The effect of online reviews on product sales: A joint sentiment-topic analysis

This research examines the business impact of online reviews. It empirically investigates the influence of numerical and textual reviews on product sales performance. We use a Joint Sentiment-Topic model to extract the topics and associated sentiments in review texts. We further propose that numerical rating mediates the effects of textual sentiments. Findings not only contribute to the knowledge of how eWOM impacts product sales, but also illustrate how numerical rating and textual reviews interplay while shaping product sales. In practice, the findings help online vendors strategize business analytics operations by focusing on more relevant aspects that ultimately drive sales.     

Performance Impacts of Web-Enabled Retail Services: An Empirical Study

Prior studies suggest that the growth in online retail is attributed to the effective implementation of media-rich and interactive services and functionalities that help overcome the para-social distance between customers and online retail stores. However, few studies have offered empirical evidence that demonstrates the specific impacts these interactive services and functionalities have on the financial performance of online retailers. Using data on the operations and financial performance of the top 500 online retailers in the U.S., this study investigates the impact that website features and functionalities have on sales performance and other intermediate performance measures. Our findings suggest that website features and functionalities have a significant direct impact on sales as well as an indirect impact through intermediate performance measures. Furthermore, our model identifies the specific performance measures that each website feature targets to improve.     

Online product review as an indicator of users’ degree of innovativeness and product adoption time: a longitudinal analysis of text reviews

Online reviews have become extremely valuable sources of information about products and their customers as electronic commerce continues to proliferate rapidly. Previous research has shown that reviews of a product change and evolve over its life. Identifying and understanding patterns of change in reviews and the forces that shape them is an underexplored topic with substantial potential for predicting and improving the market performance of products. In this study, we analyze review text of nearly 50 products over the course of their lives. Our longitudinal analysis of reviews reveals changes in certain personality-related characteristics of buyers in ways that are consistent with the predictions of product adoption and diffusion theories. The main findings and conclusions still hold when we replicate the same procedure on reviews of a different product category. Accordingly, based on online user-generated content in the form of online reviews, this research introduces a novel empirical method for identifying the product adoption and diffusion stage. Implications of the study for theory, methodology, and practice are discussed.     

Does sampling influence customers in online retailing of digital music?

Online retailers have taken recourse to many smart marketing strategies to sell digital music. This paper investigates the strategic decisions of online vendors for offering different mechanisms such as sampling and online reviews of digital music to increase their online sales. In this research we seek answers to the following research questions (1) should online retailers offer sampling for experience goods such as music CDs; (2) under what circumstances is offering sampling more important than offering reviews. Our empirical study shows that online markets behave as communication markets, and consumers learn about product quality information both passively (by reading online reviews) and actively but subjectively (by listening to music sampling). Using data from , we empirically show that sampling is a strong product quality signal that reduces product uncertainty and attracts interested shoppers. Products with the sampling option enjoy a higher conversion rate (which leads to better sales) than those without it. Second, the impact of online reviews on conversion rate is lower for experience goods with a sampling option than those without. Third, when the uncertainty of the online reviews is higher, sampling plays a more important role because it mitigates the uncertainty introduced by online reviews. We believe this paper makes an important contribution by comparing and studying the interactions between two commonly adopted online marketing strategies (i.e., sampling versus online reviews) and provides important insights on which strategy is beneficial for vendors in the context of online selling of digital music.     

Factors affecting female user information adoption: an empirical investigation on fashion shopping guide websites

With the prosperity of online shopping platforms, similar or even the same products tend to have a large variety of sources to be purchased from. More and more consumers seek the product information from online review websites before making a purchase, as they are willing to provide reviews or share their purchase experience. These behaviors turn the online review websites into vertical and community-based sales channels. Based on the Information Adoption Model, this study conducted an empirical investigation to analyze female users’ information adoption process when using fashion shopping guide website. The results show that information quality and source credibility have significant impact on information usefulness, which in turn contributes to information adoption. In addition, users with different levels of purchasing motivation demonstrate different dependence on information quality and source credibility.     

Mining consumer reviews to generate ratings of different product attributes while producing feature-based review-summary

With the massive growth of the internet, product reviews increasingly serve as an important source of information for customers to make choices online. Customers depend on these reviews to understand users’ experience, and manufacturers rely on this user-generated content to capture user sentiments about their product. Therefore, it is in the best interest of both customers and manufacturers to have a portal where they can read a complete comprehensive summary of these reviews in minimum time. With this in mind, we arrived at our first objective which is to generate a feature-based review-summary. Our second objective is to develop a predictive model to know the next week's product sales based on numerical review ratings and textual features embedded in the reviews. When it comes to product features, every user has different priorities for different features. To capture this aspect of decision-making, we have designed a new mechanism to generate a numerical rating for every feature of the product individually. The data have been collected from a well-known commercial website for two different products. The validation of the model is carried out using a crowd-sourcing technique.     

Towards effective online review systems in the Chinese context: A cross-cultural empirical study

Online reviews, as one kind of quality indicator of products or service, are becoming increasingly important in influencing purchase decisions of prospective consumers on electronic commerce websites. With the fast growth of the Chinese e-commerce industry, it is thus indispensable to design effective online review systems for e-commerce websites in the Chinese context, by taking into account cultural factors. In this paper, we conduct two empirical studies on online reviews. Firstly, we study how culture differences across countries (i.e., China and the USA) impact the way in which consumers provide online reviews. Secondly, we investigate the impact of online reviews on product sales in the Chinese context, and show that directly copying the ideas of successful online review systems in the USA will deteriorate the effectiveness of the systems in China. Finally, we propose several suggestions for the development of effective online review systems in the Chinese context based on the results of our two empirical studies and the findings in previous studies.     

The influence of information cascades on online purchase behaviors of search and experience products

Online users usually observe or refer to others’ behaviors and discount their own information when purchasing products online. This research employed a fixed-effect regression model to elucidate how information cascades could influence online purchase behaviors and how they moderated the influence of online word-of-mouth and product prices. To uncover the underlying mechanisms behind informational cascades, we compare search products and experience products. In particular, we utilize publicly available data from a B2C e-commerce site in China, i.e., Tmall.com. Our results indicate that online users’ choice of products was heavily driven by changes in product rankings after controlling for cumulative sales, online user reviews and product price, as predicted by informational cascades theory. Due to the information cascades effect, review volume had no impact on online users’ choice of products with high rankings, whereas it did exert a significant positive impact on consumer purchase decisions of products with low rankings. User rating had no impact on online users’ purchase decisions. Product price had a significant and negative impact for products with high rankings, but had a significant and positive influence on users’ choice for products with low rankings. Moreover, information cascades were more prominent for experience goods than for search goods.     

Trusting expert- versus user-generated ratings online: The role of information volume,valence, and consumer characteristics

The warranting principle, signaling theory, and theories of informational social influence suggest conditions when either user-generated information, or information originating from traditional experts, might be privileged online. A random sample of 1207 U.S.-based adults with Internet access completed an experiment that manipulated the source, volume, and valence of online movie ratings in order to test predictions derived from these perspectives. Results indicated that ratings volume is positively associated with trust of, reliance on, and confidence in user-generated content, as well as the congruence between one’s own and others’ opinions; that ratings source and volume interact to impact credibility perceptions, reliance on user-generated information, and opinion congruence, such that people tend to favor experts when there is low information volume, but favor user-generated information under conditions of high information volume; and that people’s opinions and behavioral intentions converge with the online ratings information to which they are exposed. In addition, these effects apply more strongly to people more conversant with user-generated content. Results indicate important theoretical extensions by demonstrating that social information online may be filtered through signals indicating its veracity, which may not apply equally to all social media users.     

Monotone submodular subset for sentiment analysis of online reviews

Neural Computing and Applications - Along with online social media’s prosperity, the amount of user-generated reviews dramatically increases. The kinds of text-based user-generated content...     

Preference reasoning with soft constraints in constraint-based recommender systems

A recommender system (RS) supports online users in e-commerce by proposing products that are assumed to be both useful and interesting. Knowledge-based recommendation systems form one branch of these online sales support systems that is particularly relevant for high-involvement product domains like consumer electronics, financial services or tourism. A constraint-based RS is a specific variant of a knowledge-based RS that builds on a CSP formalism for problem representation and solving. This article formalizes the different variants of a constraint-based recommendation problem based on consistency and the empirical part compares the performance of different constraint-based recommendation mechanisms in offline experiments on historical data.     

我国旅游电子商务模式创新研究与平台设计

我国旅游电子商务现有四种基本模式,即信息发布型、旅游企业自主建站在线销售型、细分型旅游产品在线销售型、综合型旅游产品在线销售型。这些模式大多还停留在旅游电子商务的表层或初级水平,存在单调、雷同、缺乏个性和深度等局限性。本文首先对这四种旅游电子商务模式进行深入研究,然后从几个方面分析现有旅游电子商务模式存在的局限性,最后探索我国旅游电子商务模式的创新和发展方向,并给出旅游电子商务平台的系统设计。     

Impact of online word-of-mouth on sales: the moderating role of product review quality

Facing with thousands of online product reviews, consumers usually pay close attention to those valuable ones which provide more specific and credible evaluations on products. Whether a close association exists between product review quality and sales is thus examined in this paper. By employing text mining techniques on multiple review features, a review is measured as one of the following two levels: high-quality or low-quality. In doing so, aggregate quality level of product’s whole reviews is also identified. Then, a two-level econometrical analysis is conducted on the real datasets from Amazon.cn. The results reveal that aggregate quality level of positive reviews and negative reviews interactively influence sales. In the situation the aggregate quality level of positive reviews is high meanwhile that of negative reviews’ is low, product sale is the highest, while in the opposite situation product sale is the lowest. The results also reveal that consumers understand product’s value from weighting positive and negative reviews of high-quality level, which then positively relates to product sales and exerts a dynamic effect on sales by the moderating role of product selling stage and popularity. The paper innovatively integrates the quantitative and qualitative characteristics of reviews to estimate their economic effect.     

Rating deviation and manipulated reviews on the Internet—A multi-method study

Manipulated reviews can mislead consumers to make inappropriate purchase decisions and reduce consumers’ dependency on online reviews, jeopardizing the platforms’ reputation. Existing studies mainly focus on the detection and the impact of manipulated reviews but are limited in examining the determinants of review legitimacy from the perspective of consumer perception. The current research introduces perceived review manipulation, defined as the extent to which an individual perceives a review as non-authentic with the goal of misleading others and influencing product sales. Using psychological reactance theory as a general framework, we investigate the impact of reviews with deviation from the average ratings on perceived review manipulation and review adoption. We future examine two boundary conditions of the above relationship—the moderating effects of review content concreteness and reviewer rating distribution. We adopt a multi-method approach to the empirical test of the research model. First, three online randomized experiments reveal that: (1) reviews with deviant ratings are more likely to be perceived as manipulated; (2) the relationship in (1) is enhanced when review content is abstract rather than concrete and when a reviewer is usually negative/positive (i.e., his/her rating distribution has positive/negative skewness) based on the deviation direction; and (3) perceived review manipulation negatively influences review adoption. Second, a field study was conducted to support the external validity of our research model. Our findings have academic and practical implications.     

Data quality and firm performance: empirical evidence from the Korean financial industry

Despite popular belief that timely and precise data are important and indispensable to good decisions and that good decisions are related to better firm performance, empirical research that examines the effect of data quality on firm performance is still scarce. How great an impact does data quality have on firm performance? This study empirically investigates the effect of firm-level data quality on firm performance in the Korean financial industry during 2008–2010. The results show that commercial banks have high-quality data, while credit unions have comparatively low-quality data. They also show that better data quality has a positive influence on sales, operating profit, and value added. Improving the level of data quality management maturity by one can increase firm performance by 33.7 % in sales, 64.4 % in operating profit, and 26.2 % in value added.     

The determinants of helpfulness of online reviews

More and more people are gravitating to reading online product reviews prior to making purchasing decisions. Because a number of reviews that vary in usefulness are posted every day, much attention is being paid to measuring their helpfulness. The goal of this paper is to investigate the various determinants of the helpfulness of reviews, and it also intends to examine the moderating effect of product type, that is, the experience or search goods in relation to the helpfulness of online reviews. The study results show that reviewer reputation, the disclosure of reviewer identity, and review depth positively affect the helpfulness of an online review. The moderating effects of product type exist for these determinants on helpfulness. That is, the number of reviews for a product and the disclosure of reviewer identity have a greater influence on the helpfulness for experience goods, while reviewer reputation, review extremity, and review depth are more important for helpfulness in relation to search goods. The interaction effects exist for average review rating and average review depth for a product with review helpfulness on product sales. The results of the study will identify helpful online reviews and assist in designing review sites effectively.     

Do online reviews affect product sales? The role of reviewer characteristics and temporal effects

Online product reviews provided by consumers who previously purchased products have become a major information source for consumers and marketers regarding product quality. This study extends previous research by conducting a more compelling test of the effect of online reviews on sales. In particular, we consider both quantitative and qualitative aspects of online reviews, such as reviewer quality, reviewer exposure, product coverage, and temporal effects. Using transaction cost economics and uncertainty reduction theories, this study adopts a portfolio approach to assess the effectiveness of the online review market. We show that consumers understand the value difference between favorable news and unfavorable news and respond accordingly. Furthermore, when consumers read online reviews, they pay attention not only to review scores but to other contextual information such as a reviewer’s reputation and reviewer exposure. The market responds more favorably to reviews written by reviewers with better reputation and higher exposure. Finally, we demonstrate that the impact of online reviews on sales diminishes over time. This suggests that firms need not provide incentives for customers to write reviews beyond a certain time period after products have been released.

Manipulation of online reviews: An analysis of ratings, readability, and sentiments

As consumers become increasingly reliant on online reviews to make purchase decisions, the sales of the product becomes dependent on the word of mouth (WOM) that it generates. As a result, there can be attempts by firms to manipulate online reviews of products to increase their sales. Despite the suspicion on the existence of such manipulation, the amount of such manipulation is unknown, and deciding which reviews to believe in is largely based on the reader's discretion and intuition. Therefore, the success of the manipulation of reviews by firms in generating sales of products is unknown. In this paper, we propose a simple statistical method to detect online reviews manipulation, and assess how consumers respond to products with manipulated reviews. In particular, the writing style of reviewers is examined, and the effectiveness of manipulation through ratings, sentiments, and readability is investigated. Our analysis examines textual information available in online reviews by combining sentiment mining techniques with readability assessments. We discover that around 10.3% of the products are subject to online reviews manipulation. In spite of the deliberate use of sentiments and ratings in manipulated products, consumers are only able to detect manipulation taking place through ratings, but not through sentiments. The findings from this research ensue a note of caution for all consumers that rely on online reviews of books for making purchases, and encourage them to delve deep into the book reviews without being deceived by fraudulent manipulation.     

Opinion evolution of online consumer reviews in the e-commerce environment

Online consumer reviews play an important role in shaping potential customers’ purchase decisions in e-commerce. Previous studies have analyzed the influence of online consumer reviews on sales, mainly considering factors such as reviewers’ and viewers’ profiles, information provided, and product features. However, there are relatively few studies that discuss how online consumer reviews interact with each other and how consumers’ opinions evolve over time. This paper proposes an opinion evolution dynamics model that is applicable to online consumer reviews in the e-commerce environment by taking into account influencing factors such as viewer reading limits, review sorting and releasing strategies, convergence parameters, review posting possibilities, and confidence thresholds. Using multi-agent simulation based on the proposed opinion evolution dynamics model, the paper discusses how these factors affect viewers’ opinions, and the opinion evolution process itself. Finally, conclusions and managerial implications of the simulation results are discussed.     

Online review manipulation by asymmetrical firms: Is a firm’s manipulation of online reviews always detrimental to its competitor?

Online reviews have a significant influence on consumers, and consequently firms are motivated to manipulate online reviews to promote their own products. This paper develops an analytical model to systematically explore the impact of online review manipulation on asymmetrical firms who sell substitutable search products in a competing market. Results show that a firm’s manipulation of online reviews is not necessary to hurt its competitor’s profit. In addition, if firms are free to choose whether to manipulate online reviews, both firms will always choose to manipulate online reviews. Moreover, there exists a prisoner’s dilemma in which online reviews are overmanipulated.