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To provide a holistic assessment of tourists’ staycation intentions, this study aims to investigate the role of secondary-stakeholder corporate social responsibility (CSR) on local engagement. Based on construal-level theory and stimulus-organism-response model, this study unpacks how and when secondary-stakeholder CSR influences staycation intention.

Data were gathered from 312 Chinese tourists with prior staycation experiences. Data were subsequently analyzed using structural equation modeling.

This study illuminates that secondary-stakeholder CSR positively influences staycation intention directly and indirectly through psychological belonging and safety. Noteworthily, immersive experience strengthens the influence of psychological belonging on staycation intention while its effect on the relationship between psychological safety and staycation intention is negligible.

All variables in this study were collected via self-report measures provided by hotel customers with staycation experiences. Although both statistical and procedural remedies are used, it would be more useful if future studies could gather time-lagged and/or multiple sources of data.

This study augments the domain of staycation research by examining CSR marketing through a stakeholder perspective. This nuanced analysis elucidates the intertwined dynamics among secondary-stakeholder CSR, psychological sense of belonging and safety, and immersive experiences that delineate the landscape of staycation markets, casting light on their epoch-defining implications for hotels’ developmental trajectories.

As the complex influencing factors for financing decisions and limited information at the early project stage often render inappropriate financing mode and scheme (FMS) selection in the large-scale urban rail transit (URT) field, this study aims to identify the multiple influencing factors and establish a revised case-based reasoning (CBR) model by drawing on experience in historical URT projects to provide support for effective FMS decisions.

Our research proposes a two-phase, five-step CBR model for FMS decisions. We first establish a case database containing 116 large-scale URT projects and a multi-attribute FMS indicator system. Meanwhile, grey relational analysis (GRA), the entropy-revised G1 method and the time decay function have been employed to precisely revise the simple CBR model for selecting high-similarity cases. Then, the revised CBR model is verified by nine large-scale URT projects and a demonstration project to prove its decision accuracy and effectiveness.

We construct a similarity case indicator system of large-scale URT projects with 11 indicators across three attributes, in which local government fiscal pressure is considered the most influential indicator for FMS decision-making. Through the verification with typical URT projects, the accuracy of our revised CBR model can reach 89%. The identified high-similarity cases have been confirmed to be effective for recommending appropriate financing schemes matched with a specific financing mode.

This is the first study employing the CBR model, an artificial intelligence approach that simulates human cognition by learning from similar past experiences and cases to enhance the accuracy and reliability of FMS decisions. Based on the characteristics of the URT projects, we revise the CBR model in the case retrieval process to achieve a higher accuracy. The revised CBR model utilizes expert experience and historical information to provide a valuable auxiliary tool for guiding the relevant government departments in making systematic decisions at the early project stage with limited and ambiguous project information.

With the rise of esports, research on the perceived fit between esports sponsors and events remains limited. This study uses the Elaboration Likelihood Model (ELM) to investigate how the perceived fit between sponsors and esports events effects brand awareness, consumer attitudes and purchasing behavior, aiming to offer insights for more effective marketing strategies.

Data were collected from 245 participants during the Taipei Game Show 2024, using purposive sampling of individuals aged 18 and above. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with Smart PLS 4.0.1.6 to examine the relationships among perceived fit, brand awareness, consumer attitudes and purchasing behavior, and to investigate the mediating effects.

The results discovered that brand awareness, perceived fit and consumer attitudes had significant positive effects on purchasing behavior, explaining 75% of its variance. Additionally, perceived fit positively affected both brand awareness and consumer attitude. Mediating effect showed that both brand awareness and consumer attitude play significant mediating roles between perceived fit and purchasing behavior, with consumer attitude having a stronger mediating effect.

This study highlights to the limited body of research on esports sponsorships by demonstrating that perceived sponsor-event fit is crucial for enhancing brand awareness, advancing positive consumer attitudes and driving purchasing behavior. The ELM framework highlights the importance of central and peripheral routes in influencing consumer decisions, offering strategies for companies to optimize sponsorship effectiveness and improve brand competitiveness.

This study aims to examine the effects of natural, cultural and urban resources on the city's tourism competitiveness from the residents' perspective and identifies the factors that city managers should prioritize.

The authors used multiple methods. The partial least squares structural equation modeling tested our theoretical assumption and the importance–performance map analysis provided an extra interpretation of the results to make them tangible to city managers. Data collection was done with residents of Viseu, Portugal.

The findings indicated that tourism competitiveness from the residents' perspective depends on the leisure activities they can enjoy as much as tourists, as the limits between tourism activities and everyday life are tenuous. The quality of leisure is part of residents' well-being; however, the results suggested that tourism development fails to engage residents in the destination's leisure offer. The cultural heritage is not attractive to them because it is part of their day-by-day living area or they need to enjoy the cultural offer. To accommodate residents' demands, destination managers need to improve the offer of outdoor activities and urban leisure opportunities.

The main limitations were the sample size and the data collection after a pandemic context, that is, in a period of economic crisis that reduced leisure consumption. The implications point out variables to managers prioritize to improve place management and rethink tourism competitiveness based on a citizen-centered view. It is also a way to understand residents as staycationers.

Our research contributed to urban and city research literature with meaningful findings that can help city managers improve the positive impacts of tourism on residents and enhance the integration of tourism and urban daily life.

The authors suggested strategies for integrating residents into cultural activities.

Theoretically, this study is set up in the frontier of rethinking and promoting society-centric tourism development. The research contributed to urban and city research literature with meaningful findings that can help city managers improve the positive impacts of tourism on residents and enhance the integration of tourism and urban daily life. In this direction, the authors suggest strategies for integrating residents into cultural activities.

Unlike previous research that primarily utilized structural equation modelling (SEM) to evaluate safety hazards in subway projects, this research aims to utilize a hybrid approach to investigate and scrutinize the key indicators of safety hazards leading to accidents, thereby hindering the progress of subway projects in China, taking into cognizance the multiple stakeholder’s perspective.

By administering a survey questionnaire to 373 highly involved stakeholders in subway projects spanning Changsha, Beijing and Qingdao, China, our approach incorporated a four-staged composite amalgamation of exploratory factor analysis (EFA), confirmatory factor analysis (CFA), covariance-based structural equation modelling (CB-SEM) and artificial neural network (ANN) to develop an optimized model that determines the causal relationships and interactions among safety hazards in subway construction projects.

The optimized model delineated the influence of individual safety hazards on subway projects. The feasibility and applicability of the model developed was demonstrated on an actual subway project under construction in Changsha city. The outcomes revealed that the progress of subway projects is significantly influenced by risks associated with project management, environmental factors, subterranean conditions and technical hazards. In contrast, risks related to construction and human factors did not exhibit a significant impact on subway construction progress.

While our study provides valuable insights, it is important to acknowledge the limitation of relying on theoretical approaches without empirical validation from experiments or the field. In future research, we plan to address this limitation by assessing the SEM using empirical data. This will involve a comprehensive comparison of outcomes derived from CB-SEM with those obtained through SEM-ANN methods. Such an empirical validation process is crucial for enhancing the overall efficiency and robustness of the proposed methodologies.

The established hybrid model revealed complex non-linear connections among indicators in the intricate project, enabling the recognition of primary hazards and offering direction to improve management of safety in the construction of subways.

Unmanned aerial vehicles (UAVs), known for their exceptional flexibility and maneuverability, have become an integral part of mobile edge computing systems in edge networks. This paper aims to minimize system costs within a communication cycle. To this end, this paper has developed a model for task offloading in UAV-assisted edge networks under dynamic channel conditions. This study seeks to efficiently execute task offloading while satisfying UAV energy constraints, and validates the effectiveness of the proposed method through performance comparisons with other similar algorithms.

To address this issue, this paper proposes a task offloading and trajectory optimization algorithm using deep deterministic policy gradient, which jointly optimizes Internet of Things (IoT) device scheduling, power distribution, task offloading and UAV flight trajectory to minimize system costs.

The analysis of simulation results indicates that this algorithm achieves lower redundancy compared to others, along with reductions in task size by 22.8%, flight time by 34.5%, number of IoT devices by 11.8%, UAV computing power by 25.35% and the required cycle for per-bit tasks by 33.6%.

A multi-objective optimization problem is established under dynamic channel conditions, and the effectiveness of this approach is validated.

Drawing on appraisal theories, this paper aims to attempt to demonstrate how augmented reality (AR) strengthens relationships in the cognitive-affective-conative mechanism to shape visitors' recommendation of awe-inspiring heritage experiences.

A mixed-method approach was undertaken. First, an exploratory pre-study (n = 98) analyzed the multidimensionality of awe appraisals, eliciting the most salient dimensions in tourism experiences for informing the main study. Subsequently, an on-site survey was used to examine the relationships in the cognitive-affective-conative sequence, based on 316 questionnaires collected at a highly acclaimed religious heritage equipped with a multisensory AR activity.

Two distinct appraisals underlined in the awe concept were elicited during the exploratory phase: altered emotions (aggregating positive self-transformative dimensions) and the need for accommodation. The survey results demonstrated that the influence of authenticity on recommendation is shaped by emotions. Altered emotions hold a central mediating role and are differentiated from other discrete emotions. AR and the need for accommodation are revealed as contingencies that regulate the mediated mechanism.

This paper provides the hospitality and tourism stakeholders with a better understanding of how immersive technologies can be used for boosting recommendations, by enhancing the emotional responses toward authenticity perceptions.

This study introduces a novel conceptual framework to broaden our understanding of how cognitive and affective mental processes are linked to visitors’ recommendations, and how AR amplifies the outcomes. To the best of the authors’ knowledge, it is the first research to distinguish and document the specific effects of two different types of awe appraisal.

This study aims to explore the intention to quit and its associations among ambulance personnel and to compare the intention to quit and its associations between paramedic and non-paramedic staff.

A cross-sectional study was conducted on 492 Australian ambulance personnel. Participants were selected by stratified random sampling. Data were collected using phone interview-administered questionnaires. Descriptive analyses, bivariate associations and structural equation modelling were performed for data analysis.

The study found that 70% of ambulance personnel intended to quit their jobs. Intention to quit was similar between paramedics and non-paramedic staff. In both staff groups, supervisors' and colleagues' support was associated with mental health symptoms; job satisfaction was associated with the intention to quit. Supervisors' and colleagues' support was indirectly associated with the intention to quit via increasing job satisfaction and reducing the experience of mental health symptoms among paramedics only. Mental health symptoms were directly associated with the intention to quit and indirectly associated with the intention to quit via reducing job satisfaction among paramedics only.

The study findings provide evidence for resource allocation in human resource management. The findings suggest that interventions to increase job satisfaction may reduce the intention to quit for all ambulance personnel. Interventions to improve supervisors' and colleagues' support and to manage depression, anxiety and stress symptoms may help to reduce the intention to quit for paramedics only.

This is the first study to model and compare the direct and indirect associations of intention to quit between paramedics and non-paramedic staff in ambulance personnel.

In recent years, new and technologically innovative financial products and services, generally subsumed under the fintech umbrella, have permeated all areas of capital markets at an exponential rate. Primarily driven by developments in Web3 and advancements in artificial intelligence (AI), fintech solutions offer valuable benefits to all existing markets and participants and are the basis for introducing wholly new segments to classic capital market ecosystems. However, this increasing fintech adaptation does not come without challenges. Due to the technologies' nascent nature and often unregulated status, many products are susceptible to manipulation and fraud. The result can be sizable investor losses and excessive regulatory and public scrutiny. This chapter highlights the most essential and prominent fintech solutions used in capital markets today, along with their features, value additiveness, and degree of adaptation.

The purpose is to investigate how to create a new premium new energy vehicle brand.

This study employed a two-stage design. Firstly, a single-case study of NIO was undertaken to execute a thematic analysis, from which propositions were proposed and a theoretical model was constructed. Subsequently, quantitative data were collected through the questionnaire method to empirically test the model developed in the first stage.

NIO creates great user experience through four aspects: product, service, digital touchpoints and lifestyle. Functional experience is shaped by product and digital touchpoints, while emotional experience is affected by service and lifestyle. NIO wins extremely high user satisfaction through great user experience. User satisfaction is affected by both functional and emotional experience. Taking extremely satisfied users as the core, NIO leverages word-of-mouth recommendations to increase brand awareness and build premium brand image, so as to achieve high performance in the long term.

This study contributes to the literature by proposing and testing a theoretical model of creating a new premium new energy vehicle brand. It highlights the significance of emotional factors in the process of creating a new premium brand. It proposes employing the “ripple model” to translate user satisfaction into financial performance. It provides a three-step guide to creating a new premium brand for managers.