Opacity behind the wheel: The relationship between transparency of algorithmic management, justice perception, and intention to quit among truck drivers

Abstract

The use of algorithms systems that manage employees is now common in many industries. Trucking is no exception since freight companies need sophisticated systems to comply with legal obligations regarding driving time limitations. However, these systems often go far beyond simple GPS tracking, with other advanced surveillance devices being linked to performance management of truck drivers. Despite the growing literature emphasizing how the opacity of these systems may lead to perceived unjust automated decisions, no studies has empirically examined the relationships between algorithmic management transparency on truck driver attitudes. Hence, this study aims to quantitatively investigate the effect of the transparency of two algorithmic management functions (i.e., surveillance and performance management) on distributive and procedural justice. Moreover, considering that this industry faces an extraordinarily high turnover rate, the indirect relationship between algorithmic management transparency and driver's intention to quit through the mediating role of perceived justice will also be examined. Data were collected from 110 respondents via online communities of truck drivers. The results show that the transparency of algorithmic surveillance is positively related to procedural justice whereas the transparency of algorithmic performance management is positively related to distributive justice. Furthermore, our results show that procedural justice mediates the negative relationship between the transparency of algorithmic surveillance and intention to quit and that distributive justice mediates the negative relationship between the transparency of algorithmic performance management and intention to quit. Implications for theory and practice are discussed.