Understanding Gig Workers Resistance to Algorithmic Control (ReACt)

What is the project about?

Organizations increasingly rely on algorithmic control (AC), which can automate control activities and replace human tasks, but diverget resistance behaviors to AC exist among gig workers.

 

Wider research context

Organizations increasingly rely on algorithmic control (AC), which leverages the capabilities of novel digital technologies and intelligent algorithms to automate control activities previously performed by human managers. Existing research points to a diverse set of resistance behavior to AC. To explain these diverse reactions, our study focuses on gig-economy work, where AC is particularly prevalent, and draws from control theory and several resistance framewors, such as resistance to change, resistance to IS, and resistance to control and domination in the workplace, and contextualizes these to the peculiarities of AC resistance.

 

Objectives

While current research has started to identify typical gig worker reactions, little is known about which factors and generative mechanisms are salient for explaining why and how gig workers show (often divergent) reactions to AC. Thus, this research project tries to uncover the mechanisms behind gig workers resistance to AC.

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Approach

This work started by conceptualizing AC and gig workers responses to AC. Based on the derived conceptual framework, which includes AC resistance profiles and their antecedents as well as rich insights into AC resistance in gig economy platforms, a multidimensional instrument for measuring AC reaction and their antecedents will be developed. This instrument will then be used to empirically explore the mechaisms behind gig workers resistance, and develop a process model to explain how and why AC resistance change over time. Next to well-established research methods, such as case study, surveys, and a field experiment the project also employs a number of innovative reseatch methods, such as experience sampling, topic modeling, Q-study, web scraping, etc.

 

Level of originality

A nuanced understanding of the mechanisms behind gig workers resistance to AC will provide a much-needed foundation for further research in this area, investigating relevant topics for society and beyond, such as socio-emotional and economic impact on workers, as well as ethical considerations of AC in use. Furthermore, the findings of this project may help practitioners using AC, not only to explain negative control consequences, such as high turnover rates, a key problem to many gig-economy firms, but also to design and implement AC that truly help improve worker treatment and satisfaction. For example, further insights into feedback loops will provide the basis for designing more social, and thus more acceptable and ethical forms of algorithmic control processes. We thus hope that this research will also contribute to balancing power asymmetries, currently prevalent in many gig platforms.