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People Analytics at Work. Intensive Case Studies of Pioneering Companies in Germany and Switzerland

Lay summary

People Analytics ist Teil eines allgemeineren Wandels im Management, der sich auf Daten stützt. Der Begriff People Analytics steht dabei für eine durch moderne Informationstechnologien und Algorithmen ermöglichte Praxis der Analyse personalbezogener Daten und Verhaltensspuren in Verbindung mit anderen Unternehmensdaten. People Analytics treten an mit dem Versprechen, durch eine systematische Erfassung, Analyse und Vorhersage von Arbeitsleistungen oder Organizational Behavior die Grundlagen für eine dem Ideal nach 'evidenzbasierte' Personalmanagement- und Entscheidungspraxis zu legen. Der hohen Relevanz von People Analytics für die Zukunft der Arbeit steht ein bis heute mangelndes empirisches Wissen über die Praktiken und Wirkungen algorithmenbasierter Entscheidungssystemen gegenüber. 

Auf Grundlage von Intensivfallstudien in Vorreiterunternehmens aus Deutschland und der Schweiz sowie einem systematischen Querschnittsvergleich analysiert das Projekt den Einsatz, die Implementierungsdynamiken und intendierten wie nicht-intendierten Effekte von People Analytics. Im Zentrum stehen dabei Fragen nach den Auswirkungen von People Analytics auf managerielle Entscheidungsprozesse, die Gestaltung betrieblicher Arbeitsbeziehungen, das Selbstmanagement und das Organizational Commitment der Beschäftigen sowie die Rolle unterschiedlicher Stakeholder wie beispielsweise von Softwareanbietern oder der Arbeitnehmervertretung.

Abstract

People Analytics is part of a more general shift in management relying on data (Davenport 2006). 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 (BI Trend Monitor 2020; Kremser/Brunauer 2019). 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 (Loi 2020; Sousa et al. 2019; Manuti/de Palma 2018; Leicht-Deobald et al. 2019; Goodell King 2016; Marler/Boudreau 2017), 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 in the area of Human Resources.

The implementation of People Analytics cannot solely be understood from a technical perspective. Quite to the contrary: in the form of a “corporate social graph” (Höller/Wedde 2018), it transfers the socially established logics of social networking to the sphere of work and organization (Staab/Geschke 2019). It relies on the ongoing transformation of work processes and organizational behavior into data, and on continuously connecting this data in manifold ways allowing ever new angles for the analysis of people and work (‘datafication’). How such an algorithm-based logic of social ordering (cf. Yeung 2017) is organizationally implemented, is one main focus of the project. To analyze the production of new visibilities, it draws on insights from the fields of the sociology of quantification (Diaz-Bone/Didier 2016) and ‘valuation studies’ (Lamont 2012) alike. From this perspective, People Analytics are not simply representing organizational reality in an objective manner. Rather, they are characterized by their performativity and reactivity (Espeland/Sauder 2007) informing the employees’ social actions, self-presentations, and identities in various ways.

Based on comparative case studies of pioneering companies in Germany and Switzerland, the project analyzes the implementation dynamics of People Analytics. It investigates how a new regime of control based on data, calculation, and - at least in part - on Artificial Intelligence/Machine Learning is actually being negotiated. On the case study level, it focuses on work which is both subjectified and algorithmically controlled, hereby revealing the micro-foundations of People Analytics. In a comparative perspective, it analyzes the influence of company-specific data cultures and external software suppliers as well as the impact of national legislation on these processes.

  Prof.Peter Kels