Abstract

In this study, we analyzed influential factors in rural travelling decision-making by using regression model. From the result, we can get that: first, food safety is the most important factor to food consumption in rural tourism, the correlation between food safety and food consumption is 0.674, which means food safety has significant positive influence to food consumption. Second, buying patterns is the second important factor to food consumption in rural tourism, the correlation relationship between food safety and food consumption is 0.642 and the constant is -0.673, which means that when the price advantage is not obvious, consumers don't engage in consumption of agricultural products. Third, local characteristics and personal characteristics are also important factor to food consumption in rural tourism, the correlation between local characteristics and food consumption is 0.549 and the correlation between personal characteristics and food consumption is 0.361, it means that the country food should be distinctive, but its consumption also decided by consumers features.

Keywords:

Customer satisfaction, food consumption, food safety, rural travelling,

References

1. Abraham, P. and U. Natan, 2000. The intensity of tourist-host social relationship and its effects on satisfaction and change of attitudes: The case of working tourists in Israel. Tourism Manage., 21(4): 395-406.
   CrossRef    
2. Chen, Q. and L. Zhou, 2013. Efficiency of finance development on improving technological innovation: Interactions with carbon markets. J. Appl. Sci., 13(24): 5700-5707.
   CrossRef    
3. Claudia, M., 2013. Prevention of travel-related food borne diseases: Microbiological risk assessment of food handlers and ready-to-eat foods in northern Italy airport restaurants. Food Control, 29(1): 202-207.
   CrossRef    
4. Deborah, S. and M.A. Eric, 2012. Urban travel in Nairobi, Kenya: Analysis, insights and opportunities. J. Transp. Geogr., 22: 65-76.
   CrossRef    
5. Erik, C. and N. Avieli, 2004. Food in tourism: Attraction and impediment. Ann. Tourism Res., 31(4): 755-778.
   CrossRef    
6. Kevin, N. and K. Metin, 2000. The role of food service in tourist satisfaction. Int. J. Hosp. Manag., 19(4): 375-384.
   CrossRef    
7. Li, Z., 2014. Energy efficiency and investments in low-carbon economy: The impact of carbon finance on sustainability development. J. Chem. Pharmaceut. Res., 6(5): 1255-1261.
   Direct Link
8. Li, Z. and Z. Ning, 2012. Research on liquidity risk and financial fragility of chinese commercial banks. Adv. Inform. Sci. Serv. Sci., 4(22): 787-793.
   Direct Link
9. Liu, Y., 2013. Under the background of food safety food industry tourism mode to explore relaxation. Food Mech., 13(1): 23-30.
   
10. Liu, Y., 2014. Beijing authenticity the economic impact of rural tourism product development. China Comm. Trade, 31(2): 53-60.
    
11. Ning, Z. and Z. Li, 2013. Principal component model for comparative evaluation of e-learning system: An empirical investigation. Int. J. Appl. Math. Stat., 42(12): 330-336.
    Direct Link
12. Qing, C. and Z. Li, 2014. Evaluation of E-commerce performance in smes based on vector auto regression model. Int. J. u- e- Serv. Sci. Technol., 7(5): 151-160.
    Direct Link
13. Shuai, Q. and W. Ning, 2004. Towards a structural model of the tourist experience: An illustration from food experiences in tourism. Tourism Manage., 25(3): 297-305.
    CrossRef    
14. Stefan, G. and G. Brian, 2011. Food management in tourism: Reducing tourism's carbon food print. Tourism Manage., 32(3): 534-543.
    CrossRef    
15. Wei, T., 2014. Rural tourism food safety problems and countermeasures research. Food Res. Develop., 18: 105-110.
    
16. Zhang, H., 2014. Rural tourism in shandong province SWOT analysis and countermeasures research. Market Mod., 24(5): 30-34.
    
17. Zhang, N. and Q. Chen, 2012. Study on how financial institutions positively impact on china's lowcarbon economy growth. Adv. Inform. Sci. Serv. Sci., 4(22): 779-786.
    Direct Link
18. Zhang, N. and C. Chen, 2013. Value-at-risk modelling for risk management of RMB exchange rate. Int. J. Appl. Math. Stat., 43: 297-304.
    Direct Link
19. Zhou, L., 2013a. Optimal compensation rate appraisal and selection based on data mining method. Int. J. Appl. Math. Stat., 50(20): 37-46.
    Direct Link
20. Zhou, G., 2013b. Rural tourism food safety management problems and countermeasures. Food Mech., 13(6): 58-62.
    
21. Zhou, L. and Q. Chen, 2013. Time series model for foreign direct investment spillover. Int. J. Appl. Math. Stat., 9: 535-543.
    

Competing interests

The authors have no competing interests.

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