Understanding the topics of export cross-border e-commerce consumers feedback: an LDA approach

Jian Mou (), Gang Ren (), Chunxiu Qin () and Kerry Kurcz ()
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Jian Mou: Xidian University
Gang Ren: Kookmin University
Chunxiu Qin: Xidian University
Kerry Kurcz: University of Illinois at Chicago

Electronic Commerce Research, 2019, vol. 19, issue 4, No 2, 749-777

Abstract: Abstract Cross-border e-commerce (CBEC) has become an important channel to help Chinese firms enter the international market. The recent influx in the development of CEBC has caused a simultaneous influx in accumulation of valuable text data such as consumer feedback. To better understand consumer feedback, we explored the topics of feedback posted directly by customers. We employed the Latent Dirichlet Allocation model to explore the topics focused on most; we found that 35 primary topics were most mentioned by both buyers and sellers. Based on our findings, the sellers regarded commission, product audit, communication between seller and buyer, order management and traffic as the most crucial. Buyers mentioned return and refund, product tracking, product description, shipping time, and seller performance significantly more than other topics. This study will help contribute to the understanding of how consumer feedback will help firms in many ways, including but not limited to recovering service and product failures, audit internal functions, and improving product quality.

Keywords: Cross-border e-commerce; Latent Dirichlet Allocation; Text mining; Consumer feedback; LDA (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (9)

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DOI: 10.1007/s10660-019-09338-7

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