Search results

The incorporation of Corporate Social Responsibility (CSR) and sustainability is essential for higher education institutions' (HEIs') future development as they deal with more societal and environmental concerns. The current status of research on CSR and sustainability in HEIs is examined in this systematic review, which also identifies major themes and trends for future development. This review emphasizes the complex characteristics of CSR and sustainability in the higher education environment, spanning aspects like university's social responsibility (education, community engagement, research, and internal management and organization), Sustainable Development Goal (environment, social, and economic), student's perceived self-efficacy, students' intention to implement sustainability, students' engagement for sustainability. It does this by assessing a wide range of scientific works. Additionally, it reveals the advantages and disadvantages of applying CSR and sustainability strategies in HEIs. The results highlight the significance of all-encompassing initiatives that cover governance, accountability, and openness in order to promote a sustainability culture inside HEIs. The paper ends with suggestions for further study, highlighting the necessity of interdisciplinary methods and successful collaborations to promote CSR and sustainability initiatives in HEIs and consequently contribute to sustainable development generally.

For sustainable strategies to be developed in business schools, it is essential to understand which sustainability drivers and barriers are identified by academic managers and coordinators in the implementation of sustainable practices. In this scenario, the study aims to understand the barriers and possible drivers for the implementation of sustainability practices in Brazilian business schools. The subjects are managers and coordinators of business schools that are part of the Principles for Responsible Executive Education Network – Principles for Responsible Management Education (PRME) Chapter Brazil/UN.

Descriptive research with a qualitative approach was conducted to achieve this objective. The study included academic managers, responsible for the general management of academic and administrative activities and coordinators, responsible for supervising and coordinating academic activities in a specific area, of schools that are part of PRME Chapter Brazil (interviews). For data analysis, the content analysis technique was used.

Taking into account the three pillars of sustainability (economic, environmental and social) as categories of analysis, as well as management, curriculum and teaching as subcategories, the study identified 10 barriers and 12 drivers.

The results reveal that both the barriers and the drivers are supported by the social pillar, which focuses on the academic community and society as a whole. The “Management” subcategory was also the most mentioned, indicating guidelines for the strategic academic management of these organizations.

Recently, autonomous vehicles (AV) acceptance has been studied intensively. This paper aims to map and analyze the bibliometric characteristics of AV acceptance literature. Furthermore, this research aims to identify research gaps and propose future research opportunities.

The bibliometric analysis was performed. Scopus database was used as the source of the literature. This study selected and analyzed 297 AV acceptance papers. The performance and science mapping analysis were performed.

The developed countries tended to dominate the topic. The publication outlet tended to be in transportation or technology journals. There were four research themes in existing literature. Technology acceptance model (TAM) and UTAUT2 tended to be used for explaining AV acceptance. AV acceptance studies tended to use two types of psychological concepts for understanding AV acceptance, namely risk related concepts and functional utilitarian benefit related concepts. In the context of research design, quantitative approach tended to be used. Self-driving feature was the most exploited feature of AV in the existing literature. Three research gaps were mapped and future research opportunities were proposed.

This paper provided a comprehensive information that allowed scientists to develop future research on AV acceptance.

There is lack of paper that discussed the bibliometric characteristics of AV acceptance literature. This paper fulfilled the gap.

Today, collaborative fashion approaches are a popular method of reimaging and positioning in contested markets. Fashion collaborations see brands, designers, artists, and companies come together, offering consumers new, alternative products. While shopping continues to be a popular leisure and touristic activity, it is becoming comparable across cities. At present, scholars and practitioners have paid scant attention to collaborative fashion consumption (Botsman & Rogers, 2010). This chapter considers how destinations should become involved with fashion collaborations and drop culture from the offset, establishing specific events for tourists to experience. Ultimately, changing the culture and behavior of shopping tourists to one of event experience and not solely product ownership.

Higher education institutions (HEIs) are critical in the sustainability agenda, not only as catalysts for promoting sustainability practices but also because their activities have substantial social, economic and environmental impacts. Yet there is limited research that examines their sustainability performance. This paper aims to investigate the factors that are associated with sustainability performance in HEIs. Specifically, drawing from the stakeholder theory and exploiting Ullmann’s (1985) conceptual framework, this study examines the association between sustainability performance and stakeholder power, strategic posture and financial slack resources.

The authors draw the sample from the People & Planet University Green League Table for the period 2011–2019 and use the generalised estimating equations for the modelling approach.

This study finds that stakeholder power, in particular, funding grant income, tuition fee income and student and staff numbers, are positively associated with sustainability performance. In relation to strategic posture, this study finds that sustainability performance is negatively associated with governing body independence and gender diversity, and positively associated with internal structures. Finally, regarding financial slack resources, this study finds that surplus income (staff costs) is positively (negatively) associated with sustainability performance.

To the best of the authors’ knowledge, this research contributes to several existing literature focusing on the not-for-profit sector by documenting, for the first time, the role of stakeholder power, strategic posture and slack financial resources on sustainability performance.

The paper includes relevant implications for HEI managers and regulators for promoting sustainability.

These results contribute to the literature on the factors influencing sustainability performance.

With the continuous growth of online education, the quality issue of online educational videos has become increasingly prominent, causing students in online learning to face the dilemma of knowledge confusion. The existing mechanisms for controlling the quality of online educational videos suffer from subjectivity and low timeliness. Monitoring the quality of online educational videos involves analyzing metadata features and log data, which is an important aspect. With the development of artificial intelligence technology, deep learning techniques with strong predictive capabilities can provide new methods for predicting the quality of online educational videos, effectively overcoming the shortcomings of existing methods. The purpose of this study is to find a deep neural network that can model the dynamic and static features of the video itself, as well as the relationships between videos, to achieve dynamic monitoring of the quality of online educational videos.

The quality of a video cannot be directly measured. According to previous research, the authors use engagement to represent the level of video quality. Engagement is the normalized participation time, which represents the degree to which learners tend to participate in the video. Based on existing public data sets, this study designs an online educational video engagement prediction model based on dynamic graph neural networks (DGNNs). The model is trained based on the video’s static features and dynamic features generated after its release by constructing dynamic graph data. The model includes a spatiotemporal feature extraction layer composed of DGNNs, which can effectively extract the time and space features contained in the video's dynamic graph data. The trained model is used to predict the engagement level of learners with the video on day T after its release, thereby achieving dynamic monitoring of video quality.

Models with spatiotemporal feature extraction layers consisting of four types of DGNNs can accurately predict the engagement level of online educational videos. Of these, the model using the temporal graph convolutional neural network has the smallest prediction error. In dynamic graph construction, using cosine similarity and Euclidean distance functions with reasonable threshold settings can construct a structurally appropriate dynamic graph. In the training of this model, the amount of historical time series data used will affect the model’s predictive performance. The more historical time series data used, the smaller the prediction error of the trained model.

A limitation of this study is that not all video data in the data set was used to construct the dynamic graph due to memory constraints. In addition, the DGNNs used in the spatiotemporal feature extraction layer are relatively conventional.

In this study, the authors propose an online educational video engagement prediction model based on DGNNs, which can achieve the dynamic monitoring of video quality. The model can be applied as part of a video quality monitoring mechanism for various online educational resource platforms.