Hasil untuk "Personnel management. Employment management"

Irina Canco, Drita Kruja, Forcim Kola

Digital skills play a central role in the ongoing transformation of tourism and hospitality. This study analyzes several issues, such as the structure of digital competencies and workforce segmentation, within the tourism sector in Albania areas where empirical evidence remains scarce. The motivation for inclusion in this study focuses on the void between advanced technologies and the skills gap, while identifying opportunities for change in this direction. The quantitative design considers a sample composed of owners, managers, and operational staff. Data analysis includes descriptive statistics, Pearson correlation analysis, heatmap visualization, cluster analysis, and workforce segmentation. The data processing reveals that while foundational digital literacy is well-evidenced, a significant gap exists in advanced analytical competencies and emerging technologies. The findings regarding workforce segmentation into three distinct groups: Digital Pioneers, Functional Users, and Digital Laggards are of high importance. Equally significant are the findings concerning disparities in digital maturity across sectors. Sectoral analysis reveals higher advanced digital skill levels in accommodation and destination management, while food and beverage businesses show lower digital proficiency. This study contributes by providing an empirical framework that links digital skill structures, workforce segmentation, and sectoral differences, offering evidence-based insights for targeted digital upskilling in tourism and hospitality. The findings highlight the need for differentiated digital training strategies tailored to workforce segments, organizational roles, and tourism subsectors.

Sitaram Sukthankar, Relita Fernandes, Sadanand Gaonkar et al.

Emerging technologies, such as artificial intelligence (AI), including chatbots, are now transforming the hospitality and tourism industry. Chatbot technology is an excellent tool for enhancing communication, boosting service delivery efficiency, reducing costs, and improving the tourist experience. Despite their potential benefits, the adoption of AI-powered chatbots in Goa’s hospitality and tourism industry remains low, underscoring the need to identify the determinants influencing tourists’ behavioural intention to adopt this technology and use behaviour. Therefore, this study examines the key determinants influencing tourists’ behavioural intentions to adopt AI-powered chatbots in the hospitality and tourism industry. In addition, the study also examines the impact of tourists’ behavioural intentions to adopt AI-powered chatbots on use behaviour. For this purpose, a revised UTAUT2 model is assessed by leveraging a quantitative research approach. Structured questionnaires were distributed to a total of 400 inbound and outbound tourists, of which 227 respondents who were aware of AI-powered chatbots were chosen as the respondents for this study based on purposive sampling. The collected data were analysed using Partial Least Squares–Structural Equation Modelling (PLS-SEM) in SmartPLS 4.0. The findings revealed that attitude, performance expectancy, effort expectancy, social influence, facilitating conditions, and perceived enjoyment significantly influence tourists’ behavioural intention to adopt AI-powered chatbots, whereas automation and habit do not significantly influence their behavioural intention to adopt AI-powered chatbots. This study has implications for tourism managers and policymakers in the tourism and hospitality industry, who can gain insights into the factors that can encourage tourists to adopt AI-based facilities.

Yan Tang, Ruochen Ma, Shixian Luo et al.

Cultural landscapes are facing increasing challenges in terms of sustainable financing, owing to fiscal austerity and limited public funding. This study explores tourists’ willingness to pay (WTP) for the conservation of cultural landscapes through Japan’s Furusato Nozei (Tax payment to hometown)—a policy that pairs tax deductions with tangible “return gifts,” institutionalising a form of mixed (or “impure”) altruism that can convert intention into action. Using a survey of 500 visitors to Shibamata, Tokyo, we estimate an integrative model that links psychological pathways (motivation → destination evaluation), behavioural investments (time, spending, and interactions with residents), and socio-demographic characteristics. To analyse the collected data, we use partial least squares structural equation modelling. Results reveal that interaction with local communities has the strongest direct effects on WTP, while motivation influences WTP indirectly through destination evaluation. Age shows a negative relationship, whereas marital status has a positive one; income and gender are not significant predictors. These findings suggest that institutional incentives embedded in Furusato Nozei can transform altruistic intention into actual financial support for heritage conservation. This study contributes theoretically by linking institutional design to behavioural intention–action gaps and practically by providing insights for participatory and incentive-based heritage financing. The findings are based on a single-site case in Shibamata, Tokyo, and should therefore be interpreted within its local and cultural context.

Juliette Galavielle, Daniel Paiva

This study contributes towards the burgeoning literature on the negative social consequences of touristification by uncovering the entanglement of gender violence and the territories produced by tourism in Lisbon’s nightlife districts. Drawing upon a perspective of body-territory, this study questions how gender-based violence affects nightlife workers in a touristified urban centre. The research is based on a year-long ethnographic study of women’s workplaces at night, which includes different forms of observation and a set of interviews with women and non-binary workers. The findings of the study describe the territorial dimension of violence for the workers of Lisbon’s tourism-oriented night life, focusing on the asymmetrical repartition of violence, which varies in its nature and intensity according to the neighborhood, the status of the venue, and the workers’ level of experience and authority in the venue. The conclusion of this study underlines the significance of territory for understanding the dynamics of gender-based violence in the nightlife and discusses future avenues of research on the topic.

Nthabeleng I. Mdhluli

Orientation: The integration of digital tools with flexible work habits has transformed modern workplaces, creating an ‘always-on’ culture that impacts employee well-being and organisational effectiveness. Research purpose: This systematic review explored how continuous connectivity affects stress, work-life boundaries and institutional standards, with an emphasis on peer-reviewed studies published from 2015 to 2024. Motivation for the study: The study aimed to explore how digital tools, such as messaging platforms and remote collaboration technologies, can lead to increased workplace stress and burnout despite their intended productivity benefits. Research approach/design and method: The review followed PRISMA principles and utilised systematic approach to ensure rigour and reproducibility. Empirical studies from emerging economies were prioritised to improve generalisability. The inclusion prioritised peer-reviewed studies with strong quantitative or qualitative evidence. Main findings: The analysis found that digital tools have exacerbated stress, burnout and mental weariness among knowledge workers and women handling caregiving obligations, despite their intended purpose of empowerment. Notably, 68% of research focus on individual coping techniques, such as digital detoxes, while less than 20% investigate organisational treatments, indicating a gap in policy formulation and implementation. The study proposes a dynamic model of flexibility, highlighting institutional standards, rather than individual habits, as the cause of unsustainable work patterns. Practical/managerial implications: The study suggests techniques for balancing productivity and well-being, such as time-sensitive communication protocols, open workload indicators and regulations enforcing the right to disengage. Contribution/value-add: The study reframes flexibility as a dual-force dynamic that requires systemic solutions. It offers evidence-based guidance for designing organisational policies.

Richard J. Abdill, Ran Blekhman

Compendium Manager is a command-line tool written in Python to automate the provisioning, launch, and evaluation of bioinformatics pipelines. Although workflow management tools such as Snakemake and Nextflow enable users to automate the processing of samples within a single sequencing project, integrating many datasets in bulk requires launching and monitoring hundreds or thousands of pipelines. We present the Compendium Manager, a lightweight command-line tool to enable launching and monitoring analysis pipelines at scale. The tool can gauge progress through a list of projects, load results into a shared database, and record detailed processing metrics for later evaluation and reproducibility.

Dongyang Lyu, Xiaoqi Li, Zongwei Li

Nowadays, environmental protection has become a global consensus. At the same time, with the rapid development of science and technology, urbanisation has become a phenomenon that has become the norm. Therefore, the urban greening management system is an essential component in protecting the urban environment. The system utilises a transparent management process known as" monitoring - early warning - response - optimisation," which enhances the tracking of greening resources, streamlines maintenance scheduling, and encourages employee involvement in planning. Designed with a microservice architecture, the system can improve the utilisation of greening resources by 30%, increase citizen satisfaction by 20%, and support carbon neutrality objectives, ultimately making urban governance more intelligent and focused on the community. The Happy City Greening Management System effectively manages gardeners, trees, flowers, and green spaces. It comprises modules for gardener management, purchase and supplier management, tree and flower management, and maintenance planning. Its automation feature allows for real-time updates of greening data, thereby enhancing decision-making. The system is built using Java for the backend and MySQL for data storage, complemented by a user-friendly frontend designed with the Vue framework. Additionally, it leverages features from the Spring Boot framework to enhance maintainability and scalability.

Wenli Yang, Rui Fu, Muhammad Bilal Amin et al.

Metadata management plays a critical role in data governance, resource discovery, and decision-making in the data-driven era. While traditional metadata approaches have primarily focused on organization, classification, and resource reuse, the integration of modern artificial intelligence (AI) technologies has significantly transformed these processes. This paper investigates both traditional and AI-driven metadata approaches by examining open-source solutions, commercial tools, and research initiatives. A comparative analysis of traditional and AI-driven metadata management methods is provided, highlighting existing challenges and their impact on next-generation datasets. The paper also presents an innovative AI-assisted metadata management framework designed to address these challenges. This framework leverages more advanced modern AI technologies to automate metadata generation, enhance governance, and improve the accessibility and usability of modern datasets. Finally, the paper outlines future directions for research and development, proposing opportunities to further advance metadata management in the context of AI-driven innovation and complex datasets.

Nikita Mukherjee, Syed Anis Haider, Satrajeet Choudhury

Zoheir Boudia, Markus C. Slevogt

The primary objective of this study is to address the limitations in existing turnaround management literature by providing managers with a reliable framework and step-by-step guidance for successful turnarounds. This research examines ten real-life turnaround experiences through primary evidence gathered from semi-structured interviews with experienced turnaround and insolvency practitioners. Using a qualitative and phenomenological approach, this study uncovers the lived experiences of these managers, exploring the nuances and intricacies of their turnaround journeys. This research aims to identify key success factors and extract practical insights from the narratives of the interviewed managers. The resulting framework consists of five essential steps: (i) analysis and diagnosis, (ii) defining tailored strategies, (iii) execution and action, (iv) leadership and change management, and (v) agile decision-making. This comprehensive framework empowers leaders to initiate the turnaround process with a deep understanding of the contextual realities. By identifying obstacles through rich qualitative data, managers can implement appropriate actions to halt the decline and pave the way for a successful recovery. The phenomenological approach adds depth to the study, ensuring that the proposed framework is not only theoretically sound but also grounded in the real-world experiences of turnaround practitioners

Inna V. Levytska, Alona O. Klymchuk, Svitlana P. Kozhushko

The issues of motivation, satisfaction and engagement of employees of the hospitality industry are central to management in the specified area, since business performance in the hospitality industry is characterized by a high level of dependence on the available human capital and, in particular, its personal qualities, skills, professionalism and administrative abilities. That is why it is vitally important for any business entity in the specified field to ensure effective personnel management and the formation of effective systems for its stimulation. The factor of remuneration of the personnel of the hospitality industry is in the field of view of both domestic and foreign scientists, which proves the universality of the specified problem for the global economic sector. However, with all the activity of researchers of the systems and factors of labor motivation in the hospitality industry, little attention is currently paid to the issue of the level of remuneration in the industry, especially in the domestic scientific field. Instead, it is the level of remuneration in the industry as a whole that is not only a motivational factor for certain individuals, but also a significant incentive for its general development, building up personnel potential, reducing the turnover rate, etc. The purpose of the study is to analyze the pay level in the field of hotel and restaurant business as a factor of staff motivation. Methods. The study analyses the pay level of personnel in the hospitality industry, following the data of the official statistics, according to the criteria of gender and profession. Systematization methods are used to select the key indicators in accordance with the selected criteria, the statistical frequency calculation method is applied to develop the distribution series of the studied indicators, as well as the methods of structural analysis and analytical study of data are implemented to process the materials of the domestic job search portal. The results. The article offers a critical review of the research on the pay level in the hotel and restaurant business as a motivational factor for productive work. The study presents a comparative and analytical assessment of the indicators of remuneration of the hospitality industry in relation to other spheres of economic activity. It suggests the constructed interval series of the distribution of the wage index of the employees of the hotel and restaurant business of Ukraine according to the criteria of gender and professional group. The paper provides a structural analysis of employment in the hospitality industry according to the criteria of gender and formal employment. The findings of the research identify the vacancy market of the hospitality industry of Ukraine during the full-scale war and the key problems with wages in the industry.

Ze Mao, Yang Xu, Erick Suarez

The quality of the data in a dataset can have a substantial impact on the performance of a machine learning model that is trained and/or evaluated using the dataset. Effective dataset management, including tasks such as data cleanup, versioning, access control, dataset transformation, automation, integrity and security, etc., can help improve the efficiency and speed of the machine learning process. Currently, engineers spend a substantial amount of manual effort and time to manage dataset versions or to prepare datasets for machine learning tasks. This disclosure describes a platform to manage and use datasets effectively. The techniques integrate dataset management and dataset transformation mechanisms. A storage engine is described that acts as a source of truth for all data and handles versioning, access control etc. The dataset transformation mechanism is a key part to generate a dataset (snapshot) to serve different purposes. The described techniques can support different workflows, pipelines, or data orchestration needs, e.g., for training and/or evaluation of machine learning models.

Narges Shadab, Pritam Gharat, Shrey Tiwari et al.

A resource leak occurs when a program fails to free some finite resource after it is no longer needed. Such leaks are a significant cause of real-world crashes and performance problems. Recent work proposed an approach to prevent resource leaks based on checking resource management specifications. A resource management specification expresses how the program allocates resources, passes them around, and releases them; it also tracks the ownership relationship between objects and resources, and aliasing relationships between objects. While this specify-and-verify approach has several advantages compared to prior techniques, the need to manually write annotations presents a significant barrier to its practical adoption. This paper presents a novel technique to automatically infer a resource management specification for a program, broadening the applicability of specify-and-check verification for resource leaks. Inference in this domain is challenging because resource management specifications differ significantly in nature from the types that most inference techniques target. Further, for practical effectiveness, we desire a technique that can infer the resource management specification intended by the developer, even in cases when the code does not fully adhere to that specification. We address these challenges through a set of inference rules carefully designed to capture real-world coding patterns, yielding an effective fixed-point-based inference algorithm. We have implemented our inference algorithm in two different systems, targeting programs written in Java and C#. In an experimental evaluation, our technique inferred 85.5% of the annotations that programmers had written manually for the benchmarks. Further, the verifier issued nearly the same rate of false alarms with the manually-written and automatically-inferred annotations.

Mert Nakıp, Onur Çopur, Emrah Biyik et al.

Smart home energy management systems help the distribution grid operate more efficiently and reliably, and enable effective penetration of distributed renewable energy sources. These systems rely on robust forecasting, optimization, and control/scheduling algorithms that can handle the uncertain nature of demand and renewable generation. This paper proposes an advanced ML algorithm, called Recurrent Trend Predictive Neural Network based Forecast Embedded Scheduling (rTPNN-FES), to provide efficient residential demand control. rTPNN-FES is a novel neural network architecture that simultaneously forecasts renewable energy generation and schedules household appliances. By its embedded structure, rTPNN-FES eliminates the utilization of separate algorithms for forecasting and scheduling and generates a schedule that is robust against forecasting errors. This paper also evaluates the performance of the proposed algorithm for an IoT-enabled smart home. The evaluation results reveal that rTPNN-FES provides near-optimal scheduling $37.5$ times faster than the optimization while outperforming state-of-the-art forecasting techniques.

Hiroaki Mori

In Japan, many academics and practitioners have focused on anime-induced tourism as one of the new alternative forms of tourism in the 21st century. Many fans have visited locations that have appeared in anime as film-induced tourists. Regarding the behavior of anime-induced tourists, many tend to be willing to contribute to the destinations they visit as eco-oriented volunteers, different from purely film-induced tourists. Therefore, anime-induced tourists possess a complex character that entails both an interest in film-induced tourism and volunteer tourism, which may conceptually be opposed to one another. This study reveals anime’s potential as a driver of new volunteer tourism and theoretically contributes to tourism research by redefining anime-induced tourism by relying on the concept of film-induced voluntourism. Using a comparative case study of the behavior of anime-induced volunteer tourists at three destinations, this study found that while anime-induced volunteer tourists have a feeling of gratitude for their host communities with a vacationer’s mindset, they can realize positive outcomes, including economic benefits and problem solutions by engaging in cleaning-up activities at the destinations they visit. In conclusion, this study clarifies that anime tourism is one of the significant alternative forms of tourism that can achieve community development associated with film-induced voluntourists, resolving the negative effects of film-induced tourism and volunteer tourism.

Muhammad Khalid

Existing studies on the foreign subsidiaries’ survival have acknowledged that the role of firm performance is critical. However, the research remains inconclusive on how institutional and organizational factors influence firm survival in foreign markets. This study aims to fill this scholarly gap by considering the role of international experience of the parent firm and cultural differences between host and home countries on firms’ survival in foreign markets. Using a sample of 680 Chinese foreign subsidiaries operating in 24 countries during the period 2011 to 2020, we find that subsidiaries having poor financial performance are more likely to withdraw from the foreign markets. This effect is more pronounced when the cultural distance between home and host countries is higher. However, the international experience of the parent company helps its foreign subsidiaries to survive in the host country for a longer period. These findings have important implications for MNCs operating in foreign markets.

Tao Wan, Buhai Shi

Offender residences have become a research focus in the crime literature. However, little attention has been paid to the interactive associations between built environment factors and the residential choices of offenders. Over the past three decades, there has been an unprecedented wave of migrant workers pouring into urban centers for employment in China. Most of them flowed into urban villages within megacities. Weak personnel stability and great mobility have led to the urban villages to be closely related to decreased public safety and the deterioration of social order. The YB district in China was selected as the study area, which is located in one of the most developed cities in Southern China and has an area of approximately 800 km² and a population of approximately four million people. This study aims to explore the associations between the neighborhood environment and the offender residences by using the geographical detector model (GeoDetector) from the perspective of interaction. The conceptual framework is based on the social disorganization theory. The results found that urban villages were the most important variable with a relatively high explanatory power. In general, taking the urban village as the carrier, various places (hotels, entertainment places, and factories) within the urban village may be more likely to include offender residences. This study also found the social disorganization theory applicable in the non-Western context. These findings may have important implications for offender residences identification, crime prevention, and the management of urban villages in Chinese cities.

Danshi Wang, Chunyu Zhang, Wenbin Chen et al.

Failure management plays a significant role in optical networks. It ensures secure operation, mitigates potential risks, and executes proactive protection. Machine learning (ML) is considered to be an extremely powerful technique for performing comprehensive data analysis and complex network management and is widely utilized for failure management in optical networks to revolutionize the conventional manual methods. In this study, the background of failure management is introduced, where typical failure tasks, physical objects, ML algorithms, data source, and extracted information are illustrated in detail. An overview of the applications of ML in failure management is provided in terms of alarm analysis, failure prediction, failure detection, failure localization, and failure identification. Finally, the future directions on ML for failure management are discussed from the perspective of data, model, task, and emerging techniques.

Aviral Chharia, Nishi Mehta, Shivam Gupta et al.

The rise of computation-based methods in thermal management has gained immense attention in recent years due to the ability of deep learning to solve complex 'physics' problems, which are otherwise difficult to be approached using conventional techniques. Thermal management is required in electronic systems to keep them from overheating and burning, enhancing their efficiency and lifespan. For a long time, numerical techniques have been employed to aid in the thermal management of electronics. However, they come with some limitations. To increase the effectiveness of traditional numerical approaches and address the drawbacks faced in conventional approaches, researchers have looked at using artificial intelligence at various stages of the thermal management process. The present study discusses in detail, the current uses of deep learning in the domain of 'electronic' thermal management.