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The success of teams in enterprise social networks (ESN) is of high importance in today's project-based and digitised work environments. In this context, onboarding of new hires or allocated team members means the adoption of group characteristics and behaviours. Studies identified cohesion and trust as part of the socialisation process and found communication behaviours that facilitate socialisation. ESN not only enable efficient communication or relationship building, they also make the socialisation processes visible and analysable. In this paper, we propose to use metrics from social network analysis (e.g. extraversion, openness and proactiveness) to operationalise communication behaviours identified as positive for socialisation. First evaluations with two ESN data sets in OLS, beta regression and multilevel models sparsely support the influence on closeness, which we expect to reflect the level of group integration.

People analytics depicts the algorithmization of human resources management characterized by the data-driven automation and support of people-related processes or tasks. On the one hand, people analytics promises productivity increases through optimizing workforce planning, hiring, or talent development. On the other hand, the extensive data collection and analysis of employees’ behaviors can be perceived as invasive, raising privacy concerns. This debate cannot only be explained by diverging norms and values, for example, practitioners realizing commercial opportunities while being criticized by academic commentaries. Instead, an alternative explanation suggests that the opposing views can be reconciled by diving into the conceptual differences regarding what analytical methods and data sources people analytics entails. Hence, this paper proposes the conceptions of operational and strategic people analytics based on a literature review of academics’ and practitioners’ literature. Four propositions about these conceptions’ privacy and performance implications are derived. Future research should empirically validate these propositions.