Can online reviews reveal a product's true quality?: empirical findings and analytical modeling of Online word-of-mouth communication

By Nan Hu, Paul A. Pavlou, Jie Jennifer Zhang. ACM Conference on Economics and Computation, 2006

443 citations recorded by the source corpus.

Subfields: Marketing, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/d9d93003b0fea15fe5456227191b675776e2bb21

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Citations in the corpus

Who is talking? An ontology-based opinion leader identification framework for word-of-mouth marketing in online social blogs (2011, background): “Sometimes the feedback is polarized, as consumers may only submit feedback when they have strong opinions – either positive or negative – on a specific issue, product, or service [19].”
Navigating by the Stars: Investigating the Actual and Perceived Validity of Online User Ratings (2016, background): “Review writers are more likely to be those that “brag” or “moan” about their product experience, resulting in a bimodal distribution of ratings for which the average does not give a good indication of the true population average (Hu, Pavlou, and Zhang 2006).”
mTrust: discerning multi-faceted trust in a connected world (2012, background): “Many studies also reported this positive ratings phenomenon in online consumer ratings [1, 7].”
Disconfirmation Effect on Online Rating Behavior: A Dynamic Analysis (2014, background): “Following the notion that online ratings are truly truthful, Hu et al. (2006) discuss whether the mean of posted ratings can represent true product quality.”
Designing novel review ranking systems: predicting the usefulness and impact of reviews (2007, unknown): “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 manufactureroriented ranking mechanism ranks the reviews according to their expected e.ect on sales.”
The Sound of Silence in Online Feedback: Estimating Trading Risks in the Presence of Reporting Bias (2008, background): “%) levels of positive feedback currently found on that system.”
Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs. review content (2012, background): “This latter implies that customers react to positive and negative reviews differently, confirming the findings of the study by Hu et al. (2008).”
Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics (2011, background): “Recent work has shown that the distribution of an overwhelming majority of reviews posted in online markets is bimodal [1].”
Opinion Mining and Sentiment Analysis (2008, background): “[132] remark that 54% of the items in a sample of Amazon book, DVD, and video products with more than 20 reviews fail both statistical normality and unimodality tests — reporting only the mean rating score may not provide enough information.”
Do online reviews affect product sales? The role of reviewer characteristics and temporal effects (2008, unknown): “Hu, Pavlou, and Zhang (2006) [28] A field study and data collected from Amazon.”
What Do Mobile App Users Complain About? (2015, background): “Users tend to write reviews when they are either extremely satisﬁed or extremely dissatisﬁed with a product [10].”
Informational Cascades and Software Adoption on the Internet: An Empirical Investigation (2009, background): “Recent research has shown that online users could be subject to both persuasion biases and extremism in that they are more likely to post reviews if they feel strongly (positively or negatively) about a product (Dellarocas and Narayan 2006; Hu et al. 2006).”