Online Restaurant Reviews Research Articles

Process vs. outcome: Effects of food photo types in online restaurant reviews on consumers’ purchase intention

User-generated photos are an important aspect of online reviews; yet they remain fragmented in academic research. This study attempts to investigate the roles of two types of food photos (process-focused and outcome-focused photos) in online restaurant reviews on consumers’ purchase intention, the associated underlying mechanism and the moderating effect of dining motivation based on mental simulation and process transparency theories. Using two experiments, this study revealed that online reviews with process-focused (vs. outcome-focused) food photos led to stronger purchase intention due to higher perceived food quality and experiential value. Moreover, hedonically motivated participants exhibited higher purchase intention towards online reviews with process-focused food photos owing to greater perceived experiential value. Participants with utilitarian motivations expressed stronger purchase intention towards online reviews with outcome-focused food photos due to better perceived food quality. This study provides theoretical insight for the online review literature and practical implications for restaurants’ digital marketing and service design.

Predicting intention to follow online restaurant community advice: a trust-integrated technology acceptance model

PurposeThis research investigates consumer intention to follow online community advice. Applying the technology acceptance model (TAM) to the context of online restaurant communities, the study empirically examines the effects of perceived usefulness, perceived ease of use, attitude and trust on the intention to follow online advice.Design/methodology/approachThe data were collected from 360 members of online restaurant communities on Facebook and analyzed using structural equation modeling (SEM).FindingsThe findings revealed that trust, perceived usefulness and attitude are key predictors of the intention to follow online restaurant community advice.Originality/valueExtant research on the influence of online reviews on consumer behavior in the restaurant industry has largely focused on the characteristics of the review, reviewers or readers. Moreover, other studies have investigated consumers' motivations to write online restaurant reviews. This study, however, takes a different approach and examines what drives consumers to follow the advice from online restaurant communities.

The Impact of Increase in Minimum Wages on Consumer Perceptions of Service: A Transformer Model of Online Restaurant Reviews

We study the impact of an increase in minimum wages on consumer perceptions of service quality in the restaurant industry.

Improving Sentiment Classification of Restaurant Reviews with Attention-Based Bi-GRU Neural Network

In the era of Web 2.0, there is a huge amount of user-generated content, but the huge amount of unstructured data makes it difficult for merchants to provide personalized services and for users to extract information efficiently, so it is necessary to perform sentiment analysis for restaurant reviews. The significant advantage of Bi-GRU is the guaranteed symmetry of the hidden layer weight update, to take into account the context in online restaurant reviews and to obtain better results with fewer parameters, so we combined Word2vec, Bi-GRU, and Attention method to build a sentiment analysis model for online restaurant reviews. Restaurant reviews from Dianping.com were used to train and validate the model. With F1-score greater than 89%, we can conclude that the comprehensive performance of the Word2vec+Bi-GRU+Attention sentiment analysis model is better than the commonly used sentiment analysis models. We applied deep learning methods to review sentiment analysis in online food ordering platforms to improve the performance of sentiment analysis in the restaurant review domain.

The interpersonal effects of distinct emotions in online reviews

ABSTRACT Emotional expressions in online reviews affect reviews’ informative value. By comparing high and low arousal emotions with a negative and positive valence, the current research demonstrates that the effects of emotional expressions in online reviews are determined not by the level of arousal, but by the perceived rationality of the reviewer and the perceived appropriateness of the emotional expression. In a lab experiment (N = 242) among university students, and an online experiment (N = 252) on Prolific Academic involving native English speakers, participants read an online restaurant review with the negative emotions anger, disappointment, or disgust, or with the positive emotions happiness, excitement, or contentment. Results showed that readers of online reviews considered expressions of anger more inappropriate than expressions of disappointment or disgust; this led them to judge the reviewer as more irrational, which decreased the informative value of the review. As a consequence, angry reviews led to less negative restaurant evaluations and stronger intentions to visit the restaurant than reviews expressing disappointment or disgust. We found no differences between contentment and happiness (Study 1), or between contentment and excitement (Study 2). Our findings underscore the importance of studying the effects of discrete emotions in online reviews.

Corrigendum to “A big data exploration of the informational and normative influences on the helpfulness of online restaurant reviews” [J. Bus. Res. 125 (2021) 354–367

Corrigendum to “A big data exploration of the informational and normative influences on the helpfulness of online restaurant reviews” [J. Bus. Res. 125 (2021) 354–367

맛집 리뷰에 나타난 ‘맛’ 표현 연구

The purpose of this study is to examine the linguistic characteristics of the expression of ‘taste’ in online restaurant review texts. Although restaurant reviews are written language, they are highly spoken, and they are written in a horizontal relationship, both producers and consumers of information. Therefore, without being bound by existing norms, they write their own experiences as if they were speaking, and freely use various new words and expressions to vividly convey the subjective evaluation of taste. Therefore, examining the linguistic characteristics related to the taste of restaurant review texts can contribute to broadening the understanding of new genres and broadening the study of Korean vocabulary. There are a lot of new vocabulary used in restaurant reviews, including abbreviations, foreign languages, dialects and North Koreans, reiterative, compound words, vowels added or syllables repeated forms, and idioms. In addition, figurative expressions are actively used, and various taste expressions related to texture, coffee, and sweetness are used.

Exploring the multi-dimensionality of authenticity in dining experiences using online reviews

The quest for authenticity in dining experiences has become increasingly important. This paper explores authenticity dimensions that are of value to customers in dining experiences, and by that gains a multi-dimensional understanding of authenticity in this context. Following an integrated learning approach using text mining and classification techniques, this paper explores and confirms different dimensions of authenticity by identifying and classifying authenticity judgements in online restaurant reviews. The results suggest that authenticity is a multi-dimensional concept encompassing Authenticity of the Other, Authenticity of the Producer , and Authenticity of the Self as first-level dimensions . Additionally, besides historical and categorical authenticity which have been previously explored in the literature, a new type of authenticity - Deviated Authenticity - emerged as a second-level dimension falling under Authenticity of the Other . This paper enhances existing conceptualisations of authenticity and establishes avenues for exploring the multi-dimensionality of other consumer research concepts using user-generated content.

Comparative study of deep learning models for analyzing online restaurant reviews in the era of the COVID-19 pandemic.

Online reviews remain important during the COVID-19 pandemic as they help customers make safe dining decisions. To help restaurants better understand customers’ needs and sustain their business under current circumstance, this study extracts restaurant features that are cared for by customers in current circumstance. This study also introduces deep learning methods to examine customers’ opinions about restaurant features and to detect reviews with mismatched ratings. By analyzing 112,412 restaurant reviews posted during January-June 2020 on Yelp.com, four frequently mentioned restaurant features (e.g., service, food, place, and experience) along with their associated sentiment scores were identified. Findings also show that deep learning algorithms (i.e., Bidirectional LSTM and Simple Embedding + Average Pooling) outperform traditional machine learning algorithms in sentiment classification and review rating prediction. This study strengthens the extant literature by empirically analyzing restaurant reviews posted during the COVID-19 pandemic and discovering suitable deep learning algorithms for different text mining tasks.

Fake Reviews or Not: Exploring the relationship between time trend and online restaurant reviews

As online reviews are becoming increasingly important for newly opened restaurants, such reviews are frequently manipulated at the startup stage of these businesses, which negatively impacts their credibility. To establish the attributes of online reviews that might be manipulated, we conducted exploratory research with a total number of 4,450 data from Yelp.com. The findings indicate that online reviews of newly opened restaurants show a time trend in which there are less negative sentiments and fewer words reflecting extreme reviews than long-running restaurants. Our findings provide five major core propositions and five sub propositions we use to advance our hypotheses. We postulate theoretical implications and practical insights in the discussion of this study.

Online review helpfulness: the moderating effects of review comprehensiveness

Purpose Consumers’ evaluation of online review helpfulness has been widely examined. The extant literature suggests that the attributes of review content (e.g. review length and extremity) influence review helpfulness. However, review length cannot fully reflect the richness of the review content. Anchoring on information diagnosticity and extremity bias, this study aims to explore the effect of review comprehensiveness on its helpfulness. Design/methodology/approach Field observations were obtained from 11,812 online restaurant reviews on a popular restaurant review platform. A controlled experiment was conducted to further delineate the effect of review comprehensiveness. Findings Review comprehensiveness moderates the effects of review length and an extremely negative review on helpfulness. It also confirms that for reviews of the same length, one covering more aspects is perceived by consumers as more helpful. Practical implications Different aspects of information in a review can efficiently assist decision-making. The results suggest that review platforms can better design their interface by providing separate areas for different product aspects. The platform can then receive more comprehensive and helpful reviews and increase the diagnosticity of these. Originality/value The study enriches the literature by introducing review comprehensiveness and examining the joint effects of review length and comprehensiveness on helpfulness. It also contributes to the literature by indicating how to reduce the effect of review extremity.

A big data exploration of the informational and normative influences on the helpfulness of online restaurant reviews

With the proliferation of user generated online reviews, uncovering helpful restaurant reviews is increasingly challenging for potential consumers. Heuristics (such as “Likes”) not only facilitate this process but also enhance the social impact of a review on an Online Opinion Platform. Based on Dual Process Theory and Social Impact Theory, this study explores which contextual and descriptive attributes of restaurant reviews influence the reviewee to accept a review as helpful and thus, “Like” the review. Utilising both qualitative and quantitative methodologies, a big data sample of 58,468 restaurant reviews on Zomato were analysed. Results revealed the informational factor of positive recommendation framing and the normative factors of strong argument quality and moderate recommendation ratings, influence the generation of a reviewee “Like”. This study highlights the important filtering function a heuristic can offer prospective customers which can also result in greater social impact for the Online Opinion Platform.

Impact of air quality on online restaurant review comprehensiveness

Comprehensiveness is one of the most important textual content features of online review and exhibits significant impacts on consumer’s buying decisions. This paper explores the effect of air quality on review comprehensiveness by using a large-scale daily restaurant review dataset. By applying panel data and text mining method, we report an emotion-based underlying mechanism for the link between ambient air pollution levels and review comprehensiveness. Specifically, we show that air pollution levels significantly decrease review comprehensiveness, and emotion arousal mediates the relationship. Besides, our analyses reveal that the effect of the changing natural environment on reviews is asymmetrical, such that the negative relationship between air pollution levels and emotional arousal is stronger among novice reviewers. This research extends our current understanding of air pollution’s psychological and behavioral effects on review writers and suggests the importance of integrating air quality information into online review management strategies.

Beyond self-selection: the multilayered online review biases at the intersection of users, platforms and culture

PurposeOnline review bias research has predominantly focused on self-selection biases on the user’s side. By collecting online reviews from multiple platforms and examining their biases in the unique digital environment of “Chinanet,” this paper aims to shed new light on the multiple sources of biases embedded in online reviews and potential interactions among users, technical platforms and the broader social–cultural norms.Design/methodology/approachIn the first study, online restaurant reviews were collected from Dianping.com, one of China's largest review platforms. Their distribution and underlying biases were examined via comparisons with offline reviews collected from on-site surveys. In the second study, user and platform ratings were collected from three additional major online review platforms – Koubei, Meituan and Ele.me – and compared for possible indications of biases in platform's review aggregation.FindingsThe results revealed a distinct exponential-curved distribution of Chinese users’ online reviews, suggesting a deviation from previous findings based on Western user data. The lack of online “moaning” on Chinese review platforms points to the social–cultural complexity of Chinese consumer behavior and online environment that goes beyond self-selection at the individual user level. The results also documented a prevalent usage of customized aggregation methods by review service providers in China, implicating an additional layer of biases introduced by technical platforms.Originality/valueUsing an online–offline design and multi-platform data sets, this paper elucidates online review biases among Chinese users, the world's largest and understudied (in terms of review biases) online user group. The results provide insights into the unique social–cultural cyber norm in China's digital environment and bring to light the multilayered nature of online review biases at the intersection of users, platforms and culture.

Which factors influence locals’ and visitors’ overall restaurant evaluations?

PurposeFew studies to date have explored factors contributing to the dining experience from a visitor’s perspective. The purpose of this study is to investigate whether different restaurant attributes are critical in evaluating the restaurant experience in online reviews for visitors (non-local) and local guests.Design/methodology/approachIn all, 100,831 online restaurant reviews retrieved from TripAdvisor are analyzed by using domain-specific aspect-based sentiment detection. The influence of different restaurant features on the overall evaluation of visitors and locals is determined and the most critical factors are identified by the frequency of their online discussion.FindingsThere are significant differences between locals and visitors regarding the impact of busyness, payment options, atmosphere and location on the overall star rating. Furthermore, the valence of the factors drinks, facilities, food, busyness and menu found in the reviews also differs significantly between the two types of guests.Practical implicationsThe findings of this study help restaurant managers to better understand the different customer needs. Based on the results, they can better decide which restaurant aspects should receive the most attention to ensure that customers are satisfied.Originality/valueResearch on online reviews has largely neglected the role of different visitation motives. This study assumes that the reviews of local and non-local restaurant visitors are based on different factors and separates them to gain a more fine-grained and realistic picture of the relevant factors for each particular group.

To follow others or be yourself? Social influence in online restaurant reviews

Purpose Online reviews are often likely to be socially influenced by prior reviews. This study aims to examine key review and reviewer characteristics which may influence the social influence process. Design/methodology/approach Restaurant review data from Yelp.com are analyzed using an ordered logit model and text mining approach. Findings This study reveals that prior average review rating exerts a positive influence on subsequent review ratings for the same restaurant, but the effect is attenuated by the variance in existing review ratings. Moreover, social influence is stronger for consumers who had a moderate dining experience or invested less cognitive effort in writing online reviews. Compared to reviewers classified by Yelp as “elite,” non-elite reviewers appear more susceptible to the social influence of prior average review rating. Practical implications This study provides guidelines for mitigating the social influence of prior reviews and improving the accuracy of online product/service ratings, which will eventually enhance business and the reputation of online review platforms. Originality/value The findings from this study contribute to the electronic word-of-mouth (eWOM) literature and social influence literature in terms of the bidirectional nature of social influence on eWOM.

Full Disclosure: How Smartphones Enhance Consumer Self-Disclosure

Results from three large-scale field studies and two controlled experiments show that consumers tend to be more self-disclosing when generating content on their smartphone versus personal computer. This tendency is found in a wide range of domains including social media posts, online restaurant reviews, open-ended survey responses, and compliance with requests for personal information in web advertisements. The authors show that this increased willingness to self-disclose on one’s smartphone arises from the psychological effects of two distinguishing properties of the device: (1) feelings of comfort that many associate with their smartphone and (2) a tendency to narrowly focus attention on the disclosure task at hand due to the relative difficulty of generating content on the smaller device. The enhancing effect of smartphones on self-disclosure yields several important marketing implications, including the creation of content that is perceived as more persuasive by outside readers. The authors explore implications for how these findings can be strategically leveraged by managers, including how they may generalize to other emerging technologies.

Halal versus Hipster: the struggle for Sydney road and the culture of complaint

PurposeSydney road in Melbourne is a multicultural experiment where Hipster youth culture contests the street space with a variety of immigrants from the Middle East and Indian subcontinent who consumer Halal food. This paper explores the relationship between these through an examination of their respective cuisines and negative online restaurant reviews. The purpose of this paper is to explore the nature of conflict as it expresses itself in this way and to consider the wider political context in passing.Design/methodology/approachGoogle online restaurant reviews for all food outlets in the area were tabulated and then the percentage of negative reviews was calculated. Restaurants were split into categories by area and by ethnicity. The results were then discussed with reference to other research in this area.FindingsHipster, Middle Eastern Halal and pubs enjoyed the lowest levels of negative online reviews. Indian, Pakistani and chain restaurants had the highest levels of negative online reviews. This could be due to many undetermined causes.Research limitations/implicationsMore powerful analytical tools were not used. Also, the text of the reviews was not examined or discussed.Practical implicationsRestaurants who attract larger percentages of negative reviews should try to improve their service offerings. This can be achieved by the standardisation of market offering.Social implicationsThe local municipality, which is Moreland council should consider focussing on promoting and expanding this precinct and encouraging the emergence of several zones so that Islamic tourism and way of life can be facilitated.Originality/valueThis is a simple and interesting approach, which encapsulates the paradoxes and dynamics of the Australian attitude to multiculturalism by focusing on a single aspect of customer response in a consumption setting. As such it is an antidote to excessive theorising and empiricism. Readers will enjoy this study and may use the approach to guide their own choice of dining venues.

Information Processing on Online Review Platforms

Online reviews represent an important decision aid for consumers. Therefore, the question of whether online reviews reflect the currently available information is of high importance. Nevertheless, previous research neglects information processing on online review platforms. We address this research gap and analyze whether restaurant health inspection results have an impact on the review generation process of online restaurant reviews. We find that while severe health inspection results lead to changes of online review star ratings, information processing depends on the current environment. We find indications for corrective actions after critical health inspections: on the one hand, the restaurant health score improves, which shows an increase in restaurant quality. On the other hand, we observe indications for an increased amount of fake reviews in case of poorly-graded restaurants. We contribute to theory by providing an understanding of the nature of information processing on online review platforms. For practitioners, our findings allow for an understanding of the dynamics of online review generation. Furthermore, we outline the importance of considering the risk of deceptive behavior.

Predicting the Helpfulness of Online Restaurant Reviews Using Different Machine Learning Algorithms: A Case Study of Yelp

Helpful online reviews could be utilized to create sustainable marketing strategies in the restaurant industry, which contributes to national sustainable economic development. This study, the main aspects (including food/taste, experience, location, and value) from 294,034 reviews on Yelp.com were extracted empirically using the Latent Dirichlet Allocation (LDA) and positive and negative sentiment were assigned to each extracted aspect. Positive sentiments were associated with food/taste, while negative sentiments were associated with value. This study further proves a robust classification algorithm based on Support Vector Machine (SVM) with a Fuzzy Domain Ontology (FDO) algorithm outperforms other traditional classification algorithms such as Naïve Bayes (MB) and SVM ontology in predicting the helpfulness of online reviews. This study enriches the literature on managerial aspects of sustainability by analyzing a large amount of plain text data that customers generated. The results of this study could be used as sustainable marketing strategy for review website developers to design sophisticated, intelligence review systems by enabling customers to sort and filter helpful reviews based on their preferences. The extracted aspects and their assigned sentiment could also help restaurateurs better understand how to meet diverse customers’ needs and maintain sustainable competitive advantages.