Research Focus:
From Service Systems and Forecasting to Human-Algorithm Interaction

The insights from the foundational work in service operations and forecasting weren't merely individual achievements; they illuminated the prelude to a broader, industry-evolving narrative. In this AI era, the spotlight isn't on algorithms superseding humans, but on reshaping their collaborative relationship: Human-Algorithm Interaction. It is advocated that with AI's ascendance, humans aren't sidelined, but elevated. This vision, molded by prior research, drives present pursuits into how businesses can cultivate a harmonious alliance between humans and algorithms, resulting in exceptional performance and a more fulfilling human experience.

Research Objectives:
What will we aim to achieve?

HAI Lab is positioned as a hub for cutting-edge AI research, generating impactful insights into the interaction between human intuition and machine intelligence. Through high-impact publications in journals such as Management Science, Production and Operations Management, and Journal of Finance, the Lab enhances Oxford’s reputation for academic excellence in AI applications across industries such as healthcare, manufacturing, finance, and policy.

The establishment of the HAI Lab addresses a critical need for a structured platform that bridges academia and industry. The Lab’s primary goals include:

Knowledge Creation and Dissemination
  • Facilitate the creation of cutting-edge research in AI for Healthcare, AI for Manufacturing, AI for Finance, and AI for Policy through dedicated academic and industrial partnerships.
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  • Disseminate findings through annual HAI International Conferences, AI Innovation Forums, and tailored AI transformation modules, ensuring the research has a tangible impact.
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  • Interdisciplinary Collaboration: 
         1) Provide a hub for collaboration between academics, government bodies, leading                enterprises, startups, and industry associations. 
         2) Foster partnerships that integrate full-time researchers, visiting scholars, and                      external academic collaborators with industry leaders and policymakers.

Effective Knowledge Transmission
  • Develop and deliver a wide range of programs, including public courses, customized corporate training, experiential learning modules, and open workshops focused on critical themes such as: 
         1) Sustainable AI
         2) Trustworthy AI
         3) AI for Finance 
         4) AI for Healthcare 
         5) Corporate Transformation and AI-Driven Business Models
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  • Enhancing Practical Impact: 
         Equip organizations with tools, frameworks, and actionable strategies for adopting AI          solutions, focusing on key challenges such as trust, ethics, sustainability, and                  effective deployment of AI systems.

Research Description & List

Service System Design: 
​Strategy and Behavioral Insights
Delving deep into the domain of service operations, We've underscored the imperative of aligning service system design with its overarching strategic vision. Recognizing the intrinsic human element in such systems, Our studies prominently feature Behavioral Service Operations. Here, we weave together human behaviour observed in service interactions, leveraging insights from economic rationally patterns to innate psychology tendencies. Moving further, my dedicated exploration in Omnichannel Service Management crystallizes the realization that a mere increase in customer engagement channels doesn't inherently translate to enhanced customer or provider benefits. Drawing from this, we've articulated strategies of channel specialization and integration, guiding entities on leveraging dedicated channels for distinct customer segments or amalgamating diverse channels for seamless service delivery.
Research List
1. "Supplier Selection Criteria under Heterogeneous Sourcing Needs: Evidence from an Online Marketplace for Selling Production Capacity," Kejia Hu, Kong Lu and ZhenzhenJia. Production and Operations Management, ​Accepted. Link
2. “Delegation with Technology Migration: An Empirical Analysis of Mobile Virtual Network Operators,” Fan Zou, Yan Dong, Kejia Hu and Sriram Venkataraman. Management Science, ​Accepted. Link
3. “WeStore or AppStore: Customer Behavior Differences in Mobile Apps and Social Commerce,” Kejia Hu and Nil Karacaoglu. Production and Operations Management, Accepted. Link
4. “Service Chains' Operational Strategies: Standardization or Customization? Evidence from the Nursing Home Industry,” Lu Kong, Kejia Hu and Rohit Verma. Manufacturing & Service Operations Management, 24, no. 6 (2022): 3099-3116. Link
5. “Reproducibility in Management Science,” Fišar, M., Greiner, B., Huber, C., Katok, E., Ozkes, A., and the Management Science Reproducibility Collaboration (Note: Member of the Reproducibility Collaboration). Management Science, 70, no. 3 (2024): 1343-1356. Link
6. “Nox Emissions from Diesel Cars Increase with Altitude,” Yuche Chen, Xuanke Wu, Kejia Hu, and Jens Borken-Kleefeld. Transportation Research Part D: Transport and Environment, 115 (2023): 103573. Link
7. “To What Extent Do Workers’ Preferences Matter?” Zhenzhen Jia, Kejia Hu, Jianqiang Hu, and Vishal Ahuja. Manufacturing & Service Operations Management, Major Revision. Link
8. “How Women Promote Greater Social Responsibility on Social Media,” Li Xiang, Kejia Hu, Kong Lu and Huibin Du. Manufacturing & Service Operations Management, Major Revision.  
9. “The Psychology of Virtual Queue: When Waiting Becomes Less Like Waiting,” Kejia Hu, Xun Xu and Ao Qu. Manufacturing & Service Operations Management, Major Revision. Link
10. Cross-Channel Marketing on E-commerce Marketplaces: Impact and Strategic Budget Allocation, Qiyuan Deng, Kejia Hu and Yun Fong Lim. Manufacturing & Service Operations Management, Major Revision.​ Link

Forecasting: Navigating Business Complexities with Predictive Precision
Armed with robust statistical training and inspired by real-world industrial challenges, we've ventured into the world of business analytics, with a specialized focus on forecasting. By harnessing both statistical and machine learning methodologies, we've crafted predictive algorithms catering to diverse challenges—from deciphering traffic data nuances in intricate networks to anticipating demand for novel products without historical sales.
Research List
1. “Forecasting Product Life Cycle Curves: Practical Approach and Empirical Analysis,” Kejia Hu, Jason Acimovic, Francisco Erize, Douglas J. Thomas, and Jan A. Van Mieghem. Manufacturing & Service Operations Management, 21, no. 1 (2018): 66-85.  Link   
2. “Product Life Cycle Data Set: Raw and Cleaned Data of Weekly Orders for Personal Computers,” Jason Acimovic, Francisco Erize, Kejia Hu, Douglas J. Thomas, and Jan A. Van Mieghem. Manufacturing & Service Operations Management, 21, no. 1 (2018): 171-176. Link
3. “Technological Growth of Fuel Efficiency in European Automobile Market 1975–2015,” Kejia Hu and Yuche Chen. Energy Policy, 98 (2016): 142-148. Link
4. “Forecast & Flex: A Double Safeguard Framework for Production Planning,” Sunil Chopra, Kejia Hu, Jan A. Van Mieghem, and Ting Wang. Working paper. Link

Human-Algorithm Interaction: The Nexus of Service and Forecasting
Human-Algorithm Interaction is a compelling intersection where our extensive studies in both forecasting and human behavior within service systems converge. These twin pillars of research have provided me with a unique lens to scrutinize the nuanced dynamics between individuals and the algorithms they interface with. The renowned Jointown case, which graces the list of top 100 MBA cases, encapsulates this perfectly. While the case reveals the sophistication of modern algorithms, it equally accentuates the indispensable role of managerial foresight—from astute strategy formulation to the design of effective incentives and team reorganization—in guiding human teams to harmoniously interact with algorithmic systems. Our ongoing investigations expand on these foundations into diverse sectors: consultancy, where computational recommendations meet human acumen; healthcare diagnosis, where AI augments but doesn't supplant the clinician's discernment; manufacturing, where algorithms streamline operations overseen by human vigilance; and sales planning, where algorithmic forecasts are enriched by human insights. In my research of human-algorithm interaction, I call for three transformational imperatives: repositioning human roles, redesigning business processes, and rethinking business models. It's pivotal for businesses to adapt and evolve, creating environments where AI and human endeavors aren't just complementary but synergistic.

​Impact: This multi-faceted delve into human-algorithm partnerships has elicited noteworthy recognition, both in academic and corporate domains. Collaborative endeavors with Fortune 500 companies have offered us unparalleled insights from real-world deployments. The acclaim of our research is further mirrored in its selection for top-tier MBA case repositories and a series of public-speaking forums, consistently captivating audiences in their hundreds. The Four-level AI framework article by Prof. Kejia Hu and PwC senior partner Jasper Xu got featured in the first edition of WAIC magazine - WAIC UP. 
Research List
1. “Analytics Applications and Strategies in the Restaurant Industry,” Morgan Swink, Kejia Hu and Xiande Zhao. Production and Operations Management, 31, no. 10 (2022): 3710-3726. Link
2. “A Hybrid ODE-Neural Network Framework for Modeling and Guiding GLP-1-Mediated Glucose Dynamics,” Zijia Wang, Sarwar Sumbal, and Christofer Toumazou. Nature Scientific Report, Minor Revision.  
3. "More or Less: How Information Richness Affects Our Choice Consistency," Kejia Hu, Xilin Li and Yixin (Iris) Wang. Working paper. Link
4. “Reducing Human Biases through AI: Empirical Evidence from a Consulting Platform,” Kejia Hu, Bowen Lou and Bilal Baloch. Working paper.
5. Jianqiang Hu and Kejia Hu. Case Study on Jointown Pharmaceutical Group Co Ltd, China. 
 🎉 Awarded as the National Top 100 MBA Case Studies, China, 2011.
6. Kejia Hu and Jasper Xu. Navigate the Four-level Framework for AI Transformation in Business. WAIC UP 1st Edition -- The official magazine of WAIC (World Artificial Intelligence Conference) Link