IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i14p8420-d859227.html
   My bibliography  Save this article

Supply Chain Risk Diffusion Model Considering Multi-Factor Influences under Hypernetwork Vision

Author

Listed:
  • Ping Yu

    (School of Science, Dalian Maritime University, Dalian 116026, China)

  • Peiwen Wang

    (School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China)

  • Zhiping Wang

    (School of Science, Dalian Maritime University, Dalian 116026, China)

  • Jia Wang

    (School of Science, Dalian Maritime University, Dalian 116026, China)

Abstract

Considering the problem of risk diffusion in increasingly complex supply chain networks, we propose using the supply chain risk diffusion model, under the hypernetwork vision, to study the influence of certain factors on risk diffusion, including the herd mentality, self-vigilance, talent recruitment, and enterprise management. First of all, the state transition probability tree is constructed to represent the state transition of each enterprise, then the Microscopic Markov Chain Approach (MMCA) is used to analyze the scale of risk spread, and the diffusion threshold of risk is discussed. We find that the herd mentality, self-vigilance, talent recruitment, and enterprise management will effectively curb the spread of risks. Directly recruiting talents and strengthening enterprise management is more effective than increasing vigilance. This study helps professionals to understand the mechanism of risk diffusion, and it provides effective suggestions on how to suppress risk diffusion in the real world.

Suggested Citation

  • Ping Yu & Peiwen Wang & Zhiping Wang & Jia Wang, 2022. "Supply Chain Risk Diffusion Model Considering Multi-Factor Influences under Hypernetwork Vision," Sustainability, MDPI, vol. 14(14), pages 1-15, July.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8420-:d:859227
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/14/8420/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/14/8420/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mary Loxton & Robert Truskett & Brigitte Scarf & Laura Sindone & George Baldry & Yinong Zhao, 2020. "Consumer Behaviour during Crises: Preliminary Research on How Coronavirus Has Manifested Consumer Panic Buying, Herd Mentality, Changing Discretionary Spending and the Role of the Media in Influencing," JRFM, MDPI, vol. 13(8), pages 1-21, July.
    2. Ran, Maojie & Chen, Jiancu, 2021. "An information dissemination model based on positive and negative interference in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    3. Guo, Jingni & Xu, Junxiang & He, Zhenggang & Liao, Wei, 2021. "Research on risk propagation method of multimodal transport network under uncertainty," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    4. Sreedevi, R. & Saranga, Haritha, 2017. "Uncertainty and supply chain risk: The moderating role of supply chain flexibility in risk mitigation," International Journal of Production Economics, Elsevier, vol. 193(C), pages 332-342.
    5. Rezapour, Shabnam & Allen, Janet K. & Mistree, Farrokh, 2015. "Uncertainty propagation in a supply chain or supply network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 73(C), pages 185-206.
    6. Hu, Ping & Geng, Dongqing & Lin, Tao & Ding, Li, 2021. "Coupled propagation dynamics on multiplex activity-driven networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
    7. Huo, Liang’an & Guo, Hongyuan & Cheng, Yingying & Xie, Xiaoxiao, 2020. "A new model for supply chain risk propagation considering herd mentality and risk preference under warning information on multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    8. Guangsheng ZHANG & Xiao WANG & Zhijun GAO & Tianyu XIANG, 2020. "Research on Risk Diffusion Mechanism of Logistics Service Supply Chain in Urgent Scenarios," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-12, February.
    9. Han, Dun & Sun, Mei, 2016. "An evolutionary vaccination game in the modified activity driven network by considering the closeness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 49-57.
    10. Tang, Liang & Jing, Ke & He, Jie & Stanley, H. Eugene, 2016. "Complex interdependent supply chain networks: Cascading failure and robustness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 58-69.
    11. An, Xuming & Ding, Li & Hu, Ping, 2020. "Information propagation with individual attention-decay effect on activity-driven networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    12. Jia, Mengqi & Li, Xin & Ding, Li, 2021. "Epidemic spreading with awareness on multi-layer activity-driven networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 579(C).
    13. Suo, Qi & Guo, Jin-Li & Sun, Shiwei & Liu, Han, 2018. "Exploring the evolutionary mechanism of complex supply chain systems using evolving hypergraphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 489(C), pages 141-148.
    14. Alessandro Rizzo & Maurizio Porfiri, 2016. "Innovation diffusion on time-varying activity driven networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 89(1), pages 1-8, January.
    15. Jianjun Zhu & Yamin Cheng & Yuhuai Zhang, 2021. "Risk Propagation Mechanism Research Based on SITR Model of Complex Supply Networks," International Journal of Information Systems and Supply Chain Management (IJISSCM), IGI Global, vol. 14(3), pages 18-38, July.
    16. Liu, Hui & Yang, Naiding & Yang, Zhao & Lin, Jianhong & Zhang, Yanlu, 2020. "The impact of firm heterogeneity and awareness in modeling risk propagation on multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    17. Lixin Wang & Xiaolong Jiao & Qian Hao & Daqing Gong, 2021. "Modeling the Incentive Mechanism of Information Sharing in a Dual-Channel Supply Chain," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-7, July.
    18. Heckmann, Iris & Comes, Tina & Nickel, Stefan, 2015. "A critical review on supply chain risk – Definition, measure and modeling," Omega, Elsevier, vol. 52(C), pages 119-132.
    19. Huo, Liang’an & Guo, Hongyuan & Cheng, Yingying, 2019. "Supply chain risk propagation model considering the herd mentality mechanism and risk preference," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 529(C).
    20. Alessandro Rizzo & Maurizio Porfiri, 2016. "Innovation diffusion on time-varying activity driven networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 89(1), pages 1-8, January.
    21. Wang, Jiepeng & Zhou, Hong & Jin, Xiaodan, 2021. "Risk transmission in complex supply chain network with multi-drivers," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    22. Estrada, Ernesto & Rodríguez-Velázquez, Juan A., 2006. "Subgraph centrality and clustering in complex hyper-networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 364(C), pages 581-594.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jianhua Chen & Ting Yin, 2023. "Transmission Mechanism of Post-COVID-19 Emergency Supply Chain Based on Complex Network: An Improved SIR Model," Sustainability, MDPI, vol. 15(4), pages 1-19, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ping Yu & Zhiping Wang & Yanan Sun & Peiwen Wang, 2022. "Risk Diffusion and Control under Uncertain Information Based on Hypernetwork," Mathematics, MDPI, vol. 10(22), pages 1-17, November.
    2. Huo, Liang’an & Guo, Hongyuan & Cheng, Yingying & Xie, Xiaoxiao, 2020. "A new model for supply chain risk propagation considering herd mentality and risk preference under warning information on multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    3. Yang, Qing & Zou, Xingqi & Ye, Yunting & Yao, Tao, 2022. "Evaluating the criticality of the product development project portfolio network from the perspective of risk propagation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    4. Ruan, Zhongyuan & Zhang, Lina & Shu, Xincheng & Xuan, Qi, 2022. "Social contagion with negative feedbacks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    5. Yang, Qihui & Scoglio, Caterina M. & Gruenbacher, Don M., 2021. "Robustness of supply chain networks against underload cascading failures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    6. Issam Laguir & Sachin Modgil & Indranil Bose & Shivam Gupta & Rebecca Stekelorum, 2023. "Performance effects of analytics capability, disruption orientation, and resilience in the supply chain under environmental uncertainty," Annals of Operations Research, Springer, vol. 324(1), pages 1269-1293, May.
    7. Li, Ruimeng & Yang, Naiding & Zhang, Yanlu & Liu, Hui & Zhang, Mingzhen, 2021. "Impacts of module–module aligned patterns on risk cascading propagation in complex product development (CPD) interdependent networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 564(C).
    8. Jin Sung Rha, 2020. "Trends of Research on Supply Chain Resilience: A Systematic Review Using Network Analysis," Sustainability, MDPI, vol. 12(11), pages 1-27, May.
    9. Ramírez-Correa, Patricio & Grandón, Elizabeth E. & Rondán-Cataluña, F. Javier, 2020. "Users segmentation based on the Technological Readiness Adoption Index in emerging countries: The case of Chile," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    10. Neungho Han & Juneho Um, 2024. "Risk management strategy for supply chain sustainability and resilience capability," Risk Management, Palgrave Macmillan, vol. 26(2), pages 1-26, May.
    11. Jia Wang & Zhiping Wang & Ping Yu & Peiwen Wang, 2022. "The SEIR Dynamic Evolutionary Model with Markov Chains in Hyper Networks," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    12. Goldbeck, Nils & Angeloudis, Panagiotis & Ochieng, Washington, 2020. "Optimal supply chain resilience with consideration of failure propagation and repair logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    13. Enrique Holgado de Frutos & Juan R Trapero & Francisco Ramos, 2020. "A literature review on operational decisions applied to collaborative supply chains," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-28, March.
    14. Jiali Wang & Xue Peng & Yunan Du & Fulin Wang, 2022. "A tripartite evolutionary game research on information sharing of the subjects of agricultural product supply chain with a farmer cooperative as the core enterprise," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(1), pages 159-177, January.
    15. Yu, Ping & Wang, Zhiping & Wang, Peiwen & Yin, Haofei & Wang, Jia, 2022. "Dynamic evolution of shipping network based on hypergraph," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    16. Wang, Zhiping & Yin, Haofei & Jiang, Xin, 2020. "Exploring the dynamic growth mechanism of social networks using evolutionary hypergraph," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 544(C).
    17. Maria Concetta Carissimi & Lorenzo Bruno Prataviera & Alessandro Creazza & Marco Melacini & Fabrizio Dallari, 2023. "Blurred lines: the timeline of supply chain resilience strategies in the grocery industry in the time of Covid-19," Operations Management Research, Springer, vol. 16(1), pages 80-98, March.
    18. Jimin Xiong & Zhanfeng Tang & Yufeng Zhu & Kefeng Xu & Yanhong Yin & Yang Xi, 2021. "Change of Consumption Behaviours in the Pandemic of COVID-19: Examining Residents’ Consumption Expenditure and Driving Determinants," IJERPH, MDPI, vol. 18(17), pages 1-15, August.
    19. Li, Dandan & Ma, Jing, 2017. "How the government’s punishment and individual’s sensitivity affect the rumor spreading in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 284-292.
    20. Ben-Ammar, Oussama & Bettayeb, Belgacem & Dolgui, Alexandre, 2019. "Optimization of multi-period supply planning under stochastic lead times and a dynamic demand," International Journal of Production Economics, Elsevier, vol. 218(C), pages 106-117.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8420-:d:859227. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.