Overview
People Analytics is part of a more general shift in management relying on data. It is focusing on the real-time acquisition, analysis, and prediction of employee performance and engagement, work and collaboration patterns. The fundamental assumption is that human experience and intuition in organizational decision-making should, to a substantial degree, be replaced by data leading to an ’evidence-based’ form of human resource management and a data-driven culture. By collecting and connecting a great variety of data, People Analytics is designed to establish new ways to predict, evaluate, and control individual and organizational behavior, leading to new visibilities and algorithmically imposed hierarchies at the workplace. The high relevance of People Analytics for the future of work stands in stark contrast to a lacking empirical knowledge about the practices and effects of algorithm-based decision-making systems. The project analyzes the application and the implementation dynamics of People Analytics and its effects on employees’ self-management, managerial decision-making, and employment relations. With a systematic cross-case comparison of pioneering companies from Germany and Switzerland, it analyzes how a new regime of control based on data, calculation, and Artificial Intelligence is being negotiated organizationally. On the case study level, it aims to reveal the micro-foundations of People Analytics. In the comparative perspective, the influence of external software suppliers as well as company-specific data culture and data governance (including the role of national labor and data protection laws) on the implementation process are analyzed, thereby revealing specific data cultures governing economic life.
The project is being jointly funded by Deutsche Forschungsgemeinschaft DFG and Swiss National Science Foundation SNF. Therefore, two teams are collaborating closely. One team is located at the Hochschule Luzern Wirtschaft, led by Prof. Dr. Peter Kels, the other one at FernUniversität in Hagen, led by Prof. Dr. Uwe Vormbusch.
