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    Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM
    Across different disciplinary boundaries, research into algorithmic surveillance (Newlands, 2020), people analytics (Gal et al., 2020; Marler & Boudreau, 2017; Tursunbayeva et al., 2018), human resource management (HRM) algorithms (Cheng & Hackett, 2021), and algorithmic control (Kellogg et al., 2020; Veen et al., 2020) is gaining traction. Moreover, these various concepts are studied alongside – and at times interchangeably with – related phenomena including Big Data (Garcia-Arroyo & Osca, 2019), artificial intelligence (Strohmeier & Piazza, 2015; Tambe et al., 2019) and online labor platforms (Duggan et al., 2020; Newlands, 2020; Veen et al., 2020). These terms and developments are often loosely linked to, or aggregated as, ‘digital HRM’ which, as a broad notion covers a multitude of topics and issues with unclear and ambiguous relations between them (Strohmeier, 2020b). Studies into HR analytics ( Marler & Boudreau, 2017; Minbaeva, 2017; Tursunbayeva et al., 2018; Van den Heuvel & Bondarouk et al., 2017 ), HRM algorithms (Cheng & Hackett, 2021; Leicht-Deobald et al., 2019), and artificial intelligence (AI) deployed in HRM practices (Strohmeier & Piazza, 2015; Vrontis et al., 2021), while beginning to coalesce around key issues, tend to use different terms to describe seemingly similar content leading to a lack of construct clarity that may prevent the scholarly community from building a collective and coherent body of knowledge (Suddaby, 2010).
      36Scopus© Citations 59