Introduction 13 practice, HR, human capital, and people analytics are frequently used to refer to analytical projects covering the entire range of HRM themes whereas workforce and talent analytics are commonly used with more narrow scopes in mind: respectively (strategic) workforce planning initiatives and analytical projects in recruitment, selection, and development. Throughout this dissertation, I will stick to the label people analytics, as this is leading label globally, and in the US tech companies, and thus the most likely label to which I expect the general field to converge. 1.2.3 People Analytics Defined What constitutes people analytics and how it differs from conventional scientific research on HRM is not well defined. People analytics has been defined as “ rigorously tracking HR investments and outcomes ” (Ulrich & Dulebohn, 2015, p. 202), as “ statistical techniques and experimental approaches […] to tease out the causal relationship between particular HR practices and […] performance metrics ” (Lawler et al., 2004, p. 4), and as “ data, metrics, statistics and scientific methods, with the help of technology, to gauge the impact of [human capital management] practices on business goals ” (Kryscynski, Reeves, Stice-Lusvardi, Ulrich, & Russell, 2017, p. 2). In reviewing people analytics literature, Marler and Boudreau (2017) synthesize multiple definitions and define people analytics as the “ HR practice enabled by information technology that uses descriptive, visual, and statistical analyses of data related to HR processes, human capital, organizational performance, and external economic benchmarks to establish business impact and enable data-driven decision-making ” (p. 15). Adding an HRM element to a general definition of analytics (Davenport & Harris, 2007, p. 7), people analytics can be defined as the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions involving personnel . Arguably, this latter definition is more in line with a balanced approach than that of Marler and Boudreau (2017), which centralizes business impact specifically. Nevertheless, both definitions highlight the two related ways in which people analytics differs from a mere application of scientific rigor and methodology in practice. In comparison to conventional HRM research – a term I use here to refer to the traditional management and psychology research on HRM issues – people analytics often serves (1) a different purpose and may thus (2) follow a different statistical modelling process. Different Purpose First, people analytics differs from conventional HRM research because of its purpose. HRM research has primarily been concerned with uncovering, forming, and/or validating theory (Locke, 2007; Shmueli, 2010; Sutton & Staw, 1995; Van Aken, 2004; Yarkoni &Westfall, 2017). This approach is in line with Herbert Simon’s (2001) definition of basic science , which seeks to describe the world and explain its observable phenomena to generate knowledge and understanding (p. 32). According toWoo, O’Boyle, and Spector (2017), “ the current zeitgeist of organizational science appears deeply vested in a ‘top- down’, deductive approach that relies primarily on testing a priori hypotheses ” (p. 255). Hence, in conventional HRM research, “ the role of theory is very strong ” and “ the reliance