International Transactions in Operational Research,
Год журнала:
2024,
Номер
unknown
Опубликована: Июль 31, 2024
Abstract
Artificial
intelligence
(AI)
as
a
disruptive
technology
is
not
new.
However,
its
recent
evolution,
engineered
by
technological
transformation,
big
data
analytics,
and
quantum
computing,
produces
conversational
generative
AI
(CGAI/GenAI)
human‐like
chatbots
that
disrupt
conventional
operations
methods
in
different
fields.
This
study
investigates
the
scientific
landscape
of
CGAI
human–chatbot
interaction/collaboration
evaluates
use
cases,
benefits,
challenges,
policy
implications
for
multidisciplinary
education
allied
industry
operations.
The
publications
trend
showed
just
4%
(
n
=
75)
occurred
during
2006–2018,
while
2019–2023
experienced
astronomical
growth
1763
or
96%).
prominent
cases
(e.g.,
ChatGPT)
teaching,
learning,
research
activities
computer
science
(multidisciplinary
AI;
32%),
medical/healthcare
(17%),
engineering
(7%),
business
fields
(6%).
intellectual
structure
shows
strong
collaboration
among
eminent
sources
business,
information
systems,
other
areas.
thematic
highlights
including
improved
user
experience
human–computer
interaction,
programs/code
generation,
systems
creation.
Widespread
usefulness
teachers,
researchers,
learners
includes
syllabi/course
content
testing
aids,
academic
writing.
concerns
about
abuse
misuse
(plagiarism,
integrity,
privacy
violations)
issues
misinformation,
danger
self‐diagnoses,
patient
applications
are
prominent.
Formulating
strategies
policies
to
address
potential
challenges
teaching/learning
practice
priorities.
Developing
discipline‐based
automatic
detection
GenAI
contents
check
proposed.
In
operational/operations
areas,
proper
CGAI/GenAI
integration
with
modeling
decision
support
requires
further
studies.
Journal of Business Research,
Год журнала:
2024,
Номер
175, С. 114542 - 114542
Опубликована: Фев. 14, 2024
This
study
outlines
the
future
research
opportunities
related
to
Generative
Artificial
Intelligence
(GenAI)
in
innovation
management.
To
this
end,
it
combines
a
review
of
academic
literature
with
results
Delphi
involving
leading
management
scholars.
Ten
major
themes
emerged
that
can
guide
developments
at
intersection
GenAI
and
management:
1)
Gen
AI
types;
2)
GenAI,
dominant
designs
technology
evolution;
3)
Scientific
artistic
creativity
GenAI-enabled
innovations;
4)
innovations
intellectual
property;
5)
new
product
development;
6)
Multimodal/unimodal
outcomes;
7)
agency
ecosystems;
8)
Policymakers,
lawmakers
anti-trust
authorities
regulation
innovation;
9)
Misuse
unethical
use
biased
10)
Organizational
design
boundaries
for
innovation.
The
paper
concludes
by
discussing
how
these
inform
theoretical
development
studies.
Data and Information Management,
Год журнала:
2024,
Номер
8(2), С. 100066 - 100066
Опубликована: Фев. 15, 2024
Generative
artificial
intelligence
(GAI)
is
a
rapidly
growing
field
with
wide
range
of
applications.
In
this
paper,
thorough
examination
the
research
landscape
in
GAI
presented,
encompassing
comprehensive
overview
prevailing
themes
and
topics
within
field.
The
study
analyzes
corpus
1319
records
from
Scopus
spanning
1985
to
2023
comprises
journal
articles,
books,
book
chapters,
conference
papers,
selected
working
papers.
analysis
revealed
seven
distinct
clusters
research:
image
processing
content
analysis,
generation,
emerging
use
cases,
engineering,
cognitive
inference
planning,
data
privacy
security,
Pre-Trained
Transformer
(GPT)
academic
paper
discusses
findings
identifies
some
key
challenges
opportunities
research.
concludes
by
calling
for
further
GAI,
particularly
areas
explainability,
robustness,
cross-modal
multi-modal
interactive
co-creation.
also
highlights
importance
addressing
security
responsible
GAI.
Business Horizons,
Год журнала:
2024,
Номер
67(5), С. 499 - 510
Опубликована: Апрель 4, 2024
The
democratization
of
powerful
artificial
intelligence
(AI)
tools,
including
ChatGPT,
has
sparked
the
interest
business
practitioners
given
their
ability
to
fundamentally
change
way
we
work.
While
AI
tools
are
positioned
augment
human
capabilities,
effective
implementation
requires
skill
understand
where,
when
and
how
best
utilize
them
efficiently.
Furthermore,
meaningful
engagement
with
content
produced
by
generative
(GenAI)
necessitates
intricacy
appropriate
prompt
engineering
optimize
learning
process.
As
field
GenAI
continues
advance,
art
developing
impactful
prompts
become
a
necessary
for
harnessing
its
full
potential.
This
research
develops
an
prompting
protocol
through
constructivist
theory
lens.
Based
on
principles
constructivism,
where
individuals
assimilate
new
knowledge
bridging
it
existing
understanding,
this
suggests
active
process
in
human-AI
co-construction
GenAI.
goal
is
empower
managers
teams
construct
validate
responses,
thereby
enhancing
user
interaction,
optimizing
workflows,
maximizing
potential
outcomes
chatbots.
Journal of Manufacturing Technology Management,
Год журнала:
2024,
Номер
35(9), С. 94 - 121
Опубликована: Май 27, 2024
Purpose
This
study
offers
practical
insights
into
how
generative
artificial
intelligence
(AI)
can
enhance
responsible
manufacturing
within
the
context
of
Industry
5.0.
It
explores
manufacturers
strategically
maximize
potential
benefits
AI
through
a
synergistic
approach.
Design/methodology/approach
The
developed
strategic
roadmap
by
employing
mixed
qualitative-quantitative
research
method
involving
case
studies,
interviews
and
interpretive
structural
modeling
(ISM).
visualizes
elucidates
mechanisms
which
contribute
to
advancing
sustainability
goals
Findings
Generative
has
demonstrated
capability
promote
various
objectives
5.0
ten
distinct
functions.
These
multifaceted
functions
address
multiple
facets
manufacturing,
ranging
from
providing
data-driven
production
enhancing
resilience
operations.
Practical
implications
While
each
identified
function
independently
contributes
under
5.0,
leveraging
them
individually
is
viable
strategy.
However,
they
synergistically
other
when
systematically
employed
in
specific
order.
Manufacturers
are
advised
leverage
these
functions,
drawing
on
their
complementarities
benefits.
Originality/value
pioneers
early
enhances
performance
framework.
proposed
suggests
prioritization
orders,
guiding
decision-making
processes
regarding
where
for
what
purpose
integrate
AI.
Technovation,
Год журнала:
2024,
Номер
133, С. 103021 - 103021
Опубликована: Апрель 23, 2024
Generative
Artificial
Intelligence
(GenAI)
is
one
of
the
popular
AI
technologies
which
can
produce
multiple
kinds
contents
including
music,
text,
image,
as
well
synthetic
data.
As
GenAI
technology
various
forms
contents,
organizations
must
face
ethical
dilemmas
to
where
this
likely
be
used.
Organizations
do
not
want
compromise
their
standards
and
compliance
policies.
Against
backdrop,
aim
study
examine
if
could
improve
future
performance
organizations.
This
deployed
environmental
dynamism
two
moderators
acting
on
different
linkages
between
adoption
organizational
performance.
With
help
literature
review
theories,
a
theoretical
model
has
been
developed
conceptually
was
validated
using
PLS-SEM
technique
with
feedback
326
responses
from
types
found
that
exploratory
exploitative
innovation
under
moderating
effects
dilemmas.
Moreover,
it
highlighted
application
Technovation,
Год журнала:
2024,
Номер
135, С. 103063 - 103063
Опубликована: Июнь 25, 2024
Recently,
Gen
AI
has
garnered
significant
attention
across
various
sectors
of
society,
particularly
capturing
the
interest
small
business
due
to
its
capacity
allow
them
reassess
their
models
with
minimal
investment.
To
understand
how
and
medium-sized
firms
have
utilised
AI-based
tools
cope
market's
high
level
turbulence
caused
by
COVID-19
pandemic,
geopolitical
crises,
economic
slowdown,
researchers
conducted
an
empirical
study.
Although
is
receiving
more
attention,
there
remains
a
dearth
studies
that
investigate
it
influences
entrepreneurial
orientation
ability
cultivate
resilience
amidst
market
turbulence.
Most
literature
offers
anecdotal
evidence.
address
this
research
gap,
authors
grounded
theoretical
model
hypotheses
in
contingent
view
dynamic
capability.
They
tested
using
cross-sectional
data
from
pre-tested
survey
instrument,
which
yielded
87
useable
responses
medium
enterprises
France.
The
used
variance-based
structural
equation
modelling
commercial
WarpPLS
7.0
software
test
model.
study's
findings
suggest
EO
influence
on
building
as
higher-order
lower-order
capabilities.
However,
negative
moderating
effect
path
joins
resilience.
results
assumption
will
positive
effects
capabilities
competitive
advantage
not
always
true,
linear
does
hold,
consistent
some
scholars'
assumptions.
offer
contributions
open
new
avenues
require
further
investigation
into
non-linear
relationship
Business Horizons,
Год журнала:
2024,
Номер
67(5), С. 561 - 570
Опубликована: Апрель 24, 2024
This
article
focuses
on
how
recent
advances
in
artificial
intelligence
(AI),
particularly
chatbots
based
large
language
models
(LLMs),
such
as
ChatGPT,
can
be
used
for
innovation
purposes.
The
begins
with
a
brief
overview
of
the
development
and
characteristics
generative
AI
(GenAI).
Elaborating
implications
GenAI,
we
provide
examples
to
demonstrate
four
mechanisms
LLMs:
translation,
summarization,
classification,
amplification.
These
inform
framework
that
highlights
LLMs
enable
creation
innovative
solutions
organizations
through
capacities
two
dimensions:
context
awareness
content
awareness.
strength
lies
combination
both
these
dimensions,
which
enables
them
comprehend
amplify
content.
Four
managerial
suggestions
are
presented,
ranging
from
starting
out
small-scale
projects
data
exploration,
scaling
integration
efforts
educating
prompt
engineers.
By
presenting
framework,
recommendations,
use
cases
various
contexts,
contributes
emerging
literature
GenAI
innovation.
