Consumer’s risk perception on the Belt and Road countries: evidence from the cross-border e-commerce
- 636 Downloads
Understanding consumer’s risk perception on the Belt and Road (B&R) countries is important for the development of cross-border e-commerce (CBEC) along these countries. However, most of the extant studies cannot properly analyze consumer’s risk perception due to the limited data collected by questionnaires. Therefore, this study proposes a text-mining-based framework to study consumer’s risk perception on the B&R countries based on massive textual online reviews collected from CBEC. In the proposed framework, the Latent Dirichlet Allocation model and sentiment analysis method are used to identify the main risk factors affecting consumer’s risk perception and calculate their sentiment score, the risk perception indicator is constructed to measure the magnitude of consumer’s risk perception. In the experiment, totally 66,661 reviews of the representative products from nine B&R countries are collected from Tmall Global. Six major risk factors are identified, and consumer’s risk perception on nine B&R countries is given.
KeywordsRisk perception Cross-border e-commerce (CBEC) Textual online reviews Latent Dirichlet Allocation (LDA) Sentiment analysis
This study was supported by grants from the National Natural Science Foundation of China (71601178,71425002) and the Youth Innovation Promotion Association of Chinese Academy of Sciences (2012137, 2017200). We sincerely thank the editor and reviewers for their very valuable and professional comments.
Compliance with ethical standards
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
- 6.Dai, B., Forsythe, S., & Kwon, W. S. (2014). The impact of online shopping experience on risk perceptions and online purchase intentions: does product category matter? Journal of Electronic Commerce Research,15, 13–24.Google Scholar
- 9.Lin, A. J., Li, E. Y., & Lee, S.-Y. (2018). Dysfunctional customer behavior in cross-border e-commerce: A justice-affect-behavior model. Journal of Electronic Commerce Research,19, 36–54.Google Scholar
- 11.Cardona, M., Duch-Brown, N., & Bartens, B. (2015). Consumer perceptions of (cross-border) e-Commerce in the EU digital single market. Joint Research Centre. Working paper. European Commission. https://ec.europa.eu/jrc/sites/jrcsh/files/JRC97231.pdf. Accessed 10 Nov 2018.
- 22.Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research,3, 993–1022.Google Scholar
- 23.George, H. (2009). Parameter estimation for text analysis. University of Leipzig, version 2.9, online.Google Scholar
- 31.Yu, Y., & Hui, J. (2017). A study on text classification based on stacked contractive auto-encoder. In Proceedings of the 1st International Conference on Electronics Instrumentation & Information Systems, (pp. 1–6), Harbin.Google Scholar
- 32.Vosecky, J., Jiang, D., Leung, K. W.-T., & Ng, W. (2013). Dynamic multi-faceted topic discovery in Twitter. In Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, (pp. 879–884). New York.Google Scholar