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Multi-Tier Supply Chain Learning Networks: A Simulation Study Based on the Experience-Weighted Attraction (EWA) Model

Author

Listed:
  • Yu Gong

    (Southampton Business School, University of Southampton, Southampton SO16 7QB, UK)

  • Xiaojiang Xu

    (College of Finance and Statistics, Hunan University, Changsha 410082, China)

  • Changping Zhao

    (Business School, Changshu Institute of Technology, Changshu 215506, China
    Tianhua College, Shanghai Normal University, Shanghai 201815, China)

  • Tobias Schoenherr

    (Broad College of Business, Michigan State University, East Lansing, MI 48824, USA)

Abstract

Supply chain learning (SCL), which is reflected in organizational learning, referring to the learning between organizations in the supply chain, carries the promise to enable sustainable competitive advantages. Many large multinational companies, such as IKEA, Nestle, and Microsoft, have therefore integrated supply chain knowledge management and continuous learning into their corporate strategies. While there is evidence in extant research about a positive correlation between both the subjective attitude and learning ability of supply chain members and their performance improvement, areas where insight is still missing pertain to the relationship between supply chain members’ subjective psychological factors, and their relationship network structures. This is a serious omission, since these dimensions likely play a key role in the dynamics underlying SCL. In order to alleviate this void, we consider a multi-tier SCL network and develop a model in which a supply chain member’s attraction is weighted based on its previous learning experience. The game mechanism underlying SCL captured in this experience-weighted attraction (EWA) model is then tested using a simulation study of IKEA China’s multi-tier supply chain network for its sustainable cotton initiative. The results suggest that learning costs can be reduced and learning spillover befits can be increased by the provision of rewards to network member companies and better communication. In addition, the perception of and preference for SCL by suppliers can be influenced by initiating sustainable advocacy and providing knowledge and technology training, as well as fostering a range of subjective factors we investigate in our study, such as the strategic attractiveness the decline ratio due to forgetting, the attractiveness improvement ratio due to preferences, and the response sensitivity to strategies. The findings offer insight into the influence mechanisms of the supply chain network structure and subjective attitude about SCL, which are especially applicable to large, multinational enterprises.

Suggested Citation

  • Yu Gong & Xiaojiang Xu & Changping Zhao & Tobias Schoenherr, 2024. "Multi-Tier Supply Chain Learning Networks: A Simulation Study Based on the Experience-Weighted Attraction (EWA) Model," Sustainability, MDPI, vol. 16(10), pages 1-25, May.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:10:p:4085-:d:1393869
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    References listed on IDEAS

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    1. Crawford, Vincent P, 1995. "Adaptive Dynamics in Coordination Games," Econometrica, Econometric Society, vol. 63(1), pages 103-143, January.
    2. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-881, September.
    3. Ritesh Ojha & Abhijeet Ghadge & Manoj Kumar Tiwari & Umit S. Bititci, 2018. "Bayesian network modelling for supply chain risk propagation," International Journal of Production Research, Taylor & Francis Journals, vol. 56(17), pages 5795-5819, September.
    4. Duncan J. Watts & Steven H. Strogatz, 1998. "Collective dynamics of ‘small-world’ networks," Nature, Nature, vol. 393(6684), pages 440-442, June.
    5. Cao, Mei & Zhang, Qingyu, 2010. "Supply chain collaborative advantage: A firm's perspective," International Journal of Production Economics, Elsevier, vol. 128(1), pages 358-367, November.
    6. Christian F. Durach & Joakim Kembro & Andreas Wieland, 2017. "A New Paradigm for Systematic Literature Reviews in Supply Chain Management," Journal of Supply Chain Management, Institute for Supply Management, vol. 53(4), pages 67-85, October.
    7. Sanne, Johan M., 2012. "Learning from adverse events in the nuclear power industry: Organizational learning, policy making and normalization," Technology in Society, Elsevier, vol. 34(3), pages 239-250.
    8. Yu Gong & Yan Jiang & Fu Jia, 2023. "Multiple multi-tier sustainable supply chain management: a social system theory perspective," International Journal of Production Research, Taylor & Francis Journals, vol. 61(14), pages 4684-4701, July.
    9. Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
    10. Maria Lai‐Ling Lam, 2011. "Challenges of sustainable environmental programs of foreign multinational enterprises in China," Management Research Review, Emerald Group Publishing Limited, vol. 34(11), pages 1153-1168, October.
    11. Zhenhua Pei & Baokui Wang & Jinming Du, 2016. "Effects of income redistribution on the evolution of cooperation in spatial public goods games," Papers 1611.01531, arXiv.org.
    12. Jeffrey H. Dyer & Kentaro Nobeoka, 2000. "Creating and managing a high‐performance knowledge‐sharing network: the Toyota case," Strategic Management Journal, Wiley Blackwell, vol. 21(3), pages 345-367, March.
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