This study aims to provide conceptual clarity for the field of Algorithmic HRM (AHRM) and map its evolution from 2000 to 2022. A multidisciplinary synthesis of algorithm concepts results in a General Framework for Algorithmic Decision-Making, which informs a science mapping review of AHRM literature. The study presents frameworks for algorithmic decision-making and AHRM tools, distinguishing between automated and augmented HR decision-making. Findings reveal AHRM's multidisciplinary nature and its current focus on specialized HR applications (workforce planning, learning & development, allocation & scheduling, recruitment), while lacking emphasis on integrative strategic HRM contexts. Implications for organizational strategic decision-making and the need for multidisciplinary research teams are discussed.

Roslyn Cameron, Heinz Herrmann, Alan Nankervis