BABYSITTING ALGORITHMS? EXPLORING MANAGERS’ SENSEMAKING OF ALGORITHMIC MANAGEMENT THROUGH STRATEGIC GAMING

Managers increasingly collaborate with algorithmic management systems that assume managerial functions. In this context, managers try to understand algorithms through algorithm sensemaking. While research has explored algorithm sensemaking of platform workers, managers’ algorithm sensemaking process remains unexplored. In this paper, we address this research gap. To this end, we collect digital trace data from forum discussions about the grand strategy video game Stellaris, where players manage a complex interconnected system while competing with others. We analyze these data by combining inductive machine and human pattern recognition through topic modeling and qualitative analysis. We find that players discern two sources of environmental tensions, share similar algorithm sensemaking with workers, but respond differently to them. Based on these insights, we propose a theoretical model that explains managers’ responses to algorithmic management. We contribute to theory and practice by enhancing our understanding of managerial responses to algorithmic management systems.