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This study aims to provide a systematic review of the existing literature on the applications of deep learning (DL) in hospitality, tourism and travel as well as an agenda for future research.

Covering a five-year time span (2017–2021), this study systematically reviews journal articles archived in four academic databases: Emerald Insight, Springer, Wiley Online Library and ScienceDirect. All 159 articles reviewed were characterised using six attributes: publisher, year of publication, country studied, type of value created, application area and future suggestions (and/or limitations).

Five application areas and six challenge areas are identified, which characterise the application of DL in hospitality, tourism and travel. In addition, it is observed that DL is mainly used to develop novel models that are creating business value by forecasting (or projecting) some parameter(s) and promoting better offerings to tourists.

Although a few prior papers have provided a literature review of artificial intelligence in tourism and hospitality, none have drilled-down to the specific area of DL applications within the context of hospitality, tourism and travel.

To the best of the authors’ knowledge, this paper represents the first theoretical review of academic research on DL applications in hospitality, tourism and travel. An integrated framework is proposed to expose future research trajectories wherein scholars can contribute significant value. The exploration of the DL literature has significant implications for industry and practice, given that this, as far as the authors know, is the first systematic review of existing literature in this research area.

This paper investigates, through the use of data mining techniques, patterns in HIV/AIDS patient data. These patterns can be used for better management of the disease and more appropriate targeting of resources.

A total of 250,000 anonymised records from HIV/AIDS patients in Thailand were imported into a database. IBM's Intelligent Miner was used for clustering and association rule discovery.

Clustering highlighted groups of patients with common characteristics and also errors in data. Association rules identified associations that were not expected in the data and were different from traditional reporting mechanisms utilised by medical practitioners. It also allowed the identification of symptoms that co‐exist or are precursors of other symptoms.

Identification of symptoms that are precursors of other symptoms can allow the targeting of the former so that the later symptoms can be avoided. This study shows that providing a pragmatic and targeted approach to the management of resources available for HIV/AIDS treatment can provide a much better service, while at the same time reducing the expense of that service. This study can also be used as a means of implementing a quality monitoring system to target available resources.

Artificial intelligence (AI) is crucial for competitive advantage in operations and supply chain management, enhancing logistics, forecasting, and customer service efficiency. Integrating AI education into social sciences curricula equips future leaders with the skills to navigate and adapt to automation and economic shifts. This chapter presents a case study on AI in supply chain education. Our findings show that incorporating generative AI in education enhances student engagement and satisfaction, aligning with prior research that AI education improves learning experiences. Students with prior AI exposure perceive these activities as more beneficial, indicating that broad AI literacy is essential before mastering domain-specific applications. Our case study calls for integrating AI education as a foundational component of curricula, preparing students to innovate and thrive in AI-enhanced professional environments.

The growing impact of artificial intelligence (AI), especially generative AI, on education introduces both challenges and opportunities. Educators and learning technologists must be prepared to integrate and evaluate the ethical implications of these technologies in teaching. This chapter demonstrates a case study on improving AI literacy among postgraduate students in a Master’s in learning, education, and technology (LET) programme. Our reflection highlights how mindsets and attitudes towards technology influence the use of AI in education. It demonstrates that these attitudes, which are crucial for AI literacy, can be developed through self-regulated and experiential learning. To further support the integration of AI in education, we propose future research and practical efforts to investigate and improve the beliefs and mindsets that underpin AI literacy.

The purpose of this paper is to propose an extensiveness intelligent hybrid model to short-term load electricity forecast that can simultaneously model the seasonal complicated nonlinear uncertain patterns in the data. For this purpose, a fuzzy seasonal version of the multilayer perceptrons (MLP) is developed.

In this paper, an extended fuzzy seasonal version of classic MLP is proposed using basic concepts of seasonal modeling and fuzzy logic. The fundamental goal behind the proposed model is to improve the modeling comprehensiveness of traditional MLP in such a way that they can simultaneously model seasonal and fuzzy patterns and structures, in addition to the regular nonseasonal and crisp patterns and structures.

Eventually, the effectiveness and predictive capability of the proposed model are examined and compared with its components and some other models. Empirical results of the electricity load forecasting indicate that the proposed model can achieve more accurate and also lower risk rather than classic MLP and some other fuzzy/nonfuzzy, seasonal nonseasonal, statistical/intelligent models.

One of the most appropriate modeling tools and widely used techniques for electricity load forecasting is artificial neural networks (ANNs). The popularity of such models comes from their unique advantages such as nonlinearity, universally, generality, self-adaptively and so on. However, despite all benefits of these methods, owing to the specific features of electricity markets and also simultaneously existing different patterns and structures in the electrical data sets, they are insufficient to achieve decided forecasts, lonely. The major weaknesses of ANNs for achieving more accurate, low-risk results are seasonality and uncertainty. In this paper, the ability of the modeling seasonal and uncertain patterns has been added to other unique capabilities of traditional MLP in complex nonlinear patterns modeling.

Despite corporate communications having an immense impact on corporate success, there is a lack of dedicated techniques for their management and visualization. A potential strategy is to apply business process management (BPM) approach with business process model and notation (BPMN) modeling techniques.

The goal of this study was to gain empirical insights into the cognitive effectiveness of BPMN-based corporate communications modeling. To this end, experimental research was performed in which subjects tested two modeling notations – standardized BPMN conversation diagrams and a BPMN extension with corporate communications-specific concepts.

Standard conversation diagrams were demonstrated to be more time-efficient for designing and interpreting diagrams. However, the subjects made significantly fewer mistakes when interpreting the diagrams modeled in the BPMN extension. Subjects also evolved positive perceptions toward the proposed extension.

BPMN-based corporate communications modeling may be applied to organizations to depict how formal communications are or should be performed consistently, effectively and transparently by following and integrating with BPM approaches and modeling techniques.

The paper provides empirical insights into the cognitive effectiveness of corporate communications modeling based on BPMN and positions the corresponding models into typical process architecture.

Over the past decade, gamification’s popularity has broadened into many industries and has become embedded in consumers’ lives. As privacy protection and how firms utilize users’ data has been at the forefront of consumers’ minds, practitioners and academics alike need to understand consumers’ perceptions of the ethics of gamification. This paper aims to explore and provide preliminary evidence on young consumers’ perceptions of gamification and the ethics involved in these strategies used by firms.

The authors conducted two studies using a mixed-methods approach to gain a foundational understanding of young consumers’ perceptions of gamification. In Study 1, interviews provided initial insights and helped inform an exploratory survey administered in Study 2 to 161 young consumers attending a university in the southwest region of the USA.

The findings indicate that consumers have positive attitudes toward gamification tactics as long as the rewards are sufficient. Further, consumers do not find gamification as unethical as long as they have control over having the ability to opt-in.

Previous research has examined gamification from several contexts including health care, education and the workplace. However, there is little research that focuses on gamification from the consumers’ perspective, specifically the young consumer. As more firms are using gamification tactics such as on their mobile applications, it is critical to understand how young consumers perceive gamification and how that can impact the consumer-brand relationship. This research offers two studies as a first step in investigating young consumers’ perceptions of gamification tactics firms use and offers several future directions.

Given the urgency of corporate engagement in sustainable development, companies seek ways to involve their employees in sustainability efforts. In this regard, gamified systems have gained attention as a novel tool to promote sustainable employee behavior. However, as the research field matures, researchers and practitioners are confronted with a scattered academic landscape that makes it difficult to grasp how gamification can be designed to engage employees in sustainable behavior and to understand how gamification effects unfold at psychological, behavioral and corporate levels of sustainability.

This paper uses a systematic literature review to consolidate the existing knowledge on gamification designs and their effects on sustainable employee behavior.

Studies have explored a variety of utilitarian and achievement-, immersion- and social-related gameful affordances to promote positive behavior- and system-related psychological effects as a basis for employee engagement in sustainable behavior. However, the evidence regarding their impact on rational decision-making processes and overcoming the intention-action gap inherent in sustainability is still limited. Nevertheless, several studies in focused areas indicate the potential to elicit behavioral changes that drive sustainability outcomes at the corporate level as well.

Our study provides three main contributions. First, we develop a conceptual framework that illustrates how gamification can drive sustainable behavior in the workplace. Second, we derive seven agenda points to guide future research on gamification for corporate sustainability. Third, we deduce three practical approaches to use gamification as a strategic intervention to promote sustainable behavior in organizations.

The study aims to propose an intelligent real-time traffic model to address the traffic congestion problem. The proposed model assists the urban population in their everyday lives by assessing the probability of road accidents and accurate traffic information prediction. It also helps in reducing overall carbon dioxide emissions in the environment and assists the urban population in their everyday lives by increasing overall transportation quality.

This study offered a real-time traffic model based on the analysis of numerous sensor data. Real-time traffic prediction systems can identify and visualize current traffic conditions on a particular lane. The proposed model incorporated data from road sensors as well as a variety of other sources. It is difficult to capture and process large amounts of sensor data in real time. Sensor data is consumed by streaming analytics platforms that use big data technologies, which is then processed using a range of deep learning and machine learning techniques.

The study provided in this paper would fill a gap in the data analytics sector by delivering a more accurate and trustworthy model that uses internet of things sensor data and other data sources. This method can also assist organizations such as transit agencies and public safety departments in making strategic decisions by incorporating it into their platforms.

The model has a big flaw in that it makes predictions for the period following January 2020 that are not particularly accurate. This, however, is not a flaw in the model; rather, it is a flaw in Covid-19, the global epidemic. The global pandemic has impacted the traffic scenario, resulting in erratic data for the period after February 2020. However, once the circumstance returns to normal, the authors are confident in their model’s ability to produce accurate forecasts.

To help users choose when to go, this study intended to pinpoint the causes of traffic congestion on the highways in the Bay Area as well as forecast real-time traffic speeds. To determine the best attributes that influence traffic speed in this study, the authors obtained data from the Caltrans performance measurement system (PeMS), reviewed it and used multiple models. The authors developed a model that can forecast traffic speed while accounting for outside variables like weather and incident data, with decent accuracy and generalizability. To assist users in determining traffic congestion at a certain location on a specific day, the forecast method uses a graphical user interface. This user interface has been designed to be readily expanded in the future as the project’s scope and usefulness increase. The authors’ Web-based traffic speed prediction platform is useful for both municipal planners and individual travellers. The authors were able to get excellent results by using five years of data (2015–2019) to train the models and forecast outcomes for 2020 data. The authors’ algorithm produced highly accurate predictions when tested using data from January 2020. The benefits of this model include accurate traffic speed forecasts for California’s four main freeways (Freeway 101, I-680, 880 and 280) for a specific place on a certain date. The scalable model performs better than the vast majority of earlier models created by other scholars in the field. The government would benefit from better planning and execution of new transportation projects if this programme were to be extended across the entire state of California. This initiative could be expanded to include the full state of California, assisting the government in better planning and implementing new transportation projects.

To estimate traffic congestion, the proposed model takes into account a variety of data sources, including weather and incident data. According to traffic congestion statistics, “bottlenecks” account for 40% of traffic congestion, “traffic incidents” account for 25% and “work zones” account for 10% (Traffic Congestion Statistics). As a result, incident data must be considered for analysis. The study uses traffic, weather and event data from the previous five years to estimate traffic congestion in any given area. As a result, the results predicted by the proposed model would be more accurate, and commuters who need to schedule ahead of time for work would benefit greatly.

The proposed work allows the user to choose the optimum time and mode of transportation for them. The underlying idea behind this model is that if a car spends more time on the road, it will cause traffic congestion. The proposed system encourages users to arrive at their location in a short period of time. Congestion is an indicator that public transportation needs to be expanded. The optimum route is compared to other kinds of public transit using this methodology (Greenfield, 2014). If the commute time is comparable to that of private car transportation during peak hours, consumers should take public transportation.