Speakers

Prof. Yeoh Ging Sun William

Hong Kong Metropolitan University

(FACS Fellow)

Brief introduction: Prof William Yeoh is a professor at Hong Kong Metropolitan University's Lee Shau Kee School of Business and Administration. Previously, he served as an associate professor at Deakin University's Deakin Business School and was the Innovation Head of Deakin Cyber Research and Innovation Centre.

He has successfully supervised 7 doctoral and 10 master theses. He is the Editor-in-Chief Emeritus of the International Journal of Business Intelligence Research and the Big Data and Analytics Educational Conference Co-Chair.

His scholarship has been published in top journals, including 8A* and 27A ABDC-ranked journals, and all top six IS conferences (e.g., ICIS, ECIS, PACIS, AMCIS, ACIS, HICSS), and has been supported by various funding bodies and industry (totalling over HKD$10M). His expert opinions have been featured on national news outlets, e.g., ABC Radio, SBS News, Australian Financial Review, The New Daily, etc.

At Deakin University, he has served as Director of the IBM Centre of Excellence in Business Analytics, Co-Director of the Faculty's Business and Technology Theme, Course Director, Director of Teaching, and Department's Director of International, Director of Industry Engagement, and HDR Coordinator.

He was a visiting scholar at the University of Ottawa (Telfer School of Management) and a visiting professor at the University of Indonesia. He currently serves as an adjunct professor at the University Tunku Abdul Rahman (UTAR) Malaysia. Previously, he was the Deputy Dean (R&D and Postgraduate Programs) at UTAR's Faculty of ICT, where he established the PhD and Master's research programs in 2009. He is a Fellow of the Australian Computer Society (ACS), an honour representing induction into the ACS Hall of Fame.

Title: Extending the understanding of critical success factors for implementing business intelligence systems

Abstract: Business intelligence (BI) systems have become essential tools for enhancing data-driven decision-making, yet their implementation remains a complex and resource-demanding process. Despite the growing importance of BI, there is still a lack of a set of critical success factors (CSFs) to guide effective implementation. This study aims to deepen the understanding of the CSFs influencing BI system success and to close the gap between academic research and industry practice.

A two-stage qualitative approach was employed. In the first stage, the Delphi method was conducted over three rounds to achieve expert consensus, resulting in the development of a comprehensive BI CSFs framework. In the second stage, multiple case studies of seven large organizations were carried out to validate and refine the framework. The findings highlight the key organizational, process, and technical factors that shape BI implementation outcomes. The resulting framework provides both theoretical insights and practical guidance, enabling BI stakeholders to better understand the contextual issues that influence successful BI system implementation.

Assoc. Prof. Zulkefli Bin Mansor

Universiti Kebangsaan Malaysia

（IEEE Senior Member)

Brief introduction: Zulkefli Mansor is an associate professor in software engineering at the Research Center for Software Technology and Management (SOFTAM), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Malaysia. He holds a BSc (Hons) in Business Information System from the University of East London, and Master of Software Engineering from Universiti Malaya, and a Ph.D. degree in Software Engineering from Universiti Teknologi MARA. He also completed the Foundation Level Software Tester, IQSTB.

He serves on the editorial and reviewer’s board for several international journals such as IEEE Access, Computer, Continua & Materials, and many more. He is also involved in several committees and associations including the IEEE, Internet Society, Project Management Institute (PMI), and Associate Machine of Computer (ACM).

He is a Senior Member of the Association of the Institute of Electrical and Electronics Engineers (IEEE). Zulkefli’s teaching covers software engineering courses such as Software Management, Software Engineering Practices Software Project Management and so forth. Her current research interests include Agile Methods, Software Management, Data Analytics, and Applied Artificial Intelligence in Software Engineering. He received several grants and industrial projects in the areas of data analytics, software engineering, and agile. He published more than 100 papers comprising more than 30 articles in ISI/WOS journals, and more than 45 articles in SCOPUS, conferences, and technical reports.He is also an Expert Panel for MQA for the Computing Program, Head of ICT Panel Cluster from MBOT, Panel for Professional Technology, Panel for MOSTI grants, and others. He was involved in curriculum development for various program levels, especially in the computing discipline. He conducted multiple workshops on curriculum development and OBE implementation.

Title: The AI Revolution in E-commerce: A Tripartite Driver of Innovation, Sustainability, and Growth

Abstract: Business intelligence (BI) systems have become essential tools for enhancing data-driven decision-making, yet their implementation remains a complex and resource-demanding process. Despite the growing importance of BI, there is still a lack of a set of critical success factors (CSFs) to guide effective implementation. This study aims to deepen the understanding of the CSFs influencing BI system success and to close the gap between academic research and industry practice.

A two-stage qualitative approach was employed. In the first stage, the Delphi method was conducted over three rounds to achieve expert consensus, resulting in the development of a comprehensive BI CSFs framework. In the second stage, multiple case studies of seven large organizations were carried out to validate and refine the framework. The findings highlight the key organizational, process, and technical factors that shape BI implementation outcomes. The resulting framework provides both theoretical insights and practical guidance, enabling BI stakeholders to better understand the contextual issues that influence successful BI system implementation.

In innovation, AI is re-engineering the customer experience through hyper-personalization, creating unique interactions by analyzing user behavior and context. It powers conversational commerce via advanced chatbots and voice assistants, while computer vision enables visual search and Augmented Reality (AR) try-ons. These technologies from virtual furniture placement to makeup try-ons dissolve traditional online shopping limitations, creating immersive and frictionless customer journeys.

Simultaneously, AI is a critical driver of sustainability. It optimizes inventory management through predictive analytics, reducing overproduction and waste. In logistics, AI algorithms minimize fuel consumption and carbon emissions by calculating the most efficient delivery routes. Furthermore, by improving product fit and accuracy through superior recommendations, AI directly reduces the high rate of returns, a significant source of logistical pollution and packaging waste.

This synergy between innovation and sustainability fuels tangible growth. AI enhances profitability through dynamic pricing and bolsters customer retention by predicting lifetime value and churn. It also secures the financial infrastructure with robust fraud detection and optimizes marketing spend for maximum return on investment.

In conclusion, the AI revolution represents a comprehensive restructuring of e-commerce. It creates a virtuous cycle which are innovative experiences engage customers, sustainable practices build resilience, and together, they unlock new frontiers of profitability. As AI technologies evolve, their role as the essential engine for a smarter, greener, and more prosperous e-commerce future is unequivocally secured.

Assoc. Prof. Hongbo Li

Shanghai University

Brief introduction: Hongbo Li is Associate Professor of Information Systems and Management Science in the School of Management at Shanghai University, Shanghai, China. He obtained his PhD degree in Management Science in July 2014 from School of Economics and Management, Beihang University, Beijing, China. He was a visiting PhD student at Research Center for Operations Management, aculty of Economics and Business, KU Leuven, Belgium from 2012 to 2013. His research interests include artificial intelligence, metaheuristics, project scheduling, robust scheduling, data science, business analytics, and information systems. He has published in a variety of refereed journals, such as Journal of Scheduling, International Journal of Production Research, Decision Support Systems, Expert Systems with Applications, and Electronic Commerce Research and Applications。

Title: Open-Source Software Project Duration Prediction Based on Ensemble Transfer Learning

Abstract: Accurately predicting the duration of open-source software (OSS) projects is critical for community coordination, resource planning, and timely delivery. However, OSS projects differ significantly from traditional software projects in terms of development dynamics, contributor diversity, and project management structures, making duration prediction a particularly challenging and underexplored task. Existing approaches often rely on private datasets from industrial environments and do not generalize well to the OSS context due to the scarcity of labelled duration data and the high variability in OSS development practices.To address these challenges, we propose a novel framework for predicting OSS project duration using an ensemble transfer learning algorithm. Our method leverages publicly available OSS data from GitHub (github.com), applies feature engineering tailored to OSS characteristics, such as contributor activity, issue resolution patterns, and repository metadata. We employ the TrAdaBoost.R2 algorithm to transfer knowledge from OSS projects in other domains to the current field's projects. Through extensive experiments on 30 cases across 8 algorithms, we demonstrate that our framework significantly outperforms traditional supervised learning and parametric approaches, offering more accurate and generalizable duration predictions for OSS projects.