Leveraging Technology to Improve Intent to Purchase

Distribution of deceptive counterfeit goods via online marketplaces such as Amazon and eBay has introduced a particularly burdensome decision making process for the consumers. The consumers need to spend additional time in the information search step, reading product and seller reviews to assist with counterfeit detection. Automated counterfeit detection could assist with this process. This paper presents the conceptual framework that employs artificial intelligence techniques, such as natural language processing and topic analysis, in order to automatically detecting counterfeit goods. Specifically, online reviews of products and sellers can be downloaded and parsed using natural language processing. Topic analysis methods can be performed against the resulting text corpus to detect the most frequent terms in the reviews and to examine the reviews for a collection of keywords related to fraudulent products. The implications of this research are to alert consumers to potentially counterfeit products thereby increasing trust and efficiency in the online marketplace.