Identifying and Detecting Patterns in Work Organization with Active Window Tracking

Abstract

Collecting data on the organization of work by individual employees has become increasingly feasible, with time-tracking applications offering valuable insights into how individuals structure their tasks and projects. Active Window Tracking (AWT) is one such method that captures data from computers on the systems the individual used and windows that were active at a certain point in time. The use of AWT data provides an opportunity to enhance research approaches by offering objective, data-driven insights into work behavior across multiple systems. In semi-structured interviews, we identified four work organization patterns within the context of academic staff: Bundling, Starting, Ending, and Dividing. We show how the work organization patterns can be detected from AWT data of three individuals covering multiple months. Using examples from the data, we demonstrate how work organization patterns can provide insights into work behavior. The findings highlight the potential of AWT data, which can be leveraged to inform strategies to optimize performance and employee well-being.