Exploring user-generated content related to dining experiences of consumers with food allergies

The purpose of this research was to explore and assess factors influencing perceptions of consumers with food allergies toward restaurants when accommodating allergen-free requests. Mixed approaches, including big data analytics (i.e., topic modeling), content analysis, and multiple regression analyses were performed to analyze user-generated reviews for restaurants listed on AllergyEats.com, an information-sharing platform for consumers with food allergies. Among the 40 topics identified, “knowledgeable staff” was the most prevalent topic. Results of topic correlation analyses revealed five groups of topics: customized orders, efforts of staff, menu options, fried foods and oil, and communication with shared latent features. Four topics in the group of “efforts of staff” had the highest positive impact on restaurant ratings, while two topics in the group of “communications” had the strongest negative impacts. Foodservice managers and educators may use the results of this study to better accommodate consumers with food allergies and develop appropriate training programs.     

Restaurant survival prediction using customer-generated content: An aspect-based sentiment analysis of online reviews

Business failure prediction or survival analysis can assist corporate organizations in better understanding their performance and improving decision making. Based on aspect-based sentiment analysis (ABSA), this study investigates the effect of customer-generated content (i.e., online reviews) in predicting restaurant survival using datasets for restaurants in two world famous tourism destinations in the United States. ABSA divides the overall review sentiment of each online review into five categories, namely location, tastiness, price, service, and atmosphere. By employing the machine learning–based conditional survival forest model, empirical results show that compared with overall review sentiment, aspect-based sentiment for various factors can improve the prediction performance of restaurant survival. Based on feature importance analysis, this study also highlights the effects of different types of aspect sentiment on restaurant survival prediction to identify which features of online reviews are optimal indicators of restaurant survival.     

Motivation and satisfaction of Chinese and U.S. tourists in restaurants: A cross-cultural text mining of online reviews

Tourists with dissimilar cultural backgrounds think and behave differently. Precisely capturing and correctly understanding the cultural difference will help tourism managers generate greater customer satisfaction and increased business revenue. To this end, this paper uncovers and compares the motivation and satisfaction of restaurant tourist customers coming from China and U.S. by investigating their online ratings and reviews. From two major online review communities, customer ratings and reviews have been retrieved, quantified, text-mined, compared, and interpreted using statistics, latent Dirichlet allocation, and frequency analysis. Results suggest that Chinese tourists are less inclined to assign lower ratings to restaurants, and are more strongly fascinated by the food offered, whereas U.S. tourists are more apt to be fun-seeking, and are less uncomfortable with crowdedness.     

The creation of memorable dining experiences: formative index construction

Memorable experiences in restaurant settings are not well understood, and little is known about what they represent and how they should be measured. This study filled this knowledge gap by developing a formative index to assess and measure memorable dining experiences (MDE). Drawing upon strategic experiential modules, MDE was conceptualized as a second-order formative construct consisting of five first-order dimensions including sensory, affect, behavioral, social, and intellectual. This study adopted a mixed methods approach combining in-depth interviews and an online survey to determine and evaluate the underlying structure of MDE. Using PLS-PM as the analytical technique, the results of the data supported that MDE was a second-order formative construct, with 37 items capturing the five first-order dimensions. The study provided a comprehensive conceptualization of the MDE and a measurement scale with strong psychometric properties to help hospitality managers create and measure such experiences to gain a competitive advantage in the market. Detailed implications for academics and practitioners are provided.     

Ecotourism experiences reported online: Classification of satisfaction attributes

This study proposed a quantitative method for evaluating ecotourism experiences reported online by U.S. travelers to Costa Rica. The user-generated content (UGC) used in this study was 373 reviews extracted from TripAdvisor^®. By applying the content analysis technique, 26 attributes that influence ecotourists’ satisfaction with their ecolodge stays were identified and further aggregated into seven categories: ecolodge settings, room, nature, service, food, location, and value for money. A two-step non-parametric statistical procedure was developed to quantitatively support the classification of attributes into satisfiers, dissatisfiers, criticals, and neutrals, the typology first proposed by Cadotte and Turgeon [(1988). Dissatisfiers and satisfiers: suggestions from consumer complaints and compliments. Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior, 1(1), 74-79]. The proposed procedure is considered an original contribution of the article to the literature. The authors hope that the results from this study can be useful to ecolodge managers to evaluate performance in critical areas and develop strategies to maximize customer satisfaction through better utilization of limited resources.     

Investigating cross-national heterogeneity in the adoption of online hotel reviews

Increasing reliance of potential guests on online hotel reviews has given rise to trepidation among some hospitality managers recently, thus necessitating a better understanding of its adoption. Literature on multicultural studies have suggested that behavioral models do not universally hold across cultures. In view of the limited generalizability and applicability of extant cross-cultural studies to this context, this study generates cross-national insights into the antecedents of travelers’ intention to use online hotel reviews to better understand the use of consumer-generated reviews across cultures. Generally, the predictive power of the model derived from the Motivation Theory and TAM holds true for both the United States and Singapore. Nonetheless, tests for structural invariance reveals some noteworthy differences between the two countries. The results highlight the complex cognitive mechanisms determining the acceptance of online hotel reviews in each country as moderated by national culture orientations. Findings hold implications for practitioners and researchers as they navigate through social media in different cultural contexts.     

Hosts’ online affinities and their impacts on the number of online reviews on peer-to-peer platforms

Different from traditional hotels, hosts' affinity is an essential feature of homestays. This study uncovers how Airbnb hosts demonstrate their affinity online through the data from 114,310 listings, and examines the relationship between hosts' affinity and the number of reviews, as well as the moderating role of hosts' affinity in the associations among sub-ratings, host listings count, and the number of reviews. The findings indicate a positive link between hosts' affinity and the number of reviews. Guests tend to assign higher overall scores to homestays with poor performance but high affinity, and may alter their homestay attribute requirements in future reservation decisions. Hosts' affinity from guest reviews has greater direct and moderating effects on the number of reviews than hosts' self-statements. Interestingly, hosts' affinity from guest reviews can reduce the guests' requirements for homestay attributes of multiple listings. Meanwhile, hosts’ affinity displayed by host self-statements exhibits the opposite moderating effects.     

Exploring the online buying behavior of specialty food shoppers

Food producers are experiencing a fast-growing need to use the Internet to enhance competitive advantage. Past researchers have urged the need to understand market segmentation mechanisms as applied to different consumer behavior models to better understand the online buying behavior of consumers. This study integrates the Theory of Planned Behavior and food-related lifestyle to explore consumer's characteristics of online specialty food buying behavior, and the differences in the online buying process among consumers with different FRL. 569 undergraduate students who purchased specialty food online were surveyed. Findings indicated that consumers have positive attitudes toward purchasing specialty food online; more are inclined to heed the suggestions of others, perceive higher levels of control when using a website, and experienced a higher intention to purchase online. The study then classified consumers into ‘traditionalists’ or ‘adventurous and healthy-conscious’ groups based on their FRL via a two-step cluster analysis. These two groups of consumers had significant differences in terms of (a) attitudes toward online specialty food buying, (b) subjective norms, (c) perceived control, (d) behavioral intention, (e) demographics and (f) online specialty food-buying behavior. This investigation explored whether there is a correlation between consumer FRL and online specialty food-buying behavior. Findings reveal relevant ways for managers to enhance their website marketing strategies.     

Finding the reviews on yelp that actually matter to me: Innovative approach of improving recommender systems

As many readers struggle with massive textual information on review websites, developing optimized recommender systems that assist readers in identifying relevant reviews is critical. The present study aims to explore and predict the relationship between a reviewer’s evaluation of distinct attributes (i.e., importance and sentiment of a restaurant aspect)² and overall satisfaction (i.e., generic numerical rating of a restaurant). Latent Aspect Rating Analysis is modified to achieve the goal. The study identifies five restaurant attributes: food & drinks, customer service, dining atmosphere, restaurant value, and location. Restaurant value contributes most from the importance perspective and food & drinks contributes most from the sentiment perspective. Restaurant value ranks the first as the overall satisfaction of attributes (i.e., combination of importance and sentiment). Accordingly, the present study suggests a supplement of the “dynamic” recommender systems. This study offers scholars and practitioners a refined approach to analyze wealthy review content.     

A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism

Online consumer reviews have been studied for various research problems in hospitality and tourism. However, existing studies using review data tend to rely on a single data source and data quality is largely anecdotal. This greatly limits the generalizability and contribution of social media analytics research. Through text analytics this study comparatively examines three major online review platforms, namely TripAdvisor, Expedia, and Yelp, in terms of information quality related to online reviews about the entire hotel population in Manhattan, New York City. The findings show that there are huge discrepancies in the representation of the hotel industry on these platforms. Particularly, online reviews vary considerably in terms of their linguistic characteristics, semantic features, sentiment, rating, usefulness as well as the relationships between these features. This study offers a basis for understanding the methodological challenges and identifies several research directions for social media analytics in hospitality and tourism.     

The view from above: the relevance of shared aerial drone videos for destination marketing

ABSTRACT

The use of drones to produce videos has generated a large amount of visually appealing footage of various destinations. They attract much attention, but there are issues that affect their production, and their relevance to destination marketing. This research examines YouTube meta-data and spatial overlay analysis of shared aerial drone videos from the United Kingdom (UK). The results suggest that shared aerial drone videos have some unique user-generated content (UGC) characteristics and their spatial distribution tends to favor more populated areas. Theoretical and practical implications for destination marketing are further discussed.     

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.     

Text mining a decade of progress in hospitality human resource management research: Identifying emerging thematic development

The authors identified the emerging research streams based on the published research literature in human resource management (HRM) from 1994 to 2003 in the International Journal of Hospitality Management. Textual data were collected and content-analyzed by a text-mining program aided by human judgments. The results from the content analysis of both the computer-aided and human judgmental methods were then integrated and conceptually graphed to map meaningful findings that were logically precise, humanly readable, and computationally tractable. Through this unique approach, nine major HRM research themes emerged and each thematic development based on time and country was interpreted and discussed.     

Exploring the underlying factors of customer value in restaurants: A machine learning approach

This study explores the underlying factors of customer value in a restaurant setting by applying machine learning-based natural language processing techniques to the analysis of a vast amount of online customer reviews. The study identifies 14 factors that reflect a holistic view of previous research on customer value. The findings suggest that the comprehensive approach incorporating cognitive and affective aspects into the experiential perspective of value gain deeper insights into the nature of customer value. Moreover, the study uniquely finds new factors that prior research has rarely investigated. This study proposes a methodological framework that enables researchers to test the quantitative measure of customer perceptions derived from unstructured online reviews. Practitioners will be able to use the findings to understand customers better and enhance service operations.     

A behavioral analysis of web sharers and browsers in Hong Kong using targeted association rule mining

With the widespread use of Internet technology, electronic word-of-mouth [eWOM] communication through online reviews of products and services has a strong influence on consumer behavior and preferences. Although prior research efforts have attempted to investigate the behavior of users regarding the sharing of personal experiences and browsing the experiences of others online, it remains a challenge for business managers to incorporate eWOM effects into their business planning and decision-making processes effectively. Applying a newly proposed association rule mining technique, this study investigates eWOM in the context of the tourism industry using an outbound domestic tourism data set that was recently collected in Hong Kong. The complete profiles and the relations of online experience sharers and travel website browsers are explored. The empirical results are useful in helping tourism managers to define new target customers and to plan more effective marketing strategies.     

Mindset matters in purchasing online food deliveries during the pandemic: The application of construal level and regulatory focus theories

Online delivery providers (ODPs), representing a growing hospitality industry sector, are playing an unprecedented role in the coronavirus pandemic. Applying construal level theory and regulatory focus theory, this research investigates how the interplay of construal mindsets and message frames affects consumers’ purchase intention regarding online food deliveries during the pandemic. Two 2 × 2 experiments were conducted in severe and mild pandemic regions and revealed different results in consumers’ responses. Specifically, promotion-framed messages are more effective when matching a “how” construal mindset in severe regions. In contrast, promotion-framed messages are more persuasive in conjecture with a “why” construal mindset in mild pandemic areas. Besides, the persuasion process is mediated through self-efficacy in severe regions but through perceived benefit in mild regions. The study also reveals different moderating effects of risk propensity in the two regions. The findings provide guidelines for ODPs in engaging consumers with online food delivery services.     

Exploring the economic,social and cultural rights of youth leaders working in Sport for Development initiatives at grassroots level in South Africa

Abstract

Sport has not only been forwarded as a basic human right but it is a powerful tool in the advocacy of human rights. Social inclusion and gender equality through sport interventions are prevalent in the developing world. Change agents working in the field of Sport for Development applaud the success of interventions in marginalised and impoverished communities as sport is viewed as an effective tool that contributes to the social inclusion and the betterment of the poor. In a developing country, such as South Africa, unemployed youth are recruited as youth leaders in an attempt to improve their economic, social and cultural standing through their involvement in sport for development interventions. This Research Note highlights the tension between Sport for Development initiatives in South Africa that carry messages of human rights and the failure of the current system to fulfil the economic, social and cultural rights of youth leaders. This study explores the experiences of youth leaders in South Africa through the lens of ‘The International Covenant on Economic, Social and Cultural Rights’ (ICESCR). A qualitative, ethnographic approach was used to address the question: How can the socio-economic rights of youth leaders working on grassroots level in Sport for Development initiatives be recognised? The qualitative research findings indicated youth leaders perceive their work as important, but do not believe that the remuneration they receive allow for a decent living; and, that they do not believe that they have an equal opportunity for career progression within the Non-Government Organisations (NGOs) that they work for. Recommendations on how to address the rights of youth leaders are provided.     

The intellectual structure of research in hospitality management: A literature review using bibliometric methods of the journal International Journal of Hospitality Management

The purpose of this study is to analyze the existing literature on hospitality management from all the research papers published in The International Journal of Hospitality Management (IJHM) between 2008 and 2014. The authors apply bibliometric methods – in particular, author citation and co-citation analyses (ACA) – to identify the main research lines within this scientific field; in other words, its ‘intellectual structure’. Social network analysis (SNA) is also used to perform a visualization of this structure. The results of the analysis allow us to define the different research lines or fronts which shape the intellectual structure of research on hospitality management.