Algorithmic management from the perspective of managers: investigating the influence of system, organizational, and task characteristics on managers supported by intelligent systems

Abstract

Data-driven algorithms enable the automation of management tasks that were previously performed by human managers. This paper examines the use of intelligent algorithmic systems to support managers in complex, human-related planning tasks in the work domains of production and logistics. The presented study aims to uncover how system, task, and organizational characteristics influence managers’ perceived system support for their planning performance. First, two research models are proposed, incorporating system-, task-, and organization-related variables based on the literature. Second, the models are evaluated using structural equation modeling (SEM) based on an empirical online study with 494 managers working in production and logistics. The results indicate that managers perceive greater system support when systems are integrated to a greater extent into the planning processes and are perceived as more intelligent and transparent. Additionally, the results show positive effects of the quality of organizational conditions on perceived planning support and job satisfaction. In the context of planning tasks, the positive effect of system use on planning support seems to diminish when tasks have a high impact on workers. In contrast, the positive effect becomes stronger the more relevant the tasks are considered to be for the company. The results provide insights into how intelligent planning support systems can be utilized effectively to enhance managerial planning performance, fostering not only high-quality outcomes but also fair and objective results for employees affected by the planning process.