Online Ratings Research Articles

A Novel Categorization of Key Predictive Factors Impacting Hotels’ Online Ratings: A Case of Makkah

In the present Internet age, customers have turned to online booking websites to meet their demand for quality hotel services and convey their experiences. As hotels can survive and succeed by satisfying consumers and obtaining high online ratings, this research predicts the key factors impacting these ratings. The study analyzes online consumer review data gleaned from the popular Booking.com website for hotel ratings near the key religious heritage site in Makkah, Saudi Arabia. The research applies the four-factor theory due to its importance in studying consumer satisfaction and prior non-application to the hospitality and tourism industry. This theory extended the two-factor theory to report four distinct sets of factors: satisfiers, dissatisfiers, criticals, and neutrals. Accordingly, the present research presents a novel categorization of key predictive factors and enriches the literature, which categorized factors as either satisfiers or dissatisfiers. Consequently, facilities and comfort are critical factors, while cleanliness, staff, and location are satisfiers; the value for the money spent is dissatisfier; the availability of breakfast and restaurants are neutral factors. The hospitality industry, specifically the hotel industry, can focus on characteristics of the four distinct sets of factors; various nations can leverage the findings to boost their hospitality and tourism sectors.

Evolution of communication policies at luxury hotels and their influence on guest ratings

ABSTRACT Strategies for responding to online reviews, particularly their evolution over time, represent an important and scarcely analysed topic in hospitality research. To address this gap, we analysed the evolution of response policies over 2010–2018 and their impact on hotels’ online ratings. We analysed 82,025 reviews and 57,164 responses on TripAdvisor for 5-star hotels in the Canary Islands (Spain). The results showed an upward trend in the volume of responses given by hotels, which seem to be increasingly concerned about communication with their guests. Second, based on their level of commitment to their online communications (low, moderate or high), the evidence showed that high and moderately involved hotels had relatively unchanged policies, whereas low-involvement hotels tended to improve their policies. Third, the findings showed some connectivity between communication policy level and affiliation to chains, the geographical span of the chain, guest traveller type and the reviewers’ experience level. However, no significant correlation was found between hotel size and communication policies. Finally, the results indicated that a positive relationship exists between hotel communication policies and the ratings given by reviewers, suggesting that effort put into communication policies is not in vain, and that even low-involvement policies are positively evaluated by guests.

‘Average’ Consumers Navigating the New Digital Food Chain: Influencers, Online Reviews and Rankings

With more and more consumers relying on online platforms to buy groceries and meals, traditional food labelling has been trumped by information arising from new features of today’s digital commerce. Instead of being affected by objective data on products’ origin, nutrition characteristics and allergens, individuals are increasingly drawn to feelings and perceptions conveyed by influencers’ experiences, online ratings, and rankings. In this sense, food information has become a different and larger notion than that on which EU Food Law is based on. This article examines the dynamics affecting the behaviour of food consumers online and how – and better yet, whether – EU law is responding to the challenges they raise. Food information, delivery platforms, behavioural consumer protection, influencers, online review, online rankings, Food Information to Consumers Regulation, Digital Services Act, Better Enforcement and Modernisation Directive, Platform-to-Business Regulation

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.

Some stay and some quit: Understanding P2P accommodation providers’ continuous sharing behavior from the perspective of feedback theory

Building on feedback theory, this study aims to understand providers' continuous sharing behavior (CSB) by integrating subjective and objective feedback in the context of peer-to-peer (P2P) accommodations. In Study 1, we test a mediation model by using survey data collected from 289 hosts of P2P accommodations. The results show that providers' positive feedback perception (PFP) is positively related to CSB and that intrinsic motivation mediates this effect. In Study 2, we collect 169 multisource data to explore the effect of objective online feedback on providers' PFP and subsequent effects in further detail. This study reveals that online ratings positively influence providers' PFP and that this effect is strengthened by the volume of online reviews. Further, we find sequential positive effects among providers’ PFP, intrinsic motivation, and CSB, which is consistent with Study 1. Implications for theory and practice are discussed.

Should patients use online reviews to pick their doctors and hospitals?

AbstractWe compare the online reviews of 221 “Questionable” Illinois and Indiana physicians with multiple paid medical malpractice claims and disciplinary sanctions with matched control physicians with clean records. Across five prominent online rating services, we find small, mostly insignificant differences in star ratings and written reviews for Questionable versus control physicians. Only one rating service (Healthgrades) reports on paid medical malpractice claims and disciplinary actions and it misses more than 90% of these actions. We also evaluate the online ratings of 171 Illinois hospitals and find that their ratings are largely uncorrelated with the share of hospital‐affiliated physicians with paid medical malpractice claims and disciplinary sanctions. Online ratings have limited utility in helping patients avoid physicians with troubled medical malpractice and disciplinary records, and steering patients away from hospitals at which more physicians have paid medical malpractice claims and disciplinary sanctions.

Aspect Based Opinion Mining & Sentiment Analysis

Opinion mining is a relatively new field that refers to the practice of collecting feedback in the form of online reviews and ratings left by users on various topics. Researchers are now able to monitor the states of consciousness of individuals in real-time because to this development. Just lately, a number of research papers for sentiment analysis were implemented, each of which was based on a unique categorization and ranking procedure. However, the amount of time necessary for the newline performing class has not decreased in any way. Sentiment Sensitivity newline word list SST was provided as a solution to the problem of function mismatch in the go-domain sentiment class across the source area and the target domain; however, achieving improved accuracy and identifying distributional similarities of words became less effective as time went on. Hidden Markov’s persistent development may be seen at the beginning. Cosine In order to achieve more effective and clean pre-processing, a method that is conceptually quite similar to HM-CPCS has been devised. The HM-CPCS methodology, which has recently been suggested, makes use of the POS tagger, a variant of which is based on the Hidden Markov algorithm. Evaluations are created using data from a wide variety of different domains. Similar to a newline, the tags that come before and after it compute the possibility of transitions and the existence of the term newline among the tags in order to increase capability. This is done in order to improve capability.

Pengaruh Ulasan Pelanggan Online Dan Rating Terhadap Keputusan Pembelian Pada Marketplace Shopee

In today's modern era, technology is developing very rapidly. The rapid development of internet technology has led to new behavioral changes. At first, people like to shop directly at the nearest market, store, and mall, now turning to online shopping. enough with a smartphone and internet connection. The purpose of this study was to determine the effect of online customer reviews and ratings on purchasing decisions on the shopee marketplace in Bandar Lampung. This research uses descriptive research with a quantitative approach. The object of this research is shopee consumers in Bandar Lampung. The method of data collection is through literature review and questionnaires. With a population of shopee consumers in Bandar Lampung and the number of samples is 100 people. The results of this study indicate that there is an influence between online customer reviews and ratings on purchasing decisions at the shopee marketplace in Bandar Lampung.

Analysing customers' reviews and ratings for online food deliveries: A text mining approach

AbstractThe purpose of this study was to explore the relationship between online reviews and ratings through text mining and empirical techniques. An Indian food delivery portal (Zomato.com) was used, where 50 restaurants on Presence Across Nation (PAN) basis were selected through stratified random sampling. A total of 2530 reviews were collected, scrutinized, and analysed. Using the NVivo software for qualitative analysis, seven themes were identified from collected reviews, out of which, the ‘delivery’ theme was explored further for identifying sub‐themes. Linear regression modelling was used to identify the variables affecting delivery ratings and sentiment analysis was also performed on the identified sub‐themes. Regression results revealed that hygiene and pricing (delivery subthemes) demonstrated lower delivery ratings. These variables can be established as indicators for restaurants and related online food delivery services to build their business model around them. Similarly, negative sentiments were observed in pricing and hygiene sub‐themes. Restaurants and online food services can enhance hygiene levels of their food delivery process in order to receive higher delivery ratings. Similarly, pricing of food items can be modified such that customers are not deterred from ordering the items—food and ordering service do not become cost‐prohibitive. This study devised a standardized methodology for analysing vast amounts of online user‐generated content (UGC). Findings from this study can be extrapolated to other sectors and service industries such as, tourism, cleaning, transportation, hospitals and engineering especially during the pandemic.

A converging reputation ranking iteration method via the eigenvector.

Ranking user reputation and object quality in online rating systems is of great significance for the construction of reputation systems. In this paper we put forward an iterative algorithm for ranking reputation and quality in terms of eigenvector, named EigenRank algorithm, where the user reputation and object quality interact and the user reputation converges to the eigenvector associated to the greatest eigenvalue of a certain matrix. In addition, we prove the convergence of EigenRank algorithm, and analyse the speed of convergence. Meanwhile, the experimental results for the synthetic networks show that the AUC values and Kendall’s τ of the EigenRank algorithm are greater than the ones from the IBeta method and Vote Aggregation method with different proportions of random/malicious ratings. The results for the empirical networks show that the EigenRank algorithm performs better in accuracy and robustness compared to the IBeta method and Vote Aggregation method in the random and malicious rating attack cases. This work provides an expectable ranking algorithm for the online user reputation identification.

Analysis of Patients' Online Reviews of Orthopaedic Surgeons.

Introduction:Physician rating websites (PRWs) are an increasingly popular interface between patient and surgeon. Despite the growing popularity of PRWs, little guidance exists for orthopaedic surgeons regarding online reviews. We analyzed online ratings and comments to provide a better understanding of patients' values and expectations so that surgeons can tailor their practice accordingly to enhance their clinical care and online reputation.Methods:Three common PRWs (Vitals, HealthGrades, and RateMDs) were queried from January 1, 2006, to May 18, 2020. Publicly available ratings, both quantitative (1 to 5 stars) and qualitative (free text comments), were collected. Comments were qualitatively tabulated as having positive or negative assessments for categories including outcome, personality, staff, surgical skill, visit time, bedside manner, wait time, diagnosis, knowledge, treatment, and advanced practice providers and analyzed using chi square goodness of fit. Quantitative comparisons of star ratings were made across surgeon years in practice, sex, practice setting, and PRW and compared using chi square independence testing.Results:In total, 81% of patient comments were found to have a positive assessment. Comments regarding outcome (P < 0.001), staff (P = 0.001), surgical skill (P < 0.001), or knowledge (P = 0.001) were more likely to be positive. Reviews regarding bedside manner (P < 0.001), wait time (P < 0.001), diagnosis (P < 0.001), treatment (P < 0.001), or advanced practice providers (P < 0.001) were more likely to be negative. Surgeon sex was not associated with a difference in quantitative ratings (P = 0.131), unlike practice setting (P < 0.001) and PRW (P < 0.001).Discussion:PRWs are a growing interface between surgeon and patient with a considerable effect on surgeon marketability. This study reveals a statistical association between certain patient-centered medical practices and positive patient reviews. This emphasizes the importance of ensuring that high standards are maintained throughout a physician's practice of maintaining a constant awareness of the fundamentals for effective patient care and of taking care to curate a physician's online presence.

Effects of emotional valence and intensity on cognitive and affective empathy after insula lesions.

The insula plays a central role in empathy. However, the complex structure of cognitive (CE) and affective empathy (AE) deficits following insular damage is not fully understood. In the present study, patients with insular lesions (n = 20) and demographically matched healthy controls (n = 24) viewed ecologically valid videos that varied in terms of valence and emotional intensity. The videos showed a person (target) narrating a personal life event. In CE conditions, subjects continuously rated the affective state of the target, while in AE conditions, they continuously rated their own affect. Mean squared error (MSE) assessed deviations between subject and target ratings. Patients differed from controls only in negative, low-intensity AE, rating their own affective state less negative than the target. This deficit was not related to trait empathy, neuropsychological or clinical parameters, or laterality of lesion. Empathic functions may be widely spared after insular damage in a naturalistic, dynamic setting, potentially due to the intact interpretation of social context by residual networks outside the lesion. The particular role of the insula in AE for negative states may evolve specifically in situations that bear higher uncertainty pointing to a threshold role of the insula in online ratings of AE.

The Effect of Digital Marketing, Online Customer Reviews and Ratings on Consumer Buying Interest Through Shopee

This study aims to analyze the effect of digital marketing, online customer review rating on consumer buying interest through shopee. The analytical method used in this study is quantitative analysis by conducting a survey on 106 respondents, namely students from Narotama University who have made purchases through shopee and tested with the help of an analytical tool, namely the SPSS program. The results of this study indicate that digital marketing, online customer reviews and ratings have a positive effect on buying interest through shopee. When consumers feel that shopee's digital marketing is attractive, online customer reviews and other user ratings provide benefits in terms of providing product-related information, this can increase purchase intention.

Analysis of Online Rating and Online Review on Hotel Buying Interest

This research is motivated by the decreased room occupancy rates in Indonesia due to the COVID-19 pandemic, where the hotel industry is one of the major industries in this country. With this pandemic, people have new considerations in choosing hotels by tending to look for information on the internet. Therefore, this research aims to analyze the effect of online ratings and online reviews on hotel buying interest. The data on this research were collected by distributing a questionnaire to 100 respondents. In determining the sample, the technique used is purposive sampling which is included in the non-probability sampling category. The research method is quantitative and the analytical method used in this research is multiple linear regression analysis, which is processed using the software SPSS. The results showed that online ratings and online reviews have a positive and significant effect on hotel buying interest partially and simultaneously.

Daily hotel demand forecasting with spatiotemporal features

PurposeGiven the importance of spatial effects in improving the accuracy of hotel demand forecasting, this study aims to introduce price and online rating, two critical factors influencing hotel demand, as external variables into the model, and capture the spatial and temporal correlation of hotel demand within the region.Design/methodology/approachFor high practical implications, the authors conduct the case study in Xiamen, China, where the hotel industry is prosperous. Based on the daily demand data of 118 hotels before and during the COVID-19 period (from January to June 2019 and from January to June 2021), the authors evaluate the prediction performance of the proposed innovative model, that is, a deep learning-based model, incorporating graph convolutional networks (GCN) and gated recurrent units.FindingsThe proposed model simultaneously predicts the daily demand of multiple hotels. It effectively captures the spatial-temporal characteristics of hotel demand. In addition, the features, price and online rating of competing hotels can further improve predictive performance. Meanwhile, the robustness of the model is verified by comparing the forecasting results for different periods (during and before the COVID-19 period).Practical implicationsFrom a long-term management perspective, long-term observation of market competitors’ rankings and price changes can facilitate timely adjustment of corresponding management measures, especially attention to extremely critical factors affecting forecast demand, such as price. While from a short-term operational perspective, short-term demand forecasting can greatly improve hotel operational efficiency, such as optimizing resource allocation and dynamically adjusting prices. The proposed model not only achieves short-term demand forecasting, but also greatly improves the forecasting accuracy by considering factors related to competitors in the same region.Originality/valueThe originalities of the study are as follows. First, this study represents a pioneering attempt to incorporate demand, price and online rating of other hotels into the forecasting model. Second, integrated deep learning models based on GCN and gated recurrent unit complement existing predictive models using historical data in a methodological sense.

Exploring the effects of official-structured managerial responses on hotel online popularity

As a burgeoning mode of service management, online managerial response has become a critical aspect driving consumer online engagement and hotel performance improvement. Nevertheless, managerial response strategy, especially presentation style, is rarely studied. With a mixed method approach that includes the use of secondary data and experimental analyses, we investigate how the official-structured presentation style of managerial response affects the volume of online reviews and online ratings, which represent hotel online popularity, and the moderating effect of managerial response speed. Results indicate that 1) responding to consumers in the official-structured text increases the volume of online reviews, 2) responding to consumers in an official-structured style increases online ratings, and 3) response speed as an indicator of management effort enhances the effect of managerial response’s official-structured style on hotel popularity. Aside from expanding the literature on managerial response strategy and hospitality marketing, our findings also provide important implications for hospitality administrators and marketers.

Machine Learning Text Analysis of Corporate Diversity Statements Predicts Employees’ Online Ratings

Machine Learning Text Analysis of Corporate Diversity Statements Predicts Employees’ Online Ratings

Online Reviews, Legitimacy, and (In)Formal Entrepreneurship

Online reviews found on websites like Yelp are an increasingly popular source of information for consumers. While an increasing number of studies find that online reviews influence firms' financial performance, less is known about their effect as a viable alternative to formal legitimating mechanisms such as licensing on the distribution of entrepreneurs between the formal and informal sectors. Using an original dataset of the California moving industry between 2004-2016, we find a curvilinear association between the number of unlicensed firms and the likelihood that informal entrepreneurs will apply for a license (i.e., transition from the informal to the formal sector). We also find that likelihood to be negatively associated with that firm’s online ratings, and with the popularity of online reviews in the area. These findings suggest that entrepreneurs may now rely more on online mechanisms to legitimate their firms than more traditional formal legitimating mechanisms such as licensing.

Working from home during COVID-19 in a Danish hospital research setting: experiences of researchers and healthcare providers, explored by Group Concept Mapping

ObjectivesThe COVID-19 pandemic has changed the working environment, how we think of it and how it stands to develop into the future. Knowledge about how people have continued to work...

Estimating impacts of logistics processes on online customer ratings: Consequences of providing technology‐enabled order tracking data to customers

AbstractRecent advances in logistics tracking technologies have enabled e‐commerce firms to both accurately track the shipments and obtain accurate estimates of delivery times. With customers constantly tracking the status of their orders, the effects of logistics processes on customers' evaluation of their online shopping experience are relatively unknown as academic literature provides little guidance. Drawing upon the expectation‐disconfirmation theory and attribution theory, and using a unique dataset assembled in collaboration with an e‐commerce firm, we empirically investigate the effects of order processing and delivery times on online customer ratings. We also estimate the impact of positive deviation (i.e., early delivery) and negative deviation (i.e., late delivery) from the promised delivery date on online customer ratings and whether the impact of delivery status (early or late) depends on order cost and freight cost. Our empirical findings demonstrate that longer order processing and delivery times are associated with lower ratings. Our results also highlight that the late delivery of an order is negatively associated with ratings and that the order cost amplifies this effect. Furthermore, freight cost reduces both the negative effect of late delivery and the positive effect of early delivery on online ratings. Our results also indicate that there exist curvilinear relationships between online ratings and the number of days an online order is late or early. Specifically, the negative effect of a late delivery follows a convex‐shape curve such that its impact decreases as the number of days an online order is late increases. Likewise, the positive effect of an early delivery increases at a decreasing rate as the number of days an online order is early increases. Our supplementary analyses further account for potential endogeneity issues and corroborate our main results with additional empirical evidence obtained through alternative model specifications and estimation strategies. We present important theoretical implications and managerial takeaways from our findings.