. Competition-based users on the other
hand will less likely share these characteristics. Therefore, we develop the following
hypothesis:
Hypothesis 5:
People who are motivated by connection-based rewards are more likely to be
opinion leaders (lead users) compared to people who are motivated by competition-based
rewards.
23
Additionally, in this thesis, we will also further investigate whether Foursquare
usage data could be a good base for segmentation by companies to understand segments
of Foursquare users or whether survey data is necessary to obtain meaningful segments of
Foursquare users (or if both should be used, survey and usage data).
Conceptual model
Connection-based
rewards
Competitionbased rewards
• + Friends and
checkins social
venues (H3)
• + Lead Users (H5)
• + Badges and
Mayorships (H2)
• + Privacy Concerns
(H4)
Foursquare
Activity
(H1)
After establishing the purpose of this research, the next chapter of this thesis deals
with how the check-in data will be collected and processed.
Chapter 3
Methodology
Research type
24
This research paper is exploratory in nature since its purpose is to find out what
can be done with check-in data both from a scientific and managerial perspective. The
problem has not been clearly defined, and much like other research into the topic of
location based marketing it will be explorative.
Data collection
The process for collecting the check-in data and demographic data will be done in
two steps. The first involves using the Foursquare Application Programming Interface
(API), which allows people to create applications that use the Foursquare application and
data. The API will be used to create a website where users invited to participate are able
to input their username. After this step the website will collect their check-in data which
can then be analyzed. Respondents will be gathered through the creation of a dummy
Foursquare account which will add as many Dutch users as possible. Only users with
more than one hundred check-ins will be taken into account to prevent collecting data
about users who stopped using the service after a short time. After collecting the check-in
data, the second step of the data collection process will involve asking the users to fill out
a survey collecting demographic and supplemental data about Foursquare usage and user
behavior. The survey will also include multi-item scales to measure privacy concerns and
opinion leadership. The sample size we aim to achieve will be around 200 Dutch
respondents, which will be gathered randomly through the dummy account. These two
methods for collecting data are well suited to studying this social media phenomenon
because it is happening now and must be studied in its natural setting (Pinsonneault,
Alain & Kraemer 1993). A survey does however come with its disadvantages, mainly,
making sure we have a large enough sample size and its inflexibility after the survey has
25
been started. What follows is a general summary of the variables to be extracted from the
check-in data.
Definitions of Variables used in analysis
Foursquare activity: Usage of Foursquare by subjects. It will be measured with
amount of check-ins, amount of places on to-do list, and tips left behind.
Motivations: amount of mayorships, badges, maximum score on leaderboard
What can be extracted from Foursquare data:
o Amount of check-ins
o Amount of badges
o Amount of mayorships
o Amount of friends
o Amount of tips
o Amount of to-do’s
o Recent and maximum scores on leaderboard
o Venue check-in history which includes its name, category7 and times been
there
Data Processing
The data gathered from the check-ins must first be processed into attributes for all
of the respondents. These attributes will be based on things such as types of places
checked in at and check in activity. From the data gathered we intend to find out what the
The different venue categories are: Arts & Entertainment, College & Education,
Food, Home/Work/Other, Nightlife Spots, Great Outdoors, Shops, Travel Spots
7
26
different factors are for competition and connection based rewards and how they
influence Foursquare activity for users. Two types of segmentation are to be carried out.
Types of segmentation:
Four types of bases can be used to obtain segments: demographic, behavioral,
psychographic and geographical. We will focus on only behavioral segmentation because
Foursquare check-ins provide good insight into a person’s habits and interests. When
conducting behavioral segmentation, we will also investigate whether Foursquare usage
data gives similar segments to segments obtained when conducting a survey. The purpose
of this investigation is to see whether Foursquare usage data can be used by companies
for targeting purposes without collecting survey data.

Behavioral Segmentation: we can use Foursquare check-in (activity) data to
extract behavioral variables. We use two methods: Apriori segments and
nonapriori segmentation. In apriori segmentation, we expect two segments. We
will create two groups based on the two motivation variables: competition and
connection based, which were explained earlier.

For nonapriori segmentation, we will conduct cluster analysis using these
variables:
o Amount of check-ins
o Amount of badges
o Amount of mayorships
o Amount of friends
o Amount of tips
27
o Maximum score on leaderboard
o Venue check-in history which includes category and times been there
We will compare segments obtained through foursquare and survey data for
behavioral segmentation. The first one is to see whether hypotheses are confirmed or not
based on segmentation results (i.e. classification of people into two groups, competitionbased versus connection-based) from two different data tools.
We will compare segments obtained with t-test (if we have two segments) or
ANOVA (if we have more than 2 segments). We will use these methods because we want
to compare some mean values across groups. Additionally, we will compare lifestyle of
extracted segments using lifestyle questions from survey data.
Reliability and Validity of constructs in survey:
To ensure the results are reliable and valid for testing hypothesis 4 and 5 dealing
with privacy concerns and opinion leaders a factor analysis and Cronbachs alpha can be
used. If they are reliable, an average can be taken when using this data to carry out a t-test
to test the hypothesis.
Data Analysis
Sample information
55 Foursquare users participated in total during the data collection. From these, 44
filled in both survey and gave their check-in information, 3 only filled in the survey and 8
only submitted their survey data. To be able to use the information from the 11 users who
did not fill in both parts of the research data was merged using demographic data to have
a complete data set with 55 respondents. From the sample, 42 were male (76.4%) and 13
28
female (23.6%). The average age of the participants was 39.5 years with a standard
deviation of 11.6. The following tables give some more general demographic information
Table 2. Highest Level of Education Achieved of Respondents
Number of Participants
Percentage
Elementary School
1
1.81%
High School or equivalent
14
25.5%
Vocation/Technical school
5
9,1%
Bachelor’s Degree
18
32.7%
Master’s Degree
12
21.8%
Doctoral Degree
1
1.8%
Professional degree (MD,
3
5.45%
1
1.81%
JD, etc)
Other
Table 3. Marital Status of Respondents
Number of Participants
Percentage
Divorced
2
3.64%
Living with another
6
10.91%
Married
24
43.64%
Separated
2
3.64%
Single
14
25,45%
In a relationship
7
12,73%
29
As for the working situation of the participants, 18.18% were students, 7.27%
were unemployed and 74.55% were employed. Interesting to know is that a good portion
of the participants had their own business or were entrepreneurs (29.1%). This group of
participants can be considered to be a varied sample with a wide range of ages, education
levels and marital status. This makes the sample good for the purposes of this research
since we intend to find out why Foursquare is being used by its users in general.
Descriptive Statistics
The survey filled out by the users contained some general questions about their
Foursquare use. 45.45% percent of the respondents first heard about or started using
Foursquare because of their friends. Another 47.27% found about it through the internet,
5.45% through other means (printed press, social media) and 1.81% because they were
using a similar service. This is of relevance when considering how online social networks
grow and can pinpoint us in the direction of why someone would start using Foursquare
in the first place. When asked how long they had been using Foursquare, 7.27% said less
than 6 months, 18.18% said between 6 months and a year, 40% between 1 and 2 years
and 34.35% longer than 2 years. This tells us the majority of the respondents are long
time Foursquare users. As to the exact reason to start using Foursquare, respondents
answered the following:
Table 4. Motivation to start using Foursquare
Why did you decide to start using Foursquare
Percent
Friends using it
26.63%
Fun way to share location (game-aspect of Foursquare)
32.72%
Fun way to explore places
1.81%
30
Social connection (to connect with friends or new people)
14.54%
Place discovery (to find new places or recommendation for new places
such as restaurants, cafes, bars)
12.72%
Keep track of places that I have visited for me and for friends
1.81%
Other, please specify
12.72%
When asked to specify their other choice, respondents filled in curiosity and
professional reasons. From this table it can be said the main motivations for users to start
using are the game aspect and their friends using Foursquare. This relates directly to
competition and connection based rewards as discussed in the hypotheses and this
question can be used for the survey segmentation later on.
A couple of questions dealt with specials. 37 out of the 55 respondents said to
have participated in a special run by a business. When asked if they would take part in a
special in the future, 30 said they would and 19 said they maybe would. Furthermore,
when asked if their decision to go somewhere was affected (negatively or positively) by
Foursquare, 16 said it did. Lastly, when asked if they were more likely to go somewhere
in the future because of a special which attracted them, 23 said yes, 6 no and 26 said
maybe. This information tells us that specials are somewhat effective and in general
Foursquare users tend to be influenced by them. The high amount of ‘maybe’ answers,
however, tells us that probably how the special is designed and whether or not it interests
the user is of high importance.
Hypothesis Testing
Hypothesis 1
Post-hoc method using only Foursquare Usage data
31
Subjects who use Foursquare motivated by competition-based rewards will be
more active foursquare users than subjects who use foursquare motivated by connectionbased rewards.
To answer this first hypothesis, we must first find out whether a user is more
motivated by a competition-based reward rather than by connection-based rewards. Then,
user activeness must be measured and compared for both groups. To segment both types
of users, a score will be given per user that says how much of a competition or
connection-based user they are. These are based upon the following variables:
Table 5. Segmentation Variables
Variable
Calculation
Friends Score (F)
Number of friends/friend average of all respondents
Badges Score (B)
Number of badges/badge average of all respondents
Mayorship Score
Number of mayorships/mayorship average of all respondents
(M)
Connection-based
((Check-ins at Arts & Entertainment locations/Average check-ins
location check-ins
at Arts and Entertainment locations for all respondents) + (Check-
Score (CON)
ins at Food locations/Average check-ins at Food locations for all
respondents) + (Check-ins at Great Outdoors locations/Average
check-ins at Great Outdoors locations for all respondents) +
(Check-ins at Nightlife spots locations/Average check-ins at
Nightlife spots locations for all respondents) + (Check-ins at Shops
& Services locations/Average check-ins at Shops & Services
locations for all respondents))/5
32
Competition-
((Check-ins at Colleges & Universities locations/Average check-
based location
ins at Colleges & Universities locations for all respondents) +
check-ins Score
(Check-ins at Professional & Other places locations/Average
(COM)
check-ins at Professional & Other places locations for all
respondents) + (Check-ins at Residencies locations/Average checkins at Residencies locations for all respondents) + (Check-ins at
Travel & Transport locations/Average check-ins at Travel &
Transport locations for all respondents))/4
The average is taken for both CON and COM so that they are weighted equally.
Using these variables the following formulas are derived for determining how much a
user responds to connection or competition based rewards:
Connection-based rewards user sensitivity (CONsen)= F + CON
Competition-based rewards user sensitivity (COMsen): (B + M)/2 + COM
The average of the badge and mayorship score is taken so that they weigh the
same as the friends score. For all the respondents, the higher sensitivity score determines
which type of user a respondent is, either connection-based or competition-based. Out of
the 55 respondents, 22 are categorized as connection-based and 33 as competition based.
Next, respondent activity will be compared to respondent category to find out if there is
any support for Hypothesis 1. The activeness score can be calculated using the following
variables:
Table 6. Activeness Score Variables
Variable
Calculation
33
Weekly high score points Maximum weekly checkin points/ Maximum weekly
score (MW)
checkin points average of all respondents
Check-in score (C)
Number of Check-ins/Check-in average of all respondents
Tips score (T)
Amount of tips/Tip average of all respondents
These variables are used to calculate how active user is in Foursquare.
Respondent activeness score (A): MW+C+T
After calculating the activeness score, the scores are aggregated for each category.
This gives an average activeness score of 2.15 for connection-based respondents and an
average activeness score of 3.56 for competition-based users. Next we conduct a t-test to
determine whether the activeness score for competition based users is higher and
statistically significant compared to connection-based users. First an f-test is carried out
which tells us whether or not the variances between both groups are significantly
different. The f-test returns a value of, 0.0008, this means the variance between both
groups is not significantly different. With this information we can carry out an
appropriate t-test where we know the variance between both groups is not significantly
different. The H1 hypothesis in this case is,
H1: Subjects who use Foursquare motivated by competition-based rewards will be more
active foursquare users than subjects who use foursquare motivated by connection-based
rewards.
The p value when carrying out this test equals 0.077 meaning we cannot
reject the null hypothesis at a 5 percent confidence level; however if we increase the
confidence level to 10%, we reject the H0. That is, competition-based users (mean=3.56)
34
are statistically significantly more active than connection-based users (mean=2.15) with
90% confidence.
Segmentation Method using Survey data
In the previous section, we categorized each subject on whether he or she is a
competition or connection-based user on given information of his or her actual
Foursquare usage. This is a post-hoc method.
Now we will investigate whether we would have obtained the same segments if
companies use surveys to understand segments of Foursquare users. For this, we can
include questions 5, 8 and 9 into a cluster analysis; because these questions are related to
motivations to use Foursquare and since these variables are categorical, we will make
dummies of each category level first. Then we can conduct a cluster analysis and decide
the number of clusters. Our expectation is to find two clusters. Then we can assign a
segment for each person indicating competition or connection-based. After that we
compare clusters(i.e. profile them). Additionally, we will test H1-H5 again based on these
segments.
The next logical step is to compare the activeness scores for the respondents with
the survey questions that deal with how active they think they are on Foursquare. Using
the survey questions, we can again determine whether a user is connection or competition
based. The questions to be used to determine this will be 5, 8 and 9 (survey can be found
in appendix). The answers to the questions will be used to come up with a score for how
connection or competition based a user is.
Question 5 will be coded like this:
35
If a user selects answer options 1, 4 or 6 then the user is categorized as
connection based. Similarly if a user selects answers 2, 3 or 5 he or she will be
categorized as competition based. Lastly, answer 7 is dependent on the answer given.
Questions 8 and 9 similarly translate to points to either competition or connection
based. Question 8 answers will be coded like this:
When a user selects options 1-3, this will be interpreted as the user being
connection based. Answers 4-6 will indicate the user is competition based.
Question 9 answers will be coded like this:
When a user selects options 2, 3, 4, 5 and 7 this will be interpreted as the user
being connection based. Answers 1, 6 and 8 will be interpreted as competition based.
Questions 8 and 9 are weighted accordingly. Using the survey data we get the
same amount of connection based users (22) and competition based users (33) that was
calculated from the check-in data.
The H1 hypothesis in this case is again,
H1: Subjects who use Foursquare motivated by competition-based rewards will be more
active foursquare users than subjects who use foursquare motivated by connection-based
rewards.
The p value when carrying out this test equals 0.02 meaning we can reject
the null hypothesis at a 5 percent confidence level. That is, competition-based users
(mean=12) are statistically significantly more active than connection-based users
(mean=9.11) with 90% confidence. The survey data therefore supports the hypothesis 1.
Segmentation Method using Check-in and Survey data
36
Next, both survey and check-in data can be used to determine whether a user is
competition or connection based. For this purpose, the connection and competition scores
are aggregated for this purpose. What this means is that we sum the scores for each user
according to the survey and the check in data. Taking the highest score determines what
type a user is (connection or competition based). This results in 28 connection based
users and 27 competition based users. The H1 hypothesis in this case is,
H1: Subjects who use Foursquare motivated by competition-based rewards will be
more active foursquare users than subjects who use foursquare motivated by connectionbased rewards.
The p value when carrying out this test equals 0.45 meaning we cannot reject the
null hypothesis at a 5 percent confidence level. This means that competition-based users
(mean=13.35) are not statistically significantly more active than connection-based users
(mean=13.17) with 95% confidence.
Cluster Analysis using only Foursquare Usage data
To make sure outliers do not influence the cluster analysis, we first look at users
with an unusually high amount of check-ins. For this purpose, the fourth spread is found
for the respondent check-ins and users with more check-ins than the upper outlier
boundary (median+1.5*fourth spread) are removed from the sample. This led to 8
respondents being removed. After running a k-means cluster analysis, where 2 clusters
are expected to be found, one cluster with 8 respondents is found, and another with 39.
Cluster number 1 will considered to be the competition-based users because of a higher
check-in score and a lower friend amount. The k-means clustering provides us with the
following ANOVA table:
37
Table 7. K-means clustering ANOVA table
Cluster
Error
F
Sig.
45
2.383
.130
609.761
45
4.466
.040
1
7674.924
45
4.166
.047
321743.136
1
5604.868
45
57.404
.000
Shops & Services
176289.712
1
10121.060
45
17.418
.000
Travel &
Transport
410310.599
1
3992.472
45
102.771
.000
8122.981
1
1667.787
45
4.871
.032
57.971
1
636.486
45
.091
.764
2887.415
1
11726.640
45
.246
.622
Mean
Square
df
1
1671.383
2722.979
1
Professional &
Other Places
31970.070
Residences
Mean Square
Df
Great Outdoors
3983.596
Nightlife Spots
Colleges &
Universities
Arts &
Entertainment
Food
The F tests should be used only for descriptive purposes because the clusters have been
chosen to maximize the differences among cases in different clusters. The observed
significance levels are not corrected for this and thus cannot be interpreted as tests of the
hypothesis that the cluster means are equal.
As can be seen from the significances, a couple of variables can be considered to
not contribute much to the separation of clusters. These are, at a 5 percent significance
level, Great Outdoors, Arts & Entertainment and Food check-ins. Even though these
variables do not contribute to the separation of clusters, they can still be used in the
analysis of the hypothesis as extra information. Now to see if there is a significant
difference in activity between both groups we can carry out a t-test comparing the
activeness score between both groups.
38
H1: Subjects who use Foursquare motivated by competition-based rewards will be more
active foursquare users than subjects who use foursquare motivated by connection-based
rewards.
The p value when carrying out this test equals 0.10 meaning we cannot
reject the null hypothesis at a 5 percent confidence level; however if we increase the
confidence level to 10%, we reject the H0. That is, competition-based users (mean=3.50)
are statistically significantly more active than connection-based users (mean=1.53) with
90% confidence.
Hypothesis 2
Competition-based rewards motivated Foursquare users will have a 2a) higher
amount of badges and 2b) mayorships and will 2c) check-in at a wider variety of venues
compared to connection-based rewards
2A)
To see which group of users has a higher amount of mayorships the mayorship
average can be used. The average mayorships are the following for both groups using
both the post-hoc method and the cluster analysis:
Table 8. Respondent Mayorship average per group
Segmentation
Connection
based Competition
based P value
users
users
Post Hoc- Mayorship average
9.13
25.78
0.03
Cluster Analysis-Mayorships
5.07
13.37
0.00003
Average
39
To find out if there is a statistically significant difference between the mayorship
scores between both groups, a t-test was carried out. The hypothesis in this case is,
H2A= Competition users have more badges than connection based users.
The corresponding p values when carrying out this tell us we can reject the null
hypothesis at a 5 percent confidence level. Therefore, there is support for hypothesis 2A.
That is, competition-based users indeed have higher mayorship score than connectionbased users.
2B)
For the hypothesis 2B, we look at the badge average for both groups using both
methods.
Table 9. Respondent Badge average per group
Segmentation
Connection
based Competition
based P
users
users
value
Post Hoc- Badge average
17.59
22.42
0.05
Cluster Analysis- Badge
16.02
23.13
0.05
Average
To find out if there is a statistically significant difference between the badge scores
between both groups, a t-test was carried out. The hypothesis in this case is,
H2B= Competition users do have more badges than connection based users.
The p value when carrying out these test tell us we can reject the null hypothesis
at a 5 percent confidence level. Therefore, there is support for hypothesis 2B.
2C)
40
To calculate the range of venues a user checks in at, all the different venue scores
can be aggregated and averaged to make a new score for variety of venues checked in at.
The aggregated venue score for each of the user groups is the following:
Table 10. Respondent Aggregated Venue score average per group
Segmentation
Connection based Competition based P
users
Post Hoc- Average Aggregated 6.13
users
value
10.91
0.063
9.89
0.046
Venue score
Cluster
Analysis-
Average 4.85
Aggregated Venue
To see if there is support for this hypothesis, a t-test is carried out once more.
H2C= Competition users do not check in at a wider variety of venues than
connection based users.
When looking at the p-value for both of these tests, it can be said there is support
for hypothesis 2C at a 10% confidence level for the post-hoc method and at a 5%
confidence level for the cluster analysis method.
Hypothesis 3
Connection-based rewards motivated Foursquare users will have 3a-) a higher
amount of friends and will have a 3b-) higher concentration of check-ins at social venues.
To test this third hypothesis, the average friends between both groups of users will
be compared. Similarly the average social venues check-ins for both groups will be
41
compared to see if they support the hypothesis or not. The following table gives the
results hereof:
Table 11. Respondent Friends average and Aggregated average social venue
check in score per group
Segmentation
Post hoc- Average Friends
Connection
Competition
based users
based users
385.18
97.24
0.00001
5.47
0.29
Post hoc- Aggregated average social 4.29
P value
venue check-in score
Cluster Analysis- Average Friends
207.59
71.88
0.001
Cluster Analysis- Aggregated
2.22
4.45
0.11
average social venue check-in score
3A)
H3A= Connection based users have more Foursquare friends than
competition based users.
The p value when carrying out these test tell us we can reject the null hypothesis
at a 5 percent confidence level. Therefore, there is support for hypothesis 3A.
3B)
H3B= Connection based users check in at more social venues than
competition based users.
The p value when carrying out these test tell us we cannot reject the null
hypothesis at a 5 percent confidence level. Therefore, there is support for hypothesis 3B.
42
Hypothesis 4:
People who are motivated by connection-based rewards have less privacy
concerns (and less perceived risk) than people who are motivated by competition-based
rewards.
Before doing any testing, the cronbach alpha is calculated to see how reliable the
privacy data is. Before doing this, question 6 ‘I am concerned about my privacy when
using Foursquare’ will be reverse coded so that it measures privacy concerns in the same
way as the rest of the privacy questions. In this case, the cronbach alpha was found to be
0.877 and deleting any question does give a higher cronbach alpha. This means the
privacy data is reliable. When testing for construct validity, a factor analysis is run. When
running a factor analysis for the 6 privacy questions, 1 component is found and the six
questions affect the variance in the following way:
Table 12. Total Variance Explained
Component
Initial Eigenvalues
Total
% of Variance
Cumulative %
1
3.806
63.432
63.432
2
.746
12.438
75.870
3
.530
8.836
84.707
4
.373
6.212
90.919
5
.358
5.959
96.877
6
.187
3.123
100.000
Extraction Method: Principal Component Analysis.
From this we can assume there is construct validity since all the questions can be
classified into one component.
43
To see if there is support for this hypothesis, question number 1 can be used to see
how privacy sensitive competition and connection-based users are. The answers to the 6
questions relating to privacy will be coded where Strongly Disagree=1, Disagree=2,
Neither Agree nor Disagree=3, Agree=4 and Strongly Agree=5. The average to the 6
questions will be taken and tested to see whether or not connection-based rewards users
have less privacy concerns than competition based users. To see if there is support for
hypothesis 4, the means for the privacy questions are compared between both groups. We
will do this both for segmentation using the post hoc method data and the cluster analysis.
The hypothesis is the following:
H4= Connection based users have less privacy concerns than people who are
motivated by competition based rewards.
Table 13. Respondent Privacy concerns average per group
Segmentation
Post-hoc method –
P value
Connection-
Competition-
based users
based users
0.07
4.16
3.89
0.052
3.88
4.31
Privacy Concerns
Cluster Analysis –
Privacy Concerns
Based on this, using the post hoc method, it can be said there is no support for
hypothesis 4. However, using cluster analysis to segment the users, we can reject the null
hypothesis and say there is support for hypothesis 4 at a 10% confidence level.
Hypothesis 5:
44
People who are motivated by connection-based rewards are more likely to be
opinion leaders (lead users) compared to people who are motivated by competition-based
rewards.
Before doing any testing, the cronbach alpha is calculated to see how reliable the
opinion leader data is. In this case, the cronbach alpha was found to be 0.874, which
means the data is reliable. To test the validity of the data a factor analysis is carried out.
This analysis found three components, which are loaded in the following way:
Table 14. Rotated Component Matrixa
Component
1
2
3
Q34_1
.635
.099
.397
Q34_2
.745
.350
-.010
Q34_3
-.031
.286
.632
Q34_4
.322
.785
.090
Q34_5
.148
.251
.852
Q34_6
.268
.003
.890
Q34_7
.116
.894
.226
Q34_8
.218
.820
.266
Q34_9
.810
.333
.062
Q34_10
.846
.061
.075
Q34_11
.879
.119
.201
Extraction Method: Principal Component
Analysis.
Rotation Method: Varimax with Kaiser
Normalization.
a. Rotation converged in 5 iterations.
45
Factor 1 includes questions:
-I am strongly attached to consumer electronics products
-I am up to date with news about new consumer electronics products
-I am knowledgeable about consumer electronic products
-I like to try out new consumer electronic products when they come out
-I am always looking out for new consumer electronic products
Factor 2 includes questions:
-I use a computer or laptop frequently during the day in my free time
-I would be at a loss without my laptop
-I am very attached to my laptop
Factor 3 includes questions:
-I use my smartphone frequently during the day
-I would be at a loss without my smartphone
-I am very attached to my smartphone
From this result we can see factor 1 relates to consumer products in general while
factor 2 and 3 to laptop and smartphone use respectively. For this reason, it would be of
interest to measure the three factors separately when looking at support for hypothesis 5.
For this hypothesis, question 18 is used and will also be coded Strongly
Disagree=1, Disagree=2, Neither Agree nor Disagree=3, Agree=4 and Strongly Agree=5.
The 11 responses to question 18 will be separated into factors, summed up and averaged
for both groups and then tested accordingly. Three hypotheses can be made in this case:
46
H5A= Connection based users are more likely to be opinion leaders (lead users)
compared to competition based users in relation to consumer products.
H5B= Connection based users are more likely to be opinion leaders (lead users)
compared to competition based users in relation to laptops and laptop use.
H5C= Connection based users are more likely to be opinion leaders (lead users)
compared to competition based users in relation to smartphones and smartphone use.
Table 15. Respondent Opinion Leaders average per group
Consumer Products
Laptop Use
Smartphone Use
Post Hoc
Connection based
Connection based
Connection based users
method
users mean: 3.65
users mean: 3.83
mean: 4.12
Competition Based
Competition Based
Competition Based
Users mean: 3.81
Users mean: 4.04
Users mean: 4.40
P-value: 0.25
P-value: 0.22
P-value: 0.086
Cluster
Connection based
Connection based
Connection based users
Analysis
users mean: 3.36
users mean: 3.87
mean: 4.16
Competition Based
Competition Based
Competition Based
Users mean: 3.58
Users mean: 4.58
Users mean: 4.79
P-value: 0.23
P-value: 0.005
P-value: 0.0004
Using the post hoc method, at a 10 percent confidence level, the null hypothesis
can be rejected. However, in this case, this means that actually competition based users
are more likely to be opinion leaders regarding smartphones and smartphone use which is
actually the opposite of what we had expected with hypothesis 5. Using cluster analysis,
at a 5 percent confidence level, the null hypothesis can also be rejected for laptop and
47
smartphone use. Again, instead of connection-based users being more likely to be opinion
leaders, competition based users are found to be so regarding laptop and smartphone use.
This is, again, the opposite as to what we had expected with hypothesis 5. The fact that
no support was found for overall consumer products use does tell us however, that
competition based users may in fact just be more intense laptop/smartphone users when
compared to connection based users and not more likely to be opinion leaders per se.
Table 16. Respondent Average per segmentation method
Size
Privacy
concerns
Segment
1(CON)
Segment
2(COM)
Opinion leader –
Consumer
Electronics
Segment
1(CON)
Segment
2(COM)
Opinion leader –
Laptops
Segment
1(CON)
Segment
2(COM)
Opinion leader –
Smartphone
Segment
A: Post-hoc
segmentation with
actual usage data
(mean, SD)
Segment 1(CON)=
22
Segment 2(COM)=
33
Segment 1(CON)=
4.01, 0.76
Segment 2(COM)=
3.90, 0.57
B: Segmentation
using survey data
(mean, SD)
Segment 1(CON)=
3.65, 0.82
Segment 2(COM)=
3.81, 0.91
Segment 1(CON)=
3.78, 0.92
Segment 2(COM)=
3.72, 0.85
Segment 1 (CON)=
3.68, 0.95
Segment 2 (COM)=
3.80, 0.80
Segment 1(CON)=
3.83, 1.07
Segment 2(COM)=
4.04, 0.91
Segment 1(CON)=
4.38, 0.72
Segment 2(COM)=
4.23, 0.77
Segment 1 (CON)=
4.29, 0.73
Segment 2 (COM)=
4.30, 0.78
Segment 1(CON)=
4.12, 0.83
Segment 2(COM)=
Segment 1(CON)=
3.8, 0.89
Segment 2(COM)=
Segment 1 (CON)=
3.89, 0.97
Segment 2 (COM)=
Segment 1(CON)=
22
Segment 2(COM)=
33
Segment 1(CON)=
3.75, 0.55
Segment 2(COM)=
4.20, 0.67
C: Segmentation
using survey and
actual usage data
(mean, SD)
Segment 1(CON)= 27
Segment 2(COM)= 28
Segment 1 (CON)=
3.88, 0.69
Segment 2 (COM)=
4.13, 0.60
48
1(CON)
Segment
2(COM)
Number of
friends
Segment
1(CON)
Segment
2(COM)
Number of
mayorships
Segment
1(CON)
Segment
2(COM)
Number of
Badges
Segment
1(CON)
Segment
2(COM)
Number of
Check-ins
Segment
1(CON)
Segment
2(COM)
Number of Tips
Segment
1(CON)
Segment
2(COM)
Gender
4.04, 0.68
4.06, 1.03
4.02, 1
Segment 1(CON)=
385, 253
Segment 2(COM)=
88, 123
Segment 1(CON)=
200, 260
Segment 2(COM)=
211, 222
Segment 1 (CON)=
270, 280
Segment 2 (COM)=
141, 159
Segment 1(CON)=
9.13, 14
Segment 2(COM)=
25.79, 46.6
Segment 1(CON)=
18.95, 40.83
Segment 2(COM)=
19.24, 36.38
Segment 1 (CON)=
6.68, 7.73
Segment 2 (COM)=
32, 50.7
Segment 1(CON)=
17.6, 10
Segment 2(COM)=
22.42, 11
Segment 1(CON)=
20.86, 10.45
Segment 2(COM)=
20.24, 11.26
Segment 1 (CON)=
18.21, 7.89
Segment 2 (COM)=
22.85, 12.98
Segment 1(CON)=
719, 1025
Segment 2(COM)=
1789, 2879
Segment 1(CON)=
1444, 3074
Segment 2(COM)=
1305, 1800
Segment 1 (CON)=
584, 728
Segment 2 (COM)=
2167, 3124
Segment 1(CON)=
14, 16
Segment 2(COM)=
16, 34
Segment 1(CON)=
15, 16
Segment 2(COM)=
16, 34
Segment 1 (CON)=
12, 15
Segment 2 (COM)=
19, 38
Segment 1(CON)=
M(19) F(3)
Segment 2(COM)=
M(23) F(10)
Segment 1(CON)=
M(15) F(7)
Segment 2(COM)=
M(27) F(6)
Segment 1 (CON)=
M(21) F(7)
Segment 2 (COM)=
M(21) F(6)
Age
Segment 1(CON)=
44.1, 6.9
Segment 2(COM)=
36.4, 13.1
Segment 1(CON)=
37.5, 13.25
Segment 2(COM)=
40.8, 10.36
Segment 1 (CON)=
38.6, 12.68
Segment 2 (COM)=
40.4, 10.54
Note: COM indicates competition-based users. CON indicates connection-based users.
Table 17. Overlap of groups between segmentation methods
49
Post Hoc –
Checkins
Post Hoc –
Checkins
Post HocSurvey
Post Hoc –
Checkins +
Survey
Cluster
Analysis
Post Hoc –
Survey
Post Hoc –
Checkins +
Survey
100%
49%
100%
67%
82%
100%
60%
55%
70%
Table 18. Hypothesis Testing Summary
H3B
H4
Post Hoc – Checkins
Reject H0 (90%)
Com Mean: 3.56
Con Mean: 2.15
Reject H0 (95%)
Com Mean: 25.78
Con Mean: 9.13
Reject H0 (95%)
Com Mean: 22.42
Con Mean: 17.59
Reject H0 (90%)
Com Mean: 10.91
Con Mean: 6.13
Reject H0 (90%)
Com Mean: 97.24
Con Mean: 385.18
No Support
No Support
H5A
H5B
No Support
No Support
H5C
Reject H0 (90%)
Com Mean: 4.4
Con Mean: 4.12
H1
H2A
H2B
H2C
H3A
Cluster Analysis
Reject H0 (90%)
Com Mean: 3.50
Con Mean: 1.53
Reject H0 (95%)
Com Mean: 13.37
Con Mean: 5.07
Reject H0 (95%)
Com Mean: 23.13
Con Mean: 16.02
Reject H0 (95%)
Com Mean: 9.89
Con Mean: 4.85
Reject H0 (95%)
Com Mean: 71.88
Con Mean: 207.59
No Support
Reject H0 (90%)
Com Mean: 4.31
Con Mean: 3.88
No Support
Reject H0 (95%)
Com Mean: 4.58
Con Mean: 3.87
*Opposite of originally
stated hypothesis
Reject H0 (95%)
Com Mean: 4.79
Con Mean: 4.16
50
*Opposite of originally stated
hypothesis.
*Opposite of originally
stated hypothesis
Lifestyle and Foursquare usage
In the survey carried out, a question with 18 sub questions about lifestyle was
asked. From these questions we can find out if there are any specific lifestyle differences
between both groups of Foursquare users. A factor analysis was run first which gave 7
components.
Factor Matrixa
Factor
1
2
3
4
5
6
7
Competing in
individual sports
(for example,
tennis, ping pong,
etc.)
.694
.020
-.103
.386
.405
-.088
-.021
Fitness
.571
-.047
.007
-.267
.047
.061
.153
Collecting or
making
something
.418
.446
-.145
.102
-.069
-.259
.103
Swimming
.493
.164
-.145
.181
-.116
-.141
.279
Attending sports
events
.662
-.564
-.205
-.267
.041
-.015
.056
Attending opera,
ballet or dance
performances
.743
.037
-.355
-.192
-.150
-.087
.056
Competing in
team sports (for
example, soccer,
baseball,
basketball, etc.)
.545
-.119
.447
-.071
.157
-.017
-.144
Going on a family
outing
.120
.032
.705
-.201
.160
-.153
.231
Going out for the
evening for drinks
and entertainment
.543
-.168
.319
-.105
-.226
.260
-.179
51
Bicycling for
leisure
.246
.467
.120
.008
.243
-.029
-.211
Going to the
movies
.252
.110
.568
.005
-.056
-.003
-.160
Listening to
music
.011
.547
.324
-.025
-.451
-.083
.076
Playing adult
games (for
example, cards,
chess, etc.)
-.100
-.546
.078
.030
.024
.097
.030
Working on the
computer
.101
-.319
.314
.642
-.058
.158
.249
Reading books for
pleasure
.094
.627
-.103
-.006
.279
.544
.146
Watching
Television
-.371
-.022
.151
-.406
.199
.003
.393
Visiting art
galleries and
museums
.341
.002
-.101
-.045
-.317
.340
.068
Surfing the Web
-.037
-.135
.096
.239
-.090
-.038
.161
Extraction Method: Principal Axis Factoring.
a. Attempted to extract 7 factors. More than 25 iterations required.
(Convergence=.008). Extraction was terminated.
The 18 activities are split into the different factors the following way and can be
labeled accordingly:
Table 20. Lifestyle Factors
Factor 1
Factor 2
Activities
Competing in individual sports (for example, tennis,
ping pong, etc.), Fitness, Swimming, Attending sports
events, Attending Opera, Ballet or dance performances,
Competing in team sports (for example, soccer,
baseball, basketball, etc.), Going out for the evening for
drinks and entertainment, Visiting art galleries and
museums
Bicycling for leisure, Collecting or making something,
Label
Sports Person
Entertainment
52
Factor 3
Factor 4
Listening to music, Playing adult games (for example,
cards, chess, etc.), Reading books for pleasure, Surfing
the Web
Going on a family outing, Going to the movies
Watching Television, Working on the computer
Person
Social Person
Regular Person
Next, we conduct t-tests for each of these factors to see if there is any statistically
significant difference between both groups of Foursquare users.
Table 21. Lifestyle factor average per group
Factor 1
Factor 2
Factor 3
Factor 4
Mean connection
based users
2.72
4.57
2.76
6.63
Mean Competition
based users
2.2
4.98
3.38
6.56
P value
0.08
0.06
0.07
0.36
From this we see there is marginal support for connection-based users having a
more sports like lifestyle. On the other hand, there is marginal support for competition
based users to have a more entertainment or social kind of lifestyle. While for connection
based users to have a more of a sports type of lifestyle (since sports are social events), it
is kind of surprising that competition based users are more interested in family outings
and going to the movies than connection based users. However, since there is only
marginal support, it cannot be concluded fully. Also, factor 1 for which the mean for
connection-based users is higher, included a variety of social activities. It is no surprise
however, that neither group participates more in regular activities such as watching
television or working on the computer. This leads us to conclude that while there are
differences between both groups of users concerning lifestyle, the differences are not big.
53
Chapter 4
Conclusion
Hypothesis Discussion
When looking at which group of users is more active we found marginal support
for competition users being more active Foursquare users through both the post-hoc
method and the cluster method. When looking at Foursquare use for both groups in
greater detail, we found support for competition based users having a higher amount of
badges and mayorships as well as checking in at a wider variety of venues. On the other
hand, we found that connection based users do have a higher amount of friends than
competition based users but do not have a higher concentration of check-ins at social
venues as was predicted. The fact that connection based users do not have a higher
amount of check-ins at social venues can be attributed to the fact that competition based
users just have an overall higher amount of check-ins anyways. When looking at privacy
concerns differences between both groups of users, we found there to be marginal support
for the fact that connection based users have less privacy concerns than competition
based users. The fact that there was no support for this hypothesis when using the posthoc method does however tell us that there may not be such a great difference between
both groups. The fact that the mean privacy concern between both groups was quite high
also could tell us that Foursquare in general may have little privacy concerns. When
investigating both groups to see whether or not connection based users where more likely
to be opinion leaders, we found that, regarding laptop and smartphone use, it is actually
competition based users who are more likely to be opinion leaders. When considering
consumer electronics overall, we found no support for any of both groups to be lead
users. The reason competition based users were found to be lead users for laptop and
54
smartphone use could be because this type of user may use these devices more
intensively than connection based users. This could also signal an increased interest in
laptops and smartphones for competition based users. However, it should be noted that
like privacy concerns, the means for laptop and smartphone use were found to be quite
high anyways and we could consider Foursquare users to be lead users in general.
Foursquare users lifestyle discussion
Even though there were no great differences in lifestyles for both types of users,
we can still use the data to create a general profile of Foursquare users. The activities
with the highest mean scores where: playing adult games (cards, chess etc.), listening to
music, working on the computer, reading books for pleasure, watching television, and
surfing the web. On the other hand, surprisingly enough, the average mean for activities
such as attending sports events, attending opera, ballet or dance performances and
visiting art galleries and museums was quite low for both groups. This tells us a little bit
about the activities that are preferred by Foursquare users in general as well as
differences between the both groups. Furthermore, using this type of lifestyle data can
provide us with a more defined profile, which will be discussed in future research.
Scientific Implications
Even though not all the hypotheses were supported by the data, almost all of them
were. We now know that it is possible to segment Foursquare users into two separate
groups depending on the way Foursquare is used by them. When choosing a
segmentation method, it is clear that cluster analysis was the best method. This can be
deduced by the fact that when using this method, there was more support for the different
hypotheses, however, a much smaller group of competition based users will be identified
55
and outliers will be have to be removed first. The post-hoc method using check-ins also
did support the hypotheses but less and at a lesser confidence level. This means that while
the method is valid, it is less reliable and should be taken into account when using checkin data for segmentation.
Furthermore, when comparing this research to existing research about location
based networks, it could be said that what was found is in line with existing results.
Foursquare users where found to be social or purpose driven as proposed by Tang et
al(2010). It was also found that Foursquare is used in a variety of ways and privacy
concerns like proposed by Lindqvist et al.(2011) were indeed found to be taken lightly by
our respondents. It can be concluded that using check-in data for carrying out
segmentation through cluster analysis is a valid method that can tell us a great deal about
why Foursquare users decide to use the application and why.
Looking at the issue of what motivates users to check-in and use the application in
more detail, we can look at what motivates both groups of users by looking at their
survey answers.
Table 22. Motivation to check in depending on user type
Competition based user
Connection based user
What motivates you the
Mayorship: 12.5%
Mayorship: 25.6%
most to check in?
Points/Leaderboard: 50%
Points/Leaderboard: 23%
Badges: 25%
Badges: 12.8%
Location Specials: 12.5%
Location Specials: 12.8%
Other: 0%
Other: 25.6%
56
‘Other’ answers included, seeing were friends are, keep track of whereabouts and
as a marketing tool. Looking at the motivation to check in, we can see competition based
users are indeed more attracted by the game aspect as can be seen by the higher
percentage for points and badges. On the other hand, connection based users are more
interested in mayorships and other aspects which relate to a social aspect. Mayorships
could also be considered to be a social as well as a game tool in Foursquare since they
could be seen as a social status at a certain location. Interestingly, both groups of users
have an almost equal percentage of users checking in for location specials. A more in
detail view of the motivation between both groups to check in is given in the following
table:
Table 23. Detailed motivation to check in by user type
Competition Based
Connection Based
Users
Users
I mainly check in when with friends
37.5%
33.33%
I mainly check in when visiting a new
62.5%
74.35%
75%
64.10%
I mainly check in to become mayor
25%
28.20%
I mainly check in to earn points or
62.5%
25.64%
37.5%
12.82%
place
I mainly check in when visiting an
interesting place
badges
I mainly check in to take part in a
special.
57
Note: Users where allowed to check multiple answers
These more in detail answers tell us what the big difference in motivation to
check in is for both groups. That is, competition based users are more motivated to check
in to earn points or badges or to take part in a special and thus more motivated by the
game aspect.
Managerial Implications
After carrying out the segmentation, it is also possible to analyze the different
marketing opportunities between both groups. The following table gives an overview of
this:
Table 24. Marketing opportunities for two types of Foursquare users using cluster
analysis
Q10. Participation in any specials run by
businesses
Q11. Are you likely to do so in the future?
Q12. Has Foursquare ever influenced your
decision to go somewhere (negatively or
positively)?
CON-users
Yes= 72%
No= 28%
COM-users
Yes= 50%
No= 50%
Yes= 54%
No= 15%
Maybe= 31%
Yes= 31%
No= 69%
Yes= 62.5%
No= 0%
Maybe= 37.5%
Yes= 25%
No= 75%
58
Q13 Would you be more likely to go somewhere
in the future if there is a location special which
attracts you?
Yes= 51%
No= 5%
Maybe= 44%
Yes= 25%
No= 12.50%
Maybe= 62.50%
Q14 Have you ever left tips (i.e., reviews or
recommendations) for venues on Foursquare?
Yes= 87%
No= 13%
Yes= 87.5%
No= 12.5%
Q15 Have you ever given feedback on previously
posted tips by adding them to the to-do list or
marking them as done?
Yes= 49%
No= 51%
Yes= 75%
No= 25%%
Q16. Have you ever participated in a marketing
event run through Foursquare by a brand?
Yes= 95%
No= 5%
Yes= 87.5%
No= 12.5%
Based on these percentages, it could be said that connection users are more
influenced by marketing actions. However, since competition users use Foursquare
because of the game aspect and because of being lead users (and therefore willing to try
new things), we can consider them to be more loyal and real users (as can be seen from
Q15 for example). Also, from Q13 we see that connection based users are more affected
by specials which further confirms this point. For this reason, competition users are a
more interesting group for Foursquare while connection based users could be considered
to be more interesting for marketers. Based on Q10, it can also be said that specials are
successful and companies would be wise to use the Foursquare platform for promotions.
Furthermore, looking at Q11, it can be seen both types of users (with competition users a
little bit more) are open minded and loyal according to the high percentage of ‘Yes’
answers.
Table 25. Length of usage for both groups of users using cluster analysis
Q4 How long have you
been using Foursquare?
Competition based users
Connection based users
Less than 6 months: 25%
Less than 6 months: 5.12%
59
Between 6 months and 1
Between 6 months and 1
year: 12.5%
year: 23%
Between 1 and 2 years:
Between 1 and 2 years:
37.5%
41%
Longer than 2 years: 25%
Longer than 2 years: 30%
From this table it can be seen that the great difference between competition based
users and connection based users is the amount of new users where competition based
users have a greater percentage of new users. This can lead us to conclude that while
competition users are lead users (as seen from the before results), connection based users
have a greater amount of early adapters. This is useful from a marketing point of view
since it is now possible to distinguish between the type of user who is an early adopter
and those who are opinion leaders. This information can be used to tailor marketing
initiatives according to the type of user needed.
Finally, when considering which type of marketing is most effective for
reaching Foursquare users, it could be argued that competition based initiatives are more
effective for increasing reaction advertising by directly engaging customers. The fact that
both groups of users are just as equally prone to participate in a special (as can be seen in
table 23) confirms this idea for both type users. Furthermore, this also tells us the current
specials, which can be setup by business are effective and could be considered to be more
effective than other types of social-network advertising.
Lastly we will discuss the different segmentation methods applied. When
comparing the post-hoc methods, a combination of the survey and check-in data had a 70
60
percent overlap with the clustering method as can be seen on table 17. On their own, the
check-in data had a higher percentage overlap with the clustering method. From this it
can be assumed that out of the post hoc methods, using both types of data for
segmentation provides for the best segmentation and only using check-in data the second
best. When considering the clustering method to be the best, the overlap information tells
us how homogenous the segments themselves are and how heterogeneous the survey and
check-in data are used compared to the clustering method. Other segmentation criteria we
can use to measure the efficiency of segmentation include if whether or not the segments
are measurable, substantial, accessible and responsive. The segments were found to be
measurable and accessible for all segmentation methods since the data is available to
measure the size of the market segment (by extrapolating the sample size to the entire
Foursquare Dutch users population) and since the Foursquare users are easy to reach.
Whether or not the segments found are substantial (and so, large enough to warrant a
firm’s attention) is out of scope for us because of the exploratory nature of the research.
Lastly, whether or not the segments found respond better to a different marketing mix
(responsive), is answered by table 24 where different approaches to reach each user are
more effective for each segment.
Considering these criteria for segmentation effectiveness, we can conclude that
from a marketing management perspective the best data to use for Foursquare research
would be a combination of both survey and check-in data (when clustering is not
possible) for the most useful information for marketing research.
Limitations
61
The first limitation faced by this paper has to do with the difficulty of finding
respondents who were willing to give up their check in history and fill in the survey. This
was a challenge, which meant the sample size was limited to 52 respondents. Whether or
not this had to do with the fact that only users with more than 100 check-ins were asked
to participate is something that could be considered. However, it should also be
considered that we did have both extensive check-in and survey data. This limitation
could be overcome in further research by either contacting Foursquare directly for check
in data (which means that users still need to be contacted for survey data if needed) or by
offering a better reward for participation. Another limitation found was the fact that only
Foursquare users where asked to fill in the survey. This means we cannot compare
privacy concerns and opinion leaders between non-Foursquare users and Foursquare
users which might have been interesting. Doing so would have also allowed for a
comparison between marketing actions sensitivity between both groups. The last
limitation to be considered is in the accuracy of the Foursquare data. Because locations
are made by users themselves, and there are not a lot of Foursquare check-in rules, the
accuracy of the check-in data is dependent on the accuracy of the locations created by the
users.
Future research
Future research which could be done with Foursquare data could include making
more in detail profiles. This could be done by carrying out a more in depth lifestyle
investigation for Foursquare users using a larger sample base. Having a larger check-in
data pool could make it possible to go further than just finding if a user is competition or
connection based. Instead, different user profiles could be made where user preferences
62
and marketing sensitivity could be found. Other future research can also look at using
more Foursquare check-ins and analyze usage patterns in more detail. Using check in
data to explore user patterns and the way people behave and move around in cities is also
another interesting application of check in data that could be done for future research.
Check in data could also be used to analyze how people move around in a city and how
new places are discovered or fall out of popularity. This is of course of interest for the
hospitality and retail industries.
63
Appendix A – Survey
Q1 Location Sharing Questions
Strongly
Disagree
Disagree
Neither Agree nor
Disagree
Agree
Strongly
Agree
I am comfortable
sharing my current
location with
friends on
Foursquare





I regularly share
my location with
friends on
Foursquare





I am comfortable
with strangers
seeing my last five
check-ins





I am comfortable
with sharing my
current location
through a public
tweet or status
update





I am comfortable
with sharing my
current location
through a private
tweet or status
update





I am concerned
about my privacy
when using
Foursquare





64
Q2 Likeliness of location sharing
Very
Likely
Likely
Somewhat
Likely
Undecided
Somewhat
Unlikely
Unlikely
Very
Unlikely
How likely
would you
be to share
your location
if it had to
do with a
marketing
initiative
(free
product,
discount)?







How likely
would you
be to share
your location
if it would
lead to
getting a
discount or
an offer
from
companies
(for instance
sharing your
location
earns you a
free drink at
a
restaurant)?







How likely
would you
be to share
your location
if companies
can offer ads
to your
mobile in
real time?
(for instance
if you are
close to an
H&M store,
you get ads
about new
products of
H&M in that







65
store)?
Q3 How did you first find about or started using Foursquare?
 Friends
 Internet
 Already used a similar service
 Other ____________________
Q4 How long have you been using Foursquare?
 Less than 6 months
 Between 6 months and 1 year
 Between 1 and 2 years
 longer than 2 years
Q5 Why did you decide to use Foursquare?
 Friends using it
 Fun way to share location (game-aspect of Foursquare)
 Fun way to explore places
 Social connection (to connect with friends or new people)
 Place discovery (to find new places or recommendation for new places such as
restaurants, cafes, bars)
 Keep track of places that I have visited for me and for friends
 Other, please specify ____________________
Q6 What motivates you the most to check in?
 Mayorship
 Points/Leaderboard
 Badges
 Location Specials
 Other, please specify ____________________
Q7 I check in:
 Always
 Most of the Time
 Sometimes
 Rarely
 Never
Q8 I mainly check-in (you may select more than one):
 When with friends
 When visiting a new place
 When visiting an interesting place
 To become mayor
66
 To earn points or badges

To take part in a special
Q9 Do you check in at any of the following type of venues? (you may select more
than one)
 Colleges/universities
 Great outdoors (parks, forests, lakes, etc.)
 Arts & entertainment
 Food
 Nightlife spots
 Home or work
 Shops
 Travel spots
 Others
Q10 Since using Foursquare have you participated in any specials run by
businesses?
 No
 Yes, specify: ____________________
Q11 Are you likely to do so in the future?
 Yes
 No
 Maybe
Q12 Has Foursquare ever influenced your decision to go somewhere (negatively or
positively)?
 Yes
 No
Q13 Would you be more likely to go somewhere in the future if there is a location
special which attracts you?
 Yes
 No
 Maybe
Q14 Have you ever left tips (i.e., reviews or recommendations) for venues on
Foursquare?
 Yes
 No
Q15 Have you ever given feedback on previously posted tips by adding them to the
to-do list or marking them as done?
 Yes
 No
67
Q16 Have you ever participated in a marketing event run through Foursquare by a
brand?
 No
 Yes, please specify: ____________________
Q17 How often do you use the following social media?
Never
Less than
Once a
Month
Once a
Month
2-3
Times a
Month
Once a
Week
2-3
Times a
Week
Daily
Facebook







Twitter







Youtube







Blogs







Online
Forums







Q18 The following questions deal with consumer electronic products (Smartphones,
computers, etc.). Please indicate what is most suitable to your use of these products:
Strongly
Disagree
Disagree
Neither Agree nor
Disagree
Agree
Strongly
Agree
I am strongly attached
to consumer electronics
products





I am up to date with
news about new
consumer electronics
products





I use my smartphone
frequently during the
day





I use a computer or
laptop frequently during
the day in my free time





I would be at a loss
without my smartphone





I am very attached to
my smartphone





I would be at a loss
without my laptop





I am very attached to
my laptop





I am knowledgeable
about consumer
electronic products





68
I like to try try out new
consumer electronic
products when they
come out





I am always looking out
for new consumer
electonic products





Q19 Please check the frequency in which you engaged in the following activities in
the past two months. Check only one answer for each possible leisure activity.
Never
Less than
Once a
Month
Once
a
Month
2-3 Times a
Month
Once a
Week
2-3
Times
a
Week
Daily
Watching
Television







Playing adult
games (for
example,
cards, chess,
etc.)







Reading books
for pleasure







Competing in
team sports
(for example,
soccer,
baseball,
basketball,
etc.)







Going on a
family outing







Competing in
individual
sports (for
example,
tennis, ping
pong, etc.)







Fitness







Going out for
the evening for
drinks and
entertainment







Bicycling for
leisure







Going to the







69
movies
Visiting art
galleries and
museums







Listening to
music







Collecting or
making
something







Swimming







Attending
sports events







Attending
opera, ballet or
dance
performances







Surfing the
Web







Working on
the computer







Q20 Gender
 Male
 Female
Q21 What is your age?
70
Q22 What is the highest level of education you have completed?
 Elementary school
 High school or equivalent
 Vocational/Technical school (2 years)
 Bachelor’s degree
 Master’s degree
 Doctoral degree
 Professional degree (MD, JD, etc)
 Other
Q23 What is your current marital status?
 Divorced
 Living with another
 Married
 Separated
 Single
 In a relationship
 Widowed
 Would rather not say
Q24 Which situation is most applicable to you?
 Student
 Unemployed
 Working, please specify: ____________________
Appendix B - References
Baines, Paul, Chris Fill, and Kelly Page (2011) "Market Segmentation and Positioning."
Marketing. Oxford UP
Bernoff, Josh, and Charlene Li (2009). "Harnessing the Power of the Oh-So-Social Web
– The Magazine - MIT Sloan Management Review." MIT Sloan Management Review The New Business of Innovation
Brandon Wed, John (2010). "Location-based Services That Reward the Enterprise."
CIO.com.
Chiu Yen-Ting Helena, Fang Shih-Chieh, Tseng Chuan-Chuan (2010). "Early versus
potential adopters: Exploring the antecedents of use intention in the context of retail
service innovations." International Journal of Retail & Distribution Management
Cranshaw, Justin, Eran Toch, Jason Hong, Aniket Kittur, and Norman Sadeh (2010).
"Bridging the Gap Between Physical Location and Online Social Networks." ACM
71
Cocoran, Cate T (2011). "Brands Use Foursquare." WWD.
Coles, Linda (2011). "Social Media for Retailers." NZBusiness.
Funk, J (2007). "The Future of Mobile Shopping: The Interaction between Lead Users
and Technological Trajectories in the Japanese Market." Technological Forecasting and
Social Change
Griner, David (2011). "Beyond Breakout." Adweek.
Howell and Bublik (2009). "Social Factors." NMA
Tussyadiah, Iis P. (2012): A Concept of Location-Based Social Network Marketing,
Journal of Travel & Tourism Marketing, 29:3, 205-220
Lindqvist,, Janne, Justin Cranshaw, Jason Wiese, Jason Hong, and John Zimmerman
(2011). "I’m the Mayor of My House: Examining Why People Use Foursquare - a SocialDriven Location Sharing Application." ACM
Liu, Nan and Chen Guanling (2009). " Multi-Layered Friendship Modeling for
Location-Based Mobile Social Networks." Mobiquitous
Liu, Yang, He Wan, and Xuecheng Yang (2011). "Social Network Based Marketing in
Mobile Phone Users' Community." ACM Digital Library.
Manchada Puneet, Xie Ying and Youn Nara (2008). " The Role of Targeted
Communication and Contagion in Product Adoption." Marketing Science.
Mangold, W. Glynn, and David J. Faulds (2009). "Social Media: The New Hybrid
Element of the Promotion Mix." Business Horizons 52.4 (2009): 357-65.
Maul, Kimberly (2010). "Location-based Networks Help Elevate Consumer Interaction."
PR Week.
Murphy, Michael S (2010). "Notes towards a Politics of Personalization." ACM
Pinsonneault, Alain and Kenneth L. Kraemer (1993) "Survey Research Methodology in
Management Information Systems: An Assessment." Journal of Management
Information Systems
Tang, Karen P., Jialiu Lin, Jason I. Hong, Daniel P. Siewiorek, and Norman Sadeh
(2010). "Rethinking Location Sharing: Exploring the Implications." ACM.
Vasconcelos Maria, Ricci Saulo, Almeida Jusarra, Benevenuto Fabricio, Almeida
Virgilio (2012). "Tips, Dones and ToDos: Uncovering User Profiles in FourSquare."
ACM
72
Ye, Mao, Peifeng Yin, and Wang-Chien Lee (2010). "Location Recommendation for
Location-based Social Networks." ACM
Ziv, Nina D., Mulloth, Bala (2006). " An Exploration on Mobile Social Networking:
Dodgeball as a Case in Point." ACM
73