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Research on Tacit Knowledge Dissemination of Automobile Consumers’ Low-Carbon Purchase Intention

Author

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  • Nan Xu

    (School of Management, Harbin University of Commerce, Harbin 150028, China)

  • Yaoqun Xu

    (Institute of System Engineering, Harbin University of Commerce, Harbin 150028, China)

Abstract

At present, domestic consumers hold a wait-and-see attitude toward new energy vehicles. Although sales are increasing year-by-year, there is still a big gap compared with traditional fuel cars. In view of this problem, this paper starts to consider the problem from the subjective internal cause. Based on the classic SIR model, the conversion rate of low-carbon purchase inclination of consumers of new energy vehicles is introduced to build a tacit knowledge dissemination model of the interaction of low-carbon and conservative purchase inclination. The system ensures that low-carbon purchase inclination is a positively advocated consumption value, and provides decision-making reference for the government’s publicity and enterprises’ technological innovation measures. For the first time, differential dynamics are combined with the purchase inclination of consumers of new energy vehicles. This article collected 1098 questionnaires, and the statistical results show that the most effective way for people to accept new energy vehicles is word of mouth from relatives and friends. This illustrates the necessity of studying tacit knowledge dissemination among consumer groups of new energy vehicles. The questionnaire also indicates what aspects of the performance of new energy vehicles consumers are concerned about, providing empirical evidence for the realization of consumption behavior. The improved SIR model dynamically depicts the evolution process of consumers’ purchasing inclination of new energy vehicles based on differential dynamics. The stable equilibrium point of the system was solved, and the main factors affecting the tacit knowledge transmission of purchase inclination included initial market parameters, conversion rate, and low-carbon and conservative transmission rates, etc. The practicality and effectiveness of the model was verified by numerical simulation. It can provide the government and enterprises with theoretical support and development suggestions in promoting the consumption and development of new energy vehicles.

Suggested Citation

  • Nan Xu & Yaoqun Xu, 2022. "Research on Tacit Knowledge Dissemination of Automobile Consumers’ Low-Carbon Purchase Intention," Sustainability, MDPI, vol. 14(16), pages 1-26, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:10097-:d:888601
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    as
    1. Bryden, Thomas S. & Hilton, George & Cruden, Andrew & Holton, Tim, 2018. "Electric vehicle fast charging station usage and power requirements," Energy, Elsevier, vol. 152(C), pages 322-332.
    2. Piergiuseppe Morone & Richard Taylor, 2004. "Knowledge diffusion dynamics and network properties of face-to-face interactions," Journal of Evolutionary Economics, Springer, vol. 14(3), pages 327-351, July.
    3. Baldini, Mattia & Trivella, Alessio & Wente, Jordan William, 2018. "The impact of socioeconomic and behavioural factors for purchasing energy efficient household appliances: A case study for Denmark," Energy Policy, Elsevier, vol. 120(C), pages 503-513.
    4. Cowan, Robin & Jonard, Nicolas & Özman, Müge, 2003. "Knowledge Dynamics in a Network Industry," Research Memorandum 003, Maastricht University, Maastricht Economic Research Institute on Innovation and Technology (MERIT).
    5. Bai, Zhenguo & Wu, Shi-Liang, 2015. "Traveling waves in a delayed SIR epidemic model with nonlinear incidence," Applied Mathematics and Computation, Elsevier, vol. 263(C), pages 221-232.
    6. Li, Jingjing & Zhang, Yumei & Man, Jiayu & Zhou, Yun & Wu, Xiaojun, 2017. "SISL and SIRL: Two knowledge dissemination models with leader nodes on cooperative learning networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 740-749.
    7. Shaofu Du & Li Hu & Li Wang, 2017. "Low-carbon supply policies and supply chain performance with carbon concerned demand," Annals of Operations Research, Springer, vol. 255(1), pages 569-590, August.
    8. Angstmann, C.N. & Henry, B.I. & McGann, A.V., 2016. "A fractional-order infectivity SIR model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 86-93.
    9. Meng, Xiaoge & Yao, Zhong & Nie, Jiajia & Zhao, Yingxue & Li, Zenglu, 2018. "Low-carbon product selection with carbon tax and competition: Effects of the power structure," International Journal of Production Economics, Elsevier, vol. 200(C), pages 224-230.
    10. Wenyi Du & Yubing Fan & Lina Yan, 2018. "Pricing Strategies for Competitive Water Supply Chains under Different Power Structures: An Application to the South-to-North Water Diversion Project in China," Sustainability, MDPI, vol. 10(8), pages 1-13, August.
    11. Geels, Frank W., 2004. "From sectoral systems of innovation to socio-technical systems: Insights about dynamics and change from sociology and institutional theory," Research Policy, Elsevier, vol. 33(6-7), pages 897-920, September.
    12. Zhang, Xian & Wang, Ke & Hao, Yu & Fan, Jing-Li & Wei, Yi-Ming, 2013. "The impact of government policy on preference for NEVs: The evidence from China," Energy Policy, Elsevier, vol. 61(C), pages 382-393.
    13. Wang, Jiajia & Zhao, Laijun & Huang, Rongbing, 2014. "SIRaRu rumor spreading model in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 398(C), pages 43-55.
    14. Yu, Yugang & Han, Xiaoya & Hu, Guiping, 2016. "Optimal production for manufacturers considering consumer environmental awareness and green subsidies," International Journal of Production Economics, Elsevier, vol. 182(C), pages 397-408.
    15. Zhao, Laijun & Xie, Wanlin & Gao, H. Oliver & Qiu, Xiaoyan & Wang, Xiaoli & Zhang, Shuhai, 2013. "A rumor spreading model with variable forgetting rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 6146-6154.
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    Cited by:

    1. Jingyang Chen & Qin Liu, 2023. "The Green Consumption Behavior Process Mechanism of New Energy Vehicles Driven by Big Data—From a Metacognitive Perspective," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    2. Mao Zheng & Ningning Cui & Yibin Zhang & Fangfang Zhang & Victor Shi, 2023. "Inventory Policies and Supply Chain Coordination under Logistics Route Disruption Risks," Sustainability, MDPI, vol. 15(13), pages 1-20, June.
    3. Shumei Wang & Yaoqun Xu, 2022. "Complex Network-Based Evolutionary Game for Knowledge Transfer of Social E-Commerce Platform Enterprise’s Operation Team under Strategy Imitation Preferences," Sustainability, MDPI, vol. 14(22), pages 1-34, November.

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