International Journal of Retail & Distribution Management,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 5, 2025
Purpose
This
study
explores
how
artificial
intelligence
(AI)
has
been
intertwined
with
rhetoric
and
the
journey
of
institutionalization
in
selected
case
firms.
The
mechanism
institutionalizing
AI
into
organizational
processes,
future
technology
transformation
driving
forces
behind
implementation
is
being
explored.
Design/methodology/approach
It
adopts
qualitative
methodology
multiple
approach,
drawing
evidence
from
ten
leading
retail
sector
organizations
that
have
practicing
for
over
a
decade.
main
data
collection
method
was
face-to-face
in-depth
interviews,
supplemented
by
focus
group
discussion
documentary
reviews.
From
theoretical
stance,
paper
draws
on
notions
institutionalism.
Findings
Empirical
findings
revealed
rhetorical
power
word
convinces
management
firm
to
embrace
AI.
In
contrast
hype
media,
real
application
not
lived
up.
Therefore,
delves
noticeable
discrepancy
between
buzz
surrounding
its
actual
use
sectors.
Originality/value
contributes
research
postulating
even
though
carries
prompt
implementation,
far
excitements.
Foregrounding
institutionalism,
it
extends
existing
institutional
theory-inspired
research.
also
offers
learning
points
practitioners
illustrating
rise
fall
story.
further
showcases
tools
techniques
could
be
used
business,
gets
implicated
firm’s
business
excellence
ensuing
control
ramifications.
Advances in environmental engineering and green technologies book series,
Год журнала:
2024,
Номер
unknown, С. 333 - 362
Опубликована: Окт. 16, 2024
Explainable
AI
(XAI)
is
important
in
situations
where
decisions
have
significant
effects
on
the
results
to
make
systems
more
reliable,
transparent,
and
people
understand
how
work.
In
this
chapter,
an
overview
of
AI,
its
evolution
are
discussed,
emphasizing
need
for
robust
policy
regulatory
frameworks
responsible
deployment.
Then
key
concept
use
XAI
models
been
discussed.
This
work
highlights
XAI's
significance
sectors
like
healthcare,
finance,
transportation,
retail,
supply
chain
management,
robotics,
manufacturing,
legal
criminal
justice,
etc.
profound
human
societal
impacts.
Then,
with
integrated
IoT
renewable
energy
management
scope
smart
cities
addressed.
The
study
particularly
focuses
implementations
solutions,
specifically
solar
power
integration,
addressing
challenges
ensuring
transparency,
accountability,
fairness
AI-driven
decisions.
Supply Chain Analytics,
Год журнала:
2024,
Номер
6, С. 100066 - 100066
Опубликована: Апрель 3, 2024
The
escalation
of
energy
prices
and
the
pressing
environmental
concerns
associated
with
excessive
consumption
have
compelled
consumers
to
adopt
a
more
optimal
approach
towards
usage
an
advanced
infrastructure
such
as
smart
grids.
Blockchain
technology
significantly
improves
management
by
creating
supply
chain
resiliency
in
distributed
grid.
This
study
proposes
blockchain-based
decision-making
framework
dynamic
pricing
model
manage
distributions,
particularly
during
crisis.
Empirical
data
from
U.S.
are
employed
show
applicability
proposed
model.
We
include
price
elasticity
address
changes
market
prices.
Findings
revealed
that
reduces
total
costs
performs
better
when
disruption
has
occurred.
provides
post
hoc
analysis
which
four
machine
learning
algorithms
used
predict
consumption.
Results
suggest
Autoregressive
Integrated
Moving
Average
(ARIMA)
algorithm
highest
accuracy
compared
other
algorithms.
International Journal For Multidisciplinary Research,
Год журнала:
2024,
Номер
6(4)
Опубликована: Июль 23, 2024
This
integrative
literature
review
investigates
the
transformative
impact
of
artificial
intelligence
(AI)
on
supply
chain
management,
addressing
pressing
need
for
efficiency
and
robustness
through
AI-driven
predictive
maintenance,
machine
learning
(ML),
decision
support
systems.
By
examining
current
literature,
study
highlights
AI's
potential
to
automate
revolutionize
operations,
enhancing
speed,
accuracy,
risk
management
capabilities
while
identifying
significant
challenges
such
as
bias
mitigation,
algorithmic
transparency,
data
privacy.
The
methodology
involves
a
comprehensive
scholarly
articles,
reports,
academic
publications,
focusing
AI
applications
in
decision-making
processes.
analysis
reveals
improvements
operational
accuracy
due
AI,
alongside
concerns
about
biases,
implementation
issues.
findings
confirm
but
emphasize
necessity
ongoing
supervision,
regular
audits,
development
models
capable
detecting
rectifying
anomalies.
proposes
creating
roles
Supply
Chain
Oversight
Officer
(AISCO),
Compliance
(AISCCO),
Quality
Assurance
(AISQAO)
ensure
responsible
utilization,
maintaining
integrity
operations
challenges.
concludes
that
is
promising
transforming
chains;
however,
careful
crucial
uphold
resilience.
Future
research
should
prioritize
longitudinal
studies
evaluate
long-term
impact,
focus
concerns,
fair
transparent
integration
technologies.
These
have
implications
practice
policy,
underscoring
robust
frameworks
regulatory
measures
guide
effective
use
chains.
