Understanding Recruiters’ Acceptance of Artificial Intelligence: Insights from the Technology Acceptance Model
Applied Sciences,
Год журнала:
2025,
Номер
15(2), С. 746 - 746
Опубликована: Янв. 14, 2025
The
integration
of
new
technologies
in
professional
contexts
has
emerged
as
a
critical
determinant
organizational
efficiency
and
competitiveness.
In
this
regard,
the
application
Artificial
Intelligence
(AI)
recruitment
processes
facilitates
faster
more
accurate
decision-making
by
processing
large
volumes
data,
minimizing
human
bias,
offering
personalized
recommendations
to
enhance
talent
development
candidate
selection.
Technology
Acceptance
Model
(TAM)
provides
valuable
framework
for
understanding
recruiters’
perceptions
innovative
technologies,
such
AI
tools
GenAI.
Drawing
on
TAM,
model
was
developed
explain
intention
use
tools,
proposing
that
perceived
ease
usefulness
influence
attitudes
toward
AI,
which
subsequently
affect
selection
processes.
Two
studies
were
conducted
Portugal
address
research
objective.
first
qualitative
exploratory
study
involving
100
interviews
with
recruiters
who
regularly
utilize
their
activities.
second
employed
quantitative
confirmatory
approach,
utilizing
an
online
questionnaire
completed
355
recruiters.
findings
underscored
transformative
role
recruitment,
emphasizing
its
potential
optimize
resource
management.
However,
also
highlighted
concerns
regarding
loss
personal
interaction
need
adapt
roles
within
domain.
results
supported
indirect
effect
via
positive
these
tools.
This
suggests
is
best
positioned
complementary
tool
rather
than
replacement
decision-making.
insights
gathered
from
perspectives
provide
actionable
organizations
seeking
leverage
Specifically,
show
importance
ethical
considerations
maintaining
involvement
ensure
balanced
effective
Язык: Английский
Human Resource Management Optimization Strategies for Diverse Work Environments Based on Artificial Intelligence
Applied Mathematics and Nonlinear Sciences,
Год журнала:
2024,
Номер
9(1)
Опубликована: Янв. 1, 2024
Abstract
Under
the
diversified
work
environment,
relationship
between
employees
and
enterprises
has
ushered
in
new
significant
changes.
How
to
adapt
changes,
form
a
human
resource
management
model,
improve
performance
is
an
urgent
issue.
In
this
paper,
we
constructed
employee
turnover
pre-analysis
measurement
model
based
on
XGBoost
artificial
intelligence,
iterated
many
times
during
training,
generated
weak
classifier
each
iteration,
trained
basis
of
residuals
previous
classifier,
finally
combined
all
classifiers
weighted
way,
reduced
bias
accuracy
final
through
continuous
completed
construction
model.
The
historical
data
six
branches
enterprise
W
are
imported
into
for
analysis,
F1
values
above
0.85,
AUCs
higher
than
0.7,
with
good
prediction
performance.
top
three
important
influencing
characteristics
their
weights
overtime
0.647,
monthly
income
0.618,
interpersonal
0.579.
Accordingly,
optimization
strategies
designed
applied
Enterprise
implement
reform.
After
reform,
average
separation
rate
been
from
2.23%
0.19%,
number
separations
only
7,
which
91.86%
lower
pre-reform
period.
This
study
proposes
feasible
paths
modern
information
technology
intelligence-enabled
management,
diverse
environment.
Язык: Английский