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Project-based organisations (PBOs) experience high labour turnover due to wellbeing issues arising from inefficient policies. An in-depth review of previous attributes this to the fact that the experiences and perspectives of project managers are considered neither in organisational policy/practice nor in wellbeing research. This study addresses this gap with two questions: (1) How do project managers experience wellbeing practices in PBOs? (2) What factors enable or restrict the implementation of these practices from their perspective?

This exploratory qualitative study collected interview data from 19 PBO employees in Western Europe across three sectors. Participants assessed the implementation and influencing factors of wellbeing practices in their organisations. Narratives were analysed thematically, followed by sentiment analysis to cross-reference the emotional tone of each theme.

The findings provided a framework showing the necessary shift in the project managers’ role in PBO wellbeing policy and devolution of voice practices. The results also provided two new conceptual themes specific to wellbeing in PBOs: (1) voice structure and policy and (2) demarcation of the project leader role.

The study introduces a new framework with two new theoretical concepts to improve wellbeing in PBOs. These concepts promote inclusivity of wellbeing concerns via new feedback structures and clarify the project manager’s role in advocating employee voice. The proposed solutions aim to improve knowledge in the research field of wellbeing providing the perspective of the project manager, which currently is lacking from theory.

This study aims to examine the effect of digitalization and sanitary measures during the COVID-19 pandemic on corporate social responsibility (CSR) in the African context. While CSR has traditionally been analyzed in developed markets, this paper explores how multinational subsidiaries can leverage CSR practices to create financial opportunities and market stability for themselves and their communities in Africa.

The authors use a panel of data from six listed African companies for the period ranking from January 2006–2022 to analyze the effect of financial performance (FP), digitalization and health measures on the social responsibility of these companies. The authors provide a robust test that improves the understanding of the impact of pandemics and innovation on CSR, using Machine Learning (ML) linear regression.

The results show that the social responsibility of African companies is highly dependent on FP and digitalization. On the other hand, the authors demonstrate that the moderating role of epidemic instability negatively affects social responsibility through FP, but on the other hand strengthens CSR in the presence of digitalization. The results of the initial analysis remain largely unchanged, demonstrating the validity and robustness of the empirical results through ML models. This article highlights some of the obstacles and opportunities for CSR adapted to the crisis context. The authors conclude that adjusting innovation strategies improves the forecasting performance of responsible companies, especially in a context of instability.

The paper clearly shows that CSR literature varies across different regions. Given that the financial market in Africa is characterized by a lack of opportunity for innovation as well as financial stability, this paper represents an important first step in the elaboration of a CSR development strategy. In light of the results presented above, the study makes an important contribution to the literature on CSR, in particular the CSR practices of multinationals in developing countries and also provides CSR managers with various insights into the types of support they will need to leverage and improve the internal underpinnings of their CSR strategies and collaboration.

The results of this study contribute to the understanding of digital transformation in responsible business, offering empirical evidence of its benefits in tackling the health crisis. In addition, the study highlights the role of an innovative approach in enhancing reputation and developing sustainable, trusting relationships with stakeholders.

This research pioneers the academic link between innovation and epidemic crisis in responsible business, filling a notable gap and introducing a new academic perspective. In concrete terms, it provides women entrepreneurs with actionable insights into the digital strategies essential to improving business performance in a context of instability. Methodologically, the study sets a benchmark for research innovation, using ML to provide a reproducible model for exposing robust results and for future research in this evolving field.

The primary goal of this research is to introduce a data-driven Problem-Solving Approach for Performance Improvement in Healthcare Systems (DPAPIH). This approach combines process mining and data mining techniques to enhance operational efficiency by identifying bottlenecks in Coronary Artery Bypass Grafting (CABG) procedures, particularly focusing on variability in Length of Stay (LOS) in the Intensive Care Unit (ICU). The study, implemented at Tehran Heart Center, aims to optimize patient flow, reduce ICU congestion and improve hospital efficiency by predicting and managing the occurrence of postoperative Atrial Fibrillation (AF), a significant cause of prolonged ICU stays.

The study introduces a data-driven problem-solving approach that integrates process mining and data mining techniques to improve performance in healthcare systems. Focusing on coronary artery bypass grafting (CABG) at Tehran Heart Center, the approach identifies bottlenecks, particularly variability in ICU length of stay (LOS) and predicts postoperative atrial fibrillation (AF). A mixed-methods approach is employed, combining quantitative process mining analyses with qualitative insights from expert consultations. The CHAID decision tree algorithm, alongside other models, is used to predict AF, enabling preemptive interventions, improving patient flow and optimizing resource allocation to reduce hospital congestion and costs.

The study reveals that postoperative Atrial Fibrillation (AF) significantly increases the length of stay (LOS) in the Intensive Care Unit (ICU), creating bottlenecks that delay subsequent surgeries and elevate hospital costs. A predictive model developed using CHAID decision tree algorithms achieved a prediction accuracy of 71.4%, allowing healthcare providers to anticipate AF occurrences. This capability enables proactive measures to reduce ICU congestion, improve patient flow and optimize resource allocation. The findings emphasize the importance of AF management in enhancing operational efficiency and improving patient outcomes in Coronary Artery Bypass Grafting (CABG) procedures.

This study presents an innovative integration of fuzzy process mining and data mining algorithms to address performance bottlenecks in healthcare systems, specifically within the coronary artery bypass surgery process. By identifying atrial fibrillation as a key factor in length of stay fluctuations and developing a robust predictive model, the research offers a novel, data-driven approach to performance improvement. The implementation at Tehran Heart Center validates the model’s practical applicability, demonstrating significant potential for enhancing patient outcomes, optimizing resource allocation and informing decision-making in healthcare management.

The purpose of this study is to understand the potential of serious gaming as an imaginative and creative method to collect data in disaster studies that address key concerns such as extractive research, power inequalities, and bridging the theory-practice gap in exploring post-disaster recovery.

Novel serious gaming approach deployed to connect theory-practice by identifying and co-analysing post-disaster recovery gaps in a workshop setting.

The serious game has value in bridging theory-practice divides, identifying and exploring gaps/solutions in post-flood recovery, and serving as a novel social science research approach for disaster studies.

Outlining a dialogic approach to knowledge construction between academics, practitioners, policymakers and community voices on post-disaster recovery.

Fostering collaboration and knowledge construction on post-disaster recovery gaps across stakeholders is valuable in improving disaster resilience strategies that benefit communities affected by disasters.

The paper proposes a creative and co-developed serious game method of data collection for disaster studies.