Exploring the remote work challenges in the era of COVID-19 pandemic: review and application model

Purpose: Remote work (RW) literature is a megatrend in HRM literature, and the COVID-19 pandemic has highlighted the importance of RW as a concept and an organisational practice. Given the large number of papers being published on remote work, there is a need for a critical review of the extant literature using bibliometric analysis. This paper examines the literature on remote working to identify the factors crucial for managing a remote workforce. This study uses the complex adaptive systems theory as a foundation to build a framework that organisations can use to manage their remote workforce, focusing on three outcomes: employee engagement, collaboration and organisational agility. Design/methodology/approach: Bibliometric analysis was conducted on the research published in Scopus journal in the area of remote work, followed by critical literature analysis. Findings: The bibliometric analysis identified five clusters that reflect five organisational factors which the management can align to achieve the desired outcomes of engagement, collaboration and agility: technology orientation, leadership, HRM practices, external processes and organisational culture. The present findings have important implications for managing the remote workforce. Originality/value: The five factors were mapped to propose a conceptual model on engaging individual employees, fostering team collaboration and building organisational agility while working remotely. We also propose an application model for using technology to achieve the outcomes of engagement, collaboration and agility in the organisation. Practitioners could use this framework to focus on the factors that can create a conducive environment to improve work efficiency in a remote workforce. © 2021, Emerald Publishing Limited.

Healthcare Operations and Black Swan Event for COVID-19 Pandemic: A Predictive Analytics

COVID-19 pandemic has questioned the way healthcare operations take place globally as the healthcare professionals face an unprecedented task of controlling and treating the COVID-19 infected patients with a highly straining and draining facility due to the erratic admissions of infected patients. However, COVID-19 is considered as a white swan event. Yet, the impact of the COVID-19 pandemic on healthcare operations is highly uncertain and disruptive making it as a black swan event. Therefore, the study explores the impact of the COVID-19 outbreak on healthcare operations and develops machine learning-based forecasting models using time series data to foresee the progression of COVID-19 and further using predictive analytics to better manage healthcare operations. The prediction error of the proposed model is found to be 0.039 for new cases and 0.006 for active COVID-19 cases with respect to mean absolute percentage error. The proposed simulated model further could generate predictive analytics and yielded future recovery rate, resource management ratios, and average cycle time of a patient tested COVID-19 positive. Further, the study will help healthcare professionals to devise better resilience and decision-making for managing uncertainty and disruption in healthcare operations.