Personnel Review,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Nov. 9, 2024
Purpose
This
study
aims
to
enhance
the
effectiveness
of
knowledge
markets
and
overall
management
(KM)
practices
within
organisations.
By
addressing
challenge
internal
stickiness,
it
seeks
demonstrate
how
machine
learning
AI
approaches,
specifically
a
text-based
method
for
personality
assessment
regression
trees
behavioural
analysis,
can
automate
personalise
market
incentivisation
mechanisms.
Design/methodology/approach
The
research
employs
novel
approach
by
integrating
methodologies
overcome
limitations
traditional
statistical
methods.
A
natural
language
processing
(NLP)-based
tool
is
used
assess
employees’
personalities,
tree
analysis
applied
predict
categorise
patterns
in
knowledge-sharing
contexts.
designed
capture
complex
interplay
between
individual
traits
environmental
factors,
which
methods
often
fail
adequately
address.
Findings
Cognitive
style
was
confirmed
as
key
predictor
knowledge-sharing,
with
extrinsic
motivators
outweighing
intrinsic
ones
market-based
platforms.
These
findings
underscore
significance
diverse
combinations
factors
promoting
sharing,
offering
insights
that
inform
automatic
design
personalised
interventions
community
managers
such
Originality/value
stands
out
first
empirically
explore
interaction
environment
shaping
actual
behaviours,
using
advanced
methodologies.
increased
automation
process
extends
practical
contribution
this
study,
enabling
more
efficient,
automated
process,
thus
making
critical
theoretical
advancements
understanding
enhancing
behaviours.
Generative
AI
(GenAI)
introduces
transformative
challenges
and
opportunities
to
Intellectual
Property
(IP)
processes
in
countries
like
Brazil,
where
existing
laws,
such
as
Copyright
IP
Laws,
do
not
explicitly
account
for
AI-generated
nuances.
This
paper
explores
the
emerging
idea
behind
GenAI’s
impact
disruptive
potential
on
current
Brazilian
landscape.
Using
a
qualitative
approach,
we
apply
McLuhan’s
tetrad
analysis,
informed
by
Sociotechnical
Theory,
identify
enhancements,
obsolescence,
retrievals,
reversals
that
GenAI
can
bring
management
Brazil.
Our
contributions
include
advancing
understanding
of
influence
offering
preliminary
insights
stakeholders
address
optimize
Brazil’s
evolving
scenario.
Advances in web technologies and engineering book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 430 - 469
Published: Aug. 22, 2024
This
study
explores
the
transformational
potential
of
generative
artificial
intelligence
(GAI)
in
commerce
fraud
detection
and
prevention
within
e-commerce,
highlighting
growing
risk
fraudulent
activities
due
to
rise
online
transactions
data-driven
various
industries,
including
finance,
healthcare.
Conventional
rule-based
systems
often
fail
keep
up
with
evolving
strategies,
whereas
GAI,
employing
tools
like
GANs
variational
autoencoders,
can
generate
synthetic
yet
realistic
data
uncover
sophisticated
schemes.
The
chapter
presents
successful
real-world
examples
GAI
applications,
emphasizing
need
for
ethical
considerations,
such
as
privacy
bias
prevention,
ensure
responsible
AI
implementation.
concludes
that
offers
a
potent,
adaptive,
strategy
combat
fraud,
promising
safer
digital
environment
if
implications
are
carefully
managed.
Personnel Review,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Nov. 9, 2024
Purpose
This
study
aims
to
enhance
the
effectiveness
of
knowledge
markets
and
overall
management
(KM)
practices
within
organisations.
By
addressing
challenge
internal
stickiness,
it
seeks
demonstrate
how
machine
learning
AI
approaches,
specifically
a
text-based
method
for
personality
assessment
regression
trees
behavioural
analysis,
can
automate
personalise
market
incentivisation
mechanisms.
Design/methodology/approach
The
research
employs
novel
approach
by
integrating
methodologies
overcome
limitations
traditional
statistical
methods.
A
natural
language
processing
(NLP)-based
tool
is
used
assess
employees’
personalities,
tree
analysis
applied
predict
categorise
patterns
in
knowledge-sharing
contexts.
designed
capture
complex
interplay
between
individual
traits
environmental
factors,
which
methods
often
fail
adequately
address.
Findings
Cognitive
style
was
confirmed
as
key
predictor
knowledge-sharing,
with
extrinsic
motivators
outweighing
intrinsic
ones
market-based
platforms.
These
findings
underscore
significance
diverse
combinations
factors
promoting
sharing,
offering
insights
that
inform
automatic
design
personalised
interventions
community
managers
such
Originality/value
stands
out
first
empirically
explore
interaction
environment
shaping
actual
behaviours,
using
advanced
methodologies.
increased
automation
process
extends
practical
contribution
this
study,
enabling
more
efficient,
automated
process,
thus
making
critical
theoretical
advancements
understanding
enhancing
behaviours.
