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Cluster Computing

, Volume 22, Supplement 3, pp 6101–6109 | Cite as

Empirical research on rural e-commerce development level index system based on catastrophe progression method

  • Hong LiuEmail author
  • Chunmei Ai
Article
  • 245 Downloads

Abstract

This paper constructs a rural e-commerce development evaluation system in terms of the preparation, application and impact of rural e-commerce development. It takes OECD e-commerce performance evaluation system as the framework of this paper’s index system and is based on diffusion of innovation theory. This paper also carries out the empirical research on development level of rural e-commerce in 14 provinces’ (autonomous regions’) of China, by using catastrophe progression method and gets a combined development index of China’s rural e-commerce. The results show that Zhejiang province is the highest echelon of the development level of rural e-commerce, the second echelon includes Guangdong province, Jiangsu province and Hebei province, the third echelon includes Shandong province, Fujian province, Henan province, the fourth echelon covers Heilongjiang province, Hunan province, Anhui province, Jiangxi province, and the fifth echelon covers Jilin province, Inner Mongolia, Liaoning province. According to the calculation results, the rural e-commerce development evaluation index system built by this paper is reasonable, and this index system can correctly measure the development level of rural e-commerce.

Keywords

Rural e-commerce Index system Catastrophe progression method Empirical Research 

Notes

Acknowledgements

Liaoning Province Social Science Planning Fund Project (Project Number: L16BGL032).

References

  1. 1.
    China international e-commerce center research institute. China rural e-commerce development report (2015–2016) (2016)Google Scholar
  2. 2.
    China Internet Network Information Center. 2015 Research report on the development of rural Internet (2016)Google Scholar
  3. 3.
    Zhang, X.: Study on rural e-commerce evaluation index system. Agric. Econ. 03, 123–125 (2016)Google Scholar
  4. 4.
    OECD: Defining and measuring electronic commerce, a provisional framework and follow-up strategy. DSTI/ICCP/IIS(99)4/Final, Paris (1999)Google Scholar
  5. 5.
    Mu, Y., Wang, D.: Empirical analysis on the development level of rural e-commerce in Heilongjiang province—take the 15 rural e-commerce demonstration villages as an example. Jiangsu Agric. Sci. 05, 608–611+619 (2016)Google Scholar
  6. 6.
    Nan, Z., Jianzheng, Y.: Research of catastrophe progression method to construct the index system of China’s e-commerce development. J. Mod. Inf. 02, 3 (2014)Google Scholar
  7. 7.
    Ma, F.: Study on measuring e-commerce development. Zhejiang Gongshang University (2015)Google Scholar
  8. 8.
    Lin, J.: Study on the statistical indicators and accounting methods of e-commerce. South China University of Technology (2015)Google Scholar
  9. 9.
    Yanghong, M., Duchun, W., Fengmin, C.: Analysis on the influence factors of rural e-commerce based on structural equation model—take the 15 rural e-commerce demonstration villages of Heilongjiang province as an example. J. Agric Tech. Econ. 08, 106–118 (2016)Google Scholar
  10. 10.
    CII Research and measurement of e-commerce index: Research on the level measure of e-commerce. Stat. Res. 12, 26–31 (2001)Google Scholar
  11. 11.
    Duan, J.: The study on measurement of e-commerce service industry development level. Central China Normal University (2012)Google Scholar
  12. 12.
  13. 13.
    Dodds, W.B., Monroe, K.B., Grewal, D.: Effects of price, brand, and store information on buyers product evaluations. J. Market. Res. 28(3), 307–319 (1991)Google Scholar
  14. 14.
    Lin, K.Y., Lu, H.P.: Why people use social networking sites: an empirical study integrating network externalities and mo-tivation theory. Comput. Hum. Behav. 27(3), 1152–1161 (2011)MathSciNetCrossRefGoogle Scholar
  15. 15.
    DeLone, W.H., McLean, E.R.: The DeLone and McLean model of information systems success: a ten-year update. J. Manag. Inf. Syst. 19(4), 9–30 (2003)CrossRefGoogle Scholar
  16. 16.
    Nahapiet, J., Ghoshal, S.: Social capital, intellectual capital, and the organizational advantage. Acad. Manag. Rev. 23(2), 242–266 (1998)CrossRefGoogle Scholar
  17. 17.
    Ellis Horwood Limited, Zhou Zhongliang translation, Zhang Guoliang proofread. Mathematical Models of Morphogensis. Shanghai Translation Publishing House, Shanghai (1989)Google Scholar
  18. 18.
    Bao, W., Zhou, H., Lu, W., Xie, F.: The system of knowledge management using web based learning and education technology. Comput. Syst. Sci. Eng. 31(6), 469–473 (2016)Google Scholar
  19. 19.
    Zhou, Q., Luo, J.: The study on evaluation method of urban network security in the big data era. Intell. Autom. Soft Comput. (2017).  https://doi.org/10.1080/10798587.2016.1267444 CrossRefGoogle Scholar
  20. 20.
    Xie, J., Luo, J., Zhou, Q.: Data mining based quality analysis on informants involved applied research. Clust. Comput. 19, 1885 (2016).  https://doi.org/10.1007/s10586-016-0657-7 CrossRefGoogle Scholar
  21. 21.
    Zhou, Q.: Research on heterogeneous data integration model of group enterprise based on cluster computing. Clust. Comput. 19, 1275 (2016).  https://doi.org/10.1007/s10586-016-0580-y CrossRefGoogle Scholar
  22. 22.
    Zhou, Q.: Multi-layer affective computing model based on emotional psychology. Electron. Commer. Res. (2017).  https://doi.org/10.1007/s10660-017-9265-8 CrossRefGoogle Scholar
  23. 23.
    Zhou, Q., Luo, J.: The risk management using limit theory of statistics on extremes on the big data era. J. Comput. Theor. Nanosci. 12, 6237–6243 (2015).  https://doi.org/10.1166/jctn.2015.4661 CrossRefGoogle Scholar
  24. 24.
    AliResearch: China’s taobao village report (2016)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Liaoning Normal UniversityDalianChina

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