Abstract
This paper proposes a conceptual framework to study and evaluate the impact of ‘Algorithmic Management’ (AM) on worker dignity. While the literature on AM addresses many concerns that relate to the dignity of workers, a shared understanding of what worker dignity means, and a framework to study it, in the context of software algorithms at work is lacking. We advance a conceptual framework based on a Capability Approach (CA) as a route to understanding worker dignity under AM. This paper contributes to the existing AM literature which currently is mainly focused on exploitation and violations of dignity and its protection. By using a CA, we expand this focus and can evaluate the possibility that AM might also enable and promote dignity. We conclude that our CA-based conceptual framework provides a valuable means to study AM and then discuss avenues for future research into the complex relationship between worker dignity and AM systems.
Highlights
In recent years, there has been a rapid growth in the use of software algorithms to automate Human Resource Management (HRM) practices (Cheng & Hackett, 2021)
On the basis that these different types of organisations will rely on algorithm-enabled HRM practices in different ways, treat workers in different ways and grant differing amounts of autonomy, we argue that future research would benefit from studying how the organisational context, in which Algorithmic Management’ (AM) is deployed, influences AM as a means, workers’ personal conversion factors and their choices in deploying their capability set
The Capability Approach (CA) was chosen as a plausible theory for assessing worker dignity as it can be applied to the work context and allows the integration of the Kantian approach to inherent dignity and the Aristotelean notion of contingent dignity
Summary
There has been a rapid growth in the use of software algorithms to automate Human Resource Management (HRM) practices (Cheng & Hackett, 2021). This increasing use of algorithmic technology to manage workforces is known in the academic literature as ‘Algorithmic Management’ (Duggan et al, 2020; Lee et al, 2015; Leicht-Deobald et al, 2019; Meijerink & Bondarouk, 2021; Möhlmann & Zalmanson, 2017). A wide range of HRM-related practices and decisions are supported or taken over by algorithmic systems including staffing in terms of automated resume screening (Cheng & Hackett, 2021; Leicht-Deobald et al, 2019), matching by automatically assigning workers to tasks (Rosenblat & Stark, 2016) and algorithmically integrating data-based performance measures for evaluation, appraisal and compensation purposes (Jarrahi et al, 2019; Kellogg et al, 2020). More specific and rather alarming concerns are linked to the instrumentalisation and dehumanisation of work(ers) that AM precipitates
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