Promotional Chat on the Internet

By Dina Mayzlin. 2006

648 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Marketing, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/b72daa4b11c117beafb8ce7881a38a8fbc6ddf8a

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In Management Science and Operations Research

In Marketing

In Sociology and Political Science

Citations in the corpus

Why are you telling me this? An examination into negative consumer reviews on the Web (2007, background): “Recognizing the significant value of consumer product reviews as a source of information and thus Web site trust for potential customers, marketers enable and encourage consumers to post product reviews and opinions on their e-retail sites (Mayzlin, 2006; Tedeschi, 1999; Yang & Peterson, 2003; Bart ”
Searching for Experience on the Web: An Empirical Examination of Consumer Behavior for Search and Experience Goods (2009, background): “Our finding that third-party opinions and recommendations do not significantly affect time per site may be due to the rarity of this mechanism in our sample or to lower consumer confidence in this retailer-controlled information (Friestad and Wright 1994; Mayzlin 2006).”
How Does the Variance of Product Ratings Matter? (2012, methodology): “…Product ratings are more influential for nonsuperstars Gao et al. (2006) Online consumer reviews exhibit remarkable community features Chevalier and Mayzlin (2006) A higher average rating leads to higher book sales, and impact of one-star reviews is bigger than impact of five-star reviews…”
The Effect of Electronic Word of Mouth Communications on Intention to Buy: A Meta-Analysis (2019, neutral): “It has been found that eWOM has an influence on individuals’ evaluation of products and services (Mayzlin 2006).”
Do Your Online Friends Make You Pay? A Randomized Field Experiment on Peer Influence in Online Social Networks (2014, background): “…playing roles that range from inspiring political action to driving viral and word-of-mouth spread of products and services (Aral and Walker 2011, Hill et al. 2006, Iyengar et al. 2011, Manchanda et al. 2008, Mayzlin 2006), and as such, represent a vast reservoir of social and economic inﬂuence.”
How Much can you Trust Online Information? Cues for Perceived Trustworthiness of Consumer-generated Online Information (2011, background): “As Mayzlin (2006) noted, marketers have incentives to disguise promotional reviews as legitimate eWOM communication in order to influence consumers' evaluation of their products, but consumers are becoming more and more aware of this kind of practice.”
Exploring determinants of voting for the "helpfulness" of online user reviews: A text mining approach (2011, background): “CNETD provides an ideal environment for this study since none of the parties (users, CNETD, and software owners) would benefit directly from the increase of the software download, hence there is no or low incentive for any of them tomanipulate the user reviews as a disguised “promotional chat” [22].”
The Influence of Product Variety on Brand Perception and Choice (2007, background): “The notion that consumers rely on cues to assess quality is well established and has been relied on in both marketing (e.g., Allison and Uhl 1964, Olson 1977, Mayzlin 2006, Purohit and Srivastava 2001) and economics (e.g., Klein and Leffler 1981, Nelson 1974).”
Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix (2004, neutral): “Mayzlin (2006) studies the credibility of the promotional messages in online chat rooms and the implication of such new information channels on sellers’ profitability.”
eWOM: Extant Research Review and Future Research Avenues (2016, methodology): “Firms can acquire, retain, and engage customers (Mayzlin, 2006) and make them happy by utilizing eWOM effectively.”
The Effects of the Social Structure of Digital Networks on Viral Marketing Performance (2008, background): “Mayzlin and her colleagues (Chevalier and Mayzlin 2006, Godes and Mayzlin 2004, Mayzlin 2006) draw on both behavioral and management science traditions and approach customer-generated characteristics and behaviors as constructs which can be utilised as known quantities in mathematical models.”