Prof. Guangming Cao, Ajman University, United Arab Emirates
Guangming Cao, BSc, MSc, PhD, is a Professor of Data Analytics and head of the Digital Transformation Research Center at Ajman University. His scholarly pursuits revolve around the impacts of ICTs such as artificial intelligence, big data analytics, and social media on organizational decision-making, capabilities, and performance. He has contributed over 100 peer-reviewed articles to the academic discourse. His scholarly contributions extend across an array of journals, including the European Journal of Operational Research, International Journal of Operations & Production Management, Journal of Business Research, Technovation, Industrial Marketing Management, IEEE Transactions on Engineering Management, Information Technology & People, International Journal of Management Review, Supply Chain Management, and Production Planning & Control. Dr. Cao’s dedication to excellence in research has been recognized by his receipt of the IMM (Industrial Marketing Management) Best Paper Award 2023.
Abstract: Digital
innovation is often
examined solely as
the use of digital
technology, although
there is evidence to
suggest that
multiple
organizational
factors play
essential roles in
the innovation
process. This study
therefore aims to
investigate how a
firm’s digital
innovation is
affected
collectively by its
digital technology
use, absorptive
capacity, digital
strategy, and
environmental
dynamism. The
research employs
partial least
squares structural
equation modeling
with data from a
survey of 250
Chinese firm
managers. The
findings reveal a
significant inverted
U-shaped
relationship between
digital technology
use and digital
innovation.
Moreover, both
absorptive capacity
and digital strategy
exert positive
influences on
digital innovation
directly and
indirectly through
digital technology
use. Furthermore,
environmental
dynamism moderates
the impact of
digital strategy on
digital innovation
positively. This
research contributes
to the literature by
developing a richer
and more nuanced
understanding of how
digital innovation
is affected jointly
by several key
organizational
factors. It also
provides actionable
managerial insights
for firms aiming to
enhance their
digital innovation
initiatives.
Assoc. Prof. Mitsunori Hirogaki, Kyushu University, Japan
Mitsunori Hirogaki graduated
with a Bachelor of Science:
Commerce from Doshisha
University and pursued his
Master's Degree in Commerce
and Ph.D.: Commerce from
Kobe University. Dr.
Hirogaki is currently an
Associate Professor of
Marketing Strategy at Kyushu
University, Graduate School
of Economics, Department of
Business and Technology
Management (QBS Business
School), where he teaches
Marketing Strategy and
International Marketing. He
also teaches marketing
research and consumer
behavior at Ehime
University.
He has served as an
administrator in various
capacities at Kyushu
University and as one of the
professors in various
training programs dealing
with Marketing in short-term
executive programs, an
Introductory Education
Program for Freshman MBA
students, and a regular
feature on QTnet "Morning
Business School" radio
educational program aired by
FM Fukuoka, and at Nikkei
Business School. As a member
of a research group at the
Center for the Study of the
Creative Economy (Doshisha
University), he works with
big data analysis to
construct systems that
identify seeds of
innovation. Dr. Hirogaki’s
current research focuses on
Cross-Cultural Consumer
Behavior in international
marketing and marketing
strategies in mature,
developed societies.
He has published numerous
papers in international
journals such as Journal of
Marketing Management;
International Journal of
Retail & Distribution
Management; International
Review of Retail,
Distribution and Consumer
Research; International
Journal of Entrepreneurship
and Small Business; Micro
and Macro Marketing;
International Journal of
Technology Transfer and
Commercialisation; and
International Journal of
Business and Globalisation.
He is a member of the
Japanese Economic
Association, Japan Society
of Marketing and
Distribution, Kyushu
Association of Economic
Science, and Japan
Association for Consumer
Studies.
Abstract: Addressing climate change and reducing greenhouse gas emissions necessitates a global transition to sustainable transportation. Japan's Green Growth Strategy aims to revolutionize its automotive sector by committing to sell electric, plug-in hybrid, and fuel cell vehicles by 2035. Despite proactive policies and significant marketing efforts, Japan's EV adoption rate lags behind other developed nations, posing challenges to its environmental goals and economic growth potential. This speech presents nationwide survey results regarding the factors influencing Japanese consumer behavior towards electric vehicles.
Assoc. Prof. João Alexandre Lobo Marques, University of Saint Joseph, Macau, China
Associate Professor, Head of Department, Research Coordinator at the University of Saint Joseph, Macau, SAR China. Founder of the Laboratory of Applied Neurosciences (LAN/USJ). PhD in Engineering Federal University of Ceará (UFC), Brazil. Post-Doctorate and Honorary Research Fellow at the University of Leicester - UK. Visiting Associate Professor at the University of the Chinese Academy of Sciences (UCAS) - Shenzhen Institutes of Advanced Technologies (SIAT). Associate Professor and Software Department Chief at University Gregorio Semedo (UGS), Angola (2009-15). Has large experience in Artificial Intelligence, Bioengineering, and Applied Computer Science, focusing on signal and image processing. RESEARCH AREAS - Neuroscience applied to management (marketing, leadership, performance) - Business Analytics - Big Data Applications - Theory of Constraints - Project management - Digital Signal Processing - Bioengineering / Computer-Aided Diagnostic Systems - Artificial Intelligence - Deep Learning - Nonlinear analysis and dynamics of time series.
Abstract: The use of
Artificial
Intelligence for
corporate
decision-making has
been consistently
and exponentially
growing in the last
few years due to the
popularization of
classification
algorithms, pattern
recognition tools
and clustering
techniques. In this
talk, the modelling
and preliminary
results of the
system called the AI
Project Manager are
presented.
The AI Project
Manager consists of
developing an
innovative
multi-stage
integrative,
intelligent system
to perform tasks and
decision-making
processes as a
corporate Project
Manager based on
multiple AI-based
subsystems and
statistical analysis
of primary and
secondary data.
A central subsystem
integrates a set of
specialized
subsystems
responsible for
specific
classifications and
analyses. These
modules communicate
and cooperate in
obtaining decisions
based on data
collections, subsets
of documents, and
previously learned
lessons, aiming to
add to the system
not only the
technical
specificities of the
company but also the
intuitive and
subjective aspects
of working on
complex projects
with multiple
constraints and a
variety of
stakeholders. The
computational
methods employed
include Large
Language Models
(LLM); Bayesian
Classifiers;
Decision Trees;
Support Vector
Machines (SVM);
Regression
techniques;
Clustering
techniques;
k-Nearest Neighbors;
Fuzzy Inferential
Systems; Artificial
Neural Networks
(ANN); and a
statistical
inferential testing
module. Each of
these techniques is
validated for
specific
decision-making
processes and aims
to provide a range
of possible
decisions with
corresponding
weights to support
the final decision
from a human Project
Manager or Project
Team. The proposed
system uses a
bi-lingual
(Chinese/English)
LLM solution for
processing external
data on project
management and
internal data
sources from the
company
knowledgebase
(technical and
financial reports,
relevant key
performance
indicators,
established
corporate policies,
human resources
management, career
plans, etc); CART
Decision Trees and
Inferential
Statistics for the
analysis of the
major Project
Constraints (Scope,
Schedule, Cost,
Resources, Quality)
and Risk Management
analysis. In
addition,
non-supervised
clustering for
variable grouping is
implemented based on
probabilistic
distance-based
approaches, such as
the Centroid
technique. A
Bayesian classifier
is considered in the
User Interface to
analyze and rank
user inputs and
requests.
The AI-PM system is
currently in the
first phase of
qualitative tests,
and the feedback
provided by
specialists in the
area is positive and
encouraging to
expand the project,
indicating that the
AI-PM can be used as
a research platform
for the area of
Project Management
and related topics
such as Change
Management,
Investment
Decisions, Career
Management, among
multiple others, and
also a high-value
tool to support
project managers in
their
decision-making
processes in a world
of increasing
uncertainty and
complexity in
projects.