Technology Analysis and Strategic Management,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 16
Published: Dec. 18, 2024
Manufacturing
enterprises
are
actively
using
big
data
analytics
to
pursue
service
innovation
opportunities
for
sustainable
development.
However,
the
mechanisms
underlying
this
influence
require
further
discussion.
Based
on
dynamic
capability
theory,
study
aims
investigate
how
affects
performance
of
manufacturing
by
exploring
mediating
effect
resource
bricolage
and
moderating
roles
various
learning
orientation
factors
(learning
commitment,
open-mindedness
shared
vision).
The
hypotheses
were
tested
questionnaire
from
245
in
China.
results
show
that
enables
manufacturers
improve
their
both
directly
via
bricolage.
In
addition,
boosts
performance,
while
commitment
vision
do
not.
Our
enriches
servitization
literature,
offers
practical
guidance
Chinese
want
engage
digital
era.
This
article
explores
the
transformative
impact
of
AI-driven
tools
on
software
development
practices,
examining
how
these
technologies
are
reshaping
developer
productivity,
code
quality,
and
overall
engineering
processes.The
analyzes
various
aspects
AI
integration
in
environments,
including
package
management
systems,
embeddings,
retrieval-augmented
generation,
while
also
investigating
ethical
considerations
evolving
role
developers.
Through
a
comprehensive
analysis
multiple
research
studies
industry
implementations,
this
demonstrates
AI-assisted
isKartheek
Medhavi
Penagamuri
Shriram
https://iaeme.com/Home/
Big Data,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Feb. 28, 2025
Decision
sciences
(DSC)
involves
studying
complex
dynamic
systems
and
processes
to
aid
informed
choices
subject
constraints
in
uncertain
conditions.
It
integrates
multidisciplinary
methods
strategies
evaluate
decision
engineering
processes,
identifying
alternatives
providing
insights
toward
enhancing
prudent
decision-making.
This
study
analyzes
the
evolutionary
trends
innovation
DSC
education
research
over
past
25
years.
Using
metadata
from
bibliographic
records
employing
science
mapping
method
text
analytics,
we
map
thematic,
intellectual,
social
structures
of
research.
The
results
identify
"knowledge
management,"
"decision
support
systems,"
"data
envelopment
analysis,"
"simulation,"
"artificial
intelligence"
(AI)
as
some
prominent
critical
skills
knowledge
requirements
for
problem-solving
before
during
period
(2000-2024).
However,
these
technologies
are
evolving
significantly
recent
wave
digital
transformation,
with
data
analytics
frameworks
(including
techniques
such
big
machine
learning,
business
intelligence,
mining,
information
visualization)
becoming
crucial.
continue
mirror
development
practice,
sustainable
through
virtual/online
learning
prominent.
Innovative
pedagogical
approaches/strategies
also
include
computer
simulation
games
("play
learn"
or
"role-playing").
current
era
witnesses
AI
adoption
different
forms
conversational
Chatbot
agent
generative
(GenAI),
chat
pretrained
transformer
teaching,
scholarly
activities
amidst
challenges
(academic
integrity,
plagiarism,
intellectual
property
violations,
other
ethical
legal
issues).
Future
must
innovatively
integrate
GenAI
into
address
resulting
challenges.
Small
and
medium
enterprises
(SMEs)
form
the
backbone
of
many
economies,
yet
they
often
struggle
to
remain
competitive
innovative
under
resource
constraints.
Rapid
advances
in
artificial
intelligence
(AI)
offer
fresh
possibilities
for
SMEs
transform
their
operations,
discover
untapped
market
segments,
foster
resilient
business
models.
AI
tools
can
enhance
decision-making
reduce
operational
inefficiencies,
from
automating
repetitive
processes
generating
predictive
insights.
At
same
time,
ethical
considerations
data
privacy
concerns
underscore
importance
implementing
responsibly.
By
embracing
cross-sector
collaboration,
developing
robust
training
programs,
advocating
supportive
policy
frameworks,
harness
AI’s
immense
potential
without
compromising
social
values
or
organizational
integrity.
This
paper
highlights
both
opportunities
challenges
poses,
proposing
actionable
strategies
that
enable
drive
sustainable,
inclusive
growth.
Systems,
Journal Year:
2025,
Volume and Issue:
13(4), P. 264 - 264
Published: April 8, 2025
This
review
synthesizes
current
knowledge
on
the
transformative
impacts
of
artificial
intelligence
(AI)—computational
systems
capable
performing
tasks
requiring
human-like
reasoning—on
business
innovation.
It
addresses
potential
AI
to
reshape
strategies,
operations,
and
value
creation
across
various
industries.
Key
themes
include
AI-driven
model
innovation,
human–AI
collaboration,
ethical
governance,
operational
efficiency,
customer
experience
personalization,
organizational
capability
development,
adoption
disparities.
enables
scalable
product
personalized
service
delivery,
data-driven
strategic
decisions.
Successful
implementations
hinge
overcoming
technical,
cultural,
barriers,
with
enhancing
consumer
trust
competitiveness,
positioning
responsible
innovation
as
a
imperative.
For
practitioners,
this
offers
evidence-based
frameworks
for
aligning
objectives.
academics,
it
identifies
research
frontiers,
including
longitudinal
impacts,
context-specific
roadmaps
small-
medium-sized
enterprises,
sustainable
pathways.
conceptualizes
driver
systemic
transformation,
continuous
learning,
foresight,
ability
competitive
advantage.
Social Sciences,
Journal Year:
2024,
Volume and Issue:
13(9), P. 475 - 475
Published: Sept. 9, 2024
This
study
aims
to
empirically
analyze
the
relationship
between
motivational
factors
of
generative
AI
users
and
intention
continue
using
service.
Accordingly,
motives
who
use
services
are
defined
as
individual,
social,
technical
motivation
factors.
research
verified
effect
these
on
tested
meditating
trust
acceptance
attitude.
We
this
through
verifying
attitudes.
An
online
survey
was
conducted
language-based
service
such
OpenAI’s
ChatGPT,
Google
Bard,
Microsoft
Bing,
Meta-Lama,
a
structural
equation
analysis
total
356
surveys.
As
result
analysis,
all
had
positive
(+)
attitude
toward
accepting
services.
Among
them,
individual
self-efficacy,
innovation
orientation,
playful
desire
were
found
have
greatest
influence
formation
In
addition,
social
identified
that
in
When
it
comes
AI,
confirmed
reputation
or
awareness
directly
affects
usability.
Frontiers in Environmental Economics,
Journal Year:
2025,
Volume and Issue:
3
Published: Jan. 9, 2025
Artificial
intelligence
(AI)
plays
a
pivotal
role
in
the
development
of
green
economy.
This
paper
examines
impact
artificial
on
economic
efficiency
(GEE)
using
panel
data
from
30
provinces
China
spanning
2011–2020.
A
multiple
linear
regression
model,
alongside
various
endogeneity
and
robustness
tests,
is
applied
to
ensure
reliable
findings.
The
empirical
results
indicate
that
AI
significantly
enhances
GEE.
However,
marginal
effect
GEE
influenced
by
different
governance
approaches.
In
terms
policy
governance,
excessive
market-based
environmental
regulation
(MER)
diminishes
AI,
while
stronger
administrative-command
regulations
(CER)
informal
(IER)
amplify
it.
Regarding
technological
substantive
innovations
(SUG)
reduce
AI's
effect,
whereas
symbolic
(SYG)
may
increase
Notably,
threshold
SUG
surpasses
SYG.
legal
both
administrative
judicial
intellectual
property
protections
though
protection
(AIP)
exhibits
more
significant
than
(JIP).
These
findings
offer
practical
insights
for
optimizing
strategies
maximize
promoting
highlight
need
balanced
sustainable
development.
Policymakers
should
tailor
encourage
regional
collaboration
harness
spatial
spillover
effects.
Enterprises
can
leverage
AI-driven
align
growth
with
ecological
goals,
fostering
coordinated
