The Role of Human Managers within Algorithmic Performance Management Systems: A Process Model of Employee Trust in Managers through Reflexivity
Keith Leavitt, Christopher M. Barnes, Debra L. Shapiro
Abstract
The introduction of algorithmic performance management has generated significant concern with regard to employee trust in organizations. Although algorithms may be viewed as sources of organizational control (and thus inhibit employee trust), we argue that the role of human managers within algorithmically managed workplaces remains undertheorized. Drawing from structuration theory and the integrated model of trust in organizations, this paper centers managers within algorithmic performance management systems to create a process model of human managers as foci and creators of trust. We articulate three heretofore overlooked properties of algorithmic performance management systems that differentiate them from other human–algorithm augmentations, including the goal of aligning third-party (i.e., employee) behavior, the tension between algorithmic accuracy and alternative logics for managing performance, and limitations of user expertise or access leading to corrective actions outside of (rather than within) the algorithm. We describe how managers may fill two roles, as translators and augmenters of algorithms, while noting challenges specific to each role (i.e., challenges of inscrutability; performance paradoxes that create logics contrary to predictive accuracy). We theorize that by engaging in reflexive behaviors within these roles, managers can increase employee perceptions of their own ability, benevolence, and integrity, despite sharing agency with algorithms.