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Abstract High levels of confusion persist around the term “algorithm” in general; and in addition to this, there is also conceptual confusion around the application of algorithms to human resource management (HRM) strategy and functions. Although there are several systematic reviews of various algorithmic applications to HRM and many of its functions, no comprehensive evolutionary map of the emergent field of algorithmic HRM (AHRM) could be found in the academic literature. This study has dual aims. The first is to provide conceptual clarity for the field of AHRM, and the second is to map the evolution of AHRM from 2000 to 2022. To address the first aim, we conduct a multidisciplinary synthesis of the concepts related to algorithms which results in a General Framework for Algorithmic Decision-Making. This framework then informs the empirical part of the study which addresses the second aim. A science mapping review is employed to chart and assess the extant literature on algorithmic HRM from 2000 to 2022. This study presents a General Framework for Algorithmic Decision-Making across all business functions and then a Framework for Algorithmic AHRM Tools. This provides conceptual clarity and distinguishes between automated and augmented HR decision-making. Findings also reveal the multidisciplinary nature of this emergent field of inquiry and point to current research, which focuses on specialized applications for HR functions such as workforce planning, learning and development, allocation and scheduling, and recruitment; but lacks emphasis on more integrative strategic HRM contexts. The study also has implications for organizational strategic decision-making. HR practitioners may need to form project teams with their information technology (IT) and data analyst colleagues when making strategic decisions about algorithmic applications for HR strategy and HR functions. This also lends itself to future research with multidisciplinary research teams including HR researchers along with computer scientists, computational engineers, and data analysts.
Cameron et al. (Fri,) studied this question.