Technology, Employment and Wellbeing

In our fifth issue of the Technology, Employment, and Wellbeing blog, we would like to centre our discussion on the growing use of algorithmic management systems in the workplace and how these systems can impact job autonomy practices in the traditional sectors of the economy.

Algorithmic management systems can be used for various functions, including monitoring, screening, performance evaluation, task allocation, scheduling, compensation, and discipline. In the last few years, policy and academic debates have predominantly focused on developing regulatory solutions to uphold the rights of platform workers. While algorithmic management can be regarded as a key building block of the platform business model, multiple pieces of evidence suggest that these tools are becoming more common in traditional sectors, such as transport, logistics, and retail, for managing workers and coordinating workflows.

To advance our understanding of how workers find new ways to assert their rights, the articles presented in this issue address different workplace contexts and examine whether algorithmic systems can be shaped by various norms that preserve the autonomy of workers.

In the first article, Antonio Aloisi  argues that it is a collective responsibility to assess the contributions these technologies make to workplace environments and reconsider how they are implemented. On the other hand, the combination of existing labour and data protection legislation can result in fairer and smarter data-driven workplaces, especially when employees’ rights are exercised collectively.

In the second article, Tiago Vieira  highlights examples that cannot be adequately addressed from a legal perspective, concluding that the debate around the introduction of new technologies in the workplace must be open to interrogating not only the most eye-catching innovations but also seemingly less sophisticated ones.

In the third article, Isabell Lippert introduces the concept of ‘job crafting’ as a promising strategy to better cope with the demanding mechanisms of algorithmic management.

Finally, Gina Glock shows that the intervention of algorithmic decision-making systems in service work can have positive effects on job autonomy, concluding that when a company’s objectives focus on process optimisation to support workflows, there is far less danger of undermining the autonomy of service workers.