How algorithmic management influences worker motivation: A self-determination theory perspective.

Abstract

The management of workers using algorithms is rapidly becoming ubiquitous across many industries. Our review of the nascent research on algorithmic management uncovers many negative effects on worker motivation. We offer recommendations to better design and implement algorithmic management in organizations.

Taylorism on steroids or enabling autonomy? A systematic review of algorithmic management

“…Furthermore, through the lens of self-determination theory, Gagné et al ( 2022 ) conducted a review of how algorithmic management influences worker motivation. The scholars found a mostly negative effects of the use of algorithmic management on worker motivation and need satisfaction, although moderating effects do exist through management features and practices (Gagné et al 2022 ).…”

Creation of the algorithmic management questionnaire: A six‐phase scale development process

“…Other authors describe AM as a system of control that directs, evaluates, and disciplines workers (Kellogg et al, 2020), “where self‐learning algorithms are given the responsibility for making and executing decisions affecting labour, thereby limiting human involvement and oversight of the labour process” (Duggan et al, 2020, p. 119). Based on these definitions, and in line with our operationalization objective which requires a concrete representation and manifestation of AM, we refer to algorithmic management as the use of programmed algorithms, often powered by artificial intelligence, by an organization to partially or completely execute workforce management functions and control (Gagné, Parent‐Rocheleau, et al, 2022, p. 248) 1 . In this context, algorithms refer to “[…] computational procedures […] drawing on some type of digital data (“big” or not) that provide some kind of quantitative output (be it a single score or multiple metrics) through a software program” (Christin, 2017, p. 2).…”

“…The literature shows how AM systems reduce workers' control over their work, and how this control is reinforced but the difficulty or impossibility to question the system and their decisions (Rani & Furrer, 2020; Rosenblat, 2018; Rosenblat & Stark, 2016; Stark & Pais, 2020). Also, because AM implies data‐driven control of the workers' goals, tasks, schedules, and compensation, it has been found to lead workers to “work for data” rather than for more intrinsic reasons (Gagné, Parent‐Rocheleau, et al, 2022; Gagné, Parker, et al, 2022; Parent‐Rocheleau et al, 2021), thereby altering perceptions of autonomy (Möhlmann et al, 2021; Shapiro, 2018; Vargas, 2021).…”

“…One of these changes is the increasing use of algorithms, often powered by artificial intelligence, to take over key activities previously accomplished by human managers. This phenomenon is referred to as algorithmic management (AM), defined as the use of programmed algorithms by an organization to partially or completely execute workforce management functions, such as monitoring the work; assigning tasks, targets or schedules; rating productivity and performance; making pay‐related decisions; and even sanctioning workers (Gagné, Parent‐Rocheleau, et al, 2022; Lee et al, 2015; Rosenblat & Stark, 2016).…”

“…In particular, previous research has mostly been conducted at the firm or workplace level, considering the use of algorithms in the management of workers or in HRM as a contextual and dichotomous phenomenon (AM vs. not). In other words, most studies have been conducted in single work contexts in which AM is known to occur, with researchers investigating workers' reactions or consequences related to AM through case studies or in‐depth qualitative studies (see Gagné, Parent‐Rocheleau, et al (2022) for a review). Other studies (e.g., Lee, 2018; Newman et al, 2020) have been conducted in research laboratories with simulated AM.…”