Delivering Value through Mobile Apps by Mark Hoskam

Master
thesis
An
Exploration
of
Delivering
Value
through
Mobile
Apps
by
Mark
Hoskam
2013

2
Abstract
This
study
contributes
to
research
on
service
quality
and
value
delivery
in
the
context
of
online
and
mobile
services,
with
emphasis
on
services
delivered
through
mobile
apps
(software
applications
for
smartphones
and
tablets).
It
explores
the
delivering
of
value
through
mobile
apps.
First,
existing
theories
are
reviewed
regarding
value-‐adding
strategies,
perceived
value,
customer
satisfaction
and
customer
loyalty.
Second,
based
on
Kano’s
model
for
measuring
perceived
value,
a
questionnaire
is
conducted
amongst
customers
of
a
Dutch
mobile
telephone
and
internet
network
provider,
asking
about
their
experiences
with
and
attitudes
towards
mobile
apps.
Results
reveal
that
different
customer
segments
have
different
attitudes
towards
mobile
apps,
with
a
significant
number
of
customers
regarding
a
mobile
app
a
must-‐have
element
of
the
overall
value
proposition
offered.
Education,
usage
experience
and
user
status
are
found
to
significantly
influence
a
customer’s
perceived
value
regarding
a
mobile
app.
First,
higher
educated
customers
regard
an
app
more
must-‐have
compared
to
lower
educated
customers.
Second,
the
more
smartphone
and
app
experience
a
customer
has,
the
more
must-‐have
an
app
is
perceived.
Third,
existing
app
users
regard
an
app
as
must-‐
have,
while
non-‐users
see
an
app
as
irrelevant.
Fourth,
on
the
app
level,
reliability
was
found
to
be
a
must-‐have
attribute
amongst
all
customer
segments,
strongly
determining
the
success
or
failure
of
an
app.
Finally,
also
on
the
app’s
attributes
level
different
customer
segments
are
found
to
have
different
needs
and
expectations,
with
age
and
education
as
significant
influencing
factors.

4
TABLE
OF
CONTENTS
1.
INTRODUCTION
...................................................................................................................
6
1.1
Background
........................................................................................................................................
6
1.1.1
Customer
service
and
competitive
advantage
..............................................................
6
1.1.2
Online
as
a
service
channel
...........................................................................................
7
1.1.3
Mobile
as
a
service
channel
..........................................................................................
8
1.1.4
Smartphones,
tablets,
apps
.........................................................................................
10
1.1.5
Core
vs.
augmented
product
.......................................................................................
12
1.1.6
Summary
.....................................................................................................................
13
1.2
Problem
definition
..........................................................................................................................
14
1.3
Theoretical
relevance
......................................................................................................................
15
2.
AIM
AND
OBJECTIVES
........................................................................................................
16
2.1
Aim
and
objectives
..........................................................................................................................
16
2.2
Research
questions
.........................................................................................................................
16
2.3
Report
structure
..............................................................................................................................
16
3.
THEORETICAL
FRAMEWORK
..............................................................................................
18
3.1
Literature
review
.............................................................................................................................
18
3.1.1
Value
...........................................................................................................................
18
3.1.2
Business
vs.
customer
perspective
...............................................................................
20
3.1.3
Utilitarian
vs.
hedonic
value
........................................................................................
20
3.1.4
Dynamics
of
perceived
value
.......................................................................................
24
3.1.5
Measuring
perceived
value
.........................................................................................
27
3.1.7
Conclusions
on
perceived
value
...................................................................................
32
3.2
Definitions
used
for
this
study
........................................................................................................
33
3.2.1
Perceived
value
...........................................................................................................
33
3.2.2
Value
proposition
........................................................................................................
34
3.2.3
Mobile
Service
App
......................................................................................................
34
3.3
Hypotheses
......................................................................................................................................
34

5
3.3.1
Demographics
.............................................................................................................
35
3.3.2
Behavior:
Usage
experience
and
user
status
...............................................................
36
4.
METHODOLOGY
................................................................................................................
38
4.1
Objectives
........................................................................................................................................
38
4.2
Research
design
...............................................................................................................................
39
4.3
Sample
strategy
and
sample
size
....................................................................................................
40
4.4
Data
collection
.................................................................................................................................
40
4.4.1
Measuring
the
dynamics
of
perceived
value:
Kano’s
measurement
model
................
40
4.4.2
Questionnaire
..............................................................................................................
42
5.
ANALYSIS
AND
RESULTS
....................................................................................................
47
5.1
Analysis
of
questionnaire
data
........................................................................................................
47
5.2
Characteristics
of
the
sample
..........................................................................................................
47
5.3
Demographics
and
perceived
value
................................................................................................
49
5.4
Behavioural
characteristics
and
perceived
value
...........................................................................
57
5.5
Main
findings
...................................................................................................................................
60
6.
DISCUSSION
AND
CONCLUSIONS
.......................................................................................
62
6.1
Main
conclusions
.............................................................................................................................
62
6.2
Reflection
on
Kano’s
model
for
measuring
perceived
value
..........................................................
65
7.
RECOMMENDATIONS
........................................................................................................
67
7.1
Limitations
and
recommendations
for
future
research
.................................................................
67
7.2
Managerial
implications
..................................................................................................................
68
BIBLIOGRAPHY
......................................................................................................................
71

1.
INTRODUCTION
In
this
chapter
the
background
of
the
research
is
described,
the
problem
definition
is
stated
and
the
research’s
theoretical
relevance
is
explained.
First,
customer
service
and
the
importance
of
customer
service
as
means
for
developing
competitive
advantage
are
stressed.
Second,
the
development
of
online
and
mobile
as
service
channels
is
explained.
Third,
the
influence
and
evolution
of
smartphones,
tablets
and
apps
is
described,
with
emphasis
on
the
usage
of
smartphones,
tablets
and
apps
as
a
service
channel.
Fourth,
the
differences
between
core
products
and
services
and
supporting
services
are
given
and
described
in
the
perspective
of
mobile
app
services.
Finally,
the
problem
on
which
this
research
focuses
is
defined
and
its
theoretical
relevance
is
explained.
6
1.1
Background
1.1.1
Customer
service
and
competitive
advantage
Over
the
last
decades,
customer
service
has
become
a
key
element
of
a
company’s
marketing
mix.
Especially
for
companies
in
high
competitive
and
matured
markets,
were
customer
acquisition
costs
are
often
significantly
higher
than
retention
costs,
resulting
in
defensive
marketing
strategies
(Fornell
and
Wernerfelt
1987;
Mullins
et
al
2010).
Contrary
to
offensive
marketing
strategies,
focusing
on
acquisition
of
new
customers,
defensive
marketing
is
concerned
with
increasing
retention
of
existing
customers.
It
focuses
on
the
creation
of
customer
loyalty,
long-‐term
relationships
and
sustainable
profit.
Companies
in
high
competitive
markets
have
shifted
from
a
transactional
focus
towards
a
relational
focus
and
have
adopted
relationship
marketing
as
new
strategy
with
customer
satisfaction
and
customer
loyalty
as
key
performance
indicators
(Anderson
et
al,
1994;
Wilson
et
al,
2012).
As
research
has
shown,
‘service
quality
is
almost
always
an
important
driver
of
customer
satisfaction
across
all
types
of
industries’
(Wilson
et
al,
2012).
Furthermore,
several
studies
concluded
that
high
quality
customer
service
has
direct
positive
effect
on
customer
satisfaction
and
leads
to
customer
loyalty
and
word-‐of-‐mouth
in
the
long-‐run
(Anderson
et
al,
1994;
Kuo
et
al,

2009).
Thus,
offering
high
quality
service
in
order
to
create
satisfied
customers
provides
businesses
with
both
a
retention
incentive
(loyalty)
and
an
acquisition
incentive
(word-‐
of-‐mouth).
Ultimately,
creating
highly
satisfied
customer
through
high
quality
customer
service
results
in
increased
financial
performance
for
businesses
(Reichfeld,
1990).
Moreover,
by
offering
high
quality
and
unique
customer
service,
companies
can
add
value
to
their
core
product
or
service,
enabling
them
to
distinguish
themselves
from
competition
in
these
highly
competitive
markets
(Riel
et
al,
2004).
As
a
result,
companies
have
rapidly
adopted
the
online
and
mobile
channel
as
a
means
to
deliver
high
quality
and
unique
customer
service,
with
mobile
service
applications
(apps)
as
the
latest
phenomenon.
7
1.1.2
Online
as
a
service
channel
‘The
service
industry
is
one
of
the
most
natural
avenues
for
e-‐commerce
because
so
much
of
the
value
in
services
is
based
on
collecting,
storing
and
exchanging
information,
something
for
which
the
Web
is
ideally
suited’
(Laudon
and
Traver,
2012).
In
this
perspective,
the
Internet
has
been
rapidly
embraced
by
companies
as
a
new
channel
to
sell
to
and
service
their
customers.
Traditional
companies
started
developing
online
extensions
of
their
offline
services
like
online
shops
and
self-‐care
portals
or
even
started
creating
new
online
services,
adding
value
to
the
core
product
or
service
offered.
A
famous
example
of
such
a
new
and
successful
service
is
Apple’s
iTunes
Store.
Next
to
traditional
players
adopting
online,
new
pure
player
brands
established
themselves;
companies
which
market,
sell
and
service
solely
through
the
online
channel.
Some
famous
examples
are
Ebay
(online
auction),
Amazon
(online
warehouse),
Bol.com
(online
warehouse),
Google
(search
engine),
Facebook
(social
network),
Spotify
(streaming
music)
and
Netflix
(streaming
movies
and
series).
Both
types
of
companies,
the
traditional
and
the
pure
players,
are
eager
to
harvest
online
customer
servicing
opportunities
and
to
conduct
their
sales
and
service
activities
online
as
much
as
possible.
Traditional
banks
like
ABN
Amro,
employment
agencies
like
Randstad
and
low-‐

cost
airlines
like
Ryanair
have
significantly
reduced
their
physical
service
encounters
and
moved
some
of
their
primary
service
activities
to
the
online
channels;
activities
like
checking
your
account
balance,
conducting
financial
transactions,
applying
for
jobs
and
booking
flights.
Main
reasons
for
this
movement
are
the
reduction
of
transaction
costs
and
the
improvement
of
margins,
but
also
meeting
up
with
changing
customer
preferences.
From
a
business
perspective,
the
online
channel
provides
them
with
the
opportunity
to
lower
costs
per
customer
interaction
(Hughes,
2005;
Laudon
and
Traver,
2012)
and
to
extend
market
reach
regionally,
nationally,
internationally
or
even
globally,
again
at
relative
low
cost
(Hughes,
2005).
This
in
contrast
to
the
pre-‐Internet
age,
when
companies
had
to
sell
and
service
customers
through
their
expensive
call
centre
and
retail
channels.
Moreover,
the
online
landscape
offers
companies
the
opportunity
to
meet
the
evolving
customer
behaviour
and
preferences.
Customer’s
usage
of
the
internet
during
the
purchase
decision
making
process
is
increasing
every
year.
Computers,
tablets
and
smartphones
are
used
to
search
for
online
information
on
products
and
services,
to
evaluate
alternatives
and
to
purchase
online.
In
the
post-‐
purchase
phase,
social
media
platforms
like
Facebook
and
Twitter,
comparison
web
sites
like
Kieskeurig.nl
and
forums
like
TripAdvisor
are
used
for
post-‐purchase
evaluation
and
to
share
user
experiences
of
the
bought
product
or
service
(Laudon
and
Traver,
2012).
The
website
of
the
company
itself
is
primarily
used
to
find
information
on
post-‐purchase
questions
and
issues,
to
up-‐
or
downgrade
subscriptions
or
to
purchase
additional
products
or
services.
In
all
these
phases
of
the
customer
lifecycle,
companies
can
add
value
by
offering
online
services.
8
1.1.3
Mobile
as
a
service
channel
With
emerging
mobile
enabling
technologies
like
4G
(high
speed
mobile
networks)
and
NFC
(near
frequency
communications),
activities
like
high
definition
video
calling
and
instant
payment
through
mobile
phones
and
tablets
become
available
at
our
fingertips.
In
line
with
the
rapid
development
of
these
mobile
technologies,
smartphone
usage

and
sales
rates
show
fast
and
ongoing
growth.
In
a
relatively
short
period
of
time,
mobile
technology
has
penetrated
significantly
into
society,
capturing
an
entire
age
spectrum
of
subscribers,
from
school
children
to
senior
citizens
(Boulos
et
al,
2011).
The
introduction
of
the
touch
screen
by
Apple
offered
customers
an
easy
to
use
graphical
user
interface
and
natural
gesture
control,
which
helped
boosting
smartphone
ownership.
As
a
result,
smartphone
sales
have
exceeded
PC
sales
in
2011
(Canalys,
2011)
and
in
emerging
markets
like
China
and
Africa,
smartphone
ownership
already
exceeds
PC
ownership
(TNS
Mobile
Life,
2013).
In
these
emerging
markets,
mobile
internet
is
often
the
only
internet
access
method
available,
resulting
in
high
mobile
internet
and
smartphone
penetration
levels
(ITU,
2012).
In
the
Netherlands,
smartphone
penetration
passed
the
50%
point
and
increased
from
42%
to
58%
from
2011
to
2012
(Telecompaper,
2012).
The
US
is
expected
to
pass
the
50%
smartphone
penetration
point
in
2013
and
Western
Europe
in
2014
(eMarketer,
2013).
In
addition,
2.1
billion
consumers
worldwide
were
actively
using
mobile
internet
subscriptions
in
2012.
This
is
29,5%
of
the
global
population,
with
adoption
rates
varying
from
11%
in
Africa
to
67,5%
in
Europe.
Mobile
internet
subscriptions
have
grown
by
40%
annually
over
three
years
and
already
outnumber
fixed
internet
connections
by
3
to
1
(ITU,
2012).
Emerging
mobile
enabling
technologies
combined
with
the
ongoing
growth
of
smartphone
and
mobile
internet
penetration
offer
businesses
new
customer
service
opportunities
based
on
mobile
technology’s
unique
characteristics.
According
to
Kleijnen
et
al
(2007);
‘M-‐commerce
is
frequently
regarded
as
an
extension
of
e-‐
commerce,
while
m-‐commerce
might
also
be
regarded
as
a
separate
channel,
because
it
can
deliver
a
unique
value
proposition
to
customers
through
the
technological
differences
it
encompasses,
including
its
communication
mode
and
protocols
and
access
devices.’
First,
activities
have
become
more
flexible
in
terms
of
time
and
space
as
a
result
of
mobile
technology
(Balasubramanian
et
al,
2002).
Since
consumers
experience
utilitarian
value
from
efficient
and
timely
service
delivery
(Childers
et
al,
2001),
exploitation
of
these
unique
factors
is
expected
to
contribute
positively
to
a
customer’s
service
experience.
Second,
urgent
and
spontaneous
customer
needs
can
be
9

serviced
instantly
as
a
result
of
these
stretches
in
space
and
time
(Anckar
and
D’Incau
2002).
Third,
the
mobile
channel
offers
unique
mobile
learning
opportunities
through
interfaces
of
voice,
text,
icons,
pictures
and
videos
(Aboelmaged
2010).
Fourth,
the
mobile
technology
significantly
improves
businesses’
contextual
information
on
their
customers
as
a
result
of
built-‐in
Global
Positioning
System
(GPS),
enabling
personalized
mobile
customer
services
(Bouwman
2008).
On
the
other
hand,
mobile
technologies
can
also
negatively
impact
a
customer’s
service
experience
due
to
‘cost’
or
‘give’
factors.
Two
of
these
potential
cost
factors
are
customer’s
perceived
risk
of
using
mobile
technologies
and
cognitive
efforts
demanded
from
the
customer
(Kleijnen
et
al,
2007).
Cognitive
effort
in
this
case
can
also
be
translated
as
information
search
costs,
the
effort
it
asks
from
the
customer
to
fulfill
his
information
needs
(Suoranta
et
al,
2005).
Thus,
from
one
perspective
,
these
unique
features
of
mobile
technology
enable
businesses
to
enhance
their
customer
services.
By
offering
their
services
through
the
mobile
channel,
businesses
aim
at
creating
added
value
for
the
customer
and
as
a
result,
create
competitive
advantage.
Although,
the
negative
consumer
beliefs
regarding
mobile
technologies
based
on
perceived
risk
and
cognitive
effort
must
not
be
overlooked.
10
1.1.4
Smartphones,
tablets,
apps
Smartphones,
in
contrast
to
feature
phones,
are
phones
which
not
only
offer
users
voice
and
texting
services
(SMS),
but
which
also
enable
mobile
internet
access,
e-‐
mailing,
voice
recording,
music
playing,
photographing,
GPS
tracking
(for
navigation)
and
measurement
of
movements
(speed,
distance,
height)
through
built-‐in
gyroscope
techniques.
Due
to
their
powerful
on-‐board
computing
capability,
capacious
memories
and
large
screens
enabling
these
unique
mobile
functions,
the
latest
generation
of
smartphones
is
increasingly
seen
as
handheld
computers
instead
of
phones.
They
can

easily
process
tasks
which
formerly
could
only
be
processed
by
PC’s
and
laptops.
An
additional
important
characteristic
distinguishing
smartphones
from
feature
phones
is
the
ability
to
download
and
install
mobile
applications,
otherwise
known
as
apps
(Dickinson
et
al,
2012).
Apps
are
tailor
made
software
packages
for
smartphones
which
improve
the
delivery
of
mobile
services
by
utilizing
the
unique
features
of
a
smartphone.
The
numbers
of
apps
available
and
used
and
the
general
popularity
of
mobile
apps
has
grown
extensively
over
the
past
years,
as
a
result
of
the
rapid
adoption
of
smartphones
and
tablets.
Portio
Research
(2013):
‘1.2
billion
people
worldwide
were
using
mobile
apps
at
the
end
of
2012.
This
is
forecast
to
grow
at
a
29.8
percent
each
year,
to
reach
4.4
billion
users
by
the
end
of
2017.
Much
of
this
growth
will
come
from
Asia,
which
will
account
for
almost
half
of
app
users
in
2017.’
In
addition,
Portio
Research
(2013)
expects
approximately
82
billion
apps
to
be
downloaded
worldwide
in
2013,
exceeding
the
point
of
200
billion
annual
downloads
by
2017.
Apple
iTunes
and
Google
Play
are
the
world’s
biggest
and
most
famous
app
stores
and
both
offer
over
800.000
apps
in
their
stores
(Canalys
2013).
Some
of
the
most
popular
apps
to
date
are
Facebook
(social
network),
WhatsApp
(instant
messaging),
Gmail
(email
client)
and
Google
Maps
(navigator).
We
can
distinguish
between
B2B
apps
and
B2C
apps.
B2B
apps
are
concerned
with
internal
business
processes
like
customer
relationship
management
(CRM),
warehouse
management
and
sales-‐force
management.
B2C
apps
are
aimed
at
consumers
and
can
be
categorized
as
content-‐,
marketing-‐
or
service
oriented
(Cortimiglia
et
al,
2011).
Content-‐oriented
apps
fulfill
individual
needs
for
information,
entertainment,
communication,
productivity,
socialization
and
instant
messaging.
Marketing-‐oriented
apps
are
mostly
used
by
companies
for
brand
advertising
or
promotion.
Service-‐oriented
apps
offer
users
with
self-‐service
functionalities
like
booking
a
flight,
buying
goods
at
an
online
shop
or
looking
up
current
mobile
data
usage
of
the
user’s
mobile
internet
subscription.
Smartphone
and
tablet
users
are
using
apps
as
a
gateway
to
online
services,
as
a
fast
and
more
convenient
alternative
to
accessing
these
services
through
their
(mobile)
web
browser
(Xu
et
al,
2011).
11

12
1.1.5
Core
vs.
augmented
product
Like
the
Internet,
smartphones
and
apps
have
changed
the
way
in
which
customers
interact
with
companies
and
created
opportunities
for
companies
to
deliver
enhanced
or
totally
new
services.
Both
companies
offering
products
and
companies
offering
services
have
adopted
apps
as
a
new
channel
to
service
their
customers,
next
to
their
(mobile)
website.
Service
companies
like
banks
(f.e.
ABN
Amro,
Rabobank)
are
offering
mobile
payment
apps,
telecom
operators
(f.e.
KPN,
Vodafone)
and
utility
providers
(f.e.
Essent,
Nuon)
are
offering
self-‐service
and
usage
management
portals
and
insurance
companies
(f.e.
Interpolis)
are
offering
self-‐service
portals.
More
product
oriented
companies
like
sports
apparel
manufacturer
Nike
and
car
manufacturer
Volkswagen
use
apps
also
in
the
post-‐purchase
phase,
offering
self-‐service
functionality
(f.e.
Volkswagen’s
Service
app).
Moreover,
they
use
apps
to
deliver
unique
value
added
services
like
the
Nike+
Running
app.
This
app
offers
the
users
of
Nike+
running
shoes
access
to
a
unique
social
network
for
runners,
enabling
the
user
to
track
their
own
performance,
set
targets,
monitor
progress
and
set-‐up
running
challenges
with
other
Nike+
users.
Looking
at
these
examples
from
service
and
product
companies
using
apps
to
deliver
services
to
their
customers,
we
can
define
these
apps
as
peripheral
services,
forming
the
augmented
product.
The
main
purpose
of
these
peripheral
services
is
to
increase
the
value
of
the
total
offer,
they
add
value
to
the
core
product
or
service
offered
(Riel
et
al,
2004).
By
adding
more
value
to
their
core
product,
companies
aim
to
improve
customer
satisfaction
and
thereby
customer
loyalty
(Ravald
and
Gronroos,
1996).
Added
value
can
be
created
by
revolutionary
or
evolutionary
means.
An
example
of
revolutionary
added
value
is
the
Nike+
example.
More
evolutionary
examples
are
the
self-‐service
apps
of
banks
and
telecom
operators,
which
in
the
basis
are
similar
to
the
traditional
services
they
offer,
but
digitalized.
The
purpose
of
peripheral
services
is
to
enhance
the
complete
product
offering.
Peripheral
services
are
often
used
to
distinguish
products
and
services
in
commodity
or
homogeneous
markets,
in
which
the
core
products
and
services
of
different
brands
look
very
familiar
to
each
other.

According
to
research
on
value
enhancing
online
services
(Riel
et
al,
2004),
these
peripheral
services
are
only
of
value
if
customers
find
them
useful
and
unique
compared
to
alternatives.
They
state:
‘Companies
need
to
investigate
what
type
of
value
they
are
currently
creating,
but
also
what
type
of
value
is
lacking.
Customers
could
be
segmented
according
to
the
type
of
experienced
value
and
services
could
be
designed
to
increase
the
preference
value
of
each
segment.’
But,
according
to
Ravald
and
Gronroos
(1996):
‘Far
too
many
companies
alienate
themselves
from
the
customers
and
the
value
added
has
consequently
nothing
to
do
with
the
actual
needs
of
the
customers’.
In
other
words,
companies
seeking
to
increase
customer
satisfaction
and
customer
loyalty
by
developing
value
added
service
often
forget
to
put
the
customer’s
needs
central
to
the
development
of
these
services.
If
companies
are
planning
to
develop
apps
to
create
sustainable
added
value
for
their
customers,
they
must
first
find
out
how
their
different
customers
are
actually
evaluating
app
services
and
how
apps
can
add
value
to
the
core
product
or
service
offered
from
different
customer
perspectives.
Additionally,
when
companies
finally
have
decided
to
start
developing
an
app
service,
they
must
understand
the
needs
and
preferences
of
the
specific
customer
segment(s)
regarding
an
app
service
in
order
to
develop
an
app
which
is
really
adds
value
for
the
customer.
13
1.1.6
Summary
As
a
result
of
the
rapid
adoption
of
smartphones
and
tablets
amongst
customers
and
based
on
the
unique
based
opportunities
offered
by
mobile
technology
and
apps,
mobile
commerce
(m-‐commerce)
and
mobile
service
(m-‐service)
have
become
the
latest
areas
of
business
interest.
The
mobile
channel
is
seen
as
a
serious
opportunity
for
businesses
to
create
extra
value
for
the
customer
and
to
reduce
operational
costs.
Businesses
have
started
adopting
the
mobile
channel
as
an
alternative
or
additional
service
channel
next
to
their
call
centers,
retail
stores
and
websites.
Examples
are
banks
like
Rabobank
and
ABN
Amro
and
telecom
providers
like
KPN.
During
the
past
decade,

banks
started
offering
mobile
banking
services
on
its
customers’
smartphones
and
tablet
computers.
This,
as
a
more
convenient
alternative
to
their
website
based
banking
services.
Telecom
providers
started
self-‐service
environments
offering
mobile
text-‐
based
chat
solutions
to
their
customers
enabling
instant
contact
with
their
service
agents
through
a
customer’s
smartphone
or
tablet
computer.
Primary
reasons
for
this
movement
are
lower
operational
costs
and
to
provide
customers
with
a
more
convenient
alternative
to
call
center
services.
But,
according
to
research
by
Riel
et
al
(2004)
on
value
enhancing
online
services,
apps
offering
peripheral
services
are
only
valuable
if
customers
find
them
useful
and
unique
compared
to
alternatives.
They
state:
‘Companies
need
to
investigate
what
type
of
value
they
are
currently
creating,
but
also
what
type
of
value
is
lacking.
Customers
could
be
segmented
according
to
the
type
of
experienced
value
and
services
could
be
designed
to
increase
the
preferred
value
of
each
segment.’
14
1.2
Problem
definition
Customers
are
adopting
smartphones
and
mobile
apps
rapidly
(Boulos
et
al,
2011;
Canalys,
2011)
and
companies
are
significantly
increasing
investments
in
mobile
strategies
(Forrester,
2011)
and
are
starting
to
develop
apps
to
create
added
value.
Costs
of
developing
apps
are
significant,
ranging
between
$
25.000
and
$
100.000
per
app
for
relative
simple
mobile
functionalities
increasing
to
$
100.000
and
more
for
complex
mobile
functionalities
(BusinessNewsDaily,
2013).
Since
customers
are
becoming
more
experienced
with
smartphones
and
apps
and
businesses
are
allocating
significant
amounts
of
resources
to
app
development
and
maintenance,
it
has
become
important
to
increase
the
understanding
of
the
value
of
an
app
within
the
total
product
or
service
package
offered
to
the
customer.
Companies
need
to
ask
themselves;
do
our
customers
demand
mobile
app
services,
and
if
so,
which
problems
do
these
app
services
need
to
solve
and
how
important
are
these
services
compared
to
other

customer
demands?
Asking
these
questions
helps
a
company
to
allocate
its
resources
to
the
development
of
services
which
have
high
impact
on
customer
satisfaction
and
loyalty.
In
addition,
when
companies
are
actually
starting
to
developing
an
app,
it
is
important
to
understand
customer
needs
and
preferences
of
the
different
user
segments
regarding
apps
and
to
develop
apps
accordingly.
This,
to
ensure
that
the
app
really
adds
value
for
its
users
by
fulfilling
a
need
or
solving
a
problem.
Only
when
developed
accordingly,
a
value
added
service
app
could
help
companies
improve
their
customer
satisfaction
and
loyalty
levels.
15
1.3
Theoretical
relevance
While
service
delivery
in
e-‐commerce
and
e-‐service
context
has
been
researched
extensively,
little
scientific
research
seems
to
be
conducted
yet
on
service
delivery
in
mobile
commerce
(m-‐commerce)
and
mobile
service
(m-‐service)
environments.
Especially,
few
studies
have
investigated
the
delivery
of
services
through
mobile
applications
(apps)
on
smartphones
and
tablets.
As
Riel
et
al
(2004)
already
stated
in
their
study
on
online
support
services:
‘Next
generation
mobile
phones
are
already
opening
up
many
new
opportunities
as
a
channel
for
online
support
and
the
value
and
enjoyment
of
receiving
various
supporting
services
through
that
channel
should
be
investigated’.
Therefore,
this
research
project
aims
to
increase
the
understanding
of
creating
added
value
by
developing
mobile
applications
(apps)
from
the
theoretical
perspective
of
perceived
value,
customer
satisfaction
and
customer
loyalty
and
from
the
perspective
of
different
customer
segments
having
different
needs,
preferences
and
expectations.

2.
AIM
AND
OBJECTIVES
In
this
chapter
the
aim
and
objectives
of
this
study
are
explained,
the
research
questions
are
stated
and
the
report’s
structure
is
briefly
explained.
2.1
Aim
and
objectives
This
research
aims
to
increase
the
understanding
of
value
creation
through
the
development
of
mobile
apps
(applications
on
smartphones
and
tablets)
from
the
theoretical
perspective
of
perceived
value,
customer
satisfaction
and
customer
loyalty
and
from
the
perspective
of
different
customer
segments
having
different
needs,
preferences
and
expectations.
First,
its
objective
is
to
determine
the
perceived
value
of
a
mobile
service
app
within
the
overall
value
proposition
offered
to
the
customer.
Second,
it
aims
to
determine
the
key
attributes
of
the
app
influencing
its
perceived
value.
16
2.2
Research
questions
• What
is
the
value
of
a
mobile
service
app
within
the
overall
value
proposition
offered
to
the
customer,
and
how
does
the
value
differ
between
customer
segments?
• What
are
the
key
attributes
to
the
app's
perceived
value,
and
how
do
these
attributes
differ
between
customer
segments?
2.3
Report
structure
The
paper
is
structured
as
follows.
First,
existing
theories
are
reviewed
regarding
value-‐
adding
strategies,
perceived
value,
customer
satisfaction
and
customer
loyalty.
Additionally,
the
definitions
of
a
value
proposition,
a
mobile
service
app
and
perceived
value
are
explained
and
a
mobile
app’s
value
attributes
are
described.
Hypotheses
are
formulated
and
tested
based
on
a
customer
survey
amongst
users
of
a
smartphone
service
application
(app).
To
conclude,
results
and
findings
will
be
discussed,

recommendations
and
managerial
implications
will
be
given
and
directions
for
future
research
are
proposed.
17

3.
THEORETICAL
FRAMEWORK
In
this
chapter,
we
summarize
existing
theories
and
literature
on
value
creation
from
a
business
perspective
and
perceived
value
from
a
customer
perspective.
Utilitarian
and
hedonistic
value
and
their
underlying
drivers
are
described
and
the
dynamics
of
perceived
value
are
explained.
In
addition,
we
establish
the
definitions
for
perceived
value,
a
value
proposition
and
a
mobile
service
app
and
describe
the
different
measurement
models
available
for
measuring
perceived
value.
Finally,
the
hypotheses
are
developed
which
must
help
in
answering
the
main
research
questions.
18
3.1
Literature
review
3.1.1
Value
Value
is
an
important
driver
of
relationship
marketing
and
delivering
superior
customer
value
to
customers
is
regarded
an
important
competitive
strategy.
Companies
are
adding
value
to
their
products
by
improving
product
quality,
developing
and
improving
supporting
services
and
by
developing
additional
services
enhancing
the
core
product
(Ravald
and
Gronroos,
1996).
In
other
words,
they
are
trying
to
improve
the
total
value
proposition
offered
to
the
customer.
This,
in
order
to
improve
customer
satisfaction
with
their
products
and
to
strengthen
customer
loyalty
(Ravald
and
Gronroos,
1996).
Research
by
Cronin
Jr.
et
al
(2000)
underline
these
cause
and
effect
links
between
value,
satisfaction
and
customers’
behavioral
intentions
‘loyalty’
and
‘word-‐of-‐mouth’,
as
shown
in
figure
1.
They
state
that
‘numerous
studies
have
specified
relationships
between
quality,
value,
satisfaction
and
consequences
as
customer
loyalty,
positive
word-‐of-‐mouth
and
repurchase
intentions’.
Their
research
amongst
six
service
industries
has
proven
direct
links
between
value,
satisfaction
and
behavioral
intentions.
Value
was
found
to
be
directly
related
to
satisfaction
and
satisfaction
directly
related
to
the
behavioral
intentions
of
re-‐purchasing
and
spreading
positive
word-‐of-‐mouth
(Cronin
Jr.
et
al,
2000).
In
other
words,
by
increasing
value,
companies
can
increase

satisfaction
and
increased
value
and
satisfaction
result
in
increased
customer
loyalty
(Ravald
and
Gronroos,
1996;
Woodruff,
1997;
Cronin
Jr.
et
al,
2000).
Figure
1:
The
relationship
between
quality,
value,
satisfaction
and
loyalty
(Cronin
Jr.
et
al,
2000)
However,
added
value
is
only
realized
when
customers
perceive
the
improved
or
added
product
or
service
as
important
to
solve
their
problem(s)
or
fulfill
their
need(s)
and
when
it
meets
or
exceeds
their
expectations
(Riel
et
al,
2004;
Witell
and
Fundin,
2005).
In
this
perspective,
Ravald
and
Gronroos
(1996)
define
customer
perceived
value
as
the
ratio
between
perceived
benefits
and
perceived
sacrifice.
Benefits
are
perceived
when
a
product
or
service
fulfils
the
customer’s
needs.
Sacrifices
are
perceived
when
a
customer
has
to
pay
in
order
to
use
the
product
or
service
(monetary
sacrifices),
when
a
customer
has
to
invest
time
or
effort
in
order
the
be
able
to
use
a
product
or
service
or
when
a
customer
perceives
inconvenience
or
risk
in
using
a
product
or
service
(non-‐
monetary
sacrifices)
(Ravald
and
Gronroos,
1996;
Boksberger
and
Melsen,
2011).
When
the
ratio
between
perceived
benefits
and
sacrifices
is
positive,
this
will
result
in
customer
satisfaction
with
the
product
or
service
(Ravald
and
Gronroos,
1996).
As
such,
perceived
value
is
found
to
directly
influence
customer
satisfaction
(Woodruff,
1997;
Cronin
Jr.
et
al,
2000).
In
addition,
previous
research
found
that
not
only
the
perceived
19

value
of
the
total
value
proposition
offered
influences
the
customer’s
satisfaction
with
a
product
or
service,
but
also
at
attribute-‐level
customer
satisfaction
is
influenced
(Oliver,
1993).
Thus,
when
analyzing
the
customer’s
perceived
value
of
a
product
or
service,
it
is
important
to
focus
on
both
the
overall
value
proposition
as
well
as
on
the
specific
attributes
included
in
the
value
proposition.
20
3.1.2
Business
vs.
customer
perspective
Since
there
is
little
consensus
with
regards
to
the
definition
and
the
concept
of
perceived
value
(Boksberger
and
Melsen,
2011),
it
is
important
to
establish
a
definition
of
perceived
value
which
fits
this
study.
Value
literature
(Zeithaml,
1988;
Ravald
and
Gronroos,
1996;
Kleijnen
et
al,
2007,
Boksberger
and
Melsen,
2011)
describes
value
from
both
a
business
and
a
customer
perspective.
The
business’
value
perspective
is
often
concerned
with
a
customer’s
lifecycle
value;
the
net
worth
of
a
customer
from
its
acquisition
phase,
through
its
development
and
retention
phases
and
finally,
to
its
exit
phase.
The
customer’s
value
perspective
is
often
described
by
the
utilitarian
perspective
of
perceived
value
where
perceived
value
is
considered
a
trade-‐off
between
a
customer’s
perceived
benefits
of
using
a
product
or
service
and
the
perceived
sacrifices
made
to
use
the
service
(Boksberger
and
Melsen,
2011).
In
line
with
the
customer’s
value
perspective,
Zeithaml
(1988)
states:
‘Value
is
the
customer’s
overall
assessment
of
the
utility
of
a
product
based
on
perceptions
of
what
is
received
and
what
is
given.’
Sacrifices
include
monetary
costs
(purchase
price,
acquisition
costs)
and
non-‐monetary
costs
of
a
service,
in
which
the
factors
‘time,
effort,
search
costs,
convenience
and
perceived
risk
are
considered
as
non-‐monetary
sacrifices
in
value
literature
(Ravald
and
Gronroos,
1996;
Boksberger
and
Melsen,
2011).
3.1.3
Utilitarian
vs.
hedonic
value
In
literature
on
value
we
find
two
types
of
customer
perceived
value;
utilitarian
value
and
hedonic
value
(Kleijnen
et
al,
2007).
First,
according
to
Kleijnen
et
al
(2007),

utilitarian
value
is
concerned
with
the
goal
of
a
customer
of
completing
a
task.
When
looking
at
literature
on
service
quality,
perceived
value,
customer
satisfaction
and
customer
loyalty
in
the
perspective
of
online
and
mobile
services,
divisions
of
utilitarian
value
into
different
dimensions
are
made.
Based
on
these
existing
studies,
we
propose
a
division
of
utilitarian
value
into
the
following
five
different
utilitarian
value
dimensions;
usefulness,
ease
of
use,
availability
and
speed,
reliability,
and
support.
An
overview
of
these
different
utilitarian
dimensions
and
its
measurements
is
given
in
figure
21
2.
As
figure
2
shows,
usefulness
is
regarded
to
as
the
functionality
of
the
service
as
perceived
by
the
user
and
is
measured
by
the
relevance
of
the
service’s
information
and
features,
the
service’s
completeness,
the
frequency
of
updates,
the
actuality
of
information
and
the
relative
advantage
of
the
service
compared
to
alternatives
(Choi
et
al,
2008;
Pura,
2005;
Sahadev
and
Purani,
2008;
Wu
and
Wang,
2005;
Yang
et
al,
2003;
Zhang
and
Von
Dran,
2002).
Ease
of
use
is
concerned
with
the
ease
if
using
the
service
as
perceived
by
the
user
and
depend
on
the
intuitiveness
of
operations
(Aladwani
and
Palvia,
2002;
Bauer
et
al,
2006;
Choi
et
al,
2008;
Cyr
et
al,
2006;
Lee
and
Lin,
2005;
Pura,
2005;
Santos,
2003;
Wu
and
Wang,
2005;
Zhang
and
Von
Dran,
2002).
Availability
and
speed
describe
the
ease
of
access
to
the
service
as
perceived
by
the
user
and
depend
on
the
availability
and
the
response
time
of
the
service
(Aladwani
and
Palvia,
2002;
Ancar
and
D'Incau,
2003;
Bauer
et
al,
2006;
Choi
et
al,
2008;
King
and
Liuo,
2004;
Lee
and
Lin,
2005;
Loiacono
et
al,
2002;
Parasuraman
et
al,
2005;
Pura,
2005;
Sahadev
and
Purani,
2008;
Yen
and
Lu,
2008;
Zhang
and
Von
Dran
2002).
Reliability
is
about
the
trustworthiness
of
the
service
as
perceived
by
the
user
and
depends
on
the
level
of
security
included
in
the
service
(Aladwani
and
Palvia,
2002;
Bauer
et
al,
2006;
Choi
et
al,
2008;
Lee
and
Lin,
2005;
Lin
and
Wang,
2006;
Parasuraman
et
al,
2005;
Sahadev
and
Purani,
2008;
Santos,
2003;
San
Martin
Gutierrez
et
al,
2012;
Wu
and
Wang,
2005;
Yang
et
al,
2003;
Yen
and
Lu,
2008;
Zhang
and
Von
Dran,
2002).
Support
is
concerned
with
the
help
provided
by
the
system
to
the
user,
in
order
to
complete
the
user’s
task
and
depends
on
the
availability
of
a
friendly
user
manual
and
service
supporting
personnel
(Bauer
et
al,
2006;
Parasuraman
et
al,
2005;
Santos,
2003;
Yen
and
Lu,
2008).

Second,
according
to
Kleijnen
et
al
(2007),
hedonic
value
is
concerned
with
emotional
involvement,
the
extent
to
which
the
user
becomes
emotionally
involved
through
interacting
with
a
product
or
service.
When
looking
at
literature
on
service
quality,
perceived
value,
customer
satisfaction
and
customer
loyalty
in
the
perspective
of
online
and
mobile
services,
divisions
of
hedonistic
value
into
different
measures
are
made.
An
overview
of
these
measurements
can
be
found
in
22
figure
2.
As
figure
2
shows,
we
propose
hedonistic
value
to
depend
on
a
user’s
emotional
involvement
which
depends
on
the
measurements
fun
of
using
a
service,
visual
appeal
of
a
service
and
innovativeness
of
a
service
(Bauer
et
al,
2006;
Cyr
et
al,
2006;
King
and
Liuo,
2004;
Lee
and
Lin,
2005;
Loiacono
et
al,
2002;
Pura,
2005;
Zhang
and
Von
Dran
2002).

Figure
2:
Value
attributes
of
a
service
mobile
app
24
3.1.4
Dynamics
of
perceived
value
Existing
theories
on
perceived
value
(Zeithaml,
1988;
Webster,
1989;
Ravald
and
Gronroos,
1996;
Mittal
and
Katrichis,
2000;
Kano,
2001;
Witell
and
Fundin,
2005;
Zhao
and
Dholakia,
2009;
Kim
and
Hwang,
2012)
state
that
perceived
value
has
a
dynamic
nature
and
is
influenced
by
demographic
variables
(f.e.
age
and
education),
psychographic
variables
(f.e.
social
class
and
lifestyle)
and
behavioral
variables
(f.e.
user
experience
and
user
status).
The
following
paragraph
covers
existing
literature
on
this
dynamic
nature
of
perceived
value.
Demographics
Zeithaml
(1988)
studied
consumer’
perceptions
of
value
and
found
that
perceived
value
is
subjective
and
individual
and
therefore
varies
amongst
customers
and
customer
segments.
Ravald
and
Gronroos
(1996)
suggest
these
differences
are
a
result
of
different
personal
values,
needs
and
preferences.
This,
in
line
with
previous
studies
on
service
quality
which
have
shown
demographics
are
influencing
service
quality
expectations
(Webster,
1998;
Kim
and
Hwang,
2012).
Webster
(1989)
studied
whether
customers
could
be
segmented
based
on
their
service
quality
expectations.
Based
on
this
study,
it
can
be
concluded
that
customers
can
be
segmented
into
different
categories
of
service
needs,
based
on
demographic
variables
like
age
and
education.
In
a
mobile
specific
context,
Kim
and
Hwang
(2012)
studied
the
effect
of
mobile
consumers’
value
tendency
on
their
perception
of
mobile
internet
service
quality.
Consumers
can
have
a
more
hedonistic
value
tendency
(pleasure-‐oriented)
or
a
more
utilitarian
value
tendency
(productivity-‐oriented).
They
studied
the
relationship
between
maturity
and
value
tendency.
Maturity
was
measured
through
the
demographic
variables
age
and
education.
The
older
of
age
and/or
the
higher
educated,
the
more
mature
a
customer
was
defined.
Their
study
found
a
direct
relationship;
the
more
mature
a
consumer,
the
higher
the
level
of
tendency
towards
utilitarian
value.
In

other
words,
mature
customers
have
a
more
productivity-‐oriented
tendency
in
contrast
to
less
mature
customers,
which
have
a
more
pleasure-‐oriented
value
tendency
(Kim
and
Hwang,
2012).
Moreover,
research
on
the
use
of
mobile
information
systems
in
the
insurance
industry
found
higher
educated
insurance
agents
to
perceive
mobile
information
systems
more
valuable
than
lower
educated
customers
(Lee
et
al,
2005).
25
Psychographics
Several
studies
distinct
between
functional
value
(utilitarian)
and
social
and
emotional
value
(Day
and
Crask,
2000;
Pura,
2005;
Boksberger
and
Melsen,
2009).
Social
and
emotional
value
are
concerned
with
psychographics
(f.e.
social
class
and
lifestyle).
First,
social
value
is
defined
as
the
perceived
utility
of
using
a
product
or
service,
through
the
association
of
that
product
or
service
with
specific
demographic,
socioeconomic
and
cultural-‐ethnic
groups.
In
this
perspective,
research
found
that
mobile
phones
are
linked
to
Maslow’s
hierarchy
of
needs,
creating
a
sense
of
belonging,
especially
amongst
the
younger
generations
which
see
‘being
mobile’
in
sense
of
‘being’
cool
(Kolsaker
and
Drakatos,
2009).
Second,
emotional
value
is
concerned
with
the
impact
of
using
a
product
or
service
on
the
consumer’s
emotional
state.
Kolsaker
and
Drakatos
(2009)
revealed
that
users
of
mobile
devices
are
emotionally
attached
to
their
devices.
This,
since
their
mobile
devices
enable
an
always
and
everywhere
online
mode,
making
it
possible
to
keep
in
touch
with
family,
friends
and
colleagues
regardless
of
their
proximity.
Next
to
that,
emotional
attachment
is
developed
by
using
the
mobile
devices
as
a
personal
management
device
for
both
personal
and
work
life.
In
other
words,
consumers
derive
emotional
value
from
using
their
mobile
devices,
as
it
as
in
integral
part
of
their
lifestyle.
Unfortunately,
psychographic
research
has
been
criticised
for
its
problems
associated
with
measurement
and
validity
and
its
practical
limitations
as
a
result
of
lacking
psychographic
segmentation
opportunities
available
for
marketers
(Gilbert
et
al,
1995).
Behavior

Woodruff
(1997)
found
differences
in
value
perceptions
between
existing
and
potential
customers
and
found
usage
experience
to
be
influencing
the
customer’s
value
perceptions.
Additionally,
Mittal
and
Katrichis
(2000)
argue
that
the
service
or
product
attributes
important
to
existing
customers
are
not
necessarily
the
same
as
for
non-‐
customers.
In
line
with
these
findings,
Zeithaml
(1988)
states
that
a
person
might
evaluate
the
same
product
differently
on
different
occasions.
Thus,
the
attributes
which
make
the
customer
purchase
are
not
similar
to
the
attributes
perceived
important
during
the
usage
of
a
product
or
service,
or
even
after
usage
of
the
product
or
service.
A
customer’s
actual
usage
experience
with
a
product
or
service
is
considered
to
be
influencing
these
perceptions,
resulting
in
different
value
perceptions
between
existing
customers
and
potential
customers
(Zhao
and
Dholakia,
2009).
Kano
(2001)
states
that
customer’s
perception
of
service
attributes
vary
over
time,
depending
on
the
customer’s
position
in
the
product
lifecycle.
His
study
found
customer’s
perception
of
remote
TV
controls
to
change
over
time.
In
1983
a
remote
control
was
considered
a
nice
to
have
attribute,
while
in
1998
it
has
become
a
must-‐have
attribute,
resulting
in
dissatisfaction
when
absent.
Based
on
these
findings,
Kano
(2001)
identified
a
specific
pattern
of
change
over
time:
indifferent
attribute
26
!
attractive
attribute
!
must-‐have
attribute
(Zhao
and
Dholakia,
2009).
In
other
words,
service
attributes
are
dynamic
and
will
change
over
time
from
being
unimportant
to
customer
satisfaction
to
ultimately
become
a
must
have
requirement
in
order
to
satisfy
customers.
In
other
words,
customers
who
do
not
have
usage
experience
with
a
certain
service
value
the
service
as
unimportant.
But,
customer’s
which
frequently
have
used
the
service
value
it
as
a
must-‐
have
requirement
(Witell
and
Fundin,
2005).
Extending
on
Kano’s
findings,
Witell
and
Fundin
(2005)
found
the
same
pattern
to
be
true
for
online
ordering
of
cinema
tickets.
Although,
in
this
context,
the
customer’s
perception
of
the
service
transforms
from
nice
to
have
to
must
have
after
only
five
times
of
usage.
In
other
words,
customers’
adoption
speed
of
services
highly
influences
their
value
perceptions
of
services.
Based
on
these
studies,
we
conclude
that
it
is
important
for
companies
to
understand
the
needs
and
expectations
of
their
different
customer
segments
(existing
and
potential

customers),
with
different
levels
of
usage
experience,
at
different
moments
in
the
product
lifecycle,
in
order
to
provide
the
right
value
to
the
right
customer
segment.
Additionally,
companies
must
be
aware
of
impact
of
adoption
speed
on
customer
value
perceptions
of
services,
in
order
to
provide
value
to
the
customer
at
the
right
time.
Then
only,
a
company
will
be
able
to
deliver
sustainable
added
value
to
its
customers
and
enhance
customer
satisfaction
and
loyalty
levels
(Ravald
and
Gronroos;
1996).
27
3.1.5
Measuring
perceived
value
Different
models
are
developed
which
can
be
applied
to
assess
the
perceived
value
of
products
and
services.
Most
models
measure
perceived
value
at
the
overall
level
of
a
value
proposition
(Zeithaml,
1988;
Cronin,
2000;
Parasuraman
and
Grewal,
2000;
Petrick,
2002).
Zeithaml
(1988)
and
Cronin
Jr.
et
al
(2000)
measure
perceived
value
as
perceived
quality
minus
perceived
monetary
and
non-‐monetary
sacrifices.
In
addition,
Parasuraman
and
Grewal
(2000)
propose
a
distinction
of
perceived
quality
in
two
sub-‐
drivers
of
quality;
product
quality
and
service
quality.
They
state:
‘In
instances
where
the
core
of
what
the
seller
offers
to
the
buyer
is
a
service,
there
is
no
tangible
product
and,
as
such,
product
quality
and
service
quality
overlap.’
Also,
their
distinction
enables
the
inclusion
of
value
added
services
(f.e.
after
sales
support)
when
determining
perceived
value.
Sweeney
and
Soutar
(2001)
developed
the
PERVAL
model,
which
measures
customer’s
perceived
value
of
a
product
at
brand
level.
Their
model
suggests
perceived
value
is
driven
by
four
sub-‐dimensions
of
value;
emotional
value,
social
value,
functional
value
in
terms
of
value
for
money
and
functional
value
in
terms
of
perceived
versus
expected
performance.
Petrick’s
SERV-‐PERVAL
(2002)
measures
perceived
value
as
the
sum
of
the
emotional
outcome
of
using
a
service,
the
quality
experienced
during
usage
of
the
service,
the
reputation
of
the
service
or
service
provider
and
the
(monetary)
sacrifices
involved
with
using
the
service.
For
measuring
perceived
value
at
the
attribute
level,
the
Kano
model
(Matzler
and
Hinterhuber,
1998)
can
be
applied.
Kano’s
theory
of
attractive
quality
helps
companies

in
analyzing
the
role
of
different
product
or
service
attributes
in
relation
to
the
customer’s
perceived
value
and
satisfaction
regarding
the
product
or
service.
Although
the
model
was
developed
by
the
Japanese
professor
Kano
back
in
1984,
over
20
years
ago,
it
still
is
relevant
and
widely
used.
From
1998
to
2012,
the
number
of
academic
articles
covering
Kano’s
model
actually
increased
(Luor
et
al,
2012).
It
has
been
used
extensively
in
quality
management,
product
and
service
development,
strategic
thinking,
employee
management,
business
planning
and
service
management
(Witell
and
Lofgren,
2007).
In
addition,
it
has
successfully
been
applied
to
assess
the
classification
of
website
attributes
(Zhang
and
Von
Dran,
2002),
web
community
attributes
(Kuo,
2004)
and
e-‐learning
services’
attributes
(Chen
and
Kuo,
2011).
According
to
Matzler
and
Hinterhuber
(1998),
the
strength
of
the
Kano
methodology,
in
relation
to
other
methods,
is
that
it
can
provide
guidance
in
trade-‐off
situations
and
it
can
point
out
opportunities
for
service
differentiation.
Moreover,
Kano’s
model
is
able
to
capture
the
dynamic
nature
of
customer
perceptions
and
expectations
regarding
products
and
services.
Thus,
Kano’s
model
is
able
to
identify
changes
in
customer’s
perception
and
expectations
over
time,
based
on
variables
like
usage
experience
and
a
user’s
status
in
the
product
lifecycle
(Zhang
and
Von
Dran,
2002).
The
model
is
based
on
Herzberg’s
Motivator-‐Hygiene
Theory
in
behavioral
science,
which
states
that
the
factors
causing
satisfaction
are
different
from
the
factors
causing
dissatisfaction
(Witell
and
Fundin,
2004).
As
28
figure
3
shows,
Kano
distinguishes
five
categories
of
product
/
service
attributes
which
influence
customer
satisfaction,
which
may
differ
between
customer
segments
and
differ
over
time,
due
to
the
dynamics
of
perceived
value;

Figure
3:
The
Kano
model
29
1. Must-‐be
attributes;
an
attribute
which
absence
will
result
in
customer
dissatisfaction,
but
whose
presence
does
not
significantly
contribute
to
customer
satisfaction.
2. Attractive
attributes;
an
attribute
that
gives
satisfaction
when
present,
but
that
produce
no
dissatisfaction
when
absent.
3. One-‐dimensional
attributes;
an
attribute
that
is
positively
and
linearly
related
to
customer
satisfaction.
4. Indifferent
attributes
an
attribute
which
presence
or
absence
does
not
cause
any
satisfaction
or
dissatisfaction
to
customers.

30
5. Reverse
attributes;
an
attribute
which
presence
causes
customer
dissatisfaction,
and
whose
absence
results
in
customer
satisfaction.
Must-‐be
attributes
Must-‐be
attributes
are
the
basic
requirements
for
a
product
and
very
important
in
the
customer’s
evaluation
of
alternatives.
If
these
requirements
are
not
fulfilled,
the
customer
will
not
purchase
and/or
use
the
product
at
all.
Or,
when
the
customer
acquires
and/or
uses
the
product,
he
or
she
will
become
extremely
dissatisfied.
On
the
other
hand,
the
customer
takes
‘must-‐be’
attributes
for
granted
and
do
not
explicitly
demand
them,
therefore
fulfilling
of
these
requirements
will
not
increase
customer
satisfaction
(Matzler
and
Hinterhuber,
1998;
Witell
and
Fundin,
2005).
An
example
of
a
must-‐be
attribute
is
the
network
coverage
of
a
mobile
telephony
and
internet
service
provider.
A
customer
takes
network
coverage
for
granted
and
expects
to
be
able
to
have
connection
everywhere,
anytime.
Customers
do
not
explicitly
demand
this.
When
the
network
coverage
is
good,
this
does
not
result
in
increased
satisfaction.
But,
when
the
coverage
is
bad,
this
will
result
in
dissatisfaction.
Attractive
attributes
Attractive
attributes
are
the
product
requirements
which
have
the
greatest
impact
on
customer
satisfaction
(Matzler
and
Hinterhuber,
1998;
Witell
and
Fundin,
2005).
These
attributes
are
not
explicitly
demanded
or
expected
by
the
customer,
fulfill
unconscious
customer
needs
and
can
be
regarded
as
surprise
and
delight
attributes.
Fulfillment
of
attractive
requirements
positively
influences
customer
satisfaction.
On
the
contrary,
when
an
attractive
attribute
is
missing
this
will
not
result
in
dissatisfaction.
This,
since
the
customer
did
not
expect
or
demand
the
requirement.
By
delivering
attractive
attributes,
companies
can
increase
the
perceived
value
of
their
offering
and
increase
customer
satisfaction
(Matzler
and
Hinterhuber,
1998;
Witell
and
Fundin,
2005).
Attractive
attributes
can
become
must-‐have
attributes
over
time
(Matzler
and
Hinterhuber,
1998).
For
example,
an
attractive
attribute
could
be
the
offering
of
free

wireless
internet
on
airports.
When
these
facilities
were
not
offered,
customers
were
not
expected
to
be
dissatisfied.
This,
since
these
facilities
were
not
fulfilling
the
travelers
primary
need;
travelling.
But,
since
free
wireless
internet
has
become
globally
available
at
almost
every
airport,
customers
are
probably
starting
to
expect
this
service
to
be
delivered.
As
a
result,
free
wireless
internet
on
airports
is
expected
to
shift
from
an
attractive
requirement
into
a
must-‐have
requirement.
Other
attributes
such
as
‘airbags
in
automobiles
have
experienced
similar
shifts
(Zhao
and
Dholakia,
2009).
31
‘One-‐dimensional’
attributes
One-‐dimensional
attributes
are
often
explicitly
demanded
by
the
customer.
These
attributes
have
a
direct
linear
relationship
with
customer
satisfaction.
When
one-‐
dimensional
requirements
are
fulfilled,
this
positively
influences
customer
satisfaction.
But,
when
unfulfilled,
customer
satisfaction
is
negatively
influenced.
In
case
of
a
negative
relationship
between
an
attribute
and
satisfaction,
the
attribute
is
regarded
as
reverse
attribute.
(Matzler
and
Hinterhuber,
1998;
Witell
and
Fundin,
2005).
An
example
of
a
one-‐dimensional
attribute
is
the
size
of
the
mobile
data
bundle
offered
by
a
mobile
internet
provider.
The
bigger
the
data
bundle,
the
higher
the
satisfaction
of
the
customer
with
the
service
offered.
An
example
of
a
reverse
attribute
is
the
cost
of
a
mobile
telephony
and
internet
subscription.
The
higher
the
monthly
costs
of
the
subscription,
the
higher
the
dissatisfaction
of
the
customer
with
the
service
offered.
‘Indifferent’
attributes
Indifferent
attributes
are
attributes
which
do
not
influence
customer
satisfaction
at
all.
These
attributes
can
become
attractive
attributes
over
time.
Therefore,
companies
should
always
take
the
development
of
indifferent
attributes
into
consideration,
since
these
can
provide
strategic
means
for
customer
acquisition
and
customer
retention
in
the
future
(Yang,
2005).

Yang
(2005)
states
that
‘for
any
quality
attribute,
its
influence
on
customer
satisfaction
is
closely
related
to
the
degree
of
importance
attached
to
it
by
customers.
For
example,
in
a
car,
an
automatic
gearbox
and
a
luggage
carrier
are
both
attractive
quality
requirements.
However,
most
customers
consider
an
automatic
gearbox
to
be
more
important
than
a
luggage
carrier.
Therefore,
adding
an
automatic
gearbox
will
create
greater
customer
value
than
adding
a
luggage
carrier’
(Yang,
2005).
In
other
words,
it
is
important
to
not
only
measure
the
Kano
category
of
an
attribute,
but
also
its
relative
importance
compared
to
other
attributes.
As
figure
4
shows,
Yang’s
refined
Kano
model
takes
importance
into
account
and
splits
attractive
attributes
into
highly
and
less
attractive,
one-‐dimensional
attributes
into
high
value-‐added
and
low-‐value
added,
must
be
attributes
into
critical
and
necessary
and
indifferent
attributes
into
potential
and
care
free,
based
on
customer’s
self-‐stated
importance.
Figure
4:
The
refined
Kano
model,
including
importance
(Yang,
2005)
32
3.1.7
Conclusions
on
perceived
value
Based
on
this
literature
study
on
perceived
value
we
find
Zeithaml’s
customer
perspective
(1988)
on
value
to
best
fit
the
research:
‘Value
is
the
customer’s
overall
assessment
of
the
utility
of
a
product
based
on
perceptions
of
what
is
received
and
what
is
given.’
This
assessment
of
value
is
based
on
two
factors;
utilitarian
value
and
hedonistic
value
(Kleijnen
et
al,
2007).
Utilitarian
value
is
concerned
with
the
goal
the
customer
wants
to
accomplish
when
using
a
service
or
product
and
the
convenience
in

achieving
this
goal
through
the
service
or
product.
Hedonistic
value
concerning
emotional
involvement
when
using
a
service
or
product.
As
33
figure
2
shows,
utilitarian
value
and
emotional
value
can
be
split
into
sub-‐dimensions,
which
enable
us
to
measure
perceived
value
on
the
attribute
level
of
a
service.
The
overall
perceived
value
of
a
customer
is
found
to
be
dynamic
and
is
influenced
by
demographic
variables
like
age
and
education,
psychographic
variables
like
social
class
and
lifestyle
and
behavioral
variables
like
usage
experience
user
status
(Zeithaml,
1988;
Webster,
1989;
Ravald
and
Gronroos,
1996;
Mittal
and
Katrichis,
2000;
Kano,
2001;
Witell
and
Fundin,
2005;
Zhao
and
Dholakia,
2009;
Kim
and
Hwang,
2012).
Therefore,
Kano’s
methodology
for
measuring
customer
value
will
used
as
analysis
tool;
it
helps
us
determine
if
specific
service
attributes
are
must-‐have,
attractive
or
irrelevant
from
a
customer’s
point
of
view
and
it
captures
the
dynamic
nature
of
perceived
value
(Matzler
and
Hinterhuber,
1998;
Zhang
and
Von
Dran,
2002).
This,
in
order
to
find
answer’s
to
our
main
research
questions:
• What
is
the
value
of
a
mobile
service
app
within
the
overall
value
proposition
offered
to
the
customer,
and
how
does
the
value
differ
between
customer
segments?
• What
are
the
key
attributes
to
the
app's
perceived
value,
and
how
do
these
attributes
differ
between
customer
segments?
3.2
Definitions
used
for
this
study
3.2.1
Perceived
value
As
the
aim
of
this
study
is
to
determine
the
perceived
value
of
an
mobile
service
app
and
its
attributes
from
a
customer’s
perspective,
the
value
definition
of
Zeithaml
(1988)
will
be
used
for
the
rest
of
this
research:
‘Value
is
the
customer’s
overall
assessment
of
the
utility
of
a
product
based
on
perceptions
of
what
is
received
and
what
is
given.’

34
3.2.2
Value
proposition
A
value
proposition
is
the
complete
product
or
service
a
company
offers
to
its
customer.
It
consists
of
a
core
product
or
service
and
is
often
extended
with
additional
services,
creating
the
augmented
product
(Riel
et
al,
2004).
The
core
product
represents
the
customer’s
minimal
purchase
conditions
(Witell
and
Fundin,
2005).
An
example
of
a
core
service
is
a
banking
offering
customers
the
opportunity
to
save
and
lend
money
and
to
conduct
financial
transactions.
The
augmented
product
exceeds
the
customer’s
basic
needs
or
expectations
(Witell
and
Fundin,
2005).
A
bank’s
augmented
product
consists
of
value
adding
services
like
online
payment
portals
and
mobile
payment
(m-‐
payment)
portals,
services
enhancing
the
customer’s
banking
experience
and
reducing
the
bank’s
costs.
The
core
and
augmented
product
together
are
considered
as
the
bank’s
value
proposition.
For
this
research,
we
will
focus
on
the
perceived
value
of
a
mobile
service
app
as
part
of
the
total
value
proposition
offered.
3.2.3
Mobile
Service
App
A
mobile
app
is
defined
as
a
software
application
on
a
smartphone
or
tablet,
enabling
anywhere,
anytime
interaction
between
a
company
and
its
customers.
It
offers
customers
a
mobile
gateway
to
online
services
(Xu
et
al,
2011).
In
light
of
this
study,
the
app
investigated
can
be
categorized
as
a
service-‐oriented
app
and
must
be
seen
as
a
peripheral
service,
part
of
the
augmented
product,
aimed
at
adding
value
to
the
core
offering
of
the
company
to
the
customer.
This,
contrary
to
stand-‐alone
apps
which
are
the
core
product
by
themselves
(f.e.
instant
messaging
apps,
game
apps).
The
research
will
focus
on
the
perceived
value
of
the
mobile
service
app
as
part
of
the
total
value
proposition
offered.
In
addition,
it
will
analyze
the
perceived
value
of
the
different
attributes
of
a
mobile
service
app.
3.3
Hypotheses

Zeithaml
(1988)
states
that
‘perceived
value
is
the
customer’s
overall
assessment
of
the
utility
of
a
product
based
on
perceptions
of
what
is
received
and
what
is
given’
and
that
it
varies
amongst
customers
as
a
result
of
the
individual
and
subjective
nature
of
perceived
value.
In
addition,
perceived
value
is
found
to
be
transforming
over
time
(Kano,
2001;
Witell
and
Fundin,
2005).
Based
on
these
definitions
of
perceived
value,
we
will
adopt
a
segmentation
approach
to
determine
the
perceived
value
of
a
mobile
service
app
and
its
attributes
within
different
customer
segments.
Segmentation
will
be
based
on
demographic
and
behavioral
characteristics.
The
effect
of
the
demographics
age
and
education
will
be
analyzed.
In
addition,
the
effect
of
the
behavioural
characteristics
user
status
and
usage
experience
will
be
investigated,
in
line
with
the
concept
of
perceived
value
transforming
over
time.
Because
of
the
theoretical
and
practical
problems
associated
with
psychographic
segmentation
(Gilbert
et
al,
1995),
we
will
not
include
psychographic
variables
in
this
study.
35
3.3.1
Demographics
Based
on
previous
studies
on
perceived
value
and
service
quality
(Zeithaml,
1988;
Webster,
1989;
Ravald
and
Gronroos,
1996;
Lee
et
al,
2005;
Kim
and
Hwang,
2012),
different
segments
of
users
are
expected
to
have
different
value
perceptions
of
a
mobile
service
app.
Age
and
education
are
expected
to
be
of
significant
influence
on
customers’
perceived
value
of
the
app.
Therefore,
we
hypothesize;
H1a:
Customers
classify
the
mobile
service
app
into
different
Kano
categories
H1b:
The
mobile
service
app’s
Kano
classification
differs
between
customer
segments
based
on
age
H1c:
The
mobile
service
app’s
Kano
classification
differs
between
customer
segments
based
on
education

In
addition,
the
mobile
service
app
consists
of
different
attributes
which
can
add
value
for
the
customer.
These
elements
are;
usefulness
of
the
service,
ease
of
use
of
the
service,
ease
of
accessing
the
service,
reliability
of
the
service,
supporting
services
and
emotional
involvement
of
the
customer
when
using
the
service
(see
literature
review,
36
figure
2).
Based
on
the
expectation
of
different
segments
of
users
having
different
value
perceptions,
we
expect
these
attributes
within
the
mobile
service
app
to
be
perceived
differently
by
different
customer
segments;
H2a:
Customers
classify
the
mobile
service
app’s
attributes
into
different
Kano
categories
H2b:
The
mobile
service
app
attributes’
Kano
classification
differs
between
customer
segments
based
on
age
H2c:
The
mobile
service
app
attributes’
Kano
classification
differs
between
customer
segments
based
on
education
3.3.2
Behavior:
Usage
experience
and
user
status
Previous
studies
(Zeithaml,
1988;
Kano,
2001;
Witell
and
Fundin,
2005;
Zhao
and
Dholakia,
2009)
found
differences
in
a
customer’s
perceived
value
of
a
service
or
technology
based
his
or
her
level
of
usage
experience
with
the
service
of
technology.
Therefore,
we
expect
smartphone
usage
to
directly
influence
the
app’s
Kano
classification
category.
We
hypothesize;
H3a:
Smartphone
usage
experience
directly
influences
the
mobile
service
app’s
Kano
classification.
H3b:
Frequency
of
app
usage
(in
general)
directly
influences
the
mobile
service
app’s
Kano
classification

H3c:
The
number
of
apps
in
use
directly
influences
the
mobile
service
app’s
Kano
classification.
In
addition,
based
on
previous
studies
on
differences
in
perceived
value
between
existing
users
and
non-‐users
of
services
(Woodruff,
1997;
Mittal
and
Katrichis,
2000),
we
expect
that
the
perceived
value
of
a
mobile
service
app
is
more
valuable
to
existing
app
users
than
to
non
app-‐users.
Therefore,
we
hypothesize:
H3d:
Existing
app
users
classify
a
mobile
service
app
into
a
different
Kano
category
compared
to
non
app-‐users
37

4.
METHODOLOGY
This
chapter
covers
the
operationalization
of
the
research.
It
explains
the
research
design,
sample
strategy
and
sample
size
and
the
data
collection
method.
Kano’s
model
for
measuring
perceived
value
is
used
to
operationalize
the
research
on
the
perceived
value
of
a
mobile
service
app
and
the
operationalization
process
is
described.
Finally,
the
development
and
execution
of
the
questionnaire
used
to
collect
the
data
is
described.
38
4.1
Objectives
This
research
aims
to
determine
the
perceived
value
of
a
mobile
service
app
within
the
overall
value
proposition
offered
to
the
customer.
Second,
it
aims
to
determine
the
key
attributes
influencing
the
app’s
perceived
value.
In
our
research,
we
suggest
a
mobile
service
app
to
be
part
of
the
augmented
product,
aimed
at
adding
value
to
a
core
product
or
service
in
order
to
distinguish
the
product
or
service
from
competition.
Kano’s
model
of
customer
satisfaction
(Matzler
and
Hinterhuber,
1998;
Yang,
2005)
will
be
used
to
find
answers
to
the
research
questions;
• What
is
the
value
of
a
mobile
service
app
within
the
overall
value
proposition
offered
to
the
customer,
and
how
does
the
value
differ
between
customer
segments?
• What
are
the
key
attributes
to
the
app's
perceived
value,
and
how
do
these
attributes
differ
between
customer
segments?
Kano’s
methodology
makes
it
possible
to
classify
service
attributes
based
on
customer
perceptions.
Using
this
model
will
enable
us
to
determine
the
perceived
value
of
a
mobile
service
app
within
the
overall
value
proposition
offered.
This,
in
order
to
find
if
a
mobile
app
is
really
adding
value
for
the
customer
and
if
the
app’s
perceived
value
varies
between
customer
segments.
Second,
it
enables
us
to
determine
the
perceived

value
of
the
different
attributes
of
a
mobile
service
app,
for
different
customer
segments.
This,
in
order
to
find
the
app’s
attributes
which
are
of
key
influence
on
the
app’s
value
for
its
users.
39
4.2
Research
design
We
tend
to
execute
the
research
in
such
a
way
that
conclusions
could
be
generally
applied,
to
different
kinds
of
businesses
and
situations.
Therefore,
this
study
adopts
a
deductive
approach,
based
on
a
survey,
with
a
descriptive
and
explanatory
aim.
The
questionnaire
approach
enables
us
to
generalizing
outcomes
and
to
find
relationships
between
variables
(Saunders
et
al,
2009),
which
fits
with
the
research
questions
and
the
wish
for
the
outcomes
to
be
generally
applicable.
This,
contrary
to
inductive
research
methods
like
focus
groups
and
interviews,
which
aim
to
build
new
theories
and
which
are
less
concerned
with
a
need
for
generally
applying
theory
to
practical
situations
(Saunders
et
al,
2009).
We
develop
and
test
hypotheses
based
on
existing
theories
and
aim
to
extend
existing
theories
on
perceived
value
with
specific
findings
on
perceived
value
regarding
mobile
apps.
Through
the
hypotheses,
different
perspectives
of
customer’
perceived
value
of
a
mobile
app
are
examined
and
described
and
relations
between
variables
are
analyzed
in
order
to
explain
these
different
perspectives.
First,
literature
review
has
been
conducted
to
establish
a
good
overview
on
existing
theories
of
perceived
value
and
to
determine
the
attributes
of
a
mobile
service
app
which
customers’
perceive
valuable.
Second,
the
measurement
method
for
measuring
perceived
value
was
determined
and
hypotheses
have
been
developed,
based
on
existing
value
theories.
Third,
in
order
to
test
these
hypotheses,
a
questionnaire
has
been
set-‐up.
Development
and
execution
of
this
questionnaire
are
explained
in
the
following
paragraphs.

40
4.3
Sample
strategy
and
sample
size
To
determine
the
perceived
value
of
a
mobile
service
app
and
the
value
of
the
mobile
app’s
attributes,
we
will
sample
amongst
customers
of
a
Dutch
mobile
network
provider
offering
mobile
telephony
and
internet
network
services.
The
company
offers
its
mobile
customers
a
mobile
service
app
for
smartphones,
which
provides
customers
information
on
monthly
bills
and
actual
usage
of
their
bundle
(call
minutes,
text
messages,
data
bundle),
and
offers
the
possibility
to
instantly
upgrade
or
downgrade
the
subscription
and
to
purchase
value
added
services.
In
that
sense,
the
mobile
service
app
should
be
considered
part
of
the
augmented
product,
aimed
at
adding
value
to
the
core
product;
a
mobile
telephony
and
internet
network
service.
This
fits
with
our
research
context.
We
have
contacted
a
random
set
of
customers
the
company’s
subscriber
base
by
email
and
through
an
online
questionnaire
we
asked
them
if
they
have
usage
experience
with
smartphones
and
with
mobile
apps
in
general.
In
addition,
we
asked
if
they
have
usage
experience
with
the
company’s
mobile
My
service
app.
The
company’s
customer
base
includes
more
than
1
million
subscribers.
For
a
95%
confidence
level
of
the
data
collected
and
5%
margin
of
error,
we
need
at
least
384
complete
responses
(Saunders
et
al,
2009).
This,
to
ensure
that
the
characteristics
of
the
sample
data
collected
will
represent
the
characteristics
of
the
total
population.
A
simple
random
sampling
approach
(probability
sampling)
has
been
adopted
to
select
the
sample.
4.4
Data
collection
4.4.1
Measuring
the
dynamics
of
perceived
value:
Kano’s
measurement
model
Kano’s
measurement
model
for
measuring
product
attribute
classifications
is
used
to
collect
and
analyze
the
data.
The
model
suggests
a
specific
method
to
collect
data
which
involves
a
functional-‐dysfunctional
form
of
asking
the
customer’s
perceived
value
of
the
different
attributes
of
a
product
or
service
(Sauerwein
et
al,
1996).
This,

reflecting
Herzberg’s
Motivator-‐Hygiene
Theory,
which
states
that
the
factors
causing
satisfaction
are
different
from
the
factors
causing
dissatisfaction
(Witell
and
Fundin,
2004).
The
functional
question
analyzes
the
customer’s
perception
if
the
product
or
service
offers
a
specific
attribute.
For
example,
it
asks;
‘How
do
you
feel
if
attribute
X
is
present
in
a
mobile
service
app?’
The
dysfunctional
question
analyzes
the
customer’s
perception
if
the
product
or
service
lacks
a
specific
attribute.
For
example,
it
asks;
‘How
do
you
feel
if
attribute
X
is
not
present
in
a
mobile
service
app?’
As
41
figure
5
shows,
respondents
can
give
five
different
answers
to
the
functional/dysfunctional
questions:
1.
I
like
it,
2.
I
require
it
(must-‐be),
3.
neutral,
4.
I
don’t
mind
(live
with),
5.
I
don’t
like
it
(dislike).
Kano’s
attribute
classification
table
(Matzler
and
Hinterhuber,
1998)
in
figure
5
combines
the
answers
to
the
functional
and
dysfunctional
question
and
classifies
an
attribute
to
one
of
the
Kano
attribute
categories;
must
have,
attractive,
one-‐
dimensional,
reverse,
indifferent
or
questionable
(see
literature
review
for
explanation).
A
questionable
classification
shows
us
that
the
question
concerning
an
attribute
has
been
phrased
incorrect
or
has
been
misunderstood
by
the
respondent
(Matzler
and
Hinterhuber,
1998).
Figure
5:
Kano’s
attribute
classification
table
Other
methodologies
for
classification
of
product
and
service
attributes
are
the
direct
classification
method
and
Kano’s
3-‐level
questionnaire
(Witell
and
Lofgren,
2007).
First,
the
direct
classification
method
directly
asks
customers
to
classify
attributes
into
Kano’s

various
categories
themselves.
Main
advantage
of
this
method
is
that
fewer
questions
need
to
be
asked,
which
shortens
the
questionnaire
length
and
stimulates
response
(Mikulic
and
Prebežac,
2011).
But,
according
to
Mikulic
and
Prebežac
(2011)
this
method
is
only
preferred
in
situations
where
the
respondents’
understanding
of
Kano’s
different
categories
is
guaranteed.
In
addition,
the
direct
classification
method
is
found
to
overestimate
the
role
of
must
be
attributes
and
underestimate
the
role
of
attractive
attributes
(Witell
and
Lofgren,
2007).
Second,
Kano’s
3-‐level
questionnaire
is
similar
to
Kano’s
5-‐level
questionnaire,
but
only
measures
on
a
3-‐point
scale
(satisfied,
neutral,
dissatisfied).
This
increases
the
ease
of
completing
the
questionnaire
for
the
respondent,
stimulating
response.
But,
like
the
direct
classification
method,
the
Kano
3-‐
level
questionnaire
is
also
is
found
to
overestimate
the
role
of
must
be
attributes
and
underestimate
the
role
of
attractive
attributes
(Witell
and
Lofgren,
2007).
Since
our
sample
is
not
expected
to
be
known
with
the
Kano
methodology,
we
chose
to
apply
the
most
commonly
used
Kano
5-‐level
classification
method,
asking
both
functional
and
dysfunctional
questions,
accepting
that
the
extra
questions
result
in
a
lengthier
questionnaire.
42
4.4.2
Questionnaire
75.000
mobile
subscribers
have
been
contacted
by
email
and
asked
if
they
would
like
to
participate
in
a
questionnaire
on
smartphone
and
app
usage.
An
incentive
was
used
to
stimulate
responses.
Data
has
been
collected
through
an
online
questionnaire
based
on
SurveyMonkey.com’s
online
questionnaire
tool.
In
total,
1.016
customers
responded
and
shared
their
smartphone
and
apps
experience
with
us.
The
questionnaire
was
constructed
according
to
the
Kano
model
(Sauerwein
et
al,
1996).
First,
in
order
to
determine
the
overall
proposition’s
attributes
to
be
investigated
through
the
questionnaire,
we
conducted
exploratory
desk
research
on
the
value
proposition
attributes
of
a
mobile
telephony
and
internet
subscription.
These
attributes

43
have
been
based
on
research
within
the
company’s
mobile
customers
base.
See
figure
6
for
an
overview
of
these
attributes.
Variable
Brand
(image
/
trustworthiness)
Price
/
monthly
subscription
costs
Network
coverage
Service
quality
(website,
helpdesk,
store)
Promotions
/
discounts
Internet
speed
Size
of
internet
bundle
(MB's
/
GB's)
Size
of
calling
&
texting
bundle
(minutes
&
SMS-‐es)
Free
WiFi
Hotspots
Mobile
Service
App
Figure
6:
Attributes
of
a
mobile
telephony
and
internet
subscription
Second,
in
order
to
determine
the
value
attributes
of
the
mobile
service
app
to
be
investigated
through
the
questionnaire,
we
conducted
exploratory
desk
research
on
value
attributes
in
online
and
mobile
service
contexts
(see
chapter
3,
theoretical
framework).
Based
on
the
value
attributes
found,
a
set
of
value
attributes
for
a
mobile
service
app
has
been
constructed.
An
overview
of
this
set
is
given
in
figure
7.
Third,
to
increase
the
respondents’
understanding
of
the
questions
asked
and
increase
the
value
of
the
data
to
be
collected,
a
couple
of
pilot
questionnaires
were
distributed
amongst
the
target
group.
Feedback
on
these
pilot
questionnaires
has
been
collected
and
used
to
develop
the
final
questionnaire.

Figure
7:
Value
attributes
of
a
service
mobile
app
46

5.
ANALYSIS
AND
RESULTS
In
this
chapter,
we
describe
the
data
analysis
process,
the
characteristics
of
the
sample
analyzed
and
the
main
results
of
our
analysis
regarding
perceived
value
and
differences
in
perceptions
based
on
demographic
factors
age
and
education
and
behavioural
factors
user
status
and
usage
experience.
47
5.1
Analysis
of
questionnaire
data
The
data
has
been
collected
through
SurveyMonkey.com’s
online
questionnaire
tool.
In
Excel,
Kano
categories
have
been
determined
based
on
the
acquired
data.
Afterwards,
the
enriched
data
was
exported
to
SPSS
20.0
for
analysis.
Where
applicable,
a
0,05
criterion
of
statistical
significance
has
been
used
to
determine
if
hypotheses
were
significantly
supported
or
not.
5.2
Characteristics
of
the
sample
Figure
8
gives
an
overview
of
the
characteristics
of
the
study’s
respondents.
The
sample
consists
of
63%
male
and
37%
female
respondents.
8%
of
the
respondents
was
between
15
and
24
years
of
age,
15%
between
25
and
34
years
of
age,
19%
between
35
and
44
years
of
age,
20%
between
45
and
54
years
of
age
and
38%
was
55
years
or
older.
This
relative
high
age
can
be
explained
by
the
relative
old
customers
of
the
population
from
which
the
sample
was
selected;
the
company’s
subscriber
base.
The
sample’s
education
level
shows
2%
of
the
respondents
did
not
have
any
higher
education
at
all,
17%
finished
high
school,
34%
finished
MBO,
35%
finished
HBO
and
13%
finished
university.
The
sample’s
general
level
of
smartphone
experience
can
be
considered
high,
with
only
13%
of
the
sample
lacking
smartphone
experience.
5%
had
less
than
half
a
year
of
smartphone
experience,
9%
half
a
year
to
a
year,
24%
one
to
two
years
and
50%
had
over
2
years
of
smartphone
experience.
This
can
also
be
explained
by
the
population
from
which
the
sample
has
been
selected;
company’s
mobile
subscriber
base.
Of
these
smartphone
experience
respondents,
only
3%
did
not
use
mobile
apps
at
all.
The
other

97%
uses
apps
at
least
once
a
month
or
more
with
22%
of
the
respondents
using
apps
over
100
times
a
month.
Amongst
the
app
users,
10%
only
uses
1
to
2
apps,
39%
uses
3
to
5
apps,
36%
uses
6
to
10
apps
and
16%
uses
more
than
10
apps
a
month.
In
addition,
59%
of
the
respondents
use
the
My
service
app
at
least
once
a
month,
while
the
other
41%
does
not
use
this
mobile
service
app.
Figure
8:
Sample
characteristics
48
Count %
Male 642 63%
Female 374 37%
15
years
or
younger 3 0%
15
-‐
24
years 77 8%
25
-‐
34
years 148 15%
35
-‐
44
years 190 19%
45
-‐
54
years 207 20%
55
years
or
older 391 38%
No
education 16 2%
High
school 168 17%
MBO 343 34%
HBO 358 35%
WO
/
University 131 13%
No
experience 134 13%
<
1/2
year 50 5%
1/2
-‐
1
year 90 9%
1
-‐
2
years 239 24%
2
years
+ 503 50%
Never 28 3%
<
1x
per
month 38 4%
1
-‐
10x
per
month 113 11%
11
-‐
50x
per
month 264 26%
51
-‐
100x
per
month 210 21%
100x
per
month
or
226 22%
N/A 137 13%
1
-‐
2
apps 82 10%
3
-‐
5
apps 331 39%
6
-‐
10
apps 301 36%
10
apps
+ 132 16%
Never 351 41%
<
1x
per
month 143 17%
1
-‐
2x
per
month 137 16%
3
-‐
5x
per
month 112 13%
6
-‐
10x
per
month 61 7%
10x
per
month
+ 42 5%
Gender
Age
Education
Smartphone
experience
Frequency
of
app
usage
Number
of
apps
in
use
MyKPN
usage

49
5.3
Demographics
and
perceived
value
The
first
set
of
hypotheses
concern
the
effects
of
demographics
on
the
perceived
value
of
a
mobile
service
app
within
the
overall
value
proposition
and
the
perceived
value
of
the
app’s
different
attributes.
First,
we
analyze
the
perceived
value
of
the
mobile
service
app
on
the
level
of
the
overall
value
proposition
offered.
As
figure
9
shows,
generally,
respondents
consider
the
mobile
service
app
a
one-‐dimensional
attribute
within
the
Kano
classification
and
a
low-‐value
added
attribute
within
Yang’s
classification.
Yang’s
classification
distinct
between
high
and
lower
importance
items,
in
which
all
items
rated
above
the
importance
mean
(7,74)
are
ranked
as
high
important
and
all
items
below
the
importance
mean
are
ranked
less
important.
But,
as
figure
10
shows,
classification
of
the
mobile
service
app
is
highly
dispersed
amongst
customers.
In
total,
49%
of
the
respondents
consider
the
app
as
an
interesting
attribute,
with
13%
classifying
the
app
as
must-‐have
attribute,
23%
as
one-‐dimensional
attribute
and
13%
as
attractive
attribute.
Based
on
these
results,
hypothesis
1a
is
supported;
customers
classify
the
mobile
service
app
into
different
Kano
categories.
Figure
9:
Value
proposition
attribute
classification

Figure
10:
Mobile
app
attribute
classification
50
In
order
to
answer
hypothesis
1b,
a
bivariate
correlation
analysis
has
been
conducted
in
SPSS,
in
order
to
determine
if
Kano
classification
of
the
mobile
service
app
is
correlated
with
a
respondent’s
age.
A
two-‐tailed
test
is
applied,
since
we
cannot
predict
if
age
has
a
positive
or
negative
effect
on
the
app’s
classification.
Kendall’s
tau
is
used
to
find
if
there
is
the
correlation
between
app
classification
and
age
exists,
which
is
suggested
a
better
estimate
of
the
correlation
in
a
population
than
the
more
popular
Spearman’s
correlation
coefficient
(Field,
2009).
Figure
11
shows
the
dispersion
in
the
app’s
classification
between
different
age
segments
and
the
outcome
of
the
analysis.
Kendall’s
tau
shows
there
is
no
significant
correlation
with
0,735
significance.
Thus,
hypothesis
1b
is
not
supported;
age
does
not
significantly
influence
the
app’s
Kano
classification.

Figure
11:
Age
vs.
app
classification
51
In
order
to
answer
hypothesis
1c,
a
bivariate
correlation
analysis
has
been
conducted
in
SPSS,
in
order
to
determine
if
Kano
classification
of
the
mobile
service
app
is
correlated

with
a
respondent’s
level
of
education.
A
two-‐tailed
test
is
applied,
since
we
cannot
predict
if
education
has
a
positive
or
negative
effect
on
the
app’s
classification.
Again,
Kendall’s
tau
is
used
to
find
if
there
is
the
correlation
between
app
classification
and
education
exists.
Figure
12
shows
the
dispersion
in
the
app’s
classification
between
different
education
segments
and
the
outcome
of
the
analysis.
Kendall’s
tau
shows
there
is
a
significant
correlation
with
0,047
significance.
Thus,
52
hypothesis
1c
is
supported;
education
does
significantly
influence
the
app’s
Kano
classification.

Figure
12:
Education
vs.
app
classification
Second,
we
analyze
the
perceived
value
of
the
app’s
attributes.
As
figure
13
shows,
generally,
respondents
consider
security
of
the
app’s
information
exchanged
as
a
must-‐
be
attribute
based
on
Kano’s
classification
and
a
critical
attribute
based
on
Yang’s
classification.
Yang’s
classification
distinct
between
high
and
lower
importance
items,
in
which
all
items
rated
above
the
importance
mean
(7,38)
are
ranked
as
high
important
and
all
items
below
the
importance
mean
are
ranked
less
important.
Availability,
53

actuality,
relevance
and
speed
are
considered
one-‐dimensional
attributes
in
Kano’s
classification
and
high
value-‐added
in
Yang’s
classification.
Ease
of
use
is
considered
an
attractive
attribute
in
Kano’s
classification
and
highly
important
according
to
Yang’s
model.
The
other
features
of
the
app
are
less
important
when
considering
the
general
perspective
of
the
respondents.
But,
as
figure
14
shows,
also
the
classification
of
the
mobile
service
app’s
attributes
is
highly
dispersed
amongst
customers.
Based
on
these
results,
54
hypothesis
2a
is
supported;
customers
classify
the
mobile
service
app’s
attributes
into
different
Kano
categories.
Figure
13:
App
attribute
classification

Figure
14:
App
attribute
classification
55
In
order
to
answer
hypothesis
2b,
a
bivariate
correlation
analysis
has
been
conducted
in
SPSS,
in
order
to
determine
if
Kano
classification
of
the
mobile
service
app’s
attributes
is
correlated
with
a
respondent’s
age.
Again,
a
two-‐tailed
Kendall’s
tau
test
is
used
to
find
if
there
is
the
correlation.
Figure
15
shows
the
correlation
coefficients
for
the
app’s
attributes
classification
and
age.
Kendall’s
tau
shows
there
is
no
significant
correlation
between
age
and
one
or
more
of
the
app’s
attributes
classifications.
Thus,
hypothesis
2b
is
not
supported;
age
does
not
significantly
influence
the
app
attributes’
Kano
classification.
Figure
15:
Age
vs.
app
attribute
classification

Although,
when
analyzing
importance
ratings
on
a
1-‐10
scale
with
Spearman’s
correlation
coefficient
for
such
parametric
scales
(Field,
2009)
shows
us
correlations
between
age
and
importance
rating.
As
56
figure
16
shows,
the
importance
of
availability
(significance
of
0,021)
and
a
user
manual
(significance
of
0,001)
are
significant
correlated
with
age.
For
the
attribute
availability,
the
correlation
is
negative,
meaning
the
younger
the
respondent
the
more
important
availability
is
rated.
For
the
attribute
user
manual,
the
correlation
is
positive,
meaning
the
older
the
respondent
the
more
important
the
user
manual
becomes.
Figure
16:
Age
vs.
importance
of
availability
and
user
manual
In
order
to
answer
hypothesis
2c,
a
bivariate
correlation
analysis
has
been
conducted
in
SPSS,
in
order
to
determine
if
Kano
classification
of
the
mobile
service
app’s
attributes
is
correlated
with
a
respondent’s
education.
Again,
a
two-‐tailed
Kendall’s
tau
test
is
used
to
find
if
there
is
the
correlation.
Figure
17
shows
the
correlation
coefficients
for
the
app’s
attributes
classification
and
education.
Kendall’s
tau
shows
there
is
a
significant
correlation
between
education
and
the
classifications
of
a
number
of
the
app’s
attributes;
relevance
(0,000
significance),
completeness
of
features
(0,007
significance),
actuality
(0,018
significance),
relative
benefit
(0,009
significance),
availability
(0,003
significance),
speed
(0,001
significance)
and
security
(0,000
significance).
Thus,
hypothesis
2c
is
supported;
education
does
significantly
influence
the
app
attributes’
Kano
classification.

Figure
17:
Education
vs.
app
attribute
classification
57
5.4
Behavioural
characteristics
and
perceived
value
The
second
set
of
hypotheses
concern
the
effects
of
behavioural
characteristics
‘usage
experience’
and
‘user
status’
on
the
perceived
value
of
a
mobile
service
app
within
the
overall
value
proposition
and
the
perceived
value
of
the
app’s
different
attributes.
First,
we
analyze
the
effects
of
the
behaviour
characteristic
‘usage
experience’
on
the
level
of
the
mobile
service
app’s
Kano
classification.
A
two-‐tailed
Kendall’s
tau
test
is
used
to
find
if
there
is
the
correlation.
Figure
18
shows
the
correlation
coefficients
for
the
app’s
classification
and
smartphone
usage
experience.
Kendall’s
tau
shows
there
is
a
significant
correlation,
with
0,004
significance.
The
more
experienced
a
user
is
with
his
or
her
smartphone,
the
more
the
app
shifts
towards
Kano’s
must-‐have
classification.
Thus,
hypothesis
3a
is
supported;
smartphone
usage
experience
directly
influences
the
app
attributes’
Kano
classification.
Figure
18:
Smartphone
usage
experience
vs.
app
classification

Delivering Value through Mobile Apps by Mark Hoskam

Delivering Value through Mobile Apps by Mark Hoskam

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