Management Decision,
Год журнала:
2024,
Номер
unknown
Опубликована: Июнь 12, 2024
Purpose
This
study
investigates
the
profound
impact
of
artificial
intelligence
(AI)
capabilities
on
decision-making
processes
and
organizational
performance,
addressing
a
crucial
gap
in
literature
by
exploring
mediating
role
speed
quality.
Design/methodology/approach
Drawing
upon
resource-based
theory
prior
research,
this
constructs
comprehensive
model
hypotheses
to
illuminate
influence
AI
within
organizations
speed,
decision
quality,
and,
ultimately,
performance.
A
dataset
comprising
230
responses
from
diverse
forms
basis
analysis,
with
employing
partial
least
squares
structural
equation
(PLS-SEM)
for
robust
data
examination.
Findings
The
results
demonstrate
pivotal
shaping
capability
significantly
positively
affects
overall
Notably,
is
critical
factor
contributing
enhanced
further
uncovered
mediation
effects,
suggesting
that
partially
mediate
relationship
between
performance
through
speed.
Originality/value
contributes
existing
body
providing
empirical
evidence
multifaceted
Elucidating
advances
our
understanding
complex
mechanisms
which
drive
success.
World Journal of Advanced Research and Reviews,
Год журнала:
2024,
Номер
1, С. 2276 - 2285
Опубликована: Янв. 27, 2024
Precision
agriculture,
facilitated
by
advancements
in
Artificial
Intelligence
(AI),
has
emerged
as
a
transformative
paradigm
modern
farming.
This
review
comprehensively
examines
the
integration
of
AI
technologies
precision
agriculture
to
enhance
sustainability
and
optimize
farming
practices.
The
paper
synthesizes
recent
research
developments
applications,
covering
key
areas
such
crop
monitoring,
resource
management,
decision
support
systems,
automation.
adoption
AI-driven
techniques,
including
machine
learning,
computer
vision,
sensor
technologies,
is
reshaping
traditional
methods
providing
farmers
with
real-time
data
actionable
insights.
Crop
monitoring
applications
utilize
satellite
imagery,
drones,
ground-based
sensors
assess
plant
health,
detect
diseases,
irrigation
strategies.
systems
empower
make
informed
choices
based
on
data-driven
predictions,
weather
forecasts,
historical
patterns,
contributing
resource-efficient
practices
minimizing
environmental
impact.
Resource
management
critical
aspect
sustainable
farming,
plays
pivotal
role
optimizing
use
water,
fertilizers,
pesticides.
Smart
enabled
algorithms,
ensure
precise
efficient
water
distribution,
reducing
wastage
promoting
conservation.
analysis
soil
conditions
helps
tailor
fertilization
practices,
enhancing
nutrient
utilization
runoff.
also
explores
automating
operations
through
robotics
autonomous
vehicles.
These
not
only
alleviate
labor
shortages
but
improve
efficiency
planting,
harvesting,
maintenance.
Additionally,
fosters
connectivity
enabling
seamless
communication
between
devices,
sensors,
equipment.
As
continues
evolve,
highlights
challenges
future
prospects.
Ethical
considerations,
security,
digital
divide
rural
are
among
that
need
attention.
Moreover,
discusses
potential
avenues
for
further
research,
emphasizing
interdisciplinary
collaboration
address
complex
issues
associated
implementation
agriculture.
provides
comprehensive
overview
impact
offering
insights
into
current
challenges,
directions.
enhances
productivity
contributes
long-term
ensuring
food
security
face
growing
global
population.
Sustainability,
Год журнала:
2024,
Номер
16(5), С. 1864 - 1864
Опубликована: Фев. 24, 2024
The
primary
purpose
of
this
study
was
to
investigate
and
present
a
theoretical
model
that
identifies
the
most
influential
factors
affecting
adoption
artificial
intelligence
(AI)
by
SMEs
achieve
sustainable
business
performance
in
Saudi
Arabia
integrating
Technology–Organization–Environment
(TOE)
framework.
authors
utilized
quantitative
method,
using
survey
instrument
for
research.
Data
research
were
collected
from
managers
working
six
different
sectors.
Subsequently,
based
on
company
size,
firms
divided
into
two
groups,
allowing
multi-group
analysis
small
medium-sized
businesses
explore
group
differences.
Hence,
firm
size
played
moderating
role
conceptualized
model.
performed
SmartPLS
3,
results
suggest
dimensions
TOE
framework,
such
as
relative
advantage,
compatibility,
human
capital,
market
customer
demand,
government
support,
play
significant
AI.
Moreover,
found
influence
AI
SMEs’
operational
economic
performance.
(MGA)
reveal
differences,
with
strengthening
relationship
between
advantage
compared
small-size
firms.
findings
lead
practical
implications
companies
how
increase
help
embrace
their
technological
challenges
KSA
obtain
contribute
economy.
Psychology Research and Behavior Management,
Год журнала:
2024,
Номер
Volume 17, С. 413 - 427
Опубликована: Фев. 1, 2024
Purpose:
The
increasing
integration
of
Artificial
Intelligence
(AI)
within
enterprises
is
generates
significant
technostress
among
employees,
potentially
influencing
their
intention
to
adopt
AI.
However,
existing
research
on
the
psychological
effects
this
phenomenon
remains
inconclusive.
Drawing
Affective
Events
Theory
(AET)
and
Challenge–Hindrance
Stressor
Framework
(CHSF),
current
study
aims
explore
“black
box”
between
challenge
hindrance
technology
stressors
employees’
AI,
as
well
boundary
conditions
mediation
relationship.
Methods:
employs
a
quantitative
approach
utilizes
three-wave
data.
Data
were
collected
through
snowball
sampling
technique
structured
questionnaire
survey.
sample
comprises
employees
from
11
distinct
organizations
located
in
Guangdong
Province,
China.
We
received
301
valid
questionnaires,
representing
an
overall
response
rate
75%.
theoretical
model
was
tested
confirmatory
factor
analysis
regression
analyses
using
Mplus
Process
macro
for
SPSS.
Results:
results
indicate
that
positive
affect
mediates
relationship
AI
adoption
intention,
whereas
anxiety
negative
intention.
Furthermore,
reveal
technical
self-efficacy
moderates
affective
reactions
indirect
anxiety,
respectively.
Conclusion:
Overall,
our
suggests
AI-driven
positively
impact
cultivation
affect,
while
impede
by
triggering
anxiety.
Additionally,
emerges
crucial
moderator
shaping
these
relationships.
This
has
potential
make
meaningful
contribution
literature
deepening
holistic
understanding
influential
mechanisms
involved.
affirms
applicability
relevance
Challenge-Hindrance
(CHSF).
In
practical
terms,
provides
actionable
insights
effectively
manage
Keywords:
stressors,
Technological Forecasting and Social Change,
Год журнала:
2024,
Номер
202, С. 123301 - 123301
Опубликована: Март 5, 2024
Research
on
the
application
of
Artificial
Intelligence
(AI)-based
technologies
in
HRM
domain
has
attracted
significant
scholarly
attention.
Yet,
few
studies
have
consolidated
key
trends
adopting
AI
for
HRM,
especially
managerial
competencies
required
AI-based
and
identifying
research
directions
HR
managers,
including
development
an
AI-focused
competency
framework
managers.
A
systematic
literature
review
(SLR)
bibliometrics
analysis
were
conducted
to
identify
current
direction
managers
HRM.
Several
themes
capabilities
identified,
utilizing
Dynamic
Capabilities
View
(DCV).
The
SLR
identified
applications
various
tools
techniques
functions,
recruitment
selection
was
one
with
broadest
use
applications.
Managerial
cognitive
capability,
human
capital,
social
capital
DCV
considered
initial
coding
categories
under
which
are
adoption
This
study
utilized
SLR,
Bibliometric,
directed
content
as
three
distinct
but
interrelated
sets
methodologies
extracting
novel
insights
into
It
highlights
associated
that
need
mapping
its
adoption.
Advances in human resources management and organizational development book series,
Год журнала:
2024,
Номер
unknown, С. 1 - 16
Опубликована: Июнь 3, 2024
In
recent
years,
the
intersection
of
artificial
intelligence
(AI)
and
talent
management
has
revolutionized
way
organizations
identify,
recruit,
retain
top
talent.
This
chapter
explores
transformative
impact
machine
learning
on
processes,
shedding
light
innovative
ways
AI
is
reshaping
recruitment
retention
strategies.
The
discourse
then
shifts
to
AI-powered
recruitment,
exploring
utilization
predictive
analytics
forecast
hiring
needs,
automation
resume
screening
for
efficiency
bias
reduction,
application
video
behavioral
analysis
refine
candidate
assessment
processes.
These
AI-driven
methodologies
not
only
enhance
precision
acquisition
but
also
ensure
a
more
profound
alignment
between
job
requirements
capabilities.Further,
addresses
role
in
bolstering
employee
retention,
with
focus
modeling
identify
turnover
risks
personalized
development
programs.
British Journal of Management,
Год журнала:
2024,
Номер
35(4), С. 1916 - 1934
Опубликована: Фев. 4, 2024
Abstract
Creativity
is
key
for
organizations’
ability
to
remain
relevant
in
today's
disruptive
world.
In
this
paper,
we
identify
new
ways
which
organizations
can
use
artificial
intelligence
(AI)
more
effectively
creativity.
Drawing
on
the
resource‐based
view
as
a
background
mechanism,
developed
and
empirically
tested
integrative
model.
We
collected
research
data
via
large
survey
of
managers
distributed
600
China.
Our
findings
show
that
coupling
AI
capability
with
strategic
agility
directly
support
It
also
mediates
effects
ambidexterity,
customer
orientation
competitor
creativity
performance
when
developing
products
services.
addition,
our
significantly
improve
firms’
product
service
development
there
high
level
government
institutional
support.
provide
theoretical
practical
implications
academics
practitioners
interested
managing
organizational
British Journal of Management,
Год журнала:
2024,
Номер
35(4), С. 1680 - 1691
Опубликована: Апрель 11, 2024
Abstract
As
businesses
and
society
navigate
the
potentials
of
generative
artificial
intelligence
(GAI),
integration
these
technologies
introduces
unique
challenges
opportunities
for
human
resources,
requiring
a
re‐evaluation
resource
management
(HRM)
frameworks.
The
existing
frameworks
may
often
fall
short
capturing
novel
attributes,
complexities
impacts
GAI
on
workforce
dynamics
organizational
operations.
This
paper
proposes
strategic
HRM
framework,
underpinned
by
theory
institutional
entrepreneurship
sustainable
organizations,
integrating
within
practices
to
boost
operational
efficiency,
foster
innovation
secure
competitive
advantage
through
responsible
development.
Central
this
framework
is
alignment
with
business
objectives,
seizing
opportunities,
assessment
orchestration,
re‐institutionalization,
realignment
embracing
culture
continuous
learning
adaptation.
approach
provides
detailed
roadmap
organizations
successfully
GAI‐enhanced
environment.
Additionally,
significantly
contributes
theoretical
discourse
bridging
gap
between
adoption,
proposed
accounting
GAI–human
capital
symbiosis,
setting
stage
future
research
empirically
test
its
applicability,
explore
implications
understand
broader
economic
societal
consequences
diverse
multi‐disciplinary
multi‐level
methodologies.
Frontiers in Artificial Intelligence,
Год журнала:
2024,
Номер
6
Опубликована: Янв. 15, 2024
The
functions
of
human
resource
management
(HRM)
have
changed
radically
in
the
past
20
years
due
to
market
and
technological
forces,
becoming
more
cross-functional
data-driven.
In
age
AI,
role
HRM
professionals
organizations
continues
evolve.
Artificial
intelligence
(AI)
is
transforming
many
practices
throughout
creating
system
process
efficiencies,
performing
advanced
data
analysis,
contributing
value
creation
organization.
A
growing
body
evidence
highlights
benefits
AI
brings
field
HRM.
Despite
increased
interest
AI-HRM
scholarship,
focus
on
human-AI
interaction
at
work
AI-based
technologies
for
limited
fragmented.
Moreover,
lack
considerations
tech
design
deployment
can
hamper
digital
transformation
efforts.
This
paper
provides
a
contemporary
forward-looking
perspective
strategic
human-centric
plays
within
as
becomes
integrated
workplace.
Spanning
three
distinct
phases
integration
(technocratic,
integrated,
fully-embedded),
it
examines
technical,
human,
ethical
challenges
each
phase
suggestions
how
overcome
them
using
approach.
Our
importance
evolving
AI-driven
organization
roadmap
bring
humans
machines
closer
together
