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Kejia Hu

Associate Professor of Management Science

  • kejia.hu@sbs.ox.ac.uk

Saïd Business School
University of Oxford
Park End Street
Oxford
OX1 1HP

Profile

Kejia Hu is an Associate Professor in Management Science at Saïd Business School, University of Oxford and a Governing Body Fellow of Exeter College, Oxford. 

Previously, she was an Assistant Professor at Vanderbilt University. Kejia earned her PhD from Kellogg School of Management in 2017, an MS from UC Davis in 2013 and a BS from Fudan University in 2011.

Her research focuses on unlocking business value from data through human-AI collaboration, emphasising human insight. Collaborations with Fortune 500 companies have led to top-tier research, multiple awards, and recognition on the Poets and Quants '40 Under 40' 2023 list. Her Jointown Pharmaceutical Group case study is a Top 100 MBA Case Study in China.

Kejia is an Academic Scholar at Cornell Institute for Healthy Futures, holds board positions at POMS College of Service Operations and INFORMS Service Science section, and has worked at Lawrence Berkeley National Lab, Morgan Stanley, and Yiwu Government.

She is on the editorial board for Journal of Operations Management and a Senior Editor for Production and Operations Management. She received the 'Rising Star' Award at QUIS for her contributions to Service Operations studies.

Domain expertise: Service system design, forecasting, human-algorithm interaction
Methodology expertise: Structural modelling and causal inference, statistical forecasting and machine learning, stochastic modelling

Please visit her personal website.

Research

Kejia specialises in human-AI interaction, exploring how humans and algorithms collaborate in business settings. She emphasises that AI should augment human capabilities rather than replace them. Her research highlights the dynamic interplay between human intuition and algorithmic precision, aiming to create synergistic business environments.

Her work includes developing forecasting algorithms and studying their implementation in real-world scenarios. Kejia's insights reveal that effective human-AI partnerships require rethinking business processes, repositioning human roles, and redesigning business models. This research has been recognised in top journals and through collaborations with Fortune 500 companies, showing its practical relevance and impact.

Publications

Delegation with Technology Migration: An Empirical Analysis of Mobile Virtual Network Operators

  • Journal article
  • Management Science
  • Fan Zou,
  • Yan Dong,
  • Kejia Hu,
  • Sriram Venkataraman
Management Science

Supplier selection criteria under heterogeneous sourcing needs: evidence from an online marketplace for selling production capacity

  • Journal article
  • Production and Operations Management
  • Kejia Hu,
  • Lu Kong,
  • Zhenzhen Jia
Management Science

WeStore or AppStore: how customers shop differently in mobile apps vs. social commerce

  • Journal article
  • Production and Operations Management
  • Kejia Hu,
  • Nil Karacaoglu
Management Science

Reproducibility in management science

  • Journal article
  • Management Science
  • Miloš Fišar,
  • Ben Greiner,
  • Christoph Huber,
  • Elena Katok,
  • Ali I Ozkes
Management Science

The Impact of Financial Incentives in Last-Mile Operations: An Empirical Investigation

  • Journal article
  • SSRN Electronic Journal
  • Ruijie Zhang,
  • Yiying Zhang,
  • Kejia Hu,
  • Xiande Zhao
Management Science
See more publications

Engagement

Kejia currently serves on the editorial review board for two flagship journals in operations management, the Journal of operations Management and Production and Operations Management. 

She also closely works with industrial partners. She has solid research partnerships with firms in service industries, such as online marketplaces, hospitality and healthcare providers, and manufacturing industries, such as automakers and high-techs, with many of them being industrial pioneers or Fortune 500 companies.

New DPhil opportunity for October 2025 entry

Kejia is looking to take on a PhD candidate from October 2025 to engage in both theoretical research and practical applications, focusing on the potential for AI to streamline the analysis of structured and unstructured credit data and automate monitoring tasks within private asset management firms. The successful candidate will have the opportunity to collaborate with industry leaders to analyse real-world data, design algorithms, and improve predictive credit analysis using AI tools. In addition to shaping their research with the guidance of supervisors, they will gain hands-on experience through live industry projects, engage in the academic community at Saïd Business School and Oxford, and present their findings at conferences. They will also have the opportunity to contribute as a teaching or research assistant, preparing them for a successful career in both academia and industry. This project is available for students admitted to the full-time DPhil programme from October 2025 - applications must have been received by Friday 13 December (23:59 UK time). 

Teaching

Kejia was named one of the '40 Under 40 Best MBA Professors' by Poets & Quants in 2023. She currently teaches:

  • MBA - Business analytics
  • DPhil - Statistical research methods
  • EMBA and Executive Education - Harness the power of AI