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This study aims to examine the efforts of two universities in Taiwan, particularly their engagement in coastal rural communities, and provides insights into the substantial contributions of University Social Responsibility (USR) projects toward achieving the sustainable development goals (SDGs) in rural settings. The objective of this research is to analyze the outcomes of these case projects on the SDGs and, consequently, to propose a theoretical model aimed at enhancing student participation in achieving SDGs through USR programs.

This study adapted the methodology comprising SDGs contribution analysis and participant observation. The SDGs contribution analysis unfolds in three steps. First, the authors conducted evaluation of the 2030 Agenda for Sustainable Development. This step involved a thorough analysis of all 17 SDGs and their respective targets to establish a foundational understanding. Second, analyzed the outcomes of the case projects to examine the specific contributions of case projects toward achieving the SDGs. Third, the information from the preceding steps is analyzed to determine the extent of the case projects’ contributions to the SDGs.

The study proposes a theoretical model to enhance student engagement in achieving SDGs, emphasizing student empowerment, community partnership and robust evaluation methods aligned with SDGs and social impacts. This model could offer guidance for higher education institutions (HEIs) globally on utilizing USR programs to contribute to the SDGs, while simultaneously enriching student learning experiences through practical engagement and empowerment.

This model can be enhanced and validated by applying more rigorous scientific methods. For instance, conducting surveys on students and community participants of events and activities, utilizing a statistically rigorous approach such as pre-post testing, can analyze the effectiveness of these programs on various SDG-related variables (e.g. awareness of SDGs). Additionally, exploring the relationships between the tested variables can be a potential research direction. For instance, examining whether community engagement can positively increase the social impacts of USR projects, or whether student empowerment can enhance community capacity building, are important issues worthy of discovery.

This model emphasizes the pivotal role of student empowerment, advocating for an educational approach that not only enhances students’ proficiency in community development but also potentially shapes their career trajectories, as evidenced by the case projects examined in this study. In essence, this model offers HEIs a structured pathway to enrich student engagement in realizing SDGs through USR initiatives. It posits student empowerment as the foundational element, fostering a learning environment where students gain valuable skills and insights into community development, potentially guiding their future professional endeavors. This research provides practical direction for those HEIs implementing USR projects, which will increase the positive impacts brought by HEIs, especially for the students and local community.

To the best of the authors’ knowledge, no previous studies have proposed a theoretical model specifically designed to engage students in achieving SDGs through USR programs in a rural context. The significance of this study lies in its potential to serve as a guide for higher education institutions globally, enabling them to effectively leverage USR programs to contribute toward SDGs. This makes the study an invaluable resource for researchers, policymakers and educators who are committed to fostering sustainability.

In this paper, a new grey Cosine New Structured Grey Model (CNSGM(1,N)) prediction power model is constructed for the small-sample modeling and prediction problem with complex nonlinearity and insignificant volatility.

Firstly, the weight of some relevant factors is determined by the grey comprehensive correlation degree, and the data are preprocessed. Secondly, according to the principle of “new information priority” and the volatility characteristics of the sequence growth rate, the ideas of damping accumulation power index and trigonometric function are integrated into the New Structured Grey Model (NSGM(1,N)) model. Finally, the non-structural parameters are optimized by the genetic algorithm, and the structural parameters are calculated by the least squares method, so a new CNSGM(1,N) predictive power model is constructed.

Under the principle of “new information priority,” through the combination with the genetic algorithm, the traditional first-order accumulation generation is transformed into damping accumulation generation, and the trigonometric function with the idea of integer is introduced to further simulate the phenomenon that the volatility is not obvious in the real system. It is applied to the simulation and prediction of China’s carbon dioxide emissions, and compared with other comparison models; it is found that the model has a better simulation effect and excellent performance.

The main contribution of this paper is to propose a new grey CNSGM(1,N) prediction power model, which can not only be applied to complex nonlinear cases but also reflect the differences between the old and new data and can reflect the volatility characteristics of the characteristic behavior sequence of the system.

Social sciences have discussed the host–guest relation from many theoretical lenses and perspectives. Violence as well as local crime has been studied as one of the major risks concerning tourism security. Anyway, less attention was given to homeless people and their interaction with foreign or local tourists. The purpose of this paper is oriented to explain how globalization has winners and losers, in which case, as noted, thousands of persons are excluded from the formal labor marketplace or the economic system year by year.

This is a conceptual paper that discusses critically not only the recent advances of sociology in urban tourism but also the connection between homeless people and tourists.

There is an urban underclass formed by those who have been excluded from the economic system. What is more important, such an underclass situates nearby luxury hotels and tourist destinations creating serious contradictions or zones of disputes. These contradictions have been approached by different sociologists since the turn of the 20th century.

The question of sustainability, as well as the idea of liveable cities, and the efficient organization of the city, have occupied a central position in the academic debate, above all after the COVID-19 pandemic. In the present paper, the authors put in dialogue the contributions of Marc Auge with Zyggy Bauman toward a new understanding of this postmodern phenomenon.

Based on the metaphor of vagabonds and tourists, we give a snapshot of the problem of homelessness in Buenos Aires city and its effects on the tourism industry. Unlike other English-speaking countries where the cities are actively organized by the state, Buenos Aires city lacks a planned program to regulate and relocate homeless people. They dwell in nonplaces nearby tourists sleeping in the streets near luxury hotels (but for sure escaping any planning or governmental control).

The goal of this study was to understand the relationships between mentoring roles, career calling, and the transmission of tacit knowledge in the setting of executive development centers. The purpose of this research was to determine if there is a link between mentoring roles and the transmission of tacit knowledge and, if so, to what extent mentoring structure and culture moderate this link.

The study used a quantitative strategy based on partial least squares structural equation modeling (PLS-SEM) to examine information gathered from 287 workers at four executive development centers. Mentoring roles, vocational calling, and the transmission of tacit knowledge were quantified using scales derived from existing research.

Results showed a beneficial association between mentoring roles and vocational interest. Tacit knowledge was more likely to be shared when it was directly related to one’s chosen profession. A person’s vocation mediated the connection between mentoring roles and the transmission of tacit knowledge. Furthermore, the form of mentoring moderated the relationship of mentoring roles and the transfer of tacit knowledge.

This study is novel because it investigates the interplay between mentoring roles, vocational vocation, and the transmission of tacit knowledge in a non-traditional business setting. This research emphasizes the mediating impact of vocation and the moderating effect of mentorship structure in influencing knowledge-sharing practices in Pakistan’s public sector.

This paper aims to provide a secure and efficient pairing protocol for two devices. Due to the large amount of data involving sensitive information transmitted in Internet of Things (IoT) devices, generating a secure shared key between smart devices for secure data sharing becomes essential. However, existing smart devices pairing schemes require longer pairing time and are difficult to resist attacks caused by context, as the secure channel is established based on restricted entropy from physical context.

This paper proposes a fuzzy smart IoT device pairing protocol via speak to microphone, FS2M. In FS2M, the device pairing is realized from the speaking audio of humans in the environment around the devices, which is easily implemented in the vast majority of Internet products. Specifically, to protect the privacy of secret keys and improve efficiency, this paper presents a single-round pairing protocol by adopting a recently published asymmetric fuzzy encapsulation mechanism (AFEM), which allows devices with similar environmental fingerprints to successfully negotiate the shared key. To instantiate AFEM, this paper presents a construction algorithm, the AFEM-ECC, based on elliptic curve cryptography.

This paper analyzes the security of the FS2M and its pairing efficiency with extensive experiments. The results show that the proposed protocol can achieve a secure device pairing between two IoT devices with high efficiency.

In FS2M, a novel cryptographic primitive (i.e., AFEM-ECC) are designed for IoT device pairing by using a new context-environment (i.e., human voice) . The experimental results show that FS2M has a good performance in both communication cost (i.e., 130 KB) and running time (i.e., 10 S).

Integrating the Chat Generative Pre-Trained Transformer-type (ChatGPT-type) model with government services has great development prospects. Applying this model improves service efficiency but has certain risks, thus having a dual impact on the public. For a responsible and democratic government, it is necessary to fully understand the factors influencing public acceptance and their causal relationships to truly encourage the public to accept and use government ChatGPT-type services.

This study used the Latent Dirichlet allocation (LDA) model to analyze comment texts and summarize 15 factors that affect public acceptance. Multiple-related matrices were established using the grey decision-making trial and evaluation laboratory (grey-DEMATEL) method to reveal causal relationships among factors. From the two opposite extraction rules of result priority and cause priority, the authors obtained an antagonistic topological model with comprehensive influence values using the total adversarial interpretive structure model (TAISM).

Fifteen factors were categorized in terms of cause and effect, and the antagonistic topological model with comprehensive influence values was also analyzed. The analysis showed that perceived risk, trust and meeting demand were the three most critical factors of public acceptance. Meanwhile, perceived risk and trust directly affected public acceptance and were affected by other factors. Supervision and accountability had the highest driving power and acted as the causal factor to influence other factors.

This study identified the factors affecting public acceptance of integrating the ChatGPT-type model with government services. It analyzed the relationship between the factors to provide a reference for decision-makers. This study introduced TAISM to form the LDA-grey-DEMATEL-TAISM method to provide an analytical paradigm for studying similar influencing factors.

Community question answering (CQA) platforms play a significant role in knowledge dissemination and information retrieval. Expert recommendation can assist users by helping them find valuable answers efficiently. Existing works mainly use content and user behavioural features for expert recommendation, and fail to effectively leverage the correlation across multi-dimensional features.

To address the above issue, this work proposes a multi-dimensional feature fusion-based method for expert recommendation, aiming to integrate features of question–answerer pairs from three dimensions, including network features, content features and user behaviour features. Specifically, network features are extracted by first learning user and tag representations using network representation learning methods and then calculating questioner–answerer similarities and answerer–tag similarities. Secondly, content features are extracted from textual contents of questions and answerer generated contents using text representation models. Thirdly, user behaviour features are extracted from user actions observed in CQA platforms, such as following and likes. Finally, given a question–answerer pair, the three dimensional features are fused and used to predict the probability of the candidate expert answering the given question.

The proposed method is evaluated on a data set collected from a publicly available CQA platform. Results show that the proposed method is effective compared with baseline methods. Ablation study shows that network features is the most important dimensional features among all three dimensional features.

This work identifies three dimensional features for expert recommendation in CQA platforms and conducts a comprehensive investigation into the importance of features for the performance of expert recommendation. The results suggest that network features are the most important features among three-dimensional features, which indicates that the performance of expert recommendation in CQA platforms is likely to get improved by further mining network features using advanced techniques, such as graph neural networks. One broader implication is that it is always important to include multi-dimensional features for expert recommendation and conduct systematic investigation to identify the most important features for finding directions for improvement.

This work proposes three-dimensional features given that existing works mostly focus on one or two-dimensional features and demonstrate the effectiveness of the newly proposed features.

The purpose of this study is to examine the intricate dynamics within the hospitality service sector, which predominantly employs low-wage and low-skilled workers. These employees frequently encounter challenges related to breaches in their psychological contracts. Despite their critical role in customer service, their experiences are often overlooked in organizational inclusion research. We investigate the relationship between employees’ perceptions of organizational inclusion and their assessments of organizational ethical virtues (OEVs), considering the potential moderating effect of psychological contract breach. Furthermore, it explores how these factors influence customer- and organizationdirected organizational citizenship behaviors (OCBs).

Our research design incorporates a two-stage moderated-mediation model to test our proposed hypotheses empirically. A two-source sample of 451 European hotel managers and employees extends the inquiry with the proposed model. Structural Equation Modeling (SEM) was employed to analyze the proposed relationships.

Findings reveal that organization inclusion is positively associated with OEV and employee-perceived OEV mediates an indirect link between organization inclusion on customer-oriented OCB. Moreover, psychological contract breach (P CB) attenuates the association between organization inclusion and OEV at the first stage and OEV, and OCB at the second stage.

The findings robustly corroborate our proposed model. The study findings culminate in a discussion accentuating the extensive implications of our findings for both research and practicality within the hospitality sector. Anchored in empirical revelations, we delineate avenues for future exploration in this pivotal domain.

With the rapid low-carbon transformation in China, the industrial approach and labor structure of mining enterprises are undergoing constant changes, leading to an increasing psychological dilemma faced by coal miners. This study aims to reveal the relationship and mechanism of factors influencing the psychological dilemma of miners, and to provide optimal intervention strategies for the safety and sustainable development of employees and enterprises.

To effectively address the complex issue of the psychological dilemma faced by miners, this study identifies and constructs five-dimensional elements, comprising 20 indicators, that influence psychological dilemmas. The relational mechanism of action of factors influencing psychological dilemma was then elucidated using an integration of interpretive structural modeling and cross-impact matrix multiplication.

Industry dilemma perception is a “direct” factor with dependent attributes. The perceptions of management response and relationship dilemmas are “root” factors with driving attributes. Change adaptation dilemma perception is a “susceptibility” factor with linkage attributes. Work dilemma perception is a “blunt” factor with both dependent and autonomous attributes.

The aforementioned findings offer a critical theoretical and practical foundation for developing systematic and cascading intervention strategies to address the psychological dilemma mining enterprises face, which contributes to advancing a high-quality coal industry and efficient energy development.

This paper aims to provide a critical reflection on the management of experiences in hospitality and tourism (H&T). The paper investigates the evolution of experience research, while discussing the emerging challenges and opportunities for management.

The study adopts a critical and reflective approach for providing future directions of experience research. Three major fields are identified to discuss advances, challenges and opportunities in experience research: conceptualization and dimensions of experiences; relational network for experience management; and theoretical and methodological approaches.

The paper proposes a mindset shift to guide experience research, but also to redirect and research thinking and managerial practices about the role of experiences in the economy and society. This proposed humanized perspective to experience research and management is deemed important given the contemporary socio-economic, environmental and technological challenges of the environment.

This paper identifies a set of theoretical and managerial implications to help scholars and professionals alike to implement the humanized perspective to experience research. Implications relate to conceptualization, relational network and theoretical and methodological approaches in experience research.

This study critically assesses research challenges and opportunities around customer experience management (CEM) in H&T contexts. This reflective and critical look at customer experiences not only informs future research for advancing knowledge and practice but also proposes a mindset shift about the role and nature of CEM in the society and economy.