Kaiyao Wu | Econometrics and Finance | Research Excellence Award

Published on by Research Awards

Prof. Dr. Kaiyao Wu | Econometrics and Finance | Research Excellence Award 

Shanghai University of International Business and Economics | China

Prof. Kaiyao Wu is a leading scholar in sustainable development economics and global value chain analysis, currently serving as a Professor at the School of Statistics and Data Science at the Shanghai University of International Business and Economics (SUIBE). He also holds several influential academic and professional roles, including Expert Committee Member and Digitalization Committee Member of the China Society of Foreign Trade and Economic Statistics, Director of the Global WiseTrade Digitalization Research Institute at SUIBE, and Evaluation Expert for the National Social Science Fund of China. With a rich academic background and extensive research experience, Prof. Wu has established himself as a prominent contributor to the fields of economic digitalization, global value-chain restructuring, energy–environment linkages, and sustainable development metrics. He holds a Ph.D. in Industrial Economics (2011) and an MBA (2007) from Shanghai Jiao Tong University, following a B.S. in Statistics from Xiamen University in 1995. After completing his doctoral studies at the Antai College of Economics and Management, he served on the faculty of Shanghai Finance University (now Lixin University of Accounting and Finance) from 2011 to 2019, while simultaneously completing postdoctoral research at Shanghai Jiao Tong University between 2012 and 2015. His international exposure includes a visiting scholar appointment at Colorado State University from 2017 to 2018, enriching his global research perspective. Since joining SUIBE in 2019, he has continued to advance impactful research and teaching. Prof. Wu has published more than 40 peer-reviewed papers in high-quality international and Chinese journals, including Energy Economics, Journal of Cleaner Production, Emerging Markets Finance and Trade, Environmental Science and Pollution Research, and Statistical Research, one of China’s top statistical journals. His publications address critical global issues such as carbon neutrality along global value chains, ESG impacts on corporate performance, energy efficiency, and the role of demographic shifts in shaping international production networks. He is also the author of six monographs and two textbooks that support both academic inquiry and practical applications, including The Global Value Chain Network of the Economy-Energy-Environment (3E) Coupling: Measurement and Application (2025) and Enterprise Data Processing with SAS EG (2023). Prof. Wu has led and contributed to major national research projects funded by the National Social Science Foundation of China, focusing on industrial correlation networks, dual-circulation system measurement, and advanced input–output analysis. His expertise further extends to statistical methods, global value chain statistics, market research, and data processing, and he teaches widely in these areas. Recognized for his outstanding mentorship, he has received multiple national-level instructor awards, underscoring his dedication to cultivating high-quality talent in statistics and data science.

Profile: Orcid

Featured Publications

Duan, J., Li, Y., Shi, W., & Wu, K. (2025). Beyond the linear: Green technology innovation, moderators, and GVC upgrading. SSRN. https://doi.org/10.2139/ssrn.5333724

Xuan, S., Wu, K., & Deng, J. (2024). The effect of producer service agglomeration externalities on urban green innovation efficiency in China. Heliyon, 10, e25085. https://doi.org/10.1016/j.heliyon.2024.e25085

Wu, K., Chen, F., Anwar, S., & Liao, L. (2024). The impact of population aging on a country’s global value chain position: Unraveling the dynamics and mechanisms. Emerging Markets Finance and Trade, 60(?), 1–?. https://doi.org/10.1080/1540496X.2023.2300636
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Wu, K., Sun, C., Zhang, J., & Duan, J. (2023). Carbon neutrality along the global value chain: An international embedded carbon network analysis. Environmental Science and Pollution Research, 30, 122051–122065. https://doi.org/10.1007/s11356-023-30680-9

Du, L., Wei, M., & Wu, K. (2023). Information technology and firm’s green innovation: Evidence from China. Environmental Science and Pollution Research, 30, ???–???. https://doi.org/10.1007/s11356-023-29320-z
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Xuan, S., Wu, K., & Deng, J. (2023). The effect of producer service agglomeration externalities on urban green innovation efficiency in China. SSRN. https://doi.org/10.2139/ssrn.4502877

Jack Ng Kok Wah | Management and Accounting | Outstanding Educator Award

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Dr. Jack Ng Kok Wah | Management and Accounting | Outstanding Educator Award 

Multimedia University Cyberjaya | Malaysia

Dr. Jack Ng Kok Wah is a dynamic and forward-thinking academic leader, researcher, and trainer, currently serving as a Senior Lecturer at the Faculty of Management, Multimedia University (MMU), Malaysia. With over three decades of experience spanning academia and industry, he has established himself as an influential figure in the fields of artificial intelligence (AI), digital healthcare, mental health innovation, agricultural technology, and consumer behaviour research. His professional journey reflects a rare blend of academic rigor and practical expertise, driven by a passion to bridge scientific insight with societal and industrial transformation. Dr. Ng holds a PhD in Business from Universiti Malaya-Wales (UMW), an MBA in Marketing Management from Nottingham Trent University, UK, and a BA (Hons) in Marketing Management from University of Northumbria at Newcastle, UK. As an HRDF-certified and accredited trainer and a PIKOM-certified professional, he brings a multidisciplinary understanding of marketing, management, AI, and human development. His academic foundation, combined with extensive professional experience, enables him to mentor students and organizations in applying digital innovation for real-world impact. A prolific scholar, Dr. Ng has published more than 10 Q1 and Q2-ranked papers in Scopus and Web of Science (WoS) indexed journals between 2024 and 2025. His research has made significant contributions across several domains. In AI and healthcare, he explores how artificial intelligence, robotics, and machine learning can enhance surgical precision, reduce risk, and personalize medical treatment—highlighted in high-impact journals such as Frontiers in Public Health and Journal of Robotic Surgery. His notable works include “The Rise of Robotics and AI-Assisted Surgery in Modern Healthcare” and “AI-Driven Robotic Surgery in Oncology: Advancing Precision, Personalization, and Patient Outcomes.” These publications not only demonstrate his academic excellence but also his commitment to improving healthcare accessibility and efficiency through technology. Beyond healthcare, Dr. Ng’s research on AI in agriculture and food innovation addresses sustainability and global food security challenges. His paper “The Role of AI in Transforming Agriculture: Toward Sustainable Growth in an Era of Climate Change” highlights how intelligent technologies can optimize farming efficiency, resource management, and crop resilience. Similarly, his study on “AI-Driven 3D and 4D Food Printing” explores futuristic applications of additive manufacturing in food production, emphasizing environmental sustainability, personalization, and global scalability.

Profile: Scopus | Orcid | Google Scholar

Featured Publications

Ng Kok Wah, J. (2025). Revolutionizing surgery: AI and robotics for precision, risk reduction, and innovation. Journal of Robotic Surgery, 19, Article 47.

Ng Kok Wah, J. (2025). Revolutionizing e-Health: The transformative role of AI-powered hybrid chatbots in healthcare solutions. Frontiers in Public Health, 13.

Ng Kok Wah, J. (2025). The rise of robotics and AI-assisted surgery in modern healthcare. Journal of Robotic Surgery, 19(1), 311.

Ng, J., Wah, K., Fitriana, M., & Arumugam, T. (2023). Assumptions for structural equation modeling (SEM), normality of data distribution analysis and model fit measures. Normality of Data Distribution Analysis & Model Fit Measures.

Wah, K., & Ng, J. (2025). AI-driven robotic surgery in oncology: Advancing precision, personalization, and patient outcomes. Journal of Robotic Surgery, 19(1), 1–11.

Ng Kok Wah, J. (2025). Decoding structural equation modeling: Insights on data assumptions, normality, and model fit in advancing digital marketing strategies. Journal of Cases on Information Technology, 27(1), 20.

Ng Kok Wah, J. (2025). AI-driven 3D and 4D food printing: Innovations for sustainability, personalization, and global applications. Food Reviews International, 1–29.

Ng Kok Wah, J. (2025). Empowering healthier decisions: The impact of consumer innovativeness on linking perceived benefits with protective health behaviors through digital marketing in the health sector. International Journal of Pharmaceutical and Healthcare Marketing.

Ng Kok Wah, J. (2025). Mental health in transition: Exploring the impact of remote and hybrid work on employee well-being in the evolving post-pandemic workplace. Xinan Jiaotong Daxue Xuebao / Journal of Southwest Jiaotong University, 60(2).