Algorithm Sensemaking: How Platform Workers Make Sense of Algorithmic Management

Abstract

Algorithmic management can create work environment tensions that are detrimental to workplace well-being and productivity. One specific type of tension originates from the fact that algorithms often exhibit limited transparency and are perceived as highly opaque, which impedes workers’ understanding of their inner workings. While algorithmic transparency can facilitate sensemaking, the algorithm’s opaqueness may aggravate sensemaking. By conducting an empirical case study in the context of the Uber platform, we explore how platform workers make sense of the algorithms managing them. Drawing on Weick’s enactment theory, we theorize a new form of sensemaking— algorithm sensemaking—and unpack its three sub-elements: (1) focused enactment, (2) selection modes, and (3) retention sources. The sophisticated, multistep process of algorithm sensemaking allows platform workers to keep up with algorithmic instructions systematically. We add to previous literature by theorizing algorithm sensemaking as a mediator linking workers’ perceptions about tensions in their work environment and their behavioral responses.