Adam Smith Business School, U. of Glasgow, United Kingdom
Using algorithms in managing people and improving productivity in the workplace, i.e., algorithmic management, is a trending topic for practitioners and academics. Digital work platforms, e.g., Uber, Upwork, and Fiverr, that facilitate buyers and sellers of services and commodities are almost fully managed by algorithms. Much of the work on algorithmic management assumes workers to be passive recipients of these practices guided by algorithms. Nevertheless, evidence shows that workers may actively manipulate and resist algorithmic control, i.e., engage in algorithmic resistance (Kellogg et al., 2020). Although still nascent, research on algorithmic resistance is largely inspired by labour process theory approaches and focuses predominantly on lack of autonomy, and to a lesser extent value creation, due to algorithmic control. Building on mutual gains vs conflicting outcomes perspectives to strategic human resource management, the paper explores explanations for platform worker resistance to algorithmic management, as conceptualised as a form of ‘high performance work system’ that achieves performance through varying degrees of worker involvement (i.e., influence over their own tasks and organisational decisions) and commitment (i.e., psychological attachment and long-term trusting relationship between the worker, the platform and the consumers). Reviewing the empirical evidence on work-related outcomes associated with high performing work systems, this paper proposes a framework for understanding the psychological mechanisms involved in digital platform worker algorithmic resistance.
