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Designing Ranking Systems for Consumer Reviews: The Impact of Review Subjectivity on Product Sales and Review Quality

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Abstract

With the rapid growth of the Internet, users' ability to publish content has created active electronic com-munities that provide a wealth of product information. Consumers naturally gravitate to reading reviews in order to decide whether to buy a product. However, the high volume of reviews that are typically pub-lished for a single product makes it harder for individuals to locate the best reviews and understand the true underlying quality of a product based on the reviews. Similarly, the manufacturer of a product wants to identify the reviews that influence the customer base, and examine the content of these reviews. In this paper we propose two ranking mechanisms for ranking product reviews: a consumer-oriented ranking mechanism ranks the reviews according to their expected helpfulness, and a manufacturer-oriented rank-ing mechanism ranks the reviews according to their expected effect on sales. Our ranking mechanism combines econometric analysis with text mining techniques in general, with subjectivity analysis in par-ticular. We show that subjectivity analysis can give useful clues about the helpfulness of a review and about its impact on sales. Our results can have several implications for the market design of online opin-ion forums.
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... Further, the Bright local's survey in 2020 found that 87% of consumer read products reviews and the time average that spend reading is 13 min and 45sec [4]. Previous researches have also caught attention in electronic word-of-mouth (eWOM), since it has a positive increment on online sales [5][6][7].Moreover, the impact of covid-19 in e-commerce has shown a peak on sales in online business from 10% to 50 %, and is expected to grow more in the following years. Therefore, ORs as a potential source of social influence, will be continuous analyze in the upcoming years. ...
... After the data collection, ORs with less than 3 Helpful votes were eliminated from the analysis because as previous researches [6] and [44], authors argued that the analysis must consider reviews that assure number of votes. ...
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Online reviews (ORs) have shown evidence to help consumers to reduce hesitation in the last stage of the purchase and has been also found that ORs help online businesses increase sales. However, ORs are increasing faster, becoming every time more robust with better ways and media to express useful and helpful information. Therefore, the way ORs help online business and consumers are constantly changing. Previous studies have intended to analyze helpfulness in different ways. However, they have not totally yet identified the most appropriate influence significance of the factors to test and predict the helpfulness of ORs due to the constant change and evolution of ORs in E-commerce platforms. I based this study on the economics of information, media richness, and negativity-bias theories, proposing a model that shows the influencing factors in the helpfulness of ORs (such as length, sentimental Analysis, score rating, number of images, video and published days). To find a closer optimal helpfulness analysis and prediction, a data set of 17,119 samples of three types of online goods have been extracted from different products on Amazon.com. For the analysis, we have considered employing a regression model to analyze the significance level of the factors in ORs for every type of online goods. The findings in this research prove that in fact there is a different perception of helpfulness for every type of good.
... El número de opiniones sobre cada producto o servicio ofertado en una web puede ser utilizado como una aproximación al número de unidades vendidas, como se ha hecho en estudios previos con Amazon (Chevalier and Mayzlin, 2006;Ghose and Ipeirotis, 2007). En el sector turístico también se ha utilizado esta técnica utilizando datos de Booking.com ...
... El número de opiniones sobre cada producto o servicio ofertado en una web puede ser utilizado como una aproximación al número de unidades vendidas, como se ha hecho en estudios previos con Amazon (Chevalier and Mayzlin, 2006;Ghose and Ipeirotis, 2007). En el sector turístico también se ha utilizado esta técnica utilizando datos de Booking.com ...
... IMDb, eBay, and many others. There is, however, a high level of subjectivity in all of the aforementioned rating systems (Ghose and Ipeirotis 2006). A simple rating system, such as the unary or binary ratings, fails to provide sufficient granularity for distinguishing products from each other, whereas a system that uses a wide rating scale, such as the 100-point rating scale, is often subject to misinterpretation of the rating scores. ...
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