When Consistency Matters: The Effect of Valence Consistency on Review Helpfulness

When evaluating the helpfulness of online reviews, review valence is a particularly relevant factor. This research argues that the influence of review valence is highly dependent on its consistency with the valence of other available reviews. Using both field and experimental data, this paper show that consistent reviews are perceived as more helpful than inconsistent reviews, independent of them being positive or negative. Experiments show that this valence consistency effect is driven by causal attributions, such that consistent reviews are more likely to be attributed to the actual product experience, while inconsistent reviews are more likely to be attributed to some reviewer idiosyncrasy. Supporting the attribution theory framework, reviewer expertise moderates the effect of consumers' causal attributions on review helpfulness.     

A study of factors that contribute to online review helpfulness

Helpfulness of online reviews is a multi-faceted concept that can be driven by several types of factors. This study was designed to extend existing research on online review helpfulness by looking at not just the quantitative factors (such as word count), but also qualitative aspects of reviewers (including reviewer experience, reviewer impact, reviewer cumulative helpfulness). This integrated view uncovers some insights that were not available before. Our findings suggest that word count has a threshold in its effects on review helpfulness. Beyond this threshold, its effect diminishes significantly or becomes near non-existent. Reviewer experience and their impact were not statistically significant predictors of helpfulness, but past helpfulness records tended to predict future helpfulness ratings. Review framing was also a strong predictor of helpfulness. As a result, characteristics of reviewers and review messages have a varying degree of impact on review helpfulness. Theoretical and practical implications are discussed.     

消费者在线评论有用性影响因素模型研究

消费者在线评论的价值已经得到消费者和在线零售商的公认,对评论有用性的研究已经成为新的研究热点。从减少消费者决策风险出发,在感知诊断性概念基础上定义了评论有用性概念,构建了一个评论有用性影响因素模型。从传播说服理论的维度考察,评论等级、评论长度、好评率和使用互联网经验是影响评论有用性的重要因素。商品类型对评论有用性具有调节作用。     

Das Rezept für die perfekte Rezension?

Online product reviews, originally intended to reduce consumers’ pre-purchase search and evaluation costs, have become so numerous that they are now themselves a source for information overload. To help consumers find high-quality reviews faster, review rankings based on consumers’ evaluations of their helpfulness were introduced. But many reviews are never evaluated and never ranked. Moreover, current helpfulness-based systems provide little or no advice to reviewers on how to write more helpful reviews. Average review quality and consumer search costs could be much improved if these issues were solved. This requires identifying the determinants of review helpfulness, which we carry out based on an adaption of Wang and Strong’s well-known data quality framework. Our empirical analysis shows that review helpfulness is influenced not only by single-review features but also by contextual factors expressing review value relative to all available reviews. Reviews for experiential goods differ systematically from reviews for utilitarian goods. Our findings, based on 27,104 reviews from Amazon.com across six product categories, form the basis for estimating preliminary helpfulness scores for unrated reviews and for developing interactive, personalized review writing support tools.     

Predicting the helpfulness of online reviews using multilayer perceptron neural networks

With the great development of e-commerce, users can create and publish a wealth of product information through electronic communities. It is difficult, however, for manufacturers to discover the best reviews and to determine the true underlying quality of a product due to the sheer volume of reviews available for a single product. The goal of this paper is to develop models for predicting the helpfulness of reviews, providing a tool that finds the most helpful reviews of a given product. This study intends to propose HPNN (a helpfulness prediction model using a neural network), which uses a back-propagation multilayer perceptron neural network (BPN) model to predict the level of review helpfulness using the determinants of product data, the review characteristics, and the textual characteristics of reviews. The prediction accuracy of HPNN was better than that of a linear regression analysis in terms of the mean-squared error. HPNN can suggest better determinants which have a greater effect on the degree of helpfulness. The results of this study will identify helpful online reviews and will effectively assist in the design of review sites.     

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.     

A concept-level approach to the analysis of online review helpfulness

Helpfulness of online reviews serves multiple needs of different Web users. Several types of factors can drive reviews' helpfulness. This study focuses on uninvestigated factors by looking at not just the quantitative factors (such as the number of concepts), but also qualitative aspects of reviewers (including review types such as the regular, comparative and suggestive reviews and reviewer helpfulness) and builds a conceptual model for helpfulness prediction. The set of 1500 reviews were randomly collected from TripAdvisor.com across multiple hotels for analysis. A set of four hypotheses were used to test the proposed model. Our results suggest that the number of concepts contained in a review, the average number of concepts per sentence, and the review type contribute to the perceived helpfulness of online reviews. The regular reviews were not statistically significant predictors of helpfulness. As a result, review types and concepts have a varying degree of impact on review helpfulness. The findings of this study can provide new insights to e-commerce retailers in understanding the importance of helpfulness of reviews.     

Helpfulness of user-generated reviews as a function of review sentiment,product type and information quality

Helpfulness of user-generated reviews has not been studied adequately in terms of the interplay between review sentiment (favorable, unfavorable and mixed) and product type (search and experience). Moreover, the ways in which information quality relates to review helpfulness remain largely unknown. Hence, this paper seeks to answer the following two research questions: (1) How does the helpfulness of user-generated reviews vary as a function of review sentiment and product type? (2) How does information quality relate to the helpfulness of user-generated reviews across review sentiment and product type? Data included 2190 reviews drawn from Amazon for three search products—digital cameras, cell phones, and laser printers—as well as three experience products—books, skin care, and music albums. Review sentiment was ascertained based on star ratings. Investigation of the research questions relied on the statistical procedures of analysis of variance, and multiple regression. Review helpfulness was found to vary across review sentiment independent of product type. Besides, the relationship between information quality and review helpfulness was found to vary as a function of review sentiment as well as product type. The paper concludes with a number of implications for research and practice.     

Effects of membership tier on user content generation behaviors: evidence from online reviews

Online shopping websites typically classify customers into different membership tiers in their customer relationship management systems. This study investigates the effects of membership tiers on user content generation behaviors in the context of an electronic commerce marketplace that has a membership tier program and an online review system. Grounded in theories related to status, our study hypothesizes the effects of membership tiers on user content generation behaviors as well as the helpfulness of the content they generated in the context of online reviews. We collected online data from a world-leading shopping website. The results from our empirical analyses indicate that membership tier has a positive effect on review rating and review delay, whereas it has a negative effect on review depth. Additionally, we tested mediation effects of review rating, depth and delay between membership tiers and review helpfulness, and found that membership tier negatively affected review helpfulness indirectly. Interestingly, reviews posted by high-status customers are perceived as more helpful than those of others when we controlled for review characteristics. This study contributes to research on online product reviews and customer relationship management.     

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.

The effect of content depth and deviation on online review helpfulness: Evidence from double-hurdle model

How does the content of a product review shape its perceived value? We propose two information theory-based constructs derived from probabilistic topic models and show their relationship with review helpfulness. The first construct, content depth, quantifies the breadth-depth tradeoff of a review and has an informational influence on readers’ voting behavior. The second construct, content deviation, indicates the deviance of the review content in comparison with others and exerts a normative influence on readers’ voting behavior. Noting the possibility that a review can get voted but has zero helpfulness score, we use a double-hurdle model to simultaneously estimate the probability of a review being voted and its helpfulness. The analyses on three product categories show that reviews with more depth and less content deviation are rated more helpful. Further, the relationships are moderated by a number of factors, including the deviation of numerical rating, recency of the review, and the reputation of the reviewer. The research contributes to the literature by showing how the content of a review and the interaction of content and numerical ratings jointly create value for consumers.     

Does source matter? Examining source effects in online product reviews

Drawing on the literature about online source classification, source credibility, and attribution theory, this study examines how the source of a product review influences people’s product judgments. Results from a between-subjects experiment suggest that the perceived source of a message (the visible source) impacts how people evaluate actual reviewer (the original source) and product. Reviews made by regular Internet users (visible sources) lead to greater trust in the actual reviewer (the original source), compared to product reviews from product makers. Results further indicate that visible sources play a crucial role in helping people judge the credibility of online reviews. Particularly, the identity of a visible source is used to consider the intention of original source of the message, which in turn determines message persuasiveness. The authors conclude that evaluating the intentions of online reviewers is a critical antecedent to forming opinions about online reviews and products.     

基于书写行为与语义特征的评论有用性评估

针对电子商务网站充斥着大量有用性较低的评论,提出一种基于用户书写行为与语义特征的中文评论有用性评估方法。方法通过在Web客户端实时监听评论文本框值的变化,识别出句尾插入、非句尾插入、句尾删除、非句尾删除等书写行为,在服务器端根据书写行为、评论的语义特征建立的线性评估模型计算用户评论的有用性。实验结果表明该方法能够较为准确地识别有用性较高的评论。     

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.     

Product type and consumers’ perception of online consumer reviews

Consumers hesitate to buy experience products online because it is hard to get enough information about experience products via the Internet. Online consumer reviews may change that, as they offer consumers indirect experiences about dominant attributes of experience products, transforming them into search products. When consumers are exposed to an online consumer review, it should be noted that there are different kinds of review sources. This study investigates the effects of review source and product type on consumers’ perception of a review. The result of the online experiment suggests that product type can moderate consumers’ perceived credibility of a review from different review sources, and the major findings are: (1) consumers are more influenced by a review for an experience product than for a search product when the review comes from consumer-developed review sites, and (2) a review from an online community is perceived to be the most credible for consumers seeking information about an experience product. The findings provide managerial implications for marketers as to how they can better manage online consumer reviews.     

Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs. review content

Online reviews have received much attention recently in the literature, as their visibility has been proven to play an important role during the purchase process. Furthermore, recent theoretical insight argue that the votes casted on how helpful an online review is (review helpfulness) are of particular importance, since they constitute a focal point for examining consumer decision making during the purchase process. In this paper, we explore the interplay between online review helpfulness, rating score and the qualitative characteristics of the review text as measured by readability tests. We construct a theoretical model based on three elements: conformity, understandability and expressiveness and we investigate the directional relationship between the qualitative characteristics of the review text, review helpfulness and the impact of review helpfulness on the review score. Furthermore, we examine whether this relation holds for extreme and moderate review scores. To validate this model we applied four basic readability measures to a dataset containing 37,221 reviews collected from Amazon UK, in order to determine the relationship between the percentage of helpful votes awarded to a review and the review text’s stylistic elements. We also investigated the interrelationships between extremely helpful and unhelpful reviews, as well as absolutely positive and negative reviews using intergroup comparisons. We found that review readability had a greater effect on the helpfulness ratio of a review than its length; in addition, extremely helpful reviews received a higher score than those considered less helpful. The present study contributes to the ever growing literature on on-line reviews by showing that readability tests demonstrate a directional relationship with average length reviews and their helpfulness and that this relationship holds both for moderate and extreme review scores.     

An information gain-based approach for recommending useful product reviews

Recently, many e-commerce Web sites, such as Amazon.com, provide platforms for users to review products and share their opinions, in order to help consumers make their best purchase decisions. However, the quality and the level of helpfulness of different product reviews are not disclosed to consumers unless they carefully analyze an immense number of lengthy reviews. Considering the large amount of available online product reviews, this is an impossible task for any consumer. Therefore, it is of vital importance to develop recommender systems that can evaluate online product reviews effectively to recommend the most useful ones to consumers. This paper proposes an information gain-based model to predict the helpfulness of online product reviews, with the aim of suggesting the most suitable products and vendors to consumers. Reviews are analyzed and ranked by our scoring model and reviews that help consumers better than others will be found. In addition, we also compare our model with several machine learning algorithms. Our experimental results show that our approach is effective in ranking and classifying online product reviews.     

Effects of consumer visit to online community and product channel on local sales of large consumer goods: Evidence from real estate industry

Online intermediaries have become important information sources for consumers’ purchase decision making. However, the economic values of consumer visits to two online intermediaries, namely, online community and product channel, have not been well studied. By collecting a dataset on consumer visits from a large real estate website to match with local offline housing sales data, we empirically explore the respective, relative, and interaction effects of consumer visits to online communities and product channels on sales of large consumer goods. We control for relevant factors, account for potential endogeneity issues, and perform various robustness checks to validate the consistency of our findings. Our results show that consumer visits to online communities have a more significant effect than those to product channels in driving sales. However, the interaction effect of consumer visits to these two online intermediaries on sales is negative. We also find that consumer website-related experience has a significant moderating effect on the relationship between consumer visits to product channels and sales. Our findings provide important theoretical contributions and managerial implications.     

An examination of the celebrity endorsements and online customer reviews influence female consumers’ shopping behavior

The goal of this study is to compare the influence of celebrity endorsements to online customer reviews on female shopping behavior. Based on AIDMA and AISAS models, we design an experiment to investigate consumer responses to search good and experience good respectively. The results revealed that search good (shoes) endorsed by a celebrity in an advertisement evoked significantly more attention, desire, and action from the consumer than did an online customer review. We also found that online customer reviews emerged higher than the celebrity endorsement on the scale of participants’ memory, search and share attitudes toward the experience good (toner). Implications for marketers as well as suggestions for future research are discussed.