A Meta-Analysis of Electronic Word-of-Mouth Elasticity

Abstract

The authors conduct a meta-analysis on the effect of electronic word of mouth on sales by examining 51 studies (involving 339 volume and 271 valence elasticities) and primary data collected on product characteristics (durability, trialability, and usage condition), industry characteristics (industry growth and competition), and platform characteristics (expertise and trustworthiness). Their analysis reveals that electronic word-of-mouth volume (valence) elasticity is .236 (.417). More importantly, the findings show that volume and valence elasticities are higher for privately consumed, low-trialability products that operate in less competitive industries and whose reviews are carried on independent review sites. Furthermore, volume elasticities are higher for durable goods and for reviews on specialized review sites, whereas valence elasticities are greater for community-based sites. Drawing on the results, they discuss several implications for managers and researchers and explain why valence elasticities are often found to be insignificant. Finally, they propose numerous directions for future research in the area on the basis of their findings.

Good, better, engaged? The effect of company-initiated customer engagement behavior on shareholder value

“…Previous literature clearly shows that word-ofmouth, especially if it uses electronic platforms, is a powerful tool to attract customers and increase sales. In particular, it outperforms traditional tools such as advertising, because of the high potential reach of (electronic) word-of-mouth, and also because customers trust the recommendations of other customers more than traditional company communication (Babić Rosario et al 2016;Trusov et al 2009;You et al 2015). …”

Do different kinds of user-generated content in online brand communities really work?

“…The second analyses how credible consumers find UGC and their goals for engaging with such content (e.g., . Finally, the third research stream focuses on the relationship between UGC and significant managerial outcomes, such as sales (e.g., You et al, 2015), the economic value of posts in online settings (e.g., Ghose and Ipeirotis, 2009), brand engagement (e.g., Luarn et al, 2015) or user reactions (e.g., Jeon et al, 2016). However, none of the previous research related to this third research stream is focused on understanding how different kinds of UGC differ in their relationship with performance measures (e.g., product success in terms of number of owners of products).…”

“…From the perspective of vividness and richness of online UGC, reviews and comments are only text messages and are characterised as low level of vividness and richness (Coursaris et al, 2016;Luarn et al, 2015). Previous research obtains mixed results regarding the influence of valence of users' reviews (i.e., whether comments are positive or negative) on sales (Floyd et al, 2014;You et al, 2015) and other psychological outcomes (Purnawirawan et al, 2015). Despite this fact, some authors underscore the significance of online customers' reviews by showing that positive/negative information encountered online can be a triggering factor to modify customer behaviour (i.e., buy/not buy, respectively) (Adjei et al, 2010;Chevalier and Mayzlin, 2006;Veloutsou et al, 2017).…”

“…Despite the importance of understanding the effects of different types of UGC in OBCs on brand/product success, this topic has received limited attention in academic literature Yadav and Pavlou, 2014). Previous research is mainly focused on volume and/or valence of customers' reviews (i.e., only text based), obtaining mixed results on their influence on sales (Floyd et al, 2014;You et al, 2015) and other psychological outcomes (Purnawirawan et al, 2015). Moreover, previous research focused on the analysis of different types of UGC merely examines their influence on brand popularity and brand engagement (Coursaris et al, 2016;Luarn et al, 2015).…”

Impact of reviewer social interaction on online consumer review fraud detection

“…A meta-analysis on online reviews [11] focuses on understanding the effect of online reviews on retailers' performance and revenue using sales elasticity. An analysis on online review elasticity [43] present that volume and valence elasticity is higher for lesser known products which are sold by a couple of online retailers and that volume elasticity is higher on the product market whereas valence elasticity is higher on community markets. A very recent approach to identify fraud in Yelp reviews addresses the economic incentives for a particular business to conduct review fraud [27], Luca and Zervas with the help of the information provided by Yelp about one of it's sting operations derive that filtered reviews on Yelp tend to lie on extremes and that a restaurant is more likely to commit review fraud when it's reputation is relatively weak, basically when it has less reviews.…”