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

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.

[1]  C. Chapman,et al.  Social Structure Facilitated the Evolution of Care-giving as a Strategy for Disease Control in the Human Lineage , 2018, Scientific Reports.

[2]  Xiaobo Zhou,et al.  Integration of Molecular Interactome and Targeted Interaction Analysis to Identify a COPD Disease Network Module , 2018, Scientific Reports.

[3]  Tingqiang Chen,et al.  SIRS contagion model of food safety risk , 2018 .

[4]  Giancarlo Ragozini,et al.  Quantifying layer similarity in multiplex networks: a systematic study , 2017, Royal Society Open Science.

[5]  Shawn Gu,et al.  From homogeneous to heterogeneous network alignment via colored graphlets , 2017, Scientific Reports.

[6]  Lei Wang,et al.  A Network Diffusion Model of Food Safety Scare Behavior considering Information Transparency , 2017, Complex..

[7]  Ha Won Kim,et al.  The analysis of food safety incidents in South Korea, 1998–2016 , 2017 .

[8]  Nanae Tanemura,et al.  The need for consumer science and regulatory science research on functional foods with health claims - What should we do to harmonize science and technology with society? , 2017 .

[9]  Lei Wang,et al.  Transparent Assessment of the Supervision Information in China’s Food Safety: A Fuzzy-ANP Comprehensive Evaluation Method , 2017 .

[10]  Katarzyna Musial,et al.  Analysis and Applications of Complex Social Networks , 2017, Complex..

[11]  Lu Xu,et al.  Dynamical evolution of trading behavior on anti-coordination game in complex networks , 2016 .

[12]  J. He,et al.  An endogenous model of the credit network , 2016 .

[13]  Kent D. Messer,et al.  Consumer Preferences Before and After a Food Safety Scare: An Experimental Analysis of the 2010 Egg Recall , 2016 .

[14]  Charles Humphrey,et al.  Seat Belt Usage Interventions for Motor Vehicle Crash Prevention on the Pine Ridge Indian Reservation, South Dakota. , 2016, Journal of environmental health.

[15]  Wendy McKelvey,et al.  Letter Grading and Transparency Promote Restaurant Food Safety in New York City. , 2015, Journal of environmental health.

[16]  Zhen Jin,et al.  Dynamic modeling and analysis of sexually transmitted diseases on heterogeneous networks , 2015 .

[17]  Arthur P.J. Mol,et al.  Governing China's food quality through transparency: A review , 2014 .

[18]  J. Sargeant,et al.  Enhancing public trust in the food safety regulatory system. , 2012, Health policy.

[19]  Lucinda L. Austin,et al.  How Audiences Seek Out Crisis Information: Exploring the Social-Mediated Crisis Communication Model , 2012 .

[20]  Maxi San Miguel,et al.  A measure of individual role in collective dynamics , 2010, Scientific Reports.

[21]  Zhang Fa,et al.  Survey of transmission models of infectious diseases , 2011 .

[22]  Arnout R. H. Fischer,et al.  Consumer perceptions of best practice in food risk communication and management: Implications for risk analysis policy , 2010 .

[23]  Mario Mazzocchi,et al.  Food Scares and Trust: A European Study , 2008 .

[24]  Alessandro Vespignani,et al.  Epidemic modeling in metapopulation systems with heterogeneous coupling pattern: theory and simulations. , 2007, Journal of theoretical biology.

[25]  Ragnar E. Löfstedt,et al.  How can we Make Food Risk Communication Better: Where are we and Where are we Going? , 2006 .

[26]  Alice M. Tybout,et al.  When Will a Brand Scandal Spill Over, and how Should Competitors Respond? , 2006 .

[27]  Alessandro Vespignani,et al.  Epidemics and immunization in scale‐free networks , 2002, cond-mat/0205260.

[28]  Reint Jan Renes,et al.  Monitoring consumer confidence in food safety: an exploratory study , 2004 .

[29]  Douwe-Frits Broens,et al.  Food safety and transparency in food chains and networks Relationships and challenges , 2004 .

[30]  T. Marsh,et al.  Does Food Safety Information Impact U.S. Meat Demand , 2004 .

[31]  Matthew T.G. Meulenberg,et al.  A Note on Modeling Consumer Reactions to a Crisis: The Case of the Mad Cow Disease , 2002 .

[32]  M. Newman,et al.  Mean-field solution of the small-world network model. , 1999, Physical review letters.

[33]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[34]  Dominic. Smith,et al.  Consumer concerns about food safety in Australia and Japan , 1999 .

[35]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[36]  Julie A. Caswell,et al.  Using Informational Labeling to Influence the Market for Quality in Food Products , 1996 .

[37]  Peter J. Williamson,et al.  Sales and Service Strategy for the Single European Market , 1992 .

[38]  Peter C. Fishburn,et al.  Convex stochastic dominance with continuous distribution functions , 1974 .

[39]  M. Rothschild,et al.  Increasing risk: I. A definition , 1970 .

[40]  H. Levy,et al.  Efficiency analysis of choices involving risk , 1969 .

[41]  Josef Hadar,et al.  Rules for Ordering Uncertain Prospects , 1969 .

[42]  J. Quirk,et al.  Admissibility and Measurable Utility Functions , 1962 .

[43]  J. Karamata.  Über einen Satz von Vijayaraghavan , 1932 .