Prof. Zhibin Lin

Durham University, UK

Lin Zhibin, Professor at Durham University Business School, UK, holds a Ph.D. and serves as a doctoral supervisor. He is the Director of the Center for Consumer and Sustainable Consumption Research. Formerly served as a senior executive at Xiamen Airlines. Ranked among the top 2% of scientists globally by Stanford University. Research spans artificial intelligence, technology and innovation, transportation and tourism management, digital marketing, and international business. Has published over 150 academic papers in leading international journals including the Journal of Product Innovation Management, Journal of Service Research, Annals of Tourism Research, Tour Management, and Journal of Travel Research. He serves as Associate Editor of Tsinghua University's Journal of Digital Economy, peer reviewer for multiple SSCI-indexed international journals, UK Vice Chair of the International E-Commerce Conference, and committee member for numerous international academic conferences. His accolades include Durham University's “Outstanding Research Supervision Award” and “Best Doctoral Supervisor Award,” the China Tourism Academy's “Outstanding Research Achievement Award in Culture and Tourism,” and multiple “Best Paper Awards” from international conferences.

Speech Title: 生成式引擎优化(GEO)：AI搜索时代的营销新战场

Abstract: 在传统搜索引擎中表现出色的企业，可能在AI推荐中鲜有身影；而擅长新式优化策略的竞争者，却主导着AI生成的答案。本报告聚焦AI搜索时代企业的核心挑战：如何在ChatGPT、DeepSeek等AI答案引擎中，让品牌被引用和推荐。传统SEO投资正逐渐失效，因为AI搜索不再依赖网页排名，而是综合多方信息生成回答。核心议题包括：从流量经济转向引用经济、在零点击环境中维持品牌影响力、实现公关与内容营销的战略融合、跨平台优化资源配置，以及建立新的价值衡量体系。面对这一根本转变，企业必须重新思考：如何在无法控制界面的情况下优化品牌呈现、如何衡量无法追踪点击的营销价值、如何在营销、公关与内容团队之间建立新型协作机制、如何应对不同AI平台的引用偏好。掌握生成式引擎优化（GEO）的企业，将在未来十年的数字竞争中占据领先；犹豫不决者，则可能面临边缘化的风险。

Prof. Jianguo Zheng

Donghua University, China

Dr. Danish is Associate Professor and his specialized areas are HRM, Organizational Behavior and Business Management. He has served as Incharge Department of Business Administration at PUGC, and has other important roles at Hailey College of Commerce before joining IBA. His contributions to the academic world, business, global non-profit organizations, and governments have been recognized by awards, research grants, invitations to provide research leadership to national universities, and governments in improving organizational management and governance.

Danish’s research program adopts a multi-disciplinary approach to provide theoretical, methodological, and empirical evidence on improving the quality of organizational performance and governance across countries such as China, Malaysia, Saudi Arabia, Australia, Italy, and Germany. He has published extensively in leading scholarly journals. For example, he has seven publications which are ranked A, B, and C by the Australian Business Deans Council. Danish’s publications are widely cited in management and OB literature and includes 2521 citations (till August 2020), h-index of 20 and i10-index of 43. He is on the editorial board of various national and international scholarly journals. He has received several research grants from Higher Education Commission of Pakistan and also HEC approved supervisor. Over the last ten years, he has successfully supervised 4 PhDs, and 120 Master by research/MPhil students to completion. These students have gone on to have their distinguished careers, both in the academic world and in business. Danish has contributed to the Harvard Business Review Group, Asian Academy of Management Group, Eurasian Business and Economics Society, Academy of Management Group, International Science Congress Association, Academy of Business and Retail Management, and Social Science and Humanities Research Association. Danish has won several awards including Research Productivity Awards by COMSATS University (Pakistan), Research Incentive Award, and Performance Evaluation Award (being at top position in the department) by University of the Punjab (Pakistan), and Best Paper Awards (for conferences in Canada and Pakistan). 

Speech Title:Application Study on Distributed Flow Shop Scheduling Based on an Improved Grey Wolf Optimizer Algorithm

Abstract: As manufacturing systems evolve toward distributed and intelligent paradigms, production scheduling is confronted with severe challenges in terms of complexity, multi-objective requirements, and dynamic uncertainty. Distributed flow shop scheduling requires coordinating job assignment and operation sequencing across multiple factories, and achieving multi-objective optimization under multiple constraints, which makes it a typical NP-hard problem. To address the insufficient adaptability of traditional methods, this report proposes a class of multi-objective collaborative scheduling and optimization approaches for distributed flow shops. First, a distributed flow shop scheduling model incorporating hybrid no-idle constraints is established, and a multi-objective Grey Wolf Optimizer is developed to improve solution quality and search efficiency. Furthermore, a Q-learning mechanism and local search strategies are introduced to better satisfy multiple objectives, including makespan, energy consumption, and total tardiness. On this basis, reinforcement learning is integrated to build a collaborative optimization scheduling framework for dynamic production environments, enabling adaptive improvement of scheduling policies. Computational examples and comparative experiments demonstrate that the proposed method achieves significant advantages in overall performance and stability, providing effective support for intelligent scheduling in complex distributed manufacturing systems.

Prof. Zhongpan Qiu

Xiamen University, China

Speech Title: 视频摘要助力论文成功录用

Prof. Junsong Zhang

Xiamen University, China

Professor, School of Artificial Intelligence, Director, Brain Cognition and Intelligent Computing Laboratory, and Deputy Director, Office of Development and Planning, Xiamen University. He earned his Ph.D. in Computer Science and Technology from Zhejiang University. He has conducted visiting research at institutions such as the University of Southern California (USC) and the California Institute of Technology (Caltech). His primary research focuses on interdisciplinary studies integrating artificial intelligence with brain science, art, medicine, and other fields. He has published over 50 papers in domestic and international journals and conferences, including Nature Communications, Science Advances, iScience, IEEE Transactions on Visualization and Computer Graphics, IEEE Transactions on Medical Imaging, ACM Transactions on Applied Perception, Computer Graphics Forum, Pattern Recognition, Brain, Brain Informatics, Brain Research, Neuroscience, and Scientia Sinica Informations. His research work was featured as a "Research Highlight" in Science Foundation in China (Issue 2, 2019) by the National Natural Science Foundation of China. He has supervised undergraduate students to win the first prize in the National Intelligent Design Competition for College Students, for which he received the Outstanding Advisor Award. He has also guided graduate students to receive the Best Paper Award at the China Conference on Computer-Aided Design and Computer Graphics. He has led multiple projects, including those funded by the National Natural Science Foundation of China and the Aviation Science Foundation, and has participated in over 20 projects, including the National 863 Program, National 973 Program, and National Natural Science Foundation projects.

Speech Title: 人力资源管理、脑科学与AI的交叉融合途径

Abstract: 报告将从脑科学与人工智能的交叉视角，探讨人力资源管理研究的创新路径。一方面，借助脑科学揭示人力资源管理背后的神经机制，为人力资源管理提供理论依据和实证方法；另一方面，AI通过数据处理与智能分析工具，为人力资源管理提供新的研究范式。报告将围绕上述两方面内容展开。

Prof. Yanhong Guo

Dalian University of Technology, China

Guo Yanhong, Professor and Doctoral Supervisor at the School of Economics and Management, Dalian University of Technology, leads the FinTech and Regulatory Innovation Research Team. Her recent research focuses on big data and artificial intelligence technologies, including: large model pre-training and stepwise collaborative reasoning; domain-specific time series large model pre-training and fine-tuning; AI-based quantitative trading and investment decision algorithms; distributed situational awareness and collaborative decision-making. She has led over 20 national and provincial-level projects, including the National Key R&D Program “Cognitive Decision-Making and Multi-Agent Collaboration in Capital Markets,” the National Natural Science Foundation of China's General Program “Research on Machine Learning-Based Analyst View Credibility Assessment and Integration Methods,” the Ministry of Education Humanities and Social Sciences Fund, and the Liaoning Provincial Social Sciences Planning Key Fund. She received the Second Prize of Liaoning Provincial Natural Science Academic Achievement Award and the Second Prize of Liaoning Provincial Philosophy and Social Sciences Outstanding Achievement Award. Her publications appear in prestigious international and domestic journals such as JOC, EJOR,　IJF, IRFA, ESWA, FI, Journal of Management Sciences, Management Review, Operations Research and Management Science, and top conferences like SIG KDD. Several papers have been cited and positively evaluated by academicians and renowned scholars worldwide, with some selected as ESI highly cited papers. He holds three invention patents. In teaching, he received the First Prize for Undergraduate Teaching Achievements in Liaoning Province and the First Prize for Teaching Achievements at Dalian University of Technology, and was recognized as a Dalian High-Level Talent. He authored multiple business cases selected for the Canadian Ivey Case Library and honored as one of China's Top 100 Outstanding Cases.

Speech Title: Collective Intelligence: How to Use Artificial Intelligence to Make Smarter Decisions

Abstract: The cross-domain value of collective intelligence mining is significant. In financial markets, it enhances prediction accuracy and reduces investment risks by integrating diverse perspectives; in healthcare and public opinion analysis, it consolidates scattered knowledge and improves public decision-making. Unlike traditional methods that often ignore differences in opinion quality, AI-driven models can distinguish expert insights from "noise," thereby boosting the reliability and efficiency of decisions .

In the digital economy, collective intelligence has become central to high-quality decision-making. For instance, securities analysts' views contain rich market information, yet conventional approaches like simple averaging fail to fully capture their value, leading to resource waste and cognitive biases. This issue is particularly pressing in markets such as China's, where over 200 million retail investors face information overload and expertise barriers . To address this, our study develops an AI-powered three-layer framework—"representation-evaluation-integration"—using event embedding, interactive learning, and uncertainty quantification. This approach bridges theory and practice, offering robust support for financial decision-making, risk management, and intelligent advisory services.

In essence, mining collective intelligence is not merely a technical innovation; it is a crucial means to improve social resource allocation and advance high-quality development in the digital era。

Assoc. Prof. Rizwan Qaiser Danish

University of the Punjab

I am Rizwan Qaiser Danish serving as Associate Professor in Institute of Business Administration Punjab University. I possess over 20 years of experience in teaching and research, including 17 years of service at the University of the Punjab and more than a decade of post-PhD academic engagement. I completed my postdoctoral fellowship at the Queensland University of Technology (QUT), Australia—an institution internationally recognized for its triple crown accreditation in business education. My academic contributions include 143 publications, all recognized by the Higher Education Commission (HEC), with several appearing in high-impact journals. Additionally, 10 manuscripts are currently under review. My scholarly work has been cited extensively, with over 6,700 citations, an h-index of 34, and an i10-index of 81. I have received multiple accolades, including three international best paper awards and the "Top Cited Article 2020–2021" award by Wiley.

Between 2020 and 2024, citation impact contributed significantly to the improved subject ranking of management sciences at my institution. I have consistently secured merit-based scholarships and earned four academic distinctions throughout my educational journey. I have participated in more than 50 international conferences across countries including the USA, UK, Japan, Italy, Singapore, Malaysia, China, Sweden, Sri Lanka, Brunei, Turkey, UAE, and Australia, with support from HEC, PHEC, and the University of the Punjab. Additionally, I have attended over 30 national conferences, often serving as a session chair. I have also organized more than 50 academic workshops and seminars, including four international conferences.

I have successfully led research projects totaling PKR 2.7 million and have been actively involved in global academic communities such as EBES (Turkey), MMIRA (USA), BAM (United Kingdom), ANZAM (Australia & New Zealand), and AIB (Worldwide). My professional development includes trainings in research, auditing, legal affairs, digital marketing, and business communication, conducted by NAHE, PIPFA, PHEC, and other reputable bodies.

Currently, I am serving on editorial boards of several peer-reviewed journals and contribute to academic committees across multiple universities. I held the position of Head of the Department of Business Administration at PUGC for four years, during which I introduced new academic programs and specializations. I also serve as an academic committee member at five public sector universities in Punjab.

In recognition of my contributions, I have received several awards, including the Research Incentive Award, the Productivity Award, and the Performance Evaluation Award.

Since joining this esteemed institution in 2008, I have undertaken diverse academic and administrative roles. At the Gujranwala Campus, I cultivated a research-focused environment that led to the publication of 30 research articles. At Hailey College, as an Assistant Professor, I mentored numerous MPhil and PhD scholars and secured international research grants.

Speech Title: Navigating Digital HRM: The Nexus of e-Recruitment, Retention, and AI-Driven Development

Abstract: Purpose: In today’s contemporary era of digital global networking, information technology has become indispensable tool in transforming the landscape of human resource management (HRM). To serve the purpose the concept of Electronic Human Resource Management known as e-HRM is becoming popular within the organizations. The domain of e-HRM covers many sub areas i-e e-recruitment, e-appraisal, e-performance management, e-trainings. This study aims to argue that e-recruitment when supported by enabling factors such as e-performance management and artificial intelligence-based training and development (AI-T&D) may act as a strategic tool for retention. This study has numerous impacts on literature in manifolds. First this study examines the impact of e-recruitment on retention in the IT sector of Pakistan. Secondly, this study examines the mediating role of e-performance management between e-recruitment and retention. Thirdly, it examines the conditional effect of AI-T&D between e-recruitment and retention.

Design/methodology/approach: The data for this study was collected from 210 employees working in IT companies comprising of Subordinate staff, Middle managers and Senior Manager Categories. This study applied structural equation modeling (SEM) to test hypotheses.

Findings: The findings reported a positive influence of e-recruitment on retention. This study also noted e-performance management as strong mediator between e-recruitment and retention. However, results did not support conditional effect of AI-T&D between e-recruitment and retention.

Originality/value: This study is also first of its kind that explores intervening effect of e-performance management between e-recruitment and retention and conditional effect of AI-T&D between e-recruitment and retention.