The duality of algorithmic management: Toward a research agenda on HRM algorithms, autonomy and value creation

Abstract

This study proposes the ‘duality of algorithmic management’ as a conceptual lens to unravel the complex relationship between human resource management (HRM) algorithms, job autonomy and the value to workers who are subject to algorithmic management. Against tendencies to present algorithmic management as having predetermined, undesired consequences (e.g. restriction of job autonomy, poor financial compensation and deteriorating working conditions), our ‘duality of algorithmic management’ perspective offers two amendments to the dominant thinking on HRM algorithms and their outcomes to workers. First, we showcase how algorithmic management simultaneously restrains and enables autonomy and value to workers – with the latter referring to both use (i.e. non-monetary benefits) and exchange value (i.e. monetary benefits) that workers derive from working (under algorithmic management). In doing so, we make the case that the desired consequences of HRM algorithms to workers co-exist alongside the undesired consequences that the literature has mostly reported on. Second, we argue that algorithmic management is shaped by, as much as it shaping, the autonomy and value to workers. We do so by highlighting the ‘recursivity’ of algorithmic management that occurs when software designers and/or self-learning algorithms reinforce or limit worker acts for (re)gaining job autonomy and/or creating value out of HRM algorithms. We conclude this paper with the presentation of avenues for future research into the duality of algorithmic management, which sets the stage for a future line of inquiry into the complex interrelationships among HRM algorithms, job autonomy and value.