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.     

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.     

Search product and experience product online reviews: An eye-tracking study on consumers' review search behavior

This study uses eye-tracking method to investigate consumers' online review search behavior by suggesting that it needs to consider the type of product reviewed. A review-product congruity proposition was testified through a self-report survey and an eye-tracking experiment. The proposition states that consumers of search products expect to seek attribute based reviews, while consumers shopping for experience products tend to seek experience based reviews. Two experiments were conducted in the human factors & ergonomics laboratory of Beihang University, China and all subjects are college students. The results of our first empirical experiment support our hypotheses by showing consumers' more active and positive responses to attribute based reviews when shopping for search products and to experience based reviews when purchasing experience products. The second experiment was conducted with eye tracking method to gain further insights. We found that consumers of search products are attracted and engaged more deeply by attribute based reviews. However, when they browse experience products, the difference of their fixations on experience based reviews and attribute based reviews is not significant, and thus the proposition is partially supported. This study extends our current understanding of consumers' online review search behavior by subsuming product type, which is necessary and helpful, and provides references on the classification and presentation of reviews to facilitate consumers' product judgement and decision making. Moreover, comparison of traditional empirical method and eye-tracking method can help deepen our understanding of complex consumer online shopping behavior.     

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.     

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.     

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.     

Scores vs. stars: A regression discontinuity study of online consumer reviews

With the rising popularity of consumer reviews, the design of the review system becomes increasingly crucial for e-commerce platforms and online retailers in their business decision-makings. Though the relationship between consumer reviews and sales has been extensively studied, only few studies have been conducted on the effects of different review designs. In this paper, we collect detailed review data from Meituan.com, a popular Chinese shopping website, to examine the effects of numerical presentation of consumer reviews (detailed to one decimal place) and graphical presentation of consumer reviews (in half-stars) on sales. By using a regression discontinuity design, we find that while consumer review scores may affect sales positively, the star presentation can create negative, rather than positive, jumps at cutoffs. Consumers restrict their attention to a star category; therefore, the “best” sellers in a lower star category are better off than the “worst” sellers in a higher star category. The incentive for review manipulation is strongly reduced, which in the long run will create trust and confidence for the review system as well as the sellers. For those sellers that are just below the cutoffs, simply crossing over the cutoffs would not lead to higher sales. Instead, they will have to substantially improve their service quality to attract consumers.     

Consumer perceptions of information helpfulness and determinants of purchase intention in online consumer reviews of services

Online consumer reviews offer an unprecedented amount of information for consumers to evaluate services before purchase. We use the dual process theory to investigate consumer perceptions about information helpfulness (IH) in electronic word-of-mouth (eWOM) contexts. Results highlight that popularity signals, two-sided reviews, and expert sources (but not source trustworthiness) are perceived as helpful by consumers to assess service quality and performance. Although two-sided reviews exercise a significant influence on perceived IH, their influence on purchase intention was indirectly mediated by IH. IH predicts purchase intention and partially mediates the relationship between popularity signals, source homophily, source expertise, and purchase intention.     

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.     

在线商品评论可信性评价方法

针对现有商品评论存在数量大、质量参差不齐、可信度差，导致用户难以快速获取有效信息并制定高效的决策，而现有评论可信性评估主要考虑评论来源和投票形式的支持度等问题，提出了一种从评论内在质量角度实现评论可信度评估方法，即通过融合评论者等级、评论支持度和评论观点一致性等实现评论可信性评估。首先基于规则库和方法库完成了评论数据的预处理；然后基于商品特征库、通用词典、情感词典以及方法库，完成了商品特征识别和特征值提取及标准化；最后基于建立的模型完成评论可信度评估。实验结果验证了该方法的可行性，该方法可以应用于其他电商平台实现商品评论可信性自动评估。     

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.     

Crowd intelligence: Analyzing online product reviews for preference measurement

The proliferation of product review websites produces a large, publicly accessible information resource for firms that seek to understand consumers’ preferences. To facilitate product design or improvement, we propose a novel econometric preference measurement model, the modified ordered choice model (MOCM), to extract aggregate consumer preferences from online product reviews. Moreover, to categorize customer requirements on the basis of the aggregate consumer preferences estimated by the MOCM model, we extend the Kano model and propose a marginal effect-based Kano model (MEKM). We empirically evaluate the effectiveness of the proposed MOCM model and demonstrate the utility of the proposed MEKM model.     

Assessing the product review helpfulness: Affective-Cognitive evaluation and the moderating effect of feedback mechanism

Product review length has been demonstrated as one of the key factors that influence the product review helpfulness. However, we have little knowledge of a deeper understanding on how consumers process the product review length to assess product review helpfulness. Anchoring on the human’s affective-cognitive model of decision-making and the prominent coping approaches, this research revisits this key issue from consumers’ affect-oriented and cognition-oriented processing perspective. Our findings show that (1) consumers apply both the affect-oriented and cognition-oriented processing to assess the helpfulness of product review, (2) a significant inverted U-shape relationship between the review length and the review helpfulness, and interestingly, (3) such a relationship is further moderated by whether the product review author has provided a response to consumers’ comments. These findings not only provide further robust evidence to the consideration of both product review length and feedback, but also suggest a refinement of the underlying mechanism on how consumers process product review length to assess product review helpfulness.     

Mining customer requirements from online reviews: A product improvement perspective

Big data commerce has become an e-commerce trend. Learning how to extract valuable and real time insights from big data to drive smarter and more profitable business decisions is a main task of big data commerce. Using online reviews as an example, manufacturers have come to value how to select helpful online reviews and what can be learned from online reviews for new product development. In this research, we first proposed an automatic filtering model to predict the helpfulness of online reviews from the perspective of the product designer. The KANO method, which is based on the classical conjoint analysis model, is then innovatively applied to analyze online reviews to develop appropriate product improvement strategies. Moreover, an empirical case study using the new method is conducted with the data we acquired from JD.com, one of the largest electronic marketplaces in China. The case study indicates the effectiveness and robustness of the proposed approach. Our research suggests that the combination of big data and classical management models can bring success for big data commerce.     

Discovering business intelligence from online product reviews: A rule-induction framework

Online product reviews are a major source of business intelligence (BI) that helps managers and marketers understand customers’ concerns and interests. The large volume of review data makes it difficult to manually analyze customers’ concerns. Automated tools have emerged to facilitate this analysis, however most lack the capability of extracting the relationships between the reviews’ rich expressions and the customer ratings. Managers and marketers often resort to manually read through voluminous reviews to find the relationships. To address these challenges, we propose the development of a new class of BI systems based on rough set theory, inductive rule learning, and information retrieval methods. We developed a new framework for designing BI systems that extract the relationship between the customer ratings and their reviews. Using reviews of different products from Amazon.com, we conducted both qualitative and quantitative experiments to evaluate the performance of a BI system developed based on the framework. The results indicate that the system achieved high accuracy and coverage related to rule quality, and produced interesting and informative rules with high support and confidence values. The findings have important implications for market sentiment analysis and e-commerce reputation management.     

The role of online seller reviews and product price on buyers' willingness-to-pay: a risk perspective

Third-party interpersonal communications such as online seller reviews play an important role in buyers’ purchase decisions in online markets. Although it is empirically clear that seller reviews (volume and valence) and product price contribute to buyers’ willingness-to-pay (WTP) differently across various studies, it is theoretically less understood why such effects qualitatively differ (e.g., positive vs negative), rendering unclear managerial implications for online marketers. In this paper, we study the role of online seller reviews and product price in buyers’ WTP. We offer a conceptual framework from a risk perspective in which we argue that the different effects of seller reviews and product price on buyers’ WTP may emerge simultaneously in an online market. We highlight two important drivers for such qualitatively different effects: a difference in buyers’ risk attitudes (averse, neutral, or seeking) and a difference in WTP measures (absolute or relative). We test our hypotheses and find good support for them both internally (an experimental study) and externally (an empirical study). Our research enhances the understanding of the relationship between online user reviews and online price dispersions while shedding light on better management of online user reviews for market makers.     

The effect of negative online consumer reviews on product attitude: An information processing view

Online consumer reviews provide product information and recommendations from the customer perspective. This study investigates the effects of negative online consumer reviews on consumer product attitude. In particular, it examines the proportion and quality of negative online consumer reviews from the perspective of information processing. The elaboration likelihood model is used to explain the persuasive effect of the proportion and quality depending on product involvement. A high proportion of negative online consumer reviews elicits a conformity effect. As the proportion of negative online consumer reviews increases, high-involvement consumers tend to conform to the perspective of reviewers, depending on the quality of the negative online consumer reviews; in contrast, low-involvement consumers tend to conform to the perspective of reviewers regardless of the quality of the negative online consumer reviews. The experiment in this study uses 248 college students in Korea. The proposed hypotheses are tested by three-way analysis of covariance.     

The influence of information overload on the development of trust and purchase intention based on online product reviews in a mobile vs. web environment: an empirical investigation

Information overload has been studied extensively by decision science researchers, particularly in the context of task-based optimization decisions. Media selection research has similarly investigated the extent to which task characteristics influence media choice and use. This paper outlines a study which compares the effectiveness of web-based online product review systems for facilitation of trust and purchase intention to those of mobile product review systems in an experiential service setting (hotel services). Findings indicate that the extensiveness of information in the review increases trust and purchase intention until that information load becomes excessive, at which point trust and purchase intention begin to decrease. The magnitude of this decline is smaller in web-environments than in mobile environments, suggesting that web-based systems are more effective in fostering focus and are less prone to navigation frustration, thus reducing information overload.     

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.