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.
Management Review Quarterly,
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 29, 2025
Abstract
The
integration
of
artificial
intelligence
(AI),
particularly
generative
AI
(GenAI)
and
large
language
models
(LLMs),
into
systematic
literature
reviews
(SLRs)
represents
a
transformative
advancement
in
research
methodologies.
This
paper
proposes
hybrid
framework
combining
AI’s
computational
power
with
the
epistemological
rigor
human
expertise,
anchored
transparency,
validity,
reliability,
comprehensiveness,
reflective
agency.
Through
three
interconnected
phases—design,
study
collection,
interpretation—the
employs
model
selection,
knowledge
base
curation,
iterative
prompt
engineering
to
enhance
scalability,
uncover
interdisciplinary
connections,
ensure
methodological
integrity
through
robust
oversight.
It
addresses
key
SLR
challenges,
including
handling
vast
datasets,
ensuring
reproducibility,
maintaining
epistemic
while
leveraging
advanced
capabilities.
Key
innovations
include
cyclical
validation,
inter-model
comparisons,
sensitivity
testing
trustworthiness
mitigate
biases.
aligns
processes
ethical
standards
objectives
by
emphasizing
domain-specific
LLMs,
reliability
metrics,
standardized
reporting
protocols.
establishes
SLRs
as
foundation
for
advancing
complex,
landscapes,
harmonizing
efficiency
expertise.
В
эпоху
информационных
технологий
управление
данными
—
основа
остается
трудоемким,
неэффективным,
в
значительной
степени
недоступным,
далеким
от
своего
потенциала.
Средства
для
значительного
скачка
вперед
управлении
уже
здесь.
Стремительное
развитие
искусственного
интеллекта
представляет
собой
возможность
смены
парадигмы
цифровом
хранении
и
данными.
этой
статье
рассматривается,
как
системы
агентного
(искусственного
интеллектa)
ИИ
могут
революционизировать
способы
хранения,
организации
извлечения
данных
организациями
людьми.
Мы
предлагаем
управления
всеми
потребностями
людей
извлечении
данных.
Используя
передовые
возможности
машинного
обучения
автономного
принятия
решений,
на
основе
обещает
превратить
из
неэффективного,
требующего
много
времени
процесса
интеллектуальную
персонализированную
услугу,
доступную
каждому.
In
the
age
of
smart
IT,
data
management
-
very
foundation
information
technology
remains
laborious,
inefficient,
largely
inaccessible,
falling
far
short
its
potential.
The
means
taking
a
major
leap
forward
in
is
here.
rapid
evolution
artificial
intelligence
presents
paradigm-shifting
opportunity
digital
storage
and
management.
This
paper
suggests
how
Agentic
AI
systems
can
revolutionize
ways
organizations
people
store,
organize,
retrieve
data.
We
propose
to
manage
all
retrieval
needs
humans.
By
leveraging
advanced
machine
learning,
autonomous
decision-making
capabilities,
AI-driven
promises
transform
from
an
inefficient
time-consuming
process
intelligent
personalized
service
accessible
everyone.
