Social Entrepreneurship (SE) benefits the society by helping to achieve social and economic goals. SE is receiving scholarly attention around the globe but its development is still moderate in Pakistan. Despite the growing trend, the dominant focus of scholars remains the ideological debate about the meaning and definition of SE. Such an approach inhibits the exploration of its other facets. Casting the gap in literature, this paper aims to find out the challenges and prospects that social entrepreneurs face in their journey, specifically in Pakistan. Keeping in view the emerging importance of this sector, this study discusses the findings of 14 in-depth semi-structured interviews conducted with leading social entrepreneurs, practitioners and academicians related to the field to understand the phenomenon at hand. Drawing upon the findings of the study, useful insights have been put forth as its theoretical contribution. Moreover, local and national government can benefit from the findings to enhance consciousness regarding the fourth sector of the economy, eventually augmenting the available social capital.

This study proposes to highlight the role of transformation leadership in enabling environmental sustainability efforts. In this regard, a mediating role of green human resource management is investigated to understand the association between transformation leadership and environmental sustainability. The study follows a quantitative and cross-sectional research approach. A self-administered questionnaire was used to collect the responses from 200 managerial-level employees of ISO-14001 certified textile organizations in Lahore, Pakistan. Furthermore, the study hypotheses were tested by applying linear regression and Hayes’ Process in SPSS to determine the interconnected dependence of the study variables. The findings of the study demonstrate that transformational leadership plays an instrumental role in the implementation of environmental sustainability strategy. The results also reveal that green human resource management significantly mediates the relationship between transformational leadership and environmental sustainability. The research outcomes portray a stringent need to apply the transforming abilities of the organizational leaders for fostering environmental initiatives; a contribution to a broader cause of global environmental sustainability

The clinical application of detecting COVID-19 factors is a challenging task. The existing named entity recognition models are usually trained on a limited set of named entities. Besides clinical, the non-clinical factors, such as social determinant of health (SDoH), are also important to study the infectious disease. In this paper, we propose a generalizable machine learning approach that improves on previous efforts by recognizing a large number of clinical risk factors and SDoH. The novelty of the proposed method lies in the subtle combination of a number of deep neural networks, including the BiLSTM-CNN-CRF method and a transformer-based embedding layer. Experimental results on a cohort of COVID-19 data prepared from PubMed articles show the superiority of the proposed approach. When compared to other methods, the proposed approach achieves a performance gain of about 1–5% in terms of macro- and micro-average F1 scores. Clinical practitioners and researchers can use this approach to obtain accurate information regarding clinical risks and SDoH factors, and use this pipeline as a tool to end the pandemic or to prepare for future pandemics.