Sustainability,
Год журнала:
2024,
Номер
16(21), С. 9246 - 9246
Опубликована: Окт. 24, 2024
Enterprise
green
innovation
(EGI)
has
become
an
essential
measure
for
manufacturing
enterprises
to
achieve
sustainable
development,
and
the
application
of
artificial
intelligence
(AI)
may
a
new
driving
solution.
This
study
empirically
analyzes
impact
internal
transmission
mechanism
AI
on
EGI
Chinese
listed
from
2010
2022.
Research
found
that
(1)
significantly
impacts
EGI,
this
basic
conclusion
passed
various
endogeneity
robustness
tests.
(2)
The
test
results
indicate
enterprise
technological
capability,
investment,
executives’
environmental
awareness
mediate
between
EGI.
(3)
Heterogeneity
analysis
shows
significant
positive
is
only
established
in
with
overseas
backgrounds,
large-scale,
highly
competitive
regional
markets,
low-carbon
pilot
cities.
above
conclusions
have
contributed
essentially
literature
AI.
Sustainability,
Год журнала:
2024,
Номер
16(19), С. 8346 - 8346
Опубликована: Сен. 25, 2024
In
the
context
of
“dual
carbon”
strategic
goal
and
sustainable
development,
digital
transformation
sports
companies
has
emerged
as
a
crucial
factor
in
overcoming
barriers
to
green
growth
addressing
institutional
efficiency
challenges.
This
study
examines
mechanism
by
which
drives
innovation,
using
sample
Chinese-listed
industry
from
2011
2022.
Fixed
effects
models
were
employed.
The
study’s
findings
are
follows:
(1)
Digital
significant
positive
impact
on
indicating
that
digitalization
plays
role
promoting
practices.
(2)
A
analysis
revealed
facilitates
innovation
enhancing
human
capital
improving
internal
control
levels.
(3)
heterogeneity
demonstrated
stricter
environmental
regulations
strengthen
driving
effect
transformation.
Moreover,
state-owned
exhibit
stronger
endogenous
impetus
for
than
non-state-owned
companies,
driven
their
alignment
with
national
planning,
thus
contributes
literature
offering
insights
into
integration
digitization
innovation.
Furthermore,
it
provides
practical
guidance
path
selection
achieving
coordinated
Chinese
within
framework
goal.
Purpose
With
the
rapid
advancement
of
artificial
intelligence
(AI)
technology
permeating
various
sectors,
corporate
management
has
increasingly
directed
their
focus
toward
AI-driven
innovation.
Particularly,
in
response
to
escalating
environmental
standards,
chief
executive
officers
(CEOs)
manufacturing
companies
are
turning
AI
as
a
strategic
tool
address
challenges
green
This
paper
aims
reveal
complex
relationship
between
CEO
orientation
and
innovation
through
attention-based
view.
Furthermore,
it
seeks
explore
strategies
enhance
leveraging
orientation.
Design/methodology/approach
The
uses
computer-assisted
text
analysis
extract
data
from
annual
reports
listed
Chinese
assesses
them
using
negative
binomial
regression.
Findings
empirical
findings
indicate
inverted
U-shaped
Initial
performance
increases
with
orientation,
reaching
peak
before
declining.
Moreover,
increases,
higher
levels
human
resource
slack
likely
reach
earlier.
Originality/value
Firstly,
this
introduces
novel
factor
within
framework
view
for
understanding
Secondly,
study
investigates
both
benefit
effect
cost
(resource
constraints)
on
innovation,
examining
Thirdly,
explores
moderating
setting
boundaries
orientation’s
impact
Sustainability,
Год журнала:
2024,
Номер
16(21), С. 9246 - 9246
Опубликована: Окт. 24, 2024
Enterprise
green
innovation
(EGI)
has
become
an
essential
measure
for
manufacturing
enterprises
to
achieve
sustainable
development,
and
the
application
of
artificial
intelligence
(AI)
may
a
new
driving
solution.
This
study
empirically
analyzes
impact
internal
transmission
mechanism
AI
on
EGI
Chinese
listed
from
2010
2022.
Research
found
that
(1)
significantly
impacts
EGI,
this
basic
conclusion
passed
various
endogeneity
robustness
tests.
(2)
The
test
results
indicate
enterprise
technological
capability,
investment,
executives’
environmental
awareness
mediate
between
EGI.
(3)
Heterogeneity
analysis
shows
significant
positive
is
only
established
in
with
overseas
backgrounds,
large-scale,
highly
competitive
regional
markets,
low-carbon
pilot
cities.
above
conclusions
have
contributed
essentially
literature
AI.
