Invited Speakers

Prof. J. Alberto Aragon-Correa, University of Granada, Spain

J. Alberto Aragon-Correa is Professor of Management and the T-Systems – University of Granada Chair of Innovation in Digital Sustainability at the University of Granada (Spain). Furthermore, he is also the current Director of the Talent Incubator. Previously, Aragon-Correa was an Honorary Professor of Management and Professor of International Business at the University of Surrey (United Kingdom). He was also a guest visiting scholar at University of California, Los Angeles (USA), University of California, Berkeley (USA), and ETH Zurich (Switzerland).
Aragon-Correa's research examines firms’ business strategies, especially the connections between innovation, governance, and sustainability in multinational firms. Aragon-Correa's work have been published in many of the most prestigious journal in the field of management, such as Academy of Management Review, Academy of Management Journal, Journal of Management, Academy of Management Annals, Journal of International Business Studies, Academy of Management Perspectives, California Management Review, among others.
Aragon-Correa has been awarded with the Academy of Management «ONE Distinguished Scholar Award», a distinction designed to recognize scholars who have provided top inspirational academic leadership. His research has gained recognition, featuring in the “List of Top Two Percent Scientists in the World” published by Prof. Ioannidis (Stanford University) and colleagues. As of May 2024, his works have received more than 14400 citations (h-index=41) according to Google Scholar.
He is the incoming Editor in Chief of the Business Research Quarterly (BRQ), the journal of the Spanish Academy of Management (ACEDE). Furthermore, he currently holds the position of Co-Editor for the Cambridge University Press book series titled ″Organizations and the Natural Environment”. Aragon-Correa also serves as a Consulting Editor for Organization & Environment, a journal focusing on sustainability and management published by SAGE Publishing.

Topic: Evolving Metrics: From Traditional Assessments to Big Data Analytics in Measuring Firm Environmental Performance

Abstract: Sustainability is crucial for ensuring the long-term health of our planet, and management research on sustainability has grown exponentially in the last decade. In the dynamic landscape of business sustainability, the methods used to evaluate environmental strategies and performance within firms have significantly evolved. This keynote presentation will delve into the historical trajectory of these measurement systems, critically examining the strengths and limitations of both traditional and contemporary approaches. We will explore the transition from anecdotal assessments to more sophisticated, quantitative methods that leverage advancements in big data analytics.
Our discussion will highlight the pressing need for emergent approaches that harness big data to provide more accurate insights into the actual environmental impact of firms. Additionally, we will describe an initiative designed by the speaker and his academic team, the TERAIN database. This innovative platform integrates comprehensive analytics from the European Pollutant Release and Transfer Register (EPRTR) and its counterpart in the USA, offering a robust tool for scholars, practitioners, and investors.
Attendees will gain a vivid understanding of how appropriate methodological approaches can improve the measurement of environmental performance in the context of firms.

 

 

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.

Topic: The Intricate Connections between Digital Strategy, Absorptive Capacity, Digital Technology Use, and Digital Innovation

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.
 

 

Prof. Lin Xiu, University of Minnesota – Duluth, USA

Dr. Lin Xiu received her PhD in Industrial Relations and Human Resources from the University of Toronto in 2010. Currently, she holds a professorship within the Department of Management Studies at the University of Minnesota – Duluth. Her research encompasses a range of subjects including labor market dynamics, HR analytics, compensation frameworks, as well as the nexus between leadership and employee well-being. Dr. Xiu has made significant scholarly contributions to her field, evidenced by the recognition of her work in prominent journals such as the British Journal of Industrial Relations, the Journal of Total Rewards, the International Journal of Manpower, the Leadership & Organization Development Journal, the Journal of Economic Psychology, and Personnel Review. Her academic impact extends to leading special issues, evaluating substantial research funding applications, and managing multiple research grants. Through her consultancy efforts, Dr. Xiu has exerted a notable influence on organizational practices (e.g., wellness programs) and labor market policy development (e.g. Paid Family and Medical Leave policies). She currently serves as an Associate Editor for the International Journal of Manpower (IJM).

Topic:  The Impact of AI on Talent Management

Abstract: Artificial Intelligence (AI) introduces a transformative shift in the nature of work. This study explores this shift through a holistic framework that moves beyond traditional views of automation, revealing AI’s extensive influence on the facets of work design, conduct, and measurement. AI fundamentally reconceptualizes the very essence of work, necessitating a paradigmatic shift in work design methodologies that underscore flexibility, adaptability, and innovative problem-solving. Through the development and deployment of HR Analytics and Talent Intelligence, organizations attain unprecedented granularity in understanding workforce dynamics, thereby revolutionizing recruitment processes and talent management strategies. Concurrently, labor platforms emerge as pivotal conduits for the provisioning of work, reshaping the contours of work execution. With AI’s ascendancy, conventional performance metrics undergo a metamorphic evolution, compelling a reassessment of entrenched notions of productivity and efficiency. Ethical considerations loom large in this landscape, prompting a critical examination of AI’s impacts on workforce dynamics, privacy, and fairness. By understanding these issues, organizations can make better decisions about how to use AI to improve work processes, drive innovation, and ensure fairness and inclusivity for all.

 

 

Assoc. Prof. Mitsunori Hirogaki, Kyushu University, Japan

Mitsunori Hirogaki graduated Bachelor of Science Major in Commerce in Doshisha University and pursued his Master's Degree in Commerce and Ph. D in Commerce in 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. 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 has been involved in big data analysis projects, 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 is a member of Japanese Economic Association, Japan Society of Marketing and Distribution, and Japan Association for Consumer Studies.

Topic: TBA

Abstract:  TBA

 

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.

Topic: The AI Project Manager

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.