Digital Support of Work-from-Home Environments: Organizational Efficiency Outcomes

Abstract

Though COVID-19 pandemic created a more need of remote working, fundamentally transforming organizational structures, work practices, and approaches to measuring employee and organizational efficiency. This study examines the potential impact of digital work-from-home (WFH) environments on organizational efficiency across different industries. To conduct the analysis, an LSTM (Long Short-Term Memory) neural network was employed for predictive analysis using Python and TensorFlow. The study utilized productivity metrics, communication patterns, and employee performance data collected from 250 organizations over an 18-month period. The findings indicate that digital WFH environments significantly influence organizational efficiency through key mediating factors such as technological infrastructure, communication quality, and work-life balance. The LSTM model demonstrated strong predictive performance, achieving an accuracy of 89.3% by effectively capturing patterns associated with digital work environments. Furthermore, organizations with robust digital infrastructure recorded 34% higher efficiency levels compared with organizations operating with inadequate digital systems. The study contributes to a deeper understanding of the complex relationship between digitalization, remote working practices, and organizational performance, while providing valuable insights for organizations seeking to optimize virtual work environments and enhance overall operational efficiency.