Page - 52 - in Critical Issues in Science, Technology and Society Studies - Conference Proceedings of the 17th STS Conference Graz 2018

matched with a nearby waiting customer, which they can accept or decline. If they accept, they are expected to pick up the customer and take them to their destination along a route suggested by the app. After the ride, the driver receives a rating from the customer. For the following discussion we apply our framework to the empirical descriptions by Lee et al. (2015), Rosenblat and Stark (2016) and Scheiber (2017). Information gathering: Firstly, our framework focuses on the ways in which the regulatees and the environment are modelled by Uber, i.e., which aspects are digitally represented, which information is gathered and how that information is then used to shape the whole work process. Uber records and processes various kinds of information through its app and the sensors integrated in smartphones. This includes the locations of drivers and customers, but also aspects of the drivers’ driving behaviour, for instance, their braking and accelerating. Furthermore, map data, traffic information and the location of other drivers are taken into account. However, other aspects such as the altruistic and non-economic motivations of drivers, the condition of the car or the road, the emotional state of the customer, the traffic policy of the city or the current weather are, to our knowledge, not taken into account. It has to be stressed at this point that it is Uber alone who defines what kinds of information are gathered — not the drivers, nor the customers. Standard setting: Uber’s general goal of maximizing revenue is broken down into a number of secondary standards which Uber deems fitting to achieve that goal: Optimally matching drivers with passengers for short pickup times, suggesting to take Uber-chosen routes, reaching a smooth driving behaviour, realizing a maximum price for the ride and improving the customers’ experience are among them. Through a universal rating system, standard setting procedures are partly delegated from the company to customers. After each ride, passengers evaluate their drivers through a five-star rating system, without being restrained in their choice of criteria. This feature renders the regulatory process decentral and dynamic because behaviour that got a driver five stars last month may not get them five stars today. Here, too, it is insightful to look at those standards that were seemingly not deemed relevant by Uber, such as the drivers’ health and happiness or ecological aspects. Behaviour modification: For the sake of brevity this analysis is limited to the excorporate behaviour modification of drivers. Interestingly, all of the four types described above can be found in the case of Uber. 1) Fear of coercion is used in the mechanism that drivers whose ratings fall below a certain level, or who repeatedly decline ride requests or cancel rides, lose access to their accounts and are excluded from the Uber market. Before this happens, the drivers receive warnings, which are — of course — directly intended to change their behaviour. 2) Uber uses monetary inducement in a highly fine-grained and dynamic manner in order to facilitate a favourable allocation between supply and demand. This is achieved through so called “surge pricing”, a temporary and local rise in fares after the system has identified a high demand in a certain area. This area will, for a limited time, be highlighted in red on the interactive map of the app. 3) The driving assistance by the Uber system corresponds to the initiation of re-interpretations. The most obvious means for this is the navigation function of the Uber driver app that ensures 52