The future of HRM has often been described as one of algorithmic HRM, i.e., automating or augmenting HRM decisions by using computational algorithms. For meaningfully integrating an emerging field (algorithmic HRM), into an existing field of knowledge (strategic HRM), it is necessary to identify ontological assumptions made in each field to uncover discrepancies and overlaps. We find substantial differences and intersections in ontological status given to central entities in algorithmic and strategic HRM. Strategic HRM focuses on aligning employee behavior to organizations’ strategies by deploying a variety of practices to incentivize the behavior desirable for the organization. Algorithmic HRM, in contrast, conceptualizes worker behavior as object to be predicted by a computational software assuming that algorithms do not influence the behavior they are designed to predict. To overcome this antagonism and meaningfully integrate algorithmic into strategic HRM, we propose a conceptual model for aligning the ontological assumptions between the two.
