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Discussion 139 7.1 RQ1: Slow Rise of People Analytics The first research question of this dissertation was “ What is the current state of people analytics? ” During this PhD project, I have dived into the scientific discourse on data analytics in general and of people analytics in specific, and I have implemented people analytics projects in practice. Unfortunately, I cannot but conclude that, in general, the HRM domain – including the HRM function in practice as well as the scientific HRM research community – is trailing behind other functional management domains when it comes to generating value through data analytics. 7.1.1 Trailing Behind In Chapter 2, I conclude that the use of analytics and machine learning is less common in relation to HRM in comparison to other functional management domains. This is visible in the scientific publication networks of studies that have examined big data, analytics, and machine learning and their implications for the performance in and of organizations. Currently, analytics is contributing business value particularly in the functional domains of marketing, finance, information technology, and supply chain. More often than in HRM, advanced data analytics applications such as text mining, predictive modelling, and machine learning are already leveraged in these domains (Chapter 2). Despite specifically searching for keywords such as “ employee- ”, “ worker- ” and “ team performance ”, similar developments were not directly visible in the field of HRM. People analytics, HRM research, the HRM function, nor employee data appeared prominently in the publication networks on data analytics. However, this does not imply that there are no developments regarding data analytics in the HRM domain at all. A closer inspection of the content of the primary studies included in Chapter 2 reinforces the assumption that applications of people analytics are becoming more widespread. A small fragment of the included studies explored the value of employee-, team-, and HRM-related data in improving business processes through analytics and machine learning (e.g., Aral, Brynjolfsson, & Wu, 2012; Baba, Kashima, Kinoshita, Yamaguchi, & Akiyoshi, 2012; Colley & Neal, 2012; Johnson & Gonzalez, 2014; Kontogiannis & Kossiavelou, 1999; Lam, Sleep, Hennig-Thurau, Sridhar, & Saboo, 2017; Robinson, Alifantis, Edwards, Ladbrook, & Waller, 2005; Tan & Netessine, 2014). Moreover, many scientific studies have explored applications of advanced analytics within an HRM context, but have been published outside of the management literature (e.g., Strohmeier & Piazza, 2013), whereas developments in practice may simply not have been published academically at all. Overall, Chapter 2 underlines that analytics and machine learning in the HRM domain is still undergoing development and that scholarly discussion on HR-related data analytics and its implications for organizations and employees is still in a startup phase. 7.1.2 Methodological Inflexibility In part, the HRM domain may be trailing behind due to the methodological inflexibility of the field. Historically, the HRM domain has not been associated with extensive quantitative analysis. Professionals and scholars have addressed the lack of analytical and statistical capabilities among contemporary HRM professionals (e.g.,