Demographic differences in perceived benefits from gamification

Abstract

In recent years, “gamification” has been proposed as a solution for engaging people in individually and socially sustainable behaviors, such as exercise, sustainable consumption, and education. This paper studies demographic differences in perceived benefits from gamification in the context of exercise. On the basis of data gathered via an online survey (N=195) from an exercise gamification service Fitocracy, we examine the effects of gender, age, and time using the service on social, hedonic, and utilitarian benefits and facilitating features of gamifying exercise. The results indicate that perceived enjoyment and usefulness of the gamification decline with use, suggesting that users might experience novelty effects from the service. The findings show that women report greater social benefits from the use of gamification. Further, ease of use of gamification is shown to decline with age. The implications of the findings are discussed.

Full text

Demographic differences in perceived benefits from gamification
Jonna Koivisto
⇑
, Juho Hamari
Game Research Lab, School of Information Sciences, University of Tampere, FIN-33014 Tampere, Finland
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Keywords:
Gamification
Social networking
Persuasive technology
Games for health
Gender
Demographics
abstract
In recent years, ‘‘gamification’’ has been proposed as a solution for engaging people in individually and
socially sustainable behaviors, such as exercise, sustainable consumption, and education. This paper
studies demographic differences in perceived benefits from gamification in the context of exercise. On
the basis of data gathered via an online survey (N= 195) from an exercise gamification service Fitocracy,
we examine the effects of gender, age, and time using the service on social, hedonic, and utilitarian
benefits and facilitating features of gamifying exercise. The results indicate that perceived enjoyment
and usefulness of the gamification decline with use, suggesting that users might experience novelty
effects from the service. The findings show that women report greater social benefits from the use of
gamification. Further, ease of use of gamification is shown to decline with age. The implications of the
findings are discussed.
Ó2014 Elsevier Ltd. All rights reserved.
1. Introduction
The question of how we understand gamer demographics and
gaming behaviors, along with use cultures of different demo-
graphic groups, has loomed over the last decade as games became
one of the main veins of entertainment and consumer culture (Yi,
2004). The deeply established perception of games being a field of
entertainment dominated by young males has been challenged.
Nowadays, digital gaming is a mainstream activity with broad
demographics. The gender divide has been diminishing, the age
span has been widening, and the average age is higher than
stereotypically assumed (Greenberg, Sherry, Lachlan, Lucas, &
Holmstrom, 2010; Griffiths, Davies, & Chappell, 2003, 2004;
Hartmann & Klimmt, 2006; Williams, Yee, & Caplan, 2008; Yee,
2006). An illustrative study commissioned by PopCap (Information
Solutions Group, 2011) reveals that it is actually women in their
30s and 40s who play the popular social games on social network-
ing services (see e.g. Hamari & Järvinen, 2011; Paavilainen, Hamari,
Stenros, & Kinnunen, 2013) most – outplaying men and younger
people. It is clear that age and gender perspectives on gaming
activities and motivations require further scrutiny.
The expansion of the game industry and the increased competi-
tion within the field has also led to two parallel developments: (1)
using game design as marketing (Hamari & Lehdonvirta, 2010) and
(2) gamification – going beyond what traditionally are regarded as
games and implementing game design there (Deterding, Dixon,
Khaled, & Nacke, 2011; Hamari, 2013; Huotari & Hamari, 2012;
McGonigal, 2011). Today, gamification is being applied in several
areas (Hamari, Koivisto, & Sarsa, 2014), often for the benefit of
users. For example, services such as Mindbloom, Fitocracy,
Zombies, Run!, and Nike+ are aimed at assisting the user toward
beneficial behavior related to lifestyle and health choices.
However, it is unclear whether we can see age and gender dis-
crepancies in use of gamified services similar to thosein other digital
gaming contexts. The main difference between games and gamifica-
tion is that gamification is commonly used to advance goals outside
the game – e.g., supporting healthier lifestyles, greener consumption,
and better financial decision-making – whereas playing games is
considered purely autotelic or intrinsically motivated. Therefore,
the motivationsand the perceived benefits fromgamification require
further examination. No data exist on user demographics for gamifi-
cation services, which further justifies the current undertaking.
This paper contributes to the growing body of literature on age
and gender in gaming by investigating age and gender differences
in perceived benefits from use of gamification. The study presents
empirical data on the effects of gender, age, and time using the ser-
vice on the social, hedonic, and utilitarian benefits from gamifying
exercise, along with the facilitating factors in a gamification service
with social features.
2. Background
2.1. Gamification
The phenomenon of creating gameful experiences has most
notably been termed gamification: the aim of gamification is to
http://dx.doi.org/10.1016/j.chb.2014.03.007
0747-5632/Ó2014 Elsevier Ltd. All rights reserved.
⇑
Corresponding author. Tel.: +358 45 126 5525.
E-mail addresses: jonna.koivisto@uta.fi (J. Koivisto), juho.hamari@uta.fi
(J. Hamari).
Computers in Human Behavior 35 (2014) 179–188
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support and motivate the users to perform tasks promoted by the
services (Deterding et al., 2011; Huotari & Hamari, 2012). This goal
is pursued by providing affordances for gameful experiences and
thus making the target activities more engaging. Gameful experi-
ences similar to those created by games, such as flow, a feeling
of mastery, and intrinsic motivations (Csíkszentmihályi, 1990; Deci
& Ryan, 1985; Ryan, Rigby, & Przybylski, 2006), have been at the
core of the discussion of gamification (Deterding et al., 2011;
Hamari, 2013; Huotari & Hamari, 2012) as a means to motivate
behavioral and psychological outcomes. For instance, in the case
of gamified exercise applications, the services at their core aim at
increasing exercise, thus serving a utilitarian purpose. In addition
to the core service, the gamification features implemented are
aimed at motivating and supporting the user for increased exercise
by adding a hedonic element to the activity: providing, for exam-
ple, feedback, achievable goals, progress, and encouragement.
The engaging elements of gameplay are employed to create more
enjoyable exercise experiences.
Many service providers have also implemented social features
and layers in their services. For example, the user community of
the service might serve the function of supporting the gamification
elements and itself providing essential functions of the gameful
experience – e.g., other users’ recognition of one’s achievements
and updates. Early results on the effects of social factors in gami-
fied services show that when a gamification implementation incor-
porates social features, the size of the community committed to
the same goals is an important prerequisite for the gamification
service’s effectiveness (Hamari, 2013; Hamari & Koivisto, 2013).
The size of the community also positively influences the perceived
benefits from social influence, recognition, and reciprocity. It
should also be noted that in the gamified services the social, utili-
tarian, and hedonic elements are often more or less intertwined
(Lin & Bhattacherjee, 2008) (e.g., social factors contribute also to
perceived usefulness and enjoyment of the service).
2.2. Demographic differences in adoption and use of digital
technologies and digital gaming, and effects of time using the service
Demographic differences are a pertinent question also in
research of technology adoption and use (Venkatesh, Morris, &
Ackerman, 2000). Differences regarding, for example, perceptions,
motivations and information processing in technology adoption
processes and use intentions may have significant consequences,
for example, in organizational contexts, where large sums are in-
vested into IT (Morris, Venkatesh, & Ackerman, 2005; Sun & Zhang,
2006; Venkatesh et al., 2000). Yet, for example, both age and gen-
der have not received great attention as moderating variables of
these processes in the information technology literature (Gefen &
Straub, 1997; Sun & Zhang, 2006). Furthermore, relevant to the
context of gamification, both age and gender represent perspec-
tives on games and gameplay wherein variation and preferences
have been long disregarded by the industry and to some degree
also by academics (Greenberg et al., 2010; Griffiths et al., 2003;
Williams et al., 2008). The current body of literature on effects of
age and gender in technology adoption and use, however, suggests
that differences do exist.
2.2.1. Age
Research regarding effects of age in technology adoption and
use has indicated that younger technology users value usefulness
of the technology more than older users when deciding on use
intentions (Venkatesh, Morris, Davis, & Davis, 2003). Furthermore,
older users are considered to be more affected by social influence
than young ones (Morris & Venkatesh, 2000; Venkatesh et al.,
2003; Wang, Wu, & Wang, 2009) in their technology adoption
processes, potentially due to higher affiliation needs (Morris &
Venkatesh, 2000; Sun & Zhang, 2006; Venkatesh et al., 2003). This
could be especially pertinent to organizational contexts where
younger employees may seek to be more autonomous (Morris
et al., 2005) than older ones. However, in an organizational context
the effect of age on social influence in IT adoption has been
reported to diminish in the long term (Morris & Venkatesh,
2000). The lesser effect of social influence on younger users may
be a result of the fact that younger users have been exposed to dig-
ital technologies at a younger age (Morris & Venkatesh, 2000). This
so called digital divide between generations (Ijsselsteijn, Nap, de
Kort, & Poels, 2007; Morris & Venkatesh, 2000; Pfeil, Arjan, &
Zaphiris, 2009) has developed as younger generations become
exposed to digital technologies earlier and earlier.
Consequently, older generations tend to experience lower
self-efficacy and more computer anxiety than younger people
and so perceive their skills in using digital technologies as lower
(Chung, Park, Wang, Fulk, & McLaughlin, 2010; Czaja et al., 2006;
Ellis & Allaire, 1999; Harrison & Rainer, 1992). Furthermore, it
has been shown that technology acceptance (Arning & Ziefle,
2007; Czaja et al., 2006) and perceived usability (Ijsselsteijn
et al., 2007), which are affected by age (Arning & Ziefle, 2007; Czaja
et al., 2006), play a major part in technology adoption and digital
gaming. Older technology-users emphasize ease of use more when
assessing the usefulness of a given system (Arning & Ziefle, 2007).
As learning to use new devices or services may become a more
time-consuming process with age, the tradeoff between perceived
ease of use and perceived benefits of the device or service becomes
relevant (Melenhorst, Rogers, & Caylor, 2001). Findings of age
having no effect on perceptions of ease of use or usefulness of,
for example, online communities have also been published (Chung
et al., 2010), which prompts examination of the effects of the ser-
vices’ content and affordances from an age perspective (Ijsselsteijn
et al., 2007).
2.2.2. Gender
With regards to gender, prior research has indicated that
genders differ in their decision making processes (Venkatesh &
Morris, 2000), for example, in terms of information processing
(Sun & Zhang, 2006; Venkatesh & Morris, 2000). Correspondingly,
results from studies on technology adoption processes and IT use
have found support for differences between genders. For instance,
research on sex and gender roles and behavior has indicated that
men display more instrumental behavior (Spence & Helmreich,
1980; Venkatesh et al., 2000), and in general, are more task- and
achievement-oriented than women (Hoffman, 1972; Minton &
Schneider, 1980). This finding has been supported in the context
of IT as studies have shown men to be more affected by the useful-
ness of the technology (Venkatesh & Morris, 2000), thus highlight-
ing the instrumentality of the system. In contrast, research has
shown that for women affiliation needs are a more influential
motivator (Hoffman, 1972), and women are more interperson-
ally-oriented than men (Minton & Schneider, 1980; Spence &
Helmreich, 1980). These perspectives would suggest that women
are more concerned with social relations and, for example, more
prone to social influence (Venkatesh et al., 2000). In accordance,
women’s IT adoption decisions have been reported to be affected
more by social factors, such as subjective norm, especially during
initial adoption (Venkatesh & Morris, 2000; Venkatesh et al.,
2000). Similarly, in the currently hugely popular online contexts
with social features (e.g., social networks), women have been indi-
cated to be more socially motivated users of the services, while
men concentrate more on pragmatic uses (Haferkamp, Eimler,
Papadakis, & Kruck, 2012; Muscanell & Guadagno, 2012). However,
in some contexts, such as mobile learning, men instead of women
have been reported to be influenced by social factors (Wang et al.,
2009).
180 J. Koivisto, J. Hamari / Computers in Human Behavior 35 (2014) 179–188

On a general level, women have been considered to be less
likely to enjoy and use computers and information technology
(Ahuja & Thatcher, 2005). This phenomenon has been explained
by women’s lower perceptions of self-efficacy and computer
aptitude, and higher perceptions of computer anxiety (Ahuja &
Thatcher, 2005; Venkatesh & Morris, 2000; Whitley, 1997), which
may be partially due to cultural considerations of IT being a
male-dominated field (Gefen & Straub, 1997). In accordance with
these perceptions, ease of use has been reported to have a greater
influence in women’s technology use and adoption processes,
especially in organizational contexts (Venkatesh & Morris, 2000).
However, as Nysveen, Pedersen, and Thorbjørnsen (2005) point
out, the context of the technology in question is potentially influ-
ential for the relationship between gender and ease of use. In their
study on mobile chat services, such relationship was not found.
They explain the results with the context of the study, and the fact
that different mobile services are already largely used by both gen-
ders (Nysveen et al., 2005).
In the context of digital gaming, results from prior research
have indicated that gender differences do exist with regard to gen-
der (Greenberg et al., 2010; Hartmann & Klimmt, 2006; Lucas &
Sherry, 2004; Williams et al., 2008). Digital gaming has been con-
nected to beneficial effects such as better computer-literacy and
increased technological skills. Thus, the gender divide in gaming
has been feared to create a more general gender gap with regard
to technology use (Cassell & Jenkins, 1998; Lucas & Sherry,
2004). Motivational aspects have been raised as a potential source
of gender differences in gaming. Researchers have considered gen-
der roles and identities (Eagly, 1987; Lucas & Sherry, 2004; Poels,
De Cock, & Malliet, 2012; Williams, Consalvo, Caplan, & Yee,
2009), self-efficacy or competence (Carr, 2005; Hartmann &
Klimmt, 2006), etc. and have examined motivational effects of con-
tentual (sexualized) representations of (female) gender (Beasley &
Collins Standley, 2002; Dietz, 1998; Kafai, 1998), violent themes
and competitiveness (Dietz, 1998; Lucas & Sherry, 2004), and lack
of meaningful social content (Hartmann & Klimmt, 2006). Though
the conceptions of the age and gender divides are persistent, in
some game environments – e.g., massively multiplayer online
(MMO) games – older players and women have been noted to
spend more time playing than younger players and men (Williams
et al., 2008, 2009). The game environment, however, seems
important, since findings to the contrary have been reported too
(Greenberg et al., 2010).
In accordance with the findings relating to IT adoption and use
in general, Williams et al. (2008) and (2009) show that in the MMO
game context, women are motivated by social factors and immer-
sion. Men have been noted to express more achievement-oriented
motivations (Williams et al., 2008, 2009) and more competitive-
ness and need for winning than women do (Hartmann & Klimmt,
2006). However, because female players might not receive as much
gratification through the social interactions related to and included
in the games as male players (e.g., social gratification from inter-
acting with friends via games, social status, and peer support
related to gameplay), lower social motivations of female players
have been reported also (Funk & Buchman, 1996; Jansz, Avis, &
Vosmeer, 2010; Lucas & Sherry, 2004). However, it should be noted
that great variety in motivations most likely exists also within gen-
ders (Carr, 2005; Kafai, 1998).
2.2.3. Time using the service
In addition to age and gender, the effects of time using the
service should be examined. The length of the involvement with
the service and in the social community are likely to affect the per-
ceptions of the benefits received. For example, in a gamification
context, Farzan et al. (2008) found that the effects of motivational
system elements may diminish with time. The discussion of the
novelty effects of gamification suggests that at first users might
feel excited about the new gameful features, but the interest
declines with time.
3. Methods and data
3.1. Data
The data were gathered via an online survey of users of Fitocra-
cy, one of the world’s largest exercise gamification services. The
service enables exercise-tracking and social networking. Features
related to these core functions of the service are gamified. The
gamification elements include badges (see Hamari & Eranti, 2011
on badges), levels, and points. Points and levels are awarded for
the tracked exercises. Achievements are rewarded through badges,
for example, for completing certain exercises or repeating them a
given number of times or within a certain timeframe. Furthermore,
the service includes elements related to social interaction, which
are commonly implemented within social network services (Baker
& White, 2010; Boyd & Ellison, 2008; Lin & Lu, 2011), such as status
updating, ‘‘liking,’’ and commenting. Users can create and join
groups focusing on some general or exercise-related topic.
Achievement badges are also rewarded for social activities, such
as posting comments and receiving ‘‘likes’’ within the service. At
the time of the data gathering, the service could be accessed via
a browser or an iPhone application. An application for the Android
operating system was released as the data gathering neared
completion.
A link to the online survey was posted on the discussion forums
of the service and further promoted through posts within a few
groups clustered around a special interest or topic. The service
requires registration, so the respondents had to have been regis-
tered users of the service to find the link to the survey. In the time-
frame of the survey (10/2012–2/2013), 195 valid responses were
gathered, making the study’s sample size N= 195.
The descriptive statistics of the data (see Table 1) present those
of the 195 respondents. The gender distribution of the sample is
equal. The service had been available for approximately 20 months
before the survey was launched. The times using the service
reported by the respondents are distributed evenly across the time
period of the service being available. The respondents’ estimates of
hours of exercise as well as how many exercise session they have
during a normal week are also reported in Table 1. Furthermore,
respondents were asked to report how they use the service: on a
mobile device and/or on a computer. Of the 195 respondents,
181 (92%) stated that they used the service on a computer. The
options were not mutually exclusive, and the respondents could
choose both mobile and browser options. In fact, of the 181 brow-
ser-users, 66 (36%) reported also using the application on a mobile
device. Therefore, no effects of the devices used are examined.
Given that Fitocracy belongs to a rather recent class of technol-
ogies, we can assume that users of Fitocracy in general are above
the average in technology-awareness and technology adoption
readiness. Furthermore, given that the service is targeted for
health-conscious people, we can assume that the users are more
health oriented than average internet users. Generally co-existing
with such characteristics is also a higher than average education.
3.2. Validity and reliability of the measurement instrument
The survey instrument consisted of constructs for facilitating
factors and social, hedonic, and utilitarian benefits. All of the
constructs were adapted from previously published sources. The
definitions, items, and sources of the constructs are provided in
the Appendix. The facilitating factors consist of two constructs:
J. Koivisto, J. Hamari / Computers in Human Behavior 35 (2014) 179–188 181

network exposure and ease of use. The social dimension is opera-
tionalized as social influence, reciprocal benefit, and recognition.
On the hedonic dimension are playfulness and enjoyment. The
utilitarian construct is usefulness. As a behavioral outcome, contin-
ued exercise intentions were measured. Most of the constructs
consisted of four items, one construct of five items, and one of eight
items. All items were measured on a seven-point Likert scale
(strongly disagree – strongly agree).
Convergent validity and reliability (see Table 2) was assessed
with three metrics: average variance extracted (AVE), composite
reliability (CR), and Cronbach’s alpha (Alpha). All of the metrics
exceeded the thresholds in relevant literature (AVE should be
greater than 0.5, CR greater than 0.6 and Cronbach’s alpha above
0.7 (Fornell & Larcker, 1981; Nunnally, 1978)). Furthermore, all
items loaded with their corresponding constructs above .650 level.
Only one item (‘curious’ of the playfulness construct) was deemed
loading too poorly with its corresponding construct, and therefore,
was omitted from the final construct structure. Furthermore,
omitting the item ensured convergent validity of the construct
(AVE above .5). We can conclude that the convergent validity and
reliability requirements are met.
Discriminant validity (see Table 3) was assessed, firstly, through
comparison of the square root of the AVE of each construct to all of
the correlation between it and other constructs (see Fornell &
Larcker, 1981), where all of the square root of the AVEs should
be greater than any of the correlations between the corresponding
construct and another construct. Secondly, in accordance with the
work of Pavlou, Liang, and Xue (2007), we determined that no
inter-correlation between constructs was higher than 0.9. Thirdly,
we assessed discriminant validity by confirming that all items had
the highest loading with its corresponding construct. All three tests
indicate that the discriminant validity and reliability are
acceptable.
The analyses of demographic differences in perceived benefits
by age, gender, and time using the service were performed via
standard multiple regression analyses (Tabachnick & Fidell,
2013). In the analyses, the ratio-scale variable of age was used,
not the ordinal scale presented in Table 1. The ordinal scale of time
using the service presented in Table 1 was utilized for time using
the service. The independent variables were centered prior to
performing the analyses and the interaction terms were created
from the centered variables (Tabachnick & Fidell, 2013). Before
the analyses, the independent variables and the interactions terms
were tested for multicollinearity. No multicollinearity between the
variables existed.
4. Results
Regression analyses on the dependent variables were
performed with age, gender, and time using the service as indepen-
dent variables as well as with the interactions of the independent
variables. The results of these analyses are reported in Table 4.
When examining the significant main effects of the indepen-
dent variables on facilitating factors, network exposure was pre-
dicted by gender (b=.146

) and time using the service
(b=.173

). Ease of use was negatively influenced by age
(b=.135

) and gender (b=.120

). Women reported perceiving
more social benefits from gamification: reciprocal benefits
(b=.156

) and recognition (b=.257

). However, social influ-
ence was only negatively affected by time using the service
(b=.158

). Of the hedonic aspects, playfulness was predicted
by gender (b=.136

) and, additionally, negatively affected by
time using the service (b=.119

). Time using the service also
Table 1
Frequencies and percentages of gender, age, time using the service, and country of residence reported by the respondents. The ‘‘Country of residence’’ category ‘‘Others’’ comprises
20 countries with under 10 respondents each.
Frequency Percent Frequency Percent
Gender Time using the service
Female 98 50.3 Less than 1 month 23 11.8
Male 97 49.7 1–3 months 38 19.5
3–6 months 28 14.4
Age, by gender (mean = 29.62, median = 28) 6–9 months 25 12.8
19 Female 4 9–12 months 32 16.4
Male 5 12–15 months 37 19.0
Total 9 4.6 More than 15 months 12 6.2
20–29 Female 58
Male 43 Country of residence
Total 101 51.8 United States 106 54.4
30–39 Female 25 United Kingdom 18 9.2
Male 37 Canada 16 8.2
Total 62 31.8 Others 55 28.2
40–49 Female 10
Male 9 Exercise sessions per week (mean = 5.2, median = 5)
Total 19 9.7 1–4 81 41.5
50–59 Female 1 5–9 104 53.3
Male 3 10–14 6 3.1
Total 4 2.1 15 or more 4 2.1
Age mean Female 28.67 Hours of exercise per week (mean = 7.1, median = 6)
Male 30.58 1–4 51 26.2
Age median Female 26 5–9 96 49.2
Male 30 10–14 38 19.5
15 or more 10 5.1
Table 2
Validity and reliability.
AVE CR Alpha
Network exposure (NE) .855 .959 .944
Ease of use (EOU) .752 .923 .887
Social influence (SOI) .735 .917 .879
Reciprocal benefit (RB) .700 .903 .857
Recognition (REC) .804 .943 .919
Playfulness (PLAY) .529 .899 .870
Enjoyment (ENJ) .779 .934 .905
Usefulness (USE) .713 .925 .899
Continued exercise intention (CEI) .548 .827 .722
182 J. Koivisto, J. Hamari / Computers in Human Behavior 35 (2014) 179–188

had a negative effect on enjoyment (b=.167

). Usefulness was
negatively predicted by time using the service (b=.167

). Con-
tinued exercise intention was predicted by gender (b=.179

).
Furthermore, some statistically significant interactions between
the independent variables were detected. A two-way interaction
between age and time using the service was significant for network
exposure (b= .145

), recognition (b= .117

), and playfulness
(b=.127

). A three-way interaction between age, gender, and
time using the service was significant for continued exercise inten-
tions, showing a negative effect (b=.161

).
5. Discussion
5.1. Theoretical contributions
This paper has examined the effects of age, gender, and time
using the service on perceived benefits from gamifying exercise
by participating in a gamification service. Perceptions of facilitat-
ing factors and social, hedonic, and utilitarian benefits were
measured.
The results indicate that age does not affect most of the benefits
significantly directly. Of the dependent variables, only ease of use
diminishes with the main effect of age. The more mature the users
are, the less they seem to experience ease of use. This finding holds
similarities with considerations of the digital divide (Ijsselsteijn
et al., 2007; Morris & Venkatesh, 2000; Pfeil et al., 2009) between
younger and older generations regarding use of digital technolo-
gies. However, through interaction, age with time using the service
had an effect on network exposure, recognition, and playfulness.
Gender differences in perceived benefits could be established
for all aspects, except the utilitarian. The results indicate that wo-
men perceive the social benefits as greater than men do. Women
reported more positive perceptions of the recognition received;
they see themselves as gaining greater benefit from the reciprocity
between users and, in general, see their network of friends in the
service as larger than men do. Thus, the findings imply that women
value the social aspects of gamification more than men do and
potentially view the associated social community more positively.
These findings are in line with the findings from, for example, orga-
nizational contexts (Venkatesh & Morris, 2000; Venkatesh et al.,
2000), online contexts such as social networking sites (Haferkamp
et al., 2012; Muscanell & Guadagno, 2012) as well as gaming con-
texts (Williams et al., 2008, 2009), where it has been found that fe-
male users perceive social benefits more positively and are shown
to be more motivated by the social factors.
No indication of men perceiving more utilitarian benefits was
found. This finding was inconsistent with prior research showing
that men value and are more motivated by the usefulness of tech-
nology (Venkatesh & Morris, 2000). Women, however, reported
greater perceived ease of use in the service, suggesting that, simi-
larly to findings from previous research, women value the ease of
use of technology more than men (Venkatesh & Morris, 2000),
and perceive its benefits as higher. Further, the findings suggest
that women perceive the gamified exercise as more playful.
Consistently with prior research on gamification (Farzan et al.,
2008; Hamari, 2013), the results show that perceived usefulness,
enjoyment, and playfulness tend to diminish with time using the
service, suggesting that gamification could have some novelty
value causing perceptions of usefulness and enjoyment to be high-
er in the beginning and to fade the longer the user continues using
the service. The interaction effects between age and time using the
service further show that the novelty effects (regarding perceived
playfulness) are stronger the younger the user is. This finding is
consistent with the general belief that younger people, while being
more susceptible to playful interactions, might also get bored more
quickly than more mature users. This finding could imply that
younger users might have more active service switching behavior
as well.
Time using the service and thus being connected with the com-
munity within the service, expectedly, affects the amount of net-
work exposure: the longer users spend time with the service, the
more likely they are to acquire new contacts. Furthermore, the
longer a user has been using the service, the greater the perceived
exposure to the community will be. There was also a positive inter-
action effect between age and time using the service on network
exposure. This finding could potentially suggest that older users
value the existence of the network higher due to higher affiliation
needs (Morris & Venkatesh, 2000; Sun & Zhang, 2006; Venkatesh
et al., 2003), and as a response, perceive the benefits from the net-
work higher. Furthermore, although neither the length of experi-
ence or age had a main effect on perceived received recognition,
a statistically significant positive interaction effect existed be-
tween them. This interaction suggests that the older the user is,
the more recognition they perceive to be receiving, the longer their
experience with the service is. This finding could also be explained
with the connection of age and affiliation needs and the satisfac-
tion received from fulfilling such needs (Morris & Venkatesh,
2000; Venkatesh et al., 2003). However, it is possible that the inter-
action effect could be partly due to the correlation between net-
work exposure and recognition, implying that network exposure
might be in this case mediating the effect between time using
the service and recognition. On the other hand, the results did
not show significant main effects between recognition and age or
time using the service. This could be considered as defense for
the interpretation that there really is an interaction effect between
age and time on perceived recognition that is not caused by a con-
founding factor discoverable within this dataset.
Moreover, the negative impact of time using the service on
social influence suggests that a longer time using the service re-
duces the impacts of social pressure or peer opinion of the service
and its use. As users gain personal experience of the service, they
may potentially rely more on their own opinions, instead of those
adopted from the community. However, this finding further under-
lines the importance of the social interaction and community for
Table 3
Correlation matrix. Square roots of AVEs are reported in bold in the diagonal.
NE EOU SOI RB REC PLAY ENJ USE CEI
Network exposure (NE) .925
Ease of use (EOU) .244 .867
Social influence (SOI) .383 .375 .856
Reciprocal benefit (RB) .448 .425 .570 .837
Recognition (REC) .464 .363 .446 .636 .896
Playfulness (PLAY) .226 .250 .392 .356 .233 .727
Enjoyment (ENJ) .334 .596 .558 .633 .561 .384 .882
Usefulness (USE) .222 .460 .621 .655 .437 .419 .714 .844
Continued exercise intention (CEI) .236 .252 .414 .420 .322 .368 .441 .469 .740
J. Koivisto, J. Hamari / Computers in Human Behavior 35 (2014) 179–188 183

gamification endeavors: social influence plays an important role in
engaging new users.
The continued exercise intentions as a behavioral outcome of
gamifying exercise suggest that women are more motivated to
keep exercising. Furthermore, an interaction effect between age,
gender and time using the service affected the continued exercise
intentions. However, these results should be considered with
caution, since there are evidently factors not examined here that
affect one’s exercise intentions. Nevertheless, the potential of
gamification in supporting and encouraging continued exercise is
still worthy of note.
5.2. Practical implications for design of gamification and gamified
services
From an industry perspective, the results suggest that design of
gamification implementations might benefit from considering the
following perspectives. Firstly, employing social features in the ser-
vice is beneficial for creating sustainable and engaging gamifica-
tion. The results suggest in particular that women might become
more engaged in the social activity than men and, therefore, the
social features might be essential especially when one seeks to
acquire female users. The community of like-minded people also
supports the core activity that is being gamified (Hamari & Koivisto,
2013). Furthermore, gamification service developers could benefit
from efforts aimed at integrating new users to the social network
of the service at an early stage.
With regard to the demographics of users of gamification ser-
vices, a few key issues emerged. Since gamification implications
rely on a variety of game mechanics, the implementations do not
necessarily share the culture and, for example, visual and narrative
representations often associated with video games, so they can
potentially be created to be more gender-neutral. For widening
the potential demographic base of the users, gamification offers
possibilities for creating services that engage users also of more
advanced ages. Implementations inducing mental and physical
activity, increasing chances of social connectedness, and offering
instant feedback and support while also increasing one’s sense of
self-efficacy could afford motivations for technology use among
older generations (Arning & Ziefle, 2007; Czaja et al., 2006;
Ijsselsteijn et al., 2007).
5.3. Limitations
Some limitations should be acknowledged in relation to the
results of the study. The responses gathered by the online survey
are, obviously, self-reported and the respondents self-selected,
which is commonplace with the Web-based-survey methodology
used. Use of self-reported data is likely to affect the results as
the users responding are most probably actively engaged with
the service, and eager to participate in activities related to it. Thus,
the results potentially represent perceptions and intentions of
active users of the service and disregard less active and unengaged
users. The perceptions of less active users could be addressed in fu-
ture studies as well as reasons for not being/becoming involved in
the service. Furthermore, the respondents in the sample most
likely differ in cultural background, which could affect perceptions
of the various elements examined in the study – e.g., perceptions
as to social benefits. Further studies would benefit from acknowl-
edging the effects of cultural differences in the use and use percep-
tions of gamified services.
As is commonplace within the industry, specific details regard-
ing the user base or its size are not public knowledge. Thus, the
study is limited with regards to reporting data concerning the
general population from which the sample was gathered, that is,
the user base of the service. This is a limitation regarding the reli-
ability of the generalizability of the results.
Table 4
Standard multiple regression analyses – the standardized b-coefficients, t-values and p-values are reported.
Facilitators Social Hedonic Utilitarian Use
NE EOU SOI RB REC PLAY ENJ USE CEI
Age b.043 .135 .099 .019 .060 .078 .074 .088 .048
t.599 1.869 1.358 .255 .858 1.086 1.026 1.218 .674
p.550 .063
*
.176 .799 .392 .279 .306 .225 .501
Gender
a
b.146 .120 .074 .156 .257 .136 .072 .065 .179
t2.044 1.664 1.017 2.137 3.679 1.890 .990 .911 2.516
p.042
**
.098
*
.310 .034
**
.000
***
.060
*
.323 .363 .013
**
Time b.173 .020 .158 .076 .092 .119 .167 .167 .081
t2.414 .277 2.177 1.041 1.307 1.653 2.295 2.324 1.140
p.017
**
.782 .031
**
.299 .193 .100
*
.023
**
.021
**
.256
Age Gender b.030 .033 .037 .003 .051 .028 .055 .099 .009
t.416 .465 .513 .042 .732 .397 .759 1.386 .123
p.678 .642 .608 .966 .465 .692 .449 .167 .902
Age Time b.145 .065 .015 .008 .117 .127 .059 .055 .069
t1.999 .891 .208 .115 1.658 1.735 .800 .761 .958
p.047
**
.374 .835 .909 .099
*
.084
*
.425 .448 .339
Gender Time b.006 .031 .002 .001 .086 .058 .037 .113 .106
t.083 .433 .032 .008 1.222 .804 .508 1.566 1.492
p.934 .666 .975 .993 .223 .422 .612 .119 .137
Age Gender Time b.047 .114 .010 .015 .065 .067 .096 .040 .161
t.659 1.568 .140 .202 .924 .923 1.313 .560 2.244
p.511 .119 .889 .840 .356 .357 .191 .576 .026
**
Network exposure (NE), Ease of use (EOU), Social influence (SOI), Reciprocal benefit (RB), Recognition (REC), Playfulness (PLAY), Enjoyment (ENJ), Usefulness (USE), Continued
exercise intention (CEI).
a
Females were coded with the lower variable value.
*
Statistical significances are boldfaced and reported as

p< 0.1.
**
Statistical significances are boldfaced and reported as

p< 0.05.
***
Statistical significances are boldfaced and reported as

p< 0.01.
184 J. Koivisto, J. Hamari / Computers in Human Behavior 35 (2014) 179–188

5.4. Further research directions
With the novelty of the gamification phenomenon, many direc-
tions for further research should be considered. This study has con-
tributed to the understanding of demographic differences in
perceptions of benefits from gamification. However, the relation-
ship of personality traits (McCrae & John, 1992) – and in the field
of game studies, player types (Hamari & Tuunanen, 2014; Yee,
2007) – to gamification should be studied as well, for better under-
standing of the psychological outcomes that gamification produces
and that attract users. Furthermore, the question of use motiva-
tions for gamification should be investigated on a larger spectrum.
The benefits examined in this study were limited, so some motiva-
tors, such as competition or mastery, that previous studies have
associated more with men (Hartmann & Klimmt, 2006; Williams
et al., 2008), were not measured. Drawing from game-studies liter-
ature, future research would benefit from widening the range of
motivations and benefits explored (Williams et al., 2008; Yee,
2007). In addition, future research should consider the differences
in derived benefits from gamification across different contexts.
This study was conducted in the context of exercise-related gami-
fication service. In line with Hamari (2013), we suggest that further
studies could consider the differences in gamifying utilitarian vs.
hedonic systems (van der Heijden, 2004) as well as differences
based upon the type of involvement users have with the service
(see Zaichkowsky, 1994).
Acknowledgements
This research has been supported by individual study grants for
both authors from the Finnish Cultural Foundation as well as car-
ried out as part of research projects (40311/12, 40134/13) funded
by the Finnish Funding Agency for Technology and Innovation
(TEKES).
Appendix A
Survey constructs, items, and sources
Construct Definition and Sources Items
Network exposure
Measurement of the motivational effect of the size of the network (Lin &
Bhattacherjee, 2008)
I have a lot of friends on Fitocracy who follow my
activities
Many people follow my activities on Fitocracy
I follow many people on Fitocracy
I have many friends in Fitocracy
Ease of use
The degree of belief that using a system would be free of effort (Davis, 1989;
Venkatesh, 2000; Venkatesh & Davis, 2000)
Using the Fitocracy interface does not require a lot of
mental effort
The interaction with Fitocracy is clear and
understandable
I find Fitocracy easy to use
I find it easy to get the interface of Fitocracy to do what I
want it to do
Social influence
The perceptions of approval for using the system (Ajzen, 1991; Fishbein &
Ajzen, 1975; Hernandez, Montaner, Sese, & Urquizu, 2011; Hsu & Lin,
2008)
People who influence my attitudes would recommend
Fitocracy
People who are important to me would think positively
of me using Fitocracy
People whom I appreciate would encourage me to use
Fitocracy
My friends would think using Fitocracy is a good idea
Reciprocal benefit
The perceived social usefulness of the service – i.e., contributing and
receiving benefit from the social community (Hsu & Lin, 2008; Lin, 2008;
Preece, 2001)
I find that participating in the Fitocracy community can
be mutually helpful
I find that my participation in the Fitocracy community
can be advantageous to me and other people
I think that participating in the Fitocracy community
improves my motivation to exercise
The Fitocracy community encourages me to exercise
Recognition
Measurement of the social motivation created by the perceptions of being
recognized by other users in the forms of ‘‘likes’’ and praise of
achievements (Cheung, Chiu, & Lee, 2001; Hamari & Eranti, 2011;
Hernandez et al., 2011; Hsu & Lin, 2008; Lin, 2008; Lin & Bhattacherjee,
2010)
I feel good when my achievements in Fitocracy are
noticed
I like it when other Fitocracy users comment and ‘‘like’’
my exercise
I like it when my Fitocracy peers notice my exercise
reports
(continued on next page)
J. Koivisto, J. Hamari / Computers in Human Behavior 35 (2014) 179–188 185

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