Barriers to E-Tendering Implementation in the Construction Industry: A Comprehensive Review and Analysis of a Decade and Beyond
Sustainability,
Год журнала:
2025,
Номер
17(5), С. 2052 - 2052
Опубликована: Фев. 27, 2025
This
study
addresses
the
scientific
issue
of
insufficient
systematization
knowledge
about
barriers
to
electronic
tendering
(E-Tendering)
in
construction
industry.
is
critical
because
it
can
potentially
promote
more
effective,
transparent,
and
environmentally
friendly
procurement
practices,
which
help
with
sustainable
development.
The
key
goals
this
are
identify
categorize
E-Tendering
adoption
across
six
(6)
geographical
regions
make
recommendations
overcome
identified
barriers.
research
used
a
systematic
literature
review
technique
these
from
relevant
databases.
categorized
main
obstacles
grouped
according
regions.
Eight
(8)
significant
were
common
locations.
They
(1)
Inadequate
technical/ICT
skilled
personnel,
(2)
data
security,
(3)
policy
or
uniform
standard
legal
framework,
(4)
resistance
change,
(5)
ICT
internet
infrastructure,
High
investment
cost
implementation,
(7)
Lack
support,
Technical
challenges.
Furthermore,
10
implementation
contributes
improving
It
creating
policies
that
long-term
reform
processes
sector.
also
supports
development
by
promoting
efficient,
processes.
Язык: Английский
Cybercrime through the public lens: a longitudinal analysis
Humanities and Social Sciences Communications,
Год журнала:
2025,
Номер
12(1)
Опубликована: Март 1, 2025
Язык: Английский
Enhancing Agricultural Cybersecurity
Advances in computational intelligence and robotics book series,
Год журнала:
2025,
Номер
unknown, С. 307 - 338
Опубликована: Апрель 8, 2025
The
rapid
digital
transformation
of
agriculture
through
smart
farming
technologies
has
introduced
new
cybersecurity
challenges
that
threaten
the
integrity,
confidentiality,
and
availability
critical
agricultural
data
systems.
As
precision
agriculture,
Internet
Things
(IoT)-enabled
sensors,
automated
decision-making
become
integral
to
modern
farming,
risks
associated
with
cyber
threats—such
as
breaches,
ransomware
attacks,
supply
chain
vulnerabilities—continue
escalate.
Unlike
traditional
security
measures,
AI-driven
solutions,
including
deep
learning
Large
Language
Models
(LLMs),
offer
real-time
threat
detection,
adaptive
defense
mechanisms,
enhanced
risk
assessment
capabilities.
This
chapter
explores
application
these
in
securing
networks,
from
intrusion
detection
incident
response.
It
also
presents
case
studies
solutions
implemented
environments.
Язык: Английский