 Perceived usefulness of online customer reviews: A review mining approach using machine learning & exploratory data analysisMadhumita Guha Majumder, Sangita Dutta Gupta and Justin Paul Journal of Business Research, 2022, vol. 150, issue C, 147-164 Abstract: Online customer reviews, considered as electronic word of mouth, have become very useful in the era of e-commerce as they facilitate future purchase decisions. The present study discusses the central and peripheral sources of influence, such as the content of the review, star rating, review length, and the total number of votes on the perceived usefulness of the review. It analyses reviews from Amazon.com on three products, namely, a videogame, digital music, and a grocery item. Using text mining, the study uncovers sentiment polarity, identifies sentiment patterns, and finally, analyses the perceived usefulness of reviews under the moderation effect. The study establishes that the impact of the central route is not significant for search goods. The study concludes that peripheral sources have a significant impact on the search products. Our study provides insights on how marketing strategies can be formulated by online retailers based on the product type. Keywords: Online customer review; Electronic word of mouth; Peripheral; Sentiment analysis; Text mining (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:150:y:2022:i:c:p:147-164 DOI: 10.1016/j.jbusres.2022.06.012 Access Statistics for this article Journal of Business Research is currently edited by A. G. Woodside More articles in Journal of Business Research from Elsevier |
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