Investigating the effect of status changes in review platforms

Review platforms often use transient status reward systems where the granted status is not permanent, and reviewers can experience gains and losses in status. To evaluate the effectiveness of such systems, review platforms need to consider a trade-off between the potential positive effects of a status gain and the potential negative effects of a status loss on the quantity and characteristics of reviewers’ contributions. This article examines this trade-off. The results of an empirical study that uses matched difference-in-differences analysis of data from Yelp show that: (i) the positive effect of a status gain on the number of reviews is about three times greater than the negative effect of a status loss; (ii) these effects are more pronounced for reviewers with less experience; and (iii) status changes also change the valence and elaborateness of reviews.     

The Effect of Low‐ versus High‐Variance in Product Reviews on Product Evaluation

This research investigates whether the effect of low‐ versus high‐variance product reviews on the evaluation of a product about which consumers have favorable or unfavorable prior expectation can vary depending on product type, the argument quality of product reviews, and the number of reviewers. The data across three laboratory experiments demonstrate that high‐variance product reviews are more likely than low‐variance product reviews to undermine product evaluation when consumers have unfavorable prior expectation about a product. When consumers have favorable prior expectation, however, high‐variance product reviews can enhance or undermine product evaluation depending on product category, the argument quality of reviews, and the number of reviewers. The findings are explained by the type of causal attribution consumers make, such that high‐variance product reviews can allow consumers to make biased product evaluation consistent with their prior expectation when the causes of variance in the product reviews are attributed to the reviewers rather than to the product. However, when the causes of variance are attributed to the product rather than the reviewers, high‐variance product reviews can undermine product evaluation regardless of the favorability of prior expectation.     

Segmentation of both reviewers and businesses on social media

Employing online consumer reviews, this research develops a market segmentation procedure that is feasible to businesses present on social media. Because online reviews typically encompass large numbers of both reviewers and businesses, this data structure allows for both reviewer segmentation and business segmentation. This two-side segmentation approach segments not only reviewers in the preferences expressed in their reviews, but also businesses in their business practices specified in the reviews. Whereas common existing segmentation approaches predominantly use survey and transaction data, the proposed procedure uses publicly available and detailed consumption information in such reviews. A large number of product features elicited from such reviews lead to rich and detailed profiling of both reviewer segments and business segments. Using restaurant reviews on Yelp, this research demonstrates how the proposed procedure can help businesses develop segmentation strategies on social media.     

The more the better? Exploring the effects of reviewer social networks on online reviews

ABSTRACT

Conventional wisdom suggests that firms leverage key influencers (e.g. individuals with high centrality) in online communities to stimulate buzz. Using a large panel dataset including 1,569,264 online Yelp reviews and the ego-network of 366,715 individual reviewers over a nine-year period, this study examines the effects of number of ties and network density on the volume and valence of online reviews. In contrast with the general belief that key influencers always generate positive buzz, the findings show that they can adversely affect future review valence. Specifically, reviewers with many connections on Yelp can reduce the positivity of reviews of the same business in the next period. This finding has implications for marketing practice in online community management and social media intervention.     

Emotional and the normative aspects of customers’ reviews

The goal of the current research is to investigate the link between the emotional aspects of hotel and travel organization customers' reviews and their normative (e.g., star rating) rankings. After filtering, the Yelp dataset generated 3,47,803 hotel and travel company reviews. Following the purification of user reviews, we used an unsupervised machine learning technique-based NRC Emotion Lexicon to study the relationships between various emotional aspects of reviews and their normative values (e.g., star rating) for the review. Customers express different sorts of feelings for different types of emotional aspects, forcing them to assign different stars, according to the study's findings. The study is the first to use a lexicon-based unsupervised learning approach to look into the emotional aspects of hotel and travel organization reviews and associated normative (e.g., star rating) rankings.     

Content analysis of fake consumer reviews by survey-based text categorization

As the influence of online consumer reviews grows, deceptive reviews are a worsening problem, betraying consumers' trust in reviews by pretending to be authentic and informative. This research identifies factors that can separate deceptive reviews from genuine ones. First, we create a novel means of detection by contrasting authentic versus fake word patterns specific to a given domain (e.g., hotel services). We use a survey on a crowdsourcing platform to obtain both genuine and deceptive reviews of hotels. We learned the word patterns from each category to discriminate genuine reviews from fake ones for positively and negatively evaluated reviews, respectively. We show that our All Terms procedure outperforms current benchmark methods in computational linguistics and marketing. Our extended analysis reveals the factors that determine fake reviews (e.g., a lack of details, present- and future-time orientation, and emotional exaggeration) and the factors influencing people's willingness to write fake reviews (including social media trust, product quality consciousness, deal proneness, hedonic and utilitarian consumption, prosocial behavior, and individualism). We also use our procedure to analyze more than 250,000 real-world hotel reviews to detect fake reviews and identify the hotel and review characteristics influencing review fakery in the industry (e.g., star rating, franchise hotel, hotel size, room price, review timing, and review rating).     

The effects of eWOM characteristics on consumer ratings: evidence from TripAdvisor.com

Abstract

Given the rise of online review communities, the management of consumer ratings has gained much attention in the recent years. In this study, we use data from Tripadvisor.com and examine the number of stars that a review receives. Specifically, we address how a star rating is determined by the components in the focal review as well as the preceding reviews of other consumers. Our qualitative and quantitative analyses provide interesting findings as follows. A star rating has a positive relationship with the focal review’s valence. That is, the more positive a review is, the greater number of stars a review receives. The reviews of other consumers also play a role in determining a star rating of the focal review suggesting social influence among consumers. Interestingly, a review with lengthy content leads to a lower star rating only when using smartphones. We conclude with theoretical and managerial implications.     

Online Review Helpfulness: Role of Qualitative Factors

Consumers are increasingly reading online reviews before making any purchasing decisions. The significance of online reviews has only grown over the years. Though in the past, scholars have emphasized the impact of quantitative factors (e.g., review ratings) on online reviews, only recently have they begun to explore the role of qualitative aspects of online reviews. Content readability and associated sentiments in text provide two important qualitative cues that influence the helpfulness of online reviews. However, the extant literature has overemphasized the linear association between these aspects and the helpfulness of reviews. Using the elaboration likelihood model and the classic ideal point concept, the current work asserts that after an ideal point is attained, lucid and sentimental reviews diminish in utility (i.e., helpfulness of an online review for consumers decreases). This may happen because consumers are wary of fraudulent reviews. This study proposes that if experienced reviewers give such extreme reviews, then consumers might still draw utility from these reviews. In other words, this study explains the moderating role of reviewer experience, which heuristically influences consumers’ trust of online reviews, thus making even too simplistic or extremely sentimental reviews helpful.     

How Online Product Reviews Affect Retail Sales: A Meta-analysis

A growing body of research has emerged on online product reviews and their ability to elicit performance outcomes desired by retailers; yet, a common understanding of the performance implications of online product reviews has eluded us. Scholars continue to navigate an array of studies assessing different design elements of online product reviews, and various research settings and data sources. We undertake a meta-analysis of 26 empirical studies yielding 443 sales elasticities to examine how these variables relate to retail sales. Building on well-established meta-analytical methods, we address the following questions: How does review valence influence the elasticity of retailer sales? What about review volume? For which product types and usage situations do online product reviews have a greater impact on retailer sales elasticity? Which types of online reviewers and websites exert the greatest influence on retailer sales elasticity? Our study answers these important questions and provides a much needed quantitative synthesis of this burgeoning stream of research.     

Assisting consumers in detecting fake reviews: The role of identity information disclosure and consensus

Current discussions in academia and in the press increase consumers’ awareness of potentially deceptive online reviews. The increasing practice of fake reviews posted online not only jeopardizes the credibility of review sites as important information sources for individuals but also endangers a valuable source of information for service providers. Two studies shed further light on the role of consensus and identity-related information in assisting consumers detect potentially faked reviews. In one preliminary study, a sample of 4826 rejected and 4881 published online reviews was analyzed to investigate the differences in the disclosure of author-related information such as name and age as well as star ratings across those reviews. In the main study, a 3 (identity disclosure) x 2 (consensus) x 2 (priming of fake reviews) experiment was carried out with 390 respondents. The results highlight the relevance of the review's consensus in relation to the overall rating of previous reviews and corroborate the results of the preliminary study from the perspective of an internet user: the value of the amount of available information on the review's author in assisting individuals detect potential fake reviews. This study complements research in computer science by highlighting the relevance of contextual—in addition to textual—indicators that assist internet users in detecting potentially deceptive online reviews.     

Status reinforcing: Unintended rating bias on online shopping platforms

Online shopping platforms have gradually begun to use hierarchical loyalty programs to distinguish customers. Previous studies have focused mainly on the effect of such programs on loyalty and repurchase behavior, and little is known about how customer statuses in hierarchical loyalty programs affect their online product evaluations. Drawing on social status and social conformity theory, this study investigates the impact of customer status on the valence of online reviews. An instrumental variable is proposed to address the endogeneity issue. The results show that a customer's need for status leads to a negative rating bias when leaving online reviews. At the same time, people encounter social pressure from crowds. The need for social conformity can alleviate such rating bias. This study contributes to the understanding of the effect of social status on post-purchase behavior and provides practical implications for both managers and platforms.     

Development of methodology for classification of user experience (UX) in online customer review

In e-commerce, customer feedback has become an essential source of insight into a product or service's user experience (UX). The study of UX helps to integrate customers' potential needs into the product's design. Because customer reviews in e-commerce are not structured and categorized, it is necessary to analyze UX based on customer opinions systematically. This study tries to structure UX in a product's positive/negative context through a neural network-based self-organizing map (SOM). As a result of analyzing 10,482 reviews on wireless earbuds in BestBuy, an electronic product e-commerce platform, it was confirmed that it is a suitable method for categorizing user experiences between reviews and deriving important factors. In particular, the difference in core UX elements by positive/negative context of the product was verified based on the star rating. The results of this study are expected to contribute to product improvement and business improvement that reflect customer needs by companies or designers who design products for end-users.     

The Recipe for the Perfect Review?

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.     

Scientific muddling: Decision making through a Lindblomian lens

This introduction reviews the history and core principles of Charles Lindblom's concept of muddling and its application across a wide array of business decisions. The approach recognizes certain decisions as sequential, strategic practices and thus finds applications in a wide variety of business decisions. This article provides a brief summary of the 15 contributions that follow and take up on this theme. The authors extend a note of appreciation to the reviewers of the papers in the issue and to the editor of Journal of Business Research, Arch Woodside.     

Text mining approach to explore determinants of grocery mobile app satisfaction using online customer reviews

In recent years, there has been proliferation of grocery mobile apps as grocery shopping on mobile has found increasing acceptance among customers accelerated by multiple factors. Maintaining high level of customer satisfaction is important for grocery mobile apps in the highly competitive app market. Online reviews have been a rich source of information to analyze customer satisfaction with a product or service. This paper explores the determinants of customer satisfaction for grocery mobile apps using online reviews. Latent Dirichlet Analysis (LDA), which is a text mining technique, is used to analyze online customer reviews of 27,337 customers to identify determinants of customer satisfaction. The determinants identified were further analyzed using a series of analysis to understand the importance of each determinant. Dominance analysis examined the relative importance of the determinants of customer satisfaction based on the overall rating. Correspondence analysis identified determinants which cause satisfaction separately from the determinants which cause dissatisfaction. The results from this study will provide insights to business managers of grocery mobile apps for decision-making on customer satisfaction management.     

Analyzing user sentiment in social media: Implications for online marketing strategy

This article examines restaurant customers’ online activity following visits to restaurants. Differences in customers’ opinions based on gender and location are discussed. Sentiment analysis was used to analyze customers’ social media behavior in terms of liking, rating, and reviewing restaurants. User‐generated reviews and comments about experiences influence potential customers’ decisions. The results of this study show that gender and location of customers influence restaurant ratings. This article shows that sentiment analysis (using Natural Language Toolkit and TextBlob) can help marketers by providing a useful tool for big data analysis. Sentiment analysis can be used to interpret customer behavior and highlight how presales, sales, and after‐sales strategies can be improved.     

浅析如何改进我国商业银行信用风险管理方法

本文以商业银行信用风险管理为切入点,对我国信用风险管理的现状和问题进行探讨。文章介绍了我国商业银行目前使用的两种信用风险管理方法——客户信用评级法和贷款风险分类法,分析其存在的问题,并对完善我国商业银行信用风险管理提出了对策建议。     

The “Invisible Hands” in Research: The Critical Roles of Reviewers and Associate Editors

In this Editorial, we underscore the critical roles fulfilled by reviewers and associate editors (AEs) in the provision of the journal's review process. The so‐called “invisible hands” of reviewers and AEs not only influence the editorial outcomes of individual works, but, more substantially, the author experience with and research impact of the journal. To that end, we offer observations and guidelines for reviewers and AEs, reinforcing the expectations we maintain for those helping to shape the work that ultimately appears in the journal.     

The power of beauty: Be your ideal self in online reviews—an empirical study based on face detection

Beauty can increase people's self-confidence and offer premium benefits in social activities. Unlike natural appearance, beauty on the internet depends greatly on people's ideal selves. Different from previous research, this study investigated the relationship between profile image's facial beauty and online reviews. Based on 25,322 face detection results, our findings showed that people's behaviors corresponded to their created beautiful self-image, which resulted in posting negative and detailed online reviews. Moreover, beauty diminished the inhibiting effect caused by clear facial disclosure. Our results enrich the understanding of online beauty, self-presentation, self-disclosure, and rating bias in online shopping platforms. The practical implications for online platforms are also discussed.