Search Results (151)

Regulatory air pollution monitoring is performed using a sparse monitoring network designed to provide background concentrations of pollutants but may miss small area variations due to local emission sources. Low-cost air pollution sensors operated by trained citizen scientists provide an opportunity to fill this gap. We describe the development and implementation of an air pollution monitoring and community engagement plan in response to resident concerns regarding excessive smoke production from a neighborhood restaurant. Particulate matter (PM_2.5) was measured using a low-cost, portable sensor. When cooking was taking place, the highest PM_2.5 readings were within 50 m of the source (mean PM_2.5 36.9 µg/m³) versus greater than 50 m away (mean PM_2.5 13.0 µg/m³). Sharing results with local government officials did not result in any action to address the source of the smoke emissions, due to lack of jurisdiction. A review of air pollution regulations across the United States indicated that only seven states regulate food cookers and six states specifically exempted cookers from air pollution regulations. Concerns about the smoke were communicated with the restaurant owner who eventually changed the cooking fuel. Following this change, less smoke was observed from the restaurant and PM_2.5 measurements were reduced to background levels. Although current environmental health regulations may not protect residents living near sources of food cooker-based sources of PM_2.5, community engagement shows promise in addressing these emissions. Full article

With the growing demand for labeled textual data in Natural Language Processing (NLP), traditional data collection and annotation methods face significant challenges, such as high cost, limited scalability, and privacy constraints. This study presents a novel web-based platform that automates text data generation and labeling by integrating Llama 3.3, an open-source large language model (LLM), with advanced prompt engineering techniques. A core contribution of this work is the Attributed Prompt Engineering Framework, which enables modular and configurable prompt templates for both data generation and labeling tasks. This framework combines zero-shot, few-shot, role-based, and chain-of-thought prompting strategies within a unified architecture to optimize output quality and control. Users can interactively configure prompt parameters and generate synthetic datasets or annotate raw data with minimal human intervention. We evaluated the platform using both benchmark datasets (AG News, Yelp, Amazon Reviews) and two fully synthetic datasets we generated (restaurant reviews and news articles). The system achieved 99% accuracy and F1-score on generated news article data, 98% accuracy and F1-score on generated restaurant review data, and 92%, 90%, and 89% accuracy and F1-scores on the benchmark labeling tasks for AG News, Yelp Reviews, and Amazon Reviews, respectively, demonstrating high effectiveness and generalizability. A usability study also confirmed the platform’s practicality for non-expert users. This work advances scalable NLP data pipeline design and provides a cost-effective alternative to manual annotation for supervised learning applications. Full article

This study investigates how pandemic-induced emotional disruption has reshaped sustainable consumer behavior in the digital age, with a focus on the continued influence of safety measures in the restaurant industry. As societies transition beyond COVID-19 restrictions, health-related anxieties persist, driving consumers to prioritize hygiene and risk reduction in their decision-making. Drawing on large-scale data from TripAdvisor and OpenTable, we analyze the effects of digitally communicated safety protocols on restaurant booking behavior across major U.S. cities. Our findings reveal that safety communication remains a salient factor in consumer choice, even after the acute phase of the pandemic. This effect is particularly pronounced in lower-tier restaurants, where visible digital safety signals help build trust and compensate for weaker brand equity. Conversely, in upscale establishments, where baseline hygiene standards are presumed, the marginal benefit of safety signaling is reduced. The study also identifies enduring patterns of emotional expression and anxiety in online reviews, indicating the long-term psychological imprint of the pandemic on consumer sentiment. By situating safety communication as both a psychological reassurance mechanism and a strategic digital marketing tool, this research contributes to the emerging discourse on sustainable marketing in post-crisis contexts. The results offer theoretical and managerial insights into how businesses can integrate health assurance into long-term brand strategies, reinforcing trust and resilience in digitally mediated, post-pandemic consumption environments. Full article

Food loss and waste are critical challenges in Taiwan’s agri-food supply chain, deteriorating security and resource efficiency. By employing the best–worst method (BWM), a multi-criteria decision-making model was developed in this study to evaluate the severity of losses and wastes. Combining literature review results with expert survey analysis results, key loss points, and mitigation strategies were identified to enhance sustainability and efficiency in Taiwan’s agricultural food system. Among the seven stages of the agricultural food supply chain, supermarket waste (16.95%) was identified as the severest, followed by government policies (16.63%), restaurant waste (15.35%), processing loss (14.71%), production site loss (13.64%), household waste (11.93%), and logistics/storage/distribution loss (10.79%). In the subcategories of each supply chain stage, the eight severe issues were identified as “Inadequate planning and control of overall production and marketing policies” under government policies, “Adverse climate conditions” and “Imbalance in production and marketing” under production site loss, “Inaccurate market demand forecasting” and “Poor inventory management at supermarkets” under supermarket waste, and “Improper storage management of ingredients leading to spoilage” as well as “Inability to accurately forecast demand due to menu diversity” under restaurant waste. The least severe issues included “Poor production techniques” under production site loss. Other minor issues included “Inefficient use of ingredients due to poor cooking skills”, “Festive culture and traditional customs”, and “Suboptimal food labeling design”, all of which contributed to household waste. Based on these findings, we proposed recommendations to mitigate food loss and waste in Taiwan’s agricultural food supply chain from practical, policy, and academic perspectives. The results of this study serve as a reference for relevant organizations and stakeholders. Full article

A sentiment analysis is a Natural Language Processing (NLP) task that identifies the opinion or emotional tone of documents such as customer reviews, either at the general or detailed level. Improving domain-specific models is important, as it provides smaller and better-suited models that can be implemented by entities that own textual data. This paper presents a deep learning model trained on Portuguese restaurant reviews using recurrent and self-attention mechanisms, which have consistently delivered strong results in prior research studies. Designing an effective model involves numerous hyperparameters and architectural choices. To address this complexity, a discrete genetic algorithm was used to find an optimal configuration, selecting the layer types, placement of self-attention, dropout rate, and model dimensions and shape. A key outcome of this study was that the optimization process produced a model that is competitive with a Bidirectional Encoder Representation from Transformers (BERT) model retrained for Portuguese, which was used as the baseline. The proposed model achieved an area under the curve of 92.1% and F1-score of 75.4%, demonstrating that a small, optimized model can compete and even outperform larger state-of-the-art models. Moreover, this work helps address the scarcity of NLP resources for Portuguese, and highlights the potential of customized architectures over generic solutions. Full article

Salt intake, specifically NaCl, should be reduced to prevent hypertension. Ramen often contains high-NaCl soup in Japan, but there are no reports of the actual sodium (Na) or potassium (K) contents. We visited ramen restaurants in Kyoto that had received high ratings on review sites and sampled the soups and toppings (n = 52). The Na and K concentrations were measured using ion electrodes, and the NaCl and K contents per serving and Na/K ratio were calculated. The results were compared among different types of “tare” (thick seasonings) (salt, soy sauce, and miso) and different types of broth (chicken, pork bone, and fish). The average NaCl and K contents per serving were 6.53 ± 1.48 g and 448 ± 141 mg, respectively, and Na/K was 10.7 ± 4.3 mmol/mmol. Considering the different broths, the Na/K ratio was the lowest for fish (9.6 ± 5.2), followed by chicken (10.0 ± 3.1) and pork bone (13.2 ± 4.8). Ramen dishes were high in both Na and the Na/K ratio. The low Na/K of fish soup ramen was thought to be due to the higher K content extracted from dried fish. Depending on the broth, it is possible to reduce the Na/K ratio. Full article

The end of the pandemic has been officially declared; however, the requirement to ensure hygienic living conditions in tourist accommodations remains a top priority for all hotel establishments and a prerequisite for every customer. Our systematic review studied the level of effectiveness of existing technological means and practices in order to limit COVID-19 infections and to protect customers from other factors aggravating their health, focusing on hotel restaurants. The PRISMA-S method was used. Database research (ABI/INFORM, ProQuest, Scopus EBSCO Business Source Premier, CBCA Business, Pubmed, and Embase) was undertaken between 6/2020 and 4/2024 with keywords comprising “hotels restaurants”, “health and safety”, “effectiveness/efficacy”, “primary analysis”, secondary analysis”, etc. In total, 1110 articles were initially identified, but eventually, 20 papers were selected comprising customer-level questionnaires, systematic reviews, and expert opinions/surveys. Different criteria were used for study assessment according to the type of study. So far, only a very limited number of studies have focused on the effectiveness of different health and safety measures in hotel restaurants. Even though the studies focusing on AI, robotics, and further technological means for enhancing customer satisfaction and the overall level of cleanliness are quite limited, the constant investment of hotels and restaurants in new technologies appears to be a one-way road. Full article

This study proposes an Aspect-Enhanced Prompting (AEP) method for unsupervised Multi-Source Domain Adaptation in Aspect Sentiment Classification, where data from the target domain are completely unavailable for model training. The proposed AEP is based on two generative language models: one generates a prompt from a given review, while the other follows the prompt and classifies the sentiment of an aspect. The first model extracts Aspect-Related Features (ARFs), which are words closely related to the aspect, from the review and incorporates them into the prompt in a domain-agnostic manner, thereby directing the second model to identify the sentiment accurately. Our framework incorporates an innovative rescoring mechanism and a cluster-based prompt expansion strategy. Both are intended to enhance the robustness of the generation of the prompt and the adaptability of the model to diverse domains. The results of experiments conducted on five datasets (Restaurant, Laptop, Device, Service, and Location) demonstrate that our method outperforms the baselines, including a state-of-the-art unsupervised domain adaptation method. The effectiveness of both the rescoring mechanism and the cluster-based prompt expansion is also validated through an ablation study. Full article

The hospitality and tourism (H&T) sector, marked by intense employee–customer interactions, dynamic labor shifts, and high physical and emotional labor demands, faces chronic talent acquisition and retention. Therefore, tailored employer branding (EB) strategies that address the unique characteristics of the H&T sector are essential for improving the current situation. Despite the critical need for tailored solutions, a clear and unified approach for H&T companies to effectively manage their EB strategies, including the development of a compelling employee value proposition (EVP) that resonates with targeted professionals, has yet to be established. Following the PRISMA reporting guidelines, a systematic literature review of 30 peer-reviewed articles from the Scopus and Web of Science databases was conducted to synthesize existing research on EB practices in the H&T sector. The results reveal a fragmented literature that lacks a cohesive framework for categorizing and measuring EVP. The use of varied and inconsistent EVP models and scales across studies hampers comparative analysis and limits the development of generalizable insights. Furthermore, the review highlights a concentration of research within the hotel industry, leaving other important H&T industries, such as the restaurant and cruise industries, underexplored. This SLR emphasizes the urgent need for a unified approach to EB in H&T. Based on these results, promising research avenues are suggested to further advance EB research in H&T, along with managerial implications for enhancing talent attraction and retention in the sector. Full article

This study investigates how review inconsistency influences perceived helpfulness in online restaurant reviews both in ratings and specific aspects of service attributes. Drawing on 106,464 Yelp reviews spanning 666 restaurants, we employed aspect-based sentiment analysis and Tobit regression to capture not only rating inconsistencies but also differences in sentiment toward décor, taste, service, and price. Results indicate that rating inconsistency negatively affects review helpfulness, suggesting that highly divergent ratings reduce credibility. However, aspect inconsistency shows mixed effects. Discrepancies in décor and taste positively influence perceived helpfulness by offering novel information, whereas service-related inconsistencies diminish review helpfulness, due to heightened consumer sensitivity to possible service failures. Reviewer expertise further strengthens the negative influence of inconsistency as readers expect experienced reviewers to provide objective feedback. These findings extend current research by shifting the analytical lens from individual reviews to sets of reviews, thereby capturing the relational dynamics that shape consumers’ perceptions of review credibility. The results also highlight the importance of analyzing review content by specific aspects to uncover nuanced effects. Practically, platforms can benefit from grouping reviews by attributes and alerting users to noteworthy inconsistencies, facilitating more informed consumer decision-making. Full article

The present study investigates the association between perceived authenticity (PAU), GCON, and behavioral intention within heritage restaurants in Hail, Saudi Arabia. By integrating cognitive appraisal theory (CAT) and the Stimulus-Organism-Response (SOR) framework, this research explores how authenticity perceptions influence both cultural and gastronomic experiences and contribute to sustainable consumption behavior. Also, it investigates the moderating role of consumer knowledge (CKNOW) in enhancing GCON and its subsequent impact on the behavioral intention (BINT) to dine at heritage restaurants. Employing a mixed-methods approach, this study first conducted a content analysis on online reviews to identify key attributes that shape authenticity perceptions. Subsequently, Smart PLS software was employed to conduct an analysis of 417 valid customers of heritage restaurants in Hail. The current study aims to investigate the customers of heritage restaurants in Hail, Saudi Arabia. The current findings showed that students formed a significant market share of heritage restaurants in Hail, Saudi Arabia. The findings confirm that perceived authenticity significantly enhances consumers’ cultural and gastronomic experiences, which in turn fosters GCON and strengthens the behavioral intention to visit authentic restaurants. Furthermore, GCON mediates the association between authenticity, cultural experiences, and purchase intention. Consumer knowledge further moderates this relationship, amplifying the positive effect of GCON on behavioral intention. This study contributes to the growing literature on sustainable gastronomy tourism by demonstrating the crucial interplay between authenticity, sustainability, and consumer knowledge in the heritage restaurant sector. It also offers practical recommendations for restaurant managers, policymakers, and tourism marketers to enhance the authentic dining experience while promoting environmentally responsible behavior. By fostering an awareness of cultural and environmental values, heritage restaurants can serve a critical role in advancing sustainable tourism development in Hail and beyond. Full article

This study integrates space syntax and big data from the catering industry to explore the impact of grid and organic street patterns on the spatial distribution of restaurants from the perspective of urban morphology. Space syntax is a set of theories and techniques for the analysis of spatial configurations. Focusing on five areas of the Macau Peninsula, this study models urban forms using space syntax. Syntactic parameters and Dianping data are analyzed through geographic visualization, correlation analysis, and descriptive statistics. The results reveal that grid-patterned streets provide a relatively equitable commercial environment through a structured hierarchy, whereas organic-patterned streets foster commercial diversity via more complex accessibility patterns. Additionally, at the local network level, a “cultural layer network” mechanism is revealed in organically shaped streets, supporting the stable distribution of different types of restaurants within specific accessibility ranges. For the first time, this study employs high precision (street-level accuracy), multidimensional analysis (number of restaurants and number of reviews), and a systematic methodology (“form-function” research framework) within the same space syntax model to uncover the effects of different urban morphologies on restaurant distribution. Collectively, these findings highlight street morphology’s key role in shaping vibrant commercial street networks in rapidly urbanizing contexts, reveal the morphological–socioeconomic synergy underpinning local catering ecosystems, and offer robust empirical guidance for integrated urban renewal, planning, and design strategies. Full article

The research of revenue management (RM) practices is widespread in the accommodation sector, but not in the restaurant industry. This study aims to ascertain which RM practices are the most used in the restaurant industry, organizing them by clusters, identifying those that imply profit maximization and describing the challenges of their implementation. Mixed methods were used as the methodology through a systematic literature review, which was submitted to a brief descriptive analysis and content analysis. Data were retrieved from the Scopus database, and, using the PRISMA diagram, 70 papers were collected for comprehensive analysis of their content. The results of the studies identified five main areas of RM and 21 practices, some specific to the restaurant industry, with reservations and meal duration management being the most used practices. Reservations have been implemented in many restaurants but are not a reality for all of them. A well-managed meal duration increases restaurant capacity. Furthermore, customer satisfaction implies the success of all other practices since customers must understand and accept the RM practices for their success. As a theoretical implication, this study contributes to the development of research into the RM practices of restaurants, and as practical implications, restaurant managers should implement the following practices: meal duration management, indicators, and table mix. This study contributes to future research, such as analyzing the relationship between sustainability and RM, applying RM to the beverages department, and including RM in consumer behavior in the context of future crises. Full article

Research published about the restaurant industry has experienced consistent growth over the past few decades. Yet, a comprehensive bibliometric review of this research has not been performed. This study aimed to perform such a review by examining peer-reviewed articles (n = 792) focused on restaurant business research published in the Scopus and Web of Science databases. Articles related to health or food science were not examined in this study. Articles were categorized as focusing on one of five types of restaurants (i.e., fast food, fast casual, casual dining, fine dining, and general restaurants), and the articles were analyzed within three research fields: consumer behavior, organizational behavior, and finance. The increasing number of research articles focused on the restaurant industry was found to have occurred during the same time as the number of hospitality scholars and the fast food restaurant sector expanded. Fast food restaurants received the most research attention and the fast casual types received the least. Consumer behavior research dominated the restaurant research, reflecting a focus on customer satisfaction and purchase intentions. Organizational behavior topics, such as workforce dynamics and organizational structure, received slightly less attention. Finance research was the least explored research field, likely due to its complexity and perceived indirect connection to consumer-facing outcomes. The study emphasizes the need for integrative research linking consumer behavior, organizational strategies, and financial decisions to provide a holistic understanding of restaurant performance and industry practices. Full article

This paper focuses on analyzing online visitor reviews of Romanian palaces and castles because these comments could represent the starting point for improving tourist offerings. Additionally, expert evaluations were included in analyzing the relevance of the information obtained for managerial decision-making at the company level. In the first stage of the analysis, 18 castles and palaces with a TripAdvisor account from three Romanian historical regions were identified: Moldavia, Muntenia (Wallachia), and Transylvania. The study used a qualitative technique to examine visitors’ cultural experiences by analyzing 1399 online evaluations from TripAdvisor. The findings reveal that visitors exhibited positive emotional reactions toward the castles and palaces in Romania and preferred integrated cultural services. The tourists expressed their opinions online related to visiting the respective castles/palaces, restaurants visited in the area, and the quality of the accommodation services. Research methods associated with expert evaluations applied periodically can become active support tools for managerial decisions in the adaptation/diversification of service packages based on the consumer category. The findings could serve as managerial resources for companies to enhance their cultural heritage offerings, better reach their target audiences, and align with their preferences and interests. Full article