Qualitative Market Research An International Journal,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 26, 2025
Purpose
This
study
aims
to
examine
the
user
experience
of
voice
assistants
(VAs)
in
different
retailing
contexts
by
highlighting
factors
that
impact
effectiveness
commerce
services.
Design/methodology/approach
follows
a
qualitative
research
method
using
30
in-depth
semi-structured
interviews
with
online
shoppers
(15
users
VAs
from
Nigeria
and
15
UK).
Following
Gioia’s
methodology
automated
content
analysis
LexiPortal,
this
paper
examined
users’
motivations
for
adopting
VAs,
their
challenges
how
might
influence
customers’
brand
trust
loyalty.
Findings
found
anthropomorphism,
convenience,
companionship
literacy
support
drove
shoppers’
adoption
VAs.
Technophobia,
audio
bias,
disparity
data
security
emerged
as
facing
VA
users.
In
addition,
Nigerian
participants
also
highlighted
unreliable
power
supply.
Despite
these
challenges,
have
developed
personal
attachment
Originality/value
is
one
few
academic
works
specifically
analyse
retail
experiences
are
shaped
through
comprative
setting
British
The
findings
enrich
extant
literature
on
granular
focus
customer
well
challenges.
The
factors
hypothesised
in
the
conceptual
model
of
chatbotChatbot
efficiency
(see
Chap.
4
,
Bialkova,
2024a)
were
tested
an
empirical
study.
UsersUser
who
had
used
a
at
least
once
their
life
invited
to
complete
survey
and
provide
opinion
about
experienceExperience
they
with
chatbotChatbot.
90%
our
respondents
have
employed
contact
customer
service,
showing
growing
importance
AIArtificial
Intelligence
(AI)
systems
substituting
human
agents
front
service
line.
results
from
regression
modelling
clearly
show
relationships
between
model.
(1)
greater
qualityQuality
ease
useEase
use
perceived
be,
higher
satisfactionSatisfaction
was
more
positive
attitudesAttitudes
toward
chatbotsChatbot
were.
(2)
was,
intention
willingness
recommend
it.
same
tendency
emerged
for
attitudesAttitudes.
(3)
Enhanced
functionalityFunctionality
led
evaluation
use.
(4)
EnjoymentEnjoyment
also
play
role
Note,
however,
some
above
parameters
may
turn
into
barriers.
Although
level
relatively
good,
consumers
are
not
satisfied
will
it
future.
Such
outcome
is
warning
call
look
appropriate
techniques
assembling
machine
learningMachine
Learning
(ML),
natural
language
processingNatural
Language
Processing
(NLP),
reasoning
build
better
systems,
prioritising
human-centred
approach.
Journal of Product & Brand Management,
Год журнала:
2024,
Номер
33(2), С. 258 - 272
Опубликована: Фев. 13, 2024
Purpose
This
study
aims
to
answer
the
following
questions:
How
do
consumers’
perceptions
of
brand
coolness
affect
relationship
outcomes
and
how
effects
differ
between
product
brands
service
brands?
Design/methodology/approach
A
quantitative
survey
was
used
collect
data
from
1,500
consumers
assigned
assess
one
20
popular
in
Vietnam.
Partial
least
square
structural
equation
modeling
analyze
data.
Findings
Data
analysis
reveals
that
both
dimensions
(i.e.
self-oriented
other-oriented
coolness)
exert
positive
impacts
on
satisfaction,
love
advocacy)
through
attitude
evaluative
mechanism)
self-brand
connection
identity
mechanism).
While
mechanism
is
more
prominent
brands,
pronounced
for
brands.
Practical
implications
research
provides
practical
guidance
managers
build
strong
customer
relationships
by
leveraging
their
mechanisms
underlying
effects.
suggests
a
tailored
application
different
branded
entities.
Originality/value
contributes
literature
validating
two-dimensional
structure
encompassing
coolness,
accordance
with
value-based
conceptualization
concept.
For
mass
studies,
this
recommends
exclusion
rebellious
subcultural
attributes,
as
well
utility
pre-determined
evaluated
objects,
measuring
coolness.
also
illuminates
dual
mediation
moderation
entity
consumer–brand
relationships.
Making
sophisticated
software
applications
economically
feasible
does
not
necessarily
mean
that
userUser
needs
and
demands
are[aut]Bialkova,
S.
met
in
regard
to
chatbotsChatbot
(Bialkova
2021,
2022a).
Creating
consumers
are
willing
use
is
an
easy
task.
In
particular,
understanding
the
key
drivers
of
chatbotChatbot
efficiency,
reflecting
consumer
satisfactionSatisfaction,
attitudesAttitudes,
use,
recommendationRecommendation
a
chatbotChatbot,
calls
further
investigation.
The
current
chapter
aims
provide
profound
literature
audit
order
identify
efficiency.
First,
evolution
research
on
discussed,
line
with
different
industries
contexts,
ranging
from
banking,
telecommunications,
retail,
travel,
tourism,
education
health
care.
main
emerging
trends
summarised
thematic
map,
raising
fundaments
build
our
theoretical
framework.
encompassed
human–computer
interaction
usabilityUsability,
cognitive
science
psychology,
as
well
behaviour
marketingMarketing
papers.
This
multidisciplinary
approach
provides
opportunity
generate
overarching
picture
could
be
used
better
understand
what
ingredients
needed
efficient
AIArtificial
Intelligence
(AI)
applications.
core
notions
organised
around
three
pillars:
acceptanceAcceptance
models,
behavioural
theories,
social
influenceSocial
influence
theories.
Fundamental
concepts
(e.g.,
qualityQuality,
functionalityFunctionality),
affective
enjoymentEnjoyment),
personal
carePersonal
care,
presenceSocial
presence)
perspectives
presented
holistic
Family Relations,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 21, 2025
ABSTRACT
Objective
In
this
brief
commentary
article,
we
outline
an
emerging
idea
that,
as
conversational
artificial
intelligence
(CAI)
becomes
a
part
of
individual's
environment
and
interacts
with
them,
their
attachment
system
may
become
activated,
potentially
leading
to
behaviors—such
seeking
out
the
CAI
feel
safe
in
times
stress—that
have
typically
been
reserved
for
human‐to‐human
relationships.
We
term
attachment‐like
behavior
,
but
future
work
must
determine
if
these
behaviors
are
driven
by
human–AI
or
something
else
entirely.
Background
is
technical
advancement
that
cornerstone
many
everyday
tools
(e.g.,
smartphone
apps,
online
chatbots,
smart
speakers).
With
generative
AI,
device
affordances
systems
increasingly
complex.
For
example,
AI
has
allowed
more
personalization,
human‐like
dialogue
interaction,
interpretation
generation
human
emotions.
Indeed,
ability
mimic
caring—learning
from
past
interactions
individual
appearing
be
emotionally
available
comforting
need.
Humans
instinctually
attachment‐related
needs
comfort
emotional
security,
therefore,
individuals
begin
met
CAI,
they
seek
source
safety
distress.
This
leads
questions
whether
truly
possible
and,
so,
what
might
mean
family
dynamics.
