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Scare Behavior Diffusion Model of Health Food Safety Based on Complex Network

Author

Listed:
  • Jun Luo
  • Jiepeng Wang
  • Yongle Zhao
  • Tingqiang Chen

Abstract

This study constructs a heterogeneous model of health food safety scare behavior diffusion through a complex network model by considering health food safety information transparency and health food consumers’ ability to process information. This study first analyzes the effects of network structure and heterogeneity of health food consumers on the health food safety scare behavior diffusion using network stochastic dominance theory. Subsequently, a computer mathematical simulation is performed to explore the characteristics and laws of the evolution of health food safety scare behavior diffusion. The following three major conclusions can be drawn from the results. First, increases in the health food safety information transparency, the health food consumers’ ability to process information, and the recovery rate of health food consumers can increase the threshold of the rate of health food safety scare behavior diffusion. The health food safety information transparency and the recovery rate of health food consumers show marginal incremental rising characteristics in relation to the rate of health food safety scare behavior diffusion, whereas the health food consumers’ ability to process information shows a marginal diminishing rising characteristic in relation to the rate of health food safety scare behavior diffusion. Second, increases in the health food safety information transparency, the health food consumers’ ability to process information, and the recovery rate of health food consumers can decrease the scale of the health food safety scare behavior diffusion. The health food safety information transparency shows a marginal diminishing decreasing characteristic in relation to the scale of the health food safety scare behavior diffusion, whereas the health food consumers’ ability to process information and the recovery rate of the health food consumers show marginal incremental decreasing characteristics in relation to the scale of the health food safety scare behavior diffusion. Finally, the network structure of health food consumers significantly affects the health food safety scare behavior diffusion. A high heterogeneity of the health food consumer network indicates a high threshold of the rate of health food safety scare behavior diffusion and low diffusion scale.

Suggested Citation

  • Jun Luo & Jiepeng Wang & Yongle Zhao & Tingqiang Chen, 2018. "Scare Behavior Diffusion Model of Health Food Safety Based on Complex Network," Complexity, Hindawi, vol. 2018, pages 1-14, November.
  • Handle: RePEc:hin:complx:5902105
    DOI: 10.1155/2018/5902105
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    1. Li, Tongzhe & Bernard, John C. & Johnston, Zachary A. & Messer, Kent D. & Kaiser, Harry M., 2017. "Consumer preferences before and after a food safety scare: An experimental analysis of the 2010 egg recall," Food Policy, Elsevier, vol. 66(C), pages 25-34.
    2. Li, Shuping & Jin, Zhen, 2015. "Dynamic modeling and analysis of sexually transmitted diseases on heterogeneous networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 192-201.
    3. Tingqiang Chen & Lei Wang & Jining Wang & Qi Yang, 2017. "A Network Diffusion Model of Food Safety Scare Behavior considering Information Transparency," Complexity, Hindawi, vol. 2017, pages 1-16, December.
    4. Ragnar E. Lofstedt, 2006. "How can we Make Food Risk Communication Better: Where are we and Where are we Going?," Journal of Risk Research, Taylor & Francis Journals, vol. 9(8), pages 869-890, December.
    5. He, Jianmin & Sui, Xin & Li, Shouwei, 2016. "An endogenous model of the credit network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 441(C), pages 1-14.
    6. Yue-tang Bian & Lu Xu & Jin-Sheng Li & Xia-qun Liu, 2016. "Dynamical evolution of trading behavior on anti-coordination game in complex networks," China Finance Review International, Emerald Group Publishing Limited, vol. 6(4), pages 367-379, November.
    7. Julie A. Caswell & Eliza M. Mojduszka, 1996. "Using Informational Labeling to Influence the Market for Quality in Food Products," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(5), pages 1248-1253.
    8. Mario Mazzocchi & Alexandra Lobb & W. Bruce Traill & Alessio Cavicchi, 2008. "Food Scares and Trust: A European Study," Journal of Agricultural Economics, Wiley Blackwell, vol. 59(1), pages 2-24, February.
    9. G. Hanoch & H. Levy, 1969. "The Efficiency Analysis of Choices Involving Risk," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 36(3), pages 335-346.
    10. James P. Quirk & Rubin Saposnik, 1962. "Admissibility and Measurable Utility Functions," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 29(2), pages 140-146.
    11. Hadar, Josef & Russell, William R, 1969. "Rules for Ordering Uncertain Prospects," American Economic Review, American Economic Association, vol. 59(1), pages 25-34, March.
    12. Cope, S. & Frewer, L.J. & Houghton, J. & Rowe, G. & Fischer, A.R.H. & de Jonge, J., 2010. "Consumer perceptions of best practice in food risk communication and management: Implications for risk analysis policy," Food Policy, Elsevier, vol. 35(4), pages 349-357, August.
    13. Papadopoulos, Andrew & Sargeant, Jan M. & Majowicz, Shannon E. & Sheldrick, Byron & McKeen, Carolyn & Wilson, Jeff & Dewey, Catherine E., 2012. "Enhancing public trust in the food safety regulatory system," Health Policy, Elsevier, vol. 107(1), pages 98-103.
    14. Nicholas E. Piggott & Thomas L. Marsh, 2004. "Does Food Safety Information Impact U.S. Meat Demand?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(1), pages 154-174.
    15. Rothschild, Michael & Stiglitz, Joseph E., 1970. "Increasing risk: I. A definition," Journal of Economic Theory, Elsevier, vol. 2(3), pages 225-243, September.
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