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 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

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

Conceptualizing the Electronic Word-of-Mouth Process: What We Know and Need to Know About eWOM Creation, Exposure, and Evaluation

Electronic word of mouth (eWOM) is a prevalent consumer practice that has undeniable effects on the company bottom line, yet it remains an over-labeled and under-theorized concept. Thus, marketers could benefit from a practical, science-based roadmap to maximize its business value. Building on the consumer motivation–opportunity–ability framework, this study conceptualizes three distinct stages in the eWOM process: eWOM creation, eWOM exposure, and eWOM evaluation. For each stage, we adopt a dual lens—from the perspective of the consumer (who sends and receives eWOM) and that of the marketer (who amplifies and manages eWOM for business results)—to synthesize key research insights and propose a research agenda based on a multidisciplinary systematic review of 1050 academic publications on eWOM published between 1996 and 2019. We conclude with a discussion of the future of eWOM research and practice

Offline and online search in used durables markets

This study examines how different information sources are used by consumers prior to their purchase of used durable goods, specifically used cars. We examine how online and offline search are related. Categories of online sources are dealer websites and resale websites, and of offline sources are print media and dealer visits. Prior research on new car purchases finds that online sources substitute for traditional, offline sources such as dealer visits. We examine whether this theory extends to used-car purchases and distinguish between dealer websites and resale websites (a distinction relevant to used-goods markets) by collecting data from a sample of used-car buyers. Because search in different sources can be interrelated, and due to data censoring, we build and estimate a simultaneous equations Tobit model. In contrast to existing research, we find that online search on dealer websites is complementary to and not a substitute for dealer visits. This complementary effect highlights the importance of dealers' web presence in used markets. © 2014 New York University

An investigation of the drivers of social commerce and e-word-of-mouth intentions: Elucidating the role of social commerce in E-business

Building on social commerce (s-commerce) perspectives and the trust transfer theory, this study develops a theoretical model that explains the indirect effects of two types of s-commerce attributes (community and platform) on behavioral outcomes (s-commerce intentions and e-Word-of-Mouth (e-WOM) intentions) through trust in community and platform. We analyze data collected from s-commerce users on travel booking websites using structural equation modeling technique. Results confirm that s-commerce intentions and e-WOM intentions are contingent upon s-commerce community and platform attributes. Moreover, the results provide evidence for the mediating effects of trust in community and platform on the relationship between s-commerce attributes and behavioral outcomes. The study provides further insights about the impact of s-commerce experience on s-commerce intention and e-WOM intention. Moreover, this study contributes to s-commerce research and practice by developing and validating the role of s-commerce community and platform attributes in forming consumers’ s-commerce behavioral outcomes

Online reviews and consumers\u27 willingness to pay: the role of uncertainty

Empirical studies of online reviews have found that valence (average rating) has a consistently positive impact on consumers’ willingness to pay (WTP), but volume does not. Although two studies tried to explain this phenomenon using different perspectives (Wu and Ayala, 2012; Sun, 2012), neither study can fully accommodate the consumer behaviors observed by the other. This dissertation adopts a theoretical framework that can explain the consumer behaviors observed in both studies as well as the varying influence of review volume at the individual level. Specifically, several studies were conducted to investigate the relationship between bidirectional online seller reviews (e.g., the eBay review format) and consumers’ WTP. Essay 1 provides an extensive review of studies that investigate online consumer reviews at the market, product, firm, consumer, and message level; special attention is given to the outcomes of consumer reviews for both products and sellers. In addition, this essay establishes the importance of the current research topic. Essay 2 combines economic and behavioral theories of decision-making under uncertainty to develop a theoretical framework. The framework proposes that review volume and valence influence a consumer’s WTP through a weighting function of outcome probability. Consumers with different preferences towards uncertainty will have different preferences toward review volume, and for some consumers, such preference can change depending on the review valence. Based on this conceptualization, the framework reconciles the current literature by explaining the inconsistent influence of review volume both across and within individuals. The internal validity of the framework is tested with an experiment and analyses carried out at the individual level provide strong support for the proposed conceptual model. Essay 3 establishes the relevance of this research for managers by applying the framework to real market data. Due to the nature of the transactional data, a finite mixture model is used to estimate the weighting function, and hypotheses are tested at the group instead of the individual level. A simulation study demonstrates the validity of using a finite mixture model to estimate the weighting function and classify groups. The results of the hypotheses testing provide adequate support for the framework

Generation, susceptibility, and response regarding negativity: an in-depth analysis of negative online reviews

Negative online reviews can drastically influence consumer behavior and business strategies. Recent attention on the subject demonstrates its importance in the consumer and marketing literature. Even so, no study quantitatively investigates the corpus of the literature. This study quantitatively and systematically investigates the foundational research streams of negative online reviews to identify influential sources and main areas of knowledge in the domain. The study employs an integration of text mining and co-citation analysis, recognizing that firms’ responses to negative online reviews cannot be analyzed without understanding the role of customers. Accordingly, this study generates insight into customers and firms in each negative online review stage, furnishing a conceptual framework that synthesizes the previous literature and highlights the most important research gaps requiring attention. Ultimately, the conceptual framework can guide future researchers in unfolding new and novel directions to expand the boundaries of the negative online review literature

Assessment, Implication, and Analysis of Online Consumer Reviews: A Literature Review

The onset of e-marketplace, virtual communities and social networking has appreciated the influential capability of online consumer reviews (OCR) and therefore necessitate conglomeration of the body of knowledge. This article attempts to conceptually cluster academic literature in both management and technical domain. The study follows a framework which broadly clusters management research under two heads: OCR Assessment and OCR Implication (business implication). Parallel technical literature has been reviewed to reconcile methodologies adopted in the analysis of text content on the web, majorly reviews. Text mining through automated tools, algorithmic contribution (dominant majorly in technical stream literature) and manual assessment (derived from the stream of content analysis) has been studied in this review article. Literature survey of both the domains is analyzed to propose possible area for further research. Usage of text analysis methods along with statistical and data mining techniques to analyze review text and utilize the knowledge creation for solving managerial issues can possibly constitute further work. Available at: https://aisel.aisnet.org/pajais/vol9/iss2/4