Exploring the individual adoption of human resource analytics: Behavioural beliefs and the role of machine learning characteristics

The technological capabilities of Human Resource Analytics (HRA), enhanced by recent innovations in Machine Learning (ML), offer exciting opportunities. However, organisations often fail to realise these potentials because of a limited understanding of why individuals choose to adopt or disregard respective tools. Prior research on innovation adoption offers preliminary insights but fails to aggregate the determinants of individual adoption into actionable suggestions for decisions in the ML adoption process. Our study applies focused interviews to examine non-ML experts' reasoning for using a specific tool tailored to a public sector organisation, which corresponds to the usual end-user perspective of ML-based HRA adoption. By drawing from the HRA adoption framework, provided by Vargas et al. (2018) , we contribute to the literature by identifying relevant beliefs and experiences influencing one's intention to adopt ML-based HRA and by qualitatively linking these beliefs to ML characteristics such as transparency, automation and fairness. For practitioners, we provide actionable guidance emphasising the need to ensure fairness proactively, as interviewees do not consider this aspect when deciding to adopt ML-based HRA. • HR Analytics adoption is rarely analysed from an individual perspective • Our study uses focused interviews to provide insights into beliefs and experiences • Perceived behavioural control, attitude and norm form the intention to adopt • Machine Learning characteristics influence attitude and PBC • Fairness does not matter for individuals deciding whether to adopt ML-based HRA