Purpose – This study examines how employees in the tourism sector perceive and engage in external whistleblowing, with a particular focus on the roles of ethical relativism, risk-taking propensity, and fear of retaliation. Methodology – Employing a quantitative research design, the study analyzes data collected from 405 tourism employees in Turkey through descriptive statistics, factor analysis, and regression-based moderation analysis. Findings – The findings indicate that the most frequently observed unethical practices include discrimination, workplace bullying (mobbing), and mismanagement of resources. Notably, the results also highlight a general reluctance among employees to report such behaviors to external authorities. Furthermore, fear of retaliation was found to moderate the relationship between individuals’ risk-taking propensity and their likelihood of engaging in external whistleblowing. Implications – In response to increasing instances of corporate misconduct and the resulting decline in public trust, the development of regulations focused on corporate and organizational ethics has become essential. Within this context, whistleblowing is recognized as a crucial mechanism for detecting and preventing unethical practices within organizations.
Hospitality industry. Hotels, clubs, restaurants, etc. Food service
Purpose - This paper proposes to detect the determinants of tourists’ loyalty to accurately modelthe asymmetric responses of tourists. Methodology/Design/Approach - Loyalty is modelled by using an asymmetric Bayesian discrete choice model in two ways: the willingness to repeat a visit and to recommend a tourism destination. We use data from two destinations with different motivations to visit: a sun and beach destination (SBD, Barbados) and a cultural heritage destination (CHD, Ethiopia).Findings - The main results highlight several aspects. First, for a CHD, the ‘repeat visit pattern’occurs when previous visits are between two and four; additional visits are less likely to happen. On the contrary, for a SBD, tourists may establish a life-long relationship. Second, distance is an issue for tourists choosing a SBD, but not for a CHD. This relates to market structure, since SBD markets work under competition, whereas CHD markets function as natural monopolies. Finally,‘High level of studies’ is a key loyalty determinant in favor of a CHD, however, when that level is low, it provides an incentive to revisit a SBD, instead.Originality of the research – The paper proposes the use of methodology never before used in this field and obtains novel determinants when explaining loyalty.
Hospitality industry. Hotels, clubs, restaurants, etc. Food service
Food systems are responsible for a third of human-caused greenhouse gas emissions. We investigate what Large Language Models (LLMs) can contribute to reducing the environmental impacts of food production. We define a typology of design and prediction tasks based on the sustainable food literature and collaboration with domain experts, and evaluate six LLMs on four tasks in our typology. For example, for a sustainable protein design task, food science experts estimated that collaboration with an LLM can reduce time spent by 45% on average, compared to 22% for collaboration with another expert human food scientist. However, for a sustainable menu design task, LLMs produce suboptimal solutions when instructed to consider both human satisfaction and climate impacts. We propose a general framework for integrating LLMs with combinatorial optimization to improve reasoning capabilities. Our approach decreases emissions of food choices by 79% in a hypothetical restaurant while maintaining participants' satisfaction with their set of choices. Our results demonstrate LLMs' potential, supported by optimization techniques, to accelerate sustainable food development and adoption.
The wastage of perishable items has led to significant health and economic crises, increasing business uncertainty and fluctuating customer demand. This issue is worsened by online food delivery services, where frequent and unpredictable orders create inefficiencies in supply chain management, contributing to the bullwhip effect. This effect results in stockouts, excess inventory, and inefficiencies. Accurate demand forecasting helps stabilize inventory, optimize supplier orders, and reduce waste. This paper presents a Third-Party Logistics (3PL) supply chain model involving restaurants, online food apps, and customers, along with a deep learning-based demand forecasting model using a two-phase Long Short-Term Memory (LSTM) network. Phase one, intra-day forecasting, captures short-term variations, while phase two, daily forecasting, predicts overall demand. A two-year dataset from January 2023 to January 2025 from Swiggy and Zomato is used, employing discrete event simulation and grid search for optimal LSTM hyperparameters. The proposed method is evaluated using RMSE, MAE, and R-squared score, with R-squared as the primary accuracy measure. Phase one achieves an R-squared score of 0.69 for Zomato and 0.71 for Swiggy with a training time of 12 minutes, while phase two improves to 0.88 for Zomato and 0.90 for Swiggy with a training time of 8 minutes. To mitigate demand fluctuations, restaurant inventory is dynamically managed using the newsvendor model, adjusted based on forecasted demand. The proposed framework significantly reduces the bullwhip effect, improving forecasting accuracy and supply chain efficiency. For phase one, supply chain instability decreases from 2.61 to 0.96, and for phase two, from 2.19 to 0.80. This demonstrates the model's effectiveness in minimizing food waste and maintaining optimal restaurant inventory levels.
Legacy floor plans, often preserved only as scanned documents, remain essential resources for architecture, urban planning, and facility management in the construction industry. However, the lack of machine-readable floor plans render large-scale interpretation both time-consuming and error-prone. Automated symbol spotting offers a scalable solution by enabling the identification of service key symbols directly from floor plans, supporting workflows such as cost estimation, infrastructure maintenance, and regulatory compliance. This work introduces a labelled Digitised Electrical Layout Plans (DELP) dataset comprising 45 scanned electrical layout plans annotated with 2,450 instances across 34 distinct service key classes. A systematic evaluation framework is proposed using pretrained object detection models for DELP dataset. Among the models benchmarked, YOLOv8 achieves the highest performance with a mean Average Precision (mAP) of 82.5\%. Using YOLOv8, we develop SkeySpot, a lightweight, open-source toolkit for real-time detection, classification, and quantification of electrical symbols. SkeySpot produces structured, standardised outputs that can be scaled up for interoperable building information workflows, ultimately enabling compatibility across downstream applications and regulatory platforms. By lowering dependency on proprietary CAD systems and reducing manual annotation effort, this approach makes the digitisation of electrical layouts more accessible to small and medium-sized enterprises (SMEs) in the construction industry, while supporting broader goals of standardisation, interoperability, and sustainability in the built environment.
This study aims to explore the impact of the influence of financial ratios on financial distress with corporate governance as a moderation variable in tourism industry, hotel and restaurant service companies listed on the Indonesia Stock Exchange during the 2019-2023 period. The method used in this study is purposive sampling from a total of 50 companies, where 23 companies were selected because they met the criteria that have been set. The analysis was carried out to involve multiple linear regression and moderating regression analysis (MRA) by utilizing the IBM SPSS Statistics 23 application as a tool for statistical and hypothesis testing. The financial distress variable was driven using the Zmijewski X-Score formula. The findings of the study show that return on equity (ROE) has a negative and significant influence on financial distress. Current ratio (CR) has a positive and significant influence on financial distress. Debt to equity ratio (DER) has a positive and insignificant effect on financial distress. In the moderation test, it can be seen that gender diversity does not positively moderate the effect of return on equity (ROE) on financial distress. Similarly, gender diversity does not positively moderate the influence of the current ratio (CR) on financial distress. However, gender diversity is able to negatively moderate/weaken the influence of debt to equity ratio (DER) on financial distress. Institutional ownership negatively moderates/weakens the effect of return on equity (ROE) on financial distress. However, institutional ownership does not negatively moderate the influence of the current ratio (CR) on financial distress. On the other hand, institutional ownership is able to positively moderate/strengthen the influence of the Debt to equity ratio (DER) on financial distress.
This study aims to measure the impact of eco-gamification on sustainable tourist behaviour. Gamification is considered one of the modern and innovative trends in the field of information technology. An integrative model was developed to explore the impact of eco-gamification on sustainable tourist behaviour, through the mediating role of word of mouth. Using Warp pls 7, data collected from 344 Arab and foreign tourists who visited Egypt were analyzed. The study found that there is a direct relationship between eco-gamification and sustainable tourist behaviour. The results also showed that the word of mouth partially mediated the relationship between eco-gamification and the sustainability of tourist behaviour. Moreover, the study provided a set of recommendations for decision-makers in the tourist destinations and for experts in the technology field to design specific visual environmental games including information about the tourist destinations. They can help in maintaining sustainable development through influencing tourists' behaviour, as well as attracting tourists via using the gamification system. Furthermore, attention should be paid to the users’ word of mouth regarding the environmental games because it has a major role in conveying their positive experience to others, which works to shape sustainable behaviour.
Hospitality industry. Hotels, clubs, restaurants, etc. Food service, Business
The Metaverse is a concept that proposes to immerse users into real-time rendered 3D content virtual worlds delivered through Extended Reality (XR) devices like Augmented and Mixed Reality (AR/MR) smart glasses and Virtual Reality (VR) headsets. When the Metaverse concept is applied to industrial environments, it is called Industrial Metaverse, a hybrid world where industrial operators work by using some of the latest technologies. Currently, such technologies are related to the ones fostered by Industry 4.0, which is evolving towards Industry 5.0, a paradigm that enhances Industry 4.0 by creating a sustainable and resilient world of industrial human-centric applications. The Industrial Metaverse can benefit from Industry 5.0, since it implies making use of dynamic and up-to-date content, as well as fast human-to-machine interactions. To enable such enhancements, this article proposes the concept of Meta-Operator: an Industry 5.0 worker that interacts with Industrial Metaverse applications and with his/her surroundings through advanced XR devices. This article provides a description of the technologies that support Meta-Operators: the main components of the Industrial Metaverse, the latest XR technologies and the use of Opportunistic Edge Computing communications (to interact with surrounding IoT/IioT devices). Moreover, this paper analyzes how to create the next generation of Industrial Metaverse applications based on Industry 5.0, including the integration of AR/MR devices with IoT/IIoT solutions, the development of advanced communications or the creation of shared experiences. Finally, this article provides a list of potential Industry 5.0 applications for the Industrial Metaverse and analyzes the main challenges and research lines. Thus, this article provides useful guidelines for the researchers that will create the next generation of applications for the Industrial Metaverse.
Georges Dubourg, Zoran Pavlović, Branimir Bajac
et al.
The application of metal oxide nanomaterials (MOx NMs) in the agrifood industry offers innovative solutions that can facilitate a paradigm shift in a sector that is currently facing challenges in meeting the growing requirements for food production, while safeguarding the environment from the impacts of current agriculture practices. This review comprehensively illustrates recent advancements and applications of MOx for sustainable practices in the food and agricultural industries and environmental preservation. Relevant published data point out that MOx NMs can be tailored for specific properties, enabling advanced design concepts with improved features for various applications in the agrifood industry. Applications include nano-agrochemical formulation, control of food quality through nanosensors, and smart food packaging. Furthermore, recent research suggests MOx's vital role in addressing environmental challenges by removing toxic elements from contaminated soil and water. This mitigates the environmental effects of widespread agrichemical use and creates a more favorable environment for plant growth. The review also discusses potential barriers, particularly regarding MOx toxicity and risk evaluation. Fundamental concerns about possible adverse effects on human health and the environment must be addressed to establish an appropriate regulatory framework for nano metal oxide-based food and agricultural products.
Purpose Robot chefs and robot waiters have emerged in the restaurant industry. Based on the curiosity theory and the stimulus–organism–response paradigm, this study aims to understand the influence of robot restaurant attributes on customer behavioral intention before purchase. Design/methodology/approach Based on research data from 289 respondents comprising undergraduates, postgraduates and non-students in China, the theoretical model is tested via the partial least squares technique. Findings Food quality perception directly affects regular patronage intention, but it has no significant influence on experience intention. Service quality perception and high-tech atmosphere perception positively affect experience intention and regular patronage intention through the mediator of interest in robot restaurants. Originality/value Different from previous studies focusing on human employee restaurants, this study, to the best of the authors’ knowledge, is the first to systematically investigate the influence of robot restaurant attributes on customer behavioral intention, and it finds that these attributes have their own uniqueness vis-à-vis influencing customer behavioral intention.
Isabel Sofia Loureiro, Helena Gomes, Vânia Costa
et al.
Health and wellness tourism had a growing interest in the Portuguese population until 2019. However, with the appearance of Covid-19, several establishments had to close over these two years, this segment being one of the most affected areas. Many water users looked forward to the opening of the spa resorts to continue their treatments. Given the importance of this theme, this work has as its main goal the definition of a consumer profile and to identify the determinants of satisfaction of the thermal tourists, as well as to gauge the degree of knowledge of thermal tourism and specifically of spas in the Portuguese context. In methodological terms, a questionnaire survey was conducted among the tourists to achieve the research objectives. The results show a younger, healthier and diversified thermal tourism practitioner. It also reveals similarities with other profile and motivations studies. However, it is possible to notice a few differences. Practitioners are seeking a combination of a leisure and health dimension and valorise factors such as location and access of the establishments, quality of the services provided and rest and tranquillity. An issue regarding their length of stay has been identified. As they are locals and live in the same country as the thermal spa, most respondents do not stay overnight at the destination. In the future, it would be interesting to research product development and market strategies for diversified thermal practitioners.
I Nengah Sandi Artha Putra, Ni Nyoman Arini, I Putu Tiana Raditya
Tourism villages in their development must have a good marketing strategy. Many tourist villages are not developing and progressing because the management and marketing aspects are not carried out properly. The development of a tourist village through an annual event strategy is one of the strategies carried out by independent tourism villages such as the Penglipuran Tourism Village. This study aims to examine the marketing strategy of Penglipuran Tourism Village through an annual event. Two objects that will be studied in the event are event management and marketing strategy. A qualitative approach is the method used in this study. The in-depth interview technique is a technique used to search for data in depth with a purposive sampling technique. The results of this study indicate that there is good event management carried out by the committee which consists of aspects of research, design, planning, coordinating and evaluating. The eight elements of the marketing mix are also applied in the event marketing strategy, such as product, price, place, promotion, people, packaging, programming and partnership aspects. Penglipuran Village Festival as an annual event is managed independently based on the community and is consistently carried out every year. This event has a good impact on the existence of tourism village branding, and provides economic, socio-cultural and environmental welfare benefits.
Hospitality industry. Hotels, clubs, restaurants, etc. Food service
This study queries the notion of slum as an anathema to the growth and prosperity of cities in sub-Saharan Africa. Slum tourism is discussed as an emerging intervention to address the challenge of slums in the global south. Using ethnographic account and personal reflection of 5 slum settlements and key institutions in Lusaka, a novel approach is proposed: Absolute Slum Tourism (AST) and Relative Slum Tourism (RST), to contribute to the discourse on slum interventions. The study shows that navigating informal settlements through RST approach could significantly influence urban rejuvenation, empower local narratives, giving voice to the marginalised in slum communities and promoting equity. The paper further proposes a new framework for the co-creation of slum
interventions, introducing a shift in how informal urban space and residents are perceived.
Hospitality industry. Hotels, clubs, restaurants, etc. Food service, Business
Purpose- This paper intends to analyze the impact of the destination image and the information
sources which are: 1 / word of mouth (Wom), 2 / electronic word of mouth (eWom) and 3 / commercial sources, on the medical image of a destination and the intention to visit it for medical
purposes.
Methodology/Design/Approach- An exploratory study was carried out with 247 people of different nationalities who have undergone surgery abroad or who are planning to do so. Three versions
of the surveys were administered on Google Forms. The data were analyzed by the method of
structural equations.
Findings- The main results are: 1 / The image of the destination significantly influences the medical image and the intention to travel. 2 / The information sources that most help in choosing a
medical destination are consecutively: 1/the eWom, 2/ the Wom, and 3/ the commercial sources.
Whereas, the sources that have the most impact on intention to travel are 1/Wom and 2/eWom.
Originality of the research- This research highlights the importance of reflecting a positive image
of the whole country (safety, attractiveness, hospitality of the people, etc.) and not just promoting
its medical image. The second contribution of this study is to show the importance of eWom, Wom
and commercial sources in the country choice process.
Hospitality industry. Hotels, clubs, restaurants, etc. Food service
This research aims to explain certain aspects of the complex legal system of personal data protection. The paper is based on the analysis of domestic and EU regulations that regulate the rights of citizens to the protection of personal data. The paper provides an overview of the obligations that must be fulfilled by tourism entities in order to ensure that the processing of personal data is carried out in accordance with the law. In addition, the risks associated with the processing of personal data and the possibility to ensure the security of data processed by providers of tourist services are listed. The research showed that there are many open questions in the tourism sector regarding the implementation of regulations on the protection of personal data.
Hospitality industry. Hotels, clubs, restaurants, etc. Food service
Food recognition has a wide range of applications, such as health-aware recommendation and self-service restaurants. Most previous methods of food recognition firstly locate informative regions in some weakly-supervised manners and then aggregate their features. However, location errors of informative regions limit the effectiveness of these methods to some extent. Instead of locating multiple regions, we propose a Progressive Self-Distillation (PSD) method, which progressively enhances the ability of network to mine more details for food recognition. The training of PSD simultaneously contains multiple self-distillations, in which a teacher network and a student network share the same embedding network. Since the student network receives a modified image from its teacher network by masking some informative regions, the teacher network outputs stronger semantic representations than the student network. Guided by such teacher network with stronger semantics, the student network is encouraged to mine more useful regions from the modified image by enhancing its own ability. The ability of the teacher network is also enhanced with the shared embedding network. By using progressive training, the teacher network incrementally improves its ability to mine more discriminative regions. In inference phase, only the teacher network is used without the help of the student network. Extensive experiments on three datasets demonstrate the effectiveness of our proposed method and state-of-the-art performance.
The global pandemic COVID-19 posed numerous challenges for U.S. restaurants and food services. Many businesses adopted contactless ordering and cashless payment policies to comply with emergency health mandates. Even with national and public health emergency mandates set to expire in May 2023, cashless payment services continue to thrive through online ordering platforms such as DoorDash and Uber Eats and social payment platforms such as Snackpass. At present, designers and policymakers must address the socioeconomic politics of cashless payment services and service accessibility for marginalized groups.
Nutrition information is crucial in precision nutrition and the food industry. The current food composition compilation paradigm relies on laborious and experience-dependent methods. However, these methods struggle to keep up with the dynamic consumer market, resulting in delayed and incomplete nutrition data. In addition, earlier machine learning methods overlook the information in food ingredient statements or ignore the features of food images. To this end, we propose a novel vision-language model, UMDFood-VL, using front-of-package labeling and product images to accurately estimate food composition profiles. In order to empower model training, we established UMDFood-90k, the most comprehensive multimodal food database to date, containing 89,533 samples, each labeled with image and text-based ingredient descriptions and 11 nutrient annotations. UMDFood-VL achieves the macro-AUCROC up to 0.921 for fat content estimation, which is significantly higher than existing baseline methods and satisfies the practical requirements of food composition compilation. Meanwhile, up to 82.2% of selected products' estimated error between chemical analysis results and model estimation results are less than 10%. This performance sheds light on generalization towards other food and nutrition-related data compilation and catalyzation for the evolution of generative AI-based technology in other food applications that require personalization.
Maritime shipping has become a trillion-dollar industry that now impacts the economy of virtually every country around the world. It is therefore no surprise that countries and companies have spent billions of dollars to modernize shipping vessels and ports with various technologies. However, the implementation of these technologies has also caught the attention of cybercriminals. For example, a cyberattack on one shipping company resulted in nearly $300 millions in financial losses. Hence, this paper describes cybersecurity vulnerabilities present in the international shipping business. The contribution of this paper is the identification and dissection of cyber vulnerabilities specific to the shipping industry, along with how and why these potential vulnerabilities exist.