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This study aims to investigate the promoting effects of the quantity and quality of online review user-generated photos (UGPs) on perceived review usefulness. The research further tests the hindering effect of human facial presence in review photos on review usefulness.

Based on review samples of restaurants in a tourist destination Las Vegas, this study used an integrated method combining a machine learning algorithm and econometric modeling.

Results indicate that the number of UGPs depicting a restaurant’s food, drink, menu and physical environment has positive impacts on perceived review usefulness. The quality of online review UGPs can also enhance perceived review usefulness, whereas facial presence in these UGPs hinders perceived review usefulness.

Findings suggest that practitioners can implement certain tactics to potentially improve consumers’ willingness to share more UGPs and UGPs with higher quality. Review websites could develop image-processing algorithms for identifying and presenting UGPs containing core attributes in prominent positions on the site.

To the best of the authors’ knowledge, this study is the first to present a comprehensive analytical framework investigating the enhancing or hindering roles of review photo quantity, photo quality and facial presence in online review UGPs on review usefulness. Using the heuristic-systematic model as a theoretical foundation, this study verifies the additivity effect and attenuation effect of UGPs’ visual elements on judgements of online review usefulness. Furthermore, it extends scalable image data analysis by adopting a deep transfer learning algorithm in hospitality and tourism.

While relevant research has considered aggregated data from mobile devices and personal computers (PCs), tourists’ search patterns on mobile devices and PCs differ significantly. This study aims to explore whether decomposing aggregated search queries based on the terminals from which these queries are generated can enhance tourism demand forecasting.

Mount Siguniang, a national geopark in China, is taken as a case study in this paper; another case, Kulangsu in China, is used as the robustness check. The authors decomposed the total Baidu search volume into searches from mobile devices and PCs. Weekly rolling forecasts were used to test the roles of decomposed and aggregated search queries in tourism demand forecasting.

Search queries generated from PCs can greatly improve forecasting performance compared to those from mobile devices and to aggregate search volumes from both terminals. Models incorporating search queries generated via multiple terminals did not necessarily outperform those incorporating search queries generated via a single type of terminal.

Major players in the tourism industry, including hotels, tourist attractions and airlines, can benefit from identifying effective search terminals to forecast tourism demand. Industry managers can also leverage search indices generated through effective terminals for more accurate demand forecasting, which can in turn inform strategic decision-making and operations management.

This study represents one of the earliest attempts to apply decomposed search query data generated via different terminals in tourism demand forecasting. It also enriches the literature on tourism demand forecasting using search engine data.

This paper aims to examine the effects of perceived authenticity at an independent, full-service mainstream ethnic restaurant and the moderating effects of diners’ cultural familiarity and cultural motivation on the influence of perceived authenticity on perceived value and behavioral intention.

A total of 417 self-administered questionnaires were collected from customers of an independent, full-service Italian restaurant in southeastern USA. The data analysis was performed using structural equation modeling.

Restaurant authenticity has a positive influence on perceived value. Respondents who are more familiar with and interested in Italian culture and food tend to attach more value to the restaurant authenticity. Respondents tend to use authenticity to convey quality judgment of the restaurant.

First, this study advances previous literature on dining authenticity by incorporating cultural familiarity and cultural motivation. Second, this study extends the theoretical framework of perceived quality of ethnic restaurants by connecting authenticity perceptions and quality assessment.

Results suggest that the managers at independent, full-service mainstream ethnic restaurants should focus on the restaurants’ environment and atmospheric authenticity, especially for customers who possess cultural familiarity and cultural motivation, while also ensuring the quality of food and service.

This study makes an initial attempt at studying the role of authenticity in a mainstream ethnic restaurant context and adds to the knowledge of restaurant authenticity from the perspectives of cultural familiarity, cultural motivation and perceived quality.

This study aims to investigate the influence of the reviewed establishment’s price level and the user’s social network size and reputation status on consumers’ tendency to post more visual imagery content. Furthermore, it explores the moderating effects of user experiences and geographic distance on these dynamics.

This study adopts a multi-method approach to explore both the determinants behind the sharing of user-generated photos in online reviews and their internal mechanisms. Using a comprehensive secondary data set from Yelp.com, the authors focused on restaurant reviews from a prominent tourist destination to construct econometric models incorporating time-fixed effects. To enhance the robustness of the authors’ findings, the authors complemented the big data analysis with a series of controlled experiments.

The reviewed establishments price level and the users reputation status and social network size incite corresponding motivations conspicuous display “reputation seeking” and social approval motivating users to incorporate more images in reviews. “User experiences can amplify the influence of these factors on image sharing.” An increase in the users geographical distance lessens the impact of the price level on image sharing, but it heightens the influence of the users reputation and social network size on the number of shared images.

As a result of this study, high-end establishments can increase their online visibility by leveraging user-generated visual content. A structured rewards program could significantly boost engagement by incentivizing photo sharing, particularly among users with elite status and extensive social networks. Additionally, online review platforms can enhance users’ experiences and foster more dynamic interactions by developing personalized features that encourage visual content production.

This research, anchored in trait activation theory, offers an innovative examination of the determinants of photo-posting behavior in online reviews by enriching the understanding of how the intricate interplay between users’ characteristics and situational cues can shape online review practices.

With the growing online review manipulation and fake reviews in the hospitality industry, it is not uncommon that a consumer encounters disconfirmation when comparing the existing online reviews with his/her own product or service evaluation. The purpose of this study is to investigate the influence of review disconfirmation on customer online review writing behavior.

This study used a mixed-method combining online secondary big data modeling and experimental design.

Review disconfirmation influences customers’ emotional responses embedded in the review; a customer who encounters review disconfirmation tends to exert more reviewing effort, manifested by writing longer reviews; negativity bias exists in disconfirmation effects, in that negative review disconfirmation shows more significant and stronger effects than positive review disconfirmation.

Findings from this study provide important managerial implications for business owners and marketers who attempt to influence online reviews. The study suggests that fictitious online review manipulation might be detrimental to the business.

This research contributes to two literature streams, including research on the social influence of online consumer reviews, and the relationship between disconfirmation and consumers’ post-consumption behavior, by extending the influence of disconfirmation from the offline context to the online context.

This paper aims to examine the influences of the number of hotel management responses and especially the textual similarity in hotel management responses to online reviews on hotel online booking.

This study used the data from 437 hotels in New York City on Expedia. The data specifically include online reviews, management responses and real-time number of online hotel bookings, which were merged to create one dataset for this study. To calculate the management response similarity, three widely recognized text mining functions of calculating textual similarity were adopted in this model. Fixed-effect panel data model was then used to examine the influence of management response to consumer online reviews on online hotel booking volume.

The empirical results demonstrate that the number of management responses to consumer online reviews does not significantly affect hotel booking; compared to none or only one management response, or management responses with low similarity, management responses with high similarity can significantly reduce the hotel booking on Expedia.

This study suggests that the similarity of management responses influences customers’ hotel booking, and hotel managers should avoid providing too similar management responses.

First, this study, for the first time, proposes the concept of management response similarity and its measurement methods. Second, this study takes an initial attempt to empirically test the influence of response similarity on hotel booking by using secondary data online.

This study aims to investigate how prior reviews posted by other consumers affect subsequent consumers’ evaluations and to what extent the review temporal distance can increase or reduce the social influence of prior reviews. In this study’s restaurant context, review temporal distance refers to the duration between dining time and review time of a dining experience.

The data of paired online restaurant reservations and reviews are analyzed using Ordered Logit Model. Two robustness checks are conducted to test the stability of the main estimation results.

The empirical results demonstrate that consumers’ restaurant evaluation is socially influenced by both the prior average review rating and number of prior reviews; review temporal distance has a direct negative effect on consumers’ restaurant evaluation; and review temporal distance increases the social influence of prior reviews.

This study suggests that online review matters. Both restaurants and the online review platforms should encourage consumers to share their experiences and post online reviews immediately after their consumption.

The study contributes to the literature on electronic word-of-mouth, social influence and psychological distance. First, the bi-directional nature of social influence on electronic word-of-mouth for experience-oriented product is documented. Second, for the first time, this study examines how review temporal distance could affect the social influence on consumers’ restaurant evaluation.

The purpose of this study is to test the local impact of COVID-19 pandemic on hotel performance at the individual property level, and further examine the roles of hotel attributes and business mix in potentially moderating or intensifying the impact of a crisis.

Using a sample of 5,090 hotel properties in Texas, USA from January 2020 to December 2021, this study estimates a monthly hotel performance model to evaluate how the pandemic affected hotels’ operational performance based on revenue per available room.

Results show that a 10% increase in the monthly number of confirmed COVID-19 cases led to a 0.522% decrease in hotel performance. Also, a series of moderators were identified within the pandemic–performance relationship: the negative impact of the pandemic was more severe among higher-end hotels and newer hotels; urbanization and localization diseconomies prevailed during the pandemic; and there was a smaller negative effect of COVID-19 on high rated hotels in the category of economy hotels.

The moderators highlighted in this paper shed light on the heterogeneity of COVID-19’s effects on hotel operations. Findings enrich the hospitality literature by considering business resilience in relation to the pandemic.

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.

Restaurant review data from Yelp.com are analyzed using an ordered logit model and text mining approach.

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.

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.

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.

Information and communication technologies (ICTs) are a key player in the food services and restaurants sector; thus, the aim of this work consists in studying the previous research on ICTs in food services and restaurants in the context of tourism and hospitality through a systematic literature review.

The systematic literature review is performed on full papers published in journals included in the Journal Citation Report of the WoS in the category of Hospitality, Leisure, Sport and Tourism. A total of 165 articles from 28 journals are analyzed, following different criteria, such as the research methods, perspectives, statistical techniques, geographical focus, topics, technologies, authors and universities.

The restaurant sector is more and more based on the creation of experiences and ICTs, through their multiple possibilities, can undoubtedly contribute to adding value to the simple meal and create and recreate experiences to attract and retain customers who are increasingly sophisticated and hooked on ICTs. ICTs are basic for managers taking decision at the highest level in food services and restaurants, so ICTs should not be seen as a technical tool but as an essential element for top management.

This paper examined articles from very well-known tourism and hospitality journals, leaving aside others as well as different publication formats such as books or papers presented at conferences.

A significant contribution made with this paper is the availability of a list of topics in the context of ICTs in food services and restaurants. These topics are classified into three areas (Consumers, Suppliers and Environment and Tendencies) that can serve as a future research framework. The paper also provides useful information to restaurant managers about ICTs, to researchers for their future projects and to academics for their courses.