{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T23:08:44Z","timestamp":1773011324955,"version":"3.50.1"},"reference-count":53,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2019,2,4]],"date-time":"2019-02-04T00:00:00Z","timestamp":1549238400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IMDS"],"published-print":{"date-parts":[[2019,2,4]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>The purpose of this paper is to identify electronic word-of-mouth (eWOM) customers from customer reviews. Thus, firms can precisely leverage eWOM customers to increase their product sales.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>This research proposed a framework to analyze the content of consumer-generated product reviews. Specific algorithms were used to identify potential eWOM reviewers, and then an evaluation method was used to validate the relationship between product sales and the eWOM reviewers identified by the authors\u2019 proposed method.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The results corroborate that online product reviews that are made by the eWOM customers identified by the authors\u2019 proposed method are more related to product sales than customer reviews that are made by non-eWOM customers and that the predictive power of the reviews generated by eWOM customers are significantly higher than the reviews generated by non-eWOM customers.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title><jats:p>The proposed method is useful in the data set, which is based on one type of products. However, for other products, the validity must be tested. Previous eWOM customers may have no significant influence on product sales in the future. Therefore, the proposed method should be tested in the new market environment.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title><jats:p>By combining the method with the previous customer segmentation method, a new framework of customer segmentation is proposed to help firms understand customers\u2019 value specifically.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>This study is the first to identify eWOM customers from online reviews and to evaluate the relationship between reviewers and product sales.<\/jats:p><\/jats:sec>","DOI":"10.1108\/imds-09-2017-0418","type":"journal-article","created":{"date-parts":[[2018,9,6]],"date-time":"2018-09-06T08:41:53Z","timestamp":1536223313000},"page":"129-147","source":"Crossref","is-referenced-by-count":14,"title":["Finding eWOM customers from customer reviews"],"prefix":"10.1108","volume":"119","author":[{"given":"Pengfei","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ji","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongsheng","family":"Hua","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shijian","family":"Fang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"issue":"8","key":"key2020092503114999200_ref001","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.1287\/mnsc.1110.1370","article-title":"Deriving the pricing power of product features by mining consumer reviews","volume":"57","year":"2011","journal-title":"Management Science"},{"issue":"3","key":"key2020092503114999200_ref002","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1086\/209118","article-title":"Social ties and word-of-mouth referral behavior","volume":"14","year":"1987","journal-title":"Journal of Consumer Research"},{"issue":"2","key":"key2020092503114999200_ref003","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.dss.2010.11.009","article-title":"Exploring determinants of voting for the \u2018helpfulness\u2019 of online user reviews: a text mining approach","volume":"50","year":"2011","journal-title":"Decision Support Systems"},{"issue":"5","key":"key2020092503114999200_ref004","doi-asserted-by":"crossref","first-page":"752","DOI":"10.1037\/0022-3514.39.5.752","article-title":"Heuristic versus systematic information processing and the use of source versus message cues in persuasion","volume":"39","year":"1980","journal-title":"Journal of Personality and Social Psychology"},{"key":"key2020092503114999200_ref005","unstructured":"Chaiken, S. 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