Why are you telling me this? An examination into negative consumer reviews on the Web

By Sercan Sen, Dawn Lerman. 2007

1,324 citations recorded by the source corpus.

Subfields: Marketing, Organizational Behavior and Human Resource Management, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/6b76c7385c5851ff57f172531dcf143d677a22eb

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In Organizational Behavior and Human Resource Management

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Citations in the corpus

Why Is the Crowd Divided? Attribution for Dispersion in Online Word of Mouth (2015, result): “In particular, given that utilitarian products tend to be taste similar and hedonic products tend to be taste dissimilar, our work supplements prior research suggesting that attributions for WOM vary across hedonic and utilitarian categories (Sen and Lerman 2007).”
Avoiding the dark side of positive online consumer reviews: Enhancing reviews' usefulness for high risk-averse travelers (2015, background): “Although previous literature investigates the persuasive nature of consumer reviews, only a few studies focuses on how travelers evaluate online consumer reviews (e.g., Sen and Lerman, 2007; Park and Lee, 2009; Mudambi and Schuff, 2010), especially in the travel industry.”
The effects of marketer- and advocate-initiated online service recovery responses on silent bystanders (2017, background): “The detrimental outcomes of NeWOM, such as negative brand evaluations, disseminating negative brand information and deteriorated brand reputation, have been repeatedly shown in past research (Chevalier &Mayzlin, 2006; Sen & Lerman, 2007).”
Curvilinear Effects of User-Generated Content on Hotels’ Market Share (2013, background): “…to demonstrate a level of trust in using product reviews (Riegner 2007), reviews and ratings of service experiences might suffer from the subjective nature of the assessment and the judgment made by the reader (Laczniak, DeCarlo, and Ramaswami 2001; Folkes 1988; Sen and Lerman 2007; Weiner 2000).”
The impact of online reviews on hotel booking intentions and perception of trust. (2011, background): “There is wide agreement (Gretzel & Yoo, 2008; Sen & Lerman, 2007) that with the advance of technology (especially the Internet) the information sources available to prospective consumers have grown.”
Extremely Negative Ratings and Online Consumer Review Helpfulness: The Moderating Role of Product Quality Signals (2020, background): “…help to advance the literature on the moderators of negative reviews, which had previously investigated the product type (Mudambi and Schuff 2010; Sen and Lerman 2007), emotion intensity (M. Lee et al. 2017), information quality (Wu 2013), objective information, and consumer’s subjective…”
Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics (2014, neutral): “Different methods can be used to extract sentiment out of text. studies use human subjects to extract the sentiment of OCR, others use automated sentiment mining to extract sentiment from the text of reviews (Bai 2011; Schindler and Bickart 2012; Sen and Lerman 2007).”
Helpfulness of Online Product Reviews as Seen by Consumers: Source and Content Features (2013, background): “When the reader is unable to fully understand the product through the reviews, it is likely that he/she may assess the customer review to be poorer since he/she may speculate that the peer may not unwilling to fully disclose the consumption experience [2, 65].”
Conceptualising electronic word of mouth activity: An input‐process‐output perspective (2011, background): “Attribution theory According to the attribution theory paradigm, in terms of whether or not to adopt eWOM communication, readers will base their decisions on the causal inferences they make regarding the reviewer’s motivation in posting eWOM communication (Sen and Lerman, 2007).”