pp 1–13 | Cite as

Spatial and big data analytics of E-market transaction in China

  • Xinyue Ye
  • Zeng LianEmail author
  • Bing She
  • Sonali Kudva


This study uses a big data approach and gravity model to quantify the scope and sources of online transactions in urban China and explore the driving forces, based on data from the Taobao platform for online cellphone transactions from June to December in 2011. Comparison among Jing-Jin-Ji Region, Yangtze River Delta, and Pearl River Delta shows that a higher level of economic development corresponds to the more developed logistics industry and more C2C Taobao shops. The regression results illustrate that distance, GDP, and population density are the three main factors which influence the volume and number of trades in the e-marketplace. The number and reputation of traders by relative value also promote the volume and numbers of trades significantly. Additionally, the big data from the Taobao platform provides evidence that the gravity model is valid in estimating the amounts of online transactions.


Big data Spatial structure E-market China 



This study was partially supported by the National Science Foundation (Grant Number 1416509).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights

This manuscript does not contain any studies with human participants or animals performed by any of the authors.


  1. Alqahtani, A. S., & Goodwin, R. (2012). E-commerce smartphone application. IJACSA International Journal of Advanced Computer Science and Applications, 3(8), 54–59.Google Scholar
  2. Anderson, J. E. (1979). A theoretical foundation for the gravity equation. The American Economic Review, 69(1), 106–116.Google Scholar
  3. Anderson, J. E., & Van Wincoop, E. (2001). Gravity with gravitas: A solution to the border puzzle. NBER Working Paper No. 8079.Google Scholar
  4. Andrews, R. L., & Currim, I. S. (2004). Behavioural differences between consumers attracted to shopping online versus traditional supermarkets: Implications for enterprise design and marketing strategy. International Journal of Internet Marketing and Advertising, 1(1), 38–61.Google Scholar
  5. Baier, S. L., & Bergstrand, J. H. (2001). The growth of world trade: Tariffs, transport costs, and income similarity. Journal of International Economics, 53(1), 1–27.Google Scholar
  6. Baier, S. L., & Bergstrand, J. H. (2007). Do free trade agreements actually increase members’ international trade? Journal of International Economics, 71(1), 72–95.Google Scholar
  7. Bergstrand, J. H. (1985). The gravity equation in international trade: Some microeconomic foundations and empirical evidence. Review of Economics & Statistics, 67(3), 474–481.Google Scholar
  8. Boisso, D., & Ferrantino, M. (1997). Economic distance, cultural distance, and openness in international trade: Empirical puzzles. Journal of Economic Integration, 12(4), 456–484.Google Scholar
  9. Brown, J. R., & Goolsbee, A. (2002). Does the Internet make markets more competitive? Evidence from the life insurance industry. Journal of Political Economy, 110(3), 481–507.Google Scholar
  10. Brown, C. H., Holman, E. W., Wichmann, S., & Velupillai, V. (2008). Automated classification of the world’s languages: A description of the method and preliminary results. STUF-Language Typology and Universals Sprachtypologie und Universalienforschung, 61(4), 285–308.Google Scholar
  11. Brun, J. F., Carrère, C., Guillaumont, P., & De Melo, J. (2005). Has distance died? Evidence from a panel gravity model. The World Bank Economic Review, 19(1), 99–120.Google Scholar
  12. Brynjolfsson, E., & Smith, M. D. (2000). Frictionless commerce? A comparison of Internet and conventional retailers. Management Science, 46(4), 563–585.Google Scholar
  13. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188.Google Scholar
  14. Chiou, J. S., & Pan, L. Y. (2009). Antecedents of internet retailing loyalty: Differences between heavy versus light shoppers. Journal of Business and Psychology, 24(3), 327.Google Scholar
  15. Choi, E. K. (2002). Trade and the adoption of a universal language. International Review of Economics & Finance, 11(3), 265–275.Google Scholar
  16. Clemes, M. D., Gan, C., & Zhang, J. (2014). An empirical analysis of online shopping adoption in Beijing, China. Journal of Retailing and Consumer Services, 21(3), 364–375.Google Scholar
  17. Clemons, E. K., Hann, I. H., & Hitt, L. M. (2002). Price dispersion and differentiation in online travel: An empirical investigation. Management Science, 48(4), 534–549.Google Scholar
  18. Cremer, R., & Willes, M. J. (1998). The tongue of the tiger: Overcoming language barriers in international trade. Singapore: World Scientific.Google Scholar
  19. Crystal, D. (2012). English as a global language. Cambridge: Cambridge University Press.Google Scholar
  20. Deardorff, A. V. (2011). Determinants of bilateral trade: Does gravity work in a neoclassical world? In R. Stern (Ed.), Comparative advantage, growth, and the gains from trade and globalization: A Festschrift in honor of Alan V Deardorff (pp. 267–293). World Scientific.Google Scholar
  21. Degeratu, A. M., Rangaswamy, A., & Wu, J. (2000). Consumer choice behavior in online and traditional supermarkets: The effects of brand name, price, and other search attributes. International Journal of Research in Marketing, 17(1), 55–78.Google Scholar
  22. Egger, P. H., & Lassmann, A. (2012). The language effect in international trade: A meta-analysis. Economics Letters, 116(2), 221–224.Google Scholar
  23. Ellickson, P. B. (2013). Supermarkets as a natural oligopoly. Economic Inquiry, 51(2), 1142–1154.Google Scholar
  24. Ellickson, P. B., Houghton, S., & Timmins, C. (2010). Estimating network economies in retail chains: A revealed preference approach. NBER Working Paper No. 15832.Google Scholar
  25. Fan, Y., Ju, J., & Xiao, M. (2016). Reputation premium and reputation management: Evidence from the largest e-commerce platform in China. International Journal of Industrial Organization, 46, 63–76.Google Scholar
  26. Fang, Y., Qureshi, I., McCole, P., & Ramsey, E. (2007). The moderating role of perceived effectiveness of third-party control on trust and online purchasing intentions. In Association for information systems - 13th Americas conference on information systems, AMCIS 2007. Reaching new heights (Vol. 2, pp. 1136–1153). Keystone, CO: 13th Americas Conference on Information Systems.Google Scholar
  27. Feenstra, R. C. (2015). Advanced international trade: Theory and evidence. Princeton: Princeton University Press.Google Scholar
  28. Forsythe, S., Liu, C., Shannon, D., & Gardner, L. C. (2006). Development of a scale to measure the perceived benefits and risks of online shopping. Journal of Interactive Marketing, 20(2), 55–75.Google Scholar
  29. Frankel, J., & Rose, A. (2002). An estimate of the effect of common currencies on trade and income. The Quarterly Journal of Economics, 117(2), 437–466.Google Scholar
  30. Gautier, P. A., & Zenou, Y. (2010). Car ownership and the labor market of ethnic minorities. Journal of Urban Economics, 67(3), 392–403.Google Scholar
  31. Gerunov, A. (2014). Big data approaches to modeling the labor market. In Proceedings of the international conference on big data, knowledge and control systems engineering (pp. 47–56).Google Scholar
  32. Ginsburgh, V., Ortuño-Ortín, I., & Weber, S. (2007). Learning foreign languages: Theoretical and empirical implications of the Selten and Pool model. Journal of Economic Behavior & Organization, 64(3–4), 337–347.Google Scholar
  33. Grimes, K. M. (2000). Democratizating international production and trade organizations. In K. Grimes & B. Milgram (Eds.), Artisans and cooperatives: Developing alternative trade for the global. Tucson, AZ: University of Arizona Press.Google Scholar
  34. Hutchinson, W. K. (2002). Does ease of communication increase trade? Commonality of language and bilateral trade. Scottish Journal of Political Economy, 49(5), 544–556.Google Scholar
  35. Hyejin, K., & Zussman, A. (2008). The role of English in international trade. Manuscript, Department of Economics, Cornell University.Google Scholar
  36. Internet World Stats. (2010). China internet usage stats and population report.Google Scholar
  37. Jin, G. Z., & Kato, A. (2007). Dividing online and offline: A case study. The Review of Economic Studies, 74(3), 981–1004.Google Scholar
  38. Leong, C. M. L., Pan, S. L., Newell, S., & Cui, L. (2016). The emergence of self-organizing E-commerce ecosystems in remote villages of China: A tale of digital empowerment for rural development. MIS Quarterly, 40(2), 475–484.Google Scholar
  39. Lohmann, J. (2011). Do language barriers affect trade? Economics Letters, 110(2), 159–162.Google Scholar
  40. Melitz, J. (2004). Distance, trade and political association. Mimeo.Google Scholar
  41. Melitz, J. (2008). Language and foreign trade. European Economic Review, 52(4), 667–699.Google Scholar
  42. Ou, C. X., & Davison, R. M. (2009). Technical opinion why eBay lost to TaoBao in China: The global advantage. Communications of the ACM, 52(1), 145–148.Google Scholar
  43. Rose, A. K. (2000). One money, one market: The effect of common currencies on trade. Economic Policy, 15(30), 8–45.Google Scholar
  44. Shaw, S. L., Tsou, M. H., & Ye, X. (2016). Human dynamics in the mobile and big data era. International Journal of Geographical Information Science, 30(9), 1687–1693.Google Scholar
  45. Tan, J., & Ludwig, S. (2016). Regional adoption of business-to-business electronic commerce in China: Role of e-readiness. International Journal of Electronic Commerce, 20(3), 408–439.Google Scholar
  46. Van Noort, G., Kerkhof, P., & Fennis, B. M. (2008). The persuasiveness of online safety cues: The impact of prevention focus compatibility of Web content on consumers’ risk perceptions, attitudes, and intentions. Journal of Interactive Marketing, 22(4), 58–72.Google Scholar
  47. Wang, J., Zhu, X., & Zhang, C. (2016b). Models of China’s E-commerce in the agricultural sector: An exploratory study. International Journal of u-and e-Service, Science and Technology, 9(4), 389–400.Google Scholar
  48. Wang, X., & Xu, S. (2011). Geographical distribution of C2C Taobao online stores in China. Progress in Geography, 30(12), 1564–1569.Google Scholar
  49. Wang, Z., Chen, C., Guo, B., Yu, Z., & Zhou, X. (2016a). Internet plus in China. It Professional, 18(3), 5–8.Google Scholar
  50. Wang, Z., Ye, X., Lee, J., Chang, X., Liu, H., & Li, Q. (2018). A spatial econometric modeling of online social interactions using microblogs. Computers, Environment and Urban Systems, 70, 53–58.Google Scholar
  51. White, T. (2012). Hadoop: The definitive guide. O’Reilly Media, Inc.Google Scholar
  52. Wolf, H. C. (2000). Intranational home bias in trade. Review of Economics and Statistics, 82(4), 555–563.Google Scholar
  53. Ye, X., & He, C. (2016). The new data landscape for regional and urban analysis. GeoJournal, 81(6), 811–815.Google Scholar
  54. Ye, X., & Liu, X. (2018). Cities as social and spatial networks. Berlin: Springer.Google Scholar
  55. Ye, X., & Xie, Y. (2012). Re-examination of Zipf’s law and urban dynamic in China: A regional approach. The Annals of Regional Science, 49(1), 135–156.Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of InformaticsNew Jersey Institute of TechnologyNewarkUSA
  2. 2.International Business SchoolBeijing Foreign Studies UniversityBeijingChina
  3. 3.Institute for Social ResearchUniversity of MichiganAnn ArborUSA
  4. 4.College of Communication and InformationKent State UniversityKentUSA

Personalised recommendations