Word-of-mouth Volume Research Articles

Post diversity: A new lens of social media WOM

Social media word of mouth (WOM) involves much interaction and engagement between users in their own networks. The feature of reposting on social media allows WOM to spread fast and easily. Original posts or reposts can be easily identified, perceived, and processed by social media users for purchase decisions. Then, we propose to investigate social media WOM post diversity, which reflects the variation of posts. Further, we propose that the relationship between social media WOM post diversity and sales can be conditional on the three previously established dimensions: volume, valence, and variance. The findings, reflecting approximately 1.5 million posts about 51 movies on a social media site, suggest that WOM post diversity, as measured by post entropy, positively associates with movie sales, contingent on low WOM volume, high valence, and high variance.

The interplay of word-of-mouth and customer value on B2B sales performance in a digital platform: an expectancy value theory perspective

PurposeBuilding on the expectancy value theory, the purpose of this paper is to examine the effects of word-of-mouth (WOM) and customer value (i.e. functional value and ease-of-doing-business value) on B2B business performance in the B2B sales process.Design/methodology/approachThe authors develop a framework to understand how B2B WOM and customer value influence supplier sales performance. This model was tested using structural equation modeling with a sample of 220 suppliers on Alibaba.com.FindingsThe empirical findings demonstrate that B2B WOM valence and volume have positive influences on the number of quotations and the number of transactions, respectively. Additionally, B2B WOM volume mediates the relationship between operational performance and the number of transactions. Response rate mediates the relationships between response time and both the number of quotations and the number of transactions, respectively.Originality/valueThis study contributes to the industrial marketing of B2B sales on the digital platform by investigating two influencers on sales performance: WOM and customer value.

The 4th V? The effect of word of mouth volatility on product performance

• WOM volatility may directly affect product performance, but the direction is an empirical question. • WOM volatility interacts with all three main characteristics of WOM: volume, valence, and variance. • Under volatile WOM volume, the positive WOM effects due to volume and valence and the negative WOM effects due to variance will be attenuated. This study investigates how word-of-mouth (WOM) volatility, a new WOM variable that measures the fluctuation over time in the WOM volume, affects product performance. Two conflicting forces of WOM volatility (i.e., positive effects on awareness and negative effects on preference and credibility) leave as an empirical question. We also investigate the moderating effects of WOM volatility on the relationship between existing WOM variables (i.e., volume, valence, and variance) and product performance. Cross-sectional regression analysis was conducted, using online WOM data in Korea movie industry. The results show that WOM volatility negatively affects the product performance only when consumers rely more on WOM information. Highly volatile WOMs weaken the positive effects of WOM volume and valence and the negative effects of WOM variance. This study suggested WOM volatility as a new characteristic of WOM. This study examined WOM volatility interacts with existing WOM variables. Managerial and academic implications are discussed.

The impact of place-of-origin on price premium for agricultural products: empirical evidence from Taobao.com

The impact of place-of-origin on price premium for agricultural products in the online marketplace has received limited attention in the existing literature. This study draws from the elaboration likelihood model and investigates whether place-of-origin affects price premium. Moreover, this study explores how other cues [seller’s reputation, positive word-of-mouth (WOM) volume, and WOM valence] moderate the relationship between place-of-origin and price premium for agricultural products in the online marketplace. The result of the empirical study reveals that place-of-origin indeed has a significant and positive impact on price premium. Furthermore, the study finds a negative interactive effect between place-of-origin and other cues (seller’s reputation, positive WOM volume, and WOM valence) on the price premium for agricultural products in an e-commerce setting. The results highlight the importance of place-of-origin in the competitive online market and have implications both for academic research and for online retailing practice.

The business model of Chinese movies

PurposeThe motion picture industry is a cultural and creative industry. Unlike its US counterpart, the Chinese motion picture industry is still developing. Therefore, learning from the US market, the purpose of this paper is to analyze the business model of Chinese movies from the perspective of new product diffusion.Design/methodology/approachBased on 66 movies released in the US and 21 movies released in China, this paper first compares the diffusion curves of Chinese and US movies through the movie life cycle and box office trends. Next, it analyzes the moviegoing behaviors of Chinese and US audiences based on the innovation and imitation coefficients in the Bass model. Finally, it compares the attention to information of Chinese and US audiences from the perspective of interpersonal word-of-mouth (WOM).FindingsIn the USA, a movie’s highest weekly box office is usually in its opening week, followed by a weekly decline in revenue; in China, there is no difference in box office performance between the first two weeks, but a weekly decline in revenue similarly follows. US audiences pay more attention to advertisements for movies than WOM recommendations, while Chinese people pay more attention to WOM recommendations. Neither the Chinese nor the US market differs in the volume of WOM between the first week before release and the opening week, and these two weeks are the most active period of WOM in both markets.Practical implicationsDuring the production phase for Chinese movies, we should satisfy opinion leaders’ needs. During the distribution phase, we should not only focus on market spending before the movie’s release, but also increase market spending in the opening week. During the theater release phase, we should stimulate WOM communication between moviegoers and thereby attract many more opinion seekers.Originality/valueFew studies have investigated the Chinese motion picture industry from the perspective of new products. This paper compares and analyzes the diffusion of Chinese and US movies using the Bass model of new product diffusion, providing systematic theoretical guidelines for the commercial operation of the Chinese motion picture industry.

Word-of-Mouth System Implementation and Customer Conversion: A Randomized Field Experiment

Online retailers often face the decision on whether they should implement a word-of-mouth (WOM) system on their websites. Some retailers opt not to have an in-site WOM system (e.g., Tiffany.com and Tjmaxx.com ), whereas others implement and manage an internal WOM system (e.g., Amazon.com and Macys.com ). According to a new study in Information Systems Research, authors Ni Huang (Arizona State University), Tianshu Sun (University of Southern California), Peiyu Chen (Arizona State University), and Joseph M. Golden ( Collage.com ) conducted a randomized field experiment to examine how implementing an in-site WOM system from ground zero influences customer conversion in an online retailing website. Their results demonstrate that implementing an in-site WOM system can be a double-edged sword to the online retailing website. Specifically, the impact of a WOM system implementation on customer conversion is moderated by WOM volume, such that its effect is positive above a threshold of volume and negative below the threshold. Additionally, the researchers find that WOM valence reinforces the impact of a WOM system on customer conversion. These results offer important implications for the hundreds-of-billion-dollar online retailing industry on the implementation and management of online WOM systems.

Marketing communication in the digital age: online ads, online WOM and mobile game adoptions

PurposeThis study aims to explore the interdependent impacts of online word-of-mouth (WOM) and online ads on digital product adoptions, as well as their dynamic changes throughout the product life cycle.Design/methodology/approachThis study adopted an empirical approach by using a unique data set of five mobile games launched between 2012 and 2014 provided by Renren Games Ltd. in China.FindingsThe results indicated that advertising generally has a positive impact on WOM. During the product life cycle, the influence on volume and variance gradually decreases, whereas the impact on valence increases over time. WOM (including WOM volume and WOM valence) and advertising both have positive impacts on game adoptions. They complement each other to shape adoptions throughout the product life cycle: advertising is more effective in encouraging adoptions in the early and later stages of the demand evolution process, whereas WOM has a greater impact on adoptions in the mid-stage.Practical implicationsThis study provided detailed managerial recommendations on how to effectively integrate different types of marketing communication and optimize the investment strategy of online ads and online WOM in different stages of the product life cycle.Originality/valueFirst, the study enriched the theory of digital marketing communication by studying the relationship between mass media (online ads), interpersonal media (online WOM) and product adoptions in the network context. Second, it provided an empirical basis for the inference of the dynamic development of media effect in the new product diffusion theory. Third, the results will be helpful to end the debate in current theoretical literature on whether there is a complementary or alternative relationship between the two effects. Last but not least, it enriched research on the antecedents and dynamic effects of online WOM.

Does Piracy Create Online Word of Mouth? An Empirical Analysis in the Movie Industry

Anecdotal evidence suggests that counterfeiting/piracy can help create online word of mouth (WOM) and through this boost demand, but how powerful is such WOM? To answer this question, we conduct a descriptive study with some attempts to establish near causality. We estimate the impact of piracy on WOM and ultimately revenue by applying a panel data method to all movies widely released in the United States from 2015 to 2017. In identifying the effects of piracy we make inventive use of Russian piracy data to construct instrument variables for piracy in the United States. This is possible because the key piracy site, The Pirate Bay, has been blocked in Russia since 2015. We find movies with prerelease piracy are associated with lower revenues despite the WOM effect. Critically, however, we show a positive correlation between postrelease piracy and WOM volume, and we extend the field by finding that the presence of postrelease piracy is associated with an approximately 3.0% increase in box office revenue. We also note the impact of a raid by the Swedish Police that temporarily took down The Pirate Bay website in December 2014. The period when the site was down experienced a decline in WOM volume and revenues, consistent with the effect of lower postrelease piracy predicted by our models. Our findings suggest approaches to target scarce antipiracy resources, such as focusing on tackling damaging prerelease piracy. This paper was accepted by Juanjuan Zhang, marketing.

The Impact of Word of Mouth via Twitter On Moviegoers' Decisions and Film Revenues

<h3>ABSTRACT</h3> This study drew on the existing decision process theory to empirically examine the effect of word of mouth (WOM) generated by social media. The prospect theory was adopted to illustrate how both the volume and valence of WOM influence a person9s decision to watch a movie through the movie quality evaluation stage. Using U.S. movie industry data and online post data from Twitter, the authors found that the effect of online WOM is well explained by the prospect theory. Particularly, findings suggest that intensively advertising a movie before its release to attract moviegoers could backfire by raising the moviegoers9 expectations.

The role of entropy of review text sentiments on online WOM and movie box office sales

Examine the role of neutral review sentiments in WOM effects.By using classification of review text, we create an entropy variable of review text sentiment.Find that entropy of review text sentiment positively moderates the relationship between online WOM and product sales. Sentiments from online word-of-mouth (WOM) are often controversial, since individuals have different preferences toward the same products. Past studies have focused on online WOM effects by measuring WOM volume and valence. However, few studies have investigated how the entropy of the review text sentiment influences the relationship between online WOM and product sales. As WOM valence and volume are usually provided at an aggregated level, consumers often do not have enough information to make a decision. In this case, reading online review text has become an important process for consumers to make purchasing decisions. However, when consumers encounter too many positive review texts, they may doubt the credibility of online WOM itself. Thus, we analyzed the entropy of the review text sentiments by conducting text-mining techniques. We classified review text sentiment into positive, negative, and neutral categories and created an entropy variable. A high level of entropy in review texts indicates that sentiment from review texts are equally distributed, but not biased, towards positive or negative sentiment. We estimated our research model with the entropy variable in a panel dataset for 204 movies over a half-year period. The results suggest that the entropy level in the review texts has a positive moderating impact on the relationship between WOM (e.g., valence and volume) and movie box office sales. The findings imply that deleting negative reviews to enhance product sales may not help online retailers or related parties.

Managing initial expectations when word-of-mouth matters

PurposeConsumer expectation not only influences purchase decision but also post-purchase satisfaction and word-of-mouth (WOM). This study aims to develop theories of initial expectation management by suggesting when it is desirable for new products to raise or lower consumer expectations. It systematically examines the interplay of product value and consumer heterogeneity in the dynamic process of new product diffusion under competition.Design/methodology/approachDrawing on traditional diffusion and choice models, this study develops an agent-based model to formalize and analyze how consumers’ initial expectations of a new product influence the interdependent processes of product sales, consumer satisfaction and WOM. The simulation analyses in controlled settings help understand the underlying mechanisms in a stepwise manner.FindingsThe results show that, although the optimal strategy for low-value products is to induce consumer expectations higher than product value, high-value products are better introduced with expectations formed close to it. The results also highlight an important drawback of “under-promising” strategies in reducing the base and volume of WOM. Further, the analysis illustrates how consumer heterogeneities in product valuation and initial expectation affect the effectiveness of expectation management. For high-value products, both heterogeneities reduce the effectiveness of the optimal strategy. For low-value products, however, value heterogeneity enhances the effectiveness, whereas expectation heterogeneity reduces it.Practical implicationsFirms introducing new products should be sensitive to how consumers value the product and form expectations about it. Different from firms that must rely on aggressive advertising to sell inferior products by building up high expectations, those with superior products can rely more on the power of consumer WOM, which is much less costly and thus gives them a competitive advantage. Firms should also pay attention to how diversified the consumers are in product valuation and expectation. The expectation management strategy is more effective when consumers form more similar expectations. Inferior firms may leverage this mechanism to neutralize their disadvantages.Originality/valueThe articulated mechanisms help push forward the research on new product diffusion and consumer expectation management. To the best of the authors’ knowledge, this is one of the first studies to systematically analyze the impact of consumer heterogeneity on the effectiveness of expectation management.

The interplay between free sampling and word of mouth in the online software market

Free sampling in digital format has become a common business practice in the online market offering consumers first-hand experience with products, due to its low marginal cost and extensive online distribution. At the same time, online word of mouth (WOM) has also been a prevalent strategy on the Internet for increasing product visibility and providing trustworthy product information. Those two online marketing strategies are generally considered to stand alone by marketers and prior research. Nevertheless, by drawing on integrated information response theory as well as theories for explaining online consumers' review sharing, we argue that free sampling complements WOM in the online market by amplifying its sales effect and facilitating its implementation. We provide supportive empirical evidence through a Bayesian analysis of software free sampling on CNET Download.com (CNETD) and sales and WOM from Amazon.com over a 25-week data set. Our results show that adoptions of CNETD free sampling positively interact with Amazon WOM in influencing Amazon software sales. In addition, more adoptions of CNETD free sampling lead to a larger volume of Amazon WOM, and this impact is more significant for less popular products. These findings contribute to our understanding of free sampling in the online market such that, in addition to its direct sales effect, free sampling can also potentially affect sales through influencing online WOM. Therefore, we suggest that marketers evaluate the free sampling strategy by including its interplay with online WOM and apply low-cost free sampling to facilitate the relatively more expensive online WOM marketing strategy, especially for unpopular products.

Television Advertising and Online Word-of-Mouth: An Empirical Investigation of Social TV Activity

In this research, we investigate the relationship between television advertising and online word-of-mouth (WOM) by examining the joint consumption of television programming and production of social media by television viewers, termed social TV. We explore how television advertising impacts the volume of online WOM about advertised brands and about the programs in which the advertisements air. We also examine what encourages or discourages viewers to engage in this particular social TV activity. Using data containing television advertising instances and the volume of minute-by-minute social media mentions, our analyses reveal that television advertising impacts the volume of online WOM for both the brand advertised and the program in which the advertisement airs. We additionally find that the programs that receive the most online WOM are not necessarily the best programs for advertisers interested in online engagement for their brands. Finally, our results highlight the brand, advertisement, and program characteristics that can encourage or discourage social TV activity. We discuss the implications of our findings for media planning strategies and advertisement design strategies. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2016.1002 .

The Paradoxes of Word of Mouth in Electronic Commerce

Challenging conventional wisdom, we unravel three paradoxes of word of mouth (WOM) in e-commerce. Specifically, the WOM valence paradox contends that higher WOM valence of a product results in a larger subsequent decrease in the WOM valence of the product, the WOM volume paradox propounds that higher WOM volume of a product results in a smaller subsequent increase in the WOM volume of the product, and the WOM spillover paradox proposes that an improvement in the WOM of a product also improves the WOM of connected products in a product network. These paradoxes caution online retailers that superior WOM may at times backfire and not boost further sales. Drawing theoretical support from expectation-confirmation theory and network theory, we collect data from China’s largest business-to-consumer platform, Tmall.com, and use linear panel data models to examine WOM evolution in a product network, controlling for relevant factors at the individual product, product network, and time unit levels. Importantly, we base our identification strategies on the use of instrumental variables and the difference-in-differences estimation approach. Numerous statistical checks confirm the robustness and consistency of our findings. We contribute to a much richer theoretical understanding of WOM, by extending the applicability of expectation-confirmation theory and network theory to novel predictions and contexts, adding a dynamic perspective, unveiling three important WOM paradoxes, and offering practical insights.

Social signals and algorithmic trading of Bitcoin.

The availability of data on digital traces is growing to unprecedented sizes, but inferring actionable knowledge from large-scale data is far from being trivial. This is especially important for computational finance, where digital traces of human behaviour offer a great potential to drive trading strategies. We contribute to this by providing a consistent approach that integrates various datasources in the design of algorithmic traders. This allows us to derive insights into the principles behind the profitability of our trading strategies. We illustrate our approach through the analysis of Bitcoin, a cryptocurrency known for its large price fluctuations. In our analysis, we include economic signals of volume and price of exchange for USD, adoption of the Bitcoin technology and transaction volume of Bitcoin. We add social signals related to information search, word of mouth volume, emotional valence and opinion polarization as expressed in tweets related to Bitcoin for more than 3 years. Our analysis reveals that increases in opinion polarization and exchange volume precede rising Bitcoin prices, and that emotional valence precedes opinion polarization and rising exchange volumes. We apply these insights to design algorithmic trading strategies for Bitcoin, reaching very high profits in less than a year. We verify this high profitability with robust statistical methods that take into account risk and trading costs, confirming the long-standing hypothesis that trading-based social media sentiment has the potential to yield positive returns on investment.

The Antecedents of Online Word-Of-Mouth

The Antecedents of Online Word-Of-Mouth Jie Feng, Jing Yang Abstract This study makes use of online word of mouth (WOM) data for automobiles to examine the antecedents of online WOM, the multiple factors that either stimulate positive or create negative online WOM, the dynamic pattern of online WOM activities, and the unique, untested influence of TPO endorsements or reviews’ on both volume and valence of online WOM. The study contributes to the growing online WOM literature, automobile industry and potentially buzzes marketing management for durable goods, such as computers, digital camera or other consumer electronics. Full Text: PDF DOI: 10.15640/jmm.v3n1a1

오피니언 마이닝과 네트워크 분석을 활용한 상품 커뮤니티 분석: 영화 흥행성과 예측 사례

오피니언 마이닝과 네트워크 분석을 활용한 상품 커뮤니티 분석: 영화 흥행성과 예측 사례

How Does the Distribution of Word-of-Mouth Across Websites Affect Online Retail Sales?

Consumers consistently resort to online Word-of-Mouth (WOM) as an effective remedy for the lack of physical trials in online shopping. Nevertheless, they are confronting relatively different levels of search costs for WOM information depending on the distribution of WOM across websites. This study investigates the sales impacts of dispersion of WOM volume and variation of WOM valence by using sales and WOM data of software programs from Amazon and download.com. Our results suggest that, conditional on the total number of WOM conversations from retailing websites and third-party websites, less evenly distributed WOM leads to more sales. And it is even more beneficial for a product’s sales if having this less dispersed WOM distribution skewed towards retailing websites. In addition, more consistent consumer evaluations across websites encourage online purchasing decisions. By comparing the volume dispersion and variance variation, we find that receiving one hundred reviews of 5-star average rating on Amazon leads to sales almost six time more than receiving fifty reviews of 5-star average rating on Amazon and another fifty reviews of 5-star average rating on download.com.

The impact of online word-of-mouth on television show viewership: An inverted U-shaped temporal dynamic

This article examines the dynamic impact of online word-of-mouth (WOM) on US television show viewership. With WOM data collected from the Internet Movie Database website, we find that the cumulative volume of online WOM has significant explanatory power for viewership over time. Consistent with the mere exposure effect theory, the dynamic impact of the volume of online WOM over time varies according to a curvilinear, inverted U-shaped curve. Due to an initial floor effect, the volume of WOM is not significant in the early episodes. The impact of volume increases over time, before peaking and starting to decrease in the latter part of a show’s life. This article demonstrates the differential effects of online WOM over time and thereby suggests that firms’ online marketing strategies, such as media planning, must adjust with the product life cycle.

Promotional Marketing or Word-of-Mouth? Evidence from Online Restaurant Reviews

The value of promotional marketing and word-of-mouth (WOM) is well recognized, but few studies have compared the effects of these two types of information in online settings. This research examines the effect of marketing efforts and online WOM on product sales by measuring the effects of online coupons, sponsored keyword search, and online reviews. It aims to understand the relationship between firms' promotional marketing and WOM in the context of a third party review platform. Using a three-year panel data set from one of the biggest restaurant review websites in China, the study finds that both online promotional marketing and reviews have a significant impact on product sales, which suggests promotional marketing on third party review platforms is still an effective marketing tool. This research further explores the interaction effects between WOM and promotional marketing when these two types of information coexist. The results demonstrate a substitute relationship between the WOM volume and coupon offerings, but a complementary relationship between WOM volume and keyword advertising.