How Does the Variance of Product Ratings Matter?

By Monic Sun. Management Sciences, 2012

661 citations recorded by the source corpus.

Subfields: Marketing, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/7107191d1d14c96ffcffcc22d537870e98a3c224

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Citations in the corpus

Online Consumer Review Factors Affecting Offline Hotel Popularity: Evidence from Tripadvisor (2016, background): “For example, some studies only investigate the review consistency to measure the degree of disagreement in consumer opinions (Godes & Mayzlin, 2004; Sun, 2012) while others focus solely on review quantity to assess product awareness among consumers (Dellarocas et al.”
Tourists’ willingness to pay for an accommodation: the effect of eWOM and internal reference price (2017, result): “We also did not account for eWOM consensus (or variance), though prior research suggests that higher consensus increases demand (Sun, 2012) and the persuasiveness of positive reviews (West & Broniarczyk, 1998).”
How Language Shapes Word of Mouth's Impact (2017, background): “Prior research has demonstrated that in categories in which tastes can vary, people attribute dispersion in reviewer ratings to heterogeneity in consumer preferences (He and Bond 2015; Sun 2012).”
Understanding the Impact of Online Reviews on Hotel Performance (2017, background): “Investigating the impact of valence on hotel attributes reveals insights into the consumer’s perceived service quality and potential purchase risks (Liu 2006; Sun 2012).”
Large-Scale Cross-Category Analysis of Consumer Review Content on Sales Conversion Leveraging Deep Learning (2019, background): “…studies have investigated simple and readily available summary statistics (e.g., review volume, average rating, variance of rating) and how they influence consumer purchase decisions (e.g., Duan, Gu, and Whinston 2008; Godes and Mayzlin 2004; Resnick and Zeckhauser 2002; Sun 2012; see Table 1).”
Too Popular to Ignore: The Influence of Online Reviews on Purchase Intentions of Search and Experience Products (2013, background): “However, as consumers increasingly use the Internet to communicate with other consumers as well as to review and purchase products, electronic word-of-mouth has gained importance (Goldsmith 2006; Trusov, Bucklin, and Pauwels 2009).”
Mining customer requirements from online reviews: A product improvement perspective (2016, background): “Online reviews have become an important source of information for consumers that significantly influence consumer choices and product sales [38,39,40,41,42,43,44,45,46,47].”
What We Know and Don't Know about Online Word-of-Mouth: A Review and Synthesis of the Literature (2014, background): “Sun (2012) found that when book reviews vary markedly, the instance of a low rating with high variance will lead to an increase in perceptions of product quality, whereas a low variance in a highly rated product will be high, as one would expect.”
Social Media Metrics — A Framework and Guidelines for Managing Social Media (2013, background): “Additionally, social media metrics and subsequently dashboards would need to cover the states, dynamics (e.g., Moe and Trusov 2011; Tirunillai and Tellis 2012), and heterogeneity (e.g., Sun 2012; Zhang, Li, and Chen 2012) in all three aspects.”
Research Note - When Do Consumers Value Positive vs. Negative Reviews? An Empirical Investigation of Confirmation Bias in Online Word of Mouth (2015, background): “…ratings can be caused by several factors, such as diversity in consumers’ tastes or product differentiation (Clemons et al. 2006, He and Bond 2015, Sun 2012), lower consensus leads consumers to be less confident in the validity of the average ratings and less certain of their initial beliefs…”
The effect of review valence and variance on product evaluations: An examination of intrinsic and extrinsic cues (2017, methodology): “Drawing from data derived from two online book retailers, Sun (2012) shows that when a product has a high valence, greater variance among reviews reduces demand for the product.”