Electronic Commerce Research

, Volume 19, Issue 4, pp 863–884 | Cite as

Analysis of launch strategy in cross-border e-Commerce market via topic modeling of consumer reviews

  • Feifei Wang
  • Yang Yang
  • Geoffrey K. F. Tso
  • Yang LiEmail author


Spurred by the policy of China’s Belt and Road Initiative, Chinese e-Commerce companies have found great opportunities in selling goods overseas. The cross-border e-Commerce shares similarities of launch and marketing strategies with domestic e-Commerce, but also has substantial differences. How to make strategic adjustments to better adapt to the overseas market is of great concern to cross-border e-Commerce companies. Analyzing behaviors of overseas consumers could offer an effective way to address this issue and has attracted great interest of researchers. Consumer comments, cheap and abundant by its nature, provides an easy access for analysis of consumer behaviors. In this paper, we focus on consumer reviews of a specific product, the cellphones, and apply topic modeling techniques to investigate the differences between behaviors of domestic and overseas consumers. We find that consumers from domestic and overseas focus on different aspects of product. In addition, the foreign consumers care more about product quality and tend to make description of technique details. On the contrary, domestic buyers pay more attention on consumer services and intend to comment in generalities. All these findings could help e-Commerce companies design better launch strategies in cross-border e-Commerce market.


Cross-border e-Commerce Consumer behavior Consumer reviews Regularized regression Topic models 



This work was supported by fund for building world-class universities (disciplines) of Renmin University of China, China Postdoctoral Science Foundation (No. 2017M18304), the Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China (No. 18XNLG02), the National Natural Science Foundation of China (No. 71771211). This work described in this paper was partial supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. 11507817).


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Center for Applied StatisticsRenmin University of ChinaBeijingChina
  2. 2.School of StatisticsRenmin University of ChinaBeijingChina
  3. 3.School of Electrical Engineering and Computer SciencePeking UniversityBeijingChina
  4. 4.Department of Management SciencesCity University of Hong KongKowloon, Hong KongChina
  5. 5.Statistical Consulting CenterRenmin University of ChinaBeijingChina

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