Search results

To contribute to achieving the Sustainable Development Goals, this study aims to explore how to encourage innovative green behaviors among college students and the mechanisms behind the formation of green innovation behavior. Specifically, this study examines the influences of schools, mentors and college students themselves.

A multilevel, multisource study involving 261 students from 51 groups generally supported this study’s predictions.

Proenvironmental and responsible mentors significantly predicted innovative green behavior among college students. In addition, creative motivation mediated the logical chain among green intellectual capital, emotional intelligence and green innovation behavior.

The study findings offer new insights into the conditions required for college students to engage in green innovation. In addition, they provide practical implications for cultivating green innovation among college students.

The authors proposed and tested a multilevel theory based on the ability–motivation–opportunity framework. In this model, proenvironmental and responsible mentors, green intellectual capital and emotional intelligence triggered innovative green behavior among college students through creative motivation.

External institutional policy and its impact on corporate social responsibility (CSR) have been widely discussed by researchers, but its effect still remains controversial. This study aims to use the minimum wage policy as an illustrative example to analyze its impact on the corporate social responsibility (CSR) of tourist enterprises. Furthermore, the research seeks to examine the boundary conditions that influence the minimum wage’s effect on CSR.

This paper takes the data of 42 listed tourism companies from 2010 to 2020 in China as samples and uses the mixed OLS regression method and the fixed effects panel model to examine the effect of the minimum wage on CSR.

Findings show that increasing wages has a significantly negative impact on their total CSR investment. Also, low-operating-capacity enterprises and private enterprises will react more adversely when faced with increasing minimum wages. And found that the increase of minimum wage has no significant negative impact on the strategic social responsibility of tourism enterprises; however, it has a significantly negative impact on their tactical social responsibility. In addition, as far as employees’ rights and interests are concerned, the minimum wage increase has effectively increased employee salaries, but the nonsalary benefits of the employees have significantly decreased.

The contribution of this paper not only expands the research on the antecedents and boundary mechanisms of CSR but also clarifies the specific effect of the rise of the minimum wage on corporate social responsibility; it further deepens the impact of institutional policy factors on CSR, which also opens new perspectives for policy evaluation and provides a theoretical basis for government policymakers.

In light of the shift in focus from information communication technology (ICT) access (access divide) and skills (skills divide) to the tangible impacts of ICT use (impact divide), a growing number of scholars have called for further investigation into the inter-territorial and multi-dimensional aspects of the digital divide in China. This study aims to address these gaps by examining the disparities across 31 provinces, particularly emphasizing the transition from the traditional access and skills divides to the impact divide.

Multivariate regression analysis extensively investigates the transition from the access and skills divides to the impact divide across 31 provinces. Additionally, ArcGIS software is used to analyze spatial agglomeration and the auto-correlation (Moran-i) and predict mapping patterns in the data corresponding to all three levels of the digital divide.

According to the study's findings, poverty is a significant factor in the digital divide between different regions in China. The research shows that provinces with advanced administrative systems, such as Guangdong, Shanghai, Beijing, Jiangsu, Shandon and Zhejiang, have high scores on the digital development index (DDI). However, regions with poverty-ridden and rural areas, primarily located in southwest, central and western China, tend to have lower DDI scores.

This study offers a novel contribution to the literature by presenting an innovative conceptual framework that explores the impact divide within China's provinces. The authors also address this lacuna in the literature by developing and testing two dimensions to examine the relationships statistically under a wide range of socioeconomic and ICT indicators.

This study aims to clarify influencing factors of overcapacity of new energy enterprises in China and accurately predict whether these enterprises have overcapacity.

Based on relevant data including the experience and evidence from the capital market in China, the research establishes a generic univariate selection-comparative machine learning model to study relevant factors that affect overcapacity of new energy enterprises from five dimensions. These include the governmental intervention, market demand, corporate finance, corporate governance and corporate decision. Moreover, the bridging approach is used to strengthen findings from quantitative studies via the results from qualitative studies.

The authors' results show that the overcapacity of new energy enterprises in China is brought out by the combined effect of governmental intervention corporate governance and corporate decision. Governmental interventions increase the overcapacity risk of new energy enterprises mainly by distorting investment behaviors of enterprises. Corporate decision and corporate governance factors affect the overcapacity mainly by regulating the degree of overconfidence of the management team and the agency cost. Among the eight comparable integrated models, generic univariate selection-bagging exhibits the optimal comprehensive generalization performance and its area under the receiver operating characteristic curve Area under curve (AUC) accuracy precision and recall are 0.719, 0.960, 0.975 and 0.983, respectively.

The proposed integrated model analyzes causes and predicts presence of overcapacity of new energy enterprises to help governments to formulate appropriate strategies to deal with overcapacity and new energy enterprises to optimize resource allocation. Ten main features which affect the overcapacity of new energy enterprises in China are identified through generic univariate selection model. Through the bridging approach, the impact of the main features on the overcapacity of new energy enterprises and the mechanism of the influence are analyzed.

Crowded urban regions pose a complex urban challenge that can adversely affect urban residents, encompassing aspects like mental and physical well-being, overall livability and quality of life. The complexity in determining the factors influencing the crowding perception, which encompass subjective and situational psychological factors alongside physical and environmental attributes, imparts ambiguity to planners' approach. This study aims to unravel the intricate interplay between crowding and the physical attributes inherent in the built environment.

This literature review analyses theories linking urban planning and environmental psychology to uncover gaps in the relationship between urban design principles and residents' perceptions of crowding. It also explores influential variables affecting crowding perception and diverse methodologies across contexts.

The study built upon a broad literature review which is expected to summarise and classify the variables of urban planning components and approaches according to their impacts on the psychological perception of crowding. Furthermore, highlighting a number of recommendations that can be considered a guide for planners and urban designers to enhance the urban experience and reduce the perception of crowding.

This study seeks to improve the overall experience of crowding in densely populated urban areas. It accomplishes this by identifying influential factors and comprehending the associated outcomes in such contexts. Furthermore, it bridges perspectives from various fields to examine relevant policies and strategies to mitigate crowding consequences.

Artificial intelligence is one of the most significant and active fields of study in the last few years. Artificial intelligence-derived robotic technologies known as chatbots are gaining interest from both academic and industry sectors. By analyzing the development and patterns of research on the chatbot phenomena within the tourism field, this study seeks to develop a theoretical framework for the interaction between chatbots and tourism.

The Web of Science (WoS) database’s 33 articles on chatbots related to travel and hospitality were examined between 2019 and 2024 using VOSviewer software for bibliometric and thematic content analysis.

Research on chatbots for tourism and hospitality appears to be in its early stages. The factors influencing tourists' intentions to use chatbots have been thoroughly researched; the attitudes, perceptions and behavioral intentions of destinations, travel agencies and restaurant patrons regarding chatbots were examined, and it was found that the quantitative research approach was dominant. In addition, the majority of the studies are based on a particular theory or model.

This is one of the first attempts to directly comprehend and depict the interconnected structures of studies on the interaction between chatbots and tourism through the use of network analysis. Furthermore, the study’s findings can offer academics a comprehensive viewpoint and a reference manual for more accurate assessment and oversight of the chatbot-tourism interaction. Regarding the lack of research on the topic and the fragmented structure of the studies that exist, it is imperative to provide both a comprehensive overview and a roadmap for future investigations into the usage of chatbots in the travel and hospitality sector.

This study aims to study the thermal identification issue by harvesting both solar energy and atmospheric thermal updraft for a solar-powered unmanned aerial vehicle (SUAV) and its electric energy performance under continuous soaring conditions.

The authors develop a specific dynamic model for SUAVs in both soaring and cruise modes. The support vector machine regression (SVMR) is adopted to estimate the thermal position, and it is combined with feedback control to implement the SUAV soaring in the updraft. Then, the optimal path model is built based on the graph theory considering the existence of several thermals distributed in the environment. The procedure is proposed to estimate the electricity cost of SUAV during flight as well as soaring, and making use of dynamic programming to maximize electric energy.

The simulation results present the integrated control method could allow SUAV to soar with the updraft. In addition, the proposed approach allows the SUAV to fly to the destination using distributed thermals while reducing the electric energy use.

Two simplified dynamic models are constructed for simulation considering there are different flight mode. Besides, the data-driven-based SVMR method is proposed to support SUAV soaring. Furthermore, instead of using length, the energy cost coefficient in optimization problem is set as electric power, which is more suitable for SUAV because its advantage is to transfer the three-dimensional path planning problem into the two-dimensional.

This study examines potential ways to break the inequality reproduction circle faced by ethnic minority health workers and sustained by key performance indicators (KPIs)-centred management in the National Health Service (NHS) in England. It does so through the lens of signalling theory.

Three years panel data for 2018–2020 covering 207 hospitals was compiled from the annual NHS staff survey and matched with relevant administrative records. Structural equation modelling was used to test the proposed hypotheses at the organisational level.

The moderated mediating model reveals that persistent racial discrimination by managers and coworkers can disadvantage the career progression of ethnic minority health workers, which in turn reinforces and reproduces economic and health inequalities among them. More importantly, we show how the collective agreement that the senior management team acts (SMTA) on staff feedback can break this vicious circle.

While our research focuses on the not-for-profit health care sector, it opens important opportunities to extend the proposed model to understand organisational inequality and how to address it.

Perceived SMTA can send strong signals to reduce deep-rooted discrimination (race, gender, age, etc.) through resource allocations and instrumental functions. This is also a way to address the current staff burnout and shortage issues in the healthcare sector.

This article reveals why the purpose of organisations that provide public service to reduce social inequality was comprised during their business-like operations and more importantly, how to reflect their foundational purpose through management practice.

This study offers a way forward to resolve one of the unintended consequences of KPI-centred management in the not-for-profit sector through unpacking the process of inequality reproduction and, more importantly, how it is possible to break this vicious circle.

This study aims to examine student perceptions regarding the mid-semester transition from face-to-face to online delivery in an accounting course during spring 2020.

Due to the COVID-19 pandemic, numerous universities and colleges worldwide transitioned from face-to-face instruction to online delivery during spring 2020. We find some evidence in line with prior literature that COVID-19 affected student learning experience from various aspects.

Thanks in part to effective teaching techniques implemented by the instructor during the transition, including online lecture videos recorded by the instructor, online class materials, early posting of answer keys, frequent communication through emails and bonus points for watching lecture videos, students still perceived their learning outcomes positively in general.

These teaching techniques can be used to enhance student learning experience and satisfaction during class modality transitions in unforeseen circumstances, for both hybrid and online business courses.

This tourism study delves into the universal impact of authenticity on tourists' preferences. This study aims to enhance understanding and present a model delineating authenticity's intricate relationship with behavioral intention, involvement and nostalgia.

The research entails administering a sample survey to travelers located in the departure lounge of Macau International Airport. Ultimately, 499 fully completed questionnaires are obtained through convenience sampling. The analysis of the collected data incorporates an innovative method, using a quadratic model.

Results undeniably validate that a destination's perceived authenticity significantly influences tourists' intentions, engagement and nostalgic sentiments. Findings strongly support the assertion that this authenticity demonstrates a nonlinear pattern in its association with behavioral intention, involvement and nostalgia.

Through quantitative analysis and interpretation, this study has advanced the comprehension of the interplay between authenticity and tourism. It introduces a novel perspective that adds depth to the broader understanding in this domain. This foundational research is crucial for acknowledging the unique contributions made by this study to the fields of tourism and authenticity.

这项旅游研究深入探讨了真实性对游客偏好的普遍影响。它旨在增强理解并提出一个模型来描述真实性与行为意图、参与和怀旧之间的复杂关系。

该研究需要对位于澳门国际机场候机室的旅客进行抽样调查。最终通过便利抽样获得完整填写的问卷499份。对收集的数据的分析采用了一种创新方法, 利用二次方模型。

结果无可否认地证实, 目的地的感知真实性会显着影响游客的意图、参与度和怀旧情绪。研究结果有力地支持了这一主张, 即这种真实性在其与行为意图、参与和怀旧的关联中表现出一种非线性模式。

通过定量分析和解释, 这项研究增进了我们对真实性与旅游之间的回应的理解。它引入了一种新颖的视角, 加深了我们对这一领域更广泛的理解。这一基础研究为承认本研究对旅游和真实性领域做出了独特贡献。

Este estudio turístico profundiza en el impacto universal de la autenticidad en las preferencias de los turistas. Pretende mejorar la comprensión y presentar un modelo que delinee la intrincada relación de la autenticidad con la intención conductual, la implicación y la nostalgia.

La investigación consistió en administrar una encuesta por muestreo a los viajeros situados en la sala de embarque del Aeropuerto Internacional de Macao. Finalmente, se obtuvieron 499 cuestionarios totalmente cumplimentados mediante un muestreo de conveniencia. El análisis de los datos recogidos incorpora un método innovador, utilizando un modelo cuadrático.

Los resultados validan sin lugar a duda que la autenticidad percibida de un destino influye significativamente en las intenciones, la implicación y los sentimientos nostálgicos de los turistas. Los hallazgos apoyan firmemente la afirmación de que esta autenticidad demuestra un patrón no lineal en su asociación con la intención conductual, la implicación y la nostalgia.

Mediante el análisis cuantitativo y la interpretación, este estudio ha hecho avanzar nuestra comprensión de la interacción entre autenticidad y turismo. Introduce una perspectiva novedosa que añade profundidad a nuestra comprensión más amplia en este ámbito. Esta investigación fundacional es crucial para reconocer las contribuciones únicas de este estudio a los campos del turismo y la autenticidad.