Online Review Platforms Research Articles

The Effects of Online Review Platforms on Restaurant Revenue, Consumer Learning, and Welfare

This paper quantifies the effects of online review platforms on restaurant revenue and consumer welfare. Using a novel data set containing revenues and information from major online review platforms in Texas, I show that online review platforms help consumers learn about restaurant quality more quickly. The effects on learning show up in restaurant revenues. Specifically, doubling the review activity increases the revenue of a high-quality independent restaurant by 5%–19% and decreases that of a low-quality restaurant by a similar amount. These effects vary widely across restaurants’ locations. Restaurants around highway exits are affected twice as much as those in nonhighway areas, implying that reviews are more useful to travelers and tourists than locals. The effects also decline as restaurants age, consistent with the diminishing value of information in learning. In contrast, chain restaurants are affected to a much lesser degree than independent restaurants. Building on this evidence, I develop a structural demand model with aggregate social learning. Counterfactual analyses indicate that online review platforms raise consumer welfare much more for tourists than for locals. By encouraging consumers to eat out more often at high-quality independent restaurants, online review platforms increased the total industry revenue by 3.0% over the period from 2011–2015. This paper was accepted by Matthew Shum, marketing. Supplemental Material: Data and the online appendix are available at https://doi.org/10.1287/mnsc.2021.4279 .

The Asymmetric Effects of Minority-Owned Markers for Businesses on Online Review Platforms

The Asymmetric Effects of Minority-Owned Markers for Businesses on Online Review Platforms

Social Interactions and Peer Evaluation on Online Review Platforms: An Empirical Analysis

Social Interactions and Peer Evaluation on Online Review Platforms: An Empirical Analysis

An Effective Model for Forecasting Travel Consumer Demand Using Big Data Analysis

As a result of gathering information from multiple consumer centers, big data (BD) assists in analyzing traveler patterns and developing a unique marketing plan tailored to the target demographic. BD tourism forecasting is a relatively new academic field because of the challenges in capturing, gathering, and modeling this sort of data due to its inherent privacy and economic importance. The growth rate of cruise tourists has slowed down after years of rapid expansion. Investing in homeports, cruise ships, and promotional activities carries a growing danger of financial loss. To make investment decisions and prepare for the future, it is necessary to predict tourism demand. We present the least-squares vector regression (LSVR) model with the gravitational search method for forecasting demand for cruise tourism (FCT) based on BD to improve forecasting performance. As a part of the proposed model forecasting demand for cruise tourism based on big data (FDCT-BD), hyper-parameters of the LSVR model are improved using an algorithm and by comparing these models with various configuration combinations. This paper forecasts tourist arrivals based on internet BD from a search engine and online review platforms and the comparative advantage of multi-platform forecasting over single-platform forecasting based on online review data. However, the results show that the methodology’s recommended framework is successful and that BD may estimate cruise tourist demand with enhanced performance and accuracy 93.8% and 97.9%, respectively.

TripAdvisor의 효과적인 호텔 온라인 평판 관리 전략

Recently, hotel companies have been struggling with low economic growth, population decline and the oversupply of hotel markets. In particular, hotels are expected to suffer from external threats such as COVID-19. In this situation, the role of online word of mouth is becoming more important due to the influence of the Internet. While the importance of online reviews is emphasized from the perspective of hotel managers, current online review studies in the hospitality industry have been conducted primarily from the user's perspective and not many studies address online reviews from the perspective of hotel managers. Therefore, this study seeks to explore factors affecting ranking on TripAdvisor, one of the online review platforms. To present implications for the effective online review management strategy of the hotel. we attempted to explore the perspectives of hotel managers rather than the perspective of hotel customers A total of 103 hotel data were collected and the fuzzy-set Qualitative Comparative Analysis (fsQCA) was applied to analyze the data, resulting in a total of 4 types of patterns. We discovered four strategies to increase the rankings of TripAdvisor. First, in the case of a large hotel whose location is far from the airport, it is necessary to focus on the positive reviews. Second, if the latest number of reviews was not large, the manager's response was the most important regardless of the size or location of the hotel. Third, when the location of the hotel was close to the airport, manager's response was the most important. Lastly, in the case of a large hotel, it was most important to focus on the number of latest reviews. Based on the above strategy, practical implications for hotels to increase TripAdvisor's rankings were drawn and future research directions were presented.

Mapping Tourists’ Destination (Dis)Satisfaction Attributes with User-Generated Content

Although the tourism industry has increasingly used social media, there has been little empirical research in terms of the attributes of tourist satisfaction and dissatisfaction with user-generated contents. The purpose of this study is to explore the attributes of tourist satisfaction and dissatisfaction through user-generated contents. We collected data from online review platforms. Our data include historical online reviews, names of reviewers, ratings, location, helpful votes, date of visits, and contributions. In terms of results, the study found 30 key topics related to tourist satisfaction and dissatisfaction. Additionally, we found three clusters (i.e., holiday experience, attractions and facilities, and food experience). Lastly, we that suggested rating levels are different based on the type of tourists (i.e., domestic and international). This study provides discussions and implications for tourism research and industry practices.

Does certainty tone matter? Effects of review certainty, reviewer characteristics, and organizational niche width on review usefulness

Given the proliferation of online review websites—e.g., Yelp.com—that prominently display a large number of online custome0r reviews, scholars have made efforts to investigate what makes a review “useful.” However, there is little research that offers insight into how review content, reviewer characteristics, and review contexts jointly influence review usefulness. We specially examine the role of review certainty on review usefulness. Drawing on dual-process and social influence theories, we examine the interaction effects of review certainty, reviewer popularity, reviewer expertise, and the niche width of a restaurant on review usefulness. In particular, this study focuses on how review certainty interacts with other contextual factors to influence users’ evaluation of the usefulness of online reviews. Utilizing a zero-inflated negative binomial Poisson regression, we empirically tested our hypotheses based on 10,097 reviews on 2,383 restaurants from Yelp.com. Our results indicated that (1) the impact of review certainty on review usefulness decreases with reviewer popularity but does not vary with reviewer expertise and (2) the niche width of a restaurant—as a contextual feature—interacts with review certainty and reviewer characteristics in influencing review usefulness. Theoretically, these findings contribute to online customer review literature and certainty literature, as well as social media research, provide new guidelines for predicting review usefulness, and add new insights into understanding the role of organizational positioning for customer evaluations. In practice, our findings can help online review platforms better understand how to screen and select useful reviews for visitors.

Engaging voluntary contributions in online review platforms: The effects of a hierarchical badges system

Employing a hierarchical badges system to sustain users' participation and content contribution has been a common practice in online review platforms. However, it is still unclear whether this incentive mechanism is effective in inducing users to provide more substantial and useful reviews. In this paper, we draw on the goal-setting theory and status hierarchies theory to study how the hierarchical badges system impacts users’ behavior such as their effort levels and expressed opinions, using the data of 234,220 reviews from a large online review platform. Our results show that users significantly increase their efforts as their status are promoted, manifesting as review frequency, length, and readability. In terms of expressed opinions, users with higher badges are less likely to post extremely negative ratings and use negative emotional words. Our results have important implications for business modes that rely on user-generated content, such as online review platforms, social question and answer sites, social media, and crowdsourcing systems.

Consumers with specialised and diverse experience produce more helpful reviews

PurposeExperienced reviewers in general can produce high-quality product reviews, and thereby get more helpful votes. This paper explores the question that whether the depth and width of the reviewers' experience distribution have effects on the helpfulness of their reviews.Design/methodology/approachAdopting the restaurant review data from Yelp, the present paper classifies the restaurants in to different categories applying the Word2Vec technique, such as Asian or fast food. By evaluating the number of a user's historical reviews in a specific category, and the evenness of such distribution in different categories, the experience specialty and experience diversity are defined respectively.FindingsThe analysis shows that users specialised in a given category can produce more helpful reviews in that category. The users with diverse historical experience, i.e. have posted reviews for many categories, also can produce helpful reviews. In addition, the experience diversity shows a positive moderation effect on the influence of experience specialty. Thus, users with diverse experience while specialized in a particular category are the source of most helpful reviews.Originality/valueWhile previous studies mostly consider the raw number of historical reviews as a reviewer's experience, we distinguish such experience by product category and focus on the width and depth of its distribution. The results not only shed lights on the mining of high-quality reviews and reviewers but also provide insights on the management of online review platforms and electronic marketing.

The Majority of Complaints About Orthopedic Sports Surgeons on Yelp Are Nonclinical

PurposeTo examine and characterize extremely negative Yelp reviews of orthopedic sports surgeons in the United States.MethodsA search for reviews was performed using the keywords “Orthopedic Sports Medicine” on Yelp.com for 8 major metropolitan areas. Single-star reviews were isolated for analysis, and individual complaints were then categorized as clinical or nonclinical. The reviews were classified as surgical or nonsurgical.ResultsA total of 11,033 reviews were surveyed. Of these, 1,045 (9.5%) were identified as 1-star, and 289 were ultimately included in the study. These reviews encompassed 566 total complaints, 133 (23%) of which were clinical, and 433 (77%) of which were nonclinical in nature. The most common clinical complaints concerned complications (32 complaints; 6%), misdiagnosis (29 complaints; 5%), and uncontrolled pain (21 complaints; 4%). The most common nonclinical complaints concerned physicians’ bedside manner (120 complaints; 21%), unprofessional staff (98 complaints; 17%), and finances (78 complaints; 14%). Patients who had undergone surgery wrote 47 reviews that resulted in 114 complaints (20.5% of total complaints), whereas nonsurgical patients were responsible for 242 reviews and a total of 452 complaints (81.3% of total complaints). The difference in the number of complaints by patients after surgery and patients without surgery was statistically significant (P < 0.05) for all categories except for uncontrolled pain, delay in care, bedside manner of midlevel staff, and facilities.ConclusionOur study of extremely negative Yelp reviews found that 77% of negative complaints were nonclinical in nature. The most common clinical complaints were complications, misdiagnoses and uncontrolled pain. Only 16% of 1-star reviews were from surgical patients.Clinical RelevancePatients use online review platforms when choosing surgeons. A comprehensive understanding of factors affecting patient satisfaction and dissatisfaction is needed. The results of our study could be used to guide future quality-improvement measures and to assist surgeons in maintaining favorable online reputations.

Innovation Analytics and Digital Innovation Experimentation: The Rise of Research-driven Online Review Platforms

Big data analytics constitute one of the driving forces of the fourth industrial revolution and represent one of the founding pillars of Industry 4.0. They are increasingly leveraged to create business insights from online reviews of products and services by a wide range of organizations and firms. In this work, we develop a typology of online review platforms (ORPs) and describe a novel platform, research-driven online review platform (RORP), that combines the science and rigor of very large-scale, low-cost, fast-paced, and complex digital experimentation using real-world customers on digital platforms with the power of modern AI-based big data analytics capabilities (BDAC) to generate novel innovation insights for the digital age. Using multiple real-world case studies, we illustrate how RORPs operate and deliver value through innovation analytics, and serve as a powerful tool for digital innovation experimentation, enabling firms to innovate more effectively and transform their business models to adapt to rapidly changing market conditions. We shed light on the BDAC requirements, as well as the benefits and challenges of using RORPs and innovation analytics, particularly in the post-COVID-19 world, and offer strategic and operational implications for entrepreneurs and innovation managers.

Artificial intelligence for hospitality big data analytics: developing a prediction model of restaurant review helpfulness for customer decision-making

Purpose Big data analytics allows researchers and industry practitioners to extract hidden patterns or discover new information and knowledge from big data. Although artificial intelligence (AI) is one of the emerging big data analytics techniques, hospitality and tourism literature has shown minimal efforts to process and analyze big hospitality data through AI. Thus, this study aims to develop and compare prediction models for review helpfulness using machine learning (ML) algorithms to analyze big restaurant data. Design/methodology/approach The study analyzed 1,483,858 restaurant reviews collected from Yelp.com. After a thorough literature review, the study identified and added to the prediction model 4 attributes containing 11 key determinants of review helpfulness. Four ML algorithms, namely, multivariate linear regression, random forest, support vector machine regression and extreme gradient boosting (XGBoost), were used to find a better prediction model for customer decision-making. Findings By comparing the performance metrics, the current study found that XGBoost was the best model to predict review helpfulness among selected popular ML algorithms. Results revealed that attributes regarding a reviewer’s credibility were fundamental factors determining a review’s helpfulness. Review helpfulness even valued credibility over ratings or linguistic contents such as sentiment and subjectivity. Practical implications The current study helps restaurant operators to attract customers by predicting review helpfulness through ML-based predictive modeling and presenting potential helpful reviews based on critical attributes including review, reviewer, restaurant and linguistic content. Using AI, online review platforms and restaurant websites can enhance customers’ attitude and purchase decision-making by reducing information overload and search cost and highlighting the most crucial review helpfulness features and user-friendly automated search results. Originality/value To the best of the authors’ knowledge, the current study is the first to develop a prediction model of review helpfulness and reveal essential factors for helpful reviews. Furthermore, the study presents a state-of-the-art ML model that surpasses the conventional models’ prediction accuracy. The findings will improve practitioners’ marketing strategies by focusing on factors that influence customers’ decision-making.

The Influence of Reviewer and Consumer Congruence in Online Word-of-Mouth Transactions

Online review platforms compete to attract and retain consumers and facilitate purchases. They invest in sophisticated algorithms that prioritize the order in which product reviews are presented, seeking to provide consumers with easy access to useful information about the ability of a product to meet a need. These algorithms collect and use review and consumer characteristics to provide different consumers with different sets of reviews, seeking to increase purchase intention and reduce returns. This study proposes a new type of factor: the congruence between the consumer and the reviewer. The authors develop a simulation-based experiment in which 281 subjects are categorized based on their travel style and socioeconomic status, then read two reviews: one that appears to be written by someone similar to them and one that appears written by someone different from them. Findings demonstrate relationships between purchase intention and both of the congruence variables, as well as disposition to trust and an interaction between uncertainty avoidance and socioeconomic status.

Crowd-sourced hospital ratings are correlated with patient satisfaction but not surgical safety

BackgroundHospital reviews posted online by patients are unsolicited and less structured than Hospital Consumer Assessment of Healthcare Providers and Systems surveys. The differences between online review platforms and their degrees of correlation with validated satisfaction and safety measures are unknown. MethodsWe identified 515 large acute care teaching hospitals in the United States. We collected patient satisfaction results and postsurgical patient safety indicators from Hospital Compare. We also collected hospital star ratings (1–5) from Facebook, Google, and Yelp. Mean ratings were compared with paired t tests. Concordance between ratings websites, Hospital Consumer Assessment of Healthcare Providers and Systems scores, and surgical safety indicators were assessed with Pearson’s correlation coefficient. ResultsMean Facebook ratings (3.81, interquartile range 3.5–4.3) were more favorable than Google (3.26, interquartile range 2.8–3.6) or Yelp (2.59, interquartile range 2.3–2.9). Facebook ratings were least strongly correlated with the Hospital Consumer Assessment of Healthcare Providers and Systems recommended hospital score (ρ = 0.356). Google was modestly correlated (ρ = 0.479), and Yelp was most strongly correlated (ρ = 0.500). The negative correlation between crowdsourced rating and composite safety indicator was too small to be meaningful on any platform. ConclusionThere is variation between platforms in consumer ratings of hospitals. Ratings on Facebook are more favorable than Google or Yelp. These are independently correlated with Hospital Consumer Assessment of Healthcare Providers and Systems scores. These findings suggest that unstructured consumer reviews generally reflect similar directionality as Hospital Consumer Assessment of Healthcare Providers and Systems satisfaction scores. Users should be aware of the significant difference between platforms. Consumer ratings platforms are not consistently correlated with postsurgical patient safety indicators, so online ratings may not reflect the safety of surgical care received.

City or beach hotel? Location as a determinant of customer satisfaction and room rate

Purpose This research aims to reveal that accessibility of hotels to major points of attraction in the urban–territorial continuum of Catalonia, with its urban and coastal landscapes, has a direct impact on the dependent variables of customer satisfaction and average room rate. Design/methodology/approach The study collected data from 84 of 4-star hotel establishments, divided into urban and coastal hotels. Both coastal and urban hotels were differentiated depending on their distance to, the beach and the urban city center, respectively. Customer satisfaction and average room rate data were retrieved from online review platforms. Findings The study proves that hotels located in the urban–territorial continuum of Catalonia have different behaviors in terms of customer satisfaction and hotel pricing according to the variables of distance and landscape. The study shows that room rate and customer satisfaction are both higher in urban landscapes than in coastal landscapes. Urban hotels present significant differences in their levels of customer satisfaction and room rates depending on their location in the city of Barcelona. However, coastal hotels do not represent significant differences in room rates depending on their location, although they do represent significant differences in terms of customer satisfaction. Originality/value The originality of this study is based on how a dynamic urban–territorial model consisting of Barcelona and the Maresme coast, the hotel location and distances to main interest points impact in the variables of customer satisfaction and average room rate.

The informational value of multi-attribute online consumer reviews: A text mining approach

In the digital age, customers use online reviews to minimize the risks associated with purchasing a product. Major online retailers help customers choose the right product by exposing reviews that received many “helpful” votes at the top of the review section. Given that reviews that have received the maximum helpfulness votes are considered more important in purchase decisions, understanding determinants of helpfulness votes offers clear benefits to online retailers and review platforms. This study focuses on the effect of review informativeness, which is measured by the number of attributes discussed in a review, and its interplay of review valence on customers' perception of review helpfulness. We applied a word-level bigram analysis to derive product attributes from review text and examined the influence of the number of attributes on the review's helpfulness votes. More importantly, we also suggested the moderating role of review valence. Estimation results of the Zero-inflated Poisson models on 21,125 reviews across 14 wireless earbuds indicated that as more attributes are discussed in a review, the more the review can earn helpfulness votes from customers. Furthermore, the positive association between the number of attributes and helpfulness was enhanced among negative reviews. This study contributes to customers' information processing literature and offers guidelines to online retailers in designing a better decision support system.

Managerial Response to Negative Online Reviews in the Service Industry: A Tactic-Based and Culture-Based Model

Given the widespread and significant impacts of negative online reviews (NORs) on service industries, this research addresses a timely and important topic—how service firms develop effective managerial response (MR) to respond NORs. We propose an integrated tactic-based and culture-based model of MR design that is especially applicable to service firms in the online review platforms. Based on attribution theory and cross-cultural literature, our research enriches the electronic word of mouth literature by proposing a series of propositions that illustrate how different types of MR can be effectively delivered by incorporating both MR source and MR target under different cultures.

Dyadic communication in online review platforms: an exploratory approach

Emerging technology and information search capabilities have the potential to enhance the efficiency of consumer recommendations for travellers. One critical factor that has been understudied in online reviews is that of the reader–poster dyadic relationship and its impact on review evaluations. This paper provides an understanding of reader–poster dyads associated with interpersonal similarities in residency and elite status that affect restaurant review assessment. An exploratory analysis is conducted using 5368 dyadic datasets collected from Yelp.com. Readers’ residency is important to assess in a review because of distinctive search behaviours that change the perceptions of review evaluations. Elite status was primarily used as a tool for socializing with similar individuals only, whereas it functioned as a quality metric for non-local readers.

Review Helpfulness Score vs. Review Unhelpfulness Score: Two Sides of the Same Coin or Different Coins?

Online review platforms have increasingly incorporated the review evaluating system, which allows users to evaluate whether reviews are helpful or unhelpful to assist review readers and encourage review contributors. Although the review helpfulness score has been studied extensively in the literature, our knowledge regarding its counterpart, the review unhelpfulness score, is lacking. Addressing this gap in the literature is important because researchers and practitioners have adopted the unhelpfulness score assuming that it is driven by intrinsic review characteristics and that it can identify low-quality reviews. This study validates this conventional wisdom by examining the factors influencing the unhelpfulness score. We found that, unlike the review helpfulness score, the unhelpfulness score is generally not driven by intrinsic review characteristics. We also found that users who receive review unhelpfulness votes are more likely to cast unhelpfulness votes to other reviews. Finally, unhelpfulness voters engage much less with the platform than helpfulness voters do.

Impact of Managerial Responses on Product Sales: Examining the Moderating Role of Competitive Intensity and Market Position

Online review platforms have become very popular in recent years, generating massive numbers of online reviews and thus enticing numerous enterprises to respond to reviews. Although the economic impact (e.g., sales impact) of managerial responses is well recognized, it is unclear whether such an impact is moderated by competitive intensity and market position. This study examines the moderating effects of competitive intensity and market position in the relationship between managerial responses and sales. Using a panel dataset from one of the largest restaurant review platforms in China, this research found that the influence of the volume of managerial responses to positive word-of-mouth (WOM) on sales declined with increasing competitive intensity and decreasing market position. Moreover, we found the volume and degree of personalization of managerial responses to negative WOM to be more important for enterprises with a low market position versus those with a high market position. Our results provide insights into the effectiveness of managerial responses in different environments. We also offer managerial implications to service providers on response strategies.