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Management samenvatting 222 This dissertation explored how the Human Resource Management (HRM) function is becoming more evidence-based through data analytics. The past decade has witnessed a tremendous rise in public interest in people analytics – the use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions involving personnel (see Chapter 1). However, scientific publication networks suggest that the use and added value of analytics is less widespread for the HRM function than for other functional management domains (e.g., marketing, IT, supply chain), at least in terms of the volume and diversity of academic research (Chapter 2). Arguably, a paradigm change is needed for HRM to fully leverage the business value of the complex data generated through modern technology. Historically, HRM scholars have focused on the development and confirmation of overall management theories through explanatory research (versus exploratory or predictive research). Additionally, complex, quantitative analyses have historically not been present in HRM research and practice. Fortunately, people analytics may facilitate a paradigm change as it involves problem- and solution-oriented research with as its main purpose the provision of local, applied insights in order to optimize HRM activities. In such research projects, the methodology is usually fit to the purpose and the data at hand (Chapters 3 and 7). In order to demonstrate how people analytics facilitates evidence-based HRM, this dissertation specifically explored its application to expatriate management. It sought to define and quantify expatriate success and uncover the impact of and best practices related to the social support provided during the expatriation cycle (Chapters 4 and 5). Additionally, this dissertation demonstrated how to quantify the local impact of, for instance, short-term international assignment for talent retention (Chapter 6). The dissertation concludes with a discussion on people analytics and the future of HRM research and practice, covering topics related to ethics, privacy, machine learning, cross- functional collaborations, and their implications (Chapter 7).