Automated governance mechanisms in digital labour platforms: how Uber nudges and sludges its drivers

Abstract

ABSTRACT Using tools like machine learning algorithms, digital platforms raise new challenges to our understanding of control-governance dynamics in organisations. In this paper, we explore a unique governance mechanism; nudging – i.e. liberty-preserving approaches that steer people in particular directions – and provide exploratory findings that extend prior research in behavioural economics and organisational control-governance dynamics towards platform markets. We surveyed 166 Uber drivers to explicate the workings and effects of Uber’s good (i.e. transparent and easy to opt-out) and evil (i.e. obscure and misleading) nudges. Our findings suggest that while drivers are more satisfied with good nudges, these nudges do not make them more productive (i.e. increase their earnings-per-hour). Evil nudges, on the other hand, seem to have no effect on driver productivity. With experience, drivers learn to respond less to nudges (as they may realise that Uber’s nudges do not seem to increase their productivity). We extend the platform governance literature by highlighting whether and when nudges could influence drivers by creating false expectations. Our exploratory approach highlights new possible boundary conditions for the traditional theories, for example, Herzberg’s hygiene-motivation theory that, while differentiating hygiene factors from motivating factors, do not have the level of specificity to show the effects we discover here.