International Journal of Physical Distribution & Logistics Management,
Journal Year:
2024,
Volume and Issue:
54(2), P. 211 - 228
Published: May 4, 2024
Navigating
excellence:
understanding
and
overcoming
common
causes
of
manuscript
rejections
in
logistics
supply
chain
management
researchIn
the
dynamic
realm
logistics,
operations,
International
Journal
Physical
Distribution
Logistics
Management
(IJPDLM)
stands
as
a
beacon
for
scholarly
excellence,
seeking
to
advance
strategic
issues
these
crucial
domains.Since
its
inception
1970,
IJPDLM
has
consistently
emphasized
intersection
rigor,
novelty,
theory
relevance.Since
early
1990,
Volume
20,
Issue
1,
with
first
online
Issue,
journal
not
only
explored
central
theory-practice
discourse
but
also
advanced
contributions
by
delving
into
rigorous
approaches,
novel
perspectives,
foundational
theoretical
frameworks
realms
strategy,
decision-making,
alignment
customers
in-depth
corporate
country
case-studies.At
heart
IJPDLM's
mission
is
commitment
publish
original
research
studies
that
are
strategically
focused,
theoretically
grounded
contribute
significantly
body
knowledge
business
physical
retail
distribution,
purchasing,
operations
management.As
custodian
empirical
methodology
stronghold
papers
strong
basis,
places
premium
on
quality,
relevance
impact
it
disseminates.The
aims
merely
provide
platform
publication
foster
community
scholars
who
engage
thoughtful
influential
research,
pushing
boundaries
our
(LSCM).In
aligning
broader
goals
IJPDLM,
editorial
team
recognizes
importance
judicious
thorough
review
process.While
essential,
faster
cycles
an
increased
volume
published
manuscripts
must
compromise
journal's
maintaining
high-quality
standards.The
decision-making
process,
exemplified
"reject
resubmit"
option,
reflects
dedication
supporting
authors
refining
enhancing
their
work
offering
details
about
weaknesses.However,
any
certain
may
face
rejection
due
specific
impede
standards.In
academic
life,
rejection,
depicted
this
editorial,
well-known
experience
every
scholar.As
Editors,
around
four
out
five
decisions
we
make
involve
rejections,
norm
shared
premier
LSCM
journals.Our
constructive,
supportive
feedback
underscores
tone
alleviating
disappointment
associated
outcomes.In
shed
light
primary
reasons
encountered
each
representing
facet
integrity
work:(1)
Superficial/inappropriate
use
theory(2)
Lack
novelty.
European Journal of Innovation Management,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 27, 2025
Purpose
This
paper
aims
to
contribute
the
discussion
on
integrating
humans
and
technology
in
customer
service
within
framework
of
Society
5.0,
which
emphasizes
growing
role
artificial
intelligence
(AI).
It
examines
how
effectively
new
generative
AI-based
chatbots
can
handle
emotions
explores
their
impact
determining
point
at
a
customer–machine
interaction
should
be
transferred
human
agent
prevent
disengagement,
referred
as
Switch
Point
(SP).
Design/methodology/approach
To
evaluate
capabilities
managing
emotions,
ChatGPT-3.5,
Gemini
Copilot
are
tested
using
Trait
Emotional
Intelligence
Questionnaire
Short-Form
(TEIQue-SF).
A
reference
is
developed
illustrate
shift
Findings
Using
four-intelligence
(mechanical,
analytical,
intuitive
empathetic),
this
study
demonstrates
that,
despite
advancements
AI’s
ability
address
service,
even
most
advanced
chatbots—such
ChatGPT,
Copilot—still
fall
short
replicating
empathetic
(HI).
The
concept
emotional
awareness
(AEA)
introduced
characterize
AI
understanding
triggering
SP.
complementary
rather
than
replacement
perspective
HI
proposed,
highlighting
Research
limitations/implications
exploratory
nature
requires
further
theoretical
development
empirical
validation.
Practical
implications
has
only
an
character
with
respect
possible
real
introduction
collaborative
approaches
integration
5.0.
Originality/value
Customer
Relationship
Management
managers
use
proposed
guide
adopt
dynamic
approach
HI–AI
collaboration
AI-driven
service.
International Journal of Production Research,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 51
Published: Aug. 19, 2024
Due
to
pressing
challenges
such
as
high
market
volatility,
complex
global
logistics,
geopolitical
turmoil
and
environmental
sustainability,
compounded
by
radical
events
the
COVID-19
pandemic,
complexity
of
supply
chain
management
has
reached
unprecedented
levels.
Together
with
increasing
data
availability
computing
power,
machine
learning
algorithms
can
help
address
these
challenges.
In
particular,
unsupervised
be
invaluable
in
extracting
new
knowledge
from
unstructured,
unlabelled
data.
This
article
systematically
reviews
current
state
research
on
techniques
management.
We
propose
a
classification
framework
that
categorises
literature
sample
based
drivers,
sectors,
sources,
UL
algorithms,
reveal
following
insights.
The
most
common
applications
are
information
processing
typical
operations
optimisation
problems
location
planning
vehicle
routing.
From
an
algorithmic
perspective,
clustering
other
traditional
dominate
recent
approaches,
owing
their
popularity
simplicity,
robustness
accessibility.
More
advanced
generative
have
been
slow
gain
acceptance.
contrast
paradigms,
mainly
plays
supporting
role.
large
number
publications
using
real-world
confirms
importance
maturity
Journal of Decision System,
Journal Year:
2025,
Volume and Issue:
unknown, P. 1 - 30
Published: Jan. 2, 2025
Generative
Artificial
Intelligence
(Gen-AI)
marks
a
pivotal
milestone
in
the
capabilities
of
machine
and
(AI)
to
create
new
content,
designs,
solutions
autonomously.
While
there
has
been
notable
surge
on
Gen-AI
research
recently,
comprehensive
systematic
reviews
remain
limited.
Motivated
by
rising
investments,
excitement
due
its
potential,
we
present
state-of-the-art
bibliometric
review
research,
utilising
5346
scholarly
articles
between
2015
2024.
The
study
employs
several
methods
such
as
citation,
co-citation
centrality
analysis
uncover
field's
intellectual
core.
We
also
trace
development
maturity
field
applying
Lotka's
law,
Bradford
critical
milestones.
reveals
four
core
themes
from
which
relate
(a)
Advancements
Architecture
Frameworks,
(b)
Applications,
(c)
Model
Validation
Benchmarking,
(d)
Systemic
Considerations.
paper
recommends
actionable
future
directions,
testable
propositions
under
each
theme.
