IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v535y2019ics037843711931355x.html
   My bibliography  Save this article

The product demand model driven by consumer’s information perception and quality perception

Author

Listed:
  • Yuan, Guanghui
  • Han, Jingti
  • Wang, YaQiong
  • liang, Hejun
  • Li, GangYuan

Abstract

This paper uses the multi-layer network to study the influence of consumer’s information perception and consumer’s quality perception on market demand. In the network society, consumer’s information perception have a greater impact on market demand, mainly because the consumer’s information perception and the consumer’s quality perception are easier to know than before, which in turn affects their market demand. This paper constructs a two-tier model of consumer’s information perception communication and consumer’s quality perception dissemination. The upper is the consumer’s information perception layer and the lower is the consumer’s quality perception layer. At the same time, individuals will also have their own behavioral habits, if they change their active state with a certain probability, it will change the information capability of multi-layer network, the dissemination of consumer’s quality perception, and the market demand (to meet the personalized needs of the market). Finally, the accuracy of the theoretical analysis is verified by the scale-free network simulation.

Suggested Citation

  • Yuan, Guanghui & Han, Jingti & Wang, YaQiong & liang, Hejun & Li, GangYuan, 2019. "The product demand model driven by consumer’s information perception and quality perception," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    • Handle: RePEc:eee:phsmap:v:535:y:2019:i:c:s037843711931355x
    DOI: 10.1016/j.physa.2019.122352
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843711931355X
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2019.122352?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sergey V. Buldyrev & Roni Parshani & Gerald Paul & H. Eugene Stanley & Shlomo Havlin, 2010. "Catastrophic cascade of failures in interdependent networks," Nature, Nature, vol. 464(7291), pages 1025-1028, April.
    2. Ru-Jen Lin & Rong-Huei Chen & Thao-Minh Ho, 2013. "Market Demand, Green Innovation, and Firm Performance: Evidence from Hybrid Vehicle Industry," Diversity, Technology, and Innovation for Operational Competitiveness: Proceedings of the 2013 International Conference on Technology Innovation and Industrial Management,, ToKnowPress.
    3. Zhang, Yi-Cheng, 2005. "Supply and demand law under limited information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 350(2), pages 500-532.
    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. Hauck, Zsuzsanna & Rabta, Boualem & Reiner, Gerald, 2021. "Joint quality and pricing decisions in lot sizing models with defective items," International Journal of Production Economics, Elsevier, vol. 241(C).
    2. Guanghui Yuan & Zhiqiang Liu & Yaqiong Wang & Dongping Pu, 2023. "Market Demand Optimization Model Based on Information Perception Control," Mathematics, MDPI, vol. 11(3), pages 1-16, 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. Wang, Chengjiang & Wang, Li & Wang, Juan & Sun, Shiwen & Xia, Chengyi, 2017. "Inferring the reputation enhances the cooperation in the public goods game on interdependent lattices," Applied Mathematics and Computation, Elsevier, vol. 293(C), pages 18-29.
    2. Tang, Liang & Jing, Ke & He, Jie & Stanley, H. Eugene, 2016. "Robustness of assembly supply chain networks by considering risk propagation and cascading failure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 459(C), pages 129-139.
    3. Shogo Mizutaka & Kousuke Yakubo, 2017. "Structural instability of large-scale functional networks," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-11, July.
    4. Balint, T. & Lamperti, F. & Mandel, A. & Napoletano, M. & Roventini, A. & Sapio, A., 2017. "Complexity and the Economics of Climate Change: A Survey and a Look Forward," Ecological Economics, Elsevier, vol. 138(C), pages 252-265.
    5. Mohammad Iranmanesh & Suhaiza Zailani & Soroush Moeinzadeh & Davoud Nikbin, 2017. "Effect of green innovation on job satisfaction of electronic and electrical manufacturers’ employees through job intensity: personal innovativeness as moderator," Review of Managerial Science, Springer, vol. 11(2), pages 299-313, March.
    6. Fridgen, Gilbert & Keller, Robert & Körner, Marc-Fabian & Schöpf, Michael, 2020. "A holistic view on sector coupling," Energy Policy, Elsevier, vol. 147(C).
    7. Hernandez-Fajardo, Isaac & Dueñas-Osorio, Leonardo, 2013. "Probabilistic study of cascading failures in complex interdependent lifeline systems," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 260-272.
    8. Yu, Haitao & Wang, Jiang & Liu, Chen & Deng, Bin & Wei, Xile, 2014. "Delay-induced synchronization transitions in modular scale-free neuronal networks with hybrid electrical and chemical synapses," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 25-34.
    9. Sgrignoli, Paolo & Metulini, Rodolfo & Schiavo, Stefano & Riccaboni, Massimo, 2015. "The relation between global migration and trade networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 245-260.
    10. Zhou, Yaoming & Wang, Junwei, 2018. "Efficiency of complex networks under failures and attacks: A percolation approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 658-664.
    11. Wang, Quan-Jing & Wang, Hai-Jie & Chang, Chun-Ping, 2022. "Environmental performance, green finance and green innovation: What's the long-run relationships among variables?," Energy Economics, Elsevier, vol. 110(C).
    12. Monsalve, Mauricio & de la Llera, Juan Carlos, 2019. "Data-driven estimation of interdependencies and restoration of infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 167-180.
    13. Liu, Huan & Tatano, Hirokazu & Pflug, Georg & Hochrainer-Stigler, Stefan, 2021. "Post-disaster recovery in industrial sectors: A Markov process analysis of multiple lifeline disruptions," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    14. Zhongju Liao & Xiang Zhu, 2022. "A configurational analysis of firms' environmental innovation: Evidence from China's key pollutant‐discharge listed companies," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(6), pages 1511-1522, December.
    15. Krawiecki, A., 2018. "Spin glass transition in a simple variant of the Ising model on multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 773-790.
    16. Shekhtman, Louis M. & Danziger, Michael M. & Havlin, Shlomo, 2016. "Recent advances on failure and recovery in networks of networks," Chaos, Solitons & Fractals, Elsevier, vol. 90(C), pages 28-36.
    17. Weihua Lei & Luiz G. A. Alves & Luís A. Nunes Amaral, 2022. "Forecasting the evolution of fast-changing transportation networks using machine learning," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    18. Wang, Gang-Jin & Chen, Yang-Yang & Si, Hui-Bin & Xie, Chi & Chevallier, Julien, 2021. "Multilayer information spillover networks analysis of China’s financial institutions based on variance decompositions," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 325-347.
    19. Leto Peel & Tiago P. Peixoto & Manlio De Domenico, 2022. "Statistical inference links data and theory in network science," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    20. Kyu-Min Lee & Kwang-Il Goh, 2016. "Strength of weak layers in cascading failures on multiplex networks: case of the international trade network," Papers 1603.05181, arXiv.org, revised May 2016.

    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:eee:phsmap:v:535:y:2019:i:c:s037843711931355x. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.