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Opening Foodora's box : an ethnography of precarious and flexible work in the platform economy

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U

NIVERSITY

O

F

A

MSTERDAM

OPENING FOODORA’S BOX:

AN ETHNOGRAPHY OF PRECARIOUS AND FLEXIBLE WORK

IN THE PLATFORM ECONOMY

Author:

Oskar WEIMAR

(11251727)

Supervisors:

Yannis TZANINIS

Dennis ARNOLD

Master’s Thesis in Sociology (General Track)

Graduate School of Social Sciences

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‘But he took the gift, and afterwards, when the evil thing was already his, he understood’ [Hesiod, 1914]

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A

BSTRACT

The emergence of the platform economy is having a profound effect on the world of work. Developments in technology have opened up new labour processes that are centred on the mediation and organisation of work through digital infrastructures. To explore the effect of these developments, an ethnography of Foodora delivery work was conducted. As a platform firm that connects restaurants, customers and workers, Foodora delivery riders were subject to working conditions typical of the platform economy: hybrid control mechanisms, ostensibly flexible working arrangements, and precarious working conditions. This study explores how riders experienced and understood the labour process in these conditions. It shows that, through computer control, riders were independently linked and suffered from a double alienation: from a collective sense of the labour process as a whole, and from each other. Paradoxically, computer control also helped to foster a sense of freedom for riders, although this is shown to stem primarily from objective job characteristics and a lack of tight surveillance and monitoring. Neo-normative control was also relatively weak and, although riders often reported that they found their work enjoyable, this also tended to derive from objective job characteristics rather than from Foodora’s formalised attempts to render work fun. Riders also reported satisfaction with the work’s flexibility. Riders were given at least some choice regarding ‘core’ aspects of their work, but this flexibility was closely attached to precarious working conditions. Riders were paid per hour and worked under zero hour contracts, which meant that they were not guaranteed work, were offered no statutory entitlements and granted only limited protection against precarious employment conditions. As a result, Foodora delivery work was precarious work, characterised by uncertainty, insecurity and high levels of risk for riders. This study shows however, that precarious working conditions do not always lead to precarious lives. This is largely determined by factors outside the workplace, which mediate the impact of precarious work by cushioning risks. Alternative sources of income, kinship networks, and to some extent, limited participation in employment, are all shown to reduce the impact of precarious conditions for riders. While these factors mediated the impact of precarious work, experiences of precarious work were understood through the prism of the past, riders’ sense of the present, and their projections for the future.

Keywords: Labour Process Theory, ethnography, Foodora, platform economy, working experiences, precarious work

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C

ONTENTS

Abstract ... iii

I. Introduction ... 1

II. Theoretical Framework ... 4

1. Labour Process Theory: Towards New Forms of Hybrid Control ... 4

2. Conceptualising the Platform Economy ... 6

3. The Platform Economy: Flexible for Whom? ... 7

4. Precariousness and the Platform Economy ... 9

III. Research Questions ... 12

Primary research question ... 13

Secondary research questions ... 13

IV. Methodology ... 14

1. Data Collection: Ethnographic Fieldwork at Foodora ... 14

2. Data Collection: Interviews ... 15

3. Data Analysis ... 16

V. Results and Discussion ... 18

1. Getting a Sense of the Work ... 18

2. Working Through the App: Computer Control ... 20

3. Workplace Culture: Normative and Neonormative Control ... 24

4. Flexibility and Resistance ... 26

5. Precariousness ... 29

VI. Conclusion ... 35

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I.

I

NTRODUCTION

There is much contention surrounding the platform economy. Even regarding it’s name, there is little agreement; the “gig-economy”, the “sharing economy”, and the “collaborative economy” are just a few examples of terms proposed by scholars, the media and international commissions (Kenney and Zysman 2016: 62). The term platform economy will be used here, not only because it is neutral, but because it highlights what is central to this emerging economy. As the name suggests, the platform economy is characterised by the platform business model. Simply put, platforms are digital intermediaries that allow two or more parties to interact (Snricek 2016: 43). Some examples include Google, Uber and Facebook. While these three platforms differ in type, they all essentially function as digital intermediaries. Uber, for example, acts as a digital intermediary between drivers and customers. They both log on to the Uber app and are matched by the platform’s digital infrastructure, which pairs the customer and the driver and facilitates a transaction for the service of taxiing a customer from one location to another.

As digital intermediaries, platforms occupy a privileged position to record and collect the enormous amount of data that these interactions produce. In contemporary capitalism, data has become a valuable resource and firms that are able to collect and record this ‘raw material’ can use it to optimise production processes, give insight into consumer preferences, discipline workers and provide the foundation for new products and services (Snricek 2016: 63).

The emergence of the platform economy is having a profound impact on the world of work. Supporters have suggested that the platform economy provides new opportunities for workers that have hitherto been excluded in the offline labour market (Eurofound 2015: 116). Workers in remote communities, or disabled workers unable to leave the house, it is argued, now have access to work and a source of income in the platform economy (Eurofound 2015: 116). Furthermore, platforms allow workers to engage in work that requires very little capital investment and training.

Supporters also contend that platforms provide workers with opportunities to unlock the commercial value of unused assets (Kenney and Zysman 2016: 62). Turo, for example, promotes the idea that one’s unused car can become a source of income (Turo 2017). Spare time can also be deployed to generate income. Amazon Mechanical Turk (AMT), for example, bills itself as a ‘marketplace for work’ where prospective workers can log on and bid to complete paid online “micro-tasks” such as creating metadata or removing duplicate entries from databases, completing surveys and transcribing audio-visual material (Eurofound 2015: 107).

Critics, however, argue that this is misleading (Webster 2016). A recent ILO study (Berg 2016) showed that for 38 per cent of American workers using Amazon Mechanical Turk, platform work constituted their main source of income (Berg 2016: 16). Furthermore, surveys indicate that the

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primary motivation for workers in such arrangements is to earn money (Schmid-Drüner 2016: 3). Workplace flexibility comes in second (Schmid-Drüner 2016: 3). These surveys seem to undermine claims that the platform economy is granting workers greater control over when and where they work, as well as what they do. Moreover, as De Stefano (2015) argues, competition in crowd-work may be so fierce that pay rates are pushed down, forcing workers to work long hours and therefore give up much of the work’s perceived flexibility (De Stefano 2015: 5-6). In any case, workplace flexibility remains to supporters one of the platform economy’s biggest advantages for workers (Eurofound 2015).

Against the claims of supporters, critics tend to argue that the platform economy is leading to the commodification of workers. DeStefano (2015), for example, argues that new digital technologies provide platforms with an ‘extremely scalable workforce’, offered to clients ‘just-in-time’ and paid on a ‘pay-as-you-go’ basis (DeStefano 2015: 7). As a result, DeStefano (2015) argues that these developments are commodifying humans by transforming them into ‘services’ (DeStefano 2015: 7). This has led many authors to define the platform economy in terms of precarious employment conditions that shift risks and responsibilities in the workplace onto the shoulders of workers (DeStefano 2015; Drahokoupil and Fabo 2016; Codagnone et al. 2016; Eurofound 2015; Schmid-Drüner 2016; ILO 2012).

There is also a fear amongst critics that the platform economy may lead to the remote provision of services and the movement of work from local to offshore labour markets (Drahokoupil and Fabo 2016: 4), although this fear has so far remained largely unrealised (Schmid-Drüner 2016: 11). Other concerns regard the breakdown of working activities into mindless individual tasks and increased inequality driven by increasingly flexible work (Schmid-Drüner 2016: 9).

A review of the literature shows that most research, while valuable, focuses on macro level developments. So far, there have been few scholarly studies on how workers are responding to these changes. It is towards this gap in the literature that this thesis is orientated. Without these accounts, our understanding of the platform economy remains unfortunately one-dimensional. A closer exploration of how workers are experiencing and understanding these changes will help to formulate a better conceptualisation of the platform economy and add clarity to the debates mentioned above.

To explore this angle, I conduct an ethnography of Foodora delivery work in the city Amsterdam. Foodora is a food delivery company, founded in 2015 by two young entrepreneurs, that now operates in more than 50 cities and 10 countries worldwide (Foodora 2017). Employees, or ‘riders’, deliver food from restaurants to customers on bicycles.

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Foodora delivery work was chosen as the focus of this thesis for several reasons. As a platform firm, Foodora delivery work displays many characteristics typical of the changing employment relations found in the platform economy. Foodora acts as an intermediary between restaurants, customers and riders. This means that the work is almost entirely organised and mediated through a digital application, RoadRunner. Furthermore, while there is no academic research into working conditions at Foodora, media reports have charged Foodora with offering riders sham contracts at pay rates below the minimum wage (Hatch 2016). These reports suggest that Foodora delivery work also displays typically precarious and ostensibly flexible working conditions characteristic of the platform economy1.

1 Some abbreviations are used to improve writing clarity and avoid repetition in this thesis. Amazon Mechanical Turk (AMT) workers are described as ‘MTurkers'. Digital infrastructures that platforms use to organise and mediate work are referred to as ‘applications’ or ‘apps’.

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II.

T

HEORETICAL

F

RAMEWORK

1. Labour Process Theory: Towards New Forms of Hybrid Control

To structure the research conducted in this paper, I take up a Labour Process Theory (LPT) perspective. LPT has its roots in Marxist political economy and has, since its conception, produced a rich seam of literature in the sociology of work (Smith 2015). According to LPT, the labour process is a transformative process — 'a conversion movement whereby the labor power of the worker enters a production process in which labor is realised to produce a concrete commodity or service that contains a use and exchange value’ (Smith 2015: 224). Building on Marx's concept, Braverman (1979) noted that this transformative process is organised by managers in control of the labour process in order to maximise the productivity of employees. To achieve this, managers must implement some form of control mechanism over employees (Elliot and Long 2016). However, control is never perfect and workers often resist and challenge strategies implemented by management (Hyman 1987).

Early literature in the LPT tradition looked to internal structures of employment relations to explore how control was implemented by management. According to Braverman (1974), the history of capitalism is defined by the degradation of work. In what has become known as the "deskilling hypothesis", Braverman (1974) argued that, in the Fordist model, management attempted to strip control from workers by deepening the division of labour. Since it’s publication, Braverman’s argument has been criticised for being both overly simplistic and objectivist (see Burawoy 1999). In Braverman's (1974) account, workers become objects of labour, appendages of machines, and, ultimately, stripped of all subjectivity (Burawoy 1999). What Burawoy (1979) so compellingly showed in his study of workers in a Chicagoan machine-tool factory was that subjective experiences of control mechanisms can obscure objective relations of exploitation, leading workers to inadvertently increase productivity. Burawoy’s (1979) analysis emphasised that it is not enough to describe systems of control; in the labour process, worker’s experiences of control mechanisms matter.

More recent work in LPT has argued that there has been a shift away from traditional forms of control towards normative forms that concentrate on the production of organisational values, as well as the manipulation of emotions (Barker 1993; Wilkinson and Willmott 1995; Sturdy et al. 2010; Fleming and Sturdy 2009). While it does seem that new organisational structures are emerging, including a shift towards more team-based work, and that there is now an increased effort on the part of management to engineer high-commitment cultures at work (Jermier 1998), what is often overstated in the literature is that these new forms of control have superseded more traditional forms (Thompson 2015). Rather, most organisations remain, at least in some part, traditionally structured (Thompson 2015). We should therefore consider these emerging regimes as a blend of traditional

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'command and control' mechanisms with new normative mechanisms that are supported by tight surveillance, a blend referred to in the literature as hybrid control (Thompson and Broek 2015; Thompson 2015).

Much of the literature on hybrid forms of control has been conducted in call centres (Taylor and Bain 1999; Taylor et al. 2003; Russell 2008) although these regimes have spread quickly into increasingly diverse industries (Newsome et al. 2013; Wright and Lund 1996; Delbridge et al. 1992; Garrahan and Stewart 1992; Sewell 1998). Elliot and Long (2016), in their work at BigBox, a grocery distribution facility, develop the concept of 'computer control’. Computer control is a hybrid form of control that occurs when software automates some dimensions of control (i.e. directing, monitoring or evaluating workers) in the workplace (Elliot and Long 2016). Computer control blends bureaucratic, technical and normative control strategies as digital data flows directly into bureaucratic standards for performance evaluations (Elliot and Long 2016: 136).

Importantly, computer control increases micro-level task control while becoming less obtrusive for the worker (Elliot and Long 2016: 136). In work that is computer-controlled, the traditional worker-manager interaction is removed. In effect, workers have an invisible supervisor ‘sitting on their shoulder’, which has been shown to have the effect of softening the oppressive experience of working under the direct control of a manager (Elliot and Long 2016: 138).

Another consequence of computer-controlled work is that social relations in the workplace tend to migrate into the digital sphere (Elliot and Long 2016). At BigBox, workers incorporated games into their work, as they did in Burawoy’s machine-tool factory (Elliot and Long 2016). The difference was, in contrast to Burawoy's operators, where the informal culture occurred within the 'execution of the production process', in the computer-controlled regime of BigBox, the production game occurred

within the digital arena, constructed from data 'about the execution of the labour process’ (Elliot and

Long 2016: 148). This migration of social relations into the digital sphere highlights the profound impact that computer-control is having on working experiences.

Surveillance has emerged as an important feature of hybrid control. Of course, surveillance is a well established form of control in the workplace (Bryson 1994). But, as technology has developed, the scope of surveillance has increased (Sewell 1998). Surveillance in the workplace has come to represent both a direct and indirect form of control, and typically functions in conjunction with normative, and more recently neo-normative control mechanisms (Sturdy et al. 2010; Fleming and Sturdy 2009). While normative control functions by encouraging workers to instil organisational cultures, neo-normative control draws upon values that are predominantly formed extra-organisationally (Sturdy et al. 2010). Workers are encouraged to be their ‘true selves’ at work, and to have fun. Sexuality, diversity, and individuality are celebrated (Sturdy et al. 2010). Work is supposed to be ‘existentially empowering’ (Fleming and Sturdy 2009: 574). Managers therefore do

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not explicitly aim to turn workers into corporate clones, but to invite extra-organisational identities into the workplace and promote values derived from outside the firm in a manner that resonates with organisational objectives (Sturdy et al. 2010: 117).

In the context of the platform economy, developments in technology and organisational structures seem to be building on existing hybrid control mechanisms and producing new experiences for workers. In their study of Uber, Rosenblat and Stark (2016) show that drivers are subjected to information asymmetries and algorithms that exert a kind of ‘soft control’ (Rosenblat and Stark 2016: 3759). High levels of surveillance and monitoring have also been reported, as well as ‘gamic elements’ designed to ‘nudge’ workers to work longer hours or into certain areas (Rosenblat and Stark 2016: 3763). This was shown to lead drivers to adopt a ‘highly individualized' sense of responsibility for [their] own job stability’ (Neff 2012; 28).

2. Conceptualising the Platform Economy

There has been much debate surrounding how best to conceptualise work in the platform economy, but some common threads run through the literature. For instance, scholars tend to agree that the platform economy consists of several different types of work (DeStefano 2015; Drahokoupil and Fabo 2016; Valenduc and Vendramin 2016). DeStefano (2015) distinguishes between two kinds: ‘crowdwork’ and ‘work on-demand via apps’ (DeStefano 2015). Crowdwork usually refers to working activities that involve completing tasks through an online platform; ‘work on-demand via apps’, on the other hand, refers to a kind of work in which the execution of traditional working activities such as transport, cleaning, and clerical work, is mediated through apps managed by firms that intervene in setting minimum quality standards of service and management of the workforce (DeStefano 2015: 2). DeStefano’s distinction is a helpful start but fails to take into consideration important differences in how and where work is performed.

Codagnone et al’s. (2016) conceptualisation is more helpful in this regard. In their recent and comprehensive overview of work in the platform economy, Codagnone et al. (2016) first distinguish between digital labour markets that allow for the remote delivery of services and those where the matching and administration processes are digital but the delivery of the services is physical and requires direct interaction (Codagnone 2016: 5). This distinction between Online Labour Markets (OLMs), the former type, and Mobile Labour Markets (MLMs), the latter type, is further dissected into two sub types. The result is a ‘two by two’ typology: (1) OLMs for micro-tasking (small and routine tasks that require low to middle levels of skill and are completed online i.e. tasks completed on Amazon Mechanical Turk); (2) OLMs for the delivery of entire and self-contained projects (i.e. tasks that require middle to high levels of skill such as CoContest); (3) MLMs for physical services (i.e. performing low skilled manual work and errands, such as in TaskRabbit); and (4) MLMs for

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interactive services (i.e. interactive services requiring high skills, such as work performed in TakeLessons) (Codagnone et al. 2016: 5). As the authors point out, most transactions in (1) and (2) are Peer-to-Business (P2B) while in (3) and (4) they are Peer-to-Peer (P2P) (Codagnone 2016: 5). Importantly, while there are typological differences, all work in the platform economy is in some way organised and mediated through the digital infrastructure of a platform.

One result of this peculiar organisational structure is that it changes the traditional relationship between mangers, workers and customers. Ratings systems are typical of the platform economy, especially in OLM work, where they play a signifiant role in determining the future success of workers in the online labour market (DeStefano 2015; Martin et al. 2014). Even in MLMs, customer ratings have a profound effect on workers. Uber drivers must maintain a minimum customer rating to keep their job, a system which has been shown to result in drivers strategically shortening rides to reduce fares, providing bottled water and other ‘extras’, and modifying their behaviour to pander to customers (Rosenblat and Stark 2016). In such rating systems, customers are empowered to act as middle managers, which directly impacts on the employment eligibility of workers (Fuller and Smith, 1991; Stark and Levy, 2015). This suggests that the platform economy is providing a framework for the redistribution of power away from formalised middle management, towards customers: from labour to capital (Rosenblat and Stark 2016: 3772).

3. The Platform Economy: Flexible for Whom?

Work in the platform economy tends to be characterised as flexible work (Codagnone et al. 2016; DeStefano 2015; Eurofound 2015). Although it is predominantly platform firms themselves that are most fervent in the proliferation of this definition rather than scholars, who tend to be more critical, perceived flexibility remains one of the primary drivers for workers entering the platform economy (Hall and Kruger 2015; Eurofound 2015). Indeed, as Rosenblat and Stark (2016) point out, the rhetorical markers of freedom, flexibility and entrepreneurship have become hallmarks of the platform economy. This is typically represented by Uber’s advertisement to drivers: ‘With Uber, you have total control. Work where you want, when you want, and set your own schedule’ (Uber 2017). Workplace flexibility is chiefly conceptualised in two distinct ways in the literature. The first conceptualisation, the organisational perspective, refers to flexibility primarily on the part of the organisation, with only secondary consideration given to workers (Hill et al. 2008: 150). This perspective defines workplace flexibility as the ‘degree to which organisational features incorporate a level of flexibility that allows them [organisations] to adapt to changes in their environment’ (Dastmalchian and Blyton 2001:1). From this perspective, the platform economy provides high levels of workplace flexibility, something that is emphasised in the literature (DeStefano 2015; Codagnone et al. 2016; Schmid-Drüner 2016; Valenduc and Vendramin 2016). Platforms provide a basis for

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workers to be called on short notice for the completion of short term contracts, as required (Valenduc and Vendramin 2016). In this way, flows of workers can be turned on and off like a tap.

The second conceptualisation of workplace flexibility is the worker perspective, which primarily emphasises individual agency in the context of organisational culture and structure (Hill et al. 2008: 150). Workplace flexibility from this standpoint is therefore defined as ‘the degree to which workers are able to make choices to arrange core aspects of their professional lives, particularly regarding where, when, and for how long work is performed’ (Hill et al 2008: 151). From this perspective, the platform economy does provide workers with some levels of workplace flexibility, but this flexibility remains in large part tightly controlled by employers.

Uber, for example, claims that drivers are entrepreneurs with the freedom to choose when, where and how they do their work, yet they constrain this freedom by a variety of regulations, design decisions, and information asymmetries deployed through the driver-application (Rosenblat and Stark 2016). For instance, drivers must accept almost all rides, even if it will be unprofitable for them — a common occurrence when the minimum fare, which Uber sets, is too low (Rosenblat and Stark 2016). Furthermore, drivers are forced to accept rides ‘blind’, without any information regarding the destination or fare (Rosenblat and Stark 2016: 3762). Such information asymmetries have also been reported in Amazon Mechanical Turk, where information regarding potential employers is made accessible to workers. This has been shown to lead MTurkers to spend much of their time on the platform conducting ‘invisible work’, searching for unavailable information as a means to make more informed decisions about their ‘tasks’ (Martin et al 2014: 233). Uber also steers drivers into specific areas by setting higher fares in ostensibly high demand zones, a system call ‘surge pricing’, despite reports by drivers that rates drop as soon as they arrive (Rosenblat and Stark 2016). Furthermore, drivers are not compensated for returning lost items to customers, and are subject to small incentives and frequent messages to encourage them to work longer shifts, when it is profitable for Uber.

The literature suggests that these constraints have been largely successful in curbing workplace flexibility from the worker perspective. In Rosenblat and Stark’s study (2016), no drivers reported thinking of themselves as entrepreneurs because of their work and most drivers, especially inexperienced ones, fell pray to Uber’s regime of ‘soft control’ and tended to work in large part, when, where and for how long Uber preferred (Rosenblat and Stark 2016).

Workers like those that drive with Uber, or Turk with Amazon Mechanical Turk, do have some choice over core aspects of their work, but these choices are constrained in large part by information asymmetries and regulations deployed by platforms. The platform economy is therefore more accurately conceptualised in terms of workplace flexibility from the organisational perspective, rather than the worker perspective. Thanks to their organisational structure, platforms (especially OLMs) have access to an ‘extremely scalable workforce’, which in turn grants them a level of flexibility

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unheard of in the past (DeStefano 2015: 7). Workers are provided ‘just-in-time’ and are generally paid per task, which means that they are offered no compensation for the signifiant amount of time they spend looking and preparing for work (DeStefano 2015:7). In part, this has led many scholars to categorise work in the platform economy as precarious (Drahokoupil and Fabo 2016; Codagnone et al. 2016; Huws 2016; Schmid-Drüner 2016).

4. Precariousness and the Platform Economy

There seems to be some confusion in the literature on precariousness about how best to conceptualise its’ central concept. Herod and Lambert (2016) appear ready to abandon hope when they echo Justice Potter Stewart, who struggled to scientifically define what constituted obscenity in film but posited that he ‘knew it when [he saw] it’ (Herod and Lambert 2016). Fortunately, they resist this temptation, but their frustration points to the difficulty scholars have had in agreeing on conceptual definitions.

Campbell and Price (2016) clarify the notion of precariousness by separating the concept into five seperate analytical levels: precariousness in employment, precarious work, precarious workers individually and as an emerging class, the precariat, and precarity as a general condition of social life (Campbell and Price 2016: 315). As the authors note, this conceptualisation helps prevent slippage from one level to another and illustrates the multidimensionality of the concept (Campbell and Price 2016: 315).

Using this framework, precariousness in employment refers to objective job characteristics that involve insecurity, such as a low level of regulatory protection, low wages, high employment insecurity and a low level of employee control over wages, hours and working conditions (Campbell and Price 2016: 315). Precarious work is waged work that exhibits several dimensions of precariousness, ‘uncertain, unpredictable, and risky from the point of view of the worker’ (Kalleberg 2008: 2). Such jobs are often non-standard jobs (Campbell and Price 2016), although precarious work exists in both the formal and informal economy (ILO 2012). Precarious workers are generally understood as persons not just engaged in precarious employment but also enveloped in a more general precariousness (Standing 2011). The concept of precarious workers is thus deeply connected to Campbell and Price’s (2016) fifth analytical level, the concept of precarity. As the authors show, the precarity of part-time employed high-school students in Australia depended on the extent to which they had ‘cushions’ like financial support from home (Campbell and Price 2016).

Work in the platform economy is varied, and it is not all precarious. CEOs and high level managers working in platform firms are some of the richest people in the world (Dolan 2017). Yet,

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using Campbell and Price’s (2016) concept, we should consider the vast majority of workers as being engaged in precarious work.

Firstly, earnings in the platform economy seem to be modest at best and very low at worst (Codagnone et al. 2016: 36). One recent study reported that average wages for both Amazon Mechanical Turk and Crowdflower workers is between $1USD and $5.50USD per hour, although 10% of MTurkers both in the U.S. and India earn hourly wages above $10USD (Berg, 2016). Workers using CoContest, an Italian OLM for individuals looking for architects and designers, have been shown to earn gross wages of roughly €5 per hour, averaged across all countries, contests and designers (Maselli and Fabo 2015). The authors conclude that CoContest therefore cannot, on average, provide a full-time salary for Italian designers, a contingent that makes up 67% of CoContests’ workers (Maselli and Fabo 2015: 9).

To compound for workers the negative effects of low wages, work in the platform economy is also very insecure. In platforms like CoContest, where users propose ‘contests’ for workers who then conduct projects in the hope that their ‘solution’ will win, most of the work is unpaid (Maselli and Fabo 2015: 3). For workers using Amazon Mechanical Turk, no guarantee is provided to workers that their labour will be remunerated and employers can retain work without justification (Codagnone et al. 2016: 37). In response, some MTurkers have established forums where workers can inform each other about dodgy employers (Martin and O’Neill 2014).

It is relatively clear that work in the platform economy is such employment; and since most workers are classified as independent contractors, ineligible for employment benefits (Risak and Warter 2015; Smith and Leberstein 2015) employees engaged in such precarious work are also particularly vulnerable to becoming precarious workers (Eurofound 2015).

Yet, this is not the case for all workers engaged in precarious work. Recent studies have stressed the importance of looking beyond the workplace when considering the extent to which precarious

work translates into precarious workers (Campbell and Price 2016; Standing 2011). These studies

have played into a broader discussion in the literature regarding the varied experiences of workers in so called ‘bad jobs’ (Knox et al. 2015).

In their study of hotel room attendants in Hawaii, Addler and Addler (2004) argue that objectively ‘bad jobs’ were subjectively perceived as ‘good jobs’ according to workers’ characteristics and preferences (Addler and Addler 2004). But as Campbell and Price (2016) point out, such case studies wrongly label individual experiences and perceptions as ‘subjective’, failing to note that the former have an objective dimension (Campbell and Price 2016: 318). Work preferences, for example, have been shown to be strongly linked to socio-structural variables such as household income and family structure (Morrison and Thurnell 2012).

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The literature therefore suggests that experiences of precarious work vary according to social structures both inside and outside the workplace (Campbell and Price 2016: 318). As Campbell and Price (2016) show, working experiences are likely to depend on specific characteristics of the employment relation, but other factors outside the workplace can activate precariousness. Alternative sources of income, the extent to which workers participate in employment, stage in the life course, and alternative career paths have all been shown to influence working experiences of precarious work (Campbell and Price 2016).

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III.

R

ESEARCH

Q

UESTIONS

A review of the literature reveals several key findings for the structure and focus of this thesis. Developments in technology seem to be producing new working arrangements centred primarily on increased workplace flexibility (Eurofound 2015). It seems that both employees and employers are seeking greater flexibility in the workplace, although current trends are favouring the interests of platforms rather than workers (Eurofound 2015; Codagnone et al. 2016; Schmid-Drüner 2016). As a result, increased flexibility has been closely attached to increasingly precarious working conditions (Codagnone et al. 2016; Schmid-Drüner 2016; Drahokoupil and Fabo 2016). In the platform economy, developments in technology have produced new opportunities for the restructuring of employment relations (Snricek 2016). Through the internet, platforms can potentially link an indefinite number of clients and workers on a global basis (DeStefano 2015). Workers can be provided ‘just-in-time’ and paid on a ‘pay-as-you-go’ basis with little to no employment protection (DeStefano 2015).

In mobile labour markets (MLMs), where workers perform physical, low-skilled tasks, work is mediated and organised through digital infrastructures, otherwise known as apps (Codagnone et al. 2016: Snricek 2016). Out of this arrangement, new, hybrid control regimes are emerging that subject workers to increased surveillance, monitoring and normative and neo-normative control mechanisms (Elliot and Long 2016; Rosenblat and Stark 2016). This has been shown to have a profound effect on the experiences of workers. The collective labour process becomes further atomised and social relations have been shown to migrate into the digital sphere (Elliot and Long 2016).

In sum, the literature suggests that the platform economy is characterised by increasingly flexible, yet precarious, forms of employment. These forms are producing new, hybrid control mechanisms that are centred on the mediation and organisation of work through digital infrastructures. These trends are well established in the literature, yet a close inspection of how workers experience and understand these developments remains understudied. As Burawoy’s (1979) research highlights, the labour process is realised through the interaction between imposed control mechanisms and how workers’ relate, respond and understand them. Without a consideration of these experiences, an understanding of labour processes in these new conditions remain incomplete. While in the context of the platform economy, there has been some notable research of working experiences in this vein, there is still considerable work to be done. Such accounts will contribute to better conceptualisations of the platform economy and deeper insights into how workers are responding to associated developments in the labour process. It is towards this gap in the literature that this thesis is orientated and the following questions formulated:

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Primary research question:

How do Foodora riders experience the labour process as mediated and organised through the Foodora platform?

Secondary research questions:

In terms of workplace flexibility from the worker perspective, how does Foodora delivery work compare to other work in the platform economy, and how do riders understand this flexibility? Compared to other work in the platform economy, how precarious is Foodora delivery work, and

how do riders mediate and understand this precariousness?

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IV.

M

ETHODOLOGY

The following section is loosely broken into two parts. In the the first part on data collection, I discuss conducting participant observation and semi-structured interviews, as well as some of the ethical challenges that this threw up. In the second part on data analysis, I discuss the thematic analysis that I conducted and my approach to coding.

1. Data Collection: Ethnographic Fieldwork at Foodora

To broach my research questions, I conducted an ethnography of Foodora delivery work that included participant observation and 6 interviews with other riders. There has been a rich tradition of ethnographic research in Labour Process Theory studies (see Smith 2001). According to Smith (2001), no single approach to the study of work has been more effective than the ethnographic in uncovering the complexities of the labour process, especially in low skilled work (Smith 2001: 221).

As a review of the literature suggests, there has been little ethnographic research into the experiences of workers in the platform economy. By taking an ethnographic approach, I aim to contribute to filling this gap and provide an in-depth and textured account of how macro-changes in the organisation of work are effecting the lives and experiences of workers.

Conducting an ethnography of Foodora work threw up some interesting challenges. As is typical of ethnographic research, the first of these was access. I had assumed that getting a job at Foodora would be easy but after my initial online application was accepted I was notified that there would be a timed trial run and that not all riders would be granted a job. During the trial I rode at a brisk, steady pace, as was instructed, and was careful to follow the instructions. When I arrived back at Foodora headquarters, confident that I had made the secret time limit, I was surprised to hear that I had only narrowly “passed” by two minutes.

After I was inducted, I spent over seven months working in the field, from the 30th of October, 2016, until the 2nd of June, 2017. This meant that I worked during two seasons and experienced many different weather conditions. Foodora delivery work is almost entirely done outside and the weather often defined shifts. As one rider told me, the difference between working during summer and winter is the difference between the best and worst job in the world.

On average, I worked two evening shifts a week. Evening shifts tended to be three hours long, although sometimes I worked for four hours in the evening. To get a sense of how shifts at various times might ‘feel’ different, I experimented with my shift schedule and made sure that I worked at least once on every day of the week, and that I also worked during the day. These variations had a

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big impact on my working experience. During the weekend, when the tourists descended on central Amsterdam, completing orders involved walking with the bike and pushing through the crowds.

During my fieldwork I kept an ethnographic diary. It was in this diary that I entered my notes and reflected on my observations. Most of my observations were taken during shifts, but most of my notes were entered afterwards. This meant that the majority of my entries were written at home, after shifts. However, if something struck me while I was riding, sometimes I would pull over and write a quick note, but these tended to be short and written mostly as reminders for when I was writing my more extensive notes later that night. Notes were taken as soon as possible, but on some occasions, I was so tired after my shift that I had to wait until morning.

The notes I took during my shift were intended to capture, the experience of working as a Foodora delivery driver. This involved writing about the experience on several different levels. I detailed the weather and the conditions of the streets, the dull ache that developed after a long shift carrying the bag, the geography of the various urban landscapes and the smell of the food. I also wrote on more practical matters. I kept track of changes in my shifts, reflected on the app, described how the shift swapping system worked and recorded how many deliveries I completed each shift.

Most of my observations were taken during my shift, but I also took some at Foodora HQ, a hub in central Amsterdam where riders would meet before and after their shift to pick-up and drop-off their gear and sometimes hang out. Taking observations at Foodora HQ threw up some difficult ethical challenges. Foodora HQ is a very busy place and on most evenings serviced more than 100 riders. While I was open about my research with riders that I interviewed, it was not feasible to get the informed consent of everyone that passed through Foodora HQ. In response, I decided to keep my observations general and make sure that the confidentiality of all the riders I spoke to was secure.

Getting the informed consent of interview participants was much more straightforward. Before the interview all participants were given detailed information on the purpose, duration, and methods of the research (Gubrium and Holstein 2012: 444). Furthermore, interviewees were aware of the risks and benefits associated with participating in the study and were guaranteed that their confidentiality would be secure. Participants were also given the right to withdraw from interviews at any time.

2. Data Collection: Interviews

In addition to the participant observation I conducted, I also completed 6 semi-structured, in-depth interviews with other Foodora riders. These interviews took place in a variety of different locations. I conducted three at riders’ homes, two at cafes and one at a University of Amsterdam library. They

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also differed in length. The shortest interview was 38 minutes and 2 seconds long and the longest interview was 57 minutes and 22 seconds long. Most interviews were around 50 minutes long.

One of the advantages of conducting ethnographic research was that it granted me access to many potential interviewees. It also allowed me to develop friendly relationships with several other riders. I followed Given (2008) in her claim that the quality of the research often depends on the quality of the researcher’s relationship with the participants (Given 2008: 729). Using a purposive sampling approach, I drew most of my interview participants from the group of riders with whom I had built up good rapport but I also used a snowball sample to top up numbers. Although I was not overly concerned with sample bias, given the small size of the sample, I did want to have a group of interviewees that was varied. I selected potential interviewees according to the rapport I had already developed, as well as varied stages in the life course, student status, whether they had other work outside of Foodora and immigrant status. As a review of the literature showed, these differences affect how workers mediate the impact of precarious work. Attention to ‘social location’ at the sampling stage was therefore important for examining how experiences of precarious working conditions differed between riders.

Semi-structured interviews were selected over other data collection methods because the concepts, as well as the relationships among them, were well understood (Given 2008: 811). My reading of the literature on the platform economy, framed by my review of the LPT literature, meant that I entered into the interviews with themes and concepts I wanted to discuss. I thus organised my interview schedule according to these themes and concepts, which were formulated into a series of ‘open’ questions (Given: 810). When participants opened up interesting discussions however, I would diverge from my transcript and follow these discussions to their end.

3. Data Analysis

To analyse the data I collected, both in my ethnographic fieldwork and in my interviews, I conducted a thematic analysis. To code the data I used a deductive approach.

I first established a ‘code template’ based on my theoretical framework and review of the literature and used these ‘a priori codes’ to organise the text for interpretation (Fereday and Muir-Cochoran 2006: 5). These preliminary codes were first tested and then applied to the data with the intent to identify meaningful units of the text (Fereday and Muir-Cochoran 2006: 6). With each re-reading of the text the codes were developed and themes emerged. These themes were then interpreted into an explanatory framework and fed back into the theory.

This process involved incorporating two types of data, the ethnographic material and the interview material, into a single framework. Most of the ethnographic material was collected before I

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had conducted the interviews so my reflections on my own observations and experiences were used in conjunction with my theoretical framework to build the code template and then to identify the themes.

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V.

R

ESULTS AND

D

ISCUSSION

1. Getting a Sense of the Work

As an introduction to the Results and Discussion section, I have inserted an extended excerpt from my ethnographic diary. Written just after my first shift, it illustrates some of the core tasks involved in Foodora delivery work; my hope is that it also conveys to the reader a fine-grained, textured sense of how it ‘feels’ to do these tasks. To add shape and scholarly depth to this detail, in this section I also present the findings of this research and discuss them in relation to the work covered in the Theoretical Framework chapter.

20 October 2016 —

I’m nervous for my first shift. No one has told me anything. When I arrive, half an hour early, I report straight to the young man at the front desk.

‘Hi my name is Oskar. It’s my first shift’, I say.

‘OK’, the young man says, scanning the piece of paper he has in his clipboard, ‘you’re with Jessa. Just wait over there.’ He motions towards the middle of the room, then adds that Jessa will accompany me for my first two orders. This is a relief. I don’t even know how to open the app yet. I wait where I’m told and take up an awkward position leaning on the foosball table. The six o’clock shift is starting in fifteen minutes and around the desk a loose queue is forming. It’s a motley crew. There are young girls and middle-aged men. There’s a man wearing a PostNL jacket. A young guy that looks west African and another with a delicately upturned moustache and a tightly twisted turban. Everyone is wearing bright, Foodora-pink jackets.

Some are chatting but the room is strangely silent. Most riders are quietly stowing their things in the lockers that line the walls and others who are already prepared are peering into their phones, silently scrolling through their Facebook feeds, or tapping out messages. Others have their headphones in.

A few minutes before the start of my shift, a young women from behind the desk tells me that Jessa is no longer coming. She looks around the room. ‘Tony, can you take Oskar out on his first shift please? Thanks’, she says in one breath. Tony and I introduce each other. My companion looks young. He’s wearing square glasses and his light blond hair is peeking out from bottom of his black beanie.

He takes me to the front desk and I’m asked what kind of jacket and bag I’d like. It’s cold outside so I take the warm jacket and the ‘plate’, an apparatus that, when attached to the bike, holds in place the Styrofoam box for the food. I wonder why anyone would prefer the heavy bag to the plate, but, looking around the room, it seems that many do. I notice that Adrian is standing outside, chatting to a colleague. His bag already on his back.

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I put the things I don’t need — my own big jacket and bag — into a locker and follow Tony outside to attach the plate. ‘It usually takes about five minutes to get this thing on’, he says, ‘so I usually arrive a bit early.’

It takes me about ten minutes to attach the plate. Tony shows me how to use the app and we head out together on our first order. On the way to the restaurant, Tony tells me a story: ‘Once I was doing a big delivery and I was riding one of those big bikes’, he says, referring to the bikes that Foodora owns and that riders use when there is a big order. ’I got to the restaurant and locked up the bike, picked up the food and then came outside to unlock the bike, but I couldn’t find the key. I looked everywhere, in the restaurant, outside, in my pockets, but I couldn’t find it anywhere. So I rang dispatch and they told me to come back to the headquarters. I think that bike is still at that restaurant. I don’t even know if they picked it up.’

We arrive at the restaurant and, showing him my phone screen, I give the waiter the name of the customer. He hands me two pizzas.

Packing them into the Foodora box, Tony gives me some tips. ‘Always remember to do this thing’, he says, pulling the bungee cord over the lid of the box and attaching it, taught, to the back of the bike. ‘It doesn’t really matter. The food won’t come out but it’s good to be sure’, he says. ‘Pizza is pretty bad but soup is the worst. Once I got an order with some soup and the lid wasn’t attached so it spilt all in my bag. It’s like, don’t order soup with Foodora!.’

When we arrive at the customer’s apartment I lock up my bike and carefully detach the bungee cord from the bike. I open the box and take out the pizzas, careful to keep the boxes level. Tony had advised me to do this earlier and I remember seeing a print out taped to the wall in the office: a picture of a pizza that had been flipped upside down in its box. Under the picture was a big yellow sad-face emoji.

I ring the doorbell and, after a moment, the buzzer sounds and the door clicks open. ‘You go in’, Tony says. ‘I’ll wait outside.’ The apartment is on the fourth floor so I begin walking up the stairs, climbing each stair with my good leg, pulling myself up with the handrail to take some weight off my injured knee. The stairwell is cold and lit like a hospital. I reach the fourth floor and see a middle aged woman standing in an open doorway. She looks tired. I say hello and apologise that the food is late. ‘It’s my first delivery’, I say. She tries to smile but does not quiet achieve it. ‘Sorry’, I repeat. ‘Here are the pizzas. I hope you have a good night.’ I hand her the two sweaty pizzas that are cooling quickly in their boxes and turn back down the stairs. ‘Thanks’ she says, shutting the door.

Outside, Tony is waiting, playing on his phone. ‘How did it go?’ he asks. ‘Yeah, pretty good. Lot of stairs though’, I say. ‘Yeah, that sucks’, Tony says. It’s the second delivery we’ve done this shift and Tony is getting ready to leave me to finish my shift alone. ‘How much longer have you got to go?’ I ask. ‘A few hours. I finish at nine’, he says. I look at my phone. It’s seven thirty. ‘I finish at nine too so I guess I’ll see you back at HQ’, I say. We shake hands and I thank him.

I do another order, picking up the food at the restaurant and dropping it off nearby. I now have an hour left and no orders to fill. I don’t know what to do so I park my bike on a bridge overlooking

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the Amstel river and wait. It’s cold and dark and a stiff breeze is blowing over the water. Underneath my jacket I’m clammy but the wind on my exposed skin is almost painfully cold. I pull the hood of my jacket over my head and fasten it tight. From the end of the bridge I can make out the figures of two boys approaching. As they come within earshot, one of the boys says: ‘I used to work for Foodora.’ He’s tall and blond and eating a Caeser Salad from Albert Hein. The other boy is shorter but also blond and eating the same Caeser Salad. ‘I didn’t like it though’, he adds. ‘We’re from Finland’, he says, nodding towards the other boy who is stuffing another fork full of lettuce into his mouth. ‘All I did was wait around in the cold’, he says.

I check my phone to see if any orders have come through but it still reads ‘no new deliveries.’ The boys from Finland are on vacation. They’d just been to the Heineken experience and tell me that Finland has the best saunas in the world, fired by birch wood and steaming rocks.

I check my phone again. I haven’t had an order for forty minutes. Then I hear a beep. It’s eight fifty five and I have a new order. I say goodbye to the Finnish boys and ride for another thirty minutes from the restaurant to the customer. By the time I get back to HQ it’s ten o’clock and I’m tired and hungry.

2. Working Through the App: Computer Control

One of the distinguishing features of the platform economy is that work is mediated and organised through online platforms (Srnicek 2016). At Foodora, riders used the specifically designed ‘Roadrunner’ app while they worked, and the ‘Shyftplan’ app to organise when they worked. It was through Roadrunner that riders were delegated deliveries and informed about the locations of restaurants and customers, as well as a host of other important information about orders.

The Roadrunner app was relatively easy to use. This is an exert taken from my diary detailing how work was organised through Roadrunner:

I’m scheduled to start at 17:30 tonight. When I arrive at headquarters a few minutes before my scheduled start, I log into the app with my phone. I watch the screen as my status changes from ‘ready’ to ‘working’. This switch means that I’m now getting paid and am available for orders. I hear my phone bleep like Roadrunner — ‘meep! meep! — with increasing frequency and volume; on my phone I can see I have a new delivery. I am asked to accept it but this is a false choice. Riders are obligated to accept all orders. I accept the delivery and the details of the order appear on the screen. I can see the name and address of both the customer and the restaurant, along with other information like what I’m picking up. I press on the customer’s name and am redirected to google maps, which appears with the location of the restaurant, as well as the fastest route from my current location. Once I arrive at the restaurant and pick up the food I select on the app ‘confirm pick-up’ and the name and address of the customer reappears. When I press on the customer’s name, I’m again taken to google maps, but this time I’m directed to the customer’s

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address. Once the food is dropped off, I select ‘confirm delivery’ on the app and the next order appears.

During most shifts, orders arrived constantly. There was a rhythm to the work that is reminiscent of Taylor and Bain’s (1999) virtual ‘assembly line in the head’, a never ending stream of customers that was fed to operators in a UK call centre (Taylor and Bains 1999). As Callaghan and Thompson (2001) point out, call centre operators are not linked like workers in traditional assembly lines. One worker cannot shut down the production line. Each worker only sees their ‘own’ labour process, which deprives them from a shared sense of the process of production. Workers are independently linked, disconnected from each other and from a sense of the labour process as a whole (Elliot and Long 2016). This linking is facilitated by what Elliot and Long (2016) call ‘computer control’ (Elliot and Long 2016: 136).

Computer control played a significant role in the labour process at Foodora. Riders were independently linked through the app, like BigBox operators, yet, compared to staff on the grocery distribution floor, riders were removed from a collective sense of the labour process in a different way. This is because, although BigBox work was individualised, and like Foodora riders, employees could go a whole shift without interacting with a single colleague, in contrast to Foodora delivery work, operators at BigBox were grouped into ‘teams’ and, before each shift, were presented with information regarding how much work was to be completed and the speed required to complete it that shift (Elliot and Long 2016). Furthermore, operators were given regular updates about both their team and individual speeds during the shift, as well as real-time projections indicating when the work would be completed (Elliot and Long 2016: 143). This meant that stronger workers pushed weaker ones to increase labour intensity so that the team as a whole might finish faster (Elliot and Long 2016: 144).

Foodora delivery riders were not similarly connected and experienced no collective sense of the labour process; unlike workers at BigBox, riders were not organised into teams and were only given information about their own orders, which typically arrived in a constant stream whether it was busy or not. At Foodora, riders could only get a vague sense of the collective labour process after shifts when they could discuss with other riders. Andy highlighted this point by describing in our interview how he arrived at Foodora HQ, after what seemed to him like a normal shift, only to realise that it had been so busy that Foodora was forced to shut down its’ entire operation in Amsterdam for the night.

As they worked, riders were monitored, surveilled and evaluated through the app. Unlike Taylor and Bains’ (1999) call centres, the monitoring that riders were subjected to at Foodora was surprisingly unstructured. During our interview, Zara told me a story about lingering outside her house for twenty minutes while she waited for her shift to end. ‘I think they’re pretty selective.

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Sometimes it doesn’t matter’, she added. During my shifts, I never got the sense that I was being closely monitored or surveilled, although riders were tracked and rider statistics were collected through the app. Typically, this data is recorded so that it can be fed back to employees by management (Callaghan and Thompson 2001). At Foodora, this did occur sometimes, but only rarely, and there was only very light pressure put on riders to improve their stats. My team captain, Domenico, who was in charge of managing around 15 riders, sent me a rundown of my stats only sometimes. One such message Domenico sent me read:

‘Hi Oskar, ‘Speed’ and ‘Time at the customer’ indices were again too low for last week. More enthusiasm [fire emoji] is required in order to improve your performance and, most importantly, enjoy your work. Do not hesitate to ask me anything you wish to know.’

Pressure to improve stats never went beyond these kinds of messages and many of the riders I spoke to did not receive them at all. Riders had access to their stats via the app. They could see their average speed, the distance that they covered during the shift and the amount of deliveries they completed. Riders were encouraged to use these stats to treat their work as a game, although there was little systematic attempt by Foodora to foster competition. Only Domenico encouraged me to gamify my work and there was not much more than a ‘leaderboard’ displayed on a small television in Foodora HQ that was supposed to encourage the same thing. These attempts seemed only modestly successful. Only one rider I interviewed ever thought of their work as a game:

[Andy]: For me, when I’m busier the time goes faster so, you know, If I’ve got an order, yeah, well, I’ll try to get there as fast as I can, end up talking to myself. I’m just like “I got to get David’s pizza in order” [laughs], like some kind of maniac riding around.

[Oskar]: That sounds like you’ve made it a game for yourself.

[Andy]: Yeah, definitely. I’m definitely like [pause] have like an imagination of who I’m delivering for, like “I’ve got to get that pizza as fast as I can!” [laughs] It just gets me through.

It was surprising to see that no production game emerged at Foodora. In similar conditions, games played a significant role in the labour process. At BigBox, Elliott and Long (2016) illustrated how management constructed a virtual social space in a computer controlled workplace that fostered a production game similar to Burawoy’s ‘making out’ (Burawoy 1979). Elliot and Long’s (2016) ‘digital arena’ engrossed BigBox employees in a game that ultimately secured greater consent to the production process. At Foodora, computer controlled and mediated work produced a similar ‘digital arena’ but no production game. This was because, compared to BigBox, the digital arena at Foodora was relatively weak. There was no minimum ‘rate’ workers were required achieve, no races to improve productivity, and no trophies for the fastest workers, all of which occurred at BigBox (Elliot and Long 2016). Informal games emerged at BigBox because the labour process was far more

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embedded in the digital arena; at Foodora, where almost no informal production games developed, the digital arena was far less developed which meant that production games lacked the necessary ground to flourish.

At Foodora, computer control gave riders a sense of freedom. Describing how it felt working through RoadRunner, Brahm explains:

[Brahm] ’It feels nice. I like that there’s no manager watching you. You have a little freedom in that. It’s not like you have to do this, now that. It’s just you have to go to the restaurant and then to the customer.’

As Brahm’s comment suggests, computer control removes the manager-worker interaction almost entirely (Elliot and Long 2016). Essentially, riders had a ‘supervisor on their shoulder at all times’ (Elliot and Long 2016); but they did not experience the work as such. As Elliot and Long (2016) suggest, riding under the direction of a computer presents workers with ways to undermine its’ perceived authority. The workings of RoadRunner puzzled many riders and I spent some time before and after shifts chatting with others about how it directed orders. As Brahm said, the app often seemed to make ‘illogical’ decisions about the allocation of orders or the quickest way to arrive at the pick-up or drop-off location. At Foodora, working through Roadrunner allowed riders to superordinate themselves above the authority of the app. Instead of a boss, riders thought of the app more, although not completely, like it was a tool. Speaking about the RoadRunner app, David explains:

[David]: ‘It doesn’t feel like my boss or manager. It feels like just something I have to use during my job. That’s all. I can express some anger towards the app sometimes depending on what mood I’m in. I don’t feel like it has any authority over me. It’s just what I have to use to get me through the job.’

BigBox operators did not report such ideas about the computer that controlled their work, but workers on Elliot and Long’s grocery distribution floor were subject to much tighter surveillance and monitoring than riders at Foodora (Elliot and Long 2016). For Foodora riders, considering the app as an instrument was underpinned by a sense of workplace autonomy not experienced by BigBox operators.

Compared to other control regimes reported in the literature, computer control at Foodora might be better considered computer control lite. Riders were only subject to loose monitoring and there was little effort by management to push riders to improve productivity by feeding them statistics about their speed and efficiency, something that was emphasised in the literature (DeStefano 2015; Elliot and Long 2016; Taylor and Bains 1999). Computer control at Foodora allowed riders to experience

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a kind of freedom in their work, yet, as workers were independently linked through RoadRunner and could only see their ‘own’ labour process, this sense of freedom coincided with fewer opportunities to make meaningful interventions in the collective labour process.

Speaking about an order that he was mistakenly sent to pick-up, Andy explains:

[Andy]: ‘You are very alienated from the ordering and coordinating process, so you have to be careful where you put your agency. When I dropped off that big order the next order that I had had for 10 minutes was just around the corner. If it had been on the other side of the city, then I’m really behind. It could have been a lot worse.’

Computer control meant that riders were unable to control the routinised pace of the work, nor the chance to develop a shared sense of the labour process; instead, riders suffered from a double alienation: alienation from the work as a whole, and from each other. As Elliot and Long (2016) argue, this leads to the subjugation of worker communication to the task of ‘doing the work’ (Elliot and Long 2016: 138). Consequently, social relations in the workplace are further degraded in order to facilitate increased productivity and profit.

3. Workplace Culture: Normative and Neonormative Control

When I first arrived at the Foodora HQ for my induction day I sat down in a room with a ping pong table in it. Seeing the table for the first time I wondered whether, during its occupation of the room, it had ever seen any ping pong. Lining the walls of the room were piles of Foodora boxes, which left little space for cross-court movement, and on its surface there was a fine film of dust. At least that day, it did get used. The powerpoint presentation was given over the table and the bats were used to prop up the projector.

In the main room, where riders would collect their jackets and bags, there was a foosball table that seemed equally neglected. Before my first shift I invited Adrian, a rider originally from Puerto Rico that I had met on the induction day, to play a game with me, but we did not play long. The handles were sticky and the surface of the playing field was so warped that the ball moved from one end to the other in great arcs.

As Kane (2004) illustrates in his analysis of the rise of the corporate play ethnic, there is an increasing trend in management towards making workplaces sites of fun. Some workplaces like KwikFit even employ a full time “Minister of Fun” (Sunday Times 2005). I follow Fleming and Sturdy (2009) and consider these developments in terms of neo-normative control, which, as the authors write, draws on extra-organisational identities to celebrate values such as difference, individuality and fun (Fleming and Sturdy 2009; Sturdy et al. 2010). As Fleming and Sturdy (2009) note in their

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study of workers at Sunray, an Australian call centre, neo-normative control is based on the idea that fun can increase worker productivity.

Like neo-normative control at Sunray, Foodora aimed to ‘existentially empower workers’ (Fleming and Sturdy 2009: 574). At Foodora HQ, riders were encouraged to express themselves by pining artwork on the walls. Yet, compared to Sunray, Foodora’s neo-normative control initiatives were far less formalised. Due to differences in workplace organisation — Foodora riders worked almost entirely outside the office — there were no workplace games and no informal dress days, as was the case at Sunray (Fleming and Sturdy 2009). However, like Sunray’s, Foodora’s recruitment strategy painted a picture of employment that emphasised fun and freedom (Fleming and Sturdy 2009). On their website, Foodora explained to potential applicants that the company was ‘bold, caring and playful’ (Foodora 2017). Backdropped behind this text was a large banner that depicted a group of fresh-faced young riders, cheering to the camera (Foodora 2017).

Like operators at Sunray, riders were invited to drink alcohol on Friday nights at Foodora HQ. Again, compared to Sunray, these parties were far less formalised. There were no posters on the walls, encouraging employees to attend, and riders were generally required to bring their own alcohol — Foodora did not typically provide drinks. So weak was the pressure to attend these parties that I only found out about their existence several months into my employment. This relaxed approach to fun contrasted with Sunray’s, where operators were strongly encouraged to attend Friday night drinks (Fleming and Sturdy 2009).

While at Foodora these events were very informal, during my fieldwork I did attend a more formal party Foodora threw at a sticky nightclub in central Amsterdam. For this event, Domenico encouraged me to attend several times via Whatsapp, although I never got the sense that it was mandatory.

That these events were given less weight at Foodora, and that management applied less pressure on workers to participate, meant that at least some of the negative effects neo-normative control exerts on employees were softened. Unlike at Sunray, where a stronger neo-normative control fostered certain corporate-friendly identities while squashing ones that were not, neo-normative control at Foodora did not seem to have the same effect (Fleming and Sturdy 2009; Sturdy et al. 2010). Being less formalised and weaker generally, neo-normative control at Foodora did not seem to significantly limit identities that were considered corporate-unfriendly. In contrast to Sunray, there was also room for workplace identities that were ‘not-fun’, or ‘not-different’ (Fleming and Sturdy 2009: 578).

Generally speaking, riders enjoyed the organised events Foodora threw, as well as this less formal approaches to encourage riders to have fun at work. Friday night drinks seemed to attract a

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