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Cynthia Qingxia  Kong
Associate Professor
Rotterdam School of Management (RSM)
Erasmus University Rotterdam

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Dr. Qingxia Kong is an Associate Professor of Operations Management at Rotterdam School of Management, Erasmus University Rotterdam. After completing her Ph.D. at the National University of Singapore, she worked as an assistant professor of Operations at Universidad Adolfo Ibanez, Santiago de Chile. She joined Erasmus University in 2016. Kong's research interests are healthcare operations management, medical decision making, and behavioral operations. 

Publications

Highlighted (6)
  • Kong, Q., Homem-de-Mello, T., & godoy-barba, R. (2022). A Simulation Optimization Approach for the Appointment Scheduling Problem with Decision-Dependent Uncertainties. INFORMS Journal on Computing, 34(5), 2845-2865. https://doi.org/10.1287/ijoc.2022.1212

  • Kong, C., Li, S., Liu, N., Teo, C., & Yan, Z. (2020). Appointment Scheduling Under Time-Dependent Patient No-Show Behavior. Management Science, 66(8), 3480-3500. https://doi.org/10.1287/mnsc.2019.3366

  • Kong, C., Granic, G., Lambert, NS., & Teo, CP. (2019). Judgment Error in Lottery Play: When the Hot-Hand Meets the Gambler's Fallacy. Management Science, 66(2), 844-862. https://doi.org/10.1287/mnsc.2018.3233

  • Liu, Y., Kong, C., & de Bekker - Grob, E. (2019). Public preferences for health care facilities in rural China: A discrete choice experiment. Social Science & Medicine, 237, Article 112396. https://doi.org/10.1016/j.socscimed.2019.112396

  • Kong, C., Lee, CY., Teo, CP., & Zheng, ZC. (2016). Appointment Sequencing: Why the Smallest-Variance-First Rule may Not Be Optimal. European Journal of Operational Research, 255(3), 809-821. https://doi.org/10.1016/j.ejor.2016.06.004

  • Kong, C., Lee, CY., Teo, CP., & Zheng, ZC. (2013). Scheduling Arrivals to a Stochastic Service Delivery System using Copositive Cones. Operations Research, 61(3), 711-726. https://doi.org/10.1287/opre.2013.1158

Academic (16)
  • van de Klundert, J., Cominetti, R., Liu, Y., & Kong, Q. (2024). The interdependence between hospital choice and waiting time — with a case study in urban China. Journal of Choice Modelling, 52, Article 100509. https://doi.org/10.1016/j.jocm.2024.100509

  • Gong, J., Kampadellis, G., Kong, Q., & Spijker, W. (2023). Factors determining non-attendance in breast cancer screening among women in the Netherlands: a national study. Health Promotion International, 38(3), Article daac009. https://doi.org/10.1093/heapro/daac009

  • Kong, Q., Homem-de-Mello, T., & godoy-barba, R. (2022). A Simulation Optimization Approach for the Appointment Scheduling Problem with Decision-Dependent Uncertainties. INFORMS Journal on Computing, 34(5), 2845-2865. https://doi.org/10.1287/ijoc.2022.1212

  • von Weinrich, P., Kong, Q., & Liu, Y. (2022). Would you zoom with your doctor? A discrete choice experiment to identify patient preferences for video and in-clinic consultations in German primary care. Journal of Telemedicine and Telecare, 30(6), 969-992. Advance online publication. https://doi.org/10.1177/1357633X221111975

  • Kong, Q., Riedewald, D., & Askari, M. (2021). Factors affecting portal usage among chronically ill patients during the COVID-19 pandemic in the Netherlands: Cross-sectional study. JMIR Human Factors, 8(3), Article e26003. https://doi.org/10.2196/26003

  • Kong, C., Li, S., Liu, N., Teo, C., & Yan, Z. (2020). Appointment Scheduling Under Time-Dependent Patient No-Show Behavior. Management Science, 66(8), 3480-3500. https://doi.org/10.1287/mnsc.2019.3366

  • Liu, P., Schmidt, M., Kong, C., Wagenaar, J., Yang, L., & Gao, Z. (2020). A Robust and Energy-Efficient Train Timetable for the Subway System. Transportation Research. Part C, Emerging Technologies, 121, Article 102822. https://doi.org/10.1016/j.trc.2020.102822

  • Liu, Y., Kong, C., Wang, S., Zhong, L., & Van de Klundert, JJ. (2019). The impact of hospital attributes on patient choice for first visit: evidence from a discrete choice experiment in Shanghai, China. Health Policy and Planning, 35(3), 267-278. Article czz159. https://doi.org/10.1093/heapol/czz159

  • Kong, C., Granic, G., Lambert, NS., & Teo, CP. (2019). Judgment Error in Lottery Play: When the Hot-Hand Meets the Gambler's Fallacy. Management Science, 66(2), 844-862. https://doi.org/10.1287/mnsc.2018.3233

  • Liu, Y., Kong, C., & de Bekker - Grob, E. (2019). Public preferences for health care facilities in rural China: A discrete choice experiment. Social Science & Medicine, 237, Article 112396. https://doi.org/10.1016/j.socscimed.2019.112396

  • Liu, Y., Kong, C., Yuan, S., & van de Klundert, J. (2018). Factors influencing choice of health system access level in China: A systematic review. PLoS One (online), 13(8), Article e0201887. https://doi.org/10.1371/journal.pone.0201887

  • Kong, C., Mondschein, S., & Pereira, A. (2018). Effectiveness of breast cancer screening policies in countries with medium-low incidence rates. Revista de Saude Publica, 52(7). https://doi.org/10.11606/S1518-8787.2018052000378

  • Kong, C., Lee, CY., Teo, CP., & Zheng, ZC. (2016). Appointment Sequencing: Why the Smallest-Variance-First Rule may Not Be Optimal. European Journal of Operational Research, 255(3), 809-821. https://doi.org/10.1016/j.ejor.2016.06.004

  • Kong, C., Lee, CY., Teo, CP., & Zheng, ZC. (2013). Scheduling Arrivals to a Stochastic Service Delivery System using Copositive Cones. Operations Research, 61(3), 711-726. https://doi.org/10.1287/opre.2013.1158

  • Chou, M., Kong, C., Teo, CP., & Zheng, H. (2009). Benford's Law and Number Selection in Fixed-Odds Numbers. Journal of Gambling Studies, 25(4), 503-521. https://doi.org/10.1007/s10899-009-9145-9

  • Kong, C., & Lu, Q. (2006). Demand Model of Stochastic Customer Choice and Its Application in Inventory Control. Logistics Research, 7, 127-130.

Academic (1)
  • Chou, M., Kong, C., Teo, CP., & Zheng, H. (2015). Managing Risk in Numbers Games: Benford’s Law and the Small-Number Phenomenon. In Steven J. Miller (Ed.), Benford's Law: Theory and Applications Princeton University Press.

Courses

IM Research clinic

  • Study year: 2024/2025, 2023/2024, 2021/2022, 2020/2021
  • Code: BM-IM-RC
  • Level: Master

Supply Chain Fundamentals

  • Study year: 2024/2025, 2023/2024, 2022/2023, 2021/2022, 2020/2021, 2019/2020, 2018/2019, 2017/2018, 2016/2017
  • Code: BM01SCM
  • Level: ERIM, Exchange, IM/CEMS, Master

Purchasing & Supply Management

  • Study year: 2024/2025, 2023/2024, 2022/2023, 2021/2022, 2020/2021
  • Code: BM05SCM
  • Level: ERIM, Exchange, IM/CEMS, Master

Decision Science & Operations

  • Study year: 2024/2025, 2023/2024, 2022/2023, 2021/2022, 2020/2021, 2019/2020
  • Code: BM27MIM
  • ECTS: 6 Level: Master

MiM Master Thesis

  • Study year: 2024/2025, 2023/2024, 2022/2023
  • Code: BMMTMIM
  • ECTS: 16 Level: Master

Supply Chain Decision Analytics

  • Study year: 2024/2025, 2023/2024, 2022/2023, 2021/2022, 2020/2021
  • Code: BMRM1SCM
  • Level: Master

Research Methods and Skills

  • Study year: 2024/2025, 2023/2024
  • Code: BMRM4SCM
  • Level: Master

Past courses

OLD STYLE - Research Methods and Skills

  • Study year: 2023/2024, 2022/2023, 2021/2022, 2020/2021
  • Code: BMRM3SCM
  • Level: Master

IM Research clinic

  • Study year: 2020/2021, 2019/2020, 2018/2019
  • Code: BM-IMRC
  • Level: Master

SCM Honours Programme

  • Study year: 2019/2020, 2018/2019
  • Code: BMHONSCM
  • ECTS: 8 Level: Master

Operations & supply chain management

  • Study year: 2018/2019, 2017/2018, 2016/2017
  • Code: BM15MIM
  • ECTS: 2 Level: Master

Featured on RSM Discovery

Discovery 41: Global Supply Chains

The latest issue of RSM Discovery magazine highlights the importance of transparency and accountability in supply chain management, as well as the need for timely and reliable availability of components.

Vaccine distribution

Explore how last-mile distribution strategies impact Covid vaccine uptake. Learn about key factors such as appointment delays, travel time, and decentralisation that influence vaccination rates, based on research from RSM.

Would you Zoom with your doctor?