Chuan (River) Zhu
AI, generative AI, AI agents, operations management, service operations, platform operations, human-AI collaboration, empirical operations management
Chuan (River) Zhu
Bio
I am a Ph.D. Candidate in Business Artificial Intelligence at the School of Management, Fudan University. My advisor is Professor Tianjun Feng. My research lies at the intersection of empirical operations management, AI-enabled service operations, and platform operations.
I study how generative AI, AI agents, and human-AI collaboration reshape operational decisions in service operations. My work uses field experiments, causal inference, platform data, machine learning, and LLM-based measurement to examine questions such as when AI systems should act autonomously, when human intervention is valuable, and how platforms can design service processes that balance efficiency, quality, and customer experience.
My current research is also grounded in industry collaboration, especially in customer service and platform contexts where AI systems interact with workers, customers, and organizational decision rules.
Research Focus
My current research focuses on AI-enabled service operations, human-in-the-loop service design, AI agents in platform operations, workforce management, service recovery, and the allocation of front-office and back-office effort in online service settings.
GenAI and Service Operations
How GenAI changes customer service workflows, service quality, escalation, and recovery in customer service and contact-center operations.
AI Agents and Platform Operations
How agentic AI systems operate inside platform-mediated service environments, including customer service operations and online service platforms.
Human-AI Collaboration
Human-in-the-loop intervention, escalation timing, AI failure recovery, and worker effort in hybrid service systems.
Selected Working Papers
Agentic AI and Human-in-the-Loop Interventions: Field Experimental Evidence from Alibaba’s Customer Service Operations
Yiwei Wang*, Chuan Zhu*, Tianjun Feng, Lauren Xiaoyuan Lu, and Bingxin Jia.
This paper studies the deployment of agentic AI in Alibaba Taobao’s customer service operations through a randomized field experiment. The findings highlight the tradeoff between service efficiency and customer experience, and show how intervention timing, AI failure type, and post-escalation human effort shape service recovery.
Managing Front-office and Back-office Effort Allocation: Unpacking the Effects of Workload on Service Performance
Chuan Zhu, Tom Fangyun Tan, and Tianjun Feng.
Using granular customer service data from a large e-commerce platform, this paper examines how workload affects agents’ allocation between customer-facing interaction and back-office problem solving. The findings show that effective service requires balancing visible communication with substantive problem-solving work.
Industry Experience
Alibaba Group
Research Scientist, Customer Operations Department, July 2022 to present. This full-time, onsite academic collaboration focuses on human-AI hybrid customer service system design, causal-inference-based workforce management, and data-driven customer service operations.
Environmental Defense Fund Climate Corps and LONGi Green Energy
Climate Corps Fellow, July 2021 to Oct. 2021. This work examined supplier-side carbon-management challenges and green supply-chain opportunities.
Teaching and Service
I have served as a teaching assistant for Operations Management, Data-Driven Market Analysis, Microeconomics, and Theories and Practices of Entrepreneurship across MBA, master’s, and undergraduate courses.
Contact
For research, collaboration, or teaching-related inquiries, please reach me by email. You can also find my academic profiles on ORCID, LinkedIn, and my SSRN author page.