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Chapter 1 14 on data and statistical modeling are strictly through the lens of the theoretical model ” (Shmueli, 2010, p. 290). This explanatory focus is not without reason or consequences. Scientific publication procedures in management and psychology fields highly favor research with a deductive approach, where theory-driven hypotheses are tested in a confirmatory way (e.g., Hambrick, 2007; Leung, 2011; Pratt, 2008; Woo et al., 2017; Van Aken, 2004). There has been such “ a strong bias towards description-driven research, even to the extent that many feel that that is the only type of research that deserves academic respectability ” (Van Aken, 2004, p. 229). As a result, there has been a “ near-exclusive focus on developing mechanistic models of cognition that hold theoretical appeal but rarely display a meaningful capacity to predict future behavior ” (Yarkoni & Westfall, 2017, p. 1101). In conventional HRM research, “management implications tend to be treated more or less as an afterthought of the analysis and are not tested as such” , resulting in doubts about the actual relevance of contemporary research (Van Aken, 2004, p. 230). People analytics serves a different purpose as highlighted in its definitions. People analytics is focused on uncovering practical insights or actions that are valuable in a specific organizational context. Here, data and statistical models are leveraged specifically to explain, predict, and/or prescribe how organizations can improve the impact of their HRM activities – be it on outcomes relevant to the business, to the employee, or to society as a whole. The insights (including predictions) generated by such research can be used directly as input for decision-making processes in local practice. Such research focused on local, practical value is still considered scientific , and not necessarily new. In Herbert Simon’s eyes, people analytics could be considered an applied science , seeking to make inferences or predictions in order to anticipate and adapt to the future and to invent and design practices (Simon, 2001, p. 32). Others would argue that people analytics as a design science , seeking to develop valid and reliable knowledge to be used in designing solutions to problems, thereby occupying the middle ground between descriptive theory and actual applications (Van Aken, 2004, p. 225). From the perspective of Gibbons and colleagues (1994), people analytics would be a form of mode 2 knowledge production: trans-disciplinary scientific research with intensive interaction between knowledge production, dissemination, and application. Furthermore, people analytics shows similarities to Action Research, collaborative (clinical) research, and case-study approaches (see Eden & Huckham, 1996; Rynes, Bartunek, & Daft, 2001; Van Aken, 2004). In sum, while people analytics seeks to generate knowledge and understanding about HRM phenomena like conventional HRM research does, its primary purpose is often more local and applied: to predict what works best in practice, in a specific context – now or in the future. 1.2.3.2 Different Statistical Modelling Process Second, people analytics may follow a different statistical modeling process than conventional HRM research, among others due to its different purpose. Any statistical modelling process will consist of several general stages: study design, data collection, data preparation, variable selection, methods and algorithms, model validation, evaluation, and selection, and model usage and reporting. Each of these stages involves several