The impact of online reviews in the presence of customer returns

Sun, Miao; Chen, Jing; Tian, Ye; Yan, Yufei

doi:10.1016/j.ijpe.2020.107929

Cited by 40 publications

(21 citation statements)

References 44 publications

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“…Online reviews disclose additional information about products and services, and in this case, they can adjust current and potential customers' pre-purchase valuations of products and services in order to help them make better purchasing decisions. Therefore, there is a high potential of reducing customer retention and satisfaction [38,39].…”

Section: Online Reviews and Consumer Satisfactionmentioning

confidence: 99%

Influence of Online Reviews on Student Satisfaction Seen through a Service Quality Model

Torabi

Bélanger

2021

JTAER

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University or college is a challenging reality leading to a sometimes elusive career path. By consulting social media and review websites, students have more alternatives to consider in their choice determination. This study develops a multifaceted model to recognize the influence of service quality and e-word of mouth on customer satisfaction and the impact of customer satisfaction on word of mouth. Data gathered from a sample of 150 university students are analyzed by SPSS and PLS-SEM. The findings indicate that service quality dimensions and e-WOM have positive impacts on customer satisfaction. Furthermore, this study reinforces that customer satisfaction has a positive influence on customers’ WOM intentions. This research recommends that university management should consider the importance of service quality factors and social media channels to meet and exceed students’ expectations in order to bolster the quality of services and boost customer satisfaction.

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Section: Online Reviews and Consumer Satisfactionmentioning

confidence: 99%

Influence of Online Reviews on Student Satisfaction Seen through a Service Quality Model

Torabi

Bélanger

2021

JTAER

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“…However, more than that, in our study, we consider all the three possible return policies offered by a seller (including the partial-refund policy), and we highlight that more sellers are motivated to transfer their return policies to a partial-refund policy in the presence of social learning. Another key finding in Sun et al (2021) is that online reviews greatly impact the high-quality seller. Interestingly, in our study, a similar effect is observed on the monopolistic seller (with either a no-refund or partialrefund policy); that is, social learning benefits the seller when the product quality is high.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In their study, Sun et al. (2021) highlight that around 90% of consumers read reviews before purchasing, and 88% trust these reviews.…”

Section: Introductionmentioning

confidence: 99%

A study about return policies in the presence of consumer social learning

Liu

Zhu

Shen

et al. 2022

Production and Operations Management

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Sellers are conventionally generous with their return policies for valuation‐uncertain products, such as experience products and new products. However, with the development of online review platforms, an increasing number of consumers are engaging in social learning by referring to others' reviews to reduce valuation uncertainty. In this study, we investigate how social learning interacts with sellers' return policies. There are three main conclusions. First, when sellers have a relatively higher expectation of product quality (or simply the product quality is high), social learning makes the sellers offering either no‐refund policies or partial‐refund policies better off in terms of the increased profit. It will cause the no‐refund sellers to choose higher prices and inventory, and the partial‐refund sellers to set lower prices and refund amounts. Second, under social learning, the partial‐refund policy tends to be more beneficial to sellers than both full‐refund and no‐refund policies; although, when the product quality is high, the no‐refund policy tends to bring more benefits to sellers than the full‐refund policy. Hence, sellers may finally switch to the partial‐refund policy. Third, for partial‐refund policies, more often than not, social learning increases social welfare when the product quality is high; specifically, in many cases, it increases not only the profit of the seller but also the welfare of consumers.

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“…Luca and Zervas [34] analyzed Yelp reviews and combined the ratings of various shops to identify fraud and other behaviors. Sun et al [35] have studied how online reviews affect the return decisions of two competing online sellers selling products of different quality. Li et al [36] extracted six factors related to comments and two incentive factors for product sales from 28 studies focusing on online comments and sales to study how they affect product sales.…”

Section: Review Of Consumer Review Researchmentioning

confidence: 99%

Competitive Product Identification and Sales Forecast Based on Consumer Reviews

Zhang

Qiu

2021

Mathematical Problems in Engineering

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Sellers readily obtain consumer product evaluations from online reviews in order to identify competitive products in detail and predict sales. Firstly, we collect product review data from shopping websites, social media, product communities, and other online platforms to identify product competitors with the help of word-frequency cooccurrence technology. We take mobile phones as an example to mine and analyze product competition information. Then, we calculate the product review quantity, review emotion value, product-network heat, and price statistics and establish the regression model of online product review forecasts. In addition, the neural-network model is established to suggest that the relationships among factors are linear. On the basis of analyzing and discussing the impact of product sales of the competitors, product price, the emotional value of the reviews, and product-network popularity, we construct the sales forecast model. Finally, to verify the validity of the factor analysis affecting the sales and the rationality of the established model, actual sales data are used to further analyze and verify the model, showing that the model is reasonable and effective.

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The impact of online reviews in the presence of customer returns

Cited by 40 publications

References 44 publications

Influence of Online Reviews on Student Satisfaction Seen through a Service Quality Model

Influence of Online Reviews on Student Satisfaction Seen through a Service Quality Model

A study about return policies in the presence of consumer social learning

Competitive Product Identification and Sales Forecast Based on Consumer Reviews

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