Journal of Organizational Behavior,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 1, 2025
ABSTRACT
As
artificial
intelligence
(AI)
becomes
increasingly
integrated
in
teams,
understanding
the
factors
that
drive
trust
formation
between
human
and
AI
teammates
crucial.
Yet,
emergent
literature
has
overlooked
impact
of
third
parties
on
human‐AI
teaming.
Drawing
from
social
cognitive
theory
teams
research,
we
suggest
how
much
a
teammate
perceives
an
as
trustworthy,
engages
behaviors
toward
AI,
determines
focal
employee's
perceptions
behavior
this
teammate.
Additionally,
propose
these
effects
hinge
trustworthiness
.
We
test
predictions
across
two
studies:
(1)
online
experiment
comprising
individuals
with
work
experience
examines
disembodied
trustworthiness,
(2)
incentivized
observational
study
investigates
embodied
AI.
Both
studies
reveal
teammate's
perceived
of,
in,
strongly
predict
behavioral
Furthermore,
relationship
vanishes
when
employees
perceive
their
less
trustworthy.
These
results
advance
our
third‐party
formation,
providing
organizations
insights
for
managing
influences
teams.
International Journal of Contemporary Hospitality Management,
Journal Year:
2024,
Volume and Issue:
36(10), P. 3324 - 3339
Published: Jan. 17, 2024
Purpose
The
technology
acceptance
model
(TAM)
is
a
widely
used
framework
explaining
why
users
accept
new
technologies.
Still,
its
relevance
questioned
because
of
evolving
consumer
behavior,
demographics
and
technology.
Contrary
to
research
paper
or
systematic
literature
review,
the
purpose
this
critical
reflection
discuss
TAM's
limitations
in
hospitality
tourism
research.
Design/methodology/approach
This
uses
reflective
approach,
enabling
comprehensive
review
synthesis
recent
academic
on
TAM.
evaluation
encompasses
historical
trajectory,
evolutionary
growth,
identified
and,
more
specifically,
context
Findings
within
revolve
around
individual-centric
perspective,
limited
scope,
static
nature,
cultural
applicability
reliance
self-reported
measures.
Research
limitations/implications
To
optimize
efficacy,
authors
propose
several
strategic
recommendations.
These
include
embedding
TAM
specific
industry,
delving
into
TAM-driven
artificial
intelligence
adoption,
integrating
industry-specific
factors,
acknowledging
nuances
using
methods,
such
as
mixed
methods
approach.
It
imperative
for
researchers
critically
assess
suitability
their
studies
be
open
exploring
alternative
models
that
can
adeptly
navigate
distinctive
dynamics
industry.
Originality/value
prompts
profound
exploration
adoption
dynamic
sector,
makes
insightful
inquiries
future
potential
presents
Journal of University Teaching and Learning Practice,
Journal Year:
2024,
Volume and Issue:
21(06)
Published: April 19, 2024
Higher
education
is
currently
under
a
significant
transformation
due
to
the
emergence
of
generative
artificial
intelligence
(GenAI)
technologies,
hype
surrounding
GenAI
and
increasing
influence
educational
technology
business
groups
over
tertiary
education.
This
commentary,
prepared
for
Special
Issue
Journal
University
Teaching
&
Learning
Practice
(JUTLP)
on
“Enhancing
student
engagement
using
Artificial
Intelligence
(AI)
chatbots,”
delves
into
complex
landscape
opportunities
threats
that
AI
chatbots,
including
ChatGPT,
introduce
realm
higher
We
argue
while
offers
promise
in
enhancing
pedagogy,
research,
administration,
support,
concerns
around
academic
integrity,
labour
displacement,
embedded
biases,
environmental
sustainability,
increased
commercialisation,
regulatory
gaps
necessitate
critical
approach.
Our
commentary
advocates
development
literacy
among
educators
students,
emphasising
necessity
foster
an
environment
responsible
innovation
informed
use
AI.
posit
successful
integration
must
be
grounded
principles
ethics,
equity,
prioritisation
aims
human
values.
By
offering
nuanced
exploration
these
issues,
our
contribute
ongoing
discourse
how
institutions
can
navigate
rise
GenAI,
ensuring
technological
advancements
benefit
all
stakeholders
upholding
core
Information Systems Frontiers,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Feb. 22, 2024
Abstract
The
present
study
addresses
two
critical
controversies
surrounding
the
emerging
Industry
5.0
agenda.
Firstly,
it
seeks
to
elucidate
driving
forces
behind
accelerated
momentum
of
agenda
amidst
ongoing
digital
industrial
transformation.
Secondly,
explores
how
agenda’s
sustainability
values
can
be
effectively
realised.
conducted
a
comprehensive
content-centric
literature
synthesis
and
identified
4.0
shortcomings
adversely
impacted
values.
Furthermore,
implements
novel
approach
that
determines
in
what
order
functions
should
leveraged
promote
objectives
5.0.
Results
reveal
has
benefited
economic
environmental
most
at
organisational
supply
chain
levels.
Nonetheless,
micro
meso-social
have
been
by
4.0.
Similarly,
worryingly
detrimental
macro
like
social
or
growth
equality.
These
contradictory
implications
pulled
However,
results
nine
that,
when
appropriately
correct
order,
offer
important
for
realising
socio-environmental
goals
For
example,
under
extreme
unpredictability
business
world
uncertainties,
first
leverage
automation
integration
capabilities
gain
necessary
cost-saving,
resource
efficiency,
risk
management
capability,
antifragility
allow
them
introduce
sustainable
innovation
into
their
model
without
jeopardising
survival.
Various
scenarios
empowering
knowledge
practice.
Journal of Business Research,
Journal Year:
2024,
Volume and Issue:
180, P. 114737 - 114737
Published: May 24, 2024
Generative
Artificial
Intelligence
(GAI)
is
witnessing
a
lot
of
adoption
across
industries,
but
literature
yet
to
fully
document
the
nuances
these
applications.
We
develop
comprehensive
framework
for
understanding
factors
that
affect
trust
in
online
grocery
shopping
(OGS)
using
GAI
chatbots.
Our
exploratory
study
was
conducted
via
interviews,
which
helped
build
our
model.
integrate
Elaboration
Likelihood
Model
(ELM)
and
Status
Quo
Bias
(SQB)
theory
Unified
Framework
Trust
on
Technology
Platforms.
In
confirmatory
study,
by
analyzing
372
responses
from
users,
structural
equation
modelling
(SEM),
we
initially
validate
path
Subsequently,
used
fuzzy
set
qualitative
comparative
analysis
(fsQCA)
check
causal
combinations
explain
different
levels.
Apart
perceived
regret
avoidance,
all
other
had
significant
effect
attitude
trust.
Perceived
anthropomorphism
moderated
associations
between
interaction
quality,
credibility,
threat,
attitude.
Education Sciences,
Journal Year:
2024,
Volume and Issue:
14(2), P. 172 - 172
Published: Feb. 7, 2024
Many
educators
and
professionals
in
different
industries
may
need
to
become
more
familiar
with
the
basic
concepts
of
artificial
intelligence
(AI)
generative
(Gen-AI).
Therefore,
this
paper
aims
introduce
some
AI
Gen-AI.
The
approach
explanatory
is
first
underlying
concepts,
such
as
intelligence,
machine
learning,
deep
neural
networks,
large
language
models
(LLMs),
that
would
allow
reader
better
understand
AI.
also
discusses
applications
implications
on
businesses
education,
followed
by
current
challenges
associated
AI and Ethics,
Journal Year:
2024,
Volume and Issue:
4(3), P. 791 - 804
Published: Feb. 23, 2024
Abstract
This
paper
examines
the
ethical
obligations
companies
have
when
implementing
generative
Artificial
Intelligence
(AI).
We
point
to
potential
cyber
security
risks
are
exposed
rushing
adopt
AI
solutions
or
buying
into
“AI
hype”.
While
benefits
of
for
business
been
widely
touted,
inherent
associated
less
well
publicised.
There
growing
concerns
that
race
integrate
is
not
being
accompanied
by
adequate
safety
measures.
The
rush
buy
hype
and
fall
behind
competition
potentially
exposing
broad
possibly
catastrophic
cyber-attacks
breaches.
In
this
paper,
we
outline
significant
threats
models
pose,
including
‘backdoors’
in
could
compromise
user
data
risk
‘poisoned’
producing
false
results.
light
these
concerns,
discuss
moral
considering
principles
beneficence,
non-maleficence,
autonomy,
justice,
explicability.
identify
two
examples
concern,
overreliance
over-trust
AI,
both
which
can
negatively
influence
decisions,
leaving
vulnerable
threats.
concludes
recommending
a
set
checklists
implementation
environment
minimise
based
on
discussed
responsibilities
concern.
Magna Scientia Advanced Research and Reviews,
Journal Year:
2024,
Volume and Issue:
10(1), P. 368 - 378
Published: Feb. 28, 2024
The
exponential
growth
of
network
complexity
and
data
volume
in
modern
digital
ecosystems
has
underscored
the
need
for
innovative
approaches
to
optimize
performance
efficiency.
This
paper
delves
into
potential
AI-driven
optimization
techniques
addressing
this
imperative.
Leveraging
artificial
intelligence
(AI)
algorithms
such
as
machine
learning
deep
learning,
study
investigates
how
AI
can
revolutionize
management
operation
achieve
higher
levels
reliability.
Through
a
comprehensive
review
existing
literature
case
studies,
elucidates
fundamental
principles,
methodologies,
applications
diverse
environments.
It
examines
analyze
vast
amounts
data,
identify
patterns,
make
data-driven
decisions
configurations,
routing
protocols,
resource
allocation
strategies.
Moreover,
explores
enhance
security,
fault
tolerance,
scalability
by
autonomously
detecting
mitigating
threats
vulnerabilities.
Review
succinctly
encapsulates
main
findings
insights
derived
from
analysis,
emphasizing
transformative
efficiency
enhancement.
underscores
benefits
automating
complex
tasks,
reducing
operational
overhead,
adapting
dynamically
changing
conditions
user
demands.
Additionally,
discusses
challenges
considerations
associated
with
implementation
techniques,
including
algorithmic
bias,
privacy
concerns,
ethical
implications.
In
conclusion,
critical
role
evolving
operation.
advocates
continued
research
development
efforts
aimed
at
harnessing
full
unlock
new
infrastructures.
By
embracing
approaches,
organizations
streamline
operations,
improve
experience,
drive
innovation
era.
