International Transactions in Operational Research,
Год журнала:
2024,
Номер
unknown
Опубликована: Июль 31, 2024
Abstract
Artificial
intelligence
(AI)
as
a
disruptive
technology
is
not
new.
However,
its
recent
evolution,
engineered
by
technological
transformation,
big
data
analytics,
and
quantum
computing,
produces
conversational
generative
AI
(CGAI/GenAI)
human‐like
chatbots
that
disrupt
conventional
operations
methods
in
different
fields.
This
study
investigates
the
scientific
landscape
of
CGAI
human–chatbot
interaction/collaboration
evaluates
use
cases,
benefits,
challenges,
policy
implications
for
multidisciplinary
education
allied
industry
operations.
The
publications
trend
showed
just
4%
(
n
=
75)
occurred
during
2006–2018,
while
2019–2023
experienced
astronomical
growth
1763
or
96%).
prominent
cases
(e.g.,
ChatGPT)
teaching,
learning,
research
activities
computer
science
(multidisciplinary
AI;
32%),
medical/healthcare
(17%),
engineering
(7%),
business
fields
(6%).
intellectual
structure
shows
strong
collaboration
among
eminent
sources
business,
information
systems,
other
areas.
thematic
highlights
including
improved
user
experience
human–computer
interaction,
programs/code
generation,
systems
creation.
Widespread
usefulness
teachers,
researchers,
learners
includes
syllabi/course
content
testing
aids,
academic
writing.
concerns
about
abuse
misuse
(plagiarism,
integrity,
privacy
violations)
issues
misinformation,
danger
self‐diagnoses,
patient
applications
are
prominent.
Formulating
strategies
policies
to
address
potential
challenges
teaching/learning
practice
priorities.
Developing
discipline‐based
automatic
detection
GenAI
contents
check
proposed.
In
operational/operations
areas,
proper
CGAI/GenAI
integration
with
modeling
decision
support
requires
further
studies.
Behaviour and Information Technology,
Год журнала:
2024,
Номер
unknown, С. 1 - 22
Опубликована: Фев. 16, 2024
The
present
research
aims
to
highlight
the
underlying
factors
that
drive
students'
adoption
of
ChatGPT
chatbot
in
higher
education.
This
study
extends
meta-UTAUT
framework
by
including
additional
exogenous
anthropomorphism,
trust,
design
novelty,
and
institutional
policy.
Empirical
examination
with
Structural
Equation
Modelling
among
355
students
Dutch
education
institutions
revealed
attitude
behavioural
intention
as
significant
positive
predictors
use
behaviour.
Institutional
policy
negatively
moderated
effect
on
Behavioural
was
significantly
positively
influenced
attitude,
performance
expectancy,
social
influence,
facilitating
conditions.
Anthropomorphism,
effort
expectancy
were
unveiled
antecedents
attitude.
central
theoretical
contributions
this
include
investigating
behaviour
instead
intention,
establishing
a
core
construct,
underlining
highlighting
importance
contributes
prior
technology
adoption,
especially
area
artificial
intelligence
findings
yield
valuable
insights
for
designers,
product
managers,
writers.
Advances in hospitality, tourism and the services industry (AHTSI) book series,
Год журнала:
2024,
Номер
unknown, С. 291 - 310
Опубликована: Март 6, 2024
The
chapter
investigates
the
impact
of
artificial
intelligence
(AI)
on
marketing
strategies
in
hospitality
industry.
It
discusses
how
AI
technologies
like
machine
learning,
natural
language
processing,
and
computer
vision
have
transformed
customer
interactions.
emphasizes
importance
learning
adapting
to
behavior
standards,
highlighting
its
significant
role
This
explores
practical
applications
industry,
potential
creating
unique
profiles
providing
personalized
tips.
Predictive
analytics
reveals
AI's
ability
anticipate
vacation
patterns
enable
dynamic
pricing,
enabling
businesses
adapt
market
changes.
significance
augmented
virtual
reality,
their
provide
immersive
experiences
influence
decisions
through
tours.
concludes
by
industry's
progress
need
for
further
research.
Frontiers in Artificial Intelligence,
Год журнала:
2024,
Номер
6
Опубликована: Янв. 5, 2024
As
the
field
of
artificial
intelligence
(AI)
continues
to
progress,
use
AI-powered
chatbots,
such
as
ChatGPT,
in
higher
education
settings
has
gained
significant
attention.
This
paper
addresses
a
well-defined
problem
pertaining
critical
need
for
comprehensive
examination
students'
ChatGPT
adoption
education.
To
examine
adoption,
it
is
imperative
focus
on
measuring
actual
user
behavior.
While
usage
behavior
at
specific
point
time
can
be
valuable,
more
holistic
approach
necessary
understand
temporal
dynamics
AI
adoption.
address
this
need,
longitudinal
survey
was
conducted,
examining
how
changes
over
among
students,
and
unveiling
drivers
change.
The
empirical
222
Dutch
students
revealed
decline
an
8
month
period.
period
defined
by
two
distinct
data
collection
phases:
initial
phase
(T1)
follow-up
conducted
months
later
(T2).
Furthermore,
results
demonstrate
that
trust,
emotional
creepiness,
Perceived
Behavioral
Control
significantly
predicted
observed
change
findings
research
carry
academic
managerial
implications,
they
advance
our
comprehension
aspects
also
provide
actionable
guidance
developers
educational
institutions
seeking
optimize
student
engagement
with
technologies.
International Transactions in Operational Research,
Год журнала:
2024,
Номер
unknown
Опубликована: Июль 31, 2024
Abstract
Artificial
intelligence
(AI)
as
a
disruptive
technology
is
not
new.
However,
its
recent
evolution,
engineered
by
technological
transformation,
big
data
analytics,
and
quantum
computing,
produces
conversational
generative
AI
(CGAI/GenAI)
human‐like
chatbots
that
disrupt
conventional
operations
methods
in
different
fields.
This
study
investigates
the
scientific
landscape
of
CGAI
human–chatbot
interaction/collaboration
evaluates
use
cases,
benefits,
challenges,
policy
implications
for
multidisciplinary
education
allied
industry
operations.
The
publications
trend
showed
just
4%
(
n
=
75)
occurred
during
2006–2018,
while
2019–2023
experienced
astronomical
growth
1763
or
96%).
prominent
cases
(e.g.,
ChatGPT)
teaching,
learning,
research
activities
computer
science
(multidisciplinary
AI;
32%),
medical/healthcare
(17%),
engineering
(7%),
business
fields
(6%).
intellectual
structure
shows
strong
collaboration
among
eminent
sources
business,
information
systems,
other
areas.
thematic
highlights
including
improved
user
experience
human–computer
interaction,
programs/code
generation,
systems
creation.
Widespread
usefulness
teachers,
researchers,
learners
includes
syllabi/course
content
testing
aids,
academic
writing.
concerns
about
abuse
misuse
(plagiarism,
integrity,
privacy
violations)
issues
misinformation,
danger
self‐diagnoses,
patient
applications
are
prominent.
Formulating
strategies
policies
to
address
potential
challenges
teaching/learning
practice
priorities.
Developing
discipline‐based
automatic
detection
GenAI
contents
check
proposed.
In
operational/operations
areas,
proper
CGAI/GenAI
integration
with
modeling
decision
support
requires
further
studies.
