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Understanding and overcoming biases in online review systems

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Abstract

This study addresses the issues of social influence and selection biases in the context of online review systems. We propose that one way to reduce these biases is to send email invitations to write a review to a random sample of buyers, and not exposing them to existing reviews while they write their reviews. We provide empirical evidence showing how such a simple intervention from the retailer mitigates the biases by analyzing data from four diverse online retailers over multiple years. The data include both self-motivated reviews, where the reviewer sees other reviews at the time of writing, and retailer-prompted reviews generated by an email invitation to verified buyers, where the reviewer does not see existing reviews. Consistent with previous research on the social influence bias, we find that the star ratings of self-motivated reviews decrease over time (i.e., downward trend), while the star ratings of retailer-prompted reviews remain constant. As predicted by theories on motivation, the self-motivated reviews are shown to be more negative (lower valence), longer, and more helpful, which suggests that the nature of self-motivated and retailer-prompted reviews is distinctively different and the influx of retailer-prompted reviews would enhance diversity in the overall review system. Regarding the selection bias, we found that email invitations can improve the representativeness of reviews by adding a new segment of verified buyers. In sum, implementing appropriate design and policy in online review systems will improve the quality and validity of online reviews and help practitioners provide more credible and representative ratings to their customers.

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... Previous research has shown that online reviews have significant impacts on buyers' expectation and behaviors (Ding et al., 2022;Hu et al., 2017;Li & Hitt, 2008). However, a major concern with online reviews is the presence of self-selection bias (Askalidis et al., 2017;Jha & Shah, 2021;Piramuthu et al., 2012;Wu et al., 2020;Zhou & Guo, 2017). ...
... For example, as the preferences of a product's early adopters can systematically differ from the broader buyer population, their online reviews can be biased in informing product information (Li & Hitt, 2008). Previous literature also found that buyers who are influenced by prior online reviews might end up writing misleading (or less informative) reviews themselves (Askalidis et al., 2017;Jha & Shah, 2021). This suggests that the information conveyed in these reviews may not accurately reflect the true information about the product's quality. ...
... The negative influence of acquisition bias can gradually decay when accurate online reviews are submitted later over time. For instance, Askalidis et al. (2017) proposed a solution to address biases in online reviews wherein suppliers can send email invitations to their buyers, encouraging them to submit online reviews. These reviews are expected to be more valuable and informative, thereby mitigating the bias caused by early and potentially inaccurate online reviews. ...
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Please cite this article as: Y. Xie, W. Yeoh and J. Wang, How self-selection Bias in online reviews affects buyer satisfaction: A product type perspective, Decision Support Systems (2024), https://doi.org/10.1016/j.dss.2024.114199 ABSTRACT Online reviews play a crucial role in shaping buyers' purchase decisions. However, previous research has highlighted the existence of self-selection biases among buyers who contribute to reviews, which in turn leads to biased distributions of review ratings. This research aims to explore the further influences of self-selection bias on buyer satisfaction through agent-based modeling, considering two product differentiations: search and experience differentiation, as well as vertical and horizontal differentiation. Our findings reveal that self-selection bias can have varying positive and negative effects on the usefulness of online reviews in suggesting product quality (i.e., review utility) to buyers, thus affecting buyer satisfaction. While self-selection bias tends to decrease review utility in most scenarios, interestingly, it can also increase review utility by enabling a "screening" function of online reviews in addition to its normal "measuring" function. We also find that the varying effects of self-selection bias on buyer satisfaction are contingent upon the type of products under scrutiny and the interaction of different types of self-selection bias. This research makes valuable contributions to the existing literature on online reviews by introducing a novel theory to explain the effects of self-selection bias on buyer satisfaction. J o u r n a l P r e-p r o o f Journal Pre-proof 2
... Online reviews representativeness is one of the critical factors for an effective internet economy (Karamana, 2021). Representativeness discrepancies are usually addressed as a problem occurring due to the joint effects of behavioral biases (Askalidis, 2017) by current reviewers, potential reviewers and managers, that can be observed on multiple stages of buying and evaluation process. Bias in reviews can lead to an unrepresentative corpus of online review information and knowledge, both through text content and ratings (Zhang et al., 2018). ...
... Similarly, posit that mean star-rating effectiveness decreases due to under-reporting bias, but that this is mainly evident in products of extremely poor or high quality. Askalidis et al. (2017) examined rating trends over time in self-motivated reviews (characterized by selection bias) and observed a downward trend because of social influence bias, in agreement with several studies. ...
... Marinescu et al. (2021) find that selection bias can be reduced by attracting customers of moderate opinion, through appropriate incentives, overcoming low initial motivations. Askalidis et al. (2017) propose that email invitations to verified customers improve representativeness, as the segment of solicited reviewers was found to be less prone to social influence bias. ...
Chapter
This study conducts a systematic literature review to identify forms of user behavioral bias, affecting the generation of content, evaluation patterns, and the impact of online reviews. Furthermore, it indicates the resulting implications for organizations' management of customer knowledge. The authors collected studies dating from 2009 to 2023 in Scopus, where a high dimensionality of bias definitions is observed. As a result of this review, a series of thematic distinctions is proposed, distinguishing sentiment and evaluation asymmetries, motivational factors, as well as on-platform effects that can amplify, adjust, or create biased behaviors. The authors draw analogies with cognitive bias literature regarding cognitive limitations, preconceptions and motives, and contextual adaptations that lead to biased behaviors. Suggested methods and analyses aiming at bias understanding and mitigation are also presented in detail. The authors provide a comprehensive framework of bias-centered management in online review platforms, incorporating customer knowledge flows and practices to monitor and address bias effects.
... To achieve this goal, we first propose to raise consumers' awareness of the self-selection bias in user ratings/reviews by making three types of information transparent, which are (1) the reviewers' experience, (2) the extremity of emotion, and (3) the reported aspects in user reviews. We distilled these pieces of information according to the literature and the definition of self-selection bias [6,12,41,59]. Next, we conduct a large-scale survey (n = 206) to assess people's perceptions of these three types of information and identify the exact facets that are critical for their decision-making under the hotel booking scenario. Then, we design a visual display of these information aspects underneath user ratings/reviews and refine the design based on the feedback from a pilot study with two visualization experts and 12 users. ...
... To improve the representativeness of user feedback, researchers in the field of business and marketing designed different strategies to mitigate biases in data [1,61,71,95]. These strategies include (1) sending emails to a random selection of users and encouraging them to write reviews [6,55], (2) offering a relative comprehensive framework for users to give feedback (e.g., commenting on the pros and cons of a subject separately) [61,95], and (3) selectively displaying a representative user feedback online by manipulating the orders [35]. However, these approaches are primarily designed for businesses with the aim of maintaining the reputation of a platform. ...
... Furthermore, later users -who refer to the ratings/reviews -may found their personal experiences with the products/services inconsistent with their expectations established based on existing user reviews, as the biased feedback might not give a complete, up-to-date picture. The big expectation-experience disparity may cause a vicious circle of extreme feedback online, which hinders people's decision-making process [6]. ...
Article
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People often take user ratings/reviews into consideration when shopping for products or services online. However, such user-generated data contains self-selection bias that could affect people's decisions and it is hard to resolve this issue completely by algorithms. In this work, we propose to raise people's awareness of the self-selection bias by making three types of information concerning user ratings/reviews transparent. We distill these three pieces of information, i.e., reviewers' experience, the extremity of emotion, and reported aspect(s), from the definition of self-selection bias and exploration of related literature. We further conduct an online survey to assess people's perceptions of the usefulness of such information and identify the exact facets (e.g., negative emotion) people care about in their decision process. Then, we propose a visual design to make such details behind user reviews transparent and integrate the design into an experimental website for evaluation. The results of a between-subjects study demonstrate that our bias-aware design significantly increases people's awareness of bias and their satisfaction with decision-making. We further offer a series of design implications for improving information transparency and awareness of bias in user-generated content.
... To achieve this goal, we first propose to raise consumers' awareness of the self-selection bias in user ratings/reviews by making three types of information transparent, which are (1) the reviewers' experience, (2) the extremity of emotion, and (3) the reported aspects in user reviews. We distilled these pieces of information according to the literature and the definition of self-selection bias [6,12,41,59]. Next, we conduct a large-scale survey (n = 206) to assess people's perceptions of these three types of information and identify the exact facets that are critical for their decision-making under the hotel booking scenario. Then, we design a visual display of these information aspects underneath user ratings/reviews and refine the design based on the feedback from a pilot study with two visualization experts and 12 users. ...
... To improve the representativeness of user feedback, researchers in the field of business and marketing designed different strategies to mitigate biases in data [1,61,71,95]. These strategies include (1) sending emails to a random selection of users and encouraging them to write reviews [6,55], (2) offering a relative comprehensive framework for users to give feedback (e.g., commenting on the pros and cons of a subject separately) [61,95], and (3) selectively displaying a representative user feedback online by manipulating the orders [35]. However, these approaches are primarily designed for businesses with the aim of maintaining the reputation of a platform. ...
... Furthermore, later users -who refer to the ratings/reviews -may found their personal experiences with the products/services inconsistent with their expectations established based on existing user reviews, as the biased feedback might not give a complete, up-to-date picture. The big expectation-experience disparity may cause a vicious circle of extreme feedback online, which hinders people's decision-making process [6]. ...
Preprint
People often take user ratings and reviews into consideration when shopping for products or services online. However, such user-generated data contains self-selection bias that could affect people decisions and it is hard to resolve this issue completely by algorithms. In this work, we propose to raise the awareness of the self-selection bias by making three types of information concerning user ratings and reviews transparent. We distill these three pieces of information (reviewers experience, the extremity of emotion, and reported aspects) from the definition of self-selection bias and exploration of related literature. We further conduct an online survey to assess the perceptions of the usefulness of such information and identify the exact facets people care about in their decision process. Then, we propose a visual design to make such details behind user reviews transparent and integrate the design into an experimental website for evaluation. The results of a between-subjects study demonstrate that our bias-aware design significantly increases the awareness of bias and their satisfaction with decision-making. We further offer a series of design implications for improving information transparency and awareness of bias in user-generated content.
... 2. Literature review 2.1 Electronic word of mouth as a data source in hospitality and tourism research Today's e-WOM has grown into one of the most powerful forces in the marketplace, considered more effective at influencing consumers' behavior than third-party website promotions, traditional advertising and information provided by businesses (Litvin et al., 2018). Compared to traditional WOM that transmits primarily within the immediate social network of users, e-WOM offers the advantage of easy access to global online reviews in an "organized and on-demand" manner (Askalidis et al., 2017). Industry practitioners use online reviews to monitor consumer feedback and preferences, track trending topics and popular sentiments and communicate with existing and potential customers (Fan and Gordon, 2014). ...
... Researchers have studied different types of online review biases, including (1) selfselection bias, which reflects various sampling bias due to users' self-selection at different stages of the review process (Hu et al., 2006(Hu et al., , 2009(Hu et al., , 2017Li and Hitt, 2008), (2) social influence bias, which results from the interactive effects among different user reviews (Askalidis et al., 2017) and leads to sequential biases when examined sequentially (e.g. Muchnik et al., 2013;Wu et al., 2017) and (3) biases related to data authenticity or credibility, which stem from deliberate manipulations of online reviews (e.g. ...
... While our results confirmed previous findings about the overall positive bias and polar self-section embedded in online ratings (e.g. Askalidis et al., 2017;Hu et al., 2009), the exponential-curved distribution observed in our study signified a notable departure from the prevalent J-shaped distribution of Western user data (e.g. Gao et al., 2015;Hu et al., 2009;Hu et al., 2017). ...
Article
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Purpose Online review bias research has predominantly focused on self-selection biases on the user’s side. By collecting online reviews from multiple platforms and examining their biases in the unique digital environment of “Chinanet,” this paper aims to shed new light on the multiple sources of biases embedded in online reviews and potential interactions among users, technical platforms and the broader social–cultural norms. Design/methodology/approach In the first study, online restaurant reviews were collected from Dianping.com, one of China's largest review platforms. Their distribution and underlying biases were examined via comparisons with offline reviews collected from on-site surveys. In the second study, user and platform ratings were collected from three additional major online review platforms – Koubei, Meituan and Ele.me – and compared for possible indications of biases in platform's review aggregation. Findings The results revealed a distinct exponential-curved distribution of Chinese users’ online reviews, suggesting a deviation from previous findings based on Western user data. The lack of online “moaning” on Chinese review platforms points to the social–cultural complexity of Chinese consumer behavior and online environment that goes beyond self-selection at the individual user level. The results also documented a prevalent usage of customized aggregation methods by review service providers in China, implicating an additional layer of biases introduced by technical platforms. Originality/value Using an online–offline design and multi-platform data sets, this paper elucidates online review biases among Chinese users, the world's largest and understudied (in terms of review biases) online user group. The results provide insights into the unique social–cultural cyber norm in China's digital environment and bring to light the multilayered nature of online review biases at the intersection of users, platforms and culture.
... Additionally, hierarchical regression analysis is used to handle multi-level data structures, inter-sample correlations, and missing data. [7,38] WBRP in the future semantic similarity score calculated with the typical scale of "willingness to buy/recommend in the future" [7,38] independent variables -halo effect BRP in the past semantic similarity score with the typical scale of "product brand reputation" [30,35,78] PHVP in the past semantic similarity score with the typical scale of "product hedonic value" [6,36,39] FBSB in the past the number of consumers' historical purchases of the same brand from consumers' historical purchase [16] independent variables -Matthew effect NPR in the past NPR accumulated before the review [22,58,63] Average ESP in the past average ESP before the review [22,69] adjustment variable PSQ provided by online stores average sentiment scores of after-sales service, logistics and product packaging mentioned in this review [20,59] control variables perceived price of the product perceived price sentiment scores mentioned by consumers in this review [50,77] perceived pragmatic value of the product average sentiment scores of pragmatic attributes (battery, system, storage, network) mentioned by consumers in this review [39,43] Content courtesy of Springer Nature, terms of use apply. Rights reserved. ...
... Additionally, hierarchical regression analysis is used to handle multi-level data structures, inter-sample correlations, and missing data. [7,38] WBRP in the future semantic similarity score calculated with the typical scale of "willingness to buy/recommend in the future" [7,38] independent variables -halo effect BRP in the past semantic similarity score with the typical scale of "product brand reputation" [30,35,78] PHVP in the past semantic similarity score with the typical scale of "product hedonic value" [6,36,39] FBSB in the past the number of consumers' historical purchases of the same brand from consumers' historical purchase [16] independent variables -Matthew effect NPR in the past NPR accumulated before the review [22,58,63] Average ESP in the past average ESP before the review [22,69] adjustment variable PSQ provided by online stores average sentiment scores of after-sales service, logistics and product packaging mentioned in this review [20,59] control variables perceived price of the product perceived price sentiment scores mentioned by consumers in this review [50,77] perceived pragmatic value of the product average sentiment scores of pragmatic attributes (battery, system, storage, network) mentioned by consumers in this review [39,43] Content courtesy of Springer Nature, terms of use apply. Rights reserved. ...
Article
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The company benefits greatly from online word-of-mouth (OWM) for its products through e-commerce platforms. However, previous research neglects the critical influence mechanism of OWM for products sold in online stores, especially the relationships between consumers’ past subjective impressions and OWM. This study innovatively explores the influence mechanism of key halo effect and Matthew effect on product OWM considering the moderating role of online store PSQ namely store trust. We propose a research model and influencing factors for the halo effect and Matthew effect in the e-commerce platform scenario. Collecting about 30,000 online reviews and 160,000 historical consumer reviews from Amazon, we use a semantic similarity model to match online reviews with a standard scale for the relevant variables, combining text mining and econometric analysis. Our results show that the selected hierarchical regression model has better fit than existing common models. And the halo effect and Matthew effect are indeed beneficial to increase product OWM including the indicators of consumer satisfaction and future consumption intention. And there is a reverse effect with low perceived service quality and star rating Matthew effect on product OWM is not supported. These findings help us understand consumers’ online comment behaviors, and have deep implications for e-commerce platforms and related companies.
... In this study, I focus on the representativeness of online review distributions to examine how extremity bias and conformity impact it and explore whether online review solicitations alter representativeness. In doing so, I answer the call by Askalidis et al. (2017) and Schoenmueller et al. (2019) for research into whether unsolicited or solicited reviews provide a more representative set of reviews. Review solicitations have the potential to change the representativeness of online review distributions through extremity bias and conformity. ...
... For instance, the leading travel website company Tri-pAdvisor (2018) reports that hotels using its review solicitation service, Review Express, achieve an average of 28% increase in the number of TripAdvisor reviews. In a recent study, Askalidis et al. (2017) demonstrated that unsolicited reviews are more negative than solicited reviews. However, it is not a priori obvious whether certain customers would be more responsive to review solicitations. ...
Article
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Representative online customer reviews are critical to the effective functioning of the Internet economy. In this study, I investigate the representativeness of online review distributions to examine how extremity bias and conformity impact it and explore whether online review solicitations alter representativeness. Past research on extreme distribution of online ratings commonly relied solely on observed public online ratings. One strength of the current paper is that I observe the private satisfaction ratings of customers regardless of whether they choose to write an online review or not. I show that both extremity bias and conformity exist in unsolicited online word-of-mouth (WOM) and introduce online review solicitations as a mechanism that can partially de-bias ratings. Solicitations increase all customers’ engagement in online WOM, but if solicited, those with moderate experiences increase their engagement more than those with extreme experiences. Consequently, although extremity bias still exists in solicited online WOM, solicitations significantly increase the representativeness of rating distributions. Surprisingly, the results demonstrate that without conformity, unsolicited online WOM would be even less representative of the original customer experiences. Furthermore, I document that both solicited and unsolicited reviews equally overstate the average customer experience (compared with average private ratings) despite stark differences in their rating distributions. Finally, I establish that solicitations for reviews on the company-owned website, on average, decrease the number of one-star reviews on a third-party review platform. This paper was accepted by Eric Anderson, marketing.
... Our analyses are based on user reviews of contraception products on a drug review website. In general, people are more likely to write an online review when they have a complaint than when they are satis ed [54]. This might also apply to birth control products. ...
Preprint
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Purpose This study aims to explore women's online descriptions and evaluations of their experiences with birth control products, utilizing natural language processing (NLP). Focusing on non-favorable reviews, the objective is to shed light on the issues and side effects discussed by women, providing additional information that could aid women and their health care providers in making informed contraception choices. Methods Employing topic modeling and descriptive statistics, this study analyzes 19,524 unique patient reviews of female contraceptive products posted on Drugs.com. The study also explores patterns in drug ratings depending on the side effects described and contraception products reviewed. Results Topic analysis identifies eight main areas of side effects: weight gain, skin problems, loss of libido, mental health issues, menstrual irregularities, cramps and pain, continuous bleeding, and multiple cause dissatisfaction. Descriptive analyses reveal that female contraceptive products vary greatly in how frequently and firmly their side effects are described by women. Drug ratings, indicative of a product’s impact on reviewers’ well-being, vary substantially with contraception type and prevalent complaints voiced in the reviews. Conclusion Although exploratory in nature, the study underscores the value of using NLP to analyze large volumes of online reviews for obtaining qualitative insights into women's experiences with contraceptive products. This method can be a useful tool for helping women and doctors make more informed decisions, despite the inherent risk of bias in online reviews. These findings serve as a preliminary guide, suggesting the need for further research to confirm the links between specific side effects and women’s well-being.
... However, the star rating and sentiment score show a positive relationship. A study on eWOM proves that consumers engaging in eWOM activities register extreme positive or negative attitudes and, hence the star rating shows J-shape distribution (Askalidis, Kim, & Malthouse, 2017a). Researcher proves that products with more number of reviews and higher star ratings are perceived as a symbol of quality by the consumers (Li, Chen, & Zhang, 2020). ...
Article
Star rating of online consumer reviews is important information for the consumers, researchers and decision-makers. In the eWOM context, the star ratings assigned in the consumer reviews often symbolise the quality of products. Instead of processing voluminous data, reading of each review, consumer predominantly depends upon the star rating to summarise the information faster. There is a direct relationship between star ratings and, quality of products and, researchers have established an inverse relationship between the helpfulness of reviews and star ratings. Even though there is a belief that online reviews are free from bias, the star ratings are the most vulnerable part in the review and is a potential candidate for bias. Research result shows that a chronological presentation of reviews as a source bias. Managers try to reduce the bias in the online review system by creating various measures like showing critical or negative reviews separately; showing reviews randomly and, providing reviews only for the products purchased in the e-retailer store. Researches addressing bias in online sources are in the preliminary stage and, few studies explore this phenomenon. This research work addresses the presence of sequential bias in the online consumer reviews by analysing star ratings of 11 products comprise of 34 brands that are commonly available in two popular e-retailers. We establish the presence of sequential bias in consumer evaluations by showing distribution patterns of star ratings are varying between two e-retailers for the same brand and the sentiment scores distributions are inconsistent from star ratings distribution. From the results, we suggest that online review system should add more robust metrics like sentiment scores to mitigate the reviewer bias.
... However, only a minority of consumers submit reviews (Hu et al., 2009). Even when reviews are written, they are typically short and lack helpful information (Askalidis et al., 2017;Mudambi & Schuff, 2010). Although review system designers would like their reviewers to spend more effort in writing textual reviews as this can be directly related to review helpfulness (Wang et al., 2012), reviewers generally do not invest sufficient effort (Cao et al., 2011). ...
Article
Full-text available
Online review systems try to motivate reviewers to invest effort in writing reviews, as their success crucially depends on the helpfulness of such reviews. Underlying cognitive mechanisms, however, might influence future reviewing effort. Accordingly, in this study, we analyze whether existing reviews matter for future textual reviews. From analyzing a dataset from Google Maps covering 40 sights across Europe with over 37,000 reviews, we find that textual reviewing effort, as measured by the propensity to write an optional textual review and (textual) review length, is negatively related to the number of existing reviews. However, and against our expectations, reviewers do not increase textual reviewing effort if there is a large discrepancy between the existing rating valence and their own rating. We validate our findings using additional review data from Yelp. This work provides important implications for online platforms with review systems, as the presentation of review metrics matters for future textual reviewing effort.
... Prior research has focused on the antecedents of helpfulness pertaining to four aspects: (1) review related factors: length , consistency (Cheung et al., 2012), text emotions (Yin et al., 2014), posting time , and readability (Korfiatis et al., 2012), (2) reviewer related factors: information disclosure, such as reviewer picture, and demographics (Gao et al., 2017), and reviewer reputation (Cheung et al., 2012), (3) review reader related factors: like how well the reader can identify themself with the review writer (Davis & Agrawal, 2018), and (4) environment-related factors: to what extent the review is visible to the readers (Hu & Chen, 2016), the voting system (Kuan et al., 2015), and the medium through which the reviews are sought (Askalidis et al., 2017). While the helpfulness of online reviews has been investigated in various domains involving products and services, such as electronics (Mousavizadeh et al., 2020), software programs (Cao et al., 2011), and hospitality (Aghakhani et al., 2021;Park et al., 2021;Shin et al., 2019;Wu, 2013), however, research in the employer review context is scanty. ...
Article
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Employer review sites have grown popular over the last few years, with 86 percent of job seekers referring to reviews on these sites before applying to job positions. Though the antecedents of review helpfulness have been studied in various contexts, it has received limited attention in the employee review context. These sites provide review text in multiple dimensions, such as pros and cons. Besides, to solicit unbiased reviews, these sites allow an option of keeping reviewer information anonymous. Rooted in the diagnosticity perspective, we investigate review helpfulness focusing on the role of review text in multiple dimensions and the anonymity of the reviewers. We use a publicly available Glassdoor dataset to model review helpfulness using a Tobit regression. The results show that the review length in multiple dimensions of review text and anonymity positively impact review helpfulness. Moreover, anonymity positively moderates the review length in the cons section. As a post-hoc analysis, we perform topic modeling to gain better insights on the review text in multiple dimensions and anonymity. The post-hoc analyses show that non-anonymous reviewers discuss firm reputation in the pros section, which anonymous reviewers do not. In the cons section, non-anonymous reviewers discuss politics, unfair and unethical treatment, and prospects of the employer, while anonymous reviewers discuss incompetency of the leadership. This research has important practical implications for online review sites’ design and crafting guidelines and policies for employees writing reviews.
... For example, Shen et al. [24] investigated the online review systems of Amazon and Barnes & Noble and found that the reviewer ordering mechanism would affect the behavior of reviewers. As it is time consuming to read the large number of usergenerated online reviews with different writing styles, tactical designs of online review system can promote the quality of online reviews and provide consumers with more credible and representative references through a laboratory study [25]. Hence, in OHRS, design features with appropriate presenting, filtering and ordering of online reviews are conducive to the judgement and decision of consumers. ...
Article
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Online hotel reviews get intensive attentions in the disciplines of hospitality and tourism. However, studies on online hotel review system (OHRS), where online hotel reviews are generated, viewed and replied are far from adequate. A variety of OHRS with different features are available online, but there is currently a lack of studies deconstructing OHRS from a consumer satisfaction standpoint, this study aims to provide an in-depth understanding on consumer's satisfactions to OHRS from a design feature perspective. Primary design features of OHRS are identified and classified based on an improved Kano method to depict consumer's quality perceptions. After quantitatively measuring the importance of design feature, we combine their implementation level to capture the overall usability of OHRS. The effectiveness of the proposed methods are verified by applying it to the evaluation of OHRS in six well-known online travel platforms. Compared with prior studies, the current study provides insights into consumers' asymmetric perceptions toward design features of OHRS and its usability structure, improves the deficiencies of the traditional Kano model, as well as provides valuable reference for online hotel vendors to optimize the design of OHRS to foster consumer's satisfaction.
... Online review systems with design feature of review tag summaries can enable users to hasten decision making (Yatani et al., 2011). Therefore, the implementation of appropriate designs and policies can improve the quality and effectiveness of online reviews and provide consumers with credible and representative ratings (Askalidis et al., 2017). In online hospitality review systems, designing features with reasonable review information content presentation, screening, and ranking can help consumers judge and make decisions. ...
Article
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Online hospitality reviews have an important impact on consumers’ travel and hospitality booking decisions in the Internet age. A well-designed online hospitality review system is crucial to reduce the uncertainty of consumers’ decision making, to grasp the actual needs of consumers, and to improve the quality experience of platforms. In this context, this research conducts an empirical study on the design features of online hospitality review systems based on the Kano model. First, the paper analyzes the design features of online hospitality review systems. Then, the paper proposes an improved method to classify design features on the basis of the Kano questionnaire design and survey data. Finally, the paper quantitatively measures their importance in online hospitality review systems. Results can provide scientific basis for online travel platforms or hospitality operators to optimize the design of online hospitality review systems and to obtain reference value to increase the satisfaction of consumers’ decision making.
... They attribute the findings to the fact that a common online goal and affiliation makes respondents repeat the attributes mentioned by previous respondents. Askalidis et al., examined the differences between email (prompted) and web (self-motivated) reviews in terms of key metrics, including review rating and volume (238,809 reviews for 27,574 unique products, across four major online retailers) [53]. Godes and Silva used the length of the written review as measured by the number of characters as a proxy of cost [54]. ...
Article
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In this paper, we study the online consumer review generation process by analyzing 37.12 million online reviews across nineteen product categories obtained from Amazon.com. This study revealed that the discrepancy between ratings by others and consumers’ post‐purchasing evaluations significantly influenced both the valence and quantity of the reviews that consumers generated. Specifically, a negative discrepancy (‘worse than what I read’) significantly accelerates consumers to write negative reviews (19/19 categories supported), while a positive discrepancy (‘better than what I read’) accelerates consumers to write positive reviews (16/19 categories supported). This implies that others’ ratings play an important role in influencing the review generation process by consumers. More interestingly, we found that this discrepancy significantly influences consumers’ neutral review generation, which is known to amplify the effect of positive or negative reviews by affecting consumers’ search behavior or the credibility of the information. However, this effect is asymmetric. While negative discrepancies lead consumers to write more neutral reviews, positive discrepancies help reduce neutral review generation. Furthermore, our findings provide important implications for marketers who tend to generate fake reviews or selectively generate reviews favorable to their products to increase sales. Doing so may backfire on firms because negative discrepancies can accelerate the generation of objective or negative reviews.
... Despite the appealing properties of our market-wide sentiment measure, online reviews are also known to be subject to biases (Li and Hitt, 2008;Askalidis et al., 2017;Hu et al., 2009). For example, polarization is typically observed in online reviews (U-shape distribution) reflecting a selection bias with extreme views being more common than moderate views, which could convey misleading information. ...
Article
We propose an aggregate measure of employee sentiment based on millions of employee online reviews and we test whether big employee data embedded in expert financial models can improve stock return predictability. In line with behavioral finance theory, our results document that the collective employee sentiment is a strong predictor of stock market returns with lower future returns following high employee sentiment. This predictive power is more pronounced when the employee sentiment index is constructed using the expectations of employees about the near-term business outlook of their employer. Our market-wide sentiment measure has superior performance compared to existing proxies of investor sentiment and commonly-studied macroeconomic variables. The forward-looking property of this data is also evident in predicting industry returns or portfolio returns sorted on characteristics, such as size, age, risk, profitability, dividend payout, tangibility, financial constraints and growth opportunities. Importantly, market-wide employee sentiment has relative power in predicting future asset returns after controlling for firm-level employee sentiment. The predictive power of aggregate employee online data is explained by investors’ biased beliefs about expected cash flows and volatility. These results indicate that financial models can be enriched with sentiment factors derived from various big data sources and stakeholders, providing insights into mispriced assets and assisting investment decisions.
... Since the 10 leisure products have to be chosen among well-known, a prior value ρ of expected taste can be elicited through an expected value computed from rating statistics of online rating platforms. Although arguably biased for both small and large samples (Askalidis, Kim, and Malthouse 2017), these priors are likely the most reliable predictors of expected taste at least from a population of subjects very interested in the product category 2 . ...
Chapter
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This book includes 25 peer-reviewed short papers submitted to the Scientific Opening Conference titled “Statistics and Information Systems for Policy Evaluation”, aimed at promoting new statistical methods and applications for the evaluation of policies and organized by the Association for Applied Statistics (ASA) and the Department of Statistics, Computer Science, Applications DiSIA “G. Parenti” of the University of Florence, jointly with the partners AICQ (Italian Association for Quality Culture), AICQ-CN (Italian Association for Quality Culture North and Centre of Italy), AISS (Italian Academy for Six Sigma), ASSIRM (Italian Association for Marketing, Social and Opinion Research), Comune di Firenze, the SIS – Italian Statistical Society, Regione Toscana and Valmon – Evaluation & Monitoring.
... Moreover, the percentage of negative reviews has a greater effect (on new product sales) than that of positive reviews, confirming the negativity bias (Cui et al., 2012). Credible and representative reviews could be achieved by implementing appropriate design and policy in online review systems (Askalidis et al., 2017). The availability of customer feedback has resulted in a separate term called online WOM, which stands for «any positive or negative statement made by potential, actual or former customers about a product or company, which is available to a multitude of people and institutions via the Internet» (Hennig-Thurau et al., 2003). ...
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Companies invest large amounts of funds to support their image as an incentive to make customers purchase the company's products. This paper's main objective is to estimate the impact of negative word-of-mouth on brand/product sustainability. As word-of-mouth represents customers' conversation regarding the quality of the company's products, the customer's voice is often analyzed to avoid negative experience outreach. History has carried several cases in which outreach could endanger a company's bottom line or even existence. The purpose of our study was to find out if this assumption could be supported. Approximately 100000 product reviews were collected in six selected categories in the Slovak market. The quantity of positive and negative word-of-mouth (PWOM/NWOM) was analyzed. It was found that there are approximately 15 times more positive reviews than negative ones. Based on previous studies' results, worst- and best-case scenarios were modeled to determine the possible impact of both PWOM and NWOM. It was found that in both cases, the direct reach of PWOM is higher than that of NWOM. On average, in the worst-case scenario, the reach of PWOM is 3.93 times higher than the reach of NWOM. In the best-case scenario, the reach of PWOM is 8.85 times higher than the reach of NWOM. According to the results, brand managers should focus on getting more positive reviews and thus positive word-of-mouth as it may have a stronger effect on the brand's sustainability. In other words, getting more ambassadors from the pool of customers satisfied with the brand might seem a reasonable strategy to avoid the potential danger from customers who were not satisfied with the products and willing to spread the word about their dissatisfaction.
... Some product testers will be among the first to review a product; others might be asked to write a review after many other reviews have been published (e.g., later in the product lifecycle, for products of great interest to customers). A review writer's exposure to previous reviews influences his or her own review (e.g., Askalidis et al. 2017;Sridhar and Srinivasan 2012;Sunder et al. 2019), because previous reviews offer insights into how others have perceived the product. According to equity theory, product testers usually try to treat the company fairly. ...
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Online reviews have profound impacts on firm success in terms of sales volume and how much customers are willing to pay, yet firms remain highly dependent on customers’ voluntary contributions. A popular way to increase the number of online reviews is to use product testing programs, which offer participants free products in exchange for writing reviews. Firms that employ this practice generally hope to increase review quality and secure higher product rating scores. However, a qualitative study, experimental study, and multilevel analysis of a field study dataset of more than 200,000 online reviews by product testers combine to reveal that product testing programs do not necessarily generate higher quality reviews, nor better product ratings. Only in certain circumstances (e.g., higher priced products) does offering a product testing program generate these benefits for the firm. Therefore, companies should consider carefully if and when they want to offer product testing programs.
... Given that the review rating is direct and easily understood, many researchers consider it to be the representative of reviewer satisfaction (Netzer, Feldman, Goldenberg, & Fresko, 2012;Rhee & Yang, 2015;Zhao, Xu, & Wang, 2019). However, a review rating is easily affected by extreme sentiments, especially extremely negative sentiments (Anderson, 1998;Askalidis, Kim, & Malthouse, 2017;Lin, Zhang, & Tan, 2019). Assuming that reviewers encounter a poor consumption experience, they are likely to give an overly low review rating to show dissatisfaction. ...
Article
Understanding the determinants of reviewer satisfaction has attracted much attention from academics and practitioners in recent years. Based on theories from information systems and personality psychology, our paper empirically analyzes how reviewer expertise and personality affect reviewer satisfaction in different contexts. Using 43,816 online reviews from TripAdvisor.com, advanced techniques of text analysis, and multiple estimation methods, reviewer expertise and personality are found to significantly affect their satisfaction with hotels. In particular, the difference in the review rating and text sentiment caused by reviewer expertise and personality is up to 0.885 and 23.43%, respectively. Second, our results show that a leisure trip positively (negatively) moderates the impact of reviewer expertise (personality) on satisfaction. By comparison, the moderating effect size of the travel type is much stronger on personality than on expertise. Third, the performance analyses display that the joint contribution of reviewer expertise and personality to the review rating and text is 50.44% and 52.89%, respectively, which demonstrates the superiority of our proposed variables in explaining reviewer satisfaction. Our findings provide important contributions to the extant literature and offer critical managerial implications to hotel managers and system developers.
... Indeed, Nielsen's Global Trust in Advertising Survey suggests that online customer reviews are second only to word-of-mouth recommendations from friends and family as a trusted source of advertising (Grimes, 2012). It is hardly surprising therefore that each of the top 10 online retailers in the United States display reviews for the products they sell (Askalidis, Kim and Malthouse, 2017). ...
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This article analyzes the relationship between ratings and review sentiment by introducing, for the first time, the tenets of prospect theory. Specifically, we test loss aversion and diminishing sensitivity on a sample of 132,486 reviews and find that: first, negative deviations in ratings (receiving a service with worse performance than expected) bring about a higher impact on review sentiment than positive deviations of equal magnitude (receiving a service with better performance than expected), thus, confirming loss aversion; and second, regardless of whether the service received is better or worse than expected, variations in ratings closer to the reference point result in higher marginal impacts on sentiment than equivalent variations further away from the reference point, thus, proving diminishing sensitivity. These results have relevant theoretical implications related to the use of relative vs absolute measures and the cognitive bias involved, and managerial implications linked to meeting expectations and service recovery.
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Purpose Physician review websites (PRW) and Medicare requirements are pressing administrators to measure, monitor and improve healthcare service delivery. Healthcare service attributes linked to patient satisfaction have received increased attention. Text analysis provides an alternative methodology to capture contemporaneous data on service delivery attributes. A Kano analysis based on these service attributes can help administrators prioritize service delivery and ultimately improve patient satisfaction. Design/methodology/approach Healthcare service attributes were defined from 4,000+ comments on a PRW using latent content text analysis. The resulting 15 attributes were validated by medical professionals using a q-sort methodology and analyzed using a Kano methodology. Findings The 15 attributes cover three domains of healthcare service – clinic operations, competency and care. The Kano analysis yields a hierarchy, or pyramid, of healthcare service attributes: (1) must-be’s: establish service operational capabilities and benchmark peer performance; (2) satisfiers: establish and increase trust through: (a) clinical competence, (b) careful management of young patients and (c) delivery of appropriate care and treatment (3) delighters: use service-dominant logic to provide patient-centered care. Originality/value This research bridges the gap between the “what” and “how” that is frequently missing in text analysis of online reviews. We provide a methodology coupled with a Kano analysis, a widely used quality improvement tool, which results in a hierarchy of service attributes that can guide administrators and researchers.
Article
This study examines the effect of firms’ participation in platform-endorsed review solicitation programs on consumers’ online review generation. We leverage a natural experiment on TripAdvisor, which launched a review solicitation program that allows hotels to collect reviews directly from guests after their stays with the aid of certified connectivity partners. Applying a two-stage difference-in-differences approach to a panel data set of online reviews for a matched set of hotels across TripAdvisor and Expedia, we find that hotels’ participation in the review solicitation program results in a 34.3% increase in review volume, a 0.151 increase in review rating, but a 16.9% decrease in review length. Review solicitation, however, generates a notable negative spillover effect on the volume of organic reviews. Specifically, the volume of organic reviews is reduced by 15.5% after hotels start soliciting reviews. We provide evidence that the motivational crowding-out effect plays an important role in driving this negative spillover. Further analyses reveal that the effects of review solicitation are heterogeneous with respect to hotels of different types and consumers with different demographic and behavioral characteristics. Finally, using a novel structural topic model, we detect a significant shift in review content from specific and concrete topics to general and abstract topics. Our findings suggest that review platforms and firms should be cautious about the unintended negative consequences of review solicitation on consumers’ review generation. This paper was accepted by Hemant Bhargava, information systems. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72371192, 72132008, 71872061, and 72061127002] and the Humanities and Social Science Fund of Ministry of Education of China [Grant 22YJA630021]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.01006 .
Article
Online hotel reviews on platforms like TripAdvisor are crucial in shaping customer choices and steering business strategies in the hospitality sector. However, the effectiveness of these platforms is partially hindered by the self-selection bias found in voluntary reviews. This bias can create false expectations and unsatisfactory experiences, mainly as the feedback generally comes from a non-representative group of self-motivated reviewers (SMRs). A common strategy to mitigate this bias is increasing the number of reviews through customer surveys, generating retailer-prompted reviews (RPRs). However, these RPRs, despite reducing selection bias, tend to lack the depth and insight of SMRs, resulting in a credibility gap and reduced representativeness. To address this, our study presents a novel approach using the propensity score adjustment (PSA) technique. This method leverages the distribution of RPRs to refine the accuracy of text data from SMRs, aiming to enhance the reliability and representativeness of online reviews. By combining the strengths of both RPRs and SMRs, we aim to create an online review environment that is both accurate and reliable. In conclusion, this research marks an important step toward improving online review platforms, aiming for a more transparent and trustworthy environment for reviews.
Article
La mise en place par Amazon d’un système de notation des biens achetés par les consommateurs est une des innovations majeures apportées par la plateforme à l’industrie du e-commerce. Reposant sur le travail des consommateurs, elle a donné naissance à une littérature sur les raisons poussant les consommateurs à faire ce travail, ces raisons étant testées à partir d’enquêtes déclaratives. L’article reprend cette question des comportements contributifs mais en l’examinant à partir des données observables sur le site d’Amazon, i. e. les informations laissées par les acheteurs sur leurs avis postés. Le nombre très élevé de données a contraint à des choix (États-Unis, biens culturels hors livres), et la nature des données à un travail d’interprétation visant à tester ou retrouver par des modèles économétriques des déterminants mentionnés dans la littérature tels que la satisfaction des consommateurs, leur engagement, leur fidélité ainsi que des variables contextuelles. Codes JEL : O35, D12
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Culture is one of the reasons that encourage many tourists to travel, one of the places par excellences to show the culture and values of a people is the museum, a monument responsible for the transmission of knowledge between generations. Additionally, visiting museums can contribute economically to local populations. In this context, this chapter intends to analyze the experience lived by the visitor in one of the two most important museums on the African continent, Ghana, and Nigeria, through the analysis of the content generated in social media, which may or may not contribute to encouraging the visit of new tourists. The methodology used was text mining with the application of sentiment analysis. As a result, it was concluded that the visitors considered the experience very positive and described it as an interesting, valuable, and beautiful/wonderful visit.
Article
Current online review systems widely suffer from rating biases. Biased ratings can lead to violations of customer trust and failures of business intelligence. Hence, both practitioners and researchers have directed massive efforts toward curbing rating biases. In this paper, we investigate bandwagon bias, the rating distortion resulting from individuals posting ratings shifted toward the displayed average rating, and propose a bias warning approach to mitigate this bias. Drawing on the flexible correction model, the theory of valuation in behavioral economics, and previous warning research, we design an effective warning strategy in two steps. First, we start with the risk-alert warning strategy, which prior research has widely employed, and rationalize its deficiencies by synthesizing theoretical analysis and extant empirical evidence. Second, considering the deficiencies, we identify a supplementary content design factor—the ranking task—and construct a risk-alert-with-ranking-task warning strategy. We then empirically test the effects of the two warning strategies on individual ratings in cases in which bandwagon bias either occurs or does not occur in individuals’ initial assessments. The results of four controlled experiments indicate that (1) the risk-alert strategy can reduce bandwagon bias in individual ratings but will elicit unwanted rating distortions when bandwagon bias does not occur in individuals’ initial assessments, and (2) the risk-alert-with-ranking-task strategy can mitigate bandwagon bias while avoiding the unwanted rating distortions above and can thus function as an effective warning strategy. Our research contributes to the literature by proposing an effective debiasing solution for bandwagon bias and a bias warning approach for online rating debiasing, which can help increase rating informativeness on online platforms.
Article
Online reviews have become increasingly important to both consumers and businesses and, as a result, have attracted considerable research attention. However, all reviews are not created equal as consumers may differ in their propensities to leave reviews, often as a function of their satisfaction. To ensure a more representative customer voice, companies often utilize different strategies to moderate the biases in online reviews. The strategies deployed by many hospitality firms differ dramatically in both how reviews are collected and where they are posted. This study investigates four review-collection strategies of major hospitality companies and analyzes how each strategy affects review ratings and length. We find that the effort required to post a review impacts review characteristics. We show that reviews collected through self-motivation methods tend to be lower rated and longer, whereas reviews solicited from companies through poststay emails tend to exhibit different characteristics.
Article
This study identifies “always-the-same-rating” reviewers (ASRs), that is, reviewers who give the same star rating for all reviewed products and who write many reviews on Amazon. This study identifies ASRs in 29 product categories by analyzing 230 million individual reviews on Amazon. The findings of this study show that: 1) all product categories contain reviews written by ASRs; 2) the majority of ASRs (99.99%) give the same star rating for all reviewed products in all categories; and 3) the rating distribution of ASRs’ reviews is extremely skewed toward the five-star rating (98.02%). The digital music category, in particular, shows a high share and volume of ASRs among all categories, making it an ideal focal category for further empirical analysis of ASRs. This study empirically demonstrates that star rating, the helpfulness of reviews, the length of headline and review, prior reviews, and holidays are potential indicators of reviews written by ASRs. The finding shows that reviews from verified and nonverified ASRs respond differently to some potential indicators. This article is the first step toward identifying irregular reviewer groups and their abnormal rating patterns, which would help in the segmentation of online consumers and a better understanding of online consumer review behaviors.
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The success of the public display of restaurant hygiene scores has encouraged online review sites to display these scores digitally on their platforms. By investigating 225,252 Yelp reviews toward 1,937 restaurants in Charlotte, North Carolina, we find that while displaying hygiene scores digitally can inform consumers in a way that reduces bias in reviews, it paradoxically can also promote the creation of more reviews that are biased, something we call the cognitive–discursive dilemma. Specifically, after the digital display on Yelp, reviews mentioning hygiene were more in line with scores, indicating an improvement in “accuracy” across reviews in general. Yet, the digital display also led to greater attention to hygiene, leading to lower scores for restaurants of lower social status as measured by price and cuisine type. Our findings thus call for more attention to a broader theoretical implication about the provision of “accurate information” on review sites.
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Online retailers frequently solicit reviews from customers who have recently purchased their products or services. This research examines how consumers react to conditional requests—wherein a retailer explicitly asks them to consider their experience but to only leave a review if this experience was favorable—versus more neutral unconditional requests. The provision of conditional requests is widespread, presumably because retailers believe that such requests will yield more positive reviews. Irrespective of whether these potential benefits materialize, the present research demonstrates that the consequences of conditional requests on customer loyalty (i.e., retailer engagement and repeat purchase behavior) are uniformly negative and surprisingly expansive. Six experiments with over 3,000 participants reveal that customers who receive conditional (vs. unconditional) requests are subsequently less loyal to the retailer, whom they perceive as manipulative and untrustworthy. This research also shows that easily implementable message modifications can attenuate (although not necessarily eliminate) the adverse effects of conditional requests on customer loyalty. Substantively, this work highlights how the messaging used in a review request affects customers’ inferences as well as their later judgments and behaviors. Managerially, the findings should exhort online retailers to exercise caution before sending conditional review requests given the risk of reputational harm.
Article
In a range of studies across platforms, online ratings have been shown to be characterized by distributions with disproportionately-heavy tails. We focus on understanding the underlying process that yields such “j-shaped” or “extreme” distributions. We propose a novel theoretical mechanism behind the emergence of “j-shaped” distributions: differential attrition, or the idea that potential reviewers with moderate experiences are more likely to leave the pool of active reviewers than potential reviewers with extreme experiences. We present an analytical model that integrates this mechanism with two extant mechanisms: differential utility and base rates. We show that while all three mechanisms can give rise to extreme distributions, only the utility-based and the attrition-based mechanisms can explain our empirical observation from a large-scale field experiment that an unincentivized solicitation email from an online travel platform reduces review extremity. Subsequent analyses provide clear empirical evidence for the existence of both differential attrition and differential utility.
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Online reviews systems try to motivate users to invest effort in writing a review since their success crucially depends on the reviews’ helpfulness. However, other factors might influence future reviewing effort as well. We analyze whether existing reviews matter for future reviewing effort. Analyzing a dataset from Google Maps which covers 40 sights across Europe with over 37,000 reviews, we find that reviewing effort – measured by the propensity to additionally write a textual review and (textual) review length – is negatively related to the number of existing reviews. Further, also the rating distribution of existing reviews matters: If there is a large discrepancy between the existing ratings and the own rating, we observe more additional textual reviews. Our findings provide important implications for review system designers regarding the presentation of review metrics: changing or omitting the display of review metrics for potential reviewers might increase their reviewing effort.
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With the rise of dependency of online shopping and service providers, consumer ratings and reviews help users decide between good and bad options. Prior studies have already shown that the layout and visual cues provided with a rating scale can affect the users’ responses. This paper aims to explore: 1) users’ reaction to certain visual cues in rating scales, and 2) users’ preference in rating scale designs and how it influences the rating scores. A survey (n = 187) was conducted to collect user ratings of popular products with six different rating scale designs, using two types of visual icons (stars and emojis) and colour-schemes (using a warm-cool and a traffic-light metaphors). Statistical analysis from the survey shows that users prefer the scale with most visually informative design (traffic-light metaphor colours with emoji icons). It also shows that users tend to give their true ratings on scales they like most, rather than the scale design they are most familiar with. Based on these results, it can be concluded that user involvement is desirable in selecting the rating scale designs, and that visual cues with cognitive metaphors can ensure more accurate (truthful) rating scores from users. Our approach has novelty because we elicited the users’ own opinion on what their accurate or “true" rating is rather than only relying on analysing the data received from the rating scores. Our work can offer insights for online rating scales designs to improve the rating decision quality of users and help online business platforms provide more credible ratings to their customers.
Article
Helpfulness prediction techniques have been widely incorporated into online decision support systems to identify high-quality reviews. Most current studies on helpfulness prediction assume that a review's helpfulness only relies on information from itself. In practice, however, consumers hardly process reviews independently because reviews are displayed in sequence; a review is more likely to be affected by its adjacent neighbors in the sequence, which is largely understudied. In this paper, we proposed the first end-to-end neural architecture to capture the missing interaction between reviews and their neighbors. Our model allows for a total of 12 (three selection × four aggregation) schemes that contextualize a review into the context clues learned from its neighbors. We evaluated our model on six domains of real-world online reviews against a series of state-of-the-art baselines. Experimental results confirm the influence of sequential neighbors on reviews and show that our model significantly outperforms the baselines by 1% to 5%. We further revealed how reviews are influenced by their neighbors during helpfulness perception via extensive analysis. The results and findings of our work provide theoretical contributions to the field of review helpfulness prediction and offer insights into practical decision support system design.
Conference Paper
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This paper focuses on the study of online customer reviews and their influence on social media marketing strategies. It seeks to analyze and understand the power of such customer reviews which may fall either in favor or against the companies practicing to promote their products and services over such platforms to increase their market reach. It includes the systematic study of available related articles and research works on Social Media Marketing, Customer Reviews, and the Electronic World of Mouth (e-WOM), etc. The analysis derived from referred cases, supported by the key finding of survey reports, signify the explicit and significant impact on reviews over companies and brands. Though most of the highlighted cases referred during the literature review process were possessing negative impact resulting in heavy loss of brand image and monetary losses as well. These monetary and non-monetary losses to the companies were derived from a single negative customer review only. To deal with or escape such failures; the unfair practice of the social media marketing strategies lured marketers to create fake and revenue-based reviews to handle the review of suffered customers or to increase the sales. But generating fake reviews without working on their own weaknesses situations in few cases referred had turned into irreversible disasters, causing the shutdown of the companies. The facts and data strongly support the importance of having a strong customer connection over social media platforms with a transparent and quick standing in real-time. Hence, the power of social media marketing is like untapped potential if channelized in an organized way with a dedicated team. If used properly it pumps the business with opportunities to expand like never before and that too in real-time. So the explicit impact compels marketers to have proper knowledge, how social media platforms work and in paying attention to online customer reviews they receive every moment.
Article
As consumers increasingly research and purchase hospitality and travel services online, new research opportunities have become available to hospitality academics. There is a growing interest in understanding the online travel marketplace among hospitality researchers. Although many researchers have attempted to better understand the online travel market through the use of analytical models, experiments, or survey collection, these studies often fail to capture the full complexity of the market. Academics often rely upon survey data or experiments owing to their ease of collection or potentially to the difficulty in assembling online data. In this study, we hope to equip hospitality researchers with the tools and methods to augment their traditional data sources with the readily available data that consumers use to make their travel choices. In this article, we provide a guideline (and Python code) for how to best collect/scrape publicly available online hotel data. We focus on the collection of online data across numerous platforms, including online travel agents, review sites, and hotel brand sites. We outline some exciting possibilities regarding how these data sources might be utilized, as well as discuss some of the caveats that have to be considered when analyzing online data.
Article
Reviewing and rating are important features of many social media websites, but they are found on many e-commerce sites too. The combination of social interaction and e-commerce is sometimes referred to as social commerce to indicate that people are supporting each other in the process of buying goods and services. Rgeviews of other consumers have a significant effect on consumer choice because they are usually considered authentic and more trustworthy than information presented by a vendor. The collaborative effort of consumers helps to make the right purchase decision (or prevent from a wrong one). The effect of reviews has often been researched in terms of helpfulness as indicated by their readers. Images are an important factor of helpfulness in reviews of experience goods where personal tastes and use play an important role. We extend this research to search goods where objective characteristics seem to prevail. In addition, we analyze potential interaction with other variables. The empirical study is performed with regression analyses on 3,483 search good reviews from Amazon.com followed by a matched pair analysis of 186 review pairs. We find that images have a significant positive effect on helpfulness of reviews of search goods too. This is especially true in case of short and ambiguous reviews.
Article
This paper focuses on consumer-generated reviews (CGRs), which are an increasingly influential source of consumer information. In particular, the paper highlights specific problems associated with CGRs, which questions their role as a reliable information source. Flowing from this, the paper calls for closer regulatory scrutiny of review platforms, which play an important intermediary role in facilitating the provision of CGRs. To this end, the paper considers possible regulatory responses in the EU which may address some of the issues highlighted.
Article
The purpose of this study is to determine guest misreport sources in Airbnb reviews. Previous studies have signalled the existence of positive bias in reviews. Here we examine the relationship between misreporting and the following factors: reciprocity, attachment, tolerance threshold, strategic behaviour and social influence. The results, obtained from a sample of 815 Airbnb users who reviewed their experience on the platform, show that the strategic behaviour of guests as well as their social influence are directly related to misreporting on Airbnb. Individual attachment is indirectly related to misreporting through the tolerance threshold. This study develops and tests a structural model which explains the factors that lead guests on the platform to misreport their actual experiences.
Conference Paper
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We study the effect of the volume of consumer reviews on the purchase likelihood (conversion rate) of users browsing a product page. We propose using the exponential learning curve model to study how conversion rates change with the number of reviews. We call the difference in conversion rate between having no reviews and an infinite number \textit{the value of reviews}. We find that, on average, the conversion rate of a product can increase by 142% as it accumulates reviews. To address the problem of simultaneity of increase of reviews and conversion rate, we explore the natural temporal trends throughout a product's lifecycle. We perform further controls by using user sessions where the reviews were not displayed. We also find diminishing marginal value as a product accumulates reviews, with the first five reviews driving the bulk of the aforementioned increase. Within categories, we find that the value of reviews is highest for Electronics (increase of 317%) followed by Home Living (increase of 182%) and Apparel (increase of 138%). We infer that the existence of reviews provides valuable signals to the customers, increasing their propensity to purchase. We also infer that users usually don't pay attention to the entire set of reviews, especially if there are a lot of them, but instead they focus on the first few available. Our approach can be extended and applied in a variety of settings to gain further insights.
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Online consumer reviews are broadly believed to be a necessary and powerful marketing tool, and as such they have attracted considerable attention from both marketers and academics. However, previous research has not sufficiently focused on the effects of various review features on sales but rather used proxy measures such as consumers’ purchase intention or perceived helpfulness of reviews. Hence, the aim of this study was to investigate the effect of review valence and volume on purchase behavior. We use data from three different e-commerce websites and study light bulbs, women’s athletic shoes, natural hair care products, and herbal vitamins. The results show that, contrary to popular belief, more positive ratings do not simply result in higher sales. We find that the effect can be nonlinear, where the probability of purchase increases with rating to about 4.2-4.5 stars, but then decreases. Also, although the majority of extant research suggests that larger numbers of reviews bring more positive outcomes, we show that it is not always the case.
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More than 4 decades of research and 9 meta-analyses have focused on the undermining effect: namely, the debate over whether the provision of extrinsic incentives erodes intrinsic motivation. This review and meta-analysis builds on such previous reviews by focusing on the interrelationship among intrinsic motivation, extrinsic incentives, and performance, with reference to 2 moderators: performance type (quality vs. quantity) and incentive contingency (directly performance-salient vs. indirectly performance-salient), which have not been systematically reviewed to date. Based on random-effects meta-analytic methods, findings from school, work, and physical domains (k = 183, N = 212,468) indicate that intrinsic motivation is a medium to strong predictor of performance (ρ = .21-45). The importance of intrinsic motivation to performance remained in place whether incentives were presented. In addition, incentive salience influenced the predictive validity of intrinsic motivation for performance: In a "crowding out" fashion, intrinsic motivation was less important to performance when incentives were directly tied to performance and was more important when incentives were indirectly tied to performance. Considered simultaneously through meta-analytic regression, intrinsic motivation predicted more unique variance in quality of performance, whereas incentives were a better predictor of quantity of performance. With respect to performance, incentives and intrinsic motivation are not necessarily antagonistic and are best considered simultaneously. Future research should consider using nonperformance criteria (e.g., well-being, job satisfaction) as well as applying the percent-of-maximum-possible (POMP) method in meta-analyses. (PsycINFO Database Record (c) 2014 APA, all rights reserved).
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This study examines the effect of online reviews on new product sales for consumer electronics and video games. Analyses of panel data of 332 new products from Amazon.com over nine months reveal that the valence of reviews and the volume of page views have a stronger effect on search products, whereas the volume of reviews is more important for experience products. The results also show that the volume of reviews has a significant effect on new product sales in the early period and such effect decreases over time. Moreover, the percentage of negative reviews has a greater effect than that of positive reviews, confirming the negativity bias. Thus, marketers need to consider the distinctive influences of various aspects of online reviews when launching new products and devising e-marketing strategies.
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Internet review forums increasingly supplement expert opinion and social networks in informing consumers about product quality. However, limited empirical evidence links digital word‐of‐mouth to purchasing decisions. We implement a regression discontinuity design to estimate the effect of positive Yelp.com ratings on restaurant reservation availability. An extra half‐star rating causes restaurants to sell out 19 percentage points (49%) more frequently, with larger impacts when alternate information is more scarce. These returns suggest that restaurateurs face incentives to leave fake reviews but a rich set of robustness checks confirm that restaurants do not manipulate ratings in a confounding, discontinuous manner.
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Follow the Leader? The Internet has increased the likelihood that our decisions will be influenced by those being made around us. On the one hand, group decision-making can lead to better decisions, but it can also lead to “herding effects” that have resulted in financial disasters. Muchnik et al. (p. 647 ) examined the effect of collective information via a randomized experiment, which involved collaboration with a social news aggregation Web site on which readers could vote and comment on posted comments. Data were collected and analyzed after the Web site administrators arbitrarily voted positively or negatively (or not at all) as the first comment on more than 100,000 posts. False positive entries led to inflated subsequent scores, whereas false negative initial votes had small long-term effects. Both the topic being commented upon and the relationship between the poster and commenter were important. Future efforts will be needed to sort out how to correct for such effects in polls or other collective intelligence systems in order to counter social biases.
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Firms' incentives to manufacture biased user reviews impede review usefulness. We examine the differences in reviews for a given hotel between two sites: Expedia.com (only a customer can post a review) and TripAdvisor.com (anyone can post). We argue that the net gains from promotional reviewing are highest for independent hotels with single-unit owners and lowest for branded chain hotels with multiunit owners. We demonstrate that the hotel neighbors of hotels with a high incentive to fake have more negative reviews on TripAdvisor relative to Expedia; hotels with a high incentive to fake have more positive reviews on TripAdvisor relative to Expedia.
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Researchers and practitioners recently have given increasing attention to the antecedents and consequences of postpurchase consumer complaint intentions and behaviors. Issues pertaining to the nature and structure of the consumer complaint behavior (CCB) concept, however, have not received such attention. The author assesses the validity of the three current operationalizations and taxonomies of CCB using intentions data from four different and independent CCB situations. None is an adequate representation of the empirical observations. Consequently a taxonomy is proposed that is based on exploratory analysis of one of the CCB situations. Confirmatory analysis of the other three CCB situations supports the proposed taxonomy. A validity analysis using complaint behavior data for the four CCB situations also supports the proposed CCB taxonomy.
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Through Web-based consumer opinion platforms (e.g., epinions.com), the Internet enables customers to share their opinions on, and experiences with, goods and services with a multitude of other consumers; that is, to engage in electronic word-of-mouth (eWOM) communication. Drawing on findings from research on virtual communities and traditional word-of-mouth literature, a typology for motives of consumer online articulation is developed. Using an online sample of some 2,000 consumers, information on the structure and relevance of the motives of consumers' online articulations is generated. The resulting analysis suggests that consumers' desire for social interaction, desire for economic incentives, their concern for other consumers, and the potential to enhance their own self-worth are the primary factors leading to eWOM behavior. Further, eWOM providers can be grouped based on what motivates their behavior, suggesting that firms may need to develop different strategies for encouraging eWOM behavior among their users.
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Online product reviews may be subject to self-selection biases that impact consumer purchase behavior, online ratings’ time series, and consumer surplus. This occurs if early buyers hold different preferences than do later consumers about the quality of a given product. Readers of early product reviews may not successfully correct for these preference differences when interpreting ratings and making purchases. In this study, we develop a model that examines how idiosyncratic preferences of early buyers can affect long-term consumer purchase behavior as well as the social welfare created by review systems. Our model provides an explanation for the structure of product ratings over time, which we empirically test using online book reviews posted on Amazon.com. Our analysis suggests that firms could benefit from altering their marketing strategies such as pricing, advertising, or product design to encourage consumers likely to yield positive reports to self-select into the market early and generate positive word-of-mouth for new products. On the other hand, self-selection bias, if not corrected, decreases consumer surplus.
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The authors document that approximately 5% of product reviews on a large private label retailer's website are submitted by customers with no record of ever purchasing the product they are reviewing. These reviews are significantly more negative than other reviews. They are also less likely to contain expressions describing the fit or feel of the items and more likely to contain linguistic cues associated with deception. More than 12, 000 of the firm's best customers have written reviews without confirmed transactions. On average, these customers have each made more than 150 purchases from the firm. This makes it unlikely that the reviews were written by the employees or agents of a competitor and suggests that deceptive reviews may not be limited to the strategic actions of firms. Instead, the phenomenon may be far more prevalent, extending to individual customers who have no financial incentive to influence product ratings.
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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.
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Consumer reviews are now part of everyday decision making. Yet the credibility of these reviews is fundamentally undermined when businesses commit review fraud, creating fake reviews for themselves or their competitors. We investigate the economic incentives to commit review fraud on the popular review platform Yelp, using two complementary approaches and data sets. We begin by analyzing restaurant reviews that are identified by Yelp’s filtering algorithm as suspicious, or fake—and treat these as a proxy for review fraud (an assumption we provide evidence for). We present four main findings. First, roughly 16% of restaurant reviews on Yelp are filtered. These reviews tend to be more extreme (favorable or unfavorable) than other reviews, and the prevalence of suspicious reviews has grown significantly over time. Second, a restaurant is more likely to commit review fraud when its reputation is weak, i.e., when it has few reviews or it has recently received bad reviews. Third, chain restaurants—which benefit less from Yelp—are also less likely to commit review fraud. Fourth, when restaurants face increased competition, they become more likely to receive unfavorable fake reviews. Using a separate data set, we analyze businesses that were caught soliciting fake reviews through a sting conducted by Yelp. These data support our main results and shed further light on the economic incentives behind a business’s decision to leave fake reviews. This paper was accepted by Lorin Hitt, information systems.
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A large number of online customer reviews greatly influences consumer purchasing decisions. Whether positive or negative, consumers regard online customer reviews as providing useful information. Based on the elaboration likelihood model (ELM), this study focuses on the factors of the central and peripheral route in online customer reviews that make readers feel they are trustworthy and helpful. In addition, the researchers are interested in the impact of social factors in the reviews on consumers. Using content analysis, the study analyzes 983 customer reviews from restaurant review websites. Results show that the larger reviewer's number of followers, the higher level of expertise of the reviewer, the larger image count and word count also make readers feel the review is more practical and useful. Further, the influence of the peripheral route, the social factors, on readers is higher than that of central route factors.
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Introduction While product review systems that collect and disseminate opinions about products from recent buyers (Table 1) are valuable forms of word-of-mouth communication, evidence suggests that they are overwhelmingly positive. Kadet notes that most products receive almost five stars. Chevalier and Mayzlin also show that book reviews on Amazon and Barnes & Noble are overwhelmingly positive. Is this because all products are simply outstanding? However, a graphical representation of product reviews reveals a J-shaped distribution (Figure 1) with mostly 5-star ratings, some 1-star ratings, and hardly any ratings in between. What explains this J-shaped distribution? If products are indeed outstanding, why do we also see many 1-star ratings? Why aren't there any product ratings in between? Is it because there are no "average" products? Or, is it because there are biases in product review systems? If so, how can we overcome them? The J-shaped distribution also creates some fundamental statistical problems. Conventional wisdom assumes that the average of the product ratings is a sufficient proxy of product quality and product sales. Many studies used the average of product ratings to predict sales. However, these studies showed inconsistent results: some found product reviews to influence product sales, while others did not. The average is statistically meaningful only when it is based on a unimodal distribution, or when it is based on a symmetric bimodal distribution. However, since product review systems have an asymmetric bimodal (J-shaped) distribution, the average is a poor proxy of product quality. This report aims to first demonstrate the existence of a J-shaped distribution, second to identify the sources of bias that cause the J-shaped distribution, third to propose ways to overcome these biases, and finally to show that overcoming these biases helps product review systems better predict future product sales. We tested the distribution of product ratings for three product categories (books, DVDs, videos) with data from Amazon collected between February--July 2005: 78%, 73%, and 72% of the product ratings for books, DVDs, and videos are greater or equal to four stars (Figure 1), confirming our proposition that product reviews are overwhelmingly positive. Figure 1 (left graph) shows a J-shaped distribution of all products. This contradicts the law of "large numbers" that would imply a normal distribution. Figure 1 (middle graph) shows the distribution of three randomly-selected products in each category with over 2,000 reviews. The results show that these reviews still have a J-shaped distribution, implying that the J-shaped distribution is not due to a "small number" problem. Figure 1 (right graph) shows that even products with a median average review (around 3-stars) follow the same pattern.
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Knowledge of how consumers react to different quality signals is fundamental for understanding how markets work. The modern electronic marketplace has revolutionized the possibilities for consumers to gather detailed information about products and services before purchase. Specifically, a consumer can easily -- through a host of online forums and evaluation sites -- estimate a product's popularity based on either i) what other users say about the product (stated preferences) or ii) how many other users that have bought the product (revealed preferences). In this paper we compare the causal effects on demand from these two quality related signals. We study the online marketplace for Android apps on Google play. The specific way that Google play presents download and average rating information to users allows for identification of the causal effects.Our data consists of daily information, for 42 consecutive days, of more than 500 000 apps from the US version of Google play. Our main result is that consumers are much more responsive to other consumers' revealed preferences, compared to others' stated preferences. A 10 percentile increase in displayed average rating only increases downloads by about 3 percent, while a 10 percentile increase in displayed number of downloads increases downloads by about 20 percent.
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Interests and goals have been identified as two important motivational variables that impact individuals' academic performances, yet little is known about how best to utilize these variables to enhance childrens' learning. We first review recent developments in the two areas and then examine the connection between interests and goals. We argue that the polarization of situational and individual interest, extrinsic and intrinsic motivation, and performance and mastery goals must be reconsidered. In addition, although we acknowledge the positive effects of individual interest, intrinsic motivation, and the adoption of mastery goals, we urge educators and researchers to recognize the potential additional benefits of externally triggered situational interest, extrinsic motivation, and performance goals. Only by dealing with the multidimensional nature of motivational forces will we be able to help our academically unmotivated children.
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The author examines consumer affective responses to product/consumption experiences and their relationship to selected aspects of postpurchase processes. In separate field studies of automobile owners and CATV subscribers, subjects reported the nature and frequency of emotional experiences in connection with product ownership and usage. Analysis confirms hypotheses about the existence of independent dimensions of positive and negative affect. Both dimensions of affective response are found directly related to the favorability of consumer satisfaction judgments, extent of seller-directed complaint behavior, and extent of word-of-mouth transmission.
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Do online consumer reviews affect restaurant demand? I investigate this question using a novel dataset combining reviews from the website Yelp.com and restaurant data from the Washington State Department of Revenue. Because Yelp prominently displays a restaurant's rounded average rating, I can identify the causal impact of Yelp ratings on demand with a regression discontinuity framework that exploits Yelp’s rounding thresholds. I present three findings about the impact of consumer reviews on the restaurant industry: (1) a one-star increase in Yelp rating leads to a 5% to 9% increase in revenue, (2) this effect is driven by independent restaurants; ratings do not affect restaurants with chain affiliation, and (3) chain restaurants have declined in market share as Yelp penetration has increased. This suggests that online consumer reviews substitute for more traditional forms of reputation. I then test whether consumers use these reviews in a way that is consistent with standard learning models. I present two additional findings: (4) consumers do not use all available information and are more responsive to quality changes that are more visible and (5) consumers respond more strongly when a rating contains more information. Consumer response to a restaurant’s average rating is affected by the number of reviews and whether the reviewers are certified as “elite” by Yelp, but is unaffected by the size of the reviewers’ Yelp friends network.
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The ubiquity of Web2.0 makes the Web an invaluable source of business information. For instance, product reviews composed collaboratively by many independent Internet reviewers can help consumers make purchase decisions and enable enterprises to improve their business strategies. As the number of reviews is increasing exponentially, opinion mining and retrieval techniques are needed to identify important reviews and opinions to answer users' queries. Most opinion mining and retrieval approaches try to extract sentimental or bipolar expressions from a large volume of reviews. However, the process often ignores the quality of each review and may retrieve useless or even noisy documents. In this paper, we propose a method for evaluating the quality of information in product reviews. We treat the evaluation of review quality as a classification problem and employ an effective information quality framework to extract representative review features. Experiments based on an expert-composed data corpus demonstrate that the proposed method outperforms state-of-the-art approaches significantly.
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Hit songs, books, and movies are many times more successful than average, suggesting that “the best” alternatives are qualitatively different from “the rest”; yet experts routinely fail to predict which products will succeed. We investigated this paradox experimentally, by creating an artificial “music market” in which 14,341 participants downloaded previously unknown songs either with or without knowledge of previous participants' choices. Increasing the strength of social influence increased both inequality and unpredictability of success. Success was also only partly determined by quality: The best songs rarely did poorly, and the worst rarely did well, but any other result was possible.
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We construct a panel of eBay seller histories and examine the importance of eBay's reputation mechanism. We find that, when a seller first receives negative feedback, his weekly sales rate drops from a positive 5% to a negative 8%; subsequent negative feedback ratings arrive 25% more rapidly than the first one and don't have nearly as much impact as the first one. We also find that a seller is more likely to exit the lower his reputation is; and that, just before exiting, sellers receive more negative feedback than their lifetime average. Copyright 2010 The Authors. Journal compilation 2010 Blackwell Publishing Ltd. and the Editorial Board of The Journal of Industrial Economics.
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Do dissatisfied customers engage in more or less word of mouth than satisfied customers? There is theoretical and empirical support for both possibilities. To better understand this issue, the authors developed a utility-based model of the relationship between customer satisfaction and word of mouth. The hypothesized functional form-an asymmetric U-shape-cannot be rejected based on data from the United States and Sweden. In addition, the estimation results based on the two samples are similar, suggesting that the proposed relationship is generalizable. The findings also indicate that although dissatisfied customers do engage in greater word of mouth than satisfied ones, common suppositions concerning the size of this difference appear to be exaggerated. Peer Reviewed http://deepblue.lib.umich.edu/bitstream/2027.42/68654/2/10.1177_109467059800100102.pdf
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We construct a panel of eBay seller histories and examine the importance of eBay s reputation mechanism. We find that, when a seller first receives negative feedback, his weekly sales rate drops from a positive7% to a negative 7%; subsequent negative feedback ratings arrive 25% more rapidly than the first one and don t have nearly as muchimpact as the first one. We also find that a seller is more likely to exit the lower his reputation is; and that, just before exiting, sellers receive more negative feedback than their lifetime average.We consider a series of theoretical models and measure them against these empirical results. Regardless of which theoretical model best explains the data, an important conclusion of our paper is that eBay sreputation system gives way to noticeable strategic responses from both buyers and sellers.
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The creation of online consumer communities to provide product reviews and advice has been touted as an important, albeit somewhat expensive component of Internet retail strategies. In this paper, we characterize reviewer behavior at two popular Internet sites and examine the effect of consumer reviews on firms' sales. We use publicly available data from the two leading online booksellers, Amazon.com and BarnesandNoble.com, to construct measures of each firm's sales of individual books. We also gather extensive consumer review data at the two sites. First, we characterize the reviewer behavior on the two sites such as the distribution of the number of ratings and the valence and length of ratings, as well as ratings across different subject categories. Second, we measure the effect of individual reviews on the relative shares of books across the two sites. We argue that our methodology of comparing the sales and reviews of a given book across Internet retailers allows us to improve on the existing literature by better capturing a causal relationship between word of mouth (reviews) and sales since we are able to difference out factors that affect the sales and word of mouth of both retailers, such as the book's quality. We examine the incremental sales effects of having reviews for a particular book versus not having reviews and also the differential sales effects of positive and negative reviews. Our large database of books also allows us to control for other important confounding factors such as differences across the sites in prices and shipping times.
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"Employing a procedure suggested by a simple theoretical model of auctions in which bidders and sellers have observable and heterogenous reputations for default, we examine the effect of reputation on price in a data set drawn from the online auction site eBay. Our main empirical result is that seller's, but not bidder's, reputation has an economically and statistically significant effect on price." Copyright 2006, The Author(s) Journal Compilation (c) 2006 Blackwell Publishing.
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We conducted the first randomized controlled study of an Internet reputation mechanism. A high-reputation, established eBay dealer sold matched pairs of items -- batches of vintage postcards -- under his regular identity and new seller identities (also operated by him). As predicted, the established identity fared better. The difference in buyers' willingness-to-pay was 8.1% of the selling price. A subsidiary experiment followed the same format, but compared sales by relatively new sellers with and without negative feedback. Surprisingly, one or two negative feedbacks for our new sellers did not affect buyers' willingness-to-pay. We gratefully acknowledge financial support from the National Science Foundation under grant number IIS-9977999. Mihir Mahajan provided valuable research assistance. The participants in seminars at the University of Michigan and the University of Arizona provided useful feedback.
Using Online Ratings as a Proxy of Word-of-Mouth in Motion Picture Revenue ForecastingRetrieved from SSRN
  • C Dellarocas
  • M Zhang
C. Dellarocas, M. Zhang, Using Online Ratings as a Proxy of Word-of-Mouth in Motion Picture Revenue ForecastingRetrieved from SSRN 2005http://ssrn.com/ab-stract=620821.
Reviews, reputation, and revenue: The case of Yelp.com, Harvard Business School NOM Unit Working Paper
  • M Luca
M. Luca, Reviews, reputation, and revenue: The case of Yelp.com, Harvard Business School NOM Unit Working Paper, 2011.
Survey Confirms the Value of Reviews, Provides New InsightsRetrieved from http://www.powerreviews.com/blog/survey-confirms-the-value-of-reviews
  • T O Neil
T. O'Neil, Survey Confirms the Value of Reviews, Provides New InsightsRetrieved from http://www.powerreviews.com/blog/survey-confirms-the-value-of-reviews/ 2015.
The future of work motivation theory Word-of-mouth communications: a motivational analysis
  • R M Steers
  • R T Mowday
  • D L Shapiro
  • D S Sundaram
  • K Mitra
  • C Webster
R.M. Steers, R.T. Mowday, D.L. Shapiro, The future of work motivation theory, Acad. Manag. Rev. 29 (3) (2004) 379-387, http://dx.doi.org/10.5465/AMR.2004.13670978. [38] D.S. Sundaram, K. Mitra, C. Webster, Word-of-mouth communications: a motivational analysis, Adv. Consum. Res. 25 (1) (1998) 527-531 Retrieved from http:// search.ebscohost.com/login.aspx?direct=true&db=buh&AN=988808&site= ehost-live.
The future of work motivation theory
  • R M Steers
  • R T Mowday
  • D L Shapiro
R.M. Steers, R.T. Mowday, D.L. Shapiro, The future of work motivation theory, Acad. Manag. Rev. 29 (3) (2004) 379-387, http://dx.doi.org/10.5465/AMR.2004.13670978.
  • The Nielsen Company
The Nielsen Company, Global Trust in Advertising, http://www.nielsen.com/content/dam/corporate/us/en/reports-downloads/2015-reports/global-trust-in-advertising-report-sept-2015.pdf 2015 Retrieved from http://www.nielsen.com/content/ dam/corporate/us/en/reports-downloads/2015-reports/global-trust-in-advertisingreport-sept-2015.pdf.
He earned his PhD in 1995 in computational statistics from Northwestern University and completed a post doc at the Kellogg marketing department. His research interests center on customer engagement and experiences; digital, social and mobile media; big data; customer lifetime value models
  • C Edward
  • R Malthouse Is The Theodore
  • Annie Laurie
Edward C. Malthouse is the Theodore R and Annie Laurie Sills Professor of Integrated Marketing Communications, and Industrial Engineering and Management Science at Northwestern University. He is the Research Director for the Spiegel Center for Digital and Database Marketing. He was the co-editor of the Journal of Interactive Marketing between 2005 and 2011. He earned his PhD in 1995 in computational statistics from Northwestern University and completed a post doc at the Kellogg marketing department. His research interests center on customer engagement and experiences; digital, social and mobile media; big data; customer lifetime value models; predictive analytics; unsupervised learning; and integrated marketing communications.
The effect of online consumer reviews on new product sales
  • Cui