"Working time reduction may be one of the great achievements of the 21st century, potentially delivering environmental, social, and economic benefits. However, implementation at the level of organizations is not straightforward. Reliable data on working hours are needed to track changes, so the limited availability or poor quality of data may prevent certain types of reductions. Here we explore the factors that influence data quality in different contexts on the basis of 28 expert interviews. We list more than ten interrelated factors that matter, including descriptors of the work context, the motivations and functions of measurements, definitions of working time applied, as well as approaches and tools of measurements. For each of these factors, we highlight cases when data quality can be expected to be high and low. At the micro level, this facilitates the implementation and assessment of reduction programmes. At the macro level, it is a first step towards more realistic models, which helps to establish policy priorities concerning the role of direct regulation as opposed to cultural and institutional changes driving reductions in working hours."

Support: Digital