Online Review Ratings Research Articles

Gender rating gap in online reviews.

Although online reviews are used by many people to make decisions, these reviews may be biased. On the basis of 1.2 billion observations across five leading online review platforms and two lab studies (n = 1,172 and n = 1,165; US respondents fluent in English), we provide evidence for a consistent and systematic gender rating gap: women's mean online review ratings are significantly more favourable than men's. We suggest that although men and women, on average, generally do not differ in their 'real' attitudes, their ratings do differ when it comes to online reviews. Our lab studies revealed that such differences are due to gender differences in the propensity to share negative attitudes online, possibly due to women's greater concern about social consequences. Our findings highlight the need for societal change to create conditions in which people, particularly women, feel comfortable publicly expressing genuine attitudes, especially in cases of dissatisfaction, without being concerned about the consequences.

Which Receives More Attention, Online Review Sentiment or Online Review Rating? Spillover Effect Analysis from JD.com.

Studies have found that competitive products' online review ratings (ORRs) have a spillover effect on the focal product's sales. However, the spillover effect of online review sentiment (ORS) as an essential component of online review analysis has yet to be studied. In this study, we analyze online review content from JD.com using the latent Dirichlet allocation to identify the product attribute topics that consumers are most concerned about. We then construct a baseline regression model of ORS and ORRs to explore the effects of online competitive product reviews on focal product sales. Moreover, we examine how the interaction between ORS and critical factors of online reviews affect sales. Our results indicate that the ORS of competitive products has a negative effect on focal product sales, and the effect is greater than the ORS and ORRs of focal products, respectively. In addition, the ORS of competitive products inhibits the sale of focal products as evaluations of product attributes become more positive or online review usefulness increases. We also find that the effect of ORRs of competitive products is not significant, which may be because clothing, as an experiential product, requires consumers to gain more information about specific usage scenarios before making a decision. This study provides a more accurate basis for consumer decision-making and offers retailers a novel approach to developing marketing strategies.

The Impact of Online Reviews on Consumers’ Purchase Intentions: Examining the Social Influence of Online Reviews, Group Similarity, and Self-Construal

Consumers often rely on evaluations such as online reviews shared by other consumers when making purchasing decisions. Online reviews have emerged as a crucial marketing tool that offers a distinct advantage over traditional methods by fostering trust among consumers. Previous studies have identified group similarity between consumers and reviewers as a key variable with a potential impact on consumer responses and purchase intention. However, the results remain inconclusive. In this study, we identify self-construal and group similarity as key factors in the influence of online review ratings on consumers’ purchase intentions. We further investigate the role of consumers’ self-construal in shaping consumers’ perceptions of online reviews in terms of belongingness and diagnosticity. To test the hypothesis, we conducted a 2 (online review rating) × 2 (group similarity) × 2 (self-construal) ANOVA on 276 subjects collected through Amazon Mechanical Turk (MTurk), and contrast analysis and PROCESS macro model 12 were used for the interaction effect analysis and moderated mediation analysis. Our findings reveal that consumers with an interdependent self-construal are sensitive to both review ratings and group similarity with regards to their purchase intentions. They demonstrate a positive purchase intention when both group similarity and online review ratings are high. However, their purchase intention is not influenced by review ratings when group similarity is low. Conversely, consumers with an independent self-construal exhibit a more positive purchase intention when the online review rating is high, irrespective of group similarity. Additionally, our study highlights the mediating roles of perceived diagnosticity and belongingness in the relationship between online review ratings, group similarity, self-construal, and purchase intentions. Results show significant indirect effects for perceived diagnosticity and belongingness, meaning that the impact of online review ratings on purchase intention is mediated by these two variables. The outcomes of our research offer theoretical and practical implications concerning online reviews and suggest new avenues for future research in the area of online consumer behavior.

Channel strategy and the management of fake reviews in a catering platform service supply chain

With the burgeoning of the platform economy, the online-to-offline catering industry has developed rapidly. However, the industry faces various challenges, such as fierce channel competition and fake reviews. This paper investigates a Catering Platform Service Supply Chain (CPSSC) consisting of a restaurant (she) and an online service platform (he). The restaurant decides whether to join the platform or not. If yes, we further delve into the issue of the restaurant’s potential fake reviews and devise a platform supervision mechanism as a viable solution. The research findings first show that if the restaurant’s offline profit is below a threshold, she would be incentivized to join the platform when the commission rate is relatively low. Furthermore, this incentive would decrease when more offline consumers switch to the online channel. Secondly, the restaurant will set a higher online pricing ratio after joining the platform if its online review rating is above the industry average, and vice versa. Thirdly, if both firms focus on their short-term benefits while neglecting their corporate reputations, they would fall into the dilemma of boiling frogs in gradually heated water. To solve this dilemma, we establish an evolutionary game model to explore the long-term strategic interaction between the restaurant and platform while concurrently formulating a robust supervision mechanism. The results suggest that reinforcing consumers’ trust in the platform’s supervision and enhancing the supervision effectiveness can significantly benefit the entire CPSSC.

Examining the negative relationship between length of stay at a hotel and customer satisfaction: evidence from online customer reviews

PurposeStudies that investigate the length of stay as a predictor of consumer post-purchase behavior are rare despite its importance in efficient hotel management. By analyzing online customer reviews, this study aims to fill this gap in the extant literature on the relationship between length of stay and customer satisfaction level.Design/methodology/approachThe authors collected and used online review data on hotels in London for this study. A series of linear regression analyses were conducted to examine the effect of length of stay on customer satisfaction as measured by review ratings. The authors used the Mahalanobis matching approach to confirm the empirical findings.FindingsThis analysis shows that length of stay is negatively associated with customer satisfaction. Additionally, the authors find that this negative relationship is stronger in high-end hotels than in low-end hotels.Research limitations/implicationsThe research findings contribute to the literature by shedding light on a new stream of research, namely, length of stay. Additionally, the research findings offer novel insights that could help hotel managers understand the trade-off between longer stays and customer satisfaction.Originality/valueTo the best of the authors’ knowledge, this is one of the first few studies to show the systematic impact of length of stay on the valence of online review ratings, as well as the moderating effect of hotel levels by analyzing customer online reviews on hotel experiences.

Artificial intelligence in service industries: customers’ assessment of service production and resilient service operations

Artificial intelligence (AI) is increasingly embedded into service firms’ operations. However, production systems and operations management scholars have not yet examined if AI-empowered service operations are positively judged by service customers. To bridge this gap, this study draws on the three-factor theory of customer satisfaction applied to online review data, to capture the effect of AI-empowered service operations on overall customer satisfaction, operationalised by means of online review ratings. Based on text analytics techniques applied to a sample of more than 50,000 TripAdvisor ORs covering 35 international hotels in Asia and America, we develop a penalty–reward contrast analysis. The findings reveal that the effects of customer interaction with mechanical AI on customer satisfaction with service operations are asymmetric: positive customer interaction with mechanical AI positively and significantly influences overall customer satisfaction with AI-empowered service operations, whereas negative customer interaction with mechanical AI does not significantly alter customer satisfaction. Taken together, these findings suggest that mechanical AI constitutes a key element of resilient AI-empowered service operations.

Are online reviews of assisted living communities associated with patient-centered outcomes?

Existing literature on online reviews of healthcare providers generally portrays online reviews as a useful way to disseminate information on quality. However, it remains unknown whether online reviews for assisted living (AL) communities reflect AL care quality. This study examined the association between AL online review ratings and residents' home time, a patient-centered outcome. Medicare beneficiaries who entered AL communities in 2018 were identified. The main outcome is resident home time in the year following AL admission, calculated as the percentage of time spent at home (i.e., not in institutional care setting) per day being alive. Additional outcomes are the percentage of time spent in emergency room, inpatient hospital, nursing home, and inpatient hospice. AL online Google reviews for 2013-2017 were linked to 2018-2019 Medicare data. AL average rating score (ranging 1-5) and rating status (no-rating, low-rating, and high-rating) were generated using Google reviews. Linear regression models and propensity score weighting were used to examine the association between online reviews and outcomes. The study sample included 59,831 residents in 12,143 ALs. Residents were predominately older (average 81.2 years), non-Hispanic White (90.4%), and female (62.9%), with 17% being dually eligible for Medicare and Medicaid. From 2013 to 2017, ALs received an average rating of 4.1 on Google, with a standard deviation of 1.1. Each one-unit increase in the AL's average online rating was associated with an increase in residents' risk-adjusted home time by 0.33 percentage points (p <0.001). Compared with residents in ALs without ratings, residents in high-rated ALs (average rating ≥4.4) had a 0.64 pp (p <0.001) increase in home time. Higher online rating scores were positively associated with residents' home time, while the absence of ratings was associated with reduced home time. Our results suggest that online reviews may be a quality signal with respect to home time.

How do online review valence and ratings interact with consumer-generated visuals?

ABSTRACT The growth of social media and technology has given online reviews more importance and popularity. Consumer-generated visuals (pictures and videos), together with words and numerical components, are increasingly being used in online reviews. However, more research is necessary to understand how these components interact. This study aims to examine the relationships between review valence, numerical ratings, and hotel booking intentions, and investigate the interactions between consumer-generated visuals and demographics on these relationships. An online questionnaire was used to collect data using a convenience sample of 418 customers from Oman. The proposed model was tested using Structural Equation Modeling. The results demonstrated that negative review valence, positive review valence, and rating usefulness are all significant predictors of hotel booking intentions. The results also show that young and female customers are more affected by review valence and rating usefulness. Consumer-generated visuals play a moderating role, where the relationships between hotel booking intentions and review valence and ratings are weaker when customers are attentive to visuals. The study’s results underline the role of negative valence, rating usefulness and visuals, and offer theoretical and practical implications.

Assessing brand performance consistency from consumer-generated media: the US hotel industry

Purpose Given the importance of performance consistency of chain hotels in customers’ decision-making and service evaluation, this study aims to explore how consistently chain hotel brands offer quality service and carry out their performance from the eyes of customers through online reviews on TripAdvisor of the top five US hotel chains (i.e. Choice, Hilton, InterContinental, Marriott and Wyndham) and their brands. Design/methodology/approach The research objectives were achieved through methodological triangulation: business intelligence, data visualization analytics and statistical analyses. First, the data collection and pre-processing of consumer-generated media (CGM) (i.e. TripAdvisor online reviews) were performed using business intelligence for further analyses. Using data visualization analytics (i.e. box-and-whisker plot by region and brand), the geographic patterns of performance attributes (i.e. online review ratings, including location, sleep, cleanliness, room and service) were depicted. Using a series of analyses of variance and regression analyses, the results were further assessed for the impacts of brand performance inconsistency on consumers’ perceived value, sentiment and satisfaction. Findings The empirical results demonstrate that there are significant performance inconsistencies in performance attributes (location, sleep, cleanliness, room and service) by brands throughout the six regions in the US hotel market. More importantly, the findings confirm that brand performance consistency significantly influences consumers’ perceived service quality (i.e. perceived value, satisfaction and sentiment). Originality This study is one of the first attempts to empirically explore hotel brand performance consistency in the US hotel market from customer reviews on CGM. To measure hotel brand performance in the US hotel market, this study collected and analyzed user-generated big data for the top 5 US hotel chains through business intelligence, visualization analytics and statistical analysis. These integrated and novel research methods would help tourism and hospitality researchers analyze big data in an innovative data analytics approach. The findings of the study contribute to the tourism and hospitality field by confirming hotel brand performance inconsistency and such inconsistent performance affected customers’ service evaluations. Practical Implications This study demonstrates the significant impact of hotel brand performance consistency on consumers’ perceived value, emotion and satisfaction. Considering that online reviews are perceived as a credible source of information, the findings suggest that the hotel industry pays special attention to brand performance consistency to improve consumers’ perceived value, emotion and satisfaction.

Pengaruh Online Customer Review dan Online Customer Rating Terhadap Minat Beli Produk Wardah Di Situs Belanja Online Shopee

The development of increasingly sophisticated technology in Indonesia has a positive impact on the ease of buying and selling transactions through e-commerce. Shopee is the pioneer of the most frequently visited Online Shopping Site as a means of purchasing products. Meanwhile, Wardah products are a cosmetic brand that has the first rank with the highest sales online through the Shopee Online Shopping Site. This study aims to determine and analyze the influence of Online Customer Reviews and Online Customer Ratings on buying interest in Wardah products on the Shopee Online Shopping Site. The type of research used is quantitative research with a descriptive analysis method approach. Data collection was carried out by distributing questionnaires in the form of google form. The population used was consumers who visited the Shopee Online Shopping Site. A sample of 100 respondents with the criteria of being more than 17 years old, consumers of Wardah products and visiting the Shopee Online Shopping Site at least more than 2 times a month. The sampling technique used is purposive sampling with non-probability methods. The data analysis technique used multiple linear regression analysis. The results of this study indicate that simultaneously Online Customer Review and Online Customer Rating have a significant effect on buying interest. And partially Online Customer Review and Online Customer Rating have a significant effect on buying interest.&#x0D; Keywords: Online Customer Review; Online Customer Rating; Buying Interest

Feeling Luxe: A Topic Modeling × Emotion Detection Analysis of Luxury Hotel Experiences

As the luxury market and associated experiential consumption continues to rise, the hospitality management literature lags somewhat behind practice in examining luxury experiences. This research presents an exploratory analysis of luxury hotel experiences via the approach of topic modeling and emotion detection in online luxury hotel reviews. Results demonstrate that “people” and “product—activity &amp; wellness” aspects of luxury hotel experiences tend to be positively associated with online review ratings. The “product—food &amp; beverage” and “physical evidence and place” aspects may produce mixed effects on rating levels. Finally, the “process” and “price and promotion” aspects tend to be negatively associated with online review ratings. Findings further reveal that such effects function via the mediation paths of various categories of discrete emotions. These results offer enriching insight for hospitality management research and practice regarding luxury hotel experiences.

Exploring environmental concerns on digital platforms through big data: the effect of online consumers’ environmental discourse on online review ratings

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.

Effect of Online Reviews and Crowd Cues on Restaurant Choice of Customer: Moderating Role of Gender and Perceived Crowding.

This study is aimed to identify the relative (direct) effect of online review ratings and perceived crowding on purchase intentions of a consumer. Our study also investigated the contingent effect of gender and perceived crowding between the relationship of exogenous and endogenous variables. This study was conducted in the Malaysian restaurant industry. We applied the purposive sampling technique to identify respondents, the mall intercept survey method was used for data collection. Smart PLS software was applied for data analysis (200 respondents). This study demonstrates through its results that online review ratings and perceived crowding have a positive effect on purchase intentions of a consumer. Moreover, if a consumer perceives crowding at a restaurant, this has a positive contingent effect on the relationship between review ratings and purchase intentions. This demonstrates that the consumer is more inclined to choose a restaurant with a high online review rating and has high perceived crowding at some unfamiliar place. Lastly, no evidence is found for the gender difference between review rating and purchase intentions; however, gender shows contingent effect and results confirmed that males preferred more crowded restaurants as compared to females. There are theoretical and practical implications for managers in the findings of this study.

Using Online Reviews for Customer Sentiment Analysis

Positive “word of mouth” is the key to successful innovation diffusion. Innovation managers pay close attention to examine customer sentiment. Online reviews are often the most accessible sources of customer feedback. Online review ratings and online review volume, the two most common metrics to interpret customer sentiment from online reviews, have some critical limitations. Online review ratings are prone to extremity bias since extremely satisfied or dissatisfied customers are most likely to leave online reviews. Online review volume can increase for any reason, and it does not necessarily indicate positive or negative customer feedback. This article explores text mining methods and proposes some alternative metrics to interpret customer sentiment. The findings show that sentiment scores might be less prone to extremity bias compared to online review ratings. Sentiment scores tended to fit a normal distribution while online review ratings were skewed to extreme values. Sentiment scores and review lengths, when combined, can provide a new angle to observe enthusiasm.

Effects of official versus online review ratings

• Both official and online review ratings have a positive impact on the clientele. • The impact of online review ratings is higher than that of the official one. • Highly rated restaurants can attract more customers and may charge higher prices.

The influence of the online community, professional critics, and location similarity on review ratings for niche and mainstream brands

Distinctions in the attributes of niche versus mainstream brands are leveraged to explain differences in the drivers of online review ratings. Specifically, we examine how customer review valence, professional critics review valence, community characteristics, location similarity, and reviewer characteristics may impact a reviewer’s rating. We use a unique dataset on the U.S. beer product category to address our research questions and find that niche brands are more impacted by OWOM activity across the board because consumers are less likely to have established brand awareness and brand imagery formed. Likewise, a reviewer is prone to rating a local niche brand more favorably. Professional critics are generally less influential than the online community for the typical focal reviewer. A prior review from the online community becomes particularly influential when its expertise is high and/or when the prior reviewer has shared geographic locational traits with the focal reviewer. Reviewers that engage more with products/brands tend to align sentiments with professional critics, while those that engage more with the online community tend to align sentiment with that community. Utilizing insights from these results, we provide several guidelines for brand managers in devising appropriate social media strategies.

Coupons or Free Shipping? Effects of Price Promotion Strategies on Online Review Ratings

Practice-Oriented Abstract

Does the dispersion of online review ratings affect review helpfulness?

The impact of online consumer reviews on online purchase decisions has increased with the growth of e-commerce. This paper tries to explain how rating dispersion impacts the process of review consumption based on the heuristic systematic model. For this research, 10,198 online consumer reviews for 516 DVD products were collected from Amazon.com using a web data crawler. Our results show that when trusting average ratings (i.e., when rating dispersion is low), the incentive to read individual reviews decreases because of the principle of least effort. In this case, consumers consider average ratings to be representative of collective intelligence, so rating inconsistency negatively impacts review helpfulness. On the other hand, when average ratings are not trusted (i.e., when rating dispersion is high), the incentive to read individual reviews increases because of the principle of sufficiency. When it happens, extreme ratings affects review helpfulness more because extreme opinions are not ambiguous. Our findings provide new perspectives to address the inconsistent findings of the previous studies on rating and review helpfulness, and the practical implications for e-commerce platforms.

Does cultural distance affect online review ratings? Measuring international customers\u2019 satisfaction with services leveraging digital platforms and big data

The advent and development of digital platforms has helped enhance the international visibility of brands, products and services, and has also introduced a proliferation of online reviews. This study develops a big data analysis of customer online reviews of hospitality services to gauge the extent to which the cultural distance among service providers and their customers influences online review ratings. By examining almost 715,000 online reviews written by hotel customers from more than 100 different nationalities, the effect of national cultural differences among service customers and providers (namely cultural distance) on online review ratings is innovatively scrutinized. The paper, by considering reviewers’ behavioral features, demographics, and trip-related factors, reveals that the effect of national cultural distance on online review ratings is negative. Several implications for practitioners are also discussed.

How do weather factors drive online reviews? The mediating role of online reviewers’ affect

PurposeThe purpose of this paper is to investigate the mediating effect of online reviewers' affect (ORA) on the relationship between weather and online review ratings (ORR).Design/methodology/approachThe consumers' online review data were collected from the third-party restaurant website, and the weather data were obtained from the weather part of Chinese e-government website. SnowNLP was utilized to analyze sentiment and further extract ORA. Furthermore, the mediating effects of ORA on temperature and ORR, rain and ORR were explored separately using PROCESS 3 Macro Model 4, and the interaction effect of temperature and rain was tested through PROCESS 3 Macro Model 7.FindingsThe findings of this work demonstrate that ORA mediates the relationship between temperature and ORR and the relationship between rain and ORR. Besides directly leading to higher ORR, a higher temperature can bring about higher ORR by elevating ORA. On the other hand, little rain and heavy rain have a direct negative influence on ORR, and they can also lead people into a bad mood state, thus leading to lower ORR. Furthermore, temperature moderates the effect of rain on ORA. When the temperature is higher, the differences of ORA are larger between different types of rain than that of lower temperature.Originality/valueThis study appears to be the first to investigate the relationship among weather, ORA and ORR using online data. The results could help managers understand when consumers are more likely to provide negative eWOM under corresponding weather conditions and adopt appropriate strategies to improve ORR.