Corporate social responsibility in higher education: concept, content, improvement

This research aims to explore the key concepts, main content, and central areas of improvements in CSR in the HE sector. Regarding this, three research questions were formulated: (1) What CSR activities are currently implemented in the HE sector? (2) What aspects of CSR should be covered in the HE sector from the stakeholders’ perspective? (3) How can CSR performance be improved in the HE sector? In research fieldwork, interview method was adopted for collecting data from 54 participants (incl. 17 HEI policymakers/executives, 17 academics, and 20 student representatives) from 20 different HEIs in China and in the UK. The data collected from these participants were analysed through thematic analysis in the forms of narrative text, thematic map, and statistical table. By discussing the findings of the data analysis, contributions were made to both theoretical development and practice. The main theoretical contribution of this research was exploring what CSR means in HE context. Other theoretical contributions made by this research include identifying characteristics of CSR in the HE sector; adding ‘instrumental aspects’ and ‘political aspects’ to explain CSR in this sector; and specifying the linkage of SDGs with CSR in the context of HE. Practical implications were made in considering the aspects of CSR in the HE sector to better serve the interests of HEI’s stakeholders. This involved taking a path to improvement of CSR performance in the HE sector, and developing CSR strategy in terms of managing the quality of CSR activities

Developing Employee Social Responsibility For CSR: Cultivating Student’s Social Responsibility as a Starting Point

No abstract available

Embedding CSR into Management Courses: A Case of Business Decision Analysis Tutorial at the Undergraduate Level

No abstract available

A Starting Point for Teaching CSR: Mapping the Content

Due to the multi-disciplinary and complex nature of Corporate Social Responsibility (CSR) as a subject, the teaching of CSR in higher education is challenging. This presentation aims to develop a framework for teaching CSR in higher education by identifying, linking, and structuring the most important topics within the widely extended subject of CSR. A content analysis and mapping approach is applied to the literature to develop a conceptual model of CSR. This model consists of detailed maps as well as a mega map to highlight the most important CSR topics and their relationships. Detailed maps are generated by identifying different CSR topics and mapping the concepts under these topics. The topics are determined by analysing the text of selected widely-recognised books and articles on CSR. To provide an overview of CSR main topics, a mega map is produced. This is achieved by merging the derived detailed maps and restructuring them into a mega map. The model implies that CSR knowledge can be classified into eleven types under three main sections. These sections are CSR Learning, CSR Application, and CSR Management. The model is used to provide educators with a curriculum framework for teaching CSR. This is done by rearranging CSR topics in a progressive and logical flow. This framework is generic enough to be used as a source and a guideline for developing customised CSR curriculums to meet different degree requirements. This research work contributes to teaching CSR in higher education by providing an informed and structured input for the design of a CSR programme. Such programmes are essential if the goal is to provide societies with graduates who can be advocates and promoters of CSR within different platforms and roles

Identification and assessment of differentially expressed necroptosis long non-coding RNAs associated with periodontitis in human

Abstract Background Periodontitis is the most common oral disease and is closely related to immune infiltration in the periodontal microenvironment and its poor prognosis is related to the complex immune response. The progression of periodontitis is closely related to necroptosis, but there is still no systematic study of long non-coding RNA (lncRNA) associated with necroptosis for diagnosis and treatment of periodontitis. Material and methods Transcriptome data and clinical data of periodontitis and healthy populations were obtained from the Gene Expression Omnibus (GEO) database, and necroptosis-related genes were obtained from previously published literature. FactoMineR package in R was used to perform principal component analysis (PCA) for obtaining the necroptosis-related lncRNAs. The core necroptosis-related lncRNAs were screened by the Linear Models for Microarray Data (limma) package in R, PCA principal component analysis and lasso algorithm. These lncRNAs were then used to construct a classifier for periodontitis with logistic regression. The receiver operating characteristic (ROC) curve was used to evaluate the sensitivity and specificity of the model. The CIBERSORT method and ssGSEA algorithm were used to estimate the immune infiltration and immune pathway activation of periodontitis. Spearman’s correlation analysis was used to further verify the correlation between core genes and periodontitis immune microenvironment. The expression level of core genes in human periodontal ligament cells (hPDLCs) was detected by RT-qPCR. Results A total of 10 core necroptosis-related lncRNAs (10-lncRNAs) were identified, including EPB41L4A-AS1, FAM30A, LINC01004, MALAT1, MIAT, OSER1-DT, PCOLCE-AS1, RNF144A-AS1, CARMN, and LINC00582. The classifier for periodontitis was successfully constructed. The Area Under the Curve (AUC) was 0.952, which suggested that the model had good predictive performance. The correlation analysis of 10-lncRNAs and periodontitis immune microenvironment showed that 10-lncRNAs had an impact on the immune infiltration of periodontitis. Notably, the RT-qPCR results showed that the expression level of the 10-lncRNAs obtained was consistent with the chip analysis results. Conclusions The 10-lncRNAs identified from the GEO dataset had a significant impact on the immune infiltration of periodontitis and the classifier based on 10-lncRNAs had good detection efficiency for periodontitis, which provided a new target for diagnosis and treatment of periodontitis