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The power of expert identity: How website-recognized expert reviews influence travelers' online rating behavior
Abstract
The objective of this paper is to study the effects of online user-generated “expert reviews” on travelers' behavior. After controlling for a large set of independent variables, the results show that as the number of expert reviews for a hotel increases, future traveler ratings for the hotel increase. However, the marginal effect of a greater number of expert reviews decreases. Further, as the reviewing expertise level of a traveler increases, the traveler posts more negative ratings, but the marginal effect of the level of reviewing expertise decreases. Our results also indicate that reviewing expertise can reinforce the positive effect of expert hotel reviews on travelers' rating behavior. The robustness of our results is confirmed by a Bayesian ordered logit model. Finally, the implications of the results are discussed in terms of research and practice.
... Second, we explain the relationship between reviewing expertise and evaluative rating patterns. Extant research shows that compared to novices, experts are generally more critical (i.e., more negative) in their evaluations (Amabile 1983;Mollick and Nanda 2016;Schlosser 2005;Zhang, Zhang, and Yang 2016). Across our three field studies, we find this to be true, but only when reviewing experts evaluate service providers that generally provide excellent experiences (i.e., above 4.0 stars). ...
... Our findings complement and refine the conventional notion that expert recommendations highly affect consumer choice (Biswas, Biswas, and Das 2006;Chocarro and Cortiñas 2013;Karmarkar and Tormala 2010). Although the actual review content generated by experts is generally favored by consumers (Racherla and Friske 2012;Zhang et al. 2016), the attenuated impact experts have on the aggregate valence metric means that reviewing experts (vs. novices) have a less important role in shaping the service providers that consumers will consider before reading individual reviews (Ghose, Ipeirotis, and Li 2012;Vermeulen and Seegers 2009). ...
... A few studies have been published on reviewing experts (Liu and Park 2015;Zhang et al. 2016). Researchers have operationalized expertise in terms of number of past reviews generated; no overarching conceptual definition has yet been provided, and no empirical link has yet been tested, between online reviewing "expertise" and the traditional literature on expertise (Alba and Hutchinson 1987). ...
This research investigates reviewing experts on online review platforms. The main hypothesis is that greater expertise in generating reviews leads to greater restraint from extreme summary evaluations. The authors argue that greater experience generating reviews facilitates processing and elaboration, and enhances the number of attributes implicitly considered in evaluations, which reduces the likelihood of assigning extreme summary ratings. This restraint-of-expertise hypothesis is tested across different review platforms (TripAdvisor, Qunar, and Yelp), shown for both assigned ratings and review text sentiment, and demonstrated both between (experts vs. novices) and within reviewers (expert vs. pre-expert). Two experiments replicate the main effect and provide support for the attributes-based explanation. Field studies demonstrate two major consequences of the restraint-of-expertise effect. (i) Reviewing experts (vs. novices), as a whole, have less impact on the aggregate valence metric, which is known to affect page-rank and consumer consideration. (ii) Experts systematically benefit and harm service providers with their ratings. For service providers that generally provide mediocre (excellent) experiences, reviewing experts assign significantly higher (lower) ratings than novices. This research provides important caveats to the existing marketing practice of service providers incentivizing reviewing experts, and provides strategic implications for how platforms should adopt rating scales and aggregate ratings.
... According to China Internet Network Information Center, as of December 2021, the size of online travel booking users in China reached 397 million, which accounts for 38.5% of the overall Internet users. Due to the popularity of social media platforms in the travel and hospitality sectors, clients are constantly searching online reviews when looking for pertinent information on websites for online travel agencies like C-Trip [3][4][5]. ...
With the economic development in China, the hotel industry has grown along with the tourism industry. However, with the emergence of hotels of the same type and the renewal of consumers' consumption habits, online travel agency platforms are becoming more and more valuable as a guideline for consumers. This study analyzes users’ tendency in the process of consumption experience and the relationship between review texts and ratings of two five-star hotels close to each other to address this phenomenon. This paper mainly uses the Jieba splitter and TF-IDF algorithm to analyze. Accroding to the analysis, users are more concerned about facilities, services, and hygiene, while high-frequency words are negatively correlated with ratings indicating that user reviews are mainly complaint-oriented. This paper explores the service focus of the hospitality industry in the new business model from the platform reviews and provides guidance for future five-star hotels to improve their ratings and positive reviews in a targeted manner.
... Some current Chinese social media platforms, such as Ctrip, Qunar, and Dianping, can provide a large number of tourists' comments on landscape preferences. The social media reviews contain tourists' comments on the characteristics, strengths and weaknesses of attractions, as well as their subjective feelings (e.g., service quality, comfort, environmental conditions, etc.), which can provide valuable data for assessing the relationship between CESs and landscape characteristics (Zhang et al., 2016). ...
Rural landscapes offer a variety of cultural ecosystem services (CESs). However, the relationship between rural landscape characteristics and different CESs is still poorly understood. Therefore, this study explored the rural areas of Huzhou city, China, as a case study to assess the main rural landscape characteristics of different CESs based on public preferences. First, 148 scenic spots were classified into four CESs (physical, experiential, intellectual and inspirational), and the public preferences for each scenic spot were determined by combining tourists' scores obtained from social media and government assessment scores. Then, the landscape characteristic indicators were constructed from the natural, infrastructural and sensory perspectives by combining geographic and social media data. Finally, the random forest model was used to evaluate the public preferences for rural landscape characteristics overall and for different CESs. The word frequency analysis showed that, in addition to the nature landscape, infrastructure and service had a strong influence on public preferences. The relationship with rural landscape characteristics varied across different CESs. For physical CESs, the convenience of infrastructure played a greater role than natural landscape characteristics. Experiential CESs, on the other hand, were affected by natural landscape characteristics themselves. Intellectual CESs had higher requirements for both infrastructure and nature. Inspirational CESs included sensory evaluation indicators, in addition to their focus on natural landscape characteristics and infrastructure, indicating that this category of CESs was more concerned with inner experience. The use of social media data has enriched the dimensions of sensory elements and provided new ideas and information supplements for comprehensively understanding different CESs, thus better supporting the management, planning and protection of rural landscapes.
... As a result, promoting them can help companies stay competitive, as it certainly influences their consumers in terms of decision making (Hennig-Thurau andWalsh 2003, Zhang, Zhang, andYang 2016). Consumer comments has much to offer to RTE in terms of customer-focused evaluation, analyzing the customer benefits in detail in the end-use segments and allowing to work retroactively from the customer to the RTE, identifying the necessary actions to improve performance (Day, Foz, and Huszagh 1988). ...
In the service industry, especially in tourism, the majority of the customer surveys and feedback take place online, in addition, the information generated by the market alone is not enough, as it does not guarantee that it will be used or well used. That is why it is important to better understand the effect that customers' online reviews, market intelligence, businesses' online visibility, and management response have on business. For this study, the authors collected information about the establishment (customer comments, management responses, night prices, visibility data (advertising expenditures) on the infomediary website, web presence expertise, number of accommodations, capacity, and rental type) from Toprural, a leading rural tourism infomediary website, related to 237 French rural tourism establishments (RTE). A complementary survey provided information about the accommodation establishments' performance (reputation and profitability) and the market intelligence. Consequently, the results reveal that our model helps to clarify how customers position RTE based on their positive perceptions of experience, accommodation strategic variables and the entrepreneur´s skills that increase the volume of positive customer messages, and will help the rural company to better understand how the customer positions RTE based on their positive perceptions and what are their characteristics. Finally, these findings has responded to the concerns and suggestions of others authors, as well as for establishment owners, regarding the need to deepen the study of the current links between the specific RTE positioning based on their perceptions, provided in review comments, the defined business strategy and entrepreneurial behavioral skills.
Purpose: This study explores the review structure of the Online Reviews Sites & Online Travel Agencies and examines the differences among the websites in terms of online travel reviews (OTRs) and their peripheral cues. This research also explores review usefulness across these platforms. Methodology: The dataset consisted of 4895 online travel reviews about five tourism products available on four platforms. The analysis was done using SPSS 28.0 through cross-tabulation, Chi-Square, and ANOVA. Findings: The results indicated a considerable difference in the review structure across the four platforms. The users of these websites adopted different approaches while revealing their identities online. However, similarities were found in the case of review type, sidedness, and rating. On all websites, the Utilitarian and Regular reviews were the highest in numbers, and the maximum of tourists gave four and five ratings to tourism products. Moreover, in terms of useful OTRs, the platforms had significant differences. Practical Implications: The results are substantial for the tourism products and service providers (the OTAs) and the review sites. Further, managerial implications are provided to assist the online platform's administrators in making the review sections of websites more user-friendly and effective for potential tourists. The study is concluded along with the future directions of research
Repeat customers are crucial for business success. Previous studies have mainly focused on those factors that affect repeat patronage but ignored how repeat customers reevaluate the same service provider after consumption. We obtained a dataset containing 637,748 reviews of restaurants in New York City and used a generalized difference-in-differences design to further explore the rating behavior of local repeat customers. The results of this study contribute to theories of customer satisfaction, repeat patronage, and customer location in the context of user-generated content as repeat customers are found to be sensitive to quality variations. Such sensitivity is even accentuated by local customers. Relevant practical implications for restaurant managers are also drawn from the results.
Social media platforms have become an important tool for travel advertisement. This study constructs the bounded confidence model to build an improved cross-platform competitive travel advertising information dissemination model based on open and closed social media platforms. Moreover, this study examines the evolution process of group opinions in cross-platform information dissemination with simulation experiments. Results reveal that based on strong relationships, the closed social media platform opinion leaders better guide in competitive travel advertising and can bring more potential consumers to follow. However, being an opinion leader on an open social media platform will not result in more consumer following.
Urban recreationists interact at various locations at various times, resulting in potential conflicts. For sustainable urban tourism, this study extends the resident–tourist comparison into that of local, domestic, and overseas tourists and applies social network analysis to compare the spatiotemporal patterns of these three types of tourists. Results show that locals are accustomed to visiting natural parks and hutongs on weekends, while out-of-town tourists might tour popular sites in the city center during holidays, where domestic and overseas tourists may still encounter each other in hot spots. Only the flow of overseas tourists contradicts the distance attenuation effect.
This study examines the price competition, experiential quality and online marketing decisions of a tourism supply chain, consisting of a tourism operator and two online retailers. The equilibrium solutions and the conditions for unique optimal solutions are derived in three supply chain strategies. This study also investigates performance improvement measures, including the operator’s involvement in online marketing and online reviews for quality improvement. An experimental analysis is conducted to compare supply chain performance and provide tourism managerial insights. The investigation provides a reference for tourism policies that could assist in improving experiential quality and operation performance.
A flourishing of the importance of customer reviews has been observed in this digital era. This is especially true in hotel sector, which allows guests to express their satisfaction towards the service in the form of open-structured online reviews and overall ratings over travel agency websites. Using reviews data of 2001 hotels from Tripadvisor.com, the chapter analyzes the overall hotel performances through linguistic features of e-WOM such as its length, readability, sentiment, and volume. The chapter develops a regression model for evaluating guest satisfaction by using overall ratings as its measure, validated through hotel review data. Data analysis result shows that review volume, sentiment index, and readability have significant positive affect over guest satisfaction whereas length shows the negative influence. This chapter discusses beneficial implications for researchers and practitioners working in this field.
Customer-to-customer (C2C) interactions can be realised through other customers’ responses (OCRs) in the online review system. However, the externality of such interactions has not been clearly understood. We address this deficiency by exploring the effects of OCRs on subsequent review volume. Drawing on social learning theory and reinforcement theory, we hypothesise that OCRs have positive effects on subsequent review volume through the visibility mechanism. We verify this hypothesis by matching restaurants across two online platforms using a difference-in-differences strategy. The results show that restaurants with OCRs show an average increase of 5.286 reviews per month, but the effect fades if subsequent customers have not observed OCRs in the last five review pages. We further validate the visibility mechanism and conduct robustness checks by examining review heterogeneity and performing within-platform analysis. Our findings highlight the importance of C2C interactions and provide implications for the design and management of online review systems.
With the prevalence of online review websites, understanding online reviewer characteristics has become important, as such an understanding provides brand managers with opportunities to segment their markets, target influencers, and develop effective marketing strategies. Nonetheless, past studies have overlooked the role of network structural positions in the characteristics of online reviewers. Accordingly, using data from Yelp websites as samples, this study attempted to explore the differences in reviewer characteristics by network structural positions. The study used multiple data collection and analysis approaches, including web scraping, network analysis, and statistical analysis. The results of this study showed that compared to peripheral reviewers, core reviewers exhibited significantly more photos and brands reviewed and included a higher proportion of early reviewers. The study has significant theoretical and practical implications for researchers and brand managers who are interested in understanding online review markets.
The analysis of the opinions and experiences of tourists is a key issue in tourist promotion. More precisely, forecasting whether a tourist will or will not recommend a given destination, based on his/her profile, is of utmost importance in order to optimize management actions. According to this idea, this research proposes the application of cutting-edge machine learning techniques in order to predict tourist recommendation of rural destinations. More precisely, classifiers based on supervised learning (namely Support Vector Machine, Decision Trees, and [Formula: see text]-Nearest Neighbor) are applied to survey data collected in the province of Burgos (Spain). Available data suffer from a common problem in real-life datasets (data unbalance) as there are very few negative recommendations. In order to address such problem, that penalizes learning, data balancing techniques have been also applied. The satisfactory results validate the proposed application, being a useful tool for tourist managers.
Purpose
Building on a small body of work, the authors' study aims to investigate some important antecedents of online review characteristics in the Chinese restaurant industry.
Design/methodology/approach
Using a data set of restaurant reviews collected from a most popular review platform in China, the authors conduct a series of analyses to examine the influence of travel experience and travel distance on travelers' review characteristics in terms of review rating and media richness. The moderating effect of restaurant price on the influence is also investigated.
Findings
Travelers with a longer travel distance and more travel experience tend to provide higher and lower online ratings, respectively, which can be explained by the construal level theory (CLT) and the expectation-confirmation theory (ECT), respectively. Furthermore, these strong feelings can then induce travelers to post enriched reviews with more pictures, more words and more affective words to release consumption tension. Besides, restaurant price can moderate these relationships.
Originality/value
Distinguished from most studies which mainly focus on the consequences of online review characteristics or antecedents of review helpfulness, the authors pay attention to the effects of travelers' individual differences in terms of travel distance and travel experience on travelers' online reviewing behavior. In addition to review rating, the authors also focus on media richness in terms of visual and textual information. The authors' research findings can benefit restaurant consumers and managers for their online word-of-mouth utilization and management.
High-dimensional limit theorems have been shown useful to derive tuning rules for finding the optimal scaling in random walk Metropolis algorithms. The assumptions under which weak convergence results are proved are, however, restrictive: the target density is typically assumed to be of a product form. Users may thus doubt the validity of such tuning rules in practical applications. In this paper, we shed some light on optimal scaling problems from a different perspective, namely a large-sample one. This allows to prove weak convergence results under realistic assumptions and to propose novel parameter-dimension-dependent tuning guidelines. The proposed guidelines are consistent with the previous ones when the target density is close to having a product form, and the results highlight that the correlation structure has to be accounted for to avoid performance deterioration if that is not the case, while justifying the use of a natural (asymptotically exact) approximation to the correlation matrix that can be employed for the very first algorithm run.
The intercultural nature of tourism makes interpersonal communication a critical element that influences individuals’ experience and, in turn, their satisfaction. The existing research predominantly focuses on indirect communication (such as advertising). This study contributes to the literature by examining the effect of the reviews of language use on overall satisfaction and by looking into the dyad formed by the extreme vs. moderate character of the reviews and their sign (positive vs. negative). The analysis of 48,491 online reviews shows the effect of language use opinions on overall satisfaction, with extreme opinions having a more significant impact than moderate opinions. A more interesting and relevant impact is the departure from the well-established cognitive negativity bias characteristics of online reviews: extreme reviews have a symmetric impact on satisfaction, and moderate reviews present asymmetric effects. Both outcomes are a deviation from this cognitive bias, and relevant implications are derived.
Many prior studies confirmed the effectiveness of applying managerial response strategy on the performance of service providers. However, studies focusing on the effect of the specific types of managerial response remain limited. Thus, based on a longitudinal dataset, the present study uses a machine-learning method to identify the apologetic responses and investigates their effectiveness on hotels’ subsequent review volume. Results show that the proportion of reviews that received apologetic responses significantly improved the hotels’ future review volume. This positive effect is amplified if hotels previously have more expert reviews, more reviews from leisure travelers, and higher overall ratings. Hotel managers can improve the effectiveness of apologetic response strategies by mostly targeting negative reviews and writing the response contends, including the information of explanation and commitment. The findings of this study can present implications to relevant literature and active hotel managers.
By deploying big data analytical techniques to retrieve and analyze a large volume of more than 2.7 million reviews, this work sheds light on how environmental concerns expressed by tourists on digital platforms, in the guise of online reviews, influence their satisfaction with tourism and hospitality services. More specifically, we conduct a multi-platform study of Tripadvisor.com and Booking.com online reviews (ORs) pertaining to hotel services across eight leading tourism destination cities in America and Europe over the period 2017–2018. By adopting multivariate regression analyses, we show that OR ratings are positively influenced by both the presence and depth of environmental discourse on these platforms. Theoretical and managerial contributions, and implications for digital platforms, big data analytics (BDA), electronic word-of-mouth (eWOM) and environmental research within the tourism and hospitality domain are examined, with a view to capturing, empirically, the effect of environmental discourse presence and depth on customer satisfaction proxied through online ratings.
Culture has been confirmed to play an important role in the process of online word-of-mouth generation. To extend the knowledge of extant literature, we answer the question that whether food culture can influence tourist reviewers’ online rating behavior considering both the food culture in their residence and that in their destination. We find that tourist reviewers are more likely to give a lower online restaurant rating when they experience a food culture in the destination consistent with that in their residence. In addition, they tend to give more weight on the taste dimension than on the environment and service dimensions when they experience a food culture consistency. We also find that reviewers’ travel experience can strengthen the negative relationship between food culture consistency and online ratings. These findings have both theoretical and managerial implications.
Purpose: This study aims to enrich the published literature on hospitality and tourism by applying big data analytics and data mining algorithms to predict travelers' online complaint attributions to significantly different hotel classes (i.e., higher star-rating and lower star-rating). Design/methodology/approach: First, 1992 valid online complaints were manually obtained from over 350 hotels located in the UK. The textual data were converted into structured data by utilizing content analysis. Ten complaint attributes and 52 items were identified. Second, a two-step analysis approach was applied via data-mining algorithms. For this study, sensitivity analysis was conducted to identify the most important online complaint attributes, then decision tree models (i.e., the CHAID algorithm) were implemented to discover potential relationships that might exist between complaint attributes in the online complaining behavior of guests from different hotel classes. Findings: Sensitivity analysis revealed that Hotel Size is the most important online complaint attribute, while Service Encounter and Room Space emerged as the second and third most important factors in each of the four decision tree models. The CHAID analysis findings also revealed that guests at higher-star-rating hotels are most likely to leave online complaints about (i) Service Encounter, when staying at large hotels; (ii) Value for Money and Service Encounter, when staying at medium-sized hotels; (iii) Room Space and Service Encounter, when staying at small hotels. Additionally, the guests of lower-star-rating hotels are most likely to write online complaints about Cleanliness, but not Value for Money, Room Space, or Service Encounter, and to stay at small hotels. Practical implications: By utilizing new data-mining algorithms, more profound findings can be discovered and utilized to reinforce the strengths of hotel operations to meet the expectations and needs of their target guests. Originality/value: The study's main contribution lies in the utilization of data-mining algorithms to predict online complaining behavior between different classes of hotel guests.
With the popularity of online reviews, brand managers have opportunities to segment their markets according to the reviews of their products or services by customers. Nonetheless, it has been suggested that traditional market segmentation methods are ineffective at analyzing online review data due to the complex features and large amount of this type of data; specifically, traditional methods fail to take into account the networked nature of interactive relationships among reviewers and brands across online review websites. Accordingly, this study proposes a network analysis approach for the market segmentation of online reviews. Collecting samples from Yelp via web scraping, this study demonstrates how network analysis techniques can be utilized to segment online reviewers through a four-step process. The results reveal the core and peripheral market segments, as well as the bridge segment in the core. The study contributes to offering marketing researchers and managers a new network structure analysis approach for the market segmentation of online reviews.
Social media and User-Generated Content contribute to the hospitality sector. Consumers usually post reviews, suggestions, or judgements on hotel booking websites regarding their accommodation during their travel and afterward. This article investigates the determinants that affect users’ trust in shared information related to travel acquired from social media or tourism sites. Data was obtained from 266 SNS users. Data was analyzed using both the Structural Equation Model (SEM) and the neural network model. Results show that perceived enjoyment and perceived value were the most critical factors that affected SNS users’ trust in the shared information about travel from social media or tourism sites. This paper provides valuable insight into travelers’ behavior and managerial implications of sharing information from SNS. From SNS, tourism managers should increase the delightful and fulfilling features of their websites to gain more shared information about travel.
This study proposes a hybrid method for producing personalized travel recommendation that better meet travellers’individual needs and also improve their online booking experience. The proposed method integrates multi-attribute collaborative filtering with social network analysis within the framework of large-scale group decision-making. It includes four modules, i.e. identification of online opinion experts, construction of a social network, detection of user communities, and interactively produced of personalized travel recommendation. Specifically, the preliminary user filtering and k-means clustering approach are utilized to identify the online opinion experts for a specific travel recommendation issue. Then, social network construction and its community detection process are adopted to alleviate the sparsity problem. Finally, the travel alternatives are ranked to select recommendations, and this is done interactively with travellers to handle the cold start problem. With the proposed method, a better online booking experience can be achieved for travellers, as they are presented with a more appropriate set of recommended options and so can make better travel decisions.
This research assesses the influence of social media usage (SMU) on the performance of the bed and breakfast (B&Bs) sector in terms of profitability during the COVID-19 Era. In addition, it tests the mediating role of owner-managers’ orientation toward social media policy implementation on the link between SMU and performance. A quantitative approach is implemented using an online questionnaire to gather the primary data from respondents in Southern Italy. The results illustrated that SMU has a positive and significant impact on top management orientation toward social media policy implementation and B&Bs performance. Moreover, it is revealed that top management orientation partially mediates the association between SMU and performance. The current study adds to knowledge within the tourism and hospitality domain by indicating the role of using social media in enhancing the performance of B&Bs sector. It also provides clear guidelines on how to benefit from social media networks in the operations of tourism and hospitality enterprises.
Hotel bookings are increasingly made online, and many travellers rely on eWOM in the form of peer hotel reviews. These reviews potentially contain information of great relevance to the tourism industry and offer a unique and ever-expanding corpus of unsolicited data. If this data is investigated systematically, it may provide insights that would enable hotel managers to be proactive in their marketing. The present study focuses on the under-researched area of the potential impact of nationality on the reviews. Using a corpus of authentic American and German hotel reviews and the qualitative, phenomenologically-inspired method of Systematic Text Condensation, this study investigates the impact of national culture on review comments in order to establish whether nationality makes a difference for the themes and attitudes expressed. The data indicate that Americans are more likely to focus on old-world charm, romance, physical comfort, personal service/relations and problem-solving than Germans are. The overall results of this qualitative study allow us to conclude that there are indeed differences between the German and the American reviews to a degree that is worth pursuing in future mixed-methods research and that may have practice implications for hotel managers.
Although many firms deem weather conditions relevant to customers’ decision making and satisfaction, firms often struggle to quantify the impact of weather on customers’ online evaluation behavior. By combining hourly weather data and online review data from an online booking platform, this study found distinct review-related effects of rainy weather on customers’ numerical review ratings versus textual review content. Specifically, customers’ ratings and engagement in textual reviews were lower when reviews were written while it was raining. Also, rainy weather had second-order interactive effects on customers’ consumption experiences: favorable experiences weakened the negative effects of rainy weather on ratings, whereas unfavorable experiences mitigated customers’ unwillingness to post longer textual reviews in rainy weather. Compared with review ratings, textual reviews were less likely to be influenced by rainy weather as indicated by the emotional intensity of reviews. Theoretical and practical implications of these findings are also discussed.
With the popularity of user-generated content (UGC), an increasing number of studies have investigated its impact on business performance. However, prior studies were limited to a single platform and showed the effects of UGC of a platform, for example, customer textual comments or customer numeral ratings, on sales/reservation of the same platform. In practice, users often refer to a UGC, for example, Instagram, and purchase it on other platforms. To incorporate the spillover effect, we considered the restaurant industry because it has active participation across various channels. Using topic modelling, we first identified from Instagram four topics of users’ interest regarding a restaurant, such as location, nightlife, food and celebration. From fixed effects models’ estimation, we found that (a) recommendation and mention of Instagram have positive effects, and (b) comments of location and food also have positive significant fixed effects, but (c) the impact of Instagram volume is curvilinear and positive significant effect the sales. Since the curvilinear effects may come from reverse causality, that is, higher reservation, might bring more customers and comments on social networking service (SNS) (echo verse effect in our paper). Therefore, we further analysed two-way Granger causality and panel vector autoregression to identify the endogeneity, and the results showed the existing Granger causality loop between OpenTable review and Instagram post volumes.
Tourism e-commerce is a multidisciplinary research area that involves tourism management and e-commerce. This paper provides a review of 1960 scientific studies published over the past two decades (1999-2018). This study presents a multidisciplinary comparative study, namely, a systematic review of tourism e-commerce, aiming to provide a reference guide for researchers. Under the methodological framework of a domain analysis, this paper analysed the scientific outputs and visualized the co-cited references, burst keywords, keyword co-occurrences and topic characteristics of journals. The results show that the tourism management discipline represents the main body of tourism e-commerce research and that this research demonstrates a rapid growth trend consistent with overall research. There are two main clusters of representative co-cited references in the tourism management discipline. However, in the e-commerce discipline, the distribution of co-cited references is scattered, lacking obvious cluster characteristics. The keyword co-occurrences in both disciplines present four themes, including network and information technology, consumer behaviour and social media as common themes. Tourism management journals can be divided into two categories, while e-commerce journals can be divided into three categories. Overall, the findings of this study can benefit researchers and practitioners.
Purpose
Can Chinese social media data (SMD) be used as an alternative to traditional surveys used to understand tourists' visitation of attractions in Chinese cities? The purpose of this paper is to explore this question.
Design/methodology/approach
Popular tourism SMD sources in China, such as Ctrip, Weibo and Dazhong Dianping (DZDP), were used as data source, and the relationships between these sources and traditional data sources were studied with statistical methods. Data from Shanghai were used in this study since it is rich in tourism resources and developed in information.
Findings
A systematic research method was followed and led to the following conclusions: There were positive correlations for attraction visitation between Chinese SMD and traditional survey data; Chinese SMD source could temporally indicate visits to Shanghai tourist attractions; Ctrip SMD generally performed less well than Weibo or DZDP, and different SMD performed differently depending on the specific attractions and time units in the visitation calculation process; and factors including visitation, distance from the city center and the grade of attractions might affect the prediction performance based on data from the SMD. The findings suggest that Chinese SMD could be used as a cost-efficient and reliable proxy for traditional survey data to predict Chinese attraction visitation.
Originality/value
This study applies and improves the methods of SMD reliability in attraction use studies, supplies the gap for premise, basis and foundation for the large amounts of tourism researches using SMD in China and could promote and inspire more efficient and advanced measures in tourism management and urban development.
With the rapid development of information technology and market economy, global e-commerce platform develops rapidly. Recently, online reviews are widely available on e-commerce platforms to express customers’ experience of products. When ranking alternative products based on online reviews, how to make full use of the information in online reviews to represent the sentiment analysis results of online reviews is an important prerequisite for decision analysis. To this end, we propose a method for measuring the time utility and support utility of online reviews. Then a method for representing the sentiment analysis results of online reviews in the form of linguistic distribution is proposed. In addition, in view of the attributes and their weights being unknown, we propose a method for extracting product attributes from online reviews by using the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm; and the objective weights of attributes are determined through the Criteria Importance through Intercriteria Correlation (CRITIC) method. Additionally, in order to highlight the differences between the alternatives, the roulette wheel selection algorithm is first used to randomly select product attributes. Then the alternative products can be ranked by the extended Multi-Attributive Border Approximation area Comparison (MABAC) method with mixed information. Finally, we illustrate the applicability of the proposed method through a case study of selecting a 5G mobile phone and simulation experiment.
Air pollution has become a major hurdle to tourists' experiences in many destinations. This study uses a large sample of TripAdvisor reviews for Chinese attractions and applies a five-way fixed-effects model to estimate the impact of air pollution on attraction ratings. Results show that an increase in the PM2.5 concentration led to a decline in foreign tourists' 5-point ratings of their experiences. Several factors appear to moderate this effect, such as tourists' past travel experience, year of travel, attraction types, tourists' national cultural traits, and air pollution in tourists' home countries. Tourists are more vulnerable to air pollution when coming from countries with more feminine and collectivist cultures and countries with lower air pollution. Across six air pollutants, only PM2.5 and PM10 concentrations are found to significantly influence foreign tourists’ experiences. Lastly, practical implications are provided.
With the development of e-commerce, online booking has become an important process for tourists in many tourism activities. Consequently, a number of online travel accommodation booking sites have emerged, allowing users to book travel-related services and leave their comments and reviews on the service. These kinds of information will influence the decision of the latecomers. Therefore, it is necessary to understand what key characteristics of online hotel reviews will affect travellers' booking decisions, and then discuss the impact of online review system on the enterprise's knowledge management in the hotel industry. This study first identified six factors from the literature, then collected data by using online questionnaire survey, and finally analysed the influencing factors of online booking decision by using multiple regression method. The experimental results showed that only three factors are important in the booking decision. They are quantity of review, content of review, and number of useful comments.
Purpose
This paper aims to analyze if and to what extent mechanical artificial intelligence (AI)-embedded in hotel service robots-influences customers’ evaluation of AI-enabled hotel service interactions. This study deploys online reviews (ORs) analytics to understand if the presence of mechanical AI-related text in ORs influences customers’ OR valence across 19 leading international hotels that have integrated mechanical AI – in the guise of service robots – into their operations.
Design/methodology/approach
First, the authors identified the 19 leading hotels across three continents that have pioneered the adoption of service robots. Second, by deploying big data techniques, the authors gathered the entire population of ORs hosted on TripAdvisor (almost 50,000 ORs) and generated OR analytics. Subsequently, the authors used ordered logistic regressions analyses to understand if and to what extent AI-enabled hospitality service interactions are evaluated by service customers.
Findings
The presence of mechanical AI-related text (text related to service robots) in ORs influences positively electronic word-of-mouth (e-WOM) valence. Hotel guests writing ORs explicitly mentioning their interactions with the service robots are more prone to associate high online ratings to their ORs. The presence of the robot’s proper name (e.g., Alina, Wally) in the OR moderates positively the positive effect of mechanical AI-related text on ORs ratings.
Research limitations/implications
Hospitality practitioners should evaluate the possibility to introduce service robots into their operations and develop tailored strategies to name their robots (such as using human-like and short names). Moreover, hotel managers should communicate more explicitly their initiatives and investments in AI, monitor AI-related e-WOM and invest in educating their non-tech-savvy customers to understand and appreciate AI technology. Platform developers might create a robotic tag to be attached to ORs mentioning service robots to signal the presence of this specific element and might design and develop an additional service attribute that might be tentatively named “service robots.”
Originality/value
The current study represents the first attempt to understand if and to what extent mechanical AI in the guise of hotel service robots influences customers’ evaluation of AI-enabled hospitality service interactions.
Extant literature has studied the relationship between identity disclosure and online consumer reviews, yet no research has investigated the effects of the introduction of an “anonymous review” option on consumers' online reviews. With a unique restaurant review data set from a most popular online review platform in China, we exploit a natural experiment setup in which the platform launched a new function of “anonymous review” that allows reviewers' choosing not to disclose their own information while posting reviews. We ground in the theories of deindividuation and social presence, and the notion of negativity bias to investigate the effects of anonymity on consumers' online review provision in terms of their rating behavior and emotional expressions in review texts. The results show that the anonymity function implementation induces a decrease in overall ratings and a decrease (increase) in positive (negative) emotions on average. Furthermore, using the reviews after the policy change, we find that, compared to non‐anonymous reviews, anonymous ones are usually with lower ratings and more (less) negative (positive) emotions, which can further induce subsequent reviewers' negative reviews. Our findings underscore the doubled‐sides of an “anonymous review” function the public policy makers, platform and restaurant managers need to balance in practice.
What is the most useful online consumer review? Consumers face the challenge of identifying useful online consumer reviews, some of which might have been manipulated. Reviewer’s review volumes is the past behavior of a reviewer. Reviews are used by many online shopping websites. While reviewer cues are receiving increasing attention, the effect of reviewer’s review volumes on the perceived usefulness of online consumer review remains under-explored. This research explored the underlying neural mechanism of reviewer’s review volumes on the perceived usefulness of online consumer reviews by applying the event-related potentials (ERPs) approach. Behaviorally, a higher perceived usefulness rate and reduced reaction time were observed for the high-review-volume condition compared to the low-review-volume condition, indicating that the high-review-volume condition was more desirable to the participants. At the neural level, the high-review-volume condition elicited an attenuated N2 and an increased LPP compared with the low-review volume condition, suggesting a more positive evaluation of the high-review volume condition. These results showed the positive impact of the reviewer’s review volumes on the perceived usefulness of online consumer reviews and its potential neural substrates.
This study investigates the effect of hotel website quality, social presence, affective commitment, and e-trust on travelers’ online hotel booking intentions. Smart-PLS software was applied to test the hypotheses. The results of this study explain how hotel website quality, social presence, affective commitment, and e-trust have a significant relationship with online hotel booking intentions. This study identifies how hotel website quality is a critical driver in developing affective commitment, e-trust, and online hotel booking intentions. Travelers who enjoy the social experience with the hotel website platform are more motivated to book a room at that hotel. This study confirms the vital role of social presence in enhancing e-trust and online hotel booking intentions and provides evidence of the importance of the holistic model in understanding travelers’ decision-making processes, especially when booking a hotel room.
Hospitality service providers' reputation and sales can be strongly damaged by low ratings on platforms such as Tripadvisor, Booking, or Yelp. In this research, we investigate how the potential damaging effects of poor ratings of a hospitality service providers' can be mitigated by corporate social responsibility (CSR) activities. Building on the self-affirmation theory, we suggest and provide evidence for the notion that CSR (vs. non-CSR) cues affect more positively guests’ evaluation of a service provider when online ratings are low. When the ratings increase, CSR cues are not better than other cues to improve evaluations. Further, we detail the underlying mechanism whereby guests allocate more weight to CSR (vs. non-CSR) cues when they form an evaluation of a low-rated (vs. higher-rated) service provider, which increases the warm-glow feelings that they anticipate and, eventually, their evaluation. We test our model with two experiments on two different samples (US and European), involving different CSR cues (environmental and social) and different types of service provider (hotel and restaurant). We then discuss the theoretical and managerial implications of our research for hospitality professionals, as well as for policy makers.
Purpose
The purpose of this study is to explore the effect of reviewer qualification and credibility (RQC) and hotel classification involving online hotel reviews (OHRs). The study examines the effects of the reviewer level as a proxy of RQC on review helpfulness and reviewing behavior (review rating, review length). The study also included hotel classification as a moderating variable.
Design/methodology/approach
Data from 1,968 reviews were collected from TripAdvisor.com using a web data-harvesting technique. Hypothesized relations in the model were tested with t -test and MANOVA analysis.
Findings
The empirical results show that the effect of reviewer level on review helpfulness is not significant. In addition, a high-level reviewer tends to leave a lower rating and a lengthier review than a low-level reviewer. Regarding the moderating effects, for the high-level reviewer, three-star independent hotels have a greater effect on review helpfulness.
Research limitations/implications
The study has several useful implications for researchers, hotel industry when managing OHR and disseminating information to their potential consumers.
Practical implications
The findings help online review website organizers manage the operation of RQC and hotel classification in a proper manner. Marketing managers, especially those of three-star independent hotels, can effectively utilize review management to the desired effect.
Originality/value
Unlike previous studies, this study explores the effect of RQC on review helpfulness and reviewing behaviors across the hotel classification. In addition, this study contributes to the hotel industry developing more effective online reviews from the reviewer level and diverse hotel types (three-star independent, four-star chain, five-star luxury hotels).
Purpose
Online hotel ratings, a form of electronic word of mouth (eWOM), are becoming increasingly important to tourism and hospitality management. Using sentiment analysis based on the big data technique, this paper aims to investigate the relationship between customer sentiment and online hotel ratings from the perspective of customers’ motives in the context of eWOM, and to further identify the moderating effects of review characteristics.
Design/methodology/approach
The authors first retrieve 273,457 customer-generated reviews from a well-known online travel agency in China using automated data crawlers. Next, they exploit two different sentiment analysis methods to obtain sentiment scores. Finally, empirical studies based on threshold regressions are conducted to establish the asymmetric relationship between customer sentiment and online hotel ratings.
Findings
The results suggest that the relationship between customer sentiment and online hotel ratings is asymmetric, and a negative sentiment score will exert a larger decline in online hotel ratings, compared to a positive sentiment score. Meanwhile, the reviewer level and reviews with pictures have moderating effects on the relationship between customer sentiment and online hotel ratings. Moreover, two different types of sentiment scores output by different sentiment analysis methods verify the results of this study.
Practical implications
The moderating effects of reviewer level and reviews with pictures offer new insights for hotel managers to make different customer service policies and for customers to select a hotel based on reviews from the online travel agency.
Originality/value
This paper contributes to the literature by applying big data analysis to the issues in hotel management. Based on the eWOM communication theories, this study extends previous study by providing an analysis framework for the relationship between customer sentiment and online hotel ratings from the perspective of customers’ motives in the context of eWOM.
Studies based on online textual data are increasingly used in the hospitality sector to gain better consumer insights. Using text mining of TripAdvisor reviews and mapping it to hard and soft attributes of SERVQUAL dimensions, this research attempts to identify service quality characteristics that influence the customers' online ratings of hotels. The moderating effects of the reviewer characteristics, namely (a) domestic vs foreign (b) novice vs experienced, (c) less vs more popular and (d) brief vs elaborate writing, on the relationship between quality attributes and online hotel ratings are also analyzed. The results imply that reviewers emphasize hard attributes of service quality in general, such as tangibles, reliability, and responsiveness, while providing online ratings to hotels. Furthermore, reviewer characteristics also influence the attributes emphasized in reviews and the resultant ratings. Finally, along with the theoretical contributions, managerial contributions, such as the usefulness of service planning outcomes, are discussed.
Whilst there is extant research focusing on the link between hotel reviews and pricing there is scant attention paid to the impact of pricing on hotel review ratings. This paper presents a large scale study based on a novel dataset of more than 44,000 guest review ratings linked to the prices paid for rooms of an European hotel group. Through applying a panel regression analysis we reveal that expectancy-disconfirmation is generally stronger than the placebo effect and higher prices have a negative effect on review ratings, not only when assessing perceived value for money but also when evaluating perceived quality. We identify the trip and guest characteristics which impact on the price-review relationship. Findings provide hotel managers with key quantitative information for simultaneously calibrating quality and revenue targets. Substantial implications include an integrated approach for segmentation as a route to better manage pricing, revenue optimization and reputation.
Tourist destinations are increasingly affected by travel-related information shared through social media. Drawing on dual-process theories on how individuals process information, this study examines the role of central and peripheral information processing routes in the formation of consumers' perceptions of the helpfulness of online reviews of mature destinations. We carried out a two-step process to address the perceived helpfulness of user-generated content, a sentiment analysis using advanced machine-learning techniques (deep learning), and a regression analysis. The database was 2023 comments posted on TripAdvisor about two iconic Venetian cultural attractions, St. Mark's Square (an open, free attraction) and the Doge's Palace (which charges an entry fee). Using deep-learning techniques, with logistic regression, we first identified which factors influenced whether a review received a “helpful” vote. Second, we selected those reviews which received at least one helpful vote to identify, through linear regression, the significant determinants of TripAdvisor users' voting behaviour. The results showed that reviewer expertise is influential in both free and paid-for attractions, although the impact of central cues (sentiment polarity, subjectivity, pictorial content) differs for both attractions. Our study suggests that managers should look beyond individual ratings and focus on the sentiment analysis of online reviews, which are shown to be based on the nature of the attraction (free vs. paid-for).
Tendencias en la Investigación Universitaria. Una visión desde Latinoamérica, es una obra de producción científica de la Colección Unión Global, adscrita al Fondo Editorial Universitario Servando Garcés de la Universidad Politécnica Territorial de Falcón Alonso Gamero (UPTAG). Es una publicación internacional, seriada, continua, arbitrada con apertura a todas las áreas del conocimiento. En el volumen IX, ofrece XV capítulos de alta calidad intelectual y científica generados por diversos investigadores que muestran los resultados del quehacer académico, científico, tecnológico y humanístico desarrollado en los diferentes escenarios de la sociedad. En este sentido el volumen IX del libro Tendencias, consciente de la dinámica compleja e incierta de las realidades que enfrenta la ciencia, presenta una diversidad temática orientada a dar respuestas a las necesidades de la sociedad actual, por lo que el CAPÍTULO I, centra su discurso en el “Turismo en el corredor de balnearios para niños con limitaciones significativas en el funcionamiento intelectual, motriz y sensorial” con el fin de identificar el potencial que tienen los balnearios particularmente los del Corredor de Balnearios en el estado de Hidalgo para atender a la población con características especiales. En el CAPÍTULO II, se aborda el área de educación superior como una de las herramientas que poseen los país para enfrentar el fenómeno de la globalización, de allí que, se planteen “Estrategias de los organismos internacionales para el sistema de educación superior”, cuyo objetivo fue reflexionar sobre cómo la educación superior es vista por los organismos internacionales analizando las directrices del Banco Mundial (BM), hasta la Organización para la Cooperación y Desarrollo Económico (OCDE) y Organización de las Naciones Unidas para la Educación, la Ciencia y la Cultura (UNESCO).
Internet techniques significantly influence the tourism industry and Internet data have been used widely used in tourism and hospitality research. However, reviews on the recent development of Internet data in tourism forecasting remain limited. This work reviews articles on tourism forecasting research with Internet data published in academic journals from 2012 to 2019. Then, the findings ae synthesized based on the following Internet data classifications: search engine, web traffic, social media, and multiple sources. Results show that among such classifications, search engine data are most widely incorporated into tourism forecasting. Time series and econometric forecasting models remain dominant, whereas artificial intelligence methods are still developing. For unstructured social media and multi-source data, methodological advancements in text mining, sentiment analysis, and social network analysis are required to transform data into time series for forecasting. Combined Internet data and forecasting models will help in improving forecasting accuracy further in future research.
“Tendencias en la Investigación Universitaria. Una visión desde Latinoamérica”, es una obra de producción científica de la Colección Unión Global, adscrita al Fondo Editorial Universitario Servando Garcés de la Universidad Politécnica Territorial de Falcón Alonso Gamero (UPTAG). Es una publicación internacional, seriada, continua, arbitrada con apertura a todas las áreas del conocimiento. En el volumen IX, ofrece XV capítulos de alta calidad intelectual y científica generados por diversos investigadores que muestran los resultados del quehacer académico, científico, tecnológico y humanístico desarrollado en los diferentes escenarios de la sociedad. En este sentido el volumen IX del libro Tendencias, consciente de la dinámica compleja e incierta de las realidades que enfrenta la ciencia, presenta una diversidad temática orientada a dar respuestas a las necesidades de la sociedad actual.
“Tendencias en la Investigación Universitaria. Una visión desde Latinoamérica”, es una obra de producción científica de la Colección Unión Global, adscrita al Fondo Editorial Universitario Servando Garcés de la Universidad Politécnica Territorial de Falcón Alonso Gamero (UPTAG). Es una publicación internacional, seriada, continua, arbitrada con apertura a todas las áreas del conocimiento. En el volumen IX, ofrece XV capítulos de alta calidad intelectual y científica generados por diversos investigadores que muestran los resultados del quehacer académico, científico, tecnológico y humanístico desarrollado en los diferentes escenarios de la sociedad. En este sentido el volumen IX del libro Tendencias, consciente de la dinámica compleja e incierta de las realidades que enfrenta la ciencia, presenta una diversidad temática orientada a dar respuestas a las necesidades de la sociedad actual.
Consumers are increasingly relying on electronic word-of-mouth, such as online reviews, before making purchase decisions. To reduce uncertainty about the validity of a review, consumers draw on different attributes of online review platforms, including the overall aggregated rating and perceived expertise of the review writer. This research aims to verify whether the aggregated rating or a single expert review is more convincing. To investigate the power of expert reviews, two scenario-based experiments were conducted in order to compare the aggregated rating with a single conflicting review. The results indicate that a single expert review can nullify the influence of the aggregated rating on purchase intention, and that the influence of the conflicting expert review differs depending on the reader’s attitude towards online reviews. The findings indicate that expert reviewers play an important role on eWOM platforms and can be helpful for service providers and customers alike.
Since the use of social networks has become part of daily life for most people, many online travel agencies (OTAs) are allowing customers to log in using account credentials for social network services like Facebook (social network interface systems, SNIS) without having to create a local account on OTA sites themselves. This research explores the roles of SNIS in review generation on OTAs. For this purpose, we collected more than 174,000 online reviews for London hotels, posted from 2015 to 2016 on TripAdvisor.com. Through a series of analyses, we find that (1) concerning review generation, the portion of online reviews posted via SNIS shows no significant increase during the period; (2) reviews posted via SNIS tend to have lower ratings and be shorter than reviews posted via local accounts; (3) the channel itself, whether SNIS or a local account, does not have a direct impact on perceived review helpfulness.
Understanding what drive users’ website stickiness is of strategic importance for e-tourism managers. This study examines the role of a neglected construct ‘perceived fashionability’ in forming users’ e-tourism website stickiness in comparison with four commonly studied rational factors: system quality, information quality, security and e-shopping value. Drawing upon dual-system theories and social influence theories, a conceptual model with hypotheses was developed and tested. A sample of 376 e-tourism website users in China participated in this study. The results indicate that perceived fashionability has a positive impact on user's stickiness to the website, and perceived fashionability also performs a mediating role between website security and stickiness. This study contributes to theory by explaining that website stickiness is not entirely driven by rationality but also perceived fashionability. Managerial implications for e-tourism strategies are provided.
The authors reflect on management of big data by organizations. They comment on service level agreements (SLA) which define the nature and quality of information technology services and mention big data-sharing agreements tend to be poorly structured and informal. They reflect on the methodologies of analyzing big data and state it is easy to get false correlations when using typical statistical tools in analyzing big data. They talk about the use of big data in management and behavior research.
While the proliferation of online review websites facilitate travellers' ability to obtain information (decrease in search costs), it makes it difficult for them to process and judge useful information (increase in cognitive costs). Accordingly, this study attempts to identify the factors affecting the perceived usefulness of online consumer reviews by investigating two aspects of online information: (1) the characteristics of review providers, such as the disclosure of personal identity, the reviewer's expertise and reputation, and (2) reviews themselves including quantitative (i.e., star ratings and length of reviews) and qualitative measurements (i.e., perceived enjoyment and review readability). The results reveal that a combination of both messenger and message characteristics positively affect the perceived usefulness of reviews. In particular, qualitative aspects of reviews were identified as the most influential factors that make travel reviews useful. The implications of these findings contribute to tourism and hospitality marketers to develop more effective social media marketing.
Critics and their reviews pervade many industries and are particularly important in the entertainment industry. Few marketing scholars, however, have considered the relationship between the market performance of entertainment services and the role of critics. The authors do so here. They show empirically that critical reviews correlate with late and cumulative box office receipts but do not have a significant correlation with early box office receipts. Although still far from any definitive conclusion, this finding suggests that critics, at least from an aggregate-level perspective, appear to act more as leading indicators than as opinion leaders.
The business case for investing in online reputation has received increasing scrutiny in recent years. This study identifies the business value of consumer reviews and management responses to hotel performance. We present a panel data analysis of online consumer reviews and management responses of 843 hotels on a hotel review website. The results show that overall rating, attribute ratings of purchase value, location and cleanliness, variation and volume of consumer reviews, and the number of management responses are significantly associated with hotel performance. In addition, variation and volume of consumer reviews moderate the relationship between overall rating and hotel performance. Management responses, together with variation and volume of consumer reviews, moderate the relationship between certain attribute ratings and hotel performance. Implications of utilizing consumer reviews and management responses to leverage hotel business are provided.
FULL TEXT AVAILABLE: https://elements.vu.edu.au/repository/files/?rep=1&pub=24583&file-url=http%3A%2F%2Fvuir.vu.edu.au%2Frt4eprints%2Ffile%2F16595%2F2008%2520JHMM%2520UGC%2520Article%2520Post%2520print.pdf
This article reports on research that investigates how websites containing user-generated content (UGC) are used by consumers and the role they have on their information search and travel behavior. The research, conducted via an online survey, represents the views of more than 12,000 hospitality and tourism consumers. The results suggest that although these sites are popular, they are not yet considered to be as credible or trustworthy as existing sources of travel information such as government-sponsored tourism websites. UGC (or Web 2.0 sites, as they are also known) appear to act as an additional source of information that travelers consider as part of their information search process, rather than as the only source of information. The study is significant in that it appears to be one of the few investigations that captures the perceptions of the travel consumer and the way they relate to the information value associated with Web 2.0 sites.
With the emergence of Web 2.0, electronic word-of-mouth (eWOM) shared through social networking sites has become the primary information source for many travelers. An enormous quantity of reviews has been posted by customers, and has become a valuable means by which hoteliers can better understand customer satisfaction and expectations. Efforts have been made to analyze these parameters in terms of customers’ backgrounds. Although customer expectations vary according to background, whether or not this is still the case across different trip modes remains unknown. In this study, an eWOM dataset was obtained from an online source and sentiment mining used to improve its quality by imputing missing values. A complete analysis of customer profiles and their contrast by trip mode was then conducted using association rule mining. The empirical results demonstrate differences in both customer expectation and satisfaction when the same traveler engages in different trip modes.
Electronic word of mouth (eWOM) communication, such as the online hotel review, is receiving increasing attention from marketing managers in the hotel industry, primarily because consumers are making considerable use of online forums and often do not book without seeking online advice on restaurants, tourist destinations, or hotels. This study of a convenience sample of 781 travelers in Las Vegas found three chief motivating factors for consumers to seek eWOM, namely, convenience and quality, risk reduction, and social reassurance. The analysis found distinct differences between the sexes regarding their motivating factors, and levels of expertise also influenced consumers’ motivations to read online reviews. Women, for instance, are more likely to read reviews for the purpose of convenience and quality and for risk reduction. Men’s use of the online reviews depended on their level of expertise.
Millennials have heavily influenced social media's evolution into an important source of product information. They are increasingly basing their product evaluations on information gathered from online reviews. Thus, companies targeting Millennials may wish to pay heed to online reviews. Which products are affected by Millennials’ online reviews? Do the reviews tend to be positive or negative? Where do Millennial customers look for information amongst the plethora of online venues? As presented herein, a survey of 227 Millennials reveals that this generation is undeniably posting reviews online and being influenced by these reviews. Respondents show a definite preference for two online venues, Facebook and company websites, when voicing their opinions. Reviews are broken down by positive and negative comments, and how product categories fare for each type. Contrary to popular thinking, the respondents were more prone to post positive reviews than negative reviews. Males voice their opinions online significantly more often than females, and specific gender differences are observed by product category. Recommendations are provided for selling to Millennials by leveraging online reviews.
The authors investigate how critics affect the box office performance of films and how the effects may be moder- ated by stars and budgets. The authors examine the process through which critics affect box office revenue, that is, whether they influence the decision of the film going public (their role as influencers), merely predict the deci- sion (their role as predictors), or do both. They find that both positive and negative reviews are correlated with weekly box office revenue over an eight-week period, suggesting that critics play a dual role: They can influence and predict box office revenue. However, the authors find the impact of negative reviews (but not positive reviews) to diminish over time, a pattern that is more consistent with critics' role as influencers. The authors then compare the positive impact of good reviews with the negative impact of bad reviews to find that film reviews evidence a negativity bias; that is, negative reviews hurt performance more than positive reviews help performance, but only during the first week of a film's run. Finally, the authors examine two key moderators of critical reviews, stars and budgets, and find that popular stars and big budgets enhance box office revenue for films that receive more neg- ative critical reviews than positive critical reviews but do little for films that receive more positive reviews than neg- ative reviews. Taken together, the findings not only replicate and extend prior research on critical reviews and box office performance but also offer insight into how film studios can strategically manage the review process to enhance box office revenue.
Recent press reports and complaints by hoteliers have called into question the credibility of travel-related user-generated content (UGC). Yet our understanding of the role of credibility perceptions in the context of UGC is limited, notwithstanding the rising interest in social media. Using an online survey of 661 travel consumers, this study applies the component-based structural equation modeling technique of partial least squares to examine online travelers’ perceptions of the credibility of UGC sources and how these perceptions influence attitudes and intentions toward UGC utilization in the travel planning process. The model also draws on the theory of homophily to make predictions about the antecedent of credibility. Results validate the Source Credibility Theory in the context of UGC but also accentuate the central mediating role of attitude. In addition, the study finds support for perceptual homophily as a critical determinant of both credibility and attitude. Theoretical and managerial implications are highlighted.
A conceptual model articulating the nature and determinants of customer expectations of service is proposed and discussed.
The model specifies three different types of service expectations: desired service, adequate service, and predicted service.
Seventeen propositions about service expectations and their antecedents are provided. Discussion centers on the research implications
of the model and its propositions.
Purpose – This study aims to examine whether and how hotel class, attributes of the room, quality, location, cleanliness, and service influence room rates in hotels. Design/methodology/approach – Regression models were developed for the hotel industry and for various grades of hotels. Findings – Using data from New York, empirical findings suggest that room quality and location are important determinants of room price for the industry, but attributes that can influence room rates differ greatly among hotel segments. Practical implications – Hotels can reap benefits from understanding customers' specific expectation of a market segment and seeking to provide amenities accordingly. Originality/value – The quality of hotel attributes is evidenced through customer reviews on a travel advice website. The theory of the hierarchy of needs is supported in the hotel industry, namely, the ascending order of accommodation needs are the quality of a room, location, and service.
Proposes to develop a service quality model, based on test of a sample of business executives, which describes how the quality of services is perceived by customers. Looks at its marketing implications, in which functional quality is seen to be a very important dimension of a perceived service. Concludes that quality dimensions are interrelated and that the importance of image should be recognised.
Analyzed the social perception process to determine whether selectivity of available stimuli is based on the informativeness of person attributes, the properties of which are the evaluative extremity (distance from the scale midpoint) and the evaluative valence (positive or negative). In a preliminary scaling study and a main weighting study, 126 undergraduates independently rated the likability of the 16 stimuli persons portrayed on slides. Negativity and extremity were manipulated on the slides across 2 behavioral dimensions: sociability and civic activism. Ss saw 2 prescaled behavior photographs for each stimulus person and controlled a slide changer switch, providing a measure of attention as looking time. Likability ratings provided a measure of relative weight derived from N. H. Anderson's (1968) information integration model. Results show that Ss preferentially weighted behaviors that were extreme or negative, and the behavioral measure of attention (looking time) replicated the predicted pattern. (58 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
The use of the WWW as a venue for voicing opinions, complaints and recommendations on products and firms has been widely reported in the popular media. However little is known how consumers use these reviews and if they subsequently have any influence on evaluations and purchase intentions of products and retailers. This study examines the effect of negative reviews on retailer evaluation and patronage intention given that the consumer has already made a product/brand decision. Our results indicate that the extent of WOM search depends on the consumer's reasons for choosing an online retailer. Further the influence of negative WOM information on perceived reliability and purchase intentions is determined largely by familiarity with the retailer and differs based on whether the retailer is a pure-Internet or clicks-and-mortar firm. Managerial implications for positioning strategies to minimize the effect of negative word-of-mouth have been discussed.
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.
Social media are playing an increasingly important role as information sources for travelers. The goal of this study is to investigate the extent to which social media appear in search engine results in the context of travel-related searches. The study employed a research design that simulates a traveler's use of a search engine for travel planning by using a set of pre-defined keywords in combination with nine U.S. tourist destination names. The analysis of the search results showed that social media constitute a substantial part of the search results, indicating that search engines likely direct travelers to social media sites. This study confirms the growing importance of social media in the online tourism domain. It also provides evidence for challenges faced by traditional providers of travel-related information. Implications for tourism marketers in terms of online marketing strategies are discussed.
The emergence of User-Generated-Content (UGC) is challenging the equilibrium of reputation management practices prompting widespread change and organizational restructuring. Formal accreditation schemes mingle with less formal mechanisms which place users in the middle making them draw their own conclusions about products and services. This paper explores how UGC reviews and ratings have intensified the contingency of organizational reputation in the travel sector. The findings are based upon a corpus of data including: a field study at the offices of the largest travel user-generated website, TripAdvisor, and a netnographic approach. In particular we discuss the shift from Word-Of-Mouth to eWOM, the consequences for the sector and provide a balanced view of the role of reviews, ratings and lists. We are concluding with a conceptual model for managing online reputation in the era of UGC, while acknowledging the current process of transformation in reputation management.
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.
Impression management, the process by which people control the impressions others form of them, plays an important role in interpersonal behavior. This article presents a 2-component model within which the literature regarding impression management is reviewed. This model conceptualizes impression management as being composed of 2 discrete processes. The 1st involves impression motivation-the degree to which people are motivated to control how others see them. Impression motivation is conceptualized as a function of 3 factors: the goal-relevance of the impressions one creates, the value of desired outcomes, and the discrepancy between current and desired images. The 2nd component involves impression construction. Five factors appear to determine the kinds of impressions people try to construct: the self-concept, desired and undesired identity images, role constraints, target's values, and current social image. The 2-component model provides coherence to the literature in the area, addresses controversial issues, and supplies a framework for future research regarding impression management.
This article describes a research agenda for investigating the relationship between tourism and the Chinese Dream, informed by the G20 First East-West Dialogue on Tourism and the Chinese Dream held in late 2014 on Australia’s Gold Coast. Six themes comprise this agenda, including (1) clarification of the “Chinese Dream” construct, (2) connections between the Dream, tourism and consumers, (3) geopolitical considerations, (4) conventional Triple Bottom Line sustainability considerations, (5) tourism and nontourism systems as open states, and (6) hybrid research perspectives. All themes are permeated by an implicit seventh theme of rapid change.
Online product reviews are increasingly important for consumer decisions, yet we still know little about how reviews are generated in the first place. In an effort to gather more reviews, many websites encourage user interactions such as allowing one user to subscribe to another. Do these interactions actually facilitate the generation of product reviews? More importantly, what kind of reviews do such interactions induce? We study these questions using data from one of the largest product review websites where users can subscribe to one another. By applying both panel data and a flexible matching method, we find that as users become more popular, they produce more reviews and more objective reviews; however, their numeric ratings also systematically change and become more negative and more varied. Such trade-off has not been previously documented and has important implications for both product review and other user-generated content websites.
In this paper, we use unique data from the market for Bordeaux wine to test the hypothesis that consumers are willing to pay for expert opinion because it is accurate. Using proprietary indicators of the quality of the vintage, which are based on both publicly and privately available information, we find that additional publicly available information on the weather improves the expert's predictions of subsequent prices. This establishes that the expert opinions are not efficient, in the sense that they can be easily improved, and that these opinions must be demanded, at least in part, for some purpose other than their accuracy. (JEL Classification: D8, Q13)
The rapid growth of information generated by consumers of tourism and hospitality services calls for a systematic review of how user-generated content (UGC) has been applied in tourism and hospitality research. This study surveyed 122 peer-reviewed journal articles and conference proceedings that used UGC as a data source. The study investigates (a) the scope of the tourism and hospitality issues that are addressed using available UGC; (b) the methods that have been applied to UGC data to achieve research objectives; and (c) the software that has been used to collect UGC and extract information from large UGC data sets. The study also presents the emerging topics and challenges in UGC research.
Third-party product reviews (TPRs) have become ubiquitous in many industries. Aided by communication technologies, particularly on the Internet, TPRs are widely available to consumers, managers, and investors. The authors examine whether and how TPRs of new products influence the financial value of firms introducing the products. An event study covering 14 major media and professional reviews of movies released by 21 studios shows that TPRs exert significant impact on stock returns in the direction of their valence. However, the impact comes from the valence of a review that is measured relative to other, previously published reviews and not from the absolute valence of the review itself. The authors further study the dynamics of TPR impact on firm value and find that the impact exists only for prerelease reviews and is the strongest on the product release date, though it disappears when sales information becomes available after product release. These results demonstrate that TPRs play significant roles as the investors update their expectation about new product sales potential. The authors also find that advertising spending increases the positive impact of TPRs on firm value and buffer the negative impact. Therefore, firms could strategically use marketing instruments such as advertising to moderate the impact of TPRs.
This book provides a compact self-contained introduction to the theory and application of Bayesian statistical methods. The book is accessible to readers havinga basic familiarity with probability, yet allows more advanced readers to quickly grasp the principles underlying Bayesian theory and methods. The examples and computer code allow the reader to understand and implement basic Bayesian data analyses using standard statistical models and to extend the standard models to specialized data analysis situations. The book begins with fundamental notions such as probability, exchangeability and Bayes' rule, and ends with modern topics such as variable selection in regression, generalized linear mixed effects models, and semiparametric copula estimation. Numerous examples from the social, biological and physical sciences show how to implement these methodologies in practice. Monte Carlo summaries of posterior distributions play an important role in Bayesian data analysis. The open-source R statistical computing environment provides sufficient functionality to make Monte Carlo estimation very easy for a large number of statistical models and example R-code is provided throughout the text. Much of the example code can be run as is' in R, and essentially all of it can be run after downloading the relevant datasets from the companion website for this book.
Customer reviews posted on the web and through social media (electronic word of mouth [eWOM]) have grown in importance for tourism businesses, but most studies have examined the effects of the content of reviews, particularly negative reviews (i.e., their valence). This study considers both the valence and the volume of eWOM using a broad and varied sample of 16,680 hotels in 249 tourist areas. The study found a relationship between valence and volume, in the sense that early reviews of a business tend to be disproportionately negative. As the number of reviews increases, the valence becomes more balanced, and the negative effect is mitigated. Moreover, the study agrees with other findings that positive comments are more common than negative reviews. Whether or not hotels actively respond to negative reviews, one implication of this study is that hoteliers should try to increase the number of reviews they receive to balance the positive and negative representations of their property (in addition to investigating and correcting the causes of negative comments). Those promoting tourist destinations should follow a similar strategy of facilitating access to customer review sites to obtain a balance of negative and favorable ratings.
The objective of the paper is to study the impact that hotel guests reviews posted on consumer-generated websites have on the consumer decision-making process and service expectations.An experimental study has been conducted to test the hypotheses and the research question. 349 young adults were involved in an online survey that asked to imagine searching for a hotel and reading other customers’ reviews of a hypothetical chosen hotel. Three scenarios were created by studying a few comments posted by customers on the main websites used by tourists.Results show a positive correlation between both hotel purchasing intention and expectations of the customers and valence of the review. On the contrary, the presence of hotel managers’ responses to guests’ reviews has a negative impact on purchasing intentions.The study enriches the stream of research on word-of-mouth in the hospitality industry and analyses a new operational problem for lodging managers. Hotels should reply to online customer reviews or not?
The notion of electronic word-of-mouth (eWOM) communication has received considerable attention in both business and academic communities. Numerous studies have been conducted to examine the effectiveness of eWOM communication. The scope of published studies on the impact of eWOM communication is large and fragmented and little effort has been made to integrate the findings of prior studies and evaluate the status of the research in this area. In this study, we conducted a systematic review of eWOM research. Building upon our literature analysis, we used the social communication framework to summarize and classify prior eWOM studies. We further identified key factors related to the major elements of the social communication literature and built an integrative framework explaining the impact of eWOM communication on consumer behavior. We believe that the framework will provide an important foundation for future eWOM research work.
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.
This paper explores effects of the emotions embedded in a seller review on its perceived helpfulness to readers. Drawing on frameworks in the emotion and cognitive processing literatures, we propose that over and above a well-known negativity bias, the impact of discrete emotions in a review will vary, and that one source of this variance is reader perceptions of reviewers’ cognitive effort. We focus on the roles of two distinct, negative emotions common to seller reviews: anxiety and anger. In Studies 1 and 2, experimental methods were utilized to identify and explain the differential impact of anxiety and anger in terms of perceived reviewer effort. In Study 3, seller reviews from Yahoo! Shopping websites were collected to examine the relationship between emotional review content and helpfulness ratings. Our findings demonstrate the importance of examining discrete emotions in online word-of-mouth, and they carry important practical implications for consumers and online retailers.
A growing reliance on the Internet as an information source when making choices about tourism products raises the need for more research into electronic word of mouth. Within a hotel context, this study explores the role of four key factors that influence perceptions of trust and consumer choice. An experimental design is used to investigate four independent variables: the target of the review (core or interpersonal); overall valence of a set of reviews (positive or negative); framing of reviews (what comes first: negative or positive information); and whether or not a consumer generated numerical rating is provided together with the written text. Consumers seem to be more influenced by early negative information, especially when the overall set of reviews is negative. However, positively framed information together with numerical rating details increases both booking intentions and consumer trust. The results suggest that consumers tend to rely on easy-to-process information, when evaluating a hotel based upon reviews. Higher levels of trust are also evident when a positively framed set of reviews focused on interpersonal service.
Travel blogs are becoming an increasingly important mechanism for exchanging information among tourists, and for destinations and businesses to learn about the attitudes of their markets. The purpose of this research was to analyse blog entries relating to trips to Austria posted to www.travelblog.org. The research sought to establish similarities and differences between those posting blog entries and Austria's tourism markets. It analysed the content of blog entries to identify positive and negative perceptions of Austria as a tourism destination, and compared these with other research into Austria's destination image. The research found a largely homogenous population of blog authors that had quite different demographic and touristic characteristics from Austria's total population of visitors. Blog authors were visiting Austria as part of a larger European tour, and they provided relatively few insights that might assist in understanding Austria's destination image. The value of monitoring blogs may depend on locating blog authors who are more likely to influence key markets.
This study presents a characterisation of online reviews for small and medium hotels (SMH) in Portugal. We collected and analysed 1500 online reviews from 50 SMH. The findings show that room, staff and location were the concepts most frequently appeared in the reviews, with cleanliness, friendliness, helpfulness, and centrality of position the terms most frequently used to qualify the concepts. This study points out the main features which SMH managers should prioritize.
In this paper, the impact of two service quality metrics (star rating and customer rating) on hotel room sales and prices is investigated. Two of the most popular tourist destinations in the world, Paris and London, are chosen. It is found that a higher customer rating significantly increases the online sales of hotels. The study results show that a 1% increase in online customer rating increases Sales per Room up to 2.68% in Paris and up to 2.62% in London. Contrary to expectations, higher stars do not increase the sales. It is also shown that higher customer ratings result in higher prices of the hotel and the prices of high star hotels are more sensitive to online customer ratings.
Extant research has shown that consumer online product ratings can significantly influence product sales. However, these ratings have also been shown to be subject to a number of social influences. In other words, posted product ratings not only reflect the customers’ experience with the product, but they also reflect the influence of others’ ratings. The objective of this paper is to model the arrival of posted product ratings in an effort to measure the impact of any social dynamics that may occur in the ratings environment on both subsequent rating behavior as well as product sales. Our modeling efforts are two fold. First, we model the arrival of product ratings and separate the effect of social influences from the underlying (or baseline) ratings behavior. Second, we model product sales as a function of posted product ratings. However, rather than simply modeling the effects of observed ratings, we decompose ratings into a baseline rating, the contribution of social influence and idiosyncratic error. From this model, we can measure the overall sales impact resulting from observed social dynamics. We consider both direct effects on sales as well as indirect effects that result from the influence of dynamics on future ratings (and thus future sales).We show that ratings behavior is significantly affected by previously posted ratings. We further show that the effect on sales resulting from this social dynamic is significant. With the increased popularity of online discussion and ratings forums, many marketers have been investing in efforts to moderate these conversations or to contribute comments of their own in order to create a more positive ratings environment. However, our results show that while these efforts can directly improve sales, the overall effect is relatively short-lived once indirect effects are considered.
We document that purchasing (selling short) stocks with the most (least) favorable consensus recommendations, in conjunction with daily portfolio rebalancing and a timely response to recommendation changes, yield annual abnormal gross returns greater than four percent. Less frequent portfolio rebalancing or a delay in reacting to recommendation changes diminishes these returns; however, they remain significant for the least favorably rated stocks. We also show that high trading levels are required to capture the excess returns generated by the strategies analyzed, entailing substantial transactions costs and leading to abnormal net returns for these strategies that are not reliably greater than zero.
Although word-of-mouth (WOM) is recognized as a powerful force in persuasion,we know little about the new communication phenomenon known as e-WOM. One of the main forms of e-WOM is the product reviews consumers post on different Web sites, and how this form of e-WOM stands up to this claim is yet unknown. For example, do consumers trust the accuracy of these reviews posted by anonymous reviewers, and, do readers trust negative and positive reviews equally? Past research has shown that people tend to weight negative information more than positive information during evaluation.Through an observation study and two laboratory experiments,we investigate the existence of this negativity effect in e-WOM consumer reviews for utilitarian versus hedonic products,and investigate the influence of the reader's attributions regarding the reviewer's motivations on this. Both types of studies show that product type moderates the effect of review valence, and readers exhibit a negativity bias for utilitarian product reviews only.Furthermore, the lab studies show that the reader's attributions about the motivations of the reviewer mediate the effect of this moderation on their attitude about the review.We find that compared with the utilitarian case, readers of negative hedonic product reviews are more likely to attribute the negative opinions expressed, to the reviewer's internal (or nonproduct related) reasons; and therefore are less likely to find the negative reviews useful. However, in the utilitarian case, readers' are more likely to attribute the reviewer's negative opinions to external (or product related) motivations, and therefore find negative reviews more useful than positive reviews on average.
Despite a substantial literature on the economics of the arts, there has been virtually no analysis of the role of criticism in the market for the arts. This paper discusses a number of problems which emerge in attempting such an analysis.
Using edited excerpts from actual negative and positive book reviews, this research examined the hypothesis that negative evaluators of intellectual products will be perceived as more intelligent than positive evaluators. The results strongly supported the hypothesis. Negative reviewers were perceived as more intelligent, competent, and expert than positive reviewers, even when the content of the positive review was independently judged as being of higher quality and greater forcefulness. At the same time, in accord with previous research, negative reviewers were perceived as significantly less likable than positive reviewers. The results on intelligence ratings are seen as bolstering the self-presentational explanation of the tendency shown by intellectually insecure individuals to be negatively critical. The present methodology is contrasted to that of previous research which obtained apparently contradictory results.