Why do people use gamification services?

Abstract

In recent years, technology has been increasingly harnessed for motivating and supporting people toward various individually and collectively beneficial behaviors. One of the most popular developments in this field has been titled gamification. Gamification refers to technologies that attempt to promote intrinsic motivations toward various activities, commonly, by employing design characteristic to games. However, a dearth of empirical evidence still exists regarding why people want to use gamification services. Based on survey data gathered from the users of a gamification service, we examine the relationship between utilitarian, hedonic and social motivations and continued use intention as well as attitude toward gamification. The results suggest that the relationship between utilitarian benefits and use is mediated by the attitude toward the use of gamification, while hedonic aspects have a direct positive relationship with use. Social factors are strongly associated with attitude, but show only a weak further association with the intentions to continue the use of a gamification service.

Full text

International
Journal
of
Information
Management
35
(2015)
419–431
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at
ScienceDirect
International
Journal
of
Information
Management
jou
rnal
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om
epage:
www.elsevier.com/locate/ijinfomgt
Why
do
people
use
gamification
services?
Juho
Hamaria,b,
Jonna
Koivistoa,∗
aGame
Research
Lab,
School
of
Information
Sciences,
FIN-33014
University
of
Tampere,
Finland
bAalto
University
School
of
Business,
P.O.
Box
21220,
00076
Aalto,
Finland
a
r
t
i
c
l
e
i
n
f
o
Article
history:
Keywords:
Gamification
Online
game
Persuasive
technology
eHealth
Technology
acceptance
a
b
s
t
r
a
c
t
In
recent
years,
technology
has
been
increasingly
harnessed
for
motivating
and
supporting
people
toward
various
individually
and
collectively
beneficial
behaviors.
One
of
the
most
popular
developments
in
this
field
has
been
titled
gamification.
Gamification
refers
to
technologies
that
attempt
to
promote
intrinsic
motivations
toward
various
activities,
commonly,
by
employing
design
characteristic
to
games.
However,
a
dearth
of
empirical
evidence
still
exists
regarding
why
people
want
to
use
gamification
services.
Based
on
survey
data
gathered
from
the
users
of
a
gamification
service,
we
examine
the
relationship
between
utilitarian,
hedonic
and
social
motivations
and
continued
use
intention
as
well
as
attitude
toward
gami-
fication.
The
results
suggest
that
the
relationship
between
utilitarian
benefits
and
use
is
mediated
by
the
attitude
toward
the
use
of
gamification,
while
hedonic
aspects
have
a
direct
positive
relationship
with
use.
Social
factors
are
strongly
associated
with
attitude,
but
show
only
a
weak
further
association
with
the
intentions
to
continue
the
use
of
a
gamification
service.
©
2015
Elsevier
Ltd.
All
rights
reserved.
1.
Introduction
In
recent
years,
technology
has
been
increasingly
harnessed
for
motivating
people
and
providing
support
toward
various
individu-
ally
and
collectively
beneficial
behaviors.
Perhaps
the
most
popular
development
in
this
area
has
been
gamification,
which
refers
to
technologies
that
attempt
to
promote
intrinsic
motivations
toward
various
activities,
commonly,
by
employing
design
characteristic
to
games
(Deterding,
Dixon,
Khaled,
&
Nacke,
2011;
Hamari,
Huotari,
&
Tolvanen,
2015;
Huotari
&
Hamari,
2012).
Typical
elements
in
gamification
include,
for
example,
points,
leaderboards,
achieve-
ments,
feedback,
clear
goals
and
narrative
(see
Hamari,
Koivisto,
&
Pakkanen
2014;
Hamari,
Koivisto,
&
Sarsa,
2014
for
reviews
of
gamification
and
persuasion
mechanics
in
related
research).
Gam-
ification
has
thus
far
been
implemented
in
a
variety
of
contexts,
from
exercise
(Fitocracy)
and
overall
wellbeing
(Mindbloom),
to
sustainable
consumption
(Recyclebank)
and
consumer
behavior
(Foursquare).
Gamification
is
a
manifold
socio-technological
phe-
nomenon
with
claimed
potential
to
provide
a
multitude
of
benefits
(Deterding
et
al.,
2011;
Huotari
&
Hamari,
2012)
such
as
enjoyment
as
well
as
social
benefits
through
communities
and
social
inter-
action.
Moreover,
as
the
goal
of
gamification
is
often
to
progress
∗Corresponding
author.
Tel.:
+358
50
318
73
63.
E-mail
addresses:
juho.hamari@uta.fi
(J.
Hamari),
jonna.koivisto@uta.fi
(J.
Koivisto).
some
external
utilitarian
goal,
therefore,
gamification
also
provides
utilitarian
benefits.
Definitions
of
gamification
(Deterding
et
al.,
2011;
Hamari
et
al.,
2015;
Huotari
&
Hamari,
2012)
focus
on
the
term
“gamefulness”,
which
implies
that
the
main
defining
factor
of
gamification
pertains
to,
in
a
similar
manner
as
games,
the
self-purposeful
nature
of
activ-
ities.
While
gamification
design,
therefore,
can
be
characterized
as
aiming
for
self-purposeful
and
hedonistic
use,
the
ultimate
goals
of
gamification
are
commonly
related
to
utilitarian
ends;
i.e.
gami-
fication
aims
to
support
extrinsic
and
valuable
outcomes
outside
the
gamification
system.
Moreover,
it
is
common
for
gamifica-
tion
services
to
also
include
strong
social
features
(e.g.
Foursquare,
Fitocracy)
common
to
various
social
media
(see
e.g.
Ngai,
Tao,
and
Moon,
2015).
Consequently,
social
factors
have
also
been
hypothe-
sized
and
examined
as
determinants
of
the
use
of
gamification
(see
e.g.
Hamari
&
Koivisto,
2015).
Another
factor
discussed
in
the
works
defining
the
concept
(Deterding
et
al.,
2011;
Hamari
et
al.,
2015;
Huotari
&
Hamari,
2012)
has
been
whether
gamification
provides
more
free-form,
playful
experiences
(paidia)
or
more
structured,
rule-driven
experiences
(ludus)
(see
Caillois,
1961
for
more
on
the
continuum
of
ludus
and
paidia).
Although
research
has
started
to
accumulate
on
the
possible
outcomes
of
gamification
(see
Section
2
and
Hamari,
Koivisto,
&
Sarsa,
2014
for
a
review),
there
is
still
a
dearth
of
empiri-
cal
evidence
regarding
which
motivations
would
actually
predict
why
people
use
gamification
services
and
what
determines
their
http://dx.doi.org/10.1016/j.ijinfomgt.2015.04.006
0268-4012/©
2015
Elsevier
Ltd.
All
rights
reserved.

420
J.
Hamari,
J.
Koivisto
/
International
Journal
of
Information
Management
35
(2015)
419–431
attitudes
toward
them.
While
obviously
relevant
from
practical
and
business
perspectives,
this
problem
is
also
connected
to
the
lack
of
theory
and
conception
around
gamification.
Therefore,
this
paper
will
focus
on
exploring
what
benefits
moti-
vate
people
to
use
gamification
services.
The
research
model
and
hypotheses
are
developed
as
a
triangulation
of
the
theories
on
human
motivations
(Deci
&
Ryan,
1985;
Lindenberg,
2001;
Ryan
&
Deci,
2000),
technology
adoption
research
(Davis,
1989;
van
der
Heijden,
2004;
Venkatesh
&
Davis,
2000;
Venkatesh,
1999,
2000),
and
previous
research
on
games
and
gamification
(see
e.g.
Deterding
et
al.,
2011;
Hamari,
Koivisto,
&
Sarsa,
2014;
Huotari
&
Hamari,
2012;
Ryan,
Rigby,
&
Przybylski,
2006;
Yee,
2006).
Based
on
this
theoretical
background,
there
is
ample
support
for
investi-
gating
three
distinct
areas
to
uncover
predictors
for
the
adoption
of
gamification;
utility,
hedonism
and
social
benefit.
On
the
basis
of
survey
data
gathered
from
users
of
a
gamification
service,
we
examine
the
relationship
between
these
predictors
and
continued
use
intentions
as
well
as
attitudes
toward
gamification.
An
empiri-
cal
analysis
of
survey
data
using
structural
equation
modeling
was
performed.
2.
Theory
and
hypotheses
While
gamification
has
been
considered
to
be
a
novel
IT
devel-
opment,
other
different
forms
of
information
technology
have
also
been
employed
for
persuasive
and
behavioral
change
purposes
in
similar
contexts,
although
potentially
differing
from
gamification
in
terms
of
their
methods
of
affecting
motivations
and
behavior
(Hamari
et
al.,
2015).
For
example,
systems
such
as
persuasive
tech-
nologies
and
behavior
change
support
systems
have
been
used
to
influence
psychological
states
and
behaviors.
These
systems
focus
mainly
on
social
and
communicative
persuasion
and
attitude
change
(Fogg,
2003;
Hamari,
Koivisto,
&
Pakkanen,
2014;
Oinas-
Kukkonen
&
Harjumaa,
2009;
Oinas-Kukkonen,
2013).
Similarly,
loyalty
programs
can
resemble
gamification
via
the
use
of
col-
lectibles
and
other
redeemable
points,
although
loyalty
programs
place
emphasis
on
economic
incentives
and
customer
loyalty
as
their
end
goal
(Sharp
&
Sharp,
1997).
Most
loyalty
programs
aim
to
offer
economic
benefits
(redeemable
by
points)
from
the
contin-
uous
use
of
services,
and
most
likely
invoke
extrinsic
motivations
(Deci,
Koestner,
&
Ryan,
1999).
Furthermore,
decision
support
sys-
tems
can
also
be
seen
as
aimed
to
affect
the
decisions
and
decision
processes
(Sprague,
1980).
While
gamification
too
functions
as
a
type
of
decision
support
system,
conceptual
developments
in
the
decision
support
system
space
mostly
focus
on
methods
of
improving
decision
making
by
making
information
more
readily
and
effectively
available,
as
well
as
by
improving
the
analysis
of
the
data
being
used
as
the
basis
of
the
decision
making
pro-
cess
(Sprague,
1980).
Gamification
on
the
other
hand,
aims
to
support
decision
making
by
means
of
affective
rather
than
cog-
nitive
processes.
Moreover,
in
many
instances,
gamification
has
been
employed
to
encourage
people
to
make
“good”
decisions,
which
relates
the
phenomenon
to
a
concept
of
“choice
architec-
ture”
defined
in
behavioral
economics.
This
concept,
which
entails
an
optimistic
view
to
behavioral
biases,
is
a
form
of
soft
pater-
nalism
that
“tries
to
influence
choices
in
a
way
that
will
make
choosers
better
off,
as
judged
by
themselves”
(Thaler
&
Sunstein,
2003).
The
perspective
guides
to
design
decision
making
situa-
tions
in
such
a
way
that
beneficial
biases
could
be
amplified,
while
harmful
biases
could
be
avoided
(Thaler
&
Sunstein,
2008).
In
summary,
as
a
departure
from
other
ISs
aiming
to
change
peo-
ple’s
behavior,
gamification
is
aimed
at
invoking
users’
intrinsic
motivations
commonly
through
design
reminiscent
from
games
(Deterding
et
al.,
2011;
Hamari
et
al.,
2015;
Huotari
&
Hamari,
2012).
Research
on
gamification
particularly
has
been
conducted
in
a
range
of
areas;
e.g.
in
the
domains
of
exercise
(Hamari
&
Koivisto,
2014,
2015;
Koivisto
&
Hamari,
2014),
health
(Jones,
Madden,
&
Wengreen,
2014),
education
(e.g.
Bonde
et
al.,
2014;
Christy
&
Fox,
2014;
Marcos,
Domínguez,
Saenz-de-Navarrete,
&
Pagés,
2014;
Denny,
2013;
Domínguez
et
al.,
2013;
Farzan
&
Brusilovsky,
2011;
Filsecker
&
Hickey,
2014;
Hakulinen,
Auvinen,
&
Korhonen,
2013;
Simões,
Díaz
Redondo,
&
Fernández
Vilas,
2013),
commerce
(Hamari,
2013,
2015a),
intra-organizational
communication
and
activity
(Farzan
et
al.,
2008a,
2008b;
Thom,
Millen,
&
DiMicco,
2012),
government
services
(Bista,
Nepal,
Paris,
&
Colineau,
2014),
public
engagement
(Tolmie,
Chamberlain,
&
Benford,
2014),
envi-
ronmental
behavior
(Lee,
Ceyhan,
Jordan-Cooley,
&
Sung,
2013;
Lounis,
Pramatari,
&
Theotokis,
2014),
marketing
and
advertising
(Cechanowicz,
Gutwin,
Brownell,
&
Goodfellow,
2013;
Terlutter
&
Capella,
2013),
and
activities
such
as
crowdsourcing
(Eickhoff,
Harris,
de
Vries,
&
Srinivasan,
2012;
Ipeirotis
&
Gabrilovich,
2014),
to
name
a
few.
A
recent
review
on
empirical
works
on
gam-
ification
(Hamari,
Koivisto,
&
Sarsa,
2014)
indicated
that
most
gamification
studies
reported
positive
effects
from
the
gamification
implementations.
Beyond
investigating
the
effects
and
benefits
of
gamification,
we
still
lack
in
understanding
on
which
factors
predict
why
people
use
gamification
services.
When
investigating
issues
related
to
why
people
use
certain
technologies
or
services,
we
deal
with
the
questions
of
technol-
ogy
adoption
and
acceptance,
on
which
a
long
vein
of
literature
exists
among
the
information
systems
research
field.
This
tech-
nology
adoption
literature
has
traditionally
distinguished
between
services
and
systems
based
on
their
use
objectives
and
functions;
services
which
aim
to
fulfill
objectives
external
to
the
service
use
itself
have
been
referred
to
as
utilitarian
(Davis,
1989;
van
der
Heijden,
2004),
while
services
used
for
entertainment
purposes
and
for
the
sake
of
using
the
service
itself
have
been
titled
hedonic
(van
der
Heijden,
2004).
According
to
established
literature
(e.g.
Davis,
1989;
van
der
Heijden,
2004),
utilitarian
systems
serve
instrumen-
tal
purposes,
such
as
the
productivity
needs
of
performing
tasks
efficiently
and
with
maximized
ease.
When
an
individual
is
intrin-
sically
motivated,
they
are
considered
to
perform
an
activity
for
the
sake
of
doing
it,
rather
than
for
any
external
goals.
The
enjoy-
ment
derived
from
the
behavior
is
thought
to
be
enough
to
incite
its
performance,
and
to
create
an
optimal
or
autotelic
experience
(see
e.g.
Csíkszentmihályi,
1990).
Therefore,
systems
that
aim
at
invok-
ing
these
kinds
of
positive
experiences
are
referred
to
as
hedonic
systems.
When
we
consider
gamification
from
the
perspective
of
this
system
type
dichotomy,
it
would
be
difficult
to
categorize
it
as
either
utilitarian
or
hedonic
since
there
is
reason
to
believe
that
gamification
provides
both
benefits;
utilitarian
benefits
such
as
productivity,
and
hedonic
benefits
such
as
enjoyment.
There-
fore,
gamification
poses
an
interesting
class
of
systems
from
the
theoretical
standpoint
of
classifying
system
types.
In
a
similar
manner
to
system
types,
human
motivation
has
been
commonly
and
popularly
abstracted
to
stem
from
two
sources,
external
and
internal.
One
of
the
most
widely
employed
theories
on
human
motivation
(self-determination
theory)
postulates
that
an
action
may
be
extrinsically
or
intrinsically
motivated
(Deci
&
Ryan,
1985).
Extrinsic
motivation
refers
to
motivations
arising
from
out-
side
goals
or
conditions
such
as
being
motivated
to
perform
a
task
in
order
to
receive
financial
compensation
for
it.
Thus,
the
source
of
motivation
is
external.
Intrinsic
motivation
on
the
other
hand,
refers
to
self-purposeful
behavior
and
being
internally
motivated,
without
external
forces
affecting
the
will
to
act.
The
more
a
behav-
ior
provides
effects
such
as
stimulation,
behavioral
confirmation
of
self
and
others,
or
status
and
self-improvement
for
the
individual,
the
more
it
is
experienced
as
enjoyable
and
the
longer
someone
will
be
willing
to
continue
with
it
without
any
external
reward
(Lindenberg,
2001).
Consequently,
being
intrinsically
motivated,

J.
Hamari,
J.
Koivisto
/
International
Journal
of
Information
Management
35
(2015)
419–431
421
for
example
during
training,
has
been
considered
as
beneficial
for
the
intervention
outcomes
(Deci,
1975;
Venkatesh,
1999),
and
may
create
more
favorable
circumstances
for
improvement
than
deriv-
ing
one’s
motivation
from
external
sources.
This
notion
regarding
the
positive
association
between
intrinsic
motivation
and
improve-
ment
in
performance
is
a
core
reason
why
gamification
is
expected
to
be
efficient
(Huotari
&
Hamari,
2012;
Ryan
et
al.,
2006).
Relating
to
the
theory
surrounding
human
motivations,
the
use
of
utilitarian
systems
is
commonly
considered
to
be
extrinsically
motivated
(van
der
Heijden,
2004).
This
means
that
the
extrinsically
motivated
user
has
an
external
goal
and
the
purpose
of
the
service
is
to
make
the
goal
more
efficiently
attainable.
Conversely,
hedonic
services
seek
to
make
the
system
entertaining
and
to
invoke
enjoy-
ment.
In
other
words,
hedonic
systems
seek
to
make
the
activity
intrinsically
motivating
and
the
user
wishes
to
use
the
system
sim-
ply
for
the
sake
of
it.
Consequently,
the
enjoyment
of
using
the
service
promotes
prolonged
use
(van
der
Heijden,
2004),
regard-
less
of
the
potential
utilitarian
benefits.
Moreover,
hedonic
systems
such
as
games
and
game-like
systems
aim
at
inducing
experiences
of
autonomy,
competence,
and
relatedness
regarding
the
activity
(Ryan
et
al.,
2006).
These
experiences
increase
the
likelihood
of
a
task
being
or
becoming
intrinsically
motivated
(Deci
&
Ryan,
1985).
Therefore,
gamification
could
be
considered
to
aim
at
motivat-
ing
the
user
toward
utilitarian
goals
through
hedonic,
intrinsically
motivated
behavior.
Hence,
gamification
can
be
seen
as
a
hedonic
tool
for
productivity.
In
addition
to
the
utilitarian
and
hedonic
characteristics
of
gami-
fied
systems,
an
aspect
commonly
affecting
contemporary
systems
is
the
implementation
of
social
features.
Following
the
success
and
popularity
of
social
networking
services,
the
power
of
social
interaction
is
being
increasingly
harnessed
in
both
utilitarian
and
hedonic-oriented
systems
in
a
variety
of
contexts.
This
develop-
ment
creates
further
blending
between
different
system
types
and
their
functions.
The
use
of
social
features
draws
from
the
fact
that
human
beings
are
social
creatures
with
the
need
to
experience
relatedness,
i.e.
being
a
part
of
something
and
longing
for
accep-
tance
(Deci
&
Ryan,
2000).
When
social
features
are
implemented
in
a
system,
the
social
community
answers
these
needs
of
related-
ness
and
further
supports
the
core
activities
of
the
service
through,
for
example,
the
recognition
and
mutual
benefits
derived
from
the
social
interaction
(Hamari
&
Koivisto,
2013).
Many
of
the
theoretical
frameworks
mentioned
above
tend
to
form
strictly
defined
categories
related
to
motivations
and
sys-
tem
types.
However,
it
should
be
noted
that
several
motivations
and
motivational
sources
may
influence
behavior
at
the
same
time.
According
to
Lindenberg
(2001),
the
strongest
motivation
becomes
predominant,
and
affects
how
the
behavior
is
framed.
This
fur-
ther
affects
the
cognitive
processes
relating
to
the
behavior.
The
weaker
motivations
do
not
disappear
despite
being
secondary
to
the
predominant
one,
but
instead,
they
continue
to
exert
back-
ground
influence
(Lindenberg,
2001).
To
exemplify,
a
behavior
may
be
mainly
motivated
by
extrinsic
motivations
(such
as
financial
compensation
for
the
activity),
but
intrinsic
motivations
(such
as
enjoyment)
may
still
act
as
a
secondary
influence.
From
the
point
of
view
of
gamification,
this
view
of
human
motivation
posits
an
interesting
perspective
as
it
reminds
us
that
several
motivational
sources,
extrinsic
as
well
as
intrinsic,
may
simultaneously
act
as
drivers
for
the
behavior.
Furthermore,
the
various
utilitarian,
hedonic
and
social
ben-
efits
derived
from
the
gamification
may
act
as
determinants
of
attitude
toward
the
acceptance
and
use
of
the
technology.
In
gen-
eral,
attitudes
are
formed
based
on
the
belief
that
certain
outcomes
are
associated
with
certain
behaviors.
These
beliefs
and
outcomes
are
valued
as
either
positive
or
negative
(Ajzen,
1991).
Moreover,
attitudes
toward
behaviors
have
been
shown
to
be
reliable
pre-
dictors
of
behavioral
intentions,
along
with
social
influence
(Ajzen,
1991;
Fishbein
&
Ajzen,
1975).
In
a
gamification
context,
an
atti-
tude
toward
the
gamified
service
can
be
similarly
be
considered
to
affect
the
intention
to
use
it.
To
investigate
the
factors
that
drive
the
use
of
gamification,
we
examine
the
relationship
between
various
antecedents
and
user
intentions
of
continuing
the
use
of
the
system.
The
stud-
ied
dependent
variable
is
therefore
continued
use,
which
refers
to
the
intentions
to
continue
using
the
system
in
the
future
(Bhattacherjee,
2001).
The
benefits
of
gamification
are
divided
into
three
categories:
(1)
utilitarian,
operationalized
as
usefulness
and
ease
of
use,
(2)
hedonic,
operationalized
as
enjoyment
and
play-
fulness,
and
(3)
social,
operationalized
as
recognition
and
social
influence.
Furthermore,
the
relationship
between
attitude
and
con-
tinued
use
intentions
is
examined.
See
Fig.
1
for
the
research
model
and
hypotheses.
2.1.
Utilitarian
aspects
In
literature
related
to
technology
use,
the
perceived
utility
of
systems
has
commonly
been
operationalized
as
perceived
useful-
ness,
which
refers
to
the
extent
of
the
belief
that
a
particular
system
enhances
the
performance
of
a
task
(Davis,
1989).
Prior
research
has
suggested
that
perceived
usefulness
mostly
predicts
the
use
inten-
tions
of
a
system
(Davis,
Bagozzi,
&
Warshaw,
1989;
Venkatesh
&
Davis,
2000),
in
contexts
such
as
organizational
and
work
environ-
ments
where
the
system
is
used
for
utilitarian
purposes
and
for
reaching
outside
goals.
Conversely,
in
contexts
with
hedonic
use
objectives,
the
usefulness
of
the
system
has
been
indicated
to
be
a
less
important
determinant
of
usage
intentions
(van
der
Heijden,
2004).
For
example,
when
examined
in
the
domain
of
online
games,
a
significant,
although
weak
relationship
was
found
between
use-
fulness
and
attitude,
while
the
connection
of
usefulness
and
use
intentions
was
insignificant
(Hsu
&
Lu,
2004).
However,
as
gam-
ified
systems
contain
a
utilitarian
dimension
in
addition
to
the
hedonic
design,
then
the
usefulness
of
the
system
is
presumed
to
be
essential
for
their
continued
use.
Therefore,
examining
the
rela-
tionship
between
usefulness
and
both
attitude
and
use
intentions
is
essential
and
highly
interesting.
If
the
gamification
is
perceived
as
easy
to
use,
it
may
promote
senses
of
efficiency
as
well
as
experiences
of
an
obstacle-free
use
of
the
system.
These
in
turn
may
generate
more
a
positive
attitude
and
an
increased
willingness
to
continue
using
the
service.
Ease
of
use
has
especially
been
proliferated
in
technology
acceptance
lit-
erature
as
one
of
the
main
antecedents
for
technology
adoption.
It
refers
to
the
individual’s
perception
of
the
required
effort
to
use
a
system
(Davis,
1989).
Moreover,
ease
of
use
has
been
regarded
an
important
predictor
of
use
for
utilitarian
information
systems,
since
perceiving
the
system
as
easy
to
use
is
considered
to
improve
the
efficiency
of
the
human–computer
interaction,
and
therefore
to
have
a
positive
impact
on
the
volume
and
quality
of
the
utilitarian
output
of
that
system.
In
other
words,
when
other
aspects
are
equal,
the
ease
of
using
a
system
may
cause
it
to
be
perceived
as
more
useful
(Venkatesh,
1999).
Consistently
with
these
considerations,
prior
research
has
shown
that
ease
of
use
has
a
positive
effect
on
the
intentions
to
use
a
system
(Davis
et
al.,
1989;
Venkatesh,
1999,
2000).
Furthermore,
ease
of
use
has
also
been
considered
impor-
tant
for
attitude
formation
(Davis,
1989).
As
gamification
refers
to
employing
elements
for
gameful
interactions,
then
gamification
may
be
considered
to
promote
hedonic
experiences,
and
conse-
quently,
hedonic
use.
In
contexts
with
hedonic
use,
ease
of
use
has
been
shown
to
positively
affect
both
attitude
(Hsu
&
Lu,
2004;
Wang
&
Scheepers,
2012)
and
continued
use
intentions
(Atkinson
&
Kydd,
1997;
van
der
Heijden,
2004).
As
hedonically
oriented
services
are
intended
to
be
enjoyable
to
use,
then
interaction
with
the
system
is
highlighted
and
ease
of
use
becomes
central
in
determining
the
user
acceptance
(van
der
Heijden,
2004).

422
J.
Hamari,
J.
Koivisto
/
International
Journal
of
Information
Management
35
(2015)
419–431
Social Hedonic Ulitarian
Usefu
lness
Enjoyment
Playf
ulness
Recognion
Ease of use
Social
influence
Connued
use
Atud e
H1.1
H1.2
H2.1
H2.2
H3.1
H3.2
H4.1
H4.2
H5.1
H5.2
H6.1
H6.1
H7
Fig.
1.
Hypothesis
model.
Therefore,
we
propose
the
following
hypotheses
regarding
the
relationships
between
the
variables
related
to
utilitarian
benefits
and
dependent
variables
in
the
dataset:
H1.1.
Usefulness
is
positively
associated
with
attitude.
H1.2.
Usefulness
is
positively
associated
with
continued
use.
H2.1.
Ease
of
use
is
positively
associated
with
attitude.
H2.2.
Ease
of
use
is
positively
associated
with
continued
use.
2.2.
Hedonic
aspects
In
the
literature
related
to
technology
use,
the
hedonic
user
experiences
have
often
been
operationalized
as
the
abstract
expe-
rience
of
perceived
enjoyment.
Perceived
enjoyment
refers
to
the
extent
to
which
the
use
of
the
system
is
perceived
as
enjoyable
on
its
own
(Davis,
1989).
In
the
context
of
games,
game-like
systems
and
other
systems
used
for
entertainment
purposes,
the
enjoyment
of
using
the
system
has
been
shown
to
be
an
important
factor
affect-
ing
use
intentions
(See
e.g.
Atkinson
&
Kydd,
1997;
Hamari,
2015b;
Mäntymäki
&
Riemer,
2014;
Moon
&
Kim,
2001;
van
der
Heijden,
2004;
Venkatesh,
1999).
Therefore,
there
is
reason
to
assume
that
similarly
to
games,
enjoyment
will
positively
influence
the
use
intentions
of
a
gamified
service.
Furthermore,
it
is
to
be
expected
that
if
a
service
is
perceived
as
enjoyable,
then
the
attitude
toward
the
system
is
likely
to
be
positive
as
well.
A
positive
relationship
between
enjoyment
and
attitude
has
been
found
in
hedonically
oriented
services
such
as
mobile
games
(Ha,
Yoon,
&
Choi,
2007),
online
video
games
(Lin
&
Bhattacherjee,
2010),
and
social
virtual
worlds
(Mäntymäki
&
Salo,
2011;
Mäntymäki,
Merikivi,
Verhagen,
Feldberg,
&
Rajala,
2014;
Shin,
2009).
In
addition
to
sheer
enjoyment,
gamification
is
often
claimed
to
aim
at
making
use
of
systems
more
playful.
The
social
contex-
tual
cues
that
frame
the
activity
may
affect
how
it
is
perceived
(Perry
&
Ballou,
1997;
Webster
&
Martocchio,
1992).
For
example,
whether
an
activity
is
framed
as
“work”
or
“play”
may
have
an
influ-
ence
on
the
attitude
formation
toward
the
activity
(Sandelands,
1988;
Venkatesh,
1999;
Webster
&
Martocchio,
1993;
see
also
Lieberoth,
2014).
Moreover,
when
an
activity
is
gamified,
the
gam-
ification
potentially
proposes
a
new,
creative
way
of
approaching
the
activity.
The
interaction
with
a
gamification
system
may,
there-
fore,
create
experiences
of
playfulness
(on
playfulness,
see
e.g.
Lieberman,
1977;
Venkatesh,
1999;
Webster
&
Martocchio,
1992).
In
the
context
of
computer
use,
the
concept
of
playfulness
has
been
defined
as
cognitive
spontaneity
in
interactions
with
the
system
(Martocchio
&
Webster,
1992).
In
other
words,
playfulness
refers
to
explorative
and
creative
behavior
when
interacting
with
the
sys-
tem.
Inducing
playful
interaction
and
experiences
from
system
use
has
been
shown
to
be
beneficial,
for
example,
in
training
contexts.
Playful
interactions
have
also
been
considered
to
promote
creative
and
exploratory
behavior,
which
benefits
the
learning
process
and
leads
to
better
learning
results
(Perry
&
Ballou,
1997).
Therefore,
we
posit
the
following
hypotheses
regarding
the
rela-
tionships
between
the
variables
related
to
the
hedonic
benefits
and
dependent
variables
in
the
dataset:
H3.1.
Enjoyment
is
positively
associated
with
attitude.
H3.2.
Enjoyment
is
positively
associated
with
continued
use.
H4.1.
Playfulness
is
positively
associated
with
attitude.
H4.2.
Playfulness
is
positively
associated
with
continued
use.
2.3.
Social
aspects
In
the
literature
surrounding
technology
adoption,
the
social
aspects
are
commonly
operationalized
as
social
influence,
which
refers
to
an
individual’s
perception
of
how
important
others
regard
the
target
behavior
and
whether
they
expect
one
to
perform
that
behavior
(Ajzen,
1991;
Fishbein
&
Ajzen,
1975).
Similarly
to
other
environments,
in
the
context
of
gamification,
such
social
influ-
ence
can
be
expected
to
be
an
important
factor
affecting
attitudes
and
use
intentions
(Ajzen,
1991;
Venkatesh
&
Davis,
2000).
In
the
service
examined
in
this
study,
the
target
behavior
is
the
use
of
gamification
to
motivate
oneself
(to
exercise).
Social
influence
is
then
likely
to
reflect
the
user’s
perceptions
of
how
other
users
per-
ceive
the
use
of
the
service.
In
line
with
Bock,
Zmud,
Kim,
and
Lee

J.
Hamari,
J.
Koivisto
/
International
Journal
of
Information
Management
35
(2015)
419–431
423
(2005),
Lewis,
Agarwal,
and
Sambamurthy
(2003)
and
Venkatesh
and
Davis
(2000),
we
propose
that
the
social
influence
directly
affects
attitude,
as
well
as
those
behavioral
intentions
that
are
mediated
by
attitude.
Furthermore,
on
a
general
level,
human
beings
inherently
long
for
relatedness
and
acceptance
from
those
near
them
(Deci
&
Ryan,
2000).
The
social
interaction
facilitated
within
a
service
may
potentially
satisfy
these
social
needs
(Zhang,
2008).
Such
social
interaction
may
also
create,
for
example,
a
sense
of
recognition,
which
refers
to
the
social
feedback
users
receive
on
their
behav-
iors
(Hernandez,
Montaner,
Sese,
&
Urquizu,
2011;
Hsu
&
Lin,
2008).
When
interacting
with
the
community,
a
user
potentially
receives
recognition
from
the
other
users
when
interacting
with
them
(Cheung,
Chiu,
&
Lee,
2011;
Lin,
2008).
In
consequence,
the
service
is
potentially
more
positively
conceived
when
it
produces
a
sense
of
recognition
from
others
(Preece,
2001),
thus
positively
affecting
the
user’s
attitude
toward
using
the
service.
Therefore,
we
hypothesize
the
following
regarding
the
rela-
tionships
between
the
variables
related
to
the
social
benefits
and
dependent
variables
in
the
dataset:
H5.1.
Recognition
is
positively
associated
with
attitude.
H5.2.
Recognition
is
positively
associated
with
continued
use.
H6.1.
Social
influence
is
positively
associated
with
attitude.
H6.2.
Social
influence
is
positively
associated
with
continued
use.
2.4.
Attitude
In
this
study,
attitude
toward
service
use
refers
to
the
overall
evaluation
of
the
system’s
usage,
be
it
favorable
or
unfavor-
able
(Ajzen,
1991;
Fishbein
&
Ajzen,
1975).
A
strong
relationship
between
attitude
and
use
intentions
has
been
shown
in
sev-
eral
studies
(e.g.
Baker
&
White,
2010;
Bock
et
al.,
2005;
Lin
&
Bhattacherjee,
2010;
Mäntymäki
et
al.,
2014).
Consequently,
in
a
gamification
context,
the
relationship
of
attitude
and
continued
use
is
suspected
to
be
similar.
Therefore,
we
propose
the
following
hypothesis
regarding
the
relationship
between
attitude
and
continued
use
in
the
dataset:
H7.
Attitude
positively
influences
continued
use.
3.
Data
and
methods
3.1.
Data
The
data
was
gathered
via
a
questionnaire
from
the
users
of
Fitocracy,
an
online
service
that
gamifies
exercise.
The
service
enables
the
tracking
of
one’s
exercise
and
the
user
enters
their
exer-
cise
details
into
the
system.
Gamification
is
further
incorporated
into
the
service
by
rewarding
the
user
with
a
point
value
allocated
to
a
given
exercise.
When
a
user
logs
an
activity,
the
system
calcu-
lates
the
point
value
that
the
user
gains
with
the
exercise.
The
point
value
is
adjusted
based
on
applicable
details,
such
as
number
of
rep-
etitions,
distance,
time,
intensity
or
weights,
provided
by
the
user.
For
example,
lifting
heavier
weights
yields
more
points
than
lifting
lighter
weights,
or
running
for
30
min
gives
more
points
than
run-
ning
for
20
min.
However,
a
greater
number
of
repeats
with
lighter
weights
may
ultimately
result
in
a
higher
point
value
than
fewer
repeats
with
heavier
weights,
etc.
Based
on
the
points
gained
by
the
user,
the
service
reports
the
profile
level
the
user
has
reached.
By
gaining
more
points,
the
service
enables
level-ups.
Furthermore,
the
service
enables
achievements
(Hamari
&
Eranti,
2011)
for
one’s
actions,
along
with
completing
quests
with
pre-set
exercise
conditions.
Other
service
users
can
provide
comments
and
‘likes’,
and
thus
offer
encour-
agement
on
the
exercise
reports,
achievements,
and
level-ups
of
other
users.
The
service
bears
similarities
to
popular
social
net-
working
services
such
as
Facebook,
as
it
offers
a
venue
for
social
activity
such
as
group-forming
and
communication,
incorporates
profile-building
and
also
the
possibility
of
sharing
content
(see
e.g.
Boyd
&
Ellison,
2007;
Lin
&
Lu,
2011).
At
the
time
of
gathering
the
data,
the
service
could
be
used
with
an
iPhone
application
or
via
a
Web
browser.
An
Android
application
was
released
while
the
data
gathering
neared
its
completion.
The
survey
was
conducted
by
posting
a
description
of
the
study
and
the
survey
link
on
a
related
discussion
forum
and
in
groups
within
the
service.
The
survey
was
accessible
only
to
users
of
the
service.
The
survey
was
active
for
three
months
during
which
200
usable
responses
were
recorded.
Respondents
were
entered
into
a
prize
draw
for
one
$50
Amazon
gift
certificate.
Table
1
outlines
the
demographic
details
of
the
respondents.
As
can
be
seen,
the
gender
divide
of
the
sample
was
fairly
equal.
Ages
between
20
and
29
are
more
represented
in
the
data
than
other
age
groups,
however,
the
age
distribution
is
wide
with
respondents
Table
1
Demographic
information
of
respondents,
including
gender,
age,
time
using
the
service
and
exercise
information.
Frequency
Percent
Frequency
Percent
Gender
Length
of
experience
Female
102
51
Less
than
1
month
24
12
Male
98
49
1–3
months
38
19
Age
(mean
=
29.5,
median
=
27.5)
3–6
months
29
14.5
Less
than
20
9
4.5
6–9
months
26
13
20–24
51
25.5
9–12
months
33
16.5
25–29
54
27
12–15
months
38
19
30–34
41
20.5
15–18
months
7
3.5
35–39
22
11
More
than
18
months
5
2.5
40–44
16
8
Exercise
sessions
per
week
(mean
=
5.3,
median
=
5.0)
45–49
3
1.5
1–4
83
41.5
50
or
more
4
2
5–9
106
53.0
10–14
6
3.0
15
or
more
5
2.5
Exercise
hours
per
week
(mean
=
7.2,
median
=
6.0)
1–4
51
25.5
5–9
99
49.5
10–14
40
20.0
15
or
more
10
5.0

424
J.
Hamari,
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/
International
Journal
of
Information
Management
35
(2015)
419–431
Table
2
Measurement
instrument.
Construct
Name
Included/total
items
Adapted
from
ATT
Attitude
4/4
Ajzen
(1991)
CUI
Continuance
intentions
for
system
use
4/4
Bhattacherjee
(2001)
ENJ
Enjoyment
4/4
van
der
Heijden
(2004)
EOU
Ease
of
use
4/4
Davis
(1989)
PLAY
Playfulness
7/9
Webster
and
Martocchio
(1992)
USE
Usefulness
5/5
Davis
(1989)
REC
Recognition
4/4
Hernandez
et
al.
(2011),
Hsu
and
Lin
(2008),
Lin
and
Bhattacherjee
(2010),
and
Lin
(2008)
SI
Social
influence 4/4
Ajzen
(1991)
featuring
in
all
of
the
categories.
The
lengths
of
experience
with
the
service
reported
by
the
respondents
are
distributed
rather
evenly.
The
details
of
the
amounts
of
exercise
reported
by
respondents
are
also
described
in
Table
1.
3.2.
Measurement
instrument
All
variables
contained
4
items,
except
for
one
which
contained
9
items
and
one
containing
5
items.
Each
variable
was
measured
with
a
7-point
Likert
scale
(strongly
disagree
–
strongly
agree).
All
operationalizations
of
the
psychometric
constructs
were
adapted
from
previously
published
sources
(see
Table
2).
The
questionnaire
items
can
be
found
in
Appendix
A.
3.3.
Validity
and
reliability
The
model-testing
was
conducted
via
the
component-based
PLS-SEM
in
SmartPLS
2.0
M3
(Ringle,
Wende,
&
Will,
2005).
Com-
pared
to
co-variance-based
structural
equation
methods
(CB-SEM),
the
key
advantage
of
component-based
PLS
(PLS-SEM)
estimation
is
that
it
is
non-parametric,
and
therefore
makes
no
restrictive
assumptions
about
the
distributions
of
the
data.
Secondly,
PLS-SEM
is
considered
to
be
a
more
suitable
method
for
prediction-oriented
studies
(such
as
the
present
study),
while
co-variance-based
SEM
is
better
suited
to
testing
which
models
best
fit
the
data
(Anderson
&
Gerbing
1988;
Chin,
Marcolin,
&
Newsted,
2003).
Convergent
validity
(see
Table
3)
was
assessed
with
three
metrics:
average
variance
extracted
(AVE),
composite
reliability
(CR)
and
Cronbach’s
alpha
(Alpha).
All
of
the
convergent
validity
metrics
were
clearly
greater
than
the
thresholds
cited
in
relevant
literature
(AVE
should
be
>0.5,
CR
>0.7
(Fornell
&
Larcker,
1981),
and
Cronbach’s
alpha
>0.7
(Nunnally,
1978)).
Only
well-established
measurement
items
were
used
(see
Appendix
A).
There
was
no
missing
data,
so
no
imputation
methods
were
used.
We
can
there-
fore
conclude
that
the
convergent
requirements
of
validity
and
reliability
for
the
model
were
met.
Discriminant
validity
was
assessed,
firstly,
through
the
compar-
ison
of
the
square
root
of
the
AVE
of
each
construct
to
all
of
the
correlations
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
(Chin,
1998;
Jöreskog
&
Sörbom,
1996).
Secondly,
in
accordance
with
the
work
of
Pavlou,
Liang,
and
Xue
(2007),
we
determined
that
no
inter-correlation
between
con-
structs
was
higher
than
0.9.
Thirdly,
we
assessed
the
discriminant
validity
by
confirming
that
each
item
had
the
highest
loading
with
its
corresponding
construct
(see
Appendix
B).
All
three
tests
indi-
cated
that
the
discriminant
validity
and
reliability
was
acceptable.
In
addition,
in
order
to
reduce
the
likelihood
of
common
method
bias,
we
randomized
the
order
of
the
measurement
items
on
the
survey
to
limit
the
respondent’s
ability
to
detect
patterns
between
the
items
(Cook,
Campbell,
&
Day,
1979).
Common
method
bias
refers
to
a
situation
where
there
is
“variance
that
is
attributable
to
the
measurement
method
rather
than
to
the
constructs
the
meas-
ures
represent”
(Podsakoff,
MacKenzie,
Lee,
&
Podsakoff,
2003).
Nevertheless,
we
tested
whether
common
method
bias
existed
in
our
data
by
“controlling
for
the
effects
of
an
unmeasured
latent
methods
factor”
as
proposed
by
Podsakoff
et
al.
(2003),
and
in
the
same
manner
as
practically
demonstrated
in
a
PLS-SEM
environ-
ment
by
Liang,
Saraf,
Hu,
and
Xue
(2007).
According
to
Williams,
Edwards,
and
Vandenberg
(2003),
if
the
loadings
of
the
“method
factor”
are
statistically
insignificant
and/or
considerably
low
in
comparison
to
indicator
loadings
of
the
substantive
factors,
there
is
no
evidence
of
common
method
bias.
Additionally,
the
square
of
the
loadings
represents
the
percentage
of
the
variance
explained.
Common
method
bias
test
was
also
run
to
provide
evidence
that
possible
high
inter-correlations
between
constructs
is
not
caused
by
a
systematic
error
caused
by
the
measurement
instrument.
As
reported
in
Appendix
C,
we
found
a
few
significant
loadings
on
the
“method
factor”,
however,
they
explain
a
negligibly
small
share
of
the
variance
(0.009
on
average).
The
indicator
loadings
however,
explain
0.726
of
variance
on
average
in
substantive
factors.
There-
fore,
we
could
be
confident
that
common
method
bias
is
not
likely
to
be
an
issue.
The
sample
size
satisfies
different
criteria
for
the
lower
bounds
of
sample
size
for
PLS-SEM:
(1)
ten
times
the
largest
number
of
structural
paths
directed
at
a
particular
construct
in
the
inner
path
model
(therefore,
the
sample
size
threshold
for
the
model
in
this
study
would
be
70
cases)
(Chin,
1998)
and
(2)
according
to
Anderson
and
Gerbing
(1984,
1988),
a
threshold
for
any
type
of
SEM
is
approximately
150
respondents
for
models
where
con-
structs
comprise
of
three
or
four
indicators.
(3)
The
sample
size
also
satisfies
stricter
criteria
relevant
for
variance-based
SEM:
for
example,
Bentler
and
Chou
(1987)
recommend
a
ratio
of
5
cases
Table
3
Convergent
and
discriminant
validity.
AVE
CR
Alpha
ATT
CUI
ENJ
EOU
PLAY
USE
REC
SI
ATT
0.795
0.939
0.914
0.892
CUI
0.735 0.917
0.880
0.658
0.857
ENJ
0.779
0.934
0.905
0.672
0.677
0.883
EOU
0.752
0.923
0.887
0.473
0.521
0.620
0.867
PLAY
0.568
0.901
0.870
0.444
0.429
0.410
0.294
0.754
USE
0.713
0.925
0.899
0.791
0.655
0.737
0.500
0.447
0.844
REC
0.804
0.943
0.919
0.587
0.397
0.566
0.371
0.267
0.464
0.897
SI
0.735
0.917
0.879
0.666
0.490
0.568
0.393
0.434
0.636
0.461
0.857

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425
Social Hedonic Ulitarian
Usefulness
Enjoyme
nt
Playfulness
Recognion
Ease
of
use
Social
influence
Connued
use
Atude
.513***
.122
.031
.126*
.015
.327***
.056
.104
.226***
-.085
.190***
-.035
.309**
Fig.
2.
The
research
model
with
significant
relationships:
*
p
<
0.1,
*
*
p
<
0.05,
*
*
*
p
<0.01.
per
observed
variable
(therefore,
the
sample
size
threshold
for
the
model
in
this
study
would
be
175).
The
independent
variables
were
tested
for
multicollinearity
issues.
No
multicollinearity
between
the
variables
existed.
4.
Results
The
path
model
could
account
for
71.6%
of
variance
for
atti-
tudes
toward
the
gamification
service
and
56.4%
of
variance
of
the
intentions
to
continue
using
the
service.
The
results
indicate
that
utilitarian
benefits
are
positively
associated
with
attitude
and
continued
use.
In
particular,
the
perceived
usefulness
is
positively
associated
with
attitude
(ˇ
=
.513***),
as
well
as
indirectly
asso-
ciated
with
continued
use
(ˇ
=
.280***)
through
attitude.
Ease
of
use
was
only
associated
with
continued
use
directly
(ˇ
=
.126*).
For
hedonic
predictors,
perceived
playfulness
did
not
significantly
predict
either
of
the
dependent
variables
directly,
but
did
have
a
weak
significant
association
with
continued
use
as
mediated
by
attitude
(ˇ
=
.122*),
whereas
enjoyment
had
a
positive
direct
rela-
tionship
with
use
(ˇ
=
.327***,
total
effect
ˇ
=
.331***).
Interestingly,
the
social
factors
in
the
study
had
a
relatively
strong
direct
rela-
tionship
with
attitude
(ˇ
=
.226***
for
recognition
and
ˇ
=
.190***
Table
4
Direct
and
indirect
effects.
Direct
effects
Total
effects
(direct
effect
+
mediated
effect
via
attitude)
Attitude
Continued
use
Continued
use
Attitude
n/a
0.309** 0.309**
Usefulness
0.513*** 0.122
0.280***
Ease
of
use
0.031
0.126*
0.136*
Enjoyment
0.015
0.327*** 0.331***
Playfulness
0.056
0.104
0.122*
Recognition
0.226*** −0.085
−0.015
Social
influence
0.190*** −0.035
0.024
*p
<
0.1.
** p
<
0.05.
*** p
<
0.01.
Table
5
Hypothesis
support.
H#
Description
Supported
H1.1
Usefulness
is
positively
associated
with
attitude.
Yes
H1.2
Usefulness
is
positively
associated
with
continued
use.
Indirectly
H2.1
Ease
of
use
is
positively
associated
with
attitude.
No
H2.2
Ease
of
use
is
positively
associated
with
continued
use.
Yes
H3.1
Enjoyment
is
positively
associated
with
attitude.
No
H3.2
Enjoyment
is
positively
associated
with
continued
use.
Yes
H4.1
Playfulness
is
positively
associated
with
attitude.
No
H4.2
Playfulness
is
positively
associated
with
continued
use.
Indirectly
H5.1
Recognition
is
positively
associated
with
attitude.
Yes
H5.2
Recognition
is
positively
associated
with
continued
use.
No
H6.1
Social
influence
is
positively
associated
with
attitude.
Yes
H6.2
Social
influence
is
positively
associated
with
continued
use.
No
H7
Attitude
is
positively
associated
with
continued
use.
Yes
for
social
influence),
however,
they
were
not
further
associated
with
intentions
to
continue
the
use
of
the
gamified
service.
Fur-
thermore,
attitude
significantly
predicted
continued
use
intentions
(ˇ
=
0.309**).
See
Table
4
and
Fig.
2
for
details
of
the
results
and
Table
5
for
a
summary
of
the
hypothesis
support.
5.
Discussion
In
this
paper,
we
examined
what
motivates
people
to
use
gamification
services.
We
investigated
which
benefits
(utilitar-
ian,
hedonic
or
social)
are
associated
with
attitudes
toward

426
J.
Hamari,
J.
Koivisto
/
International
Journal
of
Information
Management
35
(2015)
419–431
gamification
and
the
intentions
to
continue
using
gamified
services.
The
findings
have
several
theoretical
and
practical
implications,
which
are
discussed
in
detail
below.
5.1.
Conclusions
and
implications
of
the
findings
If
we
consider
gamification
from
the
perspective
of
the
technol-
ogy
acceptance
literature
(Davis,
1989;
Venkatesh
&
Davis,
2000),
and
particularly
through
the
established
perception
of
seeing
infor-
mation
systems
as
being
either
utilitarian
(Davis,
1989;
van
der
Heijden,
2004)
or
hedonic
(van
der
Heijden,
2004),
the
results
of
the
study
provide
interesting
insights.
The
established
literature
on
technology
acceptance
suggests
that
the
use
of
utilitarian
sys-
tems
is
mainly
motivated
by
perceived
usefulness
(Davis,
1989;
Venkatesh
&
Davis,
2000),
while
the
use
of
hedonic
information
systems
is
motivated
by
perceived
enjoyment
(van
der
Heijden,
2004).
The
results
of
this
paper,
however,
suggest
that
gamifica-
tion
lingers
somewhere
in
the
middle
of
these
polar
ends
(on
mixed
systems,
see
e.g.
Gerow,
Ayyagari,
Thatcher,
&
Roth,
2013),
or
alter-
natively
offers
the
benefits
of
both
systems
since
according
to
our
results
the
use
of
gamification
is
strongly
driven
by
both
utilitar-
ian
and
hedonic
benefits/motivations.
These
findings
confirm
the
a
priori
beliefs
and
hypotheses
set
forth
at
the
outset
of
this
research
that,
indeed,
both
utilitarian
and
hedonic
aspects
are
strong
deter-
minants
for
gamification
acceptance.
This
notion
could
indicate
potential
further
research
questions
about
the
nature
and
position
of
gamification
within
the
continuum
between
purely
utilitarian
and
purely
self-purposeful
hedonic
systems.
However,
as
the
results
suggest
that
the
use
of
gamified
ser-
vices
is
driven
by
both
utilitarian
and
hedonic
factors,
there
is
an
interesting
distinction
in
their
effects.
The
results
show
that
the
more
affective,
non-calculating
frame
of
enjoyment
has
a
direct
relationship
with
how
much
people
are
willing
to
use
gamification
services,
whereas
the
more
cognitive,
utility-seeking
factor
of
use-
fulness
is
mediated
by
attitudes
toward
the
gamified
system
(see
Zaichkowsky,
1994
on
affective
vs.
cognitive
involvement).
These
findings
may
suggest
that
enjoyment
functions
on
a
less
conscious,
less
cognitive
and
less
direct
level
in
determining
use
behavior,
whereas
usefulness
is
more
consciously
realized
and
calculated,
and
therefore,
also
manifests
in
more
positive
attitudes
toward
the
behavior
of
using
gamification.
In
a
similar
vein,
the
direct
influence
of
ease
of
use
is
an
interest-
ing
deviation
from
how
it
is
traditionally
perceived
in
the
literature
on
the
acceptance
of
utilitarian
systems.
Ease
of
use
is
commonly
defined
in
terms
of
a
system’s
effectiveness
in
reaching
utilitar-
ian
outcomes
through
requiring
little
effort
or
resources
to
use
the
given
system
(Davis,
1989).
However,
ease
of
use
can
also
be
unavoidably
seen
as
a
factor
that
diminishes
negative
affec-
tive
experiences
such
as
the
frustration
caused
by
a
complex
user
interface
(see
also
van
der
Heijden,
2004).
From
this
perspective,
whereas
enjoyment
measures
gaining
positive
affective
experi-
ences,
then
ease
of
use
could
measure
the
lack
of
negative
affective
experiences
stemming
from
the
user
interface
of
the
system.
This
is
why,
in
the
context
of
gamification,
it
might
not
be
so
surprising
that
enjoyment
and
ease
of
use
behave
similarly
as
determinants
for
the
continued
use
of
gamification
services;
they
both
have
a
direct
relationship
with
use
but
no
clear
relationship
with
attitude
formation.
In
previous
research
on
technology
adoption,
strong
support
for
the
effect
of
ease
of
use
on
continued
use
intentions
has
been
shown
(Davis,
Bagozzi,
&
Warshaw,
1992;
Venkatesh,
2000).
In
organizational
and
utilitarian
contexts,
previous
studies
have
shown
ease
of
use
to
affect
attitude
only
in
the
post-adoption
phase
(Davis
et
al.,
1989).
However,
in
the
contexts
of
hedonic
use
such
as
online
and
video
games,
ease
of
use
has
been
reported
to
be
a
significant
determinant
to
attitude
(Ha
et
al.,
2007;
Hsu
&
Lu,
2004;
Wang
&
Scheepers,
2012).
Another
empirically
and
theoretically
interesting
finding
con-
cerning
gamification
is
that
no
direct
relationship
between
perceived
playfulness
and
either
of
the
dependent
variables
of
atti-
tude
or
use
intention
could
be
established,
although
a
slight
indirect
relationship
with
use
intentions
as
mediated
by
attitude
could
be
found.
A
couple
of
different
interpretations
could
be
considered
for
the
lack
of
the
direct
associations.
First,
the
finding
of
playful-
ness
having
no
direct
relationship
with
attitude
or
use
intentions
could
result
from
how
the
gamification
is
perceived
by
the
users
in
terms
of
utility
or
play
(Perry
&
Ballou,
1997;
Venkatesh,
1999;
Webster
&
Martocchio,
1993).
If
the
service
is
framed
and
con-
ceived
as
serving
the
purposes
of
utility
rather
than
the
purposes
of
play,
then
the
perceived
playfulness
might
not
constitute
an
impor-
tant
benefit
to
be
derived
from
the
service,
and
therefore
would
not
affect
either
attitude
or
use
intentions
significantly.
Secondly,
we
suggest
that
this
finding
might
be
an
indicator
of
the
actual
nature
of
what
gamification
creates
in
terms
of
hedonic
experi-
ences.
According
to
theorists
on
play
and
games,
play
(paidia)
and
games
(ludus)
(see
Caillois,
1961)
can
be
situated
at
the
opposite
ends
of
a
continuum
–
paidia
representing
free-form
activity
that
is
spontaneous,
unstructured
and
not
governed
by
rules,
and
ludus
representing
structured
activity
that
contains
explicit
rules
that
need
to
be
adhered
to.
The
question
of
whether
gamification
offers
more
structured
experiences
as
opposed
to
more
explorative
and
free-form
playfulness
has
also
been
an
important
vein
of
discussion
in
theorizing
and
conceptualizing
gamification
(see
e.g.
Deterding
et
al.,
2011;
Huotari
&
Hamari,
2012).
Our
findings
provide
further
empirically
derived
evidence
on
this
issue,
at
least
in
the
context
of
the
type
of
gamification
investigated
in
this
study.
Fitocracy
can
be
seen
as
a
rather
traditional
form
of
gamification,
where
the
gamify-
ing
service
design
is
mostly
related
to
goals,
progress
and
rewards.
These
mechanics
are
prone
to
provide
structure
to
the
activities,
rather
than
enticing
users
to
independently
explore
and
experi-
ment.
Therefore,
gamification
potentially
needs
to
be
considered
more
in
terms
of
the
ludus
perspective,
as
a
system
with
rules
and
structure,
which
does
not
necessarily
invoke
playful
experiences.
However,
regardless
of
how
gamification
is
defined,
some
affor-
dances
are
more
likely
to
invoke
playfulness
than
others.
Also,
as
literature
on
playfulness
suggests,
individual
differences
in
the
ten-
dency
of
playfully
experiencing
interactions
also
exist
(Koivisto
&
Hamari,
2014;
Lieberman,
1977;
Webster
&
Martocchio,
1992).
The
results
indicated
that
social
factors
had
no
significant
associ-
ation
with
use
intentions,
but
positively
influenced
attitude
toward
the
system.
The
social
influence
having
no
association
with
use
intentions
could
potentially
be
explained
by
the
use
context
of
gamification,
which
is
mostly
voluntary.
This
finding
is
in
line
with
previous
research
suggesting
that
when
the
use
of
the
system
is
voluntary,
then
social
influence
does
not
necessarily
directly
affect
the
intentions
to
use
the
system
(Venkatesh
&
Davis,
2000).
When
adopting
mandatory
systems
however,
explicit
social
influence
in
the
form
of
compliance
has
been
considered
to
affect
use
intentions
(Hamari
&
Koivisto,
2015;
Venkatesh
&
Davis,
2000).
Compliance
refers
to
behavior
that
is
performed
due
to
normative
reasons,
i.e.
the
source
of
influence
has
the
possibility
of
rewarding
or
pun-
ishing
the
individual
(Kelman,
1958;
Venkatesh
&
Davis,
2000).
Conversely,
in
the
case
of
voluntary
system
use,
the
social
influ-
ence
functions
primarily
through
processes
of
internalization
and
identification
(Venkatesh
&
Davis,
2000),
that
is,
the
user
may
inter-
nalize
the
suggestions
and
opinions
of
respected
peers
or
consider
their
system
use
as
promoting
a
certain
image,
and
therefore
wish
to
identify
with
it
(Kelman,
1958;
Venkatesh
&
Davis,
2000).
These
processes
may
have
an
effect
on
the
perceptions
regarding
the
system,
and
thus,
affect
the
attitude
toward
it.
Accordingly,
sev-
eral
different
veins
of
literature
have
highlighted
the
relationship
between
social
influence
and
attitude,
such
as
the
theory
of
rea-
soned
action
(Ajzen,
1991;
Fishbein
&
Ajzen,
1975),
discussions

J.
Hamari,
J.
Koivisto
/
International
Journal
of
Information
Management
35
(2015)
419–431
427
on
social
proof
(Cialdini,
2001)
and
social
comparison
(Festinger,
1954).
In
addition,
research
has
also
indicated
that
while
social
influence
may
have
a
positive
effect
on
attitude,
a
so-called
attitude-behavior
gap
(also
value-action
gap)
might
exist
in
many
differing
contexts.
This
refers
to
a
situation
where
people
are
willing
to
convey
a
socially
favorable
image
of
themselves
by
communi-
cating
a
positive
attitude
toward
the
same
issues
as
their
peers
and
immediate
important
others,
while
at
the
same
time
not
compre-
hensively
increasing
the
related
behavior
(e.g.
Blake,
1999;
Flynn,
Bellaby,
&
Ricci,
2010).
One
possible
explanation
for
the
results
con-
cerning
the
relationship
of
social
factors,
attitude
and
behavioral
intention
could
stem
from
such
a
phenomenon.
Ultimately,
explor-
ing
whether
this
is
the
case
with
gamification
exceeds
the
bounds
of
this
study,
however,
it
could
provide
an
interesting
further
line
of
inquiry.
In
summary,
the
results
of
the
study
suggest
that
social
and
utilitarian
aspects
are
more
prone
to
positively
reflect
on
attitude
formation,
whereas
the
hedonic,
less
cognitive
factors
have
a
posi-
tive
direct
relationship
with
behavior,
but
a
negligible
relationship
with
attitudes.
Moreover,
the
relationship
between
perceived
use-
fulness
and
continued
use
was
shown
to
be
mediated
through
attitude.
Consequently,
the
results
suggest
that
hedonic
aspects
(i.e.
the
intrinsic
motivators)
drive
the
actual
use,
whilst
utilitarian
and
social
aspects
(i.e.
the
extrinsic
motivators)
affect
the
attitude,
and
through
attitude,
have
an
effect
on
use
intentions.
5.2.
Limitations
and
avenues
for
future
research
As
is
commonplace
with
studies
conducted
by
online
surveys,
the
data
is
self-reported
and
the
respondents
are
self-selected.
Using
self-reported
data
may
affect
the
findings
as
the
users
responding
are
potentially
more
actively
engaged
with
the
service
and
therefore
willing
to
participate
in
activities
related
to
it.
Thus,
the
results
possibly
disregard
the
perceptions
and
intentions
of
less
active
and
unengaged
users
of
the
service.
These
issues
could
be
addressed
in
future
studies,
as
well
as
the
reasons
for
not
being/becoming
involved
in
the
service.
Future
research
should
combine
survey
data
with
actual
usage
data
and
proper
experi-
ments,
in
order
to
increase
the
robustness
of
research
on
the
topic.
This
study
was
conducted
in
the
context
of
gamifying
physi-
cal
exercise.
While
there
are
no
a
priori
obvious
reasons
to
expect
that
the
context
of
gamification
would
have
a
clear
direct
effect
on
the
results,
it
is
feasible
that
the
results
might
be,
to
some
degree,
context-dependent.
Here
the
context
is
voluntary,
self-directed
and
aimed
at
motivating
the
user
toward
an
activity
that
individ-
uals
often
have
difficulties
carrying
out
without
support.
In
this
vein,
gamification
can
be
perceived
as
a
kind
of
self-help
method.
Furthermore,
the
users
of
the
service
have
decided
to
use
the
gami-
fication
service
themselves
by
registering
with
the
service.
In
a
case
where
the
gamification
would
have
been,
for
example,
imposed
on
the
users,
the
results
might
be
different.
For
example,
in
a
study
of
a
gamified
e-commerce
service
(Hamari,
2013;
Hamari,
2015a,b),
the
gamification
was
implemented
on
an
existing
service
and
the
users
had
obviously
registered
without
prior
knowledge
of
the
gameful
interaction
to
come.
Further
research
could
therefore
be
conducted
in
comparing
these
types
of
results
across
different
contexts
(see
Hamari,
Koivisto,
&
Pakkanen,
2014;
Hamari,
Koivisto,
&
Sarsa,
2014
for
contexts
studied
in
the
current
body
of
research).
Prior
research
has
demonstrated
that
individual
differences
in
how
benefits
from
gamification
are
perceived
do
exist.
However,
future
research
could
also
consider
the
effects
of
personality
differ-
ences
and
player
types
(Hamari
&
Tuunanen,
2014;
Kallio,
M¨
ayr¨
a,
&
Kaipainen,
2011;
Yee,
2006)
on
the
use
and
experiences
relat-
ing
to
gamification.
Furthering
this
line
of
research
could
refine
the
understanding
of
how
moderating
demographic
and
user
related
factors
may
impact
upon
the
topic.
Acknowledgements
The
research
has
been
partially
supported
by
individual
study
grants
for
both
authors
from
the
Finnish
Cultural
Foundation
as
well
as
carried
out
as
part
of
research
projects
(40134/13,
40111/14,
40107/14)
funded
by
the
Finnish
Funding
Agency
for
Innovation
(TEKES).
Both
authors
have
contributed
to
this
article
equally.
Appendix
A.
Survey
constructs,
items,
and
sources.
Construct
Item
Loading
Adapted
from
Attitude All
things
considered,
I
find
using
Fitocracy
to
be
a
wise
thing
to
do.
0.871 Ajzen
(1991)
All
things
considered,
I
find
using
Fitocracy
to
be
a
good
idea.
0.914
All
things
considered,
I
find
using
Fitocracy
to
be
a
positive
thing.
0.896
All
things
considered,
I
find
using
Fitocracy
to
be
favorable.
0.884
Continued
use
intentions I
predict
that
I
will
keep
using
Fitocracy
in
the
future
at
least
as
much
as
I
have
used
it
lately.
0.850 Venkatesh
and
Davis
(2000)
and
Bhattacherjee
(2001)
I
intend
to
use
Fitocracy
at
least
as
often
within
the
next
three
months
as
I
have
previously
used.
0.833
I
predict
that
I
will
use
Fitocracy
more
frequently
rather
than
less
frequently.
0.871
It
is
likely
that
I
will
use
Fitocracy
more
often
rather
than
less
often
during
the
next
couple
months.
0.875
Enjoyment I
find
the
experience
of
the
exercise
and
the
related
Fitocracy
use
enjoyable.
0.909 van
der
Heijden
(2004)
I
find
the
experience
of
the
exercise
and
the
related
Fitocracy
use
pleasant.
0.871
I
find
the
experience
of
the
exercise
and
the
related
Fitocracy
use
exciting.
0.852
I
find
the
experience
of
the
exercise
and
the
related
Fitocracy
use
interesting.
0.898

428
J.
Hamari,
J.
Koivisto
/
International
Journal
of
Information
Management
35
(2015)
419–431
Appendix
A
(Continued
)
Construct
Item
Loading
Adapted
from
Ease
of
use Using
Fitocracy
interface
does
not
require
a
lot
of
mental
effort.
0.690 Davis
(1989)
The
interaction
with
Fitocracy
is
clear
and
understandable.
0.926
I
find
Fitocracy
easy
to
use.
0.939
I
find
it
easy
to
get
the
interface
of
Fitocracy
to
do
what
I
want
it
to
do.
0.890
Playfulness Uninventive
(reversed)
0.797 Webster
and
Martocchio
(1992)
Unoriginal
(reversed)
0.858
Unimaginative
(reversed)
0.738
Playful
0.651
Curious
Dropped
(<0.6)
Creative
0.850
Flexible
0.725
Experimenting
0.624
Spontaneous
Dropped
(<0.6)
Usefulness Using
Fitocracy
makes
it
easier
for
me
to
start
exercising
0.807 Davis
(1989)
Using
Fitocracy
is
useful
for
purposes
of
exercise.
0.808
Using
Fitocracy
enables
me
to
accomplish
more
with
regards
to
exercise.
0.867
I
feel
more
effective
with
regards
to
exercise
when
using
Fitocracy.
0.901
I
find
Fitocracy
useful.
0.836
Recognition I
feel
good
when
my
achievements
in
Fitocracy
are
noticed.
0.880 Hernandez
et
al.
(2011),
Hsu
and
Lin
(2008),
Lin
and
Bhattacherjee
(2010),
and
Lin
(2008)
I
like
it
when
other
Fitocracy
users
comment
and
like
my
exercise.
0.898
I
like
it
when
my
Fitocracy
peers
notice
my
exercise
reports.
0.939
It
feels
good
to
notice
that
other
user
has
browsed
my
Fitocracy
feed.
0.869
Social
influence People
who
influence
my
attitudes
would
recommend
Fitocracy.
0.783 Ajzen
(1991)
People
who
are
important
to
me
would
think
positively
of
me
using
Fitocracy.
0.890
People
who
I
appreciate
would
encourage
me
to
use
Fitocracy.
0.894
My
friends
would
think
using
Fitocracy
is
a
good
idea. 0.858
Appendix
B.
Factor
loadings.
ATT
CUI
ENJ
EOU
PLAY
USE
REC
SI
ATT
0.914
0.642
0.577
0.375
0.403
0.738
0.480
0.585
ATT
0.896
0.595
0.614
0.423
0.394
0.656
0.576
0.536
ATT
0.871
0.529
0.548
0.385
0.408
0.749
0.440
0.638
ATT
0.884
0.579
0.654
0.499
0.380
0.681
0.594
0.617
CUI
0.567
0.833
0.564
0.481
0.308
0.515
0.389
0.318
CUI
0.557
0.850
0.634
0.485
0.379
0.510
0.374
0.361
CUI
0.580
0.871
0.593
0.456
0.411
0.635
0.333
0.536
CUI
0.550 0.875
0.529
0.363
0.369
0.582
0.262
0.454
ENJ
0.634
0.597
0.909
0.562
0.376
0.683
0.464
0.476
ENJ
0.593
0.611
0.871
0.595
0.337
0.601
0.540
0.474
ENJ
0.573
0.591
0.852
0.444
0.386
0.633
0.545
0.528
ENJ
0.571
0.593
0.898
0.586
0.349
0.686
0.449
0.529
EOU
0.410
0.458
0.567
0.939
0.267
0.435
0.298
0.342
EOU
0.417
0.478
0.567
0.890
0.273
0.441
0.348
0.363
EOU
0.482
0.554
0.617
0.926
0.292
0.498
0.362
0.374
EOU
0.304
0.253
0.349
0.690
0.166
0.341
0.274
0.275
PLAY
0.326
0.306
0.269
0.167
0.850
0.321
0.153
0.343
PLAY
0.305
0.268
0.243
0.158
0.624
0.345
0.160
0.240
PLAY
0.344
0.359
0.346
0.300
0.725
0.416
0.279
0.336
PLAY
0.350
0.286
0.326
0.151
0.651
0.363
0.261
0.350
PLAY
0.295
0.276
0.234
0.190
0.738
0.255
0.077
0.267
PLAY
0.370
0.371
0.368
0.300
0.797
0.331
0.253
0.390
PLAY
0.338
0.372
0.345
0.252
0.858
0.313
0.192
0.336
USE
0.693
0.632
0.684
0.500
0.427
0.901
0.389
0.587
USE
0.609
0.536
0.615
0.461
0.336
0.808
0.413
0.456
USE
0.623
0.443
0.524
0.279
0.261
0.807
0.312
0.490

J.
Hamari,
J.
Koivisto
/
International
Journal
of
Information
Management
35
(2015)
419–431
429
Appendix
B
(Continued
)
ATT
CUI
ENJ
EOU
PLAY
USE
REC
SI
USE
0.762
0.591
0.686
0.504
0.435
0.836
0.461
0.565
USE
0.635
0.544
0.582
0.338
0.405
0.867
0.369
0.577
REC
0.476
0.329
0.528
0.299
0.250
0.423
0.880
0.349
REC
0.607
0.410
0.552
0.377
0.268
0.455
0.939
0.444
REC
0.502
0.272
0.436
0.305
0.207
0.360
0.898
0.383
REC
0.506 0.394 0.506 0.341 0.228 0.415 0.869
0.466
SI
0.594
0.422
0.517
0.360
0.361
0.558
0.441
0.858
SI
0.593
0.443
0.485
0.304
0.427
0.551
0.424
0.894
SI
0.574
0.441
0.500
0.375
0.416
0.589
0.404
0.890
SI
0.520
0.370
0.443
0.310
0.274
0.479
0.302
0.783
Appendix
C.
Common
method
bias
test.
Item
Construct
Substantive
factor
loading t-Value Variance
explained Method
factor
loading
t-Value
Variance
explained
1
ATT
0.761
10.124
0.579
0.140
1.947
0.020
2
ATT
0.891
12.311
0.794
−0.022
0.314
0.000
3
ATT
0.931
13.734
0.867
−0.04
0.542
0.002
4
ATT
0.98
22.585
0.960
−0.075
1.492
0.006
1
CUI
0.829
13.768
0.687
0.026
0.419
0.001
2
CUI
0.789
12.454
0.623
0.105
1.416
0.011
3
CUI
0.861 13.926
0.741
−0.035
0.476
0.001
4
CUI
0.949
25.759
0.901
−0.096
1.753
0.009
1
ENJ
0.767
9.721
0.588
0.101
1.253
0.010
2
ENJ
0.846
11.221
0.716
0.027
0.325
0.001
3
ENJ
0.955
13.900
0.912
−0.056
0.748
0.003
4
ENJ
0.956
13.749
0.914
−0.065
0.815
0.004
1
EOU
0.966
39.900
0.933
−0.046
1.217
0.002
2
EOU
0.852 25.606
0.726
0.050
1.150
0.003
3
EOU
0.778
11.441
0.605
−0.092
1.439
0.008
4
EOU
0.869
22.565
0.755
0.071
1.565
0.005
1
PLAY
0.765
10.239
0.585
0.063
0.851
0.004
2
PLAY
0.92
28.287
0.846
−0.100
2.141
0.010
3
PLAY
0.832
11.115
0.692
−0.121
1.858
0.015
4
PLAY
0.569
7.350
0.324
0.104
1.528
0.011
5
PLAY
0.638
6.679
0.407
0.113
1.413
0.013
6
PLAY
0.597
6.606
0.356
0.022
0.268
0.000
7
PLAY
0.892
26.955
0.796
−0.037
0.755
0.001
1
USE
0.816
9.486
0.666
−0.010
0.130
0.000
2
USE
1.013
14.129
1.026
−0.242
3.060
0.059
3
USE
0.914
17.175
0.835
−0.049
0.802
0.002
4
USE
0.611
7.564
0.373
0.257
3.045
0.066
5
USE
0.867
13.742
0.752
0.041
0.593
0.002
1
REC
0.924
20.579
0.854
−0.054
1.219
0.003
2
REC
0.883
27.467
0.780
0.072
1.925
0.005
3
REC
0.980
33.308
0.960
−0.108
3.482
0.012
4
REC
0.798
18.250
0.637
0.091
2.006
0.008
1
SI
0.811
10.706
0.658
0.056
0.739
0.003
2
SI
0.879
19.827
0.773
0.015
0.277
0.000
3
SI
0.890
19.616
0.792
0.006
0.115
0.000
4
SI
0.851
11.383
0.724
−0.084
1.089
0.007
On
average:
0.726
On
average:
0.009
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Juho
Hamari
(D.Sc.
Econ)
is
a
post-doctoral
researcher
at
the
Game
Research
Lab,
University
of
Tampere
and
at
the
Aalto
University
School
of
Business.
His
research
is
focused
on
the
intersection
of
economic
and
psychological
phenomena
in
gameful
environments.
He
has
authored
several
seminal
empirical
and
theoretical
schol-
arly
articles
on
gamification
and
games
from
perspectives
of
consumer
behavior,
human-computer
interaction
and
information
technology.
Hamari’s
research
has
been
published
in
a
variety
of
respected
journals
such
as
Journal
of
the
Associa-
tion
for
Information
Science
and
Technology,
International
Journal
of
Information
Management,
Computers
in
Human
Behavior,
Cyberpsychology,
Behavior
and
Social
Networking,
Electronic
Commerce
Research
and
Applications,
Simulation
&
Gaming
as
well
as
in
books
published
by
e.g.
MIT
Press.
http://juhohamari.com
Jonna
Koivisto
is
a
researcher
and
a
doctoral
candidate
at
the
Game
Research
Lab,
University
of
Tampere.
Her
research
concentrates
on
the
use
of
motivational
and
gameful
elements
in
various
systems.
Koivisto’s
research
has
been
published
on
venues
such
as
Computers
in
Human
Behavior,
European
Conference
on
Information
Systems
Science
and
Hawaii
International
Conference
on
Systems
Sciences.
http://
jonnakoivisto.com