why people use running apps: a study based on the uses and
TRANSCRIPT
UNIVERSITEIT GENT
FACULTEIT ECONOMIE EN BEDRIJFSKUNDE
ACADEMIEJAAR 2014 – 2015
Why people use running apps: A study based on the uses and
gratifications theory
Masterproef voorgedragen tot het bekomen van de graad van Master of Science in de Bedrijfseconomie
door
Dave Maertens
onder leiding van
Prof. Bart Larivière en Arne De Keyser
UNIVERSITEIT GENT
FACULTEIT ECONOMIE EN BEDRIJFSKUNDE
ACADEMIEJAAR 2014 – 2015
Why people use running apps: A study based on the uses and
gratifications theory
Masterproef voorgedragen tot het bekomen van de graad van Master of Science in de Bedrijfseconomie
door
Dave Maertens
onder leiding van Prof. Bart Larivière en Arne De Keyser
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III
Vertrouwelijkheidsclausule
PERMISSION: Ondergetekende verklaart dat de inhoud van deze masterproef mag
geraadpleegd en/of gereproduceerd worden, mits bronvermelding.
Dave Maertens
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Nederlandse samenvatting Achtergrond: Één van de meest gedownloade smartphone applicaties zijn loopapps. Veel van deze apps zijn echter nauwelijks theoretisch onderbouwd en het onderzoek naar loopapps staat nog in zijn kinderschoenen. Objectief: Het doel van deze studie is een helder beeld te verkrijgen waarom mensen een loopapp gebruiken. Hiervoor werden verschillende variabelen opgenomen: de concrete inhoud en het plezier van de applicatie zelf, de band die het creëert met de loper zijn/haar omgeving of met het merk, het effect op de loper zijn/haar zelfwaarde, persoonlijkheidstrekken en motivationele of gezondheidsredenen. We gebruiken hiervoor het uses and gratification model en gaan na of de genoemde variabelen een effect hebben op zowel de gebruiksintensiteit als het engagement van de app. We gaan eveneens na of het verhogen van de gebruiksintensiteit en engagement op zich, een invloed heeft op de loyaliteit van de loper naar het merk toe. Methode: Via een cross-sectionele online survey werden 127 volledig ingevulde vragenlijsten bekomen. De survey bevroeg de bovenvermelde variabelen door vorig onderzoek van Jahn en Kunz (2012) te adopteren en psychosociale variabelen zoals motivatie (ESR-Q), persoonlijkheid (BFI-10) en ervaren gezondheid (SF-12) toe te voegen. Resultaat: Gebruiksintensiteit en engagement hadden een positief effect op merkloyaliteit. Zowel de inhoud en het plezier van de app, alsook de band die het creëert met de sociale omgeving van de loper, hangen positief samen met gebruiksintensiteit en engagement. Van de psychosociale variabelen was er enkel een significant effect voor motivatie, waarbij meer autonome doelen gelinkt werden met meer gebruiksintensiteit en engagement. Conclusie: Loopapps hebben een significant effect op merkloyaliteit en zijn daardoor voor een bedrijf een effectief middel om dichter en meer in contact te komen met hun klanten. Deze studie is echter slechts een eerste stap en toekomstig onderzoek is bijgevolg nodig om de eigenlijke drivers van gebruiksintensiteit en engagement uit te diepen.
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Preface This dissertation is the ideal way to conclude my studies of business economics at the
University of Ghent. In turn, it also represents the starting point for working - and
more learning - outside the university walls. A lot of time and effort went into this
work and it only managed to reach your eyes thanks to a few people. I’d like to take
this opportunity to acknowledge everyone who has assisted me in creating this
dissertation.
First, I’d like to acknowledge Arne De Keyser, for serving as my advisor and for his
patience and feedback. I truly appreciate all his assistance as I wrote this dissertation.
Further I’d like to acknowledge Dirk Roelens and Robert Coppens for helping me
distribute the survey. Finally, I’d like to thank my girlfriend, parents and friends for
their encouragement, enthusiasm and continued support.
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Table of content
Vertrouwelijkheidsclausule ............................................................................................ III
Nederlandse samenvatting ................................................................................................ V
Preface ........................................................................................................................... VII
Table of content .............................................................................................................. IX
List of Figures ................................................................................................................. XI
List of Tables .................................................................................................................. XI
Introduction ...................................................................................................................... 1
Running apps ................................................................................................................ 2
Theoretical framework ................................................................................................. 6
Gratification .................................................................................................................. 7
Content ..................................................................................................................... 7
Interaction ................................................................................................................. 8
Self-oriented ........................................................................................................... 10
Other psychological variables .................................................................................... 10
Perceived Health ..................................................................................................... 10
Motivation .............................................................................................................. 11
Personality .............................................................................................................. 12
Participation ................................................................................................................ 13
Brand loyalty .............................................................................................................. 14
Method ............................................................................................................................ 16
Data collection and sampling ..................................................................................... 16
Measurement development ......................................................................................... 16
Results ............................................................................................................................ 19
Internal consistency .................................................................................................... 19
Descriptive variables .................................................................................................. 19
Analysis ...................................................................................................................... 22
Hypothesis 1 ........................................................................................................... 22
Hypothesis 2 ........................................................................................................... 22
Hypothesis 3 ........................................................................................................... 24
Hypothesis 4 ........................................................................................................... 25
Hypothesis 5 ........................................................................................................... 25
Hypothesis 6 ........................................................................................................... 25
Hypothesis 7 ........................................................................................................... 26
Hypothesis 8 ........................................................................................................... 26
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Discussion ....................................................................................................................... 30
Management implications .......................................................................................... 33
Strength and limitations .............................................................................................. 35
Conclusion .................................................................................................................. 35
References ......................................................................................................................... I
Appendix ........................................................................................................................ IX
XI
List of Figures
1. Nike+ and Runkeeper interface…………………………………………………………………………3
2. Framework of the uses and gratification model, augmented with psychological variables…………….……………………………………………….………………………………………..….9
3. Significant (green) and non-significant (red) effects of framework………………...27
List of Tables
1. Internal consistency, average, standard deviation and source of constructs.…20
2. Correlation matrix of all constructs………………………………………………………….…….21
3. Summary of linear regression analysis results………………………………………..…23-24
4. Final overview of (un)confirmed hypotheses………………………………………...……….29
5. Internal consistency, average, standard deviation and source of constructs and items……………………………………………………………………………………………………Appendix
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Introduction
Smartphones are on the rise. In 2014, smartphone subscriptions were estimated at 2,7
billion, with a prediction of 6,1 billion for 2020 (Ericsson Mobility Report, 2014). Armed
with a powerful processor, a large resolution screen and capacious memory,
smartphones offer a myriad of functions to its user, such as mobile internet, banking
and email, as well as GPS navigation. As a result, the smartphone has taken up a
central role in most of our modern lives.
Perhaps the most important aspect of smartphones is that they serve as a platform for
applications, so-called ‘apps’. These are small bundles of software that can be directly
downloaded to the smartphone (Cowan et al, 2012). They greatly expand a
smartphone’s functionality and utility and range from healthcare, fitness and lifestyle
apps that help people adopt a healthier lifestyle, to news feeds, travelling and online
shopping aides, games and books. Apple was the first to introduce apps through their
iTunes app store, not long after the development of Apple’s iPhone, the first easy-to-
use smartphone (Wortham, 2009).
Apps are certainly popular. As of September 2014, the iTunes store offers a staggering
amount of over 1.3 million different applications. By 2016, it is anticipated that more
than 44 billion apps will have been downloaded (International Association for the
Wireless Telecommunications Industry, 2011, cited in Cowan et al, 2012). Rival
companies like Google were quick to follow with their own app store. Google offers
their applications in the Google Play store with a list of over 1.5 million different apps,
as of December 2014 (Appbrain, 2014).
Of particular interest are health and fitness-related apps. These apps focus on
improving the health of their users. Examples are widespread and include apps that
track your calorie intake (Calorie Counter), your glucose readings (HelpDiabetes), your
blood pressure (Heartwise Blood pressure Tracker) and apps that provide you with
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fitness instructions (iFitness) or track you while you run (Runkeeper, Nike+, Runtastic).
In recent years, these apps are being more and more perceived as a new, unique
opportunity to connect users – patients, family members, high-risk groups and
healthcare providers – in ways that can improve individual and population health
(Atienza & Patrick, 2011). Indeed, these apps are now broadly recognized as a form of
“mHealth” – mobile health applications that offer several advantages compared to
more traditional interventions. These applications use personal information and
directly interact with the user, with or without the presence of a healthcare
professional. In this way, they can quickly and efficiently be used to provide
information or help to the user. mHealth has emerged as an important field for not
only disease management, but health behavior assessment and interventions as well
(O’Reilly & Spruijt-Metz, 2013). It is now perceived as a new way to reduce the cost of
health care and improve health research and outcomes (Kumar et al., 2013).
The number of mHealth apps that are being published has reached more than 100.000
apps in 2014 (Research2guidance, 2014). Fox (2010) states that nearly 1 in 10
smartphone users have downloaded a health and fitness related app. Indeed, fitness
apps are the lion’s share of mHealth apps. More than 30% of all apps that are listed in
the Health & fitness and Medical app sections of Apple App Store and Google Play are
fitness trackers or exercise guides. The mHealth app market has made some significant
progress along the industry hype cycle. The market revenue reached 2.4 billion USD in
2013 and is projected to grow to 26 billion USD by the end of 2017
(Research2guidance, 2014). Over the period of the last two years, the perception of
mHealth has become increasingly business oriented. In other words, the mHealth app
market has already entered the commercialization phase (Research2guidance, 2014).
Running apps
Our study focuses on a certain type of health and fitness mHealth applications: running
applications. These apps can be downloaded to the user’s phone and aide him/her
while doing a cardio activity. The popularity of these applications is without question -
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running applications like Nike+ and Runkeeper have both been downloaded over 20
million times. For the runner, the added benefit of the app is that they track a myriad
of variables: speed, distance, time, amount of calories burned. Combined with on-body
equipment, it can even keep track of your heart rate (Figure 1). Furthermore, it
enables the user to see the route they have taken and it offers audio-feedback. The
ability to collect health-related information in real-time, provides a way to offer
reminders and immediate feedback in a just-in-time manner (Bickmore, Gruber, &
Intille, 2008). For example, users can instantly receive feedback on their performance
or when they have to slow down/speed up. Another benefit is the ability for the user
to form his/her own online community. By combining running apps with social media,
users can interact with other people, share information and encourage each other.
Moreover, some running applications offer specific training programs while storing all
the information on the application, enabling the user to track their progress.
Figure 1. Example of Nike+ (left) and Runkeeper (middle and right) interface.
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Most of the research up to now has only focused on the effectiveness (Patrick,
Griswold, Raab, & Intille, 2008), accuracy (Kane, Simmons, Thompson & Bassett, 2010),
privacy risks (Anderson & Agarwal, 2011; Raij, Kumar, & Srivastava, 2011) and scientific
basis of health and fitness apps (Verhagen & Bolling, 2015; Cowan et al, 2012).
Remarkably, there is little research being done on the drivers and motivation to use
these apps. Yet, it is obvious that the reasons why people use a running app can differ
greatly. One can use a running app simply for obtaining running statistics, the pleasure
of tracking your run, sharing your run with friends, connecting with the wider running
community or keeping yourself motivated to keep on running. In order to gain a better
understanding why some people stop using a running app, while others sustain and
adopt the running app into their lifestyle, these drivers have to be examined closely.
Furthermore, sustaining behaviour has shown to be affected by other psychosocial
variables as well. First of all, the self-determination theory proposes that more self-
determined behaviour (i.e. when someone acts for the inherent pleasure derived from
that activity) leads to positive behavioural (i.e. sustaining behaviour) (Gagne, 2003)
and physiological (Deci, Ryan, Gagne, et al., 2001) outcomes. Indeed, motivation has
been found to be predictive of changes in exercise level in a school setting (Wilson,
Evans, et al., 2005). Secondly, differences in personality characteristics have been
found to be associated with smartphone and app use (Kim, Briley and Ocepek (2015).
Based on these findings and the limited amount of studies that combine motivation
and personality with app use, we opted to include these variables in this study as
additional drivers.
It’s plain that there is a considerable research gap surrounding running apps. Our study
aims to fill this gap and gain a thorough understanding of what drives people to use a
running app. More specifically, we intend to understand what aspects of the app are
the key drivers to engage people in a meaningful way and increase their likelihood of
using the app. To this effect, we based our research on Jahn and Kunz (2012), and
adopted the uses and gratification model to examine three kinds of drivers: content-
oriented (i.e. using a running app for its content), relationship-oriented (i.e. using a
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running app for social reasons) and self-oriented (i.e. using a running app to promote
oneself). Furthermore, we wish to examine if the personality characteristics and the
type of running goals a person sets for himself have a connection with using a running
app.
As a result, we contribute to the current scientific literature by establishing what drives
people to use running apps, how this affects their use of the app itself and how this is
connected with brand loyalty. Furthermore, we provide practical value on two fronts
as well. From a public health point of view, by gaining a better understanding on the
drivers of using a running app, we can design the running apps in ways that get more
people to use one and therefore engage in exercise. Studies have found that non-
activity is the fourth leading risk factor for premature death (World Health
Organization, 2015a). However, the duration of physical activities required to reduce
these risks doesn’t necessarily have to be long. In fact, the World Health Organization
reported that participation in just 150 minutes of moderate physical activity each week
is estimated to reduce the risk of ischemic heart disease by 30%, the risk of diabetes by
27% and the risk of breast and colon cancer by 21-25% (World Health Organization,
2015b).
From a marketing point of view, running apps can serve as a new way for companies to
establish a customer-brand relationship. A debate has risen over the activities of
brands and companies in social media (Laroche, Habibi, & Richard, 2012). Indeed,
more and more marketers are focusing on building a ‘brand community’, which is a
community not bound by geography and based on the social relations among
supporters of a brand. Companies that manage to create good running apps and
engage their customers have an advantage over their competitors, as branded
applications offer a high level of user engagement and a positive impact on the
attitudes towards the brand (Hutton & Rodnick, 2009, In Bellman, Potter, Treleaven-
Hassard, Robinson, and Varan, 2011). Bellman et al. (2011) confirmed that using these
branded apps has a positive persuasive impact, increasing interest in the brand and
also the brand’s product category.
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This dissertation is organized as follows. First, we give an explanation on the uses &
gratification model we adopted for this study, followed by a brief overview of the
literature regarding our drivers (content, relationship and self-oriented). As stated
above, we decided to augment our model with a fourth kind of driver that might have
an impact on usage intensity and engagement. These include the psychosocial drivers
perceived health, motivation and personality. We formulated eight hypotheses based
on these constructs. Finally, we report the results of our study. We conclude with a
discussion of our findings, limitations of our research and avenues for future research.
Theoretical framework The framework we used is the uses and gratification theory, first proposed by Katz
(1959). This theory has been found to be useful when examining new media like the
internet and online communities (Raacke & Bonds-Raacke, 2008; Sheldon, 2008). It’s
used to research how the specific needs of individual users can be satisfied by specific
media. The model assumes that individuals are active agents and only choose the
medium that meets their desires and enables them to achieve gratification (Perse &
Courtright, 1993). By using this model, we can explicate users’ various goals when
engaging with a running app, allowing for a better understanding of differing behavior,
outcomes and perceptions (Smock, Ellison, Lampe, & Wohn, 2011). The basic idea of
our framework is that, if a running app can fulfill certain needs of a person
(gratification), this should lead to adoption of the app and in time, higher usage
intensity rate and engagement (participation), which in turn makes a consumer more
loyal to the app (brand relationship and loyalty) (Jahn & Kunz, 2012). An overview of
the model can be found below.
The most pronounced needs can be categorized into 3 categories: a content-oriented
area based on the information delivered by the app, a relationship-oriented area based
on social interaction with others and a self-oriented area based on particular needs of
individuals.
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Gratification
Content
For this driver, we decided to differentiate between functional and hedonic values that
are being delivered by the running app, since researchers have found that both
information (Foster, Francescucci, & West, 2010; cited in Jahn & Kunz, 2012) and
entertainment (Sheldon, 2008) play important roles for social media users.
From a functional point of view, people use a running app because it provides them
with feedback about their run, such as heart rate, running speed and time/distance
ran. In this way, the information given by the app benefits a person during his run,
since it enables them to monitor their speed and heart rate and adjust their run
accordingly. Research often mentions this biofeedback as one of the most powerful
means for facilitating the learning of self-regulation. Indeed, biofeedback has been
found to be effective in reducing athletic performance anxiety, as well as increasing
muscle strength, reducing pain and fatigue (Blumenstein, 2002). Furthermore, the app
benefits a person outside of their workout too, as it allows them to monitor their
progress.
However, from a more hedonic point of view, we can assume people also use a
running app because they simply enjoy using it when running. Although the exact
definition of fun is rather elusive, there is general agreement in sport literature that
the role of fun is a key aspect in sport participation. ‘Having fun’ is one of the most
frequent reasons young athletes participate in sport (Kolt et al., 1999). Contrarily, lack
of fun is one of the reasons that some youngsters cite for sport withdrawal (Gould &
Petlichkoff, 1988). As stated above, great care is required when defining ‘fun’. Running
apps were long deemed to work because researchers thought they imply exercise can
be a game. However, in one of the first studies to research the relationship between
social and motivation variables with mHealth activities such as running, Spillers and
Asimakopoulos (2014) demonstrated that gamification (using game-like elements)
does not have a significant relationship with mHealth fitness usage activities. Running
apps such as “Zombies, Run, 2”, where the runner pretends to run away from zombies,
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have little scientific evidence that they actually motivate people to run more often. So
while fun can be a reason to use a running app, it is simplistic to assume that adding a
game to a running app makes running instantly fun and thus attracts more people to
use the app. Based on these findings, we propose the following hypotheses:
H1a. Higher functional value of the running app leads to higher usage intensity and
engagement.
H1b. Higher hedonic value of the running app leads to higher usage intensity and
engagement.
Interaction
Running apps offer the user two kinds of interaction. Firstly, it enables the user to
connect and share their workouts through Facebook or Twitter with friends and other
online peers. Research on social networks acknowledges that growing and maintaining
relationships with others are a major motivation to use a social networking site
(Sheldon, 2008). Indeed, people tend to join social media to fulfill a need of
belongingness (Gangadharbhatla, 2008) and are motivated to engage in content
creation activities to fulfill their desire for social interaction (Hennig-Thurau, Gwinner,
Walsh, & Gremler, 2004). Sherwood and Jeffery (2000) found that social support can
be a crucial element in determining exercise adherence. Based on this, we propose the
following:
H2a. Higher social interaction value of the running app leads to higher usage intensity
and engagement.
Secondly, from a marketing point of view, running apps help a company create a brand
community. These are places where people who admire a brand can socialize with
each other and the brand itself, in a context provided by the brand (McAlexander,
Schouten, & Koenig, 2002). A growing list of companies is spending resources on these
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communities as research has shown that they influence their members and affect
adoption behaviour (Thompson & Sinha, 2008). Furthermore, members gain utilitarian
and hedonic values from their participation in brand communities (Schau, Muñiz &
Arnould, 2009). Additionally, research concerning brand relationships shows that
consumers tend to invest in a relationship with a brand (Algesheimer, Dholakia, &
Herrmann, 2005, cited in Jahn & Kunz, 2012). Based on this, we propose the following:
H2b. Higher brand interaction value of the running app leads to higher usage intensity
and engagement.
Figure 2. Framework of the uses and gratification model, augmented with
psychological variables.
Gratification Participation Brand loyalty
Content-Oriented
Brand Commitment
Functional Value Usage Intensity
Hedonic Value Brand Word-of-mouth
Relationship-Oriented
Social Interaction Value Engagement Brand Purchase
Brand Interaction Value
Self-Oriented
Self concept value
Psychological Variables
Perceived Health Physical component summary Mental component summary
Motivation
External regulation Introjected regulation Identified regulation Intrinsic motivation
Personality
Extraversion Agreeableness
Conscientiousness Openness to experience
Neuroticism
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Self-oriented
People can also decide to use a running app because they expect it will impact their
image or status. In this case, the ability to track your runs and share your best runs
with friends and peers serves as a powerful incentive to keep using a running app. By
doing this, the users attempt to convey a certain positive image of themselves. This
behaviour is explained by the self-presentation theory (Goffman, 2002; cited in
Stragier & Mechant, 2013), a framework to explain motivations for sharing content
online. The theory stipulates that people can use a variety of strategies to impress
others and influence the perception of their image. For example, by sharing their runs
with others, they aim to increase their social attractiveness by highlighting their
running achievements. Spillers and Asimakopoulos (2014) found that most runners are
indeed interested in sharing small goals with their friends, while they usually want to
share a milestone with a broader audience. Similarly, Peluchette and Karl (2009) have
found that Facebook users use their profiles to send out a more positive picture of
them. Based on this, we propose the following:
H3. Using running apps as a way to promote yourself leads to higher usage intensity
and engagement.
Other psychological variables
As stated above, we decided to include other factors, such as health, motivation and
personality, since these might have an influence on the usage intensity and
engagement of running apps.
Perceived Health
Perceived health refers to the perception of a person's health in general, with health
not only meaning the absence of disease or injury but also physical, mental and social
well-being (Statistics Canada, 2014). It’s been long established that sport participation
has a substantial direct effect on perceived health (Thorlindsson, Vilhjalmsson, &
Valgeirson, 1990), with participation and physical exercise directly affecting self-
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assessed health, even when controlling for other variables (Mechanic & Hansell, 1987).
This variable was included in our study, as it’s possible that a person will use a running
app more often when they think their health has improved since they started using it.
In this way, the runner attributes the positive health changes to the running app,
which makes the user use the app more often. Based on this, we propose the
following:
H4. A person perceiving his mental and physical health as good is more likely to have
higher usage intensity and engagement.
Motivation
Research on motivation and sports has been refined since the proliferation of the self-
determination theory, which distinguishes various types of motivation. The self-
determination theory proposes that motivation is multidimensional and resides along
a continuum of self-determination ranging from being not motivated (i.e. when
someone has no motivation to act) through extrinsic motivation (i.e. when a person
acts in response to external cues) to intrinsic motivation (i.e. when someone acts for
the inherent pleasure derived from that activity) (Gillison, Standage, & Skevington,
2006). In recent years, this continuum has been expanded on to reveal autonomous
and controlled motivation, both consisting of two separate sorts of motivation.
Autonomous motivation encompasses both intrinsic motivation and identified
regulation. The first involves doing an activity because one gets pleasure from the task
itself, or from completing the task. We speak of identified regulation when a person
has identified with the importance of a certain behavior and recognizes the beneficial
effects of adopting the behavior. Controlled motivation includes external regulation,
where one’s behavior is a function of external rewards or punishments, and
introjected regulation, where the action has only been internalized partially. The main
motivational driver for behavior with this type of motivation is guilt, shame or worry.
Vast amount of research has confirmed that autonomous motivation and controlled
motivation will lead to very different outcomes. Autonomous motivation tends to yield
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greater psychological health and more effective performance on heuristic types of
activities. It also leads to greater persistence in maintaining changes made toward
healthier behavior (Deci & Ryan, 2008). Other research suggests that self-determined
motives to exercise predict intended and actual behavioural frequency (Wilson &
Rodgers, 2004). Indeed, holding intrinsic (e.g., health) as opposed to extrinsic (e.g.,
attractiveness) goals is associated with adaptive consequences, including sustained
exercise behaviour (Vansteenkiste, Simons, Soenens, & Lens, 2004; cited in Wilson,
Mack, & Grattan, 2008). Based on this, we propose the following:
H5: The more autonomous the user’s goals to working out (i.e. more identified
regulation and intrinsic motivation), the higher usage intensity and engagement.
Personality
Interestingly, there has been some research on the association between smartphone
use and personality. The most widely used psychological model to describe human
personality is the five-factor model, more commonly named the “Big 5” (McCrae &
Costa, 1999). These 5 broad factors - extraversion, agreeableness, conscientiousness,
openness to experience and neuroticism - represent individual differences of thoughts,
feelings and behavior. In a study by Kim, Briley and Ocepek (2015) the researchers
explored predictors of app use in a large representative South Korean Sample.
Although socio-demographic factors were by far the strongest predictor of
smartphone use, individual differences in personality were found to be associated with
smartphone and application use. Extraversion was associated with an increased
probability of using a smartphone. Extraverts also place more emphasis on the
communication features of a smartphone (Lane & Manner, 2011; cited in Kim, Briley, &
Ocepek, 2014) and may adopt more quickly to novel technologies and influence others
in doing the same (Gnambs & Batinic, 2013; cited in Kim, Briley, & Ocepek, 2014), since
extraversion is linked to opinion leadership (Gnambs & Batinic, 2012, cited in Kim,
Briley, & Ocepek, 2014). Conscientiousness was associated an increased likelihood of
owning a smartphone, using apps and a decreased use e-commerce applications. A
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research question is whether the other three constructs are associated with the use of
running apps. Based on this, we propose the following:
H6. A higher score on the Extraversion and Conscientiousness subscale leads to higher
usage intensity and engagement.
RQ1: Does Agreeableness, Openness to experience and Neuroticism have a relationship
with usage intensity and engagement?
Participation
To reiterate, the uses and gratification model stipulates that if a running app manages
to satisfy the various needs a runner has, this should lead to a higher approach to the
running app. However, this approach can be defined in two ways. First of all, since it
fulfills his needs, the user can simply decide to use the running app more often. In this
manner, there would just be an increase in his usage intensity of the app. However,
the user can also engage with the running app in a different, more distinct way. The
user can create a meaningful relationship with the app and its community, interacting
with them and being an integrative part of the community. Hollebeek (2011) defines
this engagement as: “the level of a customer’s cognitive, emotional and behavioral
investment in specific brand interactions”. This definition clearly shows that the
concept of engagement has several facets. If a runner thinks (cognitive) he’s a central
part of a wider community, this relationship will come with positive emotions
(emotional) and will encourage the runner to elicit behaviour that will make him an
even more integral part of the community. When transferring this engagement
construct to the context of running apps, we define running app engagement as being
a participant and integrative user in the running app’s community. As Jahn and Kunz
(2012) suggest, it is unlikely that the constructs ‘usage intensity’ and ‘app engagement’
are not independent from each other and we can assume usage leads to engagement.
Based on this, we propose the following:
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H7: Running app usage intensity influences app engagement positively.
Brand loyalty
Lastly, we can assume an increase in the usage intensity of the app, combined with a
deeper engagement with the app and its wider community will have a positive effect
on brand loyalty, a central concept of marketing. Kotler and Keller (2007) define brand
loyalty as:
“(…) the extent of consumer faithfulness towards a specific brand and this faithfulness
is expressed through repeat purchases and other positive behaviours such as word of
mouth advocacy, irrespective of the marketing pressures generated by the other
competing brands.”
Brand loyalty symbolizes a consumer’s final relationship and deep level of
identification with a brand (Keller, Parameswaran, & Jacob, 2011) as the brand gains
an exclusive and positive place in the minds of the consumer. Research has shown that
brand communities operating on social media can enhance brand loyalty by improving
the customer relationship with the brand, the company, other consumers and the
products (Laroche, Habibi, & Richard, 2013). Brand loyalty is an important concept in
marketing, as it has been shown to increase revenues and market share and help
companies grow in the marketplace (Keller, Parameswaran, & Jacob, 2011). Based on
the definition above, we see that brand loyalty consists of two components, an
attitudinal and a behavioural one. The attitudinal component consists of brand
commitment, which can be understood as an “enduring desire to maintain a valued
relationship” (Moorman, Zaltman, & Deshpande, 1992; cited in Jahn & Kunz, 2012). For
this study, we defined brand commitment as the degree of belongingness a person has
towards a certain running app.
The behavioural component of brand loyalty can be understood as the actions a
customer, who is loyal to a brand, perform. For this study, we broke the concept down
to two factors: customer word-of-mouth and purchase behavior of the brand. The first
15
refers to the actions a customer engages in to actively promote the app. The second
refers to the intention of the customer to remain loyal and keep on buying the brand.
Research has shown that brand communities clearly influence their members’
behaviour. Members not only develop a social identification with the community, but
being a member also influences word-of-mouth behavior and purchase intentions
(Algesheimer, Dholakia, and Herrmann 2005). In addition, members experience
normative pressure to remain loyal to the brand and the community (Algesheimer,
Dholakia, & Herrmann 2005; Muniz, & O'Guinn, 2001).
On the one hand, runners who show high usage intensity come in regular contact with
the app. This should have an effect on the relationship they have with the app and its
community. In term, this should increase their general commitment to the brand and
their tendency to repurchase and recommend the app to others.
On the other hand, runners who show high engagement with the app and its
community are deemed to have a strong emotional bond with the former. Thus, the
basis of brand loyalty is not only usage intensity, but also engagement. Jahn and Kunz
(2012) stipulate that this association is supported by the involvement theory, as both
usage and engagement are indicators for a high involvement with the brand. Indeed,
studies have shown a relationship between involvement and product/brand loyalty
(Olsen, 2007; cited in Jahn & Kunz, 2012). Based on this, we propose the following
hypotheses:
H8a: Running app usage intensity and engagement influences word of mouth
promotion positively.
H8b: Running app usage intensity and engagement influences purchase intentions
positively.
H8c: Running app usage intensity and engagement influences brand commitment
positively.
16
Method
Data collection and sampling
The target population consisted of people who have experience with using a running
app. Experience was defined as having used a running app at least once. We executed
a survey and distributed it online through various channels. Firstly, we invited
members through the Facebook fan page of the different running apps and various
running events to participate in the online survey by posting the survey link. Secondly,
e-mails were sent out to every Flemish athletics club, urging them to spread the survey
in their club. Lastly, students at the University of Ghent were encouraged to fill in the
survey as well. In order to increase the response rate, there was a chance to win a gift
certificate for Amazon, Bol, Fnac or Kinepolis when the survey was completely filled in.
Surveys that were filled in in less than 1 minute were excluded from the analysis. After
this, we obtained a sample of 161 surveys started, with 127 fully completed
questionnaires. A demographic overview can be found in the results section below.
Measurement development
The survey adapted questionnaire items from previous literature. As the study
incorporated many variables, we opted for choosing questionnaires that were not only
known for the strong validity and reliability, but for their brevity as well.
The survey started with questions regarding which running app people use. In the
second section, questions were asked regarding functional/hedonic value, social/brand
interaction and the users’ self-concept. These questions were adopted from Jahn and
Kunz (2012) and were answered on a 7-point Likert scale ranging from “Totally
disagree” to “Totally agree”. In the same way, usage intensity, fan engagement and
brand loyalty were all examined by adopting Jahn & Kunz’ (2012) questionnaire items.
We urged the participants to only consider the running app that they used most often.
In the third section, the users’ motivation to exercise was examined with the Exercise
Self-regulation Questionnaire (Brown, Miller, & Lawendowski, 1999). This
17
questionnaire uses the same 7-point Likert scale and is based on the self-
determination theory. It concerns the reasons why a person exercises regularly or
engages in other such physical activities. It is structured so that it provides responses
that represent the constructs external regulation, introjected regulation, identified
regulation and intrinsic motivation.
Next, the runner’s health was analyzed by using the Short Form-12 health survey (SF-
12), a multipurpose short-form with only 12 questions. It’s a shorter version of the SF-
36 Health Survey (Ware, Kosinski, & Keller, 1996) and measures functional health and
well-being from the patient’s point of view. Specifically, it measures eight domains of
well-being: physical functioning (PF), bodily pain (BP), role limitations due to physical
problems (RP), general health perceptions (GH), energy and vitality (VT), role
limitations due to emotional problems (RE), social functioning (SF) and mental health
(MH) (Ware & Sherbourne, 1992). These domains are then further summarized into a
physical component summary (PCS), which gives more weight to the first 3
components, and a mental component summary (MCS), which gives more weight to
mental health, social functioning and role limitations due to emotional problems
(Ware, Kosinksi, & Keller, 1996). To calculate these summary scores, test items are
scored and normalized. They have a range of 0 to 100 with a mean score of 50 and a
standard deviation of 10. When deriving the summary scores, we opted for oblique
rotation as this is more consistent with changes in individual scales (Fleishman, Selim &
Kazis, 2010). It is important to note that this questionnaire only asks about the
participant’s health during the last month.
Furthermore, we measured personality by using the BFI-10, an abbreviated form of the
Big Five Inventory (BFI-44) (Rammstedt & John, 2006). This questionnaire consists of
10 items – two for each constructs -scored on a 5-point Likert scale ranging from
“Disagree Strongly” to “Agree strongly”. Not much reliability and validity is lost by
reducing the BFI scales to just 2 items, since the BFI-10 scales captured 70% of the full
BFI variance and retained 85% of the retest reliability on average. However, the
reduction to two items is noticeable for the construct Agreeableness. Nonetheless, as
18
this construct had limited value in our research and since Rammstedt and John (2006)
mention that the BFI-10 is only to be used in research settings in which time is a factor,
we favored the BFI-10. Finally, demographic factors such as sex, age, ethnicity,
country, country-of-origin, highest degree received, marital & employment status were
asked about, as well as running frequency.
19
Results
In order to find support for an effect of the above-mentioned drivers, we started with
assessing the internal consistency of the constructs by calculating Cronbach alpha’s
(Cronbach, 1970). Next, we give an overview of the descriptive variables. We end with
discussing our results for every hypothesis individually.
Internal consistency As can been seen from Table 1, the Cronbach alpha for the Agreeableness (α = 0.47),
Conscientiousness (α = 0.18) and Openness to Experience (α = 0.15) subscale of the
BDI-10 personality scale were found to be lower than .60. This means that the scales
are less reliable and are probably not one-dimensional. This will have to be taken into
account when analyzing the results. All the other constructs were found to have a
good internal consistency (α > .60) and therefore we used the mean scores for the
analysis. The correlation matrix of all constructs is shown in table 2. A full summary of
the constructs, the items they consist of, their averages and standard deviations can be
found in the Appendix.
Descriptive variables
The final sample consisted of 127 participants. Gender was distributed evenly, with 68
participant being male (54%) compared to 59 being female (46%). The most
represented age was the 25-34 age group (50%), followed by the 18-24 age group
(30%) and the 35-44 age group (14%). Only seven participants were older than 45 (6%).
Most of the participants were from Belgium (84%), with others residing in the United
States of America (4%), Sweden (2%), the UK (2%) and France (2%). 88% of the
participant were frequent runners, reporting that they run at least once a week. The
most used running app was Runkeeper (46%) followed by Nike+ (27%), Runtastic (20%)
Strava Run (13%), Endomondo (9%), Garmin Connect (4%) and Start to Run (3%). Only
a few participants reported using other running apps, such as MapMyRun, Run The
Map, RunTrainer, TomTom My Sports, Caledos, Jawbone and Polar Loop (8%
combined).
20
Table 1
Internal consistency, average, standard deviation and source of constructs
Construct items α M SD Source
Functional value 4 0.88 6.07 0.71 Jahn & Kunz (2012)
Hedonic Value 4 0.92 5.12 1.12
Social Interaction Value 4 0.93 3.50 1.64
Brand interaction value 4 0.93 3.45 1.55
Self-concept value 4 0.93 4.33 1.54
Usage intensity 3 0.89 5.18 1.65
Engagement 5 0.96 2.90 1.53
Brand attitude 4 0.91 5.86 0.87
Word of mouth 3 0.92 5.49 1.28
Purchase 3 0.91 4.97 1.46
Brand Commitment 3 0.81 3.86 1.53
Brand Loyalty 3 0.88 4.78 1.28
Perceived Health SF12v2
Ware, Kosinski, &
Keller (1996)
Physical Component Summary 4 0.71 50.93 8.11
Mental Component Summary 4 0.71 50.11 8.70
Motivation (“I work out…”) SRQ-E
Brown, Miller, &
Lawendowski (1999)
External Regulation 3 0.79 4.41 1.30
Introjected Regulation 3 0.65 4.61 1.17
Identified Regulation 3 0.70 5.74 0.96
Intrinsic Motivation 3 0.82 4.81 1.39
Personality (“I see myself as
someone who…”)
BFI-10
Rammstedt & John
(2006) Extraversion 2 0.68 3.30 1.01
Agreeableness 2 0.45 3.56 0.77
Conscientiousness 2 0.20 3.59 0.77
Neuroticism 2 0.60 2.71 1.02
Openness to Experience 2 0.13 3.60 0.81
Note. α = Cronbach’s alpha , M = mean, SD = standard deviation.
21
Note: *. Correlation is significant at the 0.05 level (2-tailed). **Correlation is significant at the 0.01 level (2-tailed). FV = Functional Value; HV = Hedonic Value; SIV = Social Interaction Value; BIV= Brand interaction Value; SCV = Self Concept Value; PCS = Physical Component Summary; MCS = Mental Component Summary; IR = Introjected Regulation; ER = External Regulation; IdR = Identified Regulation; IM = Intrinisc Motivation; E = Extraversion; A = Agreeableness; C = Conscientiousness ; N = Neuroticism ; O = Openness to Experience; UI = Usage Intensity; Eng = Engagement; BC = Brand Commitment; WOM= Brand Word-Of-Mouth; BP = Brandpurchase.
Table 2
Correlation matrix of all constructs
FV HV SIV BIV SCV PCS MCS IR ER IdR IM E A C N O UI Eng BC WOM BP
FV
HV .39**
SIV .22* .35**
BIV .11 .25** .46**
SCV .11 .21* .40** .35**
PCS -.07 .03 .12 .01 -.04
MCS .31** .09 .13 .16 .09 -.30**
IR .03 .05 .05 .04 .12 .11 -.21*
ER -.11 -.02 .08 .05 .22* .09 -.13 .56**
IdR .35** .35** .251** .191* .09 .33** .08 .29** .08
IM .24** .33** .39** ,183* 0,15 .44** .04 .13 .05 .66**
E .05 .20* .10 .09 .23** -.16 .17 .04 .07 .00 .05
A .09 .02 .13 .04 .02 .02 .25** -.16 -.08 .10 .14 .23**
C .08 -.04 .03 .05 .07 .01 .07 .04 -.05 .24** .12 .20* .07
N -.01 .01 -.03 -.15 -.10 -.03 -.32** .08 -.06 -.05 -.02 -.17 -.33** .00
O -.06 .10 .04 .12 .08 .16 -.12 .01 .09 .08 .12 .30** .07 .21* -.07
UI .42** .27** .25** .07 .04 .08 .15 .00 -.15 .40** .30** -.03 .00 .11 .15 -0,12
Eng .18* .26** .49** .27** .34** .02 .22* .06 .14 .13 .22* .10 .12 -.07 .00 -.08 .41**
BC .40** .45** .53** .45** .42** .01 .25** -.03 .02 .29** .39** .07 .07 .04 .00 .00 .47** .68**
WOM .57** .45** .36** .23** .21* -.01 .31** -.04 -.08 .43** .42** .14 .16 .10 .00 -.05 .61** .42** .67**
BP .56** .49** .31** .25** .17 -.04 .27** -.08 -.11 .30** .30** .14 .04 .05 -.05 -.01 .56** .40** .70** .78**
22
Analysis
To find evidence for our hypotheses, we performed a linear regression analysis for
every single hypothesis. In a first step, we ran our analysis with only the focus
variables. If a significant effect was found, a second analysis was run to control for
variables such as age and sex. The results of our analysis are listed below separately for
each hypothesis. An overview of our results can be found below in Table 3, while figure
3 provides a visual view of our predicted model. Finally, a quick summary of our
hypotheses can be found at the end of the results section (Table 4).
Hypothesis 1
Our first hypothesis predicted that (a) higher functional value and (b) higher hedonic
value of the running app leads to higher usage intensity and engagement. A simple
linear regression was calculated to predict usage intensity and engagement based on
functional and hedonic value of the app. After controlling for age and gender,
functional value significantly predicted usage intensity ( = .41, t(120) = 4.97, p < .000)
and engagement ( = .18, t(120) = 2.05, p = .043), and the model explained a
significant proportion of variance in usage intensity, R² = .21. F(1,120)= 10.52, p < .000.
Controlling for age and gender, hedonic value also significantly predicted usage
intensity ( = .27, t(120) = 3.08, p = .003) and engagement ( = .26, t(120) = 2.97, p =
.004) , and the model explained a significant proportion of variance in usage intensity,
R² = .12. F(1,120)= 5.20, p = .002, and engagement, R² = .07. F(1,120)= 3.04, p = .032.
We confirmed the hypothesis that higher functional and hedonic value of the app
leads to higher usage intensity and engagement.
Hypothesis 2 Our second hypothesis predicted that (a) higher social interaction value and (b) higher
brand interaction value of the running app leads to higher usage intensity and
engagement. After controlling for age and sex, social interaction value significantly
predicted usage intensity ( = .22, t(120) = 2.42, p = .017) and engagement ( = .52,
23
t(120) = 6.58, p < .000), respectively explaining a significant portion of variance for
respectively usage intensity, R²= .09. F(1,120)=3.94, p = .010) and engagement R²= .26.
F(1,120)=14.55, p < .000). Brand interaction value did not significantly predict usage
intensity ( = .071, t(122) = .79, p = .433), but after controlling for sex and age, brand
interaction value did significantly predict engagement ( = .27, t(120) = 3.11, p = .002),
explaining a significant portion of variance for engagement R²= .075. F(1,120)=3.32, p =
.022). We confirmed the hypothesis that higher social interaction value leads to higher
usage intensity and engagement. However, we could not confirm the hypothesis that
higher brand interaction value predicted higher usage intensity, as it only predicted an
increase in engagement.
Table 3
Summary of linear regression analysis results
IV DV B SE B t p
Functional Value Usage Intensity .95 .19 .41 4.97 .000
Functional Value Engagement .40 .19 .18 2.05 .043
Hedonic Value Usage Intensity .39 .13 .27 3.08 .003
Hedonic Value Engagement .35 .12 .26 2.97 .004
Social Interaction Value Usage Intensity .22 .09 .22 2.15 .017
Social Interaction Value Engagement .49 .07 .52 6.58 .000
Brand Interaction Value Usage Intensity .08 .10 .07 .79 .433
Brand Interaction Value Engagement .27 .09 .27 3.11 .002
Self-Concept Value Usage Intensity .04 .10 .04 .42 .673
Self-Concept Value Engagement .35 .09 .35 4.04 .000
Physical Component Summary Usage Intensity .16 .18 .08 .88 .383
Physical Component Summary Engagement .04 .17 .02 .21 .837
Mental Component Summary Usage Intensity .29 .17 .15 1.70 .092
Mental Component Summary Engagement .39 .16 .22 2.54 .012
Introjected Regulation Usage Intensity .00 .13 .00 .00 1
Introjected Regulation Engagement .08 .12 .06 .69 .500
External Regulation Usage Intensity -.19 .12 -.15 -1.63 .110
External Regulation Engagement .16 .10 .14 1.53 .130
Identified Regulation Usage Intensity .66 .14 .38 4.61 .000
24
IV DV B SE B t p
Identified Regulation Engagement .21 .14 .13 1.46 .150
Intrinsic Motivation Usage Intensity .34 .10 .29 3.42 .001
Intrinsic Motivation Engagement .25 .10 .23 2.62 .010
Extraversion Usage Intensity -.04 .15 -.03 -.29 .777
Extraversion Engagement .16 .13 .10 1.17 .244
Agreeableness Usage Intensity .01 .20 .00 .05 .961
Agreeableness Engagement .23 .18 .12 1.30 .197
Conscientiousness Usage Intensity .24 .19 .11 1.27 .206
Conscientiousness Engagement -.13 .17 -.07 -.74 .463
Neuroticism Usage Intensity .25 .15 .15 1.69 .094
Neuroticism Engagement -.01 .13 .00 -.03 .973
Openness to Experience Usage Intensity -.24 .18 -.12 -1.31 .193
Openness to Experience Engagement -.15 .17 -.08 -.91 .363
Usage Intensity Engagement .39 .08 .43 5.06 .000
Usage Intensity Word-Of-Mouth .46 .06 .60 8.13 .000
Usage Intensity Brand purchase .48 .07 .54 7.05 .000
Usage Intensity Brand Commitment .43 .08 .47 5.67 .000
Engagement Word-Of-Mouth .35 .07 .42 5.28 .000
Engagement Brand purchase .38 .08 .40 4.91 .000
Engagement Brand Commitment .68 .07 .68 10.17 .000
Note: IV = Independent Variable; DV = Dependent Variable; B = unstandardized coefficient; SE B = standard
deviation of B; Beta = Standardized coefficient;
Hypothesis 3
Our third hypothesis predicted that higher self-concept value leads to higher usage
intensity and engagement. Self-concept value did not significantly predict usage
intensity ( = .04, t(122) = .423, p = .673). However, after controlling for age and
gender, a significant effect was found between self-concept value and engagement (
=.35, t(120) = 4.04 p < .000), explaining a significant portion of variance for
engagement R²= .12. F(1,120)= 5.53, p = .001). We could not confirm the hypothesis
that higher self-concept value leads to higher usage intensity, as it only predicted an
increase in engagement.
25
Hypothesis 4
Our fourth hypothesis predicted that perceived health is positively linked with usage
intensity and engagement. The aggregated score for physical health was found not to
predict both usage intensity ( = .08, t(120) = 8.8, p = 0.38) and engagement ( = .02,
t(120) = .21, p = .84). The aggregated score for mental health was not found to predict
usage intensity ( = .15, t(120) = 1.70, p = .092), but did predict engagement, even
after controlling for age and sex ( = .22, t(120) = 2.54, p = .012). We could not confirm
the hypothesis that good physical health leads to an increase in usage intensity and
engagement. However, one of these relationships was found for mental health, as an
increase in mental health leads to higher engagement.
Hypothesis 5
Our fifth hypothesis predicted that more self-determined goals were linked with higher
usage intensity and engagement. Of the four motivational constructs, only identified
regulation ( = .38, t(120) = 4.61 p < .000) and intrinsic motivation (( =.29, t(120) =
3.42 p = .001) had a significant effect on usage intensity after controlling for age and
gender), explaining a significant amount of variance for respectively identified
regulation R²= .19. F(1,120)= 9.32, p < .000) and intrinsic motivation R²= .13. F(1,120)=
5.97, p = .001). After controlling for age and gender, only intrinsic motivation had a
significant effect on engagement ( = .23, t(120) = 2.62 p = .010). We confirmed the
hypothesis that more autonomous motivation is related to an increase in usage
intensity and engagement.
Hypothesis 6
Our sixth hypothesis predicted that higher Extraversion and Conscientiousness leads to
higher usage intensity and engagement. Extraversion did not significantly predict usage
intensity ( = -.06, t(122) =-.28 p = .777) and engagement ( = .10, t(122) =1.17 p =
.24). Conscientiousness did neither significantly predict usage intensity ( = .11, t(122)
=1.27 p = .206), nor engagement ( = -.07, t(122) =-.74 p = .463). We disconfirmed the
26
hypothesis that a higher score on the Extraversion and Conscientiousness subscale was
related to higher usage intensity and engagement.
An open research question was asked regarding the relationship between the other
personality characteristics, Agreeableness, Openness to experience and Neuroticism,
and usage intensity and app engagement. No significant effects were found for the
three personality characteristics.
Hypothesis 7
Our seventh hypothesis predicted that higher usage intensity leads to higher
engagement. After controlling for age and sex, a significant effect was found. Usage
intensity significantly predicted engagement ( = .43, t(120) = 5.06, p < .000), and also
explained a significant proportion of variance in engagement, R² = .17. (F(1,120)= 8.54,
p < .000). We confirmed the hypothesis that higher usage intensity predicts higher
levels of engagement.
Hypothesis 8
Our final hypothesis stipulates that both usage intensity and engagement predicted
word of mouth promotion (a), purchase intentions (b) and brand commitment (c).
H8a: Word of mouth
Both usage intensity ( = .60, t(120) = 8.13, p < .000) and engagement ( = .42, t(120)
= 5.28, p < .000) predicted word of mouth promotion. Usage intensity explained a
significant proportion of variance, R² = .39. F(1,120)= 25.37, p < .000, and so did
engagement, R² = .22. F(1,120)= 11.78, p < .000).
27
(red) effects of framework. Constructs coloured red have no significant effects on both usage intensity and engagement.
Gratification Participation Brand Loyalty
Content Oriented
Functional Value
Relationship Oriented
Hedonic Value
Self Oriented
Social Interaction Value
Perceived Health
Brand Interaction Value
Self-Concept Value
Motivation
Perceived Physical Health
Perceived Mental Health
External Regulation
Introjected Regulation
Personality
Identified Regulation
Intrinsic Motivation
O C E A N
Engagement
Usage Intensity
Other Psychological Variables
Figure 3. Significant (green) and non-significant (red) effects of framework. Constructs colored red have no significant effects on
both usage intensity and engagement.
Note. * p < .05; ** p < .01 ; *** p < .001.
Brand Commitment
Purchase
Word of Mouth (H8a) .60***
(H8a) .42***
(H1a) .41***
(H1b) .27**
(H1b) .26**
(H1a) .18*
(H5) .38***
(H5) .29***
(H2a) .22*
(H2a) .52***
(H2b) .27***
(H3) .35***
(H4) .22*
(H5) .29***
(H7) .43***
(H8c) .68***
(H8c) .47***
(H8b) .54***
(H8b) .40***
28
H8b: Purchase intentions
Both usage intensity ( = .54, t(120) = 7.05, p < .000) and engagement ( = .40, t(120)
= 4.91, p < .000) predicted purchase intentions. Their explained proportion of variance
is respectfully R² = .33. F(1,120)= 19.73, p < .000) and R² = .20. F(1,120)= 10.51, p <
.000).
H8c: Brand commitment
Both usage intensity ( = .47, t(120) = 5.66, p < .000) and engagement ( = .68, t(120)
= 10.17, p < .000) predicted brand commitment. Their explained proportion of variance
is respectfully R² = .21. F(1,120)= 10.92, p < .000) and R² = .46. F(1,120)= 34.64, p <
.000).
Based on these results, we confirmed the hypothesis that higher usage intensity and
engagement predicts an increase in word of mouth promotion, purchase intentions
and brand commitment.
29
Table 4
Final overview of (un)confirmed hypotheses
Hypothesis Confirmed?
H1a Higher functional value of the running app leads to higher usage intensity and
engagement.
H1b Higher hedonic value of the running app leads to higher usage intensity and
engagement.
H2a Higher social interaction value of the running app leads to higher usage intensity
and engagement.
H2b Higher brand interaction value of the running app leads to higher usage intensity
and engagement.
H3 Using running apps as a way to promote yourself leads to higher usage intensity
and engagement.
Partly
H4 A person perceiving his mental and physical health as good is more likely to have
higher usage intensity and engagement
Partly
H5 The more autonomous the user’s goals to working out (i.e. more identified
regulation and intrinsic motivation), the higher usage intensity and engagement.
H6 A higher score on the Extraversion and Conscientiousness subscale leads to higher
usage intensity and engagement.
X
RQ1 Does Agreeableness, Openness to experience and Neuroticism have a relationship
with usage intensity and engagement?
X
H7 Running app usage intensity influences app engagement positively.
H8a Running app usage intensity and engagement influences word of mouth promotion
positively.
H8b Running app usage intensity and engagement influences purchase intentions
positively.
H8c Running app usage intensity and engagement influences brand commitment
positively.
Note: = Confirmed, X = not-confirmed.
30
Discussion
In our study, we analyzed the role running apps can play as a new way to form a
meaningful customer-brand relationship. To this effect, we implemented the uses and
gratification model and adapted the drivers for successful brand fan pages proposed
by Jahn and Kunz (2012), while inserting other psychosocial variables such as
motivation, perceived health and personality in our model. We identified both running
app engagement and usage intensity as important drivers for the customer-brand
relationship. Several significant variables for successful running apps are described.
Based on these results, we can derive several implications for the management of
running apps.
Hypothesis eight stated that usage intensity and engagement to the app were linked
with brand loyalty, as measured by word-of-mouth promotion, purchase intentions
and brand commitment. By confirming this hypothesis, we can draw the first major
conclusion that running apps can be a useful tool for companies in establishing a
relationship with their customers. Brand loyalty is positively affected by both the usage
intensity and engagement towards the app and its wider community. Therefore, we
confirm previous research by Thompson and Sinha (2008), who show that a higher
level of participation in a brand community will lead to brand loyalty. Other research
(Erdoğmuş & Mesut Cicek, 2012) already established that companies may want to
work on creating more engaging and participative applications in order to draw their
customer’s interest. Our research further proves the importance of this
recommendation. This effect is significant, as it shows that running apps can be used
to establish a relationship with a customer and engage them to a brand in a
meaningful way. However, the question remains: what drives a person to use a
running app? We look at our other results to answer this.
First of all, based on our data, we could confirm hypothesis one that valuable
functional and hedonic content of the running app were both important drivers for
getting people to use the app more frequently. This corroborates and expands
31
previous research by Foster et al. (2010) and Sheldon (2008) who found those two
drivers to be important in the use of social media. This implies that runners are likely
to use running apps more if they can derive pleasure from it and it provides them with
adequate information.
However, unlike Jahn and Kunz’s research (2012), our results show that both
functional and hedonic values also predict engagement in a positive way. A number of
reasons could explain this relationship. Firstly, their research was based on simply
liking brand fan pages, distinctive from using running apps since these require more
commitment and effort of the user. Secondly, it is not unlikely that, with our sample
consisting of 88% frequent runners, the users were already using the app for quite
some time and had had enough experience with the app to form a deeper connection
with the brand behind it. Previous research suggested that membership duration in a
brand community is indeed positively associated with brand engagement - meaning
that the longer a person considers himself a member of a brand community, the more
likely he is to be engaged to the brand (Thompson & Sinha, 2008).
What these results suggest is that running app users are more likely to engage in a
brand relationship when the brand offers relevant content, confirming previous
research by Erdoğmuş and Mesut Cicek (2012). This is important because it shows that,
if companies want to attract more users for their running apps, they have to deliver
both interesting and entertaining content to its users. However, as said above, what
constitutes as entertaining and fun (Spillers & Asimakopoulos, 2014) is rather elusive.
Therefore, we urge future, qualitative research to examine these constructs more
closely. We should try to understand what type of content the users value the most
and in what way the application is fun to them.
Secondly, we could confirm hypothesis two that states users who feel connected to
other runners by the running app were likely to use the app more often. This
corroborates previous research (Sherwood & Jeffery, 2000) that states social support
can be a crucial element in determining exercise adherence. The desire for social
interaction proved to be an important motivator for using the app more (Hennig-
32
Thurau, Gwinner, Walsh, & Gremler, 2004). It’s noteworthy that all of the running apps
used by our participants had the ability to connect to Facebook and Twitter to share
runs and goals.
Additionally, social interaction was positively associated with brand engagement. This
implies that runners, who use the app to find likeminded people, see themselves as an
engaged member in the brand community. Not surprisingly, we also found that
runners, who feel they can interact and communicate with the brand, are more likely
to report being an engaged and integrated member of the app’s community. In this
way, we continued and confirmed previous research by Laroche, Habibi and Richard
(2012), who found that brand communities based around social media can have
positive effects on engagement towards a brand. These results imply that runners are
more engaged towards a running app if it enables them to communicate with the
brand or developers.
Thirdly, hypothesis three could only partly be confirmed, as the desire for online self-
presentation was not associated with an increase in the use of the running app.
However, self-presentation was linked to more engagement towards the brand. These
results confirm previous research by Stragier and Mechant (2013), who found that only
community identification, receiving feedback and sharing information positively
influenced posting a workout on Twitter. It seems that building a reputation of a
runner is not a reason by itself why people share their workouts. These results suggest
again that running should be designed in a way that enables runners to easily join
communities and interact with each other.
Fourthly, the inclusion of psychosocial variables only resulted in a few significant
relationships. With regard to perceived health, we could only partly confirm hypothesis
four that physical and mental health is associated with usage intensity and
engagement. Only the aggregated score for mental health was positively associated
with engagement. This construct is based on the same factors as the PCS, but gives
more weight to role limitations due to emotional problems, social functioning and
33
mental health. This could imply that runners who feel mentally healthy are more likely
to use a running app to interact and engage with a brand.
Regarding motivation, there was a clear association between motivation, usage
intensity and engagement, confirming hypothesis five. Runners who have more self-
determined goals (i.e. who exercise for the inherent pleasure), use the application
more often and are more likely to be engaged towards the brand. The association
could however not be found for runners who are extrinsically motivated. This confirms
previous research by Deci and Ryan (2008), who found that self-determined goals lead
to greater persistence in maintaining healthy behaviour.
Concerning personality, we could neither confirm hypothesis seven nor our research
question, as personality traits were not related to usage intensity and engagement
with the running app community. Indeed, none of the five personality characteristics
significantly predicted usage intensity and engagement. A number of reasons can be
given. Firstly, the constructs Agreeableness, Conscientiousness and Openness to
Experience were not reliable and were therefore probably not one-dimensional.
Rammstedt and John (2006) mentioned that the reduction of the BFI-44 questionnaire
to a questionnaire with only two items per personality trait could provide a problem
for the Agreeableness subscale. Additionally, the Conscientiousness scale consists of
two items: one regarding laziness and the other regarding how thorough one does his
job. It’s possible we biased this scale since the majority of participants were frequent
runners and thus less likely to feel lazy, while thoroughness can vary more.
Management implications
Management should view running apps as a new, unique opportunity to establish a
customer-brand relationship. Companies are more and more interested in creating a
brand community and this study shows that running apps can help with that.
As stated above, management should make sure that the running app provides
relevant content and entertainment. Furthermore, by enabling the running app to be
paired with social media, companies effectively turn these apps into interactive
communication channels between users and the brand (Kaplan & Haenlein, 2010; cited
34
in Laroche, Habibi, & Richard, 2012). The inclusion of social media in running apps
clearly has an effect on a firm’s marketing strategy, as it completely shifts from trying
to sell a product to one where establishing a relationship with the user is the number
one priority (Gordhamer, 2009). Thus, from a marketing point of view, the goal of a
running app should be to completely engage users in an active community. Companies
should support and design the app in such a way that facilitates interaction among
users. To this effect, they should try and create a brand community around the app,
creating a sense of belonging in its users. This study confirms previous research by
Laroche, Habibi and Richard (2012), who found that brand communities based around
social media can have positive effects on brand loyalty.
Marketeers should note that our results indicate the way people are motivated, will
influence their use and engagement towards an app. This implies that running apps
should be designed in a way that stimulates runners to think about running as self-
autonomous activity. It could also indicate that, if running apps are too focused on
counting calories and urging people to lose weight, this could have an adverse effect
on the engagement towards a brand. Furthermore, studies have shown that
motivation processes are influenced by self-serving bias (Shepperd, Malone, &
Sweeny, 2008), a tendency where people reject the validity of negative events and
attribute their cause to something external. It’s possible that externally motivated
runners, who fail in the goals they set up, attribute this failure to the running app
instead of themselves. In the end, this could cause them to stop using the application,
which is obviously something to be avoided. We find further evidence for this in
previous research on engagement (Algesheimer, Dholakia, & Herrmann 2005), which
stated that engagement is comprised of multiple components, with some leading to
positive outcomes and others to negative states. By viewing engagement as a double
edged sword, normative pressure and extrinsic goals could lead to a withdrawal from a
brand. Finally, we urge marketeers to remember that previous research indicates
companies should only consider developing a product or service if it’s congruent with
their brand identity, as otherwise it could lead to a gap between brand identity and
brand image (De Chernatony, 1999).
35
Strength and limitations
Our present research makes contributions to the existing literature regarding running
apps. To our knowledge, this study is the first to examine running apps in a uses and
gratification framework. This way, it contributes to the existing literature regarding
running apps by identifying the drivers for running app use.
Nevertheless, this study has some limitations. First of all, since this study was based on
data collected through surveys in a cross-sectional design, we can only demonstrate a
connection between constructs, not the existence of causal relationships. Future,
longitudinal research is needed to fully understand the causal relationships between
the proposed drivers and the other variables. Additionally, qualitative research is
required to further explore what constitutes as an ‘informational’ or ‘fun’ running app.
Finally, the majority of our participants were relatively young (< 34 years) and most of
the participants were from Belgium (84%), urging caution in generalizing these results.
Conclusion
Our findings show that running apps are an effective tool for companies, as they have
measurable effects on the customer-brand relationship. By combining running apps
with social media, companies have a new and unique way to interact with their
customers. This study is a first step in gaining a better understanding why people use
running apps.
XI
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Appendix
Table 5
Internal consistency, average, standard deviation and source of constructs and items
Source Construct α M SD Jahn & Kunz
(2012) Functional value The content of the application is helpful for me 0.88 5.96 0.89 The content of the application is useful for me 6.18 0.74
The content of the application is functional for me 6.10 0.81 The content of the application is practical for me 6.06 0.89
Jahn & Kunz (2012)
Hedonic Value
The content of the application is fun 0.92 5.39 1.25 The content of the application is exciting 4.89 1.26
The content of the application is pleasant 5.25 1.15 The content of the application is entertaining 4.96 1.33
Jahn & Kunz (2012)
Social Interaction Value
I can meet people like me through this application 0.93 3.51 1.84 I can meet new people like me through this application 3.16 1.69 I can find out about people like me through this application 3.60 1.82
I can interact with people like me through this application 3.72 1.82 Jahn & Kunz
(2012) Brand interaction value
I can interact with the brand/developer through the application 0.93 3.48 1.78 I can communicate with the brand/developer through the application 3.45 1.70 I can give feedback to the brand/developer through the application 3.61 1.74
I can get answers from the brand/developer through the application 3.28 1.61 Jahn & Kunz
(2012) Self-concept value
Through the app, I can make a good impression on others 0.93 4.47 1.75 Through the app, I can improve the way I am perceived 4.52 1.67
Through the app, I can present others who I am 4.27 1.68 Through the app, I can present others who I want to be 4.07 1.70
Jahn & Kunz (2012)
Usage intensity
I frequently use the application
0.89 5.30 1.79 I often use the application
5.06 1.88 I regularly use the application
5.19 1.82 Jahn & Kunz
(2012) Engagement I am an active member of this app's community. 0.96 3.17 1.86 I am an engaged member of this app's community. 2.85 1.62 I am an integrated member of this app's community. 2.72 1.51 I am a participating member of this app's community 3.02 1.70
I am an interacting member of this app's community 2.72 1.55 Jahn & Kunz
(2012) Brand attitude In my opinion, this app is good. 0.91 5.89 0.92 In my opinion, this app is positive. 5.95 0.88
I like this app. 5.89 0.96 I think favorably of this app. 5.71 1.17
Jahn & Kunz (2012)
Word of mouth I recommend app to other people. 0.92 5.75 1.19 I introduce this app to other people. 5.21 1.64
I say positive things about this app to other people. 5.52 1.24 Jahn & Kunz
(2012) Purchase I intend to remain loyal to this app in the future. 0.91 5.30 1.43 I will not stop buying/ supporting this app. 4.94 1.56
I think of myself as a loyal consumer of this app. 4.67 1.76 Jahn & Kunz
(2012) Brand Commitment I feel I am part of a community around this brand 0.81 3.38 1.80 I am an active supporter of this brand 4.35 1.79
I interact with this brand 3.87 1.81 Jahn & Kunz
(2012) Brand loyalty Brand Commitment 0.88 3.86 1.53
XI
Source Construct α M SD Purchase 4.97 1.46
Word of Mouth 5.49 1.28
SF12v2 Ware, Kosinski, &
Keller (1996)
Perceived Health 0.71 Physical Functioning 91.73 18.11 Role Functioning Physical 81.29 23.98 Role Functioning Emotional 82.00 21.23 Bodily Pain 81.29 26.16
General Health 65.04 25.10 Vitality 65.35 19.43 Social Functioning 84.06 22.64 Mental Health 73.62 16.98
SRQ-E
Brown, Miller, & Lawendowski
(1999)
Motivation (“I work out…”) External Regulation Because others like me better when I am in shape. 0.79 4.11 1.54 Because it helps my image. 4.60 1.50 Because I want others to see me as physically fit. 4.53 1.62
Introjected Regulation Because I would feel bad about myself if I didn’t do it. 0.65 5.14 1.48 Because I’d be afraid of falling too far out of shape. 5.35 1.35 Because I feel pressured to work out. 3.33 1.72 Identified Regulation Because working out is important and beneficial for my health and lifestyle. 0.70 6.24 0.85 Because it is personally important to me to work out. 5.54 1.33 Because I have a strong value for being active and healthy. 5.45 1.38 Intrinsic Motivation Because I simply enjoy working out. 0.82 5.36 1.66 Because it is fun and interesting. 5.29 1.43 For the pleasure of discovering and mastering new training techniques. 3.79 1.79
BFI-10 Rammstedt &
John (2006)
Personality (“I see myself as someone who…”) Extraversion … is reserved* 0.68 2.75 1.30 … is outgoing, sociable 3.86 1.01 Agreeableness
… is generally trusting 0.45 4.07 0.88 … tends to find fault with others* 3.06 1.02 Conscientiousness … tends to be lazy* 0.20 3.23 1.16 … does a thorough job 3.94 0.90 Neuroticism … is relaxed, handles stress well* 0.60 2.44 1.19 … gets nervous easily 2.98 1.22 Openness to Experience … has few artistic interests* 0.13 3.06 1.30 … has an active imagination 4.14 0.90
Note. α = Cronbach alpha , M = mean, SD = standard devation, * = reverse scored.