Abstract

In this study, we set an empirical analysis model about urban residents’ rural tourism decision-making and analyze influential factors mechanism based on food circulation channels. The result shows that: first, personal disposable income is the most important factor that will affect consumption decision-making in rural tourism, the regression coefficient of urban residents personal disposable income is 0.071; sec, discretionary time is the most significant variable in urban residents' personal characteristics, the sig value is 0.007 and the regression coefficient is positive; third, infrastructure condition variables' sig value is less than 0.1, the regression coefficient value is -0.029, probably because the city residents to participate in the study area of rural tourism is more focus on rural tourism ecological environment quality and less attention on the infrastructure; finally, the environment quality has high coefficient value as 0.048, means that rural personnel's service quality plays a positive effect. On this basis, we put forward relevant suggestions.

Keywords:

Decision-making, food circulation channels, rural tourism, urban residents,

References

1. Anderson, L.F. and M.A. Littrell, 1995. Souvenir-purchase behavior of women tourists. Ann. Tourism Res., 22(2): 328-348.
   Direct Link
2. Chen, Q. and L. Zhou, 2013. Efficiency of finance development on improving technological innovation: Interactions with carbon markets. J. Appl. Sci., 13: 5700-5707.
   CrossRef    
3. Claire, H. and J. Eleri, 2012. Local leadership for rural tourism development: A case study of Adventa, Monmouthshire, UK. Tourism Manage. Persp., 4: 28-35.
   Direct Link
4. Elisabeth, K. and J. Maria, 2012. Understanding and managing the rural tourism experience: The case of a historical village in Portugal. Tourism Manage. Persp., 4: 207-214.
   
5. Elspeth, A. and N. Robin, 1990. The relationship between personality gender and tourist behavior. Tourism Manage., 20: 193-202.
   Direct Link
6. François, B., L. Anne, L. Sandrine, R. Tina and T. Nadine, 2015. Domestic demand for tourism in rural areas: Insights from summer stays in three French regions. Tourism Manage., 46: 562-570.
   Direct Link
7. Li, Y., 2004. Urban residents travel consumer behavior analysis. J. Tourism Sci., 3: 42-44.
   
8. 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
9. Li, J. and F. Zhang, 2008. Chinese tourism consumption behavior research. J. Taiyuan Teach. Coll., 3(7): 93-95.
   
10. 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
11. Li, Z., Z. Ning and C. Qing-yi, 2012. Study on how financial institutions positively impact on China's low-carbon economy growth. Adv. Inform. Sci. Serv. Sci., 4(22): 779-786.
    Direct Link
12. Littrell, M.A., L. Anderson and P. Brown, 1993. What makes a craft souvenir authentic? Ann. Tourism Res., 20: 197-215.
    
13. Mansour, G. and M. Omid, 2014. An application of European performance satisfaction index towards rural tourism: The case of Western Iran. Tourism Manage. Persp., 11: 77-82.
    Direct Link
14. Neil, C., 1990. A study of gender differences: Young tourist behavior in a UK coastal resort. Tourism Manage., 20: 220-225.
    Direct Link
15. Ning, Z. and Z. Li, 2013. Value-at-risk modelling for risk management of RMB exchange rate. Int. J. Appl. Math. Stat., 43(13): 297-304.
    Direct Link
16. Nyaupane, G.P. and S. Poudel, 2012. Application of appreciative inquiry in tourism research in rural communities. Tourism Manage., 33: 978-987.
    Direct Link
17. OECD (Organization for Economic Cooperation and Development), 1994. Selection of indicators for state of the environment monitoring. OECD, Paris.
    
18. Qing, C. and Z. Li, 2013. Time series model for foreign direct investment spillover. Int. J. Appl. Math. Stat., 49: 535-543.
    Direct Link
19. Raija, K., 2014. The role of individual entrepreneurs in the development of competitiveness for a rural tourism destination-a case study. Tourism Manage., 40: 361-371.
    Direct Link
20. Samuel Folorunso, A.O., K.L. Catheryn and N. Vikneswaran, 2014. A framework for rural tourism destination management and marketing organisations. Proc. Soc. Behav. Sci., 144: 151-163.
    
21. Tomas, B., K. Irena, B. Alvydas and G. Ron, 2012. Rural tourism development in Lithuania (2003-2010)-a quantitative analysis. Tourism Manage. Persp., 2: 1-6.
    Direct Link
22. Zheng, Q., 2007. Rural tourism consumer behavior based on travel frequency differentiation research. J. Hunan Bus. Sch., 4(14): 56-59.
    Direct Link
23. Zhou, L., 2013. Optimal compensation rate appraisal and selection based on data mining method. Int. J. Appl. Math. Stat., 50: 37-46.
    Direct Link
24. Zhou, L. and Q.Y. Chen, 2013. A dynamic model for evaluating the impact of FDI on macro economy based on panel data method. Int. J. Appl. Math. Stat., 49: 499-506.
    Direct Link
25. Zhou, L. and Q. Chen, 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
26. Zou, S., 2013. Study on tourism consumption behavior. J. Guilin Tourism Coll., 10: 31-33.
    

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.