Do Online Reviews Affect Product Sales? The Role of Reviewer Characteristics and Temporal Effects

Hu, Nan; Liu, Ling; Zhang, Jie

doi:10.2139/ssrn.1324190

Cited by 141 publications

(177 citation statements)

References 35 publications

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“…The importance of online social media, particularly online customer review forums, has attracted significant attention in recent studies (Chen et al 2007, Chevalier and Mayzlin 2006, Clemons et al 2006Dellarocas 2003, Duan et al 2009, Li and Hitt 2008. Existing studies consistently show that consumer reviews have significant impacts on consumer purchase decisions (Chevalier and Mayzlin 2006, Duan et al 2008, Forman et al 2008, Hu et al 2008, Zhu and Zhang 2010, and they have increasingly been viewed as a new element in the marketing communications mix (Chen and Xie 2008, Chen et al 2004, Dellarocus et al 2007.…”

Section: Background and Theorymentioning

confidence: 99%

“…Online social media, however, is a double-edged sword for businesses. Although positive customer comments can increase sales, negative comments are often more influential than positive ones (Chevalier and Mayzlin 2006), and customers with strong negative views are more motivated to post in online social media than customers with average views (Hu et al 2008). Although extensive academic research has examined the influence of consumer reviews and their implications for marketing and product diversification strategies Xie 2008, Clemons et al 2006), little research has been done to understand how management should respond to customer reviews in online social media.…”

Section: Background and Theorymentioning

confidence: 99%

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First Step in Social Media: Measuring the Influence of Online Management Responses on Customer Satisfaction

2014

Production and Operations Management

321

220

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With the growing influence of online social media, firms increasingly take an active role in interacting with consumers in social media. For many firms, their first step in online social media is management responses, where the management responds to customers' comments about the firm or its products and services. In this article, we measure the impact of management responses on customer satisfaction using data retrieved from a major online travel agency in China. Applying a panel data model that controls for regression toward the mean and heterogeneity in individual preference for hotels, we find that online management responses are highly effective among low satisfaction customers but have limited influence on other customers. Moreover, we show that the public nature of online management responses introduces a new dynamic among customers. Although online management responses increase future satisfaction of the complaining customers who receive the responses, they decrease future satisfaction of complaining customers who observe but do not receive management responses. The result is consistent with the peer‐induced fairness theory.

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Section: Background and Theorymentioning

confidence: 99%

Section: Background and Theorymentioning

confidence: 99%

First Step in Social Media: Measuring the Influence of Online Management Responses on Customer Satisfaction

2014

Production and Operations Management

321

220

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“…On the other hand, source identity and source reputation have been acknowledged as key factors in health communications (e.g., Eastin, 2001), marketing (e.g., Homer & Kahle, 1990), and persuasive communications generally (e.g., DeBono & Harnish, 1988). Also, recent studies of online reviews have found that online review readers use source cues (such as reviewers' identity disclosures, and community-rated reputation) to help filter and process review content (Forman, Ghose, & Wiesenfeld, 2008;Hu, Liu, & Zhang, 2008;Vermeulen & Seegers, 2009). These studies generally show that source identity disclosure and source reputation have significant effects on evaluations of information in various contexts.…”

Section: Studymentioning

confidence: 99%

“…However, work in this area is relatively fragmented and the empirical findings are sometimes inconclusive or conflicting (Dellarocas, 2003;Hu, Liu, & Zhang, 2008). This is due to a variety of reasons including the complexity of consumer decision marking (de Valck, van Bruggen, & Wierenga, 2009;Park, Lee, & Han, 2007), lack of accurate sales data (Chevalier & Mayzlin, 2006), difficulty of performing qualitative analyses with large text corpora (Ghose & Ipeirotis, 2009), and variations in products, consumers, and online shopping contexts (Forman, Ghose, & Wiesenfeld, 2008;Huang, Lurie, & Mitra, 2009;Weathers, Sharma, & Wood, 2007).…”

mentioning

confidence: 99%

In Search of Negativity Bias: An Empirical Study of Perceived Helpfulness of Online Reviews

2013

Psychology and Marketing

202

129

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A basic tenet of psychology is that the psychological effects of negative information outweigh those of positive information. Three empirical studies show that the negativity bias can be attenuated or even reversed in the context of electronic word-of-mouth (eWoM). The first study analyzes a large sample of customer reviews collected from Amazon.com and concludes that negative reviews are no more helpful than positive ones when controlling for review quality The second study follows up with a virtual experiment that confirms the lack of negativity bias in evaluating the helpfulness of online reviews. The third study demonstrates that the negativity effect can be reversed by manipulating the baseline valences. This work challenges the conventional wisdom of "bad is stronger than good" and contributes to the understanding of the eWoM phenomenon.

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“…Previous studies suggest that eWOM valence, which refers to customer opinions (positive or negative) about product quality [20], is likely to have an impact on product sales [3], [8]- [9], [21]- [22]. Positive eWOM fascinates new consumers and thus increases sales, whereas negative eWOM might hinder trust in a product that consumers want to purchase [23]- [24].…”

Section: Impact Of Negative Tweets On Box Office Revenuementioning

confidence: 99%

Impact of Tweets on Box Office Revenue: Focusing on When Tweets are Written

Baek

Ahn

2014

ETRI J

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This study investigates the impact of tweets on box office revenue. Specifically, the study focuses on the times when tweets were written by examining the impact of pre‐ and post‐consumption tweets on box office revenue; an examination that is based on Expectation Confirmation Theory. The study also investigates the impact of intention tweets versus subjective tweets and the impact of negative tweets on box office revenue. Targeting 120 movies released in the US between February and August 2012, this study collected tweet information on a daily basis from two weeks before the opening until the closing and box office revenue information. The results indicate that the disconfirmation that occurs in relation to the total number of pre‐consumption tweets for a movie has a negative impact on box office revenue. This premise suggests that the formation of higher expectations of a movie does not always result in positive results in situations where tweets on perceived movie quality after watching spread rapidly. This study also reveals that intention tweets have stronger effects on box office revenue than subjective tweets.

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Do Online Reviews Affect Product Sales? The Role of Reviewer Characteristics and Temporal Effects

Cited by 141 publications

References 35 publications

First Step in Social Media: Measuring the Influence of Online Management Responses on Customer Satisfaction

First Step in Social Media: Measuring the Influence of Online Management Responses on Customer Satisfaction

In Search of Negativity Bias: An Empirical Study of Perceived Helpfulness of Online Reviews

Impact of Tweets on Box Office Revenue: Focusing on When Tweets are Written

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