(p.432) In the United Kingdom, the pronounced increase in self-employment, around the turn of the century, has more recently been accompanied by the rise of so-called ‘gig work’. In particular, the spread of Uber – often hailed as the flagship of the gig economy – has given rise to a spirited debate. On the one hand, it has been argued that Uber extends the opportunity to become a ‘micro-entrepreneur’ to groups often marginalized in the traditional labour market. By giving individuals full autonomy over working time, it allows drivers to achieve work–life balance and provides opportunities to earn additional income when...

(p.467) The preference for flexible work expressed by Uber drivers, which we find to be correlated with their SWB, suggests that evaluations of the gig economy preferably should go beyond monetary metrics. Notably, an important role of non-monetary factors also extends to more traditional work arrangements. Indeed, the latest British Social Attitudes survey shows that less than half feel that work is only about monetary compensation, and the importance people attach to income has been falling in recent years. Thus, happiness economics seemingly has an important role to play in the context of evaluating workers’ welfare in the future of work.

(p.316) The underlying issue explored here concerns the implications of precarious work for individual workers – their physical, mental and social well-being and also, more broadly, their agency and the way in which they integrate paid work into other domains of social life. To what extent is the precariousness of work transmitted to the worker? This difficult but important area requires careful conceptualisation in order to extend research on precarious work and develop policies to combat its effects.

(p.217) Going back to Arendt’s suggestion to “think what we are doing” , I propose to do just that to make sense of the changing nature of “work,” to study what we are doing. Considering whether the activities people engaged in are work or not will allow us to unpack the social and political implications of the emerging activities, new organizational arrangements, and sociotechnical practices they include.

(p.721) Gig work is usually typified by four characteristics: irregular work schedules; workers providing some or all capital (e.g. mobile phones, cars, or bikes); piece-rate work remuneration; and work being arranged and/or facilitated by digital platforms.

(p.46) Postcapitalism within the context of platform-based sharing economy as we have revealed, may not be an anti-capitalist alternative but it has presented new challenges, insights, and configurations of capitalism. One such expression of postcapitalism that this article has reflected on is the work and lives of Uber drivers. Driving for Uber is a personalized, commercial endeavor, established in the dark spaces that current business models and legislation are only starting to venture into. It does not remedy threats to instability, precariousness or work intensification. Rather, for some drivers, it exacerbates these realities.

(p.687) Originally considered a curious novelty, labor platforms have now established themselves as important players in domestic and, increasingly, global labor markets. Scholars have produced several insightful theorizations of them. We identified four controlling images in the literature; however, each has weaknesses. We have added a fifth, which contends that from the point of view of labor and the future of work, platforms should be thought of as a new economic form, distinct from markets, firms, and networks.

(p.64) A far more effective means of control was the ‘algorithmic management’ enabled by platform-based rating and reputation systems (Lee et al., 2015; Rosenblat and Stark, 2016). Workers were rated by their clients following the completion of tasks. Workers with the best scores and the most experience tended to receive more work due to clients’ preferences and the platforms’ algorithmic ranking of workers within search results. This form of control was very effective, as informants stressed the importance of maintaining a high average rating and good accuracy scores. Whereas Uber’s algorithmic management ‘deactivates’ (dismisses) workers with ratings deemed low (Rosenblat and...