The Sales Impact of Word-of-Mouth Distribution across Retail and Third-Party Websites

With online search tools and users’ Internet experiences, online consumers are shown to rely on Word-of-Mouth (WOM) information hosted by both retail and third-party websites. Nevertheless, will online consumers conduct the same comprehensive level of WOM search, if the distribution of WOM across websites differs? This study is intrigued by this question to propose that the distribution of WOM across websites affects the search cost of WOM information during consumers’ decision making, and thus influences online retail sales. By using sales and WOM data of software programs from Amazon and a third-party website download.com, we find negative sales impacts of WOM volume dispersion and valence variation. Our results show that less dispersed 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 retail websites. In addition, more consistent consumer evaluations across websites encourage online purchase decisions

Product Variety, Online Word of Mouth and Long Tail: An Empirical Study on the Internet Software Market

This study examines the impact of both demand and supply side factors on long tail and superstar effects in the context of online software download. Our descriptive analysis suggests the coexistence of a steeper head and a longer but slimmer tail. Employing a novel empirical approach via the quantile regression model, we find a significant interaction effect between the demand-side factor (online user reviews) and the supply-side factor (product variety) on users’ software download. The influence of the two factors and their interplay on long tail and superstar effects vary significantly across different product popularity level. The results highlight the importance of incorporating both supply and demand factors in long tail research. The findings also offer an explanation for the mixed results reported in extant studies on the influence of online user reviews

DOES IT MATTER WHERE THE WORD-OF-MOUTH OCCURS?: AN EMPIRICAL STUDY ON THE SALES IMPACT OF THE DISTRIBUTION OF ONLINE USER REVIEWS

Consumers consistently resort to online Word-of-Mouth (WOM) in online shopping, thanks to the reach of the Internet and various web tools. Nevertheless, they are confronting relatively different levels of search costs for WOM information available on the Internet, 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 less evenly distributed WOM leads to more sales, conditional on the total number of WOM conversations across websites. 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 greater than receiving fifty reviews of 5-star average rating on Amazon and another fifty reviews of 5-star average rating on download.com

CUSTOMER JOURNEYS ON ONLINE PURCHASE: SEARCH ENGINE, SOCIAL MEDIA, AND THIRD-PARTY ADVERTISING

As the technologies and better practices become broadly available, companies are moving more quickly from a single-click or search-only model toward greater sophisticated models of informing and influencing the customer online shopping journeys. This study scrutinizes the predictive relationship between three referral channels, search engine, social medial, and third-party advertising, and online consumer search and purchase. The results derived from vector autoregressive models suggest that the three channels have differential predictive relationship with sale measures. Such differential relationship is even more pronounced for the long-term, accumulative effects. The predictive power of the three channels is also considerably different in referring customers among competing online shopping websites. This study offers new insights for IT and marketing practitioners in respect to how different channels perform in order to optimize the media mix and overall performance

Online User Reviews and Professional Reviews: A Bayesian Approach to Model Mediation and Moderation Effects

We propose a Bayesian analysis of mediation and moderation effects embedded within a hierarchical structure to examine the impacts of two sources of WOM information — online user reviews and professional reviews in the context of software download. Our empirical results indicate that the impact of user reviews on software download varies over time and such variation is moderated by product variety. The increase in product variety strengthens the impact of positive user reviews, while weakening the impact of negative user reviews. Furthermore, professional reviews influence software download both directly and indirectly, partially mediated by volume of online user reviews. Receiving positive professional reviews leads to more software download, yet receiving very negative professional reviews has a negative impact on the number of download. The increase in professional ratings not only directly promotes software download but also leads to more active user WOM interactions, which in turn leads to more download

The Impact of Free Sampling of Information Goods on the Dynamics of Online Word-of-Mouth and Retail Sales

Free sampling of information goods has become a common business practice in expectation of reducing consumers’ uncertainty of product quality and helping product diffusion, yet receiving limited investigation of how consumers process free sampling and online word-of-mouth (WOM) and its consequences on retail sales. In this research, we examine the impact of free sampling of information goods on the dynamics of online WOM and retail sales by analyzing a simultaneous equation system in a Bayesian hierarchical framework in online software market. We find that free sampling of information goods asymmetrically moderates the positive feedback mechanism between online WOM and retail sales. More adoptions of free trial not only directly lead to more retail sales but also enhance online WOM effect. Nevertheless, more adoptions of free trial generate fewer WOM and weaken the impact of past sales on WOM, which could potentially have a negative impact on future sales

The Impact of Third-Party Information on the Dynamics of Online Word-of-Mouth and Retail Sales

Consumers have been widely searching information on third-party and retail websites before making product choices, yet receiving limited systematic investigation of how consumers process third-party information and retailer-hosted (internal) word-of-mouth (WOM) and its consequences on retail sales. In this research, we examine the impact of third party information on the dynamics of internal WOM and retail sales by analyzing a simultaneous equation system in a Bayesian hierarchical framework in online software market. We find that third-party information moderates the positive feedback mechanism between internal WOM and retail sales. Receiving third-party reviews positively interact with retail sales to increase volume of internal WOM, thus leading to more sales; whereas consumer adoption of free-trial services negatively moderates the impact of retail sales on internal WOM, which may potentially have a negative impact on future sales indirectly. The findings imply that third-party information interact with retail website information in influencing consumers’ product choices

The Role of Dealers in Electronic Markets: Empirical Insights from Online Auctions

This study examines the impact of intermediaries (dealers) in online Consumer-to-Consumer (C2C) market. Online C2C transactions, such as the Internet auctions on eBay, are one of the most successful forms of electronic commerce (e-commerce). It has been suggested by many scholars that the Internet or electronic markets will eliminate intermediaries by lowering search cost and allowing direct and efficient interactions between sellers and buyers. However, a close examination of the market mechanism indicates that many functions provided by intermediaries are indispensable. Specifically, we consider intermediaries’ role in price discovery and trust building in electronic markets. Intermediaries provide a buffer for temporary misalignment between supply and demand by buying low and selling high, which provides product liquidity to buyers and sellers in online markets. Intermediaries also help build trust by engaging in transactions with risk-averse buyers and sellers who otherwise will not participate in the market. Using a dataset from eBay’s online auctions, we examine empirically these two functions in online C2C auction markets. We find that the presence of dealers has a significant impact on market liquidity, resulting in more successful trades and higher auction prices. In addition, we find that dealers are more likely to engage in transactions with less established sellers. Their presence reduces the reputation penalty faced by these players and further facilitates the success of auctions

Investigating Determinants of Voting for the “Helpfulness” of Online Consumer Reviews: A Text Mining Approach

The “helpfulness” feature of online user reviews helps consumers cope with information overloads and facilitates decision making. However, many online user reviews lack sufficient helpfulness votes for other users to evaluate their true helpfulness level. This study empirically examines the impact of the various features, that is, basic, stylistic, and semantic, of online user reviews on the number of helpfulness votes those reviews receive. Text mining techniques are employed to extract semantic characteristics from review texts. Our findings show that the semantic characteristics are more influential than other characteristics in affecting how many helpfulness votes reviews receive. Our findings also suggest that reviews with extreme opinions receive more helpfulness votes than those with mixed or neutral opinions. This paper sheds light on the understanding of online users’ helpfulness voting behavior and the design of a better helpfulness voting mechanism for online user review systems

An Empirical Investigation of Herding on the Internet: The Case of Software Downloading

Online shopping often requires consumers to choose among multiple products without detailed information about the quality. Herding is common in such situations which require consumers to infer product quality from other consumers’ choices and incorporate that information into their own decision-making process. The Internet affects the herding phenomenon in two ways. On the one hand, it provides more information about other consumers’ choices, making herding more feasible. On the other hand, the Internet provides more details about product quality, thus making herding less desirable. In this paper, we empirically examine those two effects in the context of online software downloading. We collected data on daily software downloads and studied how the daily download market share is related to the cumulative number of downloads and to the professionals’ and users’ ratings. We find significant herd behavior in our analysis of customers’ software choices. Surprisingly, the provision of professional product reviews or user reviews does not have a significant influence on the herding phenomenon. Our results suggest that consumers are in favor of information inferred from others’ behavior, but choose to ignore other sources of information. Such results are consistent with the predictions of the informational cascades literature. Our results also indicate that the vast amount of information provided on the Internet may not have as great an impact on consumer decision-making as previously expected. This paper contributes to e-commerce and Internet marketing research by investigating and offering a more in-depth understanding of online consumer behavior. This paper also contributes to the emerging literature on the impact of virtual communities