When Online Reviews Meet Hyperdifferentiation: A Study of Craft Beer Industry

By E. Clemons, Guodong (Gordon) Gao, Lorin M. Hitt. Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06), 2006

838 citations recorded by the source corpus.

Subfields: Marketing, Sociology and Political Science, Tourism, Leisure and Hospitality Management

View Paper: https://www.semanticscholar.org/paper/d2b60a6c79e98a7a8c5536268e214e54bf6f4fce

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Citations in the corpus

Perceived 'usefulness' of online consumer reviews: An exploratory investigation across three services categories (2012, background): “In a different but relevant study of craft beer sales, Clemons et al. (2006) discover that the mean of top quartile review ratings was positively and significantly associated with sales growth.”
Helpfulness of Online Product Reviews as Seen by Consumers: Source and Content Features (2013, background): “such as valence of review [17, 21], volume of review [44], source [5], and review content [47].”
How Does the Variance of Product Ratings Matter? (2012, background): “Along the same lines, Clemons et al. (2006) find that beer brands with higher variances of ratings grow fastest in terms of sales. West and Broniarczyk (1998) also consider how others’ opinions influence consumers’ evaluations of product attributes.”
Do online reviews affect product sales? The role of reviewer characteristics and temporal effects (2008, neutral): “Clemons, Gao, and Hitt (2006) [19] Sales data from the craft beer industry and review data from Ratebeer.”
eWOM: Extant Research Review and Future Research Avenues (2016, methodology): “…Television viewership ratings Entropy of posts, number of posts Chevalier and Mayzlin (2006) Books Sales rank Average rating, number of ratings Clemons, Gao, and Hitt (2006) Beer Sales growth rate Average rating, standard deviation of ratings Liu (2006) Movies Sales Number of posts Dellarocas…”
Why Is the Crowd Divided? Attribution for Dispersion in Online Word of Mouth (2015, positive): “Adopting the approach of others (Clemons et al. 2006), we operationalize WOM dispersion in terms of statistical variance—that is, the second moment of product ratings.”
Are Consumers More Likely to Contribute Online Reviews for Hit or Niche Products? (2010, background): “Other studies have examined the value of various metrics of online consumer content in influencing and predicting future sales [7, 10, 12, 15, 21, 28, 31].”
The impact of electronic word-of-mouth communication: A literature analysis and integrative model (2012, background): “, the rate and the valence of consumer reviews) extracted from the websites or online product review platforms to examine the impact of eWOM messages on product sales [14,20,22,24,27,29,79].”
Measuring the Value of Social Dynamics in Online Product Ratings Forums (2010, background): “The result is that online customer ratings has the potential to significantly affect product sales (Chevalier and Mayzlin 2006; Dellarocas, Zhang and Awad 2008; Clemons, Gao and Hitt 2006).”
What drives purchase intention for paid mobile apps? - An expectation confirmation model with perceived value (2015, background): “Subsequent empirical studies have found that a strong positive rating can positively influence product sales (Basuroy et al. 2003, Clemons et al. 2006, Gu et al. 2013).”
Opinion Mining and Sentiment Analysis (2008, background): “05 level to be classed as “significant”, then many studies find that review polarity has a significant economic effect [13, 14, 23, 31, 35, 47, 59, 62, 68, 72, 75, 76, 81, 82, 128, 136, 145, 180, 194, 196, 198, 207, 208, 214, 237, 251, 254, 279, 298, 332].”
Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation (2017, background): “As a consequence, in the past decade, there have been an increasing number of studies examining the phenomenon of online consumer reviews (e.g., Clemons et al. 2006; Dellarocas et al. 2007; Ho-Dac et al., 2013).”