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

人员分析是依赖数据的管理更普遍转变的一部分(Davenport，2006)。它专注于对员工绩效和参与度、工作和协作模式的实时获取、分析和预测。基本假设是，组织决策中的人类经验和直觉应该在很大程度上被数据所取代，从而形成一种以证据为基础的人力资源管理形式和数据驱动的文化(BI趋势监测2020；克里姆瑟/布鲁诺2019)。通过收集和连接各种数据，People Analytics旨在建立预测、评估和控制个人和组织行为的新方法(LOI 2020；Sousa等人)。2019年；Manuti/De Palma 2018；Leicht-Deobald等人。2019年；Goodell King 2016；Marler/Boudreau 2017)，导致了工作场所新的可见性和算法强加的等级制度。人员分析与未来工作的高度相关性与缺乏关于人力资源领域基于算法的决策系统的做法和影响的经验知识形成鲜明对比。恰恰相反：它以“企业社交图”的形式将社交网络的社会既定逻辑转移到工作和组织领域(Staab/Geschke，2019)。它依赖于正在进行的工作流程和组织行为到数据的转换，以及以多种方式持续连接这些数据，从而允许从新的角度分析人和工作(数据通信)。这种基于算法的社会秩序逻辑(参见Yeung 2017)是组织实施的，是该项目的主要重点之一。为了分析新的可见性的产生，它借鉴了量化社会学(Diaz-bone/Didier 2016)和估值研究(Lamont 2012)等领域的见解。从这个角度来看，People Analytics不仅仅是以一种客观的方式代表组织的现实。相反，他们的特点是他们的表演性和反应性(Espland/Sauder 2007)，以各种方式告知员工的社交行为、自我展示和身份认同。基于德国和瑞士创业公司的比较案例研究，该项目分析了People Analytics的实施动态。它调查了基于数据、计算以及(至少部分)人工智能/机器学习的新控制制度实际上是如何谈判的。在案例研究层面，它聚焦于既主观化又受算法控制的工作，从而揭示了人的分析的微观基础。从比较的角度分析了公司特有的数据文化和外部软件供应商的影响，以及国家立法对这些过程的影响。

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.