Abstract
The digital transformation has enabled the detailed and real-time datafication of work activities, allowing organizations to analyze and understand digital work performance. This study introduces a data-driven methodology that leverages an unsupervised Random Forest-based algorithm to cluster observations, identify key features using tree topology metrics, and visualize results with optimized graphical tools. We applied the proposed framework to data from a digital consulting agency, incorporating metadata from Microsoft365 and survey responses on work attitudes. The analysis produced a novel metric for digital work performance, which was linked to employee attitudes using tailored visual tools to explore the relationships between work activities and attitudes in digital workplaces.
Cite as:
Demaria, F., Cavicchioli, M., Fabbri, T., Papana Dagiasis, A., and Scapolan, A.C. (2025). A non-parametric approach to employee performance in digital workplaces. In: Castellano, R., De Luca, G., Bruno, E. (eds), Book of Short Papers 3rd Italian Conference on Economic Statistics (ICES 2025). [ISBN: 9791280655523]