Objective:
This
research
examines
the
transformative
potential
of
AI
in
fostering
entrepreneurial
innovation,
highlighting
its
augmentation
capabilities,
integration
strategies,
and
ethical
concerns
that
are
essential
for
sustainable
development.
study
is
contextualized
against
backdrop
fast-changing
ecosystems
United
Arab
Emirates
(UAE),
with
applications
redefining
business
landscape.Methods:
Quantitative
metrics
related
to
adoption
were
analyzed
alongside
qualitative
insights
from
entrepreneurs
owners.
Using
a
sound
theoretical
base
innovation
technology
frameworks,
they
applied
structural
equation
modeling
delineate
direct,
indirect,
mediated
relationships
use
innovationResults:
AI's
influence
on
entrepreneurship
complex,
shaped
through
various
mediators,
including
operational
efficiency,
ethics,
innovative
strategies.
By
building
businesses
around
these
dimensions,
companies
able
both
innovate
sustain
competitive
advantages
an
increasingly
digital
world.Novelty:
helps
filling
gap
between
understanding
practical
entrepreneurship.
As
focuses
UAE,
territory
which
prides
itself
being
global
leader
AI-driven
will
be
unique
leveraging
emerging
technologies
ethically
drive
growth.Research
Implications:
The
highlights
critical
role
intentional
within
academic
settings
necessity
standards.
It
important
reference
policymakers,
entrepreneurs,
academics
working
maximize
practices
Sustainability,
Journal Year:
2025,
Volume and Issue:
17(7), P. 3009 - 3009
Published: March 28, 2025
This
systematic
review
examines
digital
transformation
in
post-pandemic
supply
chains
through
a
bibliometric
analysis
of
literature
from
2020
to
2024.
Using
the
PRISMA
protocol,
we
analyzed
publications
Scopus,
Web
Science,
and
ScienceDirect
databases.
Results
show
that
sustainability
has
become
dominant
keyword
research,
with
China,
United
States,
India
forming
main
research
triangle.
The
most
influential
technologies
driving
are
big
data,
blockchain,
artificial
intelligence,
Internet
Things
(IoT).
Co-citation
network
revealed
three
major
clusters:
green
cluster
led
by
Gunasekaran
Angappa
focusing
on
chain
management;
red
Rahman
Muhammad
Saddiq
addressing
implementation
aspects;
blue
Calatayud
Rodriguez
examining
innovation
adaptation.
Organizations
shifting
purely
operational
approaches
more
holistic
transformations
integrate
strategic
organizational
dimensions.
We
identified
important
gaps
developing
regions
integration
emerging
existing
systems.
enhances
understanding
digitization
while
providing
framework
for
future
this
rapidly
evolving
field.
Humanities and Social Sciences Communications,
Journal Year:
2024,
Volume and Issue:
11(1)
Published: April 8, 2024
Abstract
Due
to
the
extraordinary
capacity
of
artificial
intelligence
(AI)
process
rich
information
from
various
sources,
an
increasing
number
enterprises
are
using
AI
for
development
ecosystem-based
business
models
(EBMs)
that
require
better
orchestration
multiple
stakeholders
a
dynamic,
sustainable
balance
among
people,
plant,
and
profit.
However,
given
nascency
relevant
issues,
there
exists
scarce
empirical
evidence.
To
fill
this
gap,
research
follows
affordance
perspective,
considering
technology
as
object
EBM
use
context,
thereby
exploring
how
whether
technologies
afford
EBMs.
Based
on
data
Chinese
A-share
listed
companies
between
period
2014
2021,
our
findings
show
inverted
U-shape
quadratic
relationship
EBM,
moderated
by
knowledge
spillover.
Our
results
enhance
understanding
role
in
configuring
EBMs,
thus
providing
novel
insights
into
mechanisms
specific
practice
with
societal
concerns
(i.e.,
EBM).
Sustainability,
Journal Year:
2025,
Volume and Issue:
17(3), P. 827 - 827
Published: Jan. 21, 2025
Addressing
resource
scarcity
and
climate
change
necessitates
a
transition
to
sustainable
consumption
circular
economy
models,
fostering
environmental,
social,
economic
resilience.
This
study
introduces
deep
learning-based
ensemble
framework
optimize
initial
public
offering
(IPO)
performance
prediction
while
extending
its
application
processes,
such
as
recovery
waste
reduction.
The
incorporates
advanced
techniques,
including
hyperparameter
optimization,
dynamic
metric
adaptation
(DMA),
the
synthetic
minority
oversampling
technique
(SMOTE),
address
challenges
class
imbalance,
risk-adjusted
enhancement,
robust
forecasting.
Experimental
results
demonstrate
high
predictive
performance,
achieving
an
accuracy
of
76%,
precision
83%,
recall
75%,
AUC
0.9038.
Among
methods,
Bagging
achieved
highest
(0.90),
outperforming
XGBoost
(0.88)
random
forest
(0.75).
Cross-validation
confirmed
framework’s
reliability
with
median
0.85
across
ten
folds.
When
applied
scenarios,
model
effectively
predicted
sustainability
metrics,
R²
values
0.76
for
both
reduction
low
mean
absolute
error
(MAE
=
0.11).
These
highlight
potential
align
financial
forecasting
environmental
objectives.
underscores
transformative
learning
in
addressing
challenges,
demonstrating
how
AI-driven
models
can
integrate
goals.
By
enabling
IPO
predictions
enhancing
outcomes,
proposed
aligns
Industry
5.0’s
vision
human-centric,
data-driven,
industrial
innovation,
contributing
resilient
growth
long-term
stewardship.
Journal of Small Business and Enterprise Development,
Journal Year:
2025,
Volume and Issue:
unknown
Published: March 13, 2025
Purpose
While
the
literature
on
artificial
intelligence
(AI)
capability
is
expanding,
gaps
remain
in
understanding
how
this
internally
developed
technology-based
startups
(TBS)
across
different
life
cycle
phases.
This
study,
grounded
resource
orchestration
theory
(ROT),
investigates
pathway
through
which
TBS
use
organizational
creativity
to
build
AI
and
achieve
performance.
Design/methodology/approach
A
conceptual
framework
based
ROT
emphasizes
role
of
structuring
bundling
processes.
Data
were
collected
a
survey
166
managers
employees
operating
Brazil
international
markets,
using
multiple
linear
regressions
Sobel
test
for
analysis.
The
study
validated
scale
context.
Findings
fully
mediates
relationship
between
performance,
confirming
that
critical
development.
These
findings
advance
by
deepening
TBS.
offers
dynamic,
process-based
view
performance
trajectories
TBS,
demonstrating
synchrony
creates
cyclical
process,
maximizing
company
Originality/value
research
identifies
an
alternative
develop
highlighting
synchronization
co-evolution
resources
capabilities.
It
provides
novel
insights
into
capability’s
mediating
expands
management
Sustainability,
Journal Year:
2025,
Volume and Issue:
17(8), P. 3535 - 3535
Published: April 15, 2025
The
circular
bioeconomy
(CBE)
is
an
evolving
paradigm
that
promotes
sustainable
economic
development.
Artificial
intelligence
(AI)
emerges
as
important
enabler
within
this
paradigm,
offering
capabilities
could
significantly
enhance
operational
efficiencies
and
innovation.
Despite
its
recognized
potential,
the
full
value
of
Al
across
diverse
areas
CBE
remains
underexplored.
This
paper
introduces
a
novel
framework
for
assessing
harnessing
role
to
facilitate
transition
towards
CBE.
was
developed
through
interdisciplinary
literature
review
conceptual
modeling.
maps
ten
key
domains
against
eight
core
AI
functions
(such
prediction,
optimization,
discovery)
can
be
leveraged
circularity
bioeconomic
processes.
A
case
study
on
biowaste
valorization,
employing
framework-guided
methodology,
demonstrates
framework’s
utility
in
identifying
research
gaps
opportunities
using
AI.
reveals
current
emphasis
prediction
optimization
tasks,
while
highlighting
significant
underutilization
discovery
design
functions.
help
guide
researchers,
policymakers,
industry
stakeholders
deploying
AI-driven
solutions
support
more
bioeconomy.
