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Understanding and overcoming biases in online review systems
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.
... 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]. ...
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]. ...
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). ...
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.
... Within-website bias, as generally used by researchers in the context of online consumer reviews, pertains to a strong and systematic inclination of reviews (i.e., ratings and comments) within a website toward the positive or negative extreme of a rating scale or sentiment continuum [2]. Whereas, between-website bias refers to the systematic differences between reviews (i.e., ratings and comments) associated with the same objects, persons, entities, etc. posted on two or more websites [16]. While numerous studies in the past have evaluated within-website bias [2,17], very few studies have assessed betweenwebsite bias in health or non-health contexts. ...
... Our results extend the existing literature on socially-enabled information systems [24], eWOM in healthcare [8,25], social media-enabled patient decision-making [19], and bias in user-generated content in online environments [2,16,26]. From a practical standpoint, the findings provide insights into the quality, reliability, and accuracy of the physician reviews posted on HOWs. Administrators of those websites, directors of patient-centered information systems, and healthcare quality managers in clinics and hospitals can use the results of this study to improve the processes of administering patient satisfaction surveys and publishing the results on their websites. ...
... Acquisition bias denotes that online reviews tend to be skewed toward positive values and sentiments because consumers with a positive predisposition toward a product or service are more likely to acquire it and hence, write reviews about it [26]. As a special case of acquisition bias, the preferences and attitudes of a product's early adopters toward the product are likely to be more positive than those of followers and late-adopters [30]; consequently, the product's overall rating scores and review sentiments may systematically decrease over time [16]. The second form of self-selection bias, namely underreporting bias, postulates that people with extreme opinions about a product or service, either positive or negative, are more likely to write reviews about it [33]. ...
Making healthcare decisions is often difficult, challenging, and consequential for individuals. To facilitate and inform provider selection decisions, healthcare organizations offer physician review services and publish the provided reviews on their websites. However, if such reviews are highly skewed, significantly inaccurate, and systematically biased, they could mislead patients. This is against healthcare organizations' responsibility of acting in the best interests of patients. To shed light on this phenomenon, this study examines two types of bias, namely between-website and within-website biases, in the online reviews of physicians posted on healthcare organizations' websites. Reviews belonging to 1921 physicians were collected from two data sources: (1) websites of the hospitals that the physicians were affiliated with and (2) four commercial physician rating websites including HealthGrades, RateMDs, Vitals, and Google Reviews. To test between-website biases, the ratings posted on the healthcare organizations' websites were compared against the corresponding ratings on the commercial websites. Our results suggest that the ratings provided by healthcare institutions are generally, more skewed toward scores favorable to physicians, and less dispersed, than corresponding ratings reported on commercial websites. The within website results also supported the argument that physicians with different rating scores systematically receive different number of ratings and comments. Our findings highlight the fact that hospitals that publish physician ratings should try to identify and reduce rating biases to help patients make more informed and judicious clinical decisions, realizing the responsibility of healthcare providers in protecting patients' rights and safety.
... 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. ...
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.
... Studies have identified a number of reasons behind individuals posting online comments in traditional asynchronous online environment, including economic incentives (Burtch et al., 2017;Sun et al., 2017), social norms (Askalidis et al., 2017;Burtch et al., 2017), altruism (Hennig-Thurau et al., 2004;Mathwick & Mosteller, 2017), and social interaction (de Vries et al., 2017;Sun et al., 2017). However, on the live streaming platforms, the usergenerated comments are displayed as streams of moving subtitles overlaid on the video screen, whereby users can simultaneously communicate with other viewers and the streamer by sending Danmu comments. ...
... This implies that the underlying motivation in sending Danmu is different from posting traditional comments. Previous studies have investigated the antecedents of review posting behavior in traditional online websites including economic incentives (Burtch et al., 2017;Kuem et al., 2017), social norms (Askalidis et al., 2017), altruism (Hennig-Thurau et al., 2004), and social interaction (de Vries et al., 2017;Sun et al., 2017), whereas these studies did not consider the effects of technical features of websites on the motivation of posting behavior. ...
Danmu function as an augmented comment feature has been adopted by almost all live streaming platforms to foster interaction between viewers and the streamer in China. However, few studies have been conducted to understand the determinants of users’ Danmu sending behavior on live streaming platforms. This study examines this phenomenon from the lens of effectance theory and the S-O-R framework. We propose that two effectances – Danmu effectance and live streaming effectance – play an essential role in active Danmu participation. In addition, we explore the effects of time-enhanced (synchronicity) and space-enhanced technical characteristic (visibility) of Danmu on live streaming platforms on two effectances. Data analysis of 877 participations from Douyu platform in mainland China indicates that active Danmu participation is positively associated with Danmu effectance and live streaming effectance which are influenced by both time-enhanced technical feature (synchronicity) and space-enhanced technical feature (visibility). In addition, the study finds that demographic characteristics, namely education and income, also affect active Danmu participation.
... The effect of reviewer motivation and helpfulness can be negatively moderated by implementing a design feature that allows asking consumers to become a reviewer [Review Elicitation]. It has been found that asking past consumers to write a review by email leads to reviews that are perceived as less helpful than reviews that consumers have provided spontaneously (Askalidis et al., 2017). ...
... This design feature, then, positively moderates the taste match effect [Rating Dimensions]. To reduce the impact of previous ratings and address the social influence bias, designers can design their system so that it emails consumers asking them to write reviews on a page where they are not exposed to existing reviews (Askalidis et al., 2017) [Review Elicitation]. Allowing friendships between reviewers on the system can also positively moderate the strength of the social influence bias [Reviewer Reputation]. ...
As online reviews play a decisive role in consumers’ purchase decisions, e-commerce platforms are using review systems strategically to obtain a competitive advantage. However, the strategic potential can only be leveraged if the review system is designed appropriately. Research on the design of review systems and the effects of design choices has not yet been summarized or synthesized in a review article. We aim to close this gap by providing a scoping review. In our synthesis we posit that the design of review systems moderates the impact of online reviews on economic outcomes and the factors that drive the formation of reviews. After reviewing current research findings, we identify gaps and provide a research agenda covering three key themes: Design features, environments, and devices.
... The effect of reviewer motivation and helpfulness can be negatively moderated by implementing a design feature that allows asking consumers to become a reviewer [Review Elicitation]. It has been found that asking past consumers to write a review by email leads to reviews that are perceived as less helpful than reviews that consumers have provided spontaneously (Askalidis et al., 2017). ...
... Askalidis et al., 2017) [Review Elicitation]. Allowing friendships between reviewers on the system can also positively moderate the strength of the social influence bias [Reviewer Reputation]. ...
... Because most of these platforms (e.g., Amazon Customer Reviews, Tripadvisor, Yelp etc.) are free and open to access, anyone can benefit. Recent research even suggests that appropriate design and policy in online review systems is needed to improve the quality and validity of online reviews, resulting in representative and more trustful ratings (Askalidis, Kim, & Malthouse, 2017). ...
... While this general pattern should hold across contexts, context-specific variations could be identified, such as the relation between motivations and WOM content. Writers in online system context have different motivations to spread the word, this motivation may have been strengthened by the online review context (Askalidis et al., 2017). In other contexts, such as email or social media platforms, self-enhancement or other motives may play a larger role (Berger & Milkman 2012). ...
Online consumer reviews provide relevant information about products and services for consumers. In today's networked age, the online consumer review platform market is hyper-competitive. These platforms can easily change different design characteristics to get more reviewers and to nudge reviewers to deliver higher quality reviews. This study explored the relation between online consumer review platforms' design characteristics and the reviewers' construal level. A psycholinguistic coding scheme was used to assess which social and physical design characteristics impact the language abstraction in accompanying online consumer reviews. To this end, we content analyzed reviews of services and products posted on eight different online consumer review platforms (N = 400). This resulted in a number of key design characteristics (e.g., reviewer identification, reviewer status, order of instructions and length instructions) that led to a decrease in language abstraction used in online consumer reviews. Moreover, results showed that language abstraction mediated the relationship between the four design characteristics and valence. The findings and their broader theoretical, methodological and practical implications are discussed. Online consumer review platforms could capitalize on our findings in adaptive design choices.
... 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). ...
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). ...
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. ...
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. ...
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. ...
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]. ...
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. ...
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 . ...
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). ...
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. ...
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. ...
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). ...
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.
... For UGC websites, they may need to balance the need to induce more user efforts and the need to avoid potential biases [19]. In addition to the incentive with reevaluation mechanism, the UGC websites can resort to some approaches from the literature (e.g., retailer-prompted reviews [55]) to mitigate the biases. For businesses that try to examine the market response to their products, or consumers that try to evaluate the products, they should carefully take into account such induced biases as well [19]. ...
Content sharing platforms such as product review websites largely depend on reviewers' voluntary contributions. In order to motivate reviewers to contribute more, many platforms established incentive mechanisms, either reputation-based or financial. Yet most of the existing research has focused on reputations that are everlasting, such as badges and virtual points, or financial rewards where no evaluation exists about the users' contributed content, such as rebates. There is still a significant gap in our understanding of how incentives with reevaluation mechanism actually influence reviewers' behaviors such as their contribution levels, the opinion they express, and how they express. In this paper, we fill this gap using data collected from Yelp Elite Squad where reviewers with good reviewing history are awarded into the elite group and most importantly reevaluated each year. We draw from the accountability theory and conduct a difference-in-differences analysis to empirically study the effect of incentives with reevaluation mechanism on reviewers' behaviors in both short term and long term. The results show that in short term, reviewers significantly increase their contribution levels, become more conservative with lower percentage of extreme ratings, and also increase the readability of their reviews. In long term, they continue improving the quality of reviews though their numerical rating behaviors stabilize. Our research has significant implications for business models that rely on user contributions.
... In Zhihu Live, customers can rate the Lives after their participation, along with review texts. Numerical ratings on goods and services are deemed to well reflect the customer's overall satisfaction [52,53]. Therefore, numerical ratings were used to measure the individual satisfaction of each customer. ...
In this study, we investigate what factors are influential to customer satisfaction of paid knowledge, especially among different customer segments, by integrating user activities on both free and paid platforms. Considering the complexity of knowledge acquisition, we first propose a novel measurement of “customer expertise” based on text mining, as a criterion for customer segmentation. Drawing upon the value-percept diversity theory, we then postulate a conceptual model proposing that customers with different expertise would react differently to the price of knowledge and historical knowledge-consuming transactions, in terms of customer satisfaction. We test the model empirically through the hierarchical OLS regression with data collected from Zhihu and Zhihu Live. Distinguishing expert and novice customers, we have findings that (1) expert customers are less sensitive to price; (2) historical price positively influences the satisfaction of novice customers, but negatively for expert customers; (3) expert customers are less influenced by historical satisfaction, which have important implications for market targeting and knowledge pricing strategy.
... Muchnik, Aral, and Taylor (2013) named this phenomenon social influence bias and showed that previous positive ratings increased the probability of posting favourable reviews. Askalidis, Kim, and Malthouse (2017) also tested this phenomenon and found differences in ratings made by individuals who had read existing reviews and those who had not accessed this information. Bridges and Vásquez (2016) have also identified a possible cause for positivity bias on Airbnb: the lack of anonymity. ...
One issue that has been identified in the literature is the relatively high average of guest client ratings that properties receive on sharing accommodation platforms. High ratings seem to be the norm in most online platforms that include consumer reviews, but the case of Airbnb seems more extreme than the others. Several reasons have been proposed to explain this apparently positively-biased eWOM. However, none of these proposals have taken into account the guests’ perspective on the matter. In this study, we develop a two-step methodology to research and verify the reasons for this issue. First, with a sample of 391 Airbnb guests, we analyse the specific causes that explain these high ratings. Second, we carry out in-depth interviews with 20 additional guests who did not rate or who recognized that they were not fully accurate in their reviews. Not wanting to harm a reputed host that performed well in stays that did not involve serious problems was the main reason behind these behaviours. Theoretical and managerial implications are discussed.
... Previous research on OCRs has found that the distribution of star ratings of a given product exhibits a J-shape because consumers who are either very satisfied or very disappointed tend to write about their consumption experiences (Hu, Zhang, & Pavlou, 2009). A study by Askalidis, Kim, and Malthouse (2017) also found that self-motivated reviews are more negative than retailer-prompted reviews, which confirms the finding that some organic reviewers are motivated to write their reviews due to their disappointment with a product. ...
Online consumer reviews (OCRs) have become one of the most influential persuasive messages with respect to purchase decisions. Knowing this, marketers have started incentivizing consumers to write reviews, hoping that they can increase the volume of positive reviews. However, little research exists on the content characteristics and effects of sponsored OCRs. This paper examines the different characteristics and effects of sponsored and organic OCRs, and the mechanisms by which consumers recognize and process these two types of reviews, using mixed methods in two studies. The findings of a text mining analysis (Study 1) suggest that sponsored reviews provide more elaborate and evaluative content; however, they are perceived as less helpful than organic reviews. The findings of a randomized experiment (Study 2) suggest that sponsorship disclosure increases suspicions about the reviewer's ulterior motives and decreases consumers' attitudes and purchase intentions when a review is positive. Sponsorship disclosure does not hurt attitudes or purchase intentions when a review is negative.
... While previous research has focused on how reading reviews influences purchase-related behaviors, it has mostly ignored the impact of review writing activity on reviewers themselves or the review writing process (with a few exceptions, e.g. Askalidis et al. 2017). Hence, one of the biggest questions still unanswered in the literature concerns the creation of reviews. ...
In this chapter we define online reviews as a form of customer engagement behavior and provide an overarching framework for understanding the relationships between different elements of customer review ecosystem, its effects on purchase behaviors and consumption experiences, and moderators of these relationships. We discuss what has been examined in the extant literature about the effects of customer reviews and what research questions still need to be addressed. Finally, we provide directions for future research on customer reviews that will help us understand the mechanisms and effects of customer engagement.
... In the context of social media-initiated web surveys, those who are self-motivated to participate in surveys are considered to be intrinsically motivated. On the other hand, Askalidis et al. (2017) suggested that intrinsically motivated reviews are more elaborate and helpful compared to extrinsically motivated reviews. This supports the findings of Cerasoli et al. (2014) which indicates that an intrinsically motivated individual is more likely to have a higher performance level. ...
The use of web-based surveys is currently increasing due to its cost-effectiveness and agility as it provides access to market researchers to web-connected populations who are unlikely to answer through traditional survey methods. However, survey response rates in market research are in general decline and among survey platforms, web-based surveys have the highest rates of non-response. Thus, there is a need to address the declining response rates for web-based surveys particularly unit response rates – the likelihood the respondent would answer the survey. This paper proposes a respondent prequalifying framework that reduces unit non-response rates of web-based non-probabilistic surveys. A checklist of respondent characteristics influencing the likelihood of unit non-response was developed. The framework was then adopted for its applicability by replicating the recruitment phase of two case studies wherein the prequalifying checklist was applied with consideration to the respondent profile requirements of each study. While this paper does not intend to provide robust empirical evidence to the proposed framework, it demonstrates a promising framework that can be used to increase unit non-response rate by comprehensively integrating the pre-qualifying factors in the domain literature and carefully developing such framework to the most plausible direction a web-survey can be implemented. Findings suggest that (1) the proposed respondent prequalifying framework increases the unit response by prequalifying the sample in the recruitment stage, and (2) increasing the threshold value may increase unit response rates with careful consideration to some significant issues such as the weights assigned to the prequalifying factors, the quality of the background information of the respondents in relation to the prequalifying factors, and the sensitivity of the survey topic. The proposed framework is developed with strong theoretical grounding and detailed discussion for its practical use is also provided. The framework benefits market researchers by reducing unit non-response costs and increasing efficiency in social media-based market surveys.
... In the context of social media-initiated web surveys, those who are self-motivated to participate in surveys are considered to be intrinsically motivated. On the other hand, Askalidis et al. (2017) suggested that intrinsically motivated reviews are more elaborate and helpful compared to extrinsically motivated reviews. This supports the findings of Cerasoli et al. (2014) which indicates that an intrinsically motivated individual is more likely to have a higher performance level. ...
The use of web-based surveys is currently increasing due to its cost-effectiveness and agility as it provides access to market researchers to web-connected populations who are unlikely to answer through traditional survey methods. However, survey response rates in market research are in general decline and among survey platforms, web-based surveys have the highest rates of non-response. Thus, there is a need to address the declining response rates for web-based surveys particularly unit response rates – the likelihood the respondent would answer the survey. This paper proposes a respondent prequalifying framework that reduces unit non-response rates of web-based non-probabilistic surveys. A checklist of respondent characteristics influencing the likelihood of unit non-response was developed. The framework was then adopted for its applicability by replicating the recruitment phase of two case studies wherein the prequalifying checklist was applied with consideration to the respondent profile requirements of each study. While this paper does not intend to provide robust empirical evidence to the proposed framework, it demonstrates a promising framework that can be used to increase unit non-response rate by comprehensively integrating the pre-qualifying factors in the domain literature and carefully developing such framework to the most plausible direction a web-survey can be implemented. Findings suggest that (1) the proposed respondent prequalifying framework increases the unit response by prequalifying the sample in the recruitment stage, and (2) increasing the threshold value may increase unit response rates with careful consideration to some significant issues such as the weights assigned to the prequalifying factors, the quality of the background information of the respondents in relation to the prequalifying factors, and the sensitivity of the survey topic. The proposed framework is developed with strong theoretical grounding and detailed discussion for its practical use is also provided. The framework benefits market researchers by reducing unit non-response costs and increasing efficiency in social media-based market surveys.
... In the recent era, online review systems are making the biases on social influence and product selection. In order to reduce the biases, Askalidis et al. (2017) have differentiated and investigated the retailer promoted reviews and self-motivated reviews for the same product. ...
Recently, Retail 4.0 is progressively demanding the accurate prediction of consumer's purchase intention. In this regard, an attribute level decision support prediction model has been developed for providing an influential e-commerce platform to the customers. In order to build the prediction model, brands' social perception score and reviews' polarity are computed from social network mining and sentiment analysis, respectively. Afterward, an appropriate regression analysis and suitable instances have been identified for each attribute to predict the appropriate product attributes. One of the key findings, the camera attributes: sensor, display, and image stabilization pursue the customer attention at the end of the search. The outcomes of this analysis can be beneficial to e-commerce retailers and prepare an efficient search platform for the customers to obtain the desired durable goods in an adorable form. Finally, the sensitivity analysis has also been performed to test the robustness of the proposed model.
... Hence, companies should consider engaging in conversations with customers through webcare. Third, companies should actively solicit reviewers from customers who actually purchased and used a product (Askalidis, Kim, and Malthouse 2017). The problem of verified purchase is that it only recognizes consumers who bought an item from a particular retailer that provides the online review system. ...
The role of electronic word-of-mouth (eWOM) has been recognized by marketers and academics, but little research has examined the impact of eWOM on purchase behavior. Building on dual-process models of persuasion, this study aims to disentangle the effect of different online review features (i.e. argument quality, review valence, review helpfulness, message sidedness, source credibility and reviewer recommendation). Using product reviews and purchase data from an online retailer website, we investigate the financial impact of online product reviews on purchase decisions. The results demonstrate the persuasive power of different review features that are derived from dual-process models of information processing. Managerial implications on how advertisers and companies should design and manage online product reviews are offered.
... Hence, companies should consider engaging in conversations with customers through webcare. Third, companies should actively solicit reviewers from customers who actually purchased and used a product (Askalidis, Kim, and Malthouse 2017). The problem of verified purchase is that it only recognizes consumers who bought an item from a particular retailer that provides the online review system. ...
Online customer reviews help consumers make decisions, such as purchasing products, watching movies, or joining a sports club. Online reviews have become a major driving force in marketing (Cui et al., 2012) and are a common feature on many websites. Information from other consumers, such as online reviews, is thought to be more persuasive because it is allegedly written by other consumers rather than brands, and is therefore perceived as being more credible and trustworthy (Willemsen et al., 2012).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
The voluntary nature of online customer review platforms self-selects customers with strong opinions, resulting in an underreporting bias. However, little research has been conducted on the relationship between postpurchase satisfaction and the propensity to share one’s opinion. The goal of this study is to empirically examine the relationship between customer satisfaction and reporting motivation in online review platforms. The results of this study demonstrate that customer intention to post an online hotel review varies depending on the level of customer satisfaction. Online reviewers are more motivated to post extreme and negative ratings. However, this underreporting bias is mitigated when ratings are generated by reviewers who are familiar with the online review posting process. The relationship between individual familiarity with the review platform and the underreporting bias can be explained using the benefit-cost theory.
The fast growth in online word of mouth (online WOM) reviews has witnessed their wide applications in assisting customers in their purchases. While many positive factors about these reviews have been identified, biases may result from reviewers’ self-selection behavior. To examine whether online WOM ratings reflect authentic customer purchase feelings, this study focuses on the relation between customer dissatisfaction and negative reviews. First, the reviews are fine grained into feature-opinion pairs, after which three explanatory variables are designed to represent the individual, collective, and comprehensive (individual and collective) complaints, with the online rating being considered as the explained variable. Finally, a simple linear regression model is built to determine the relation between customer dissatisfaction (represented by discounting marks of ratings) and negative reviews. Our analysis finds that personal complaints have a positive effect on rating and the most frequent customer complaints have no significant effect on customer ratings.
This study proposes an effective way of using retailer-prompted review data from TripAdvisor to measure the social network effect in self-motivated online reviews by overcoming the reflection problem. After applying the network effect model, we find that self-motivated review ratings are positively associated with previous corresponding peer reviews. We further show that the size of this peer effect attenuates as the peer reviews are located further away from the first page. This study suggests that reviewer ratings are more strongly influenced by peer ratings located on the visible page.
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.
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.
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.
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.
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.
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.
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.
This study examines the persuasive effect and awareness effect of online user reviews on movies' daily box office performance. In contrast to earlier studies that take online user reviews as an exogenous factor, we consider reviews both influencing and influenced by movie sales. The consideration of the endogenous nature of online user reviews significantly changes the analysis. Our result shows that the rating of online user reviews has no significant impact on movies' box office revenues after accounting for the endogeneity, indicating that online user reviews have little persuasive effect on consumer purchase decisions. Nevertheless, we find that box office sales are significantly influenced by the volume of online posting, suggesting the importance of awareness effect. The finding of awareness effect for online user reviews is surprising as online reviews under the analysis are posted to the same website and are not expected to increase product awareness. We attribute the effect to online user reviews as an indicator of the intensity of underlying word-of-mouth that plays a dominant role in driving box office revenues.
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.
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.
Human beings can be proactive and engaged or, alternatively, passive and alienated, largely as a function of the social conditions in which they develop and function. Accordingly, research guided by self-determination theory has focused on the social-contextual conditions that facilitate versus forestall the natural processes of self-motivation and healthy psychological development. Specifically, factors have been examined that enhance versus undermine intrinsic motivation, self-regulation, and well-being. The findings have led to the postulate of three innate psychological needs--competence, autonomy, and relatedness--which when satisfied yield enhanced self-motivation and mental health and when thwarted lead to diminished motivation and well-being. Also considered is the significance of these psychological needs and processes within domains such as health care, education, work, sport, religion, and psychotherapy.
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.
This research documents a substantial disconnect between the objective quality information that online user ratings actually convey and the extent to which consumers trust them as indicators of objective quality. Analyses of a dataset covering 1,272 products across 120 vertically-differentiated product categories reveal that average user ratings (1) lack convergence with Consumer Reports scores, the most commonly used measure of objective quality in the consumer behavior literature, (2) are often based on insufficient sample sizes which limits their informativeness, (3) do not predict resale prices in the used-product marketplace, and (4) are higher for more expensive products and premium brands, controlling for Consumer Reports scores. However, when forming quality inferences and purchase intentions, consumers heavily weight the average rating compared to other cues for quality like price and the number of ratings. They also fail to moderate their reliance on the average user rating as a function of sample size sufficiency. Consumers’ trust in the average user rating as a cue for objective quality appears to be based on an “illusion of validity.”
We investigate the evolution of online ratings over time and sequence. We first establish that there exist two distinct dynamic processes, one as a function of the amount of time a book has been available for review and another as a function of the sequence of reviews themselves. We find that, once we control for calendar date, the residual average temporal pattern is increasing. This is counter to existing findings that suggest that without this calendar-date control, the pattern is decreasing. With respect to sequential dynamics, we find that ratings decrease: the nth rating is, on average, lower than the n.1th when controlling for time, reviewer effects, and book effects. We test and find some support for existing theories for this decline based on motivation. We then offer two additional explanations for this "order effect." We find support for the idea that one's ability to assess the diagnosticity of previous reviews decreases: when previous reviewers are very different, more reviews may thus lead to more purchase errors and lower ratings.
Studies of online word of mouth have frequently posited that the level of disagreement between existing product reviews can impact the propensity to review and the valence of future reviews. However, due to purchasing and reporting biases that result from unique facets of consumer behavior, the distribution of online reviews is frequently an amalgamation of two distributions: consumers who liked the product and consumers who did not. Consequently, statistical measures capturing only the dispersion of reviews, such as standard deviation, can be improved by a measure that specifically classifies reviews as belonging to these disjunct populations of consumers. We theoretically develop and empirically test a new measure of disagreement for online word of mouth using a new data set containing nearly 300,000 reviews for 425 movies over three years. We find this measure results in lower standard errors and has higher predictive power than standard deviation. Using this measure, we show that higher levels of disagreement among previously posted reviews lead to a higher propensity to post future product reviews. This effect is amplified by the average length of prior reviews but is decreased by the product's availability in the market. Further, we show that increased disagreement leads to future reviews of lower valence.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
"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.
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.
If it is funny, it is mean: understanding social perceptions of yelp online reviews
- S Bakhshi
- P Kanuparthy
- D A Shamma
S. Bakhshi, P. Kanuparthy, D.A. Shamma, If it is funny, it is mean: understanding social perceptions of yelp online reviews, Paper presented at the Proceedings of the
18th International Conference on Supporting Group Work, Sanibel Island, Florida,
USA, 2014.
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.
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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.