It’s the Algorithm, It Decides: An Autoethnographic Exploration of Algorithmic Systems of Management In On-Demand Food Delivery Work in Amsterdam

In Partial Fulfillment of: Master of Arts in Media Studies New Media and Digital Culture

Written by: Under the Supervision of: Date of Submission: Emma Knight Dr. Niels van Doorn June 28, 2019 ID: 12149888 Second Reader: Dr. Thomas Poell

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Table of Contents

Abstract 3

Acknowledgements 4

Chapter 1 | Introduction 5

Chapter 2 | The Origins of Platform Labor & Algorithmic Management 7 2.1 Surveying the Platform Landscape 7 2.2 The Rise of Workforce Capture 11 2.3 Algorithmic Management in Platform Labor 16 2.4 Developing Algorithmic Competencies 20

Chapter 3 | Methodological Framework 23 3.1 The Current Landscape of On-Demand Food Delivery Platforms 23 3.1.1 Deliveroo 24 3.1.2 Uber Eats 25 3.2 Qualitative Research Design 27 3.3 Onboarding Process 30 3.4 Interview Protocol 31 3.5 Rider Recruitment Strategies 32 3.6 Overview of Participants 33 3.7 Ethical Protections for Participants 33 3.8 Limitations of Research Design 34

Chapter 4 | The Generation of Algorithmic Knowledge 37 4.1 Capture in the Context of Deliveroo and Uber Eats 37 4.2 Deliveroo’s Shift Booking Algorithm 40 4.3 Order Assignment Algorithms 43 4.4 Dynamic Pricing Algorithm 48 4.5 Algorithmic Limitations and Automated Errors 52

Chapter 5 | Conclusion 57

References 60

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Abstract

Companies that operate in the ‘on-demand’ platform or ‘gig’ economy rely upon machine-learning algorithms to facilitate interactions between service providers and customers. This use of algorithms has come under scrutiny within the burgeoning field of platform labor studies, particularly with regard to the ways in which platform companies utilize algorithmic systems to manage and coordinate their semi-autonomous and disaggregated workforces. However, much of this existing scholarship has avoided critical analysis of the highly subjective and individualized experiences of platform service workers who conduct their work at the intersection of digital and urban space. In turn, this thesis contributes to this growing body of knowledge by investigating the following question from an autoethnographic perspective: how has the redistribution of managerial duties to algorithmic systems impacted the experience of work for on-demand food delivery riders in Amsterdam? Specifically, this thesis investigates the lived experiences of ‘riders’ (who conduct their work primarily on bicycles) and explores how riders are impacted by the algorithms they come into contact with while working, as well as how they make sense of and develop strategic responses to these algorithmic workforce management systems. Through the use of autoethnographic research methods and in-depth, semi-structured interviews with Deliveroo and Uber Eats riders, I argue that the platforms’ redistribution of managerial duties to algorithmic systems has negatively impacted the working experiences of Amsterdam-based riders. By using algorithmic systems to govern riders’ labor, Deliveroo and Uber Eats have successfully conditioned their allegedly free marketplaces for their own profit-maximization purposes and at the detriment of riders. Furthermore, riders are negatively impacted when these algorithmic systems fail to account for the complexity of their work, and are unduly punished as a result.

Keywords: platform labor, on-demand food delivery, algorithmic management, Deliveroo, ​ Uber Eats, autoethnography, workforce management

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Acknowledgements

First and foremost, I am thankful to the riders who indulged me by answering my many questions and opened up a wealth of knowledge by sharing their insights. I am extremely appreciative of their collaboration in this research and thank them for allowing me to learn from them. I am also indebted to my thesis supervisor, Dr. Niels van Doorn. His guidance, expertise, and constructive feedback were extremely helpful throughout the thesis writing process, and I thank him for his candor and support. I would also like to thank my parents and siblings for their love and reassurance, and for always motivating me to reach my academic goals throughout my life. Finally, I am thankful to my partner, Max, who spent countless hours reading my work and encouraging me when I doubted myself. Thank you for always believing in me.

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Chapter 1 | Introduction

It is a warm Friday night and throughout Amsterdam, cyclists sporting teal, lime green, and neon orange backpacks dart in and out of restaurants, their faces illuminated by the hazy blue glow of their smartphones. As I pull up to a restaurant for my next order, I see another rider I know, smoking a cigarette, his head bobbing to the music playing through his headphones. We chat, asking how the evening rush is going, are you getting good orders? Before long, my phone vibrates and I navigate through the sea of waiting riders to collect my order inside the restaurant. Once I get back outside, I realize my friend has left; must have gotten another order request. Such is the life of a Deliveroo rider in Amsterdam. So-called ‘on-demand work,’ in which workers sell ad hoc services to nearby customers via digital platforms, has become an increasingly popular and visible form of labor in recent years (Hunt and Samman 7; Ticona et al. 21). While accurate estimates of the platform labor economy are hard to come by, some scholars posit that around one and a half percent of the global workforce is engaged in some form of platform-mediated work (Hunt and Samman 11). This trend has prompted researchers to study the conditions faced by people engaged in platform-based ridehailing work (Lee et al.; Möhlmann and Zalmanson; Rosenblat and Stark; Ticona et al.), food delivery (Ivanova et al.; Shapiro; Sun; Veen et al.), as well as domestic labor and care work (Hunt and Machingura; Ticona et al.). In particular, these scholars have critiqued the role algorithms play in platform-based work, as platform companies increasingly wield these allegedly neutral machine-learning systems as managerial tools. For example, on-demand ridehailing platforms such as Uber and Lyft use algorithms to match passengers with drivers and determine the fees drivers receive for providing their services. Thus, in using these algorithmic systems, platform companies do more than neutrally mediate interactions on their platforms, as they claim to. Rather, they “shape the experience of work itself,” (Ticona et al. 20). My curiosity about the nature of platform labor reached new heights in February 2019 when I became a ‘rider’ for the on-demand food delivery platform Deliveroo. Soon after, I began riding for Uber Eats and started recording my personal reflections and thoughts on the work. In doing so, I realized that much of the existing literature that explores how algorithms shape the experience of platform service work utilizes qualitative research methods, such as ethnographic interviews with platform workers. However, these studies avoid any discussion Knight 6 of the affective, highly subjective and individualized nature of platform work, and particularly on-demand food delivery work, from an autoethnographic perspective. Autoethnography, as opposed to traditional ethnography, situates the researcher as a self-reflexive actor in a particular social setting or group and uses the personal experiences of the researcher to “illustrate facets of cultural experience” (Ellis et al., 276). Thus, I seek to contribute to this growing body of knowledge by combining both autoethnographic and interview-based research methods, all in an effort to answer the following question:

How has the redistribution of managerial duties to algorithmic systems impacted the work experiences of on-demand food delivery riders in Amsterdam?

In understanding how algorithms impact riders’ work, I also seek to explore how food delivery riders for these two platforms make sense of the algorithmic systems that influence their jobs. In this sense, my aim is to critically investigate how riders produce working knowledges and competencies, and how this generation of knowledge influences their interactions with both Deliveroo and Uber Eats’ algorithmic systems. This component is worthy of study because algorithms are not one-dimensional, authoritarian objects that solely act upon platform service workers. Rather, they are “embedded within local, contextual, and multi-layered sociotechnical relations,” meaning workers’ interactions with algorithms contribute to their remaking and reshaping (Sun 14). Thus, a study of the effect of algorithms on platform service workers must also include a rigorous examination of workers’ resilience and resourcefulness as they navigate algorithmically-mediated working environments. The remainder of this thesis is divided into four chapters. Chapter 2 establishes a theoretical framework by reviewing existing literature about the nature of platform service work. In Chapter 3, I outline my research design and methodological approach. I present the findings and analysis of my research in Chapter 4 by delving deep into my personal working experiences and the experiences of other Deliveroo and Uber Eats riders. In Chapter 5, I summarize my work and connect my findings to broader themes in platform labor studies, as well as identify avenues for future research. Knight 7

Chapter 2 | The Origins of Platform Labor & Algorithmic Management

This chapter comprises the theoretical framework of my research. I first explore what platforms are, and analyze the current landscape of on-demand platform service work. I then draw from Philip Agre’s capture model of privacy and explain how this theoretical model is applicable to the ways in which corporations attempt to optimize the activities of industrial and service workers (Agre 744). Next, I analyze the capture model in the context of platform labor and introduce the concept of algorithmic management. With this established, I review literature that explores how platform workers make sense of algorithms and negotiate the constant capture of their on-the-job activities. I show that much of the existing literature which explores the effects of algorithmic systems on platform workers lacks methodological diversity, and thus outline my intended contributions to the broader research area of platform labor studies.

2.1 Surveying the Platform Landscape

The rise of the modern technology industry has heralded an era in which companies increasingly label themselves as ‘platforms.’ What are platforms, and how do they function? While the etymology of the word platform stems from its architectural definition — a raised, level surface on which people can stand — its computational meaning as “an infrastructure that supports the design and use of particular applications” has become a mainstream connotation in recent years (Gillespie, “The Politics of ‘Platforms’” 349). Not only do platforms offer a technological infrastructure for designing and building other digital products, such as software applications, but they facilitate exchanges and interactions between users, thereby orchestrating multi-sided marketplaces (Gawer 1240). Yet in facilitating interactions, platforms also shape such exchanges in important ways. As Benjamin Bratton notes, platforms “set the terms of participation according to fixed protocols,” meaning that interactions between users, and between a user and the platform itself, are governed by the technical and infrastructural choices made by the platform owners (44). Due to this capacity to mediate interactions and serve as a technological infrastructure, platforms have gained prominence as the “dominant infrastructural and ​ economic model of the social web,” (Helmond 5). In this sense, by interacting on and with a Knight 8 platform, every action taken by users generates valuable data (Srnicek 99). For example, Facebook users generate valuable data every time they click the ‘like’ button, as Facebook can use these aggregated likes to serve users targeted advertisements (Helmond 4). By owning the marketplace in which users operate, platform companies occupy a prime position ​ to accumulate, analyze, and monetize data that are produced from users’ interactions (Srnicek 44). These data may be used to train and improve machine-learning algorithms, optimize production processes, sell advertisements, or support a variety of profitable activities for platform companies (Srnicek 40). In his book Platform Capitalism, Nick Srnicek identifies five varieties of platforms ​ ​ and discusses the corresponding business model for each type. These include advertising ​ platforms, such as Google and Facebook, which gather and analyze data on their users and ​ sell these data to advertisers; cloud platforms like Amazon Web Services which rent their ​ hardware and software infrastructure to other businesses; industrial platforms, such as ​ ​ ​ Siemens and Intel, which sell hardware and software that enable traditional manufacturing production to incorporate Internet connectivity (i.e, ‘the Internet of Things’); product ​ platforms, such as Zipcar, which rent their physical assets to customers; and finally, lean ​ platforms, such as Uber, Airbnb and TaskRabbit, which mediate the exchange of goods and ​ services without actually owning any of these goods or services themselves (Srnicek 49). For the purposes of brevity and applicability to my object of study, on-demand food delivery platforms, I focus on the lean platform business model. Lean platform companies first emerged in the late 2000s during an era of pivotal technological, societal, and economic change (Srnicek 85). To begin, mobile computing technology was rapidly improving; the first iPhone was released in 2007, and with its subsidized price point, more consumers than ever before could own a smartphone (Manjoo; “Demographics of Mobile Device Ownership”). At the same time, the 2008 global financial crisis was in full swing and taking its toll on working class Americans who quickly found themselves with few opportunities for secure employment (Srnicek 81). Further still, the post-crisis years were marked by widespread financial speculation, as venture capitalists were hungry for new investment vessels that could offer higher rates of return than those yielded by traditional financial investments (Srnicek 86). Thus, this confluence of factors – burgeoning technological advancements, a lack of employment opportunities for working Knight 9 class folks, and available venture capital for innovative yet high-risk companies – created the conditions for lean platform companies to emerge. Lean platform companies operate by outsourcing a majority of their business-related operating expenses (Srnicek 76). These include fixed capital assets, like software and hardware, which lean platform companies rent from other technology companies (Srnicek 83). For example, Uber uses Google’s mapping software as the backbone of its navigation system and Amazon’s Web Services for its cloud hosting (Srnicek 83). Lean platform companies also outsource their need for labor by utilizing self-employed contractors (Srnicek 76). By labeling themselves as ‘platforms,’ these companies argue they are simply intermediaries or marketplaces that connect independent providers of a service with a customer base (van Doorn, “Platform Labor” 902; Shapiro 2954). In turn, workers can be hired as self-employed contractors who are paid for each gig or task they complete rather than as employees who receive an hourly or salary wage (Srnicek 76). This designation frees platform companies from paying costly employee benefits and taxes required in traditional employment relationships, as well as offloads the responsibility of equipment supply and maintenance to contractors themselves (Aloisi 653). Indeed, classifying workers as independent contractors has stoked significant debate over the legal and ethical obligations platform companies have to individuals who use their platforms to obtain work (De Stefano 5). While this debate continues, it is essential to recognize the tenuous economic conditions American workers were facing when lean platform companies such as Uber and TaskRabbit began operating in the United States in the late 2000s. The precarious labor market conditions during and after the 2008 financial crisis allowed lean platform companies to emerge at a time when American workers were perhaps more willing to enter nontraditional employment contracts in order to make ends meet (Srnicek 81). In addition to their hyper-outsourcing of labor and business assets, lean platform companies rely on substantial up-front capital investments to sustain their operations. Indeed, the lean platform business model is premised on the assumption that a monopoly position in the market will eventually generate substantial profits (Srnicek 87). In turn, lean platform companies use venture capital financing to subsidize the cost of attempting to achieve a monopoly position by offering customers artificially low prices and recruiting workers with large sign-on incentives and earnings, all while promising investors this allegedly temporary loss will later be made up in volume (Griswold). As the potential returns for venture Knight 10 capitalists who invest in lean platform companies are significantly larger than the returns offered by traditional stock or corporate investments, there has been widespread speculation in on-demand platforms in recent years (Srnicek 86). Uber is widely recognized as the archetype of the lean platform model, and its prominence has inspired a wave of other technology companies to develop ‘gig work’ platforms on which customers hire service providers for one-off or repeat jobs (Madrigal; Alkhatib et al. 4599). Uber’s ridehailing competitors include Didi Chuxing, Grab, and Lyft operate by connecting drivers with customers at the tap of a button (Schelifer). Similarly, on-demand courier and food delivery platforms such as Caviar, Deliveroo, DoorDash, Eleme, Grubhub, Meituan, Postmates, and Uber Eats operate multi-sided marketplaces in which fleets of service workers transport restaurant meals to customers. Domestic work has also been subject to disruption, as platforms like Handy, Care.com, and TaskRabbit have established digital marketplaces where clients can find workers who will clean their homes, care for their children and complete household repairs and tasks (Mateescu and Nguyen 8). Finally, so-called ‘crowdwork’ platforms such as Upwork, Fiverr and Amazon Mechanical Turk connect clients with geographically dispersed workers who typically complete “skilled knowledge work” tasks such as copywriting, graphic design and data entry (Jarrahi and Sutherland 2). In this vein, ridehailing and food delivery are considered ‘on-demand’ platforms in that they give customers the ability to buy services immediately from undifferentiated workers, whereas domestic and crowdwork platforms require customers to pre-schedule and select the worker who fulfills a service (Ticona et al. 21). Moreover, this divergence in platform type influences the nature of platform service work in the sense that on-demand workers are more fungible than pre-scheduled workers (van Doorn, “Platform Labor” 904). As van Doorn aptly summarizes, lean platform companies must constantly recruit new workers, thereby creating a “‘surplus population’ of underemployed gig workers whose fungibility and superfluity is orchestrated through digital ​ ​ platform architectures,” (“Platform Labor” 904). In this sense, some forms of platform service work are treated as plentiful and easily substitutable (van Doorn, “Platform Labor” 904). For example, on-demand food delivery workers and ridehailing drivers are highly fungible, as it does not matter much for either the platform companies nor the customers who the worker is. Domestic service workers who obtain work through platforms, on the other hand, are less fungible as they must attain high levels of trust from clients (van Doorn 904). It Knight 11 is important to keep in mind the varying degrees of fungibility across different forms of platform service work, as it also impacts the way platform workers interact with and are impacted by algorithms, which will be discussed in greater depth in Chapter 4. In this section, I have established the origins of modern lean platforms and explored the varying nature of on-demand platform service work. In the following section, I expand upon how platform companies are able to capture valuable data from workers, and how this data capture supports the redistribution of management functions to non-human management systems.

2.2 The Rise of Workforce Capture

For on-demand platform companies, the need to manage dispersed, semi-autonomous labor forces necessitates various methods of monitoring and controlling work activities efficiently. In this section, I historicize the workforce management practices of on-demand platforms by examining precursory managerial philosophies such as scientific management and Fordism. I then introduce Philip Agre’s capture model of privacy and describe how contemporary companies capture and aggregate data about workers’ activities to remotely manage labor processes. In doing so, I attempt to illustrate the evolution of capitalist workforce management practices and establish a theoretical grounding for a deeper investigation into how platform companies use algorithmic systems to remotely manage workers. Within capitalist societies such as the United States, efforts to centralize managerial control and optimize workers’ activities have dominated the organizational calculus of industrial companies since the late 19th century (Rosenblat et al. 1). An early means of achieving these goals was introduced by Frederick Winslow Taylor, who believed that managers should scientifically study production processes (Alkhatib et al. 4965). Such scrutiny of worker performance and formalization of tasks, popularly deemed ‘scientific management,’ thus allowed companies to mitigate inefficiencies and control workers’ activities for maximum productivity (Agre 752). Industrialist Henry Ford’s approach to workforce management, now known as Fordism, similarly sought to standardize and optimize manufacturing work (Alkhatib et al. 4965). In this sense, Taylorism and Fordism not only presented novel approaches to managing production processes, but also codified bureaucratic systems of managing labor power under capitalist conditions (Braverman 90). Knight 12

Deconstructing, formalizing and monitoring the activities of blue-collar, industrial workers paved the way for the use of piecework in mass manufacturing settings. Piecework reconfigured manufacturing by segmenting work activities into discrete tasks whereby workers were paid for output produced rather than time spent working (Alkhatib et al. 4961). Critics of the Taylorization of the labor process, such as Harry Braverman, argued that the standardization of piecework in manufacturing led to the subordination and exploitation of workers, whose knowledge over the production process as a whole was now reduced to his or her knowledge over one regimented and repetitive task deemed by his or her employer as the most efficient use of labor (Braverman 80). Others argued that this shift towards piecework manufacturing required managers to exercise significant oversight of workers’ activities to ensure compliance, maximum productivity, and uniformity of output (Sewell and Wilkinson 275). Without such oversight, according to a Taylorist perspective, workers would purposefully attempt to reduce their productivity and thereby output (Rosenblat et al. 2). However, other critics of capitalist labor processes suggest that Taylorist perspectives do not consider the “organization of consent,” or the need for companies to obtain blue-collar workers’ cooperation throughout the labor process (Burawoy 27). In this sense, as Michael Burawoy argues, modern corporations do less to coerce manual laborers into achieving production targets through intensive monitoring than they do to compel workers into producing surplus labor by organizing their consent to the “legitimacy of the capitalist profit-making system,” (Zabala 282). To organize consent, firms configure labor processes such that manufacturing workers feel a certain degree of autonomy and can receive rewards for their individual effort (Burawoy 81). This configuration creates the conditions for the game of “making out,” whereby workers engaged in piecework production are financially incentivized to produce more than the required quota and employ production-maximizing strategies such as stockpiling finished goods (Burawoy 82). In other words, by organizing labor activities such that workers believe they are being presented with real choices and opportunities to ‘win,’ corporations can generate workers’ consent to production processes in which workers produce surplus labor, and thereby value, for the company (Burawoy 27). In turn, the need for companies to actively monitor workers’ production diminishes. Yet while the need for intensive monitoring may actually diminish in industrial production work through the organization of workers’ consent, corporations must still quantify and analyze the work employees do. Particularly with the evolution of Knight 13 manufacturing work in the 1980s and 1990s, in which ‘just-in-time’ production processes and lean manufacturing became the norm, the need to make workflows (and manufacturing workers) more flexible and adaptive led companies to adopt information management systems that enabled the real-time monitoring of quality and productivity (Sewell and Wilkinson 280). As the computing capabilities of such information management systems have improved, the use of software and automated systems in modern production and service industries has achieved greater ubiquity. For example, software has revolutionized modern warehousing work, as companies like Amazon have adopted complex technical systems to track workers’ activities and increase their overall efficiency (Rosenblat et al. 6). In turn, the influx of automated management systems, facilitated by the permeation of software through all facets of contemporary life, has led to the creation of “capture systems that actively reshape behavior by altering the performance of a task,” (Kitchin and Dodge 85). Kitchin and Dodge’s mention of “capture systems” references Philip Agre’s seminal concept of the capture model of privacy. In introducing this theory, Agre characterizes ‘capture’ in terms of its use in computing vernacular, referring to the act of acquiring data to be used as an input for a computational system, such as when an order is captured by an employee at a restaurant’s point of sale station (Agre 744). In turn, he defines the capture model as a linguistic metaphor that facilitates the “parsing of human activities” for representation and practical application in computational systems (Agre 744). In this sense, by capturing human activities and translating them to fit within the confines of a computer’s representational language, these activities can be analyzed and reorganized by various actors, such as companies (Agre 744). Agre juxtaposes the capture model of privacy with the surveillance model, traditionally expressed through visual metaphors of omnipresent ‘watching’ by nefarious state actors such as the Nazi-era Gestapo or the Soviet Union’s KGB (Agre 743). Thus, while both the surveillance and capture models serve as metaphorical systems for understanding information gathering practices, the capture model offers a contemporary conceptualization that takes into account modern technology’s capacity to track, represent, and modify the activities of individuals with computers (Agre 744). In theorizing the capture model, Agre argues that when human activity is captured for the purposes of computational expression, it must adhere to a specific ‘grammar of action,’ (Agre 746). We can think of grammars of action as linguistic rules that assign words to human activities, which in turn allows computers to express and connect these words or Knight 14 activities like a sentence (Agre 746). Agre identifies a cycle of five stages that occur, often concurrently, when a grammar of action is imposed upon an activity for the purposes of capturing and representing it computationally (Agre 746). The first stage of this cycle is analysis, in which an existing activity is analyzed and broken down into ontological units, ​ which may or may not be the same ontology participants of the activity use (Agre 746). Following this is articulation, in which a grammar of action is defined so the units may be ​ ​ connected to create “sensible stretches of activity,” (Agre 746). The third phase is imposition, ​ ​ during which the grammar of action is imposed upon the participants of the activity. In this imposition, participants are compelled to organize themselves so that their actions can be parsed in terms of the articulated grammar (Agre 747). Fourth, the instrumentation phase ​ takes place, in which social and technical normative forces are instituted to ensure the ongoing activity can be continuously captured and parsed (Agre 747). Finally, in the elaboration phase, these captured activity records can be statistically analyzed and monitored ​ en masse for quality control, performance tracking, and error detection purposes (Agre 747). The capture model cycle reveals a truly cybernetic process in that both the grammar of action and the activity system that has been grammatized undergo constant revision (Agre 743). Such revision is necessary, because even when highly technical systems and sophisticated grammars of action are employed to capture human activities, humans will continuously interpret, develop workarounds, and circumvent such systems (Agre 748). More importantly, the capacity for people to interpret and circumvent capture is a desired effect by those who make use of this captured data, as it allows for the creation of new efficiencies. In this vein, the capture model is reminiscent of Nikolas Rose’s concept of ‘governing through freedom,’ in that the self-governing and self-enterprising capabilities of individuals upon whom a grammar of action is imposed actually support the consolidation of power and control for those who implement the grammar (Rose 147). If we view the capture model in the context of workforce management, a clear technological evolution emerges from the Taylorist and Fordist managerial philosophies that preceded it. The segmentation of tasks, as well as the introduction of piecework and worker monitoring systems in the early and mid-twentieth century, paved the way for more sophisticated computer-aided methods of capturing and reorganizing blue collar workers’ activities through technical means such as location tracking devices or identity cards (Agre 749). However, where the capture model clearly diverges from traditional Taylorist and Knight 15

Fordist managerial practices is in its reflexiveness; under Taylorism, workers had little to no flexibility to negotiate the imposed task, whereas workers who engage in captured activities can take an “infinite variety of sequences of action,” so long as these sequences fit within the prescribed grammar of action (Agre 752). This difference is important, because it concerns the ways workers are disciplined and their activities reorganized. As Agre notes,

“Capture does not require that control be exercised through the fragmentation of jobs and the a priori specification of their forms. Instead, ​ capture permits work activities to be disciplined through aggregate measures derived from captured information,” (752).

In a labor context, then, a worker is not disciplined by their manager for individual instances of non-compliance with a prescribed work method, but rather by the computationally assigned meaning to the aggregated measurements of their work (Agre 752). It is in the elaboration phase that workers’ captured activities are scrutinized, which supports the development of ‘tighter’ capture systems overall. As will be discussed in Chapter 4, the capacity for software powered by algorithms to carry out the elaboration phase of capture is crucial for on-demand food delivery companies in their efforts to manage riders from afar. Furthermore, Chapter 4 will analyze the capture model in the specific context of Deliveroo and Uber Eats, and explore how each platform grammaticizes their respective on-demand food delivery activity systems. In this section, I have established the historical evolution of workforce management practices that aim to reorganize workers’ activities for the purposes of efficiency and control. Furthermore, I have introduced Agre’s capture model of privacy and differentiated its reflexive nature from earlier workforce management philosophies in an effort to demonstrate how the introduction of software has revolutionized modern workforce management. In the following section, I analyze capture in the context of platform labor generally and explore the ways in which platform companies use capture systems and algorithms to track and manage platform service workers. Knight 16

2.3 Algorithmic Management in Platform Labor

As the capture of industrial and service work activities has become increasingly common, platform companies have embraced algorithms as tools to make sense of and act upon this captured data. In this section, I define what algorithms are, introduce the concept of algorithmic management, and review literature that explores how platform companies operationalize control of their fragmented workforces through algorithms. In turn, I discuss how algorithmic systems help platform companies monitor workers, evaluate performance, enforce automated decisions, and leverage indirect communication channels in their efforts to manage platform workers. In a basic computational sense, an algorithm is “a series of steps undertaken in order to solve a particular problem or accomplish a defined outcome,” (Diakopoulos 3). When considered in a broad sociological perspective, algorithms are computational objects that can condition how humans consume information and make decisions (Kitchin and Dodge 109). Yet for the most part, both an algorithm’s presence and its effects are largely invisible to human users despite near daily interaction with algorithms, such as in one’s Facebook newsfeed (Eslami et al. 153). Algorithms, like other highly sophisticated computational technologies, are frequently described as ‘black boxes,’ because their technical complexity prevents the average user from understanding how they operate (Diakopoulos 13). Moreover, companies whose business models depend on algorithms, such as on-demand platform companies, typically refuse to disclose the ‘rules’ their algorithms follow, claiming such information constitutes a proprietary trade secret (Möhlmann and Zalmanson 5). When thinking critically about algorithms and their effects in society, it is crucial to avoid a technologically deterministic view. While algorithms may be discussed as abstract or black box-like, the “warm human and institutional choices that lie behind these cold mechanisms” must always be taken into account (Gillespie, “The Relevance of Algorithms” 169). Furthermore, when attempting to determine the power algorithms possess in governing and ordering the social world, one cannot divorce the technical properties of algorithms from their social processes (Beer 4). In other words, a critical study of algorithms requires nuanced consideration of how algorithms are intertwined with the social world they are coded in, and the ways in which human social power is exerted through algorithms (Beer 4). Particularly within the growing body of research that examines the role of algorithms in shaping labor Knight 17 processes, scholars have correctly called for a reconceptualization of algorithms as “algorithms in everyday labor,” which take into consideration the “human and non-human, as well as technical and social” elements that form algorithms (Sun 3). In the context of platform labor, the capacity for computer software powered by algorithms to enforce managerial decisions on gig workers has gained significant attention from policymakers and scholars alike. Scholars at Carnegie Mellon University’s Human-Computer Interaction Institute have introduced the phrase ‘algorithmic management’ to describe the ways in which gig work platforms deploy data capture technologies and computer software to systematically monitor and evaluate workers (Lee et al. 1603). In this ​ ​ sense, by designing and redistributing management functions to algorithmic systems, platform companies are changing the experience of work itself (Ticona et al. 20). Multiple factors characterize algorithmic management in the context of platform labor. To begin, algorithmic systems that carry out managerial decisions depend upon the constant input of information to function, which in turn requires platform companies to implement technical data capture infrastructures to track and analyze workers’ activities (Mateescu and Nguyen 13; Möhlmann and Zalmanson 4). As Agre notes, in the age of ​ ​ captured work, tracking occurs when an individual completes a causal chain between the tracked entity — in this case, the worker providing a service — and a centralized computational system (Agre 742). In turn, as platform companies seek to remotely manage and coordinate their workforces, they must collect enormous amounts of data on workers’ activities, which can run the gamut from an Uber driver’s acceleration or braking patterns (Ticona et al. 22) to an UpWork freelancer’s keyboard presses and mouse movements (Wood et al. 64). Tracking workers with the use of GPS technology is a quintessential way platform companies collect data on workers’ activities. For example, on-demand food delivery workers have their location constantly tracked while working, thus providing the company with data about workers’ mobility patterns and time spent on each step of the grammaticized delivery process (Ivanova et al. 23). The computational parsing of these captured activities subsequently allows platform companies to catalog them, extrapolate meaning from them with the use of algorithms, and restructure work tasks in ways that benefit a company’s bottom line. By capturing and collecting data on a worker’s activity in any given situation, platform companies can also constantly evaluate a worker’s performance (Möhlmann and ​ Knight 18

Zalmanson 4). With some on-demand platforms, customers take a central role in evaluating ​ worker performance. Rosenblat and Stark note that Uber passengers assume the role of quasi-managers by rating drivers’ performance out of five stars (Rosenblat and Stark 3774). With this rating as an input, platforms can then utilize algorithms which take into account a driver’s cumulative rating and, if necessary, discipline them based on this aggregated information (Agre 752). To be clear, human managers within platform companies determine the performance thresholds upon which a platform’s algorithms will act, such as Uber’s decision to remove drivers from its platform if they maintain less than a 4.6 out of 5 stars rating (Rosenblat and Stark 3774). Moreover, if these thresholds are communicated to platform workers, which they often are not, their enforcement varies across regional markets, as platform companies depend upon adequate labor ‘reserves’ to meet demand (Veen et al. 12). By obscuring the parameters and consequences of their performance management systems, platform companies can utilize captured customer ratings as “a bureaucratic control lever” to “elicit particular behaviors” from workers (Veen et al. 12). Thus, by using technical systems to impose a grammar of action, such as a five-star rating system, and capture the rating, platforms can then use algorithms to automate workers’ performance evaluations and enforce disciplinary actions at will. A platform worker’s performance is similarly evaluated through the use of aggregated ratings of a worker’s compliance with a platform’s requests or requirements. For example, Lyft and Uber drivers are encouraged to maintain high acceptance rates of incoming ride requests (Lee et al. 1605). While both companies have been known to deactivate riders with low acceptance rates (without informing riders up front of any acceptance rate requirements), recent reports reveal that Uber and Lyft have stopped enforcing acceptance rate deactivations to avoid appearing as true employers (“Have You Been Sidelined by Uber?”). Instead, Lyft drivers with acceptance rates below ninety percent over a week-long period can be disqualified from receiving certain bonuses and pricing incentives, and Uber drivers who decline multiple rides in a row may be temporarily logged out of the app (Campbell). So-called ‘Taskers,’ or service workers who complete home repair and household tasks on the platform TaskRabbit must maintain an acceptance rate of at least seventy five percent, and falling below this threshold reduces a Tasker’s ability to appear in the recommendations section of the client-facing app (“Acceptance Rate”). In this manner, the algorithmic ranking Knight 19 and rating of workers based on their adherence to a platform’s desired behaviors can significantly influence how platform workers conduct themselves while working. Certainly, all service jobs require workers to adhere to rules and standards or otherwise risk punitive consequences. However, the use of algorithms to automate such punishments enables platform companies to enforce highly subjective judgement calls that promote platform companies’ profit maximization at the detriment of the worker. For example, platforms such as Handy frequently charge workers with steep fines for cancelling appointments less than forty-eight hours in advance, even if the reason for cancellation was no fault of their own (Ticona et al. 32; van Doorn, “Late for a Job”). In turn, situations in which human managers may be more lenient with punishments become opportunities to “identify the weakest link [...] and cull workforces,” as the decision to punish is no longer in the hands of a human, but rather an algorithm (Mateescu and Nguyen 17). Indeed, this culling potential does not impact all platform service workers equally. For example, on-demand food delivery riders whose labor is considered abundant are more easily replaced than in-home care workers, who are considered less fungible due to the highly personal nature of their work (van Doorn, “Platform Labor” 904). Platform companies do permit workers to appeal these decisions, but routinely make use of indirect communication channels, automated responses, and outsourced customer service representatives (van Doorn, “Platform Labor” 903). This allows platform companies to avoid any direct interaction with workers, as well as puts the onus on workers to spend additional unpaid labor hours advocating for themselves (van Doorn, “Platform Labor” 903). In this manner, platform companies often attempt to absolve themselves of responsibility for harsh decisions by blaming their own algorithmic systems, essentially dissolving “their authority into the disinterested medium of a software program,” (Tomassetti 46, qtd. in van Doorn, “Platform Labor” 903). Although human managers at platform companies are the ones delineating the rules that govern workers’ activities, they make use of algorithms within their management decisions to obfuscate their role and justify the ways in which subjective managerial rules are enforced. In this section, I have outlined the ways in which algorithmic management tactics enable platforms to track and monitor workers’ activities, evaluate performance, and enforce disciplinary actions en masse. At the same time, however, platform workers maintain a significant degree of autonomy and freedom within this capture system. In the next section, I Knight 20 review literature that discusses how platform workers make sense of and negotiate algorithmic management tactics.

2.4 Developing Algorithmic Competencies

While platform workers may not know the inner workings of the algorithmic systems which govern their work processes, they do develop nuanced perceptions of how these algorithms affect their working lives. In this section, I review existing literature that explores how platform workers attempt to make sense of the algorithms they come into contact with while working. In doing so, I establish the research context of my study and outline my intended contribution. The ways in which platform workers experience, engage with and negotiate algorithms while working has received increased attention in recent years. Much of the academic research in this area has focused on how drivers for ridehailing platforms, namely Uber and Lyft, are impacted by algorithmically-assigned work (Lee et al.; Rosenblat and Stark; Ticona et al.; Möhlmann and Zalmanson). Similar studies of so-called “crowdworkers” on platforms such as UpWork and Fiverr have sought to evaluate the extent to which algorithmic control influences remote platform workers’ behaviors (Wood et al.; Jarrahi and Sutherland). Within the scope of on-demand food delivery work, recent publications have explored the influence algorithms exert on the experiences of on-demand food delivery workers in China (Sun), Australia (Veen et al.), Philadelphia, USA (Shapiro), and Berlin, Germany (Ivanova et al.). As these aforementioned studies make quite clear, the experience of working under algorithmic management is not a passive one and workers do not simply acquiesce to control operationalized through algorithmic systems (Shapiro 2965; Veen et al. 12). Platform workers adapt and leverage algorithmic working environments based on their functional understandings, whether consciously or unconsciously formed, of how a platform’s algorithms work (Jarrahi and Sutherland 2). Agre reminds us that individuals who participate in captured activities are likely to adjust their conduct “based on their understanding of what will become of the data and what this entails for their own lives,” (Agre 748). In this sense, platform workers who develop these understandings not only calculatively adjust their behaviors to maximize their own financial earnings while working, but also qualitatively weigh past experiences and their own “sense of moral economy” when deciding to accept a Knight 21 gig (Shapiro 2967). In this manner, workers engage in “on-the-job bodily and affective” sensemaking of a platform’s algorithms based on accumulated work experiences (Shapiro 2966). Multiple studies of platform labor have analyzed workers’ sensemaking practices as they negotiate a platform’s use of algorithmic management tactics. Since platform workers operate in working environments in which “few work rules are communicated outright” but rather “enforced through indirect and automated means,” they often infer how a platform’s algorithms function by experiencing the effects such algorithms have on their work (Mateescu and Nguyen 12). Such sensemaking practices are evident among Uber drivers who were found to prioritize maintaining a high acceptance rate after learning one’s acceptance rate substantially influences the quality of future rides in terms of price and distance (Lee et al.; Rosenblat and Stark). Similarly, Jarrahi and Sutherland identified sensemaking activities among UpWork freelancers who reported registering for the client-facing side of the site in an effort to better understand and improve their ranking positions on the platform (5). In the context of food delivery, Sun noted the communal sensemaking practices of food delivery workers in China who utilized the social network WeChat to share platform-specific information and strategies with one another (13). By attempting to make sense of the constantly changing algorithmic systems that platform companies use, workers can develop functional understandings of the effects a platform’s algorithms have on their work. Moreover, platform workers’ sensemaking practices allow them to develop methods of manipulating and appropriating a platform’s algorithmic systems to suit their own needs (Jarrahi and Sutherland 8). These “algorithmic competencies” result from a person’s repeated interactions with algorithms and subsequent construction of a “data infrastructure literacy,” (Jarrahi and Sutherland 9). For example, an Uber driver may notify a passenger that they have arrived before actually arriving in order to encourage the passenger to walk outside earlier, thereby reducing the driver’s unpaid waiting time (Ticona et al. 31). Similarly, an UpWork freelancer may split a large project into multiple small projects in an effort to accumulate more ratings and rank higher in the platform’s algorithmically-determined search results (Jarrahi and Sutherland 8). More practically, workers may learn to ignore certain algorithmically-mediated cues, as illustrated by delivery workers in China who used their own knowledge to navigate from restaurants to customers rather than follow the route recommended by the app (Sun 12). Thus, as platform companies modify their algorithmic Knight 22 management systems based on the feedback loops supported by the platforms’ capture of workers’ activities, so do workers, who continually adjust their competencies to cope with these changes. In this vein, the reflexive manner in which competencies are formed represents the continuous work done in the elaboration phase of Agre’s capture model (Agre 747). Platform workers’ development of algorithmic competencies resembles an iterative learning process in which workers continuously adjust their activities depending on the modifications platform companies make to their algorithmic management systems. Many researchers have utilized ethnographic research methods, such as semi-structured interviews, to uncover how platform service workers engage in sensemaking and develop algorithmic competencies. Indeed, such a methodological approach allows researchers to gather qualitative data and compare workers’ competencies across platforms, as Ivanova et al., Lee et al., Shapiro, Sun, Veen et al., and Wood et al. have done. However, to the best of my knowledge, there have been no studies to date that purposefully utilize autoethnography in combination with semi-structured interviews to compare workers’ resilience and resourcefulness across platforms. To be sure, Shapiro did participate in food delivery work in his study of on-demand food couriers in Philadelphia (2958). However, Shapiro did not highlight or emphasize his subjective experiences as a courier, and instead characterized his participation in food delivery work as means of collecting “ethnographic observations,” (2958). Thus, I seek to add to contribute to the growing body of research that analyzes the experiences of on-demand food delivery workers by triangulating the perspectives of workers with my own autoethnographic reflections and existing theoretical frameworks, such as Agre’s capture model. Incorporating autoethnography within platform labor studies is valuable because it allows researchers to experience for themselves the working environments they seek to understand. In this section, I have established the theoretical basis of my study and situated my intended contribution to the growing body of literature that seeks to understand the dynamics of algorithmic managerial control in the context of platform labor. In the section that follows, I outline my methodology and qualitative research design.

Knight 23

Chapter 3 | Methodological Framework

To understand how the redistribution of management functions to algorithmic systems has impacted the work experiences of on-demand food delivery riders in Amsterdam, I employ a multimethod qualitative research design. In this chapter, I first present an overview of the platform-mediated food delivery industry and situate Deliveroo and Uber Eats within this landscape. Following this, I discuss my research methods, which include the use of autoethnography, semi-structured interviews with riders, and secondary research. I also review my method of analysis and coding procedure. Afterwards, I explain how I began working with Deliveroo and Uber Eats, outline my interview protocol, review participant recruitment strategies, provide an overview of the participants, and discuss steps taken to ensure the ethical treatment of participants. Finally, I review the limitations of my research design.

3.1 The Current Landscape of On-Demand Food Delivery Platforms

On-demand food delivery platforms have emerged quite recently within the broader industry of delivering restaurant meals to customers. According to McKinsey & Company, the traditional model of delivery, in which customers place their order directly with a restaurant and the restaurant manages the delivery process, is still the most common globally (Hirschberg et al 1). However, shifting consumer preferences and technological developments have facilitated the platformization of the food delivery industry (Hirschberg et al. 1). Online ‘aggregator’ platforms such as Grubhub and Just Eat developed first in this space by creating platforms that allow customers to order from a variety of restaurants through a single online portal or app (Blumtritt 2). In this manner, aggregator platforms offer restaurants access to more customers and automate the order process, but the restaurants still manage the logistics of delivering the food (Blumtritt 2). More recently, on-demand platforms that manage the logistics of delivering food while also functioning as aggregator platforms have gained a small portion of the food delivery market. These on-demand food delivery platforms have enticed higher-end restaurants to utilize the flexible delivery workforces offered by the platforms and participate in on-demand delivery (Hirschberg et al. 2). While consumer trends indicate that customers prefer on-demand delivery, the substantial capital investments required to sustain lean Knight 24 platform companies operating in this space has largely prevented the on-demand delivery model from capturing a larger share of the overall food delivery market (Vergauwen and Akkermans). Within the Netherlands, Thuisbezorgd (the Dutch name for Takeaway), Deliveroo and Uber Eats are currently the only on-demand food delivery platforms. Thuisbezorgd, which was founded in the Netherlands in 2000, is the market leader and most established player (“Takeaway.com Annual Report 2017”). However, the company remains primarily an aggregator platform, as less than two percent of its total orders are completed by Thuizbezorgd’s own riders in the Dutch market (“Takeaway.com Annual Report 2017” 41). Additionally, Thuisbezorgd riders are employed directly by the company and are paid hourly wages, whereas Deliveroo and Uber Eats riders are self-employed contractors paid piece-rate wages for each order delivered. Thus, for the purposes of this research, I compare Deliveroo and Uber Eats due to their similar business models and operating practices in the Netherlands.

3.1.1 Deliveroo

Deliveroo was founded in London in 2013 and as of 2019, offers delivery in more than five hundred cities around the world (“Ambitieuze plannen”). The company entered the Dutch delivery market in September 2015 and reports to work with more than two thousand riders in the Netherlands (“Over Deliveroo”). As a lean platform company, Deliveroo relies on massive venture capital investments to subsidize the true cost of its operations in its efforts to monopolize markets. These venture capital investments allow Deliveroo to set artificially low delivery fees to attract new customers when it enters new geographic markets, as well as spend heavily on marketing and recruitment efforts to attract new riders. Since its founding, Deliveroo has raised more than one and a half billion dollars from venture capital investors, including a $575 million investment from Amazon in May 2019 (“Deliveroo - Crunchbase”). While Deliveroo’s revenues doubled in 2017, its overall financial losses before tax rose to nearly $240 million due to the company’s expansion into new markets and higher operating expenses (Ram and Hodgson). Despite this, Deliveroo is rumored to be exploring an initial public offering in 2020 (Boland). Deliveroo’s business model resembles a multi-sided marketplace in which it must manage customers’ demand for food, riders’ supply of labor, and working relationships with Knight 25 partner restaurants. Customers can order from a selection of “high-quality and diverse” restaurants via the Deliveroo mobile app or website (“Deliveroo FAQ”). Once the customer places an order with a restaurant, Deliveroo’s order algorithm, which the company calls “Frank,” matches the order with a rider who either accepts or rejects the order request (“Meet Frank!”). Throughout the delivery process, riders must notify the rider app when they complete certain actions, including when they arrive at the restaurant, when they collect the order, when they are approaching the customer’s address and when the delivery is completed (“Tech round-up”). These stages indicate Deliveroo’s grammatization of the delivery process and efforts to capture riders’ work activities for subsequent computational parsing, which will be expanded upon further in Chapter 4. In addition, Deliveroo utilizes a shift booking system which requires riders to book one-hour shifts in a city’s various booking zones one week in advance (“Self-serve booking”). Riders who provide “the most consistent, quality service” are given early access to reserve shifts in the system. This early access is determined by a rider’s ‘statistics’ which are based on the percentage of booked sessions a rider attends (attendance rate), the number of sessions the rider has worked during the busiest periods (super-peak participation rate), and if a rider cancels a booked session less than twenty four hours before it starts (cancellation rate) (“Self-serve booking). In turn, Deliveroo utilizes an algorithm to rank riders based on these statistics, which will be further explored in Chapter 4. Finally, the employment status of Netherlands-based riders has risen as a point of contention for Deliveroo in recent years. In the past, Deliveroo employed Netherlands-based riders directly, but has since reclassified riders as self-employed contractors (“Deliveroo ​ FAQ”). With this employment classification, riders must register with the Dutch Chamber of Commerce and pay business taxes if they earn more than €596 in a four week period (“Regular Riding”). As of August 2018, all Deliveroo riders earn money according to a distance fee model which compensates riders based on the distance and time needed to complete a delivery (“Distance Fees”).

3.1.2 Uber Eats

Uber, the on-demand car service platform based in San Francisco, launched Uber Eats in late 2015 in select cities in the United States and Canada (Hempel). The company’s existing international infrastructure allowed the service to quickly expand to cities around the Knight 26 world, such as in Amsterdam, where Uber Eats began operating in 2016 (van den Outenaar). Currently, Uber Eats works with more than twelve hundred restaurants in the Netherlands, and aims to double this offering as it aggressively expands in large and medium-sized Dutch cities (“Uber Eats Plans”). The experience of riding with Uber Eats is very similar to that of Deliveroo, with a few key differences. To begin, Uber does not require riders to reserve sessions in advance. Rather, riders can simply go online at any time and, depending on consumers’ demand, will receive order requests from nearby restaurants (“How do I receive delivery requests?”). Another difference concerns the information Uber Eats riders receive prior to accepting an incoming order request. When Deliveroo riders receive a new delivery request, they are informed of the drop-off location and the fee they will receive for delivering the order. Uber Eats riders, on the other hand, only learn the delivery location after they confirm they have collected the order from the restaurant, and payment information is displayed after the delivery is completed. These information asymmetries and their effects on riders’ work will be further investigated in Chapter 4. Uber’s reliance on investment capital is very similar to Deliveroo’s, albeit at a much greater scale. In May 2019, Uber became a publicly traded company following a highly anticipated initial public offering that led to the company’s current valuation of $82 billion (Davies and Wong). Despite this valuation, however, Uber has only once turned a quarterly profit in its ten years of operations, and has sustained net losses of almost $6 billion in the past two years alone (Isaac and Conger). Uber has not revealed any losses associated with its Uber Eats business specifically, but has self-reported that Uber Eats’ revenues grew by nearly one hundred and fifty percent in 2018 and constitute thirteen percent of the company’s overall revenue (Newcomer). In the competitive on-demand food delivery market, Deliveroo and Uber Eats face numerous challenges in terms of profitability, competition, and appeasing its myriad of stakeholders. Particularly within the Netherlands, where aggregator platform Thuisbezorgd maintains a dominant position, Uber Eats and Deliveroo face significant market pressure to expand their operations while keeping costs low for customers and earnings high for riders. This overview of the on-demand food delivery landscape in the Netherlands has sought to establish the technical and operational contexts in which Deliveroo and Uber Eats riders work. In the following section, I outline my qualitative research design. Knight 27

3.2 Qualitative Research Design

This qualitative research design makes use of semi-structured, in-depth interviews in conjunction with autoethnography to gather information on the working experiences of Amsterdam-based food delivery riders. As Taina Bucher recognizes, “accessing people’s personal stories and experiences with data and algorithms can be tricky,” (32). Indeed, algorithms are intangible and largely invisible to those who come into contact with them (Bucher 31). Yet in this invisibility, algorithms hold tremendous power; as Eslami et al. note, algorithms influence how individuals receive information and can affect how they behave (153). This power is deserving of interrogation, especially considering the increasingly influential role algorithms play in fundamental parts of life, such as one’s work. To access the personal stories and perspectives of food delivery riders in Amsterdam, I follow in the methodological footsteps of platform labor scholars (see Ivanova et al., Jarrahi and Sutherland, Lee et al., Möhlmann and Zalmanson, Rosenblat and Stark, Shapiro, Sun, Ticona et al., Veen et al., and Wood et al.) who utilize qualitative, semi-structured interviews and ethnographic participant observation research methods. Where I diverge from these previous studies, however, is in my intentional use of autoethnography as opposed to ethnographic and participant observation research methods. Participant observation, in its traditional scope, concerns a researcher’s presence in a social group or setting for the purposes of scientific investigation (Schwartz and Schwartz 344). In doing so, the intended outcome of both observing and participating is the objective and empirical generation of “human understanding” about that particular group’s behaviors (Tedlock 70). Autoethnography, on the other hand, subverts the notion of participant observation by emphasizing the researcher’s own “observation of participation,” (Berger 506). In this sense, autoethnography allows researchers to reflexively interrogate their own subjectivity, emotionality, and influence on their research, thus resulting in a more narrative than scientific assessment (Ellis et al. 274). Moreover, autoethnography as a research method uses the researcher’s own embodied experiences and perspectives to explore and problematize broader theoretical claims, as the researcher’s experiences must constantly be in dialogue with existing theoretical concepts (Ellis et al. 276). In my view, the aforementioned studies provide important theoretical and empirical contributions with regard to the nature of platform work. Yet what these studies seemingly Knight 28 avoid is discussion of the affective, highly subjective, and individualized nature of platform work – and particularly on-demand food delivery work – from a first-person perspective. From my experiences as a rider, I found food delivery work in Amsterdam to be isolating and solitary. Long stretches of cycling alone were dotted with brief interactions with restaurant staff, customers, and the occasional fellow rider. Thus, pursuing participant observation as a research method in the context of on-demand food delivery work is pragmatically challenging. Not only is it difficult to observe the experiences and behaviors of riders who are constantly in motion as they work, but it is nearly impossible to participate in any larger social group, as this collective work dynamic does not exist for riders in Amsterdam. Therefore, to explore how the redistribution of management functions to algorithmic systems has impacted the nature of work for food delivery riders in Amsterdam, it was necessary to experience the work for myself. In doing so, I could observe and analyze how I, as a rider, was impacted by this redistribution, as well as compare my experiences with those of other riders whom I interviewed. Finally, I could triangulate my experiences and those of other riders with existing theoretical concepts, all in an effort to most effectively investigate my research question. Autoethnography as a social science research method is not without its own politics. Some scholars seek to distance autoethnography from the positivist tendencies of contemporary ethnographic research, arguing instead that autoethnography “was designed to be unruly, dangerous, vulnerable, rebellious and creative,” (Ellis and Bochner 433). In this view, autoethnography offers a highly aesthetic and intimate method of inquiry that allows researchers to acknowledge and give importance to their own personal and interpersonal experiences that result from their participation in a social group or setting (Ellis et al. 277). Another camp of scholars, however, believe that autoethnography should be considered as a method for gathering “empirical data to gain insights into some broader set of social phenomena,” (Anderson 387). In this sense, so-called analytic autoethnography requires researchers to demonstrate a “commitment to an analytic agenda,” (Anderson 387). Ultimately, the debate between these two epistemological views centers upon how the self should be positioned, and if this positioning should be expressed in an evocative, artistic narrative or more sociological, scientific manner (Ellis and Bochner 438). My use of autoethnography falls somewhere in between these two schools of thought. While participating in food delivery work over the course of five months as a rider for Knight 29

Deliveroo and Uber Eats, I recorded field notes and took many app screenshots. These field notes took into account quantifiable components of my work as a rider, such as the number of deliveries made, earnings per order, and restaurant waiting times, but also my own emotional responses and interpretations of personal interactions with other riders, customers and restaurant staff. In this sense, my intention was document moments that would provide insight into the broader working experiences of food delivery riders in Amsterdam, as well as produce “aesthetic and evocative thick descriptions of personal and interpersonal experiences,” (Ellis et al. 277). Thus, situating myself within my field notes was not a “decorative flourish” nor “exposure for its own sake,” but rather an essential component of my field research (Behar 13-14). In addition to autoethnography, I conducted semi-structured, in-depth interviews with riders to learn how they experience their work and how, if at all, they perceive each platform’s use of algorithms. Semi-structured interviews allow researchers to gather “descriptions of the life world of the interviewee in order to interpret the meaning of the described phenomena,” (Kvale and Brinkmann 3). The semi-structured aspect of such an interview relates to both the format and flow of the interview, meaning that the interviewer prepares questions which target certain issues, as well as conducts the interview in a manner that is “flexible enough for interviewees to be able to raise questions and concerns in their own words and from their own experiences,” (Brinkmann 285). In this vein, semi-structured interviews can resemble a conversation between the researcher and interviewee, which requires the researcher to maintain a significant degree of reflexivity in their role as a co-constructor of meaning (Heyl 370). Semi-structured interviews were best suited for my purposes as they allowed the participants and I to engage in thoughtful, free-flowing dialogue while still ensuring specific topic areas within my broader research area were addressed. Furthermore, all interviews were conducted face-to-face, which allowed me to witness participants’ non-verbal cues and facial expressions, thus providing for a richer interview experience (Brinkmann 290). Lastly, I conducted primary and secondary research by consulting online resources, conducting interface analyses of each platforms’ rider applications, and communicating directly with Uber Eats and Deliveroo rider support representatives. In particular, I consulted Deliveroo and Uber Eats’ rider ‘Frequently Asked Questions’ webpages to gather publicly available information about each companies’ policies, payment models, and algorithmic Knight 30 systems (“FAQ English”; “Frequently Asked Questions from Delivery Partners”). I also consulted various online forums, such as Deliveroo and Uber Eats rider Facebook groups and Reddit forums, to familiarize myself with challenges faced by riders in other locations. Finally, I communicated directly with Deliveroo and Uber Eats rider support representatives via email to ask clarifying questions, and conducted interface analyses of each platforms’ rider applications to compare how they grammaticize the activity system that is food delivery. To analyze these complementary qualitative data, I utilized the constant comparative method developed by Barney Glaser. The constant comparative method is a systematic coding procedure used to support the generation of theory (Glaser 437). This method allows researchers to systematically interpret qualitative data, such as interview transcripts and autoethnographic reflections, categorize and code the data to identify meaning and patterns, and connect these patterns to theoretical arguments (Boeije 393). I first conducted close readings of each interview transcript and embarked on the process of open coding in which I assigned summative labels to sections of the transcripts that represented a phenomenon (Tracy 189). This coding process was iterative and reflexive, meaning each transcript was read through and coded multiple times, and the codes were modified in order to accurately capture phenomena in the data (Tracy 189). I also created a coding index to summarize the meaning of each code, and wrote research memos in which I recorded reflections on the coding process, thus allowing me to capture the reasoning behind coding decisions made (Glaser 440). Following this, I compared the phenomena identified in the interview transcripts with my autoethnographic reflections and information gathered from my primary and secondary research. In this sense, I utilized the themes established from the interview transcripts analysis to construct an evocative and sensory narrative stemming from my autoethnographic reflections. Chapter 4 of this thesis illustrates the interweaving of themes from participants’ interviews with my own experiences as a food delivery rider. I turn, I use these narrative expressions to support and problematize existing literature regarding the redistribution of management duties to algorithmic systems.

3.3 Onboarding Process

To begin delivering for Deliveroo and Uber Eats, I signed up for onboarding sessions at the companies’ respective offices. For Deliveroo, this process entailed registering online Knight 31 and providing contact information, followed by an online test in which I watched informational videos and answered multiple choice questions. Upon completing the test, I was invited to schedule an appointment at the Deliveroo office. In this meeting, I gave my passport information to the onboarding coordinator, signed an “assignment agreement” which stipulated my contractual status as a rider, and ordered my complementary Deliveroo riding gear, which included a backpack, jacket and helmet. The entire meeting lasted around fifteen minutes and there were only two other new riders in the meeting. The Uber Eats onboarding process followed a similar process. I first registered online and made a “driver” account, as Uber aggregates its onboarding process for both its Uber Eats and Uber ridehailing operations. Following this, I visited Uber’s Amsterdam Greenlight Hub where I completed an online video-based test. Following the test, I was instructed to fill out my value added tax (VAT) registration form, as Uber requires all riders to be classified as self-employed contractors. With Deliveroo, I was able to register without a VAT as long as I did not exceed €596 in earnings in a four-week period. After completing my VAT ​ registration form, an Uber employee reviewed the form and asked me to modify some of my responses. After revisions, the employee stuffed my VAT registration form into a pre-addressed envelope which was sent to the Dutch Tax and Customs Administration Office. Following this, I met with another employee who verified my passport. She also gave me an Uber Eats-branded thermal backpack which cost €50. I questioned the necessity of buying the bag, as I already had a Deliveroo-branded backpack, but the employee informed me it was non-negotiable, which struck me as odd considering my independent contractor status. Four weeks later I received my VAT number in the mail from the tax authorities and was able to begin riding with Uber Eats.

3.4 Interview Protocol

Prior to interviewing participants, I developed an interview protocol and questions. Interview questions were structured to interrogate seven key areas and were inspired by Jarrahi and Sutherland’s interview protocol (4). The areas of inquiry focused on: 1. A rider’s general background and experience with Deliveroo and/or Uber Eats; 2. Riders’ perceptions of their personal statistics (for Deliveroo) and/or ratings (for Uber Eats); 3. Strategies, tactics, or ‘tricks’ riders use (if any) while working, and why; Knight 32

4. What riders know about the platforms’ algorithms, namely Deliveroo’s ranking algorithm which determines access to the shift booking system, as well as both platforms’ order assignment algorithms and pricing algorithms. Moreover, this area probed how riders believe these algorithmic systems function and how they interact with these algorithmic systems; 5. What riders think about communicating with Deliveroo and/or Uber Eats rider support; 6. What riders’ interactions with other riders are like; 7. How riders experience the rider app interface(s). As interviews were completed, I revised my list of questions and honed in on topic areas that were found to stimulate participants’ thoughts on Deliveroo or Uber Eats’ use of algorithms. Each interview was recorded in full, and I took notes during the interviews to ensure “better recollection of the body language, the atmosphere, and other non-transcribable features of the interaction,” (Brinkmann 290). All interviews were conducted in English and transcribed word-for-word with the exclusion of filler words such as ‘like’ and ‘um.’ Interviews lasted fifty-one minutes on average.

3.5 Rider Recruitment Strategies

To recruit riders for interviews, I employed a convenience sampling method. Convenience sampling is typically used when attempting to recruit ‘hard-to-reach’ populations and involves recruiting participants “who are readily available and accessible to the researcher,” (Abrams 542). Considering the sensitive employment status of riders, who could be deactivated from the platforms for their participation, convenience sampling was the most practical method for recruiting riders who were comfortable with the potential risks. I primarily recruited riders while working, particularly in moments when we were waiting for our orders to be prepared. I made small talk and depending on the other rider’s level of engagement, I told them about the study. Once a fellow rider expressed interest in participating, we exchanged contact details and later coordinated the time and location of the interview. I also recruited participants while not working by canvassing outside busy delivery restaurants. I explained the study to riders who were waiting for their orders and took down riders’ phone numbers on a clipboard. While this recruitment strategy did result in one interview, riders expressed much more skepticism about my intentions and affiliation. To Knight 33 ease concerns, I provided riders with condensed versions of the information brochure and wore my Deliveroo-branded jacket. These two tactics resulted in the initial recruitment of twenty four riders. However, only seven riders responded to my messages and phone calls to coordinate interviews, thus indicating a significant drop-off point in the recruitment process. Lastly, I attempted to recruit riders online by posting in private Facebook groups for Uber Eats riders in the Netherlands and on r/deliveroos, a subreddit on Reddit.com which Deliveroo riders around the world use to communicate. Prior to posting in these forums, I obtained approval from the groups’ administrators and disclosed my researcher status. While I was unable to recruit any riders with this strategy, these online forums did provide a wealth of information which I used in the initial exploratory research phase of this study.

3.6 Overview of Participants

Seven individuals in total were interviewed. The table below presents an overview of the participants.

Participant Age Primary Secondary Avg. Hours Tenure with Bike Type Pseudonym Range Platform Platform Worked per Uber and/or Week Deliveroo

Amit 30-35 Uber Deliveroo 10-15 7 months Electric bike

Harvey 20-25 Deliveroo Uber 40-50 9 months Electric bike

Julian 35-40 Deliveroo Uber 0-10 2 years, 2 Electric bike months and city bike

Mateo 20-25 Deliveroo N/A 15-20 7 months City bike

Martijn 35-40 Deliveroo N/A 35-40 2 years, 2 Race bike months

Raj 25-30 Uber N/A 15-20 7 months Mountain bike

Rodrigo 30-35 Deliveroo N/A 35-45 7 months City bike

Fig 1.1. An overview of the riders interviewed. Source: Author.

3.7 Ethical Protections for Participants

Considering the fundamentally sensitive nature of qualitative studies involving voluntary research participants, this research design was informed by a number of ethical Knight 34 considerations and protections. Prior to gathering data, I received approval from the University of Amsterdam’s Ethics Committee, which seeks to safeguard the ethical standards of research involving human participants and provides guidelines for carrying out ethical research projects (“Ethics Committee”). This process included developing an informed consent form which all participants read and signed before being interviewed, as well as an information brochure explaining the purpose of the research and contact details for both myself and my supervisor, Dr. Niels van Doorn. In recruiting riders, I always fully disclosed my intentions as a researcher. During the interview process, participants were re-informed of the study’s scope and how their interviews would be used (Brennen 19). All participants were given pseudonyms to protect their identities and guaranteed anonymity, and all communications and interview recordings were digitally protected and anonymized in accordance with modern data protection standards (“Data Storage and Data Security”). Anonymity was essential because riders, like most platform workers, are subject to Terms of Service agreements which allow platform companies to terminate workers’ contracts at will, should the company decide a worker has violated the agreement (van Doorn, “Platform Labor” 902). As such, the riders who participated in this study could be ‘deactivated’ or fired from the platforms if their identities were ever revealed. Finally, interviewees were reminded before and after the interview of the voluntary nature of their participation and that they retained the right to end the interview at any time for any reason, and had eight days following the interview to withdraw from the study.

3.8 Limitations of Research Design

A few limitations exist with regard to the methods and design of this study. Indeed, most qualitative research designs are subject to significant criticism due to their limited adherence to strict scientific methodologies commonly found in quantitative research. Autoethnography in particular has been critiqued as “lacking academic rigour” due to its introspective nature (Allen-Collinson 206). Others note that autoethnography as a qualitative research method has been labeled as too hypothetical, theoretical, aesthetic and emotional (Ellis et al. 283). At an extreme, some have branded autoethnography as egotistical, as it gives a researcher permission to focus “more on oneself than on the research question and culture of study,” (Doloriert and Sambrook 85). Knight 35

On the whole, I disagree with these dismissive generalizations, but do acknowledge important limitations in my use of autoethnography. Autoethnography certainly emphasizes the researcher’s experience, which reduces the researcher’s capacity to relinquish their personal biases and objectively document social phenomenon. However, in my efforts to understand how the redistribution of managerial functions to algorithmic systems has influenced the nature of on-demand food delivery work, my intention was to experience that redistribution for myself so I could better understand and empathize with the riders I interviewed. Without having experienced the work as a rider myself, I would have little idea about the issues riders communicated to me during our interviews. Furthermore, I do not claim my autoethnographic reflections speak on behalf of all Amsterdam food delivery riders. Indeed, my researcher status impacted my experiences as a rider in that I approached the work with my existing theoretical knowledge. In turn, I viewed some aspects of food delivery work more critically than the riders I interviewed. Despite these limitations, I believe my use of autoethnography was justified because it allowed me to connect more deeply with other riders. Moreover, my use of autoethnography supports the exploration of the highly individualized and solitary nature of on-demand food delivery work, which has not received significant scholarly attention to date. Semi-structured interviews also have limitations which diminish the overall strength of this qualitative research design. To begin, the use of semi-structured interviews inherently means that a researcher’s theoretical knowledge and subjective perspectives will influence the types of questions asked, as well as how an interviewee’s responses are interpreted (Diefenbach 891). Other scholars argue that the interpretive element of qualitative interviewing is a limitation. For example, researchers can either take a positivist and phenomenological approach by choosing to emphasize an interviewee’s explicitly stated meaning, or by choosing to interpret an interviewee’s meaning as the result of a localized, dialogical process that situates the researcher as a co-constructor of meaning (Brinkmann 288). Indeed, these criticisms are justified; interviewing as a qualitative research method is highly subjective and influenced by the researcher’s judgement of meaning and importance. To mitigate these limitations, I make explicit my interviewing and analysis processes, operationalized through the use of the constant comparative method. Furthermore, I make explicit my worldview as both a rider and researcher to my interview participants and to my Knight 36 readers. In this sense, my interpretation of interviewees’ responses stems from my own status as a rider, which I believe enables me to make better interpretive judgements of interviewees’ meanings. Finally, I take a decidedly reflexive approach in my interviewing style and analysis of interview transcripts, meaning I remain critical and cognizant of my own socialization as a rider, and make this socialization explicit in the discussion of my findings (Allen-Collinson 194). My coding procedure and use of the constant comparative method also presents limitations which I have attempted to mitigate. As Glaser notes, during the coding process, the researcher will likely experience “a conflict in emphasis of thought,” or question her initial coding decisions or extrapolations of meaning (440). To remedy this conflict, Glaser highlights the importance of recording research memos which are “designed to tap the initial freshness of the analyst’s theoretical notions and to relieve the conflict in thought,” (440). In this sense, recording research memos helped me ensure my coding choices were purposeful and rigorously reviewed, as well as supported the consistency of codes through constant examination of these coding choices. A final and significant limitation of this qualitative research design is its limited sample size and use of convenience sampling. As convenience sampling only allows the researcher to collect information from individuals who voluntarily participate, it is difficult to determine if key perspectives were unintentionally excluded (Abrams 546). Indeed, the perspectives of seven riders barely begins to scratch the surface when it comes to understanding such a diverse group of people. Yet despite the small sample size, my use of convenience sampling yielded valuable qualitative data from a hard-to-reach population of platform workers. Moreover, by incorporating autoethnographic and secondary research methods into the overall research design, I could counterbalance the limitations of convenience sampling and supplement my data with those gathered from other methods (Abrams 547). The use of convenience sampling also enabled me to have informal conversations with riders, which helped situate and substantiate my interview questions and recruitment strategies. While I make no claim that these seven accounts constitute a representative sample of riders’ perspectives, they do add to the growing canon of platform labor studies that investigate on-demand food delivery work. I submit these narrative accounts as an exploratory endeavor which hopefully supports future studies that examine on-demand food delivery work in different geographic and platform contexts. Knight 37

Chapter 4 | The Generation of Algorithmic Knowledge

This chapter summarizes my findings and analysis of how Deliveroo and Uber Eats food delivery workers are impacted by the redistribution of managerial duties to algorithmic systems. Throughout this chapter, I attempt to illustrate the discrepancies between the intended operational functions of each platforms’ algorithmic systems and the realities riders face when interacting with these systems in their work. I first apply Agre’s five-stage capture model to the activity system that is on-demand food delivery, and explore how Deliveroo and Uber Eats’ capture of riders’ activities sustains the platforms’ use of algorithmic management systems. Following this, I present my subjective experiences and those of other riders to demonstrate how the lived realities of this activity system are much more complex than these algorithms currently account for. Specifically by examining riders’ interactions with Deliveroo’s shift booking algorithm, as well as each platforms’ order assignment and pricing algorithms, I identify how Uber Eats and Deliveroo riders produce working knowledges and competencies for negotiating the capture and algorithmic restructuring of their labor. Despite riders’ production of knowledge, however, certain aspects of these algorithmic systems remain outside riders’ capacity to understand or negate. Considering this, I discuss the ways in which Deliveroo and Uber Eats’ use of algorithmic systems severely limits riders’ alleged autonomy, especially when these systems malfunction or fail to computationally represent complex aspects of the food delivery process. In turn, I argue that although riders develop nuanced understandings for negotiating many algorithmic management tactics, the overall redistribution of managerial duties to algorithmic systems has impacted food delivery riders negatively. The opaque, non-negotiable, and ever-changing qualities of each platforms’ algorithms have conditioned important aspects of riders’ work in ways that benefit the platform companies’ own control and profit generation, and fundamentally disadvantaged riders.

4.1 Capture in the Context of Deliveroo and Uber Eats

Deliveroo and Uber Eats capture a plethora of data on riders’ activities to inform their algorithmic management systems. In doing so, both platforms structure the activity system of food delivery in such a way that riders’ actions can be computationally represented and restructured, much akin to Agre’s five-stage capture model. In this section, I briefly review Knight 38 how Deliveroo and Uber Eats structure their respective food delivery activity systems based on my critical analysis of each platforms’ rider application interface. The first step in this process is analysis, in which the food delivery activity system is ​ ​ studied and broken down into ontological units (Agre 746). This allows the platforms to articulate grammars of action, or stretches of units that represent the food delivery activity system as a whole (Agre 746). Deliveroo’s grammar of action for the delivery activity system is illustrated below:

Figure 1.2. Deliveroo’s grammaticization of the food delivery activity system. The boxes in green indicate steps at which riders must either ‘swipe’ or manually interact with the rider application to complete the step. Source: Author.

Deliveroo’s grammaticization of the delivery process includes two components; the shift-booking component in which riders must reserve their shifts, and the delivery component. To ensure riders adhere to the articulated grammars of action for each component, Deliveroo requires riders to swipe a button or otherwise notify the application that they have completed each step. This reveals the third stage of the capture model, imposition, in which riders are induced to “organize their actions so that they are readily ​ ‘parseable’ in terms of the grammar,” (Agre 747). Indeed, as Agre notes, participants in the Knight 39 activity system may or may not comply with the imposed grammar, and often find methods of resisting or circumventing it (747). This exemplifies the flexible nature of capture as a workforce management practice and riders’ ability to negotiate the capture of their labor. Uber, on the other hand, does not require riders to reserve shifts in order to participate in food delivery. In turn, its grammaticization of the food delivery activity system is slightly less complex, as shown below:

Figure 1.3. Uber Eats’ grammaticization of the food delivery activity system. The boxes in green indicate steps at which riders must either ‘swipe’ or manually notify the application that they have completed the step. Source: Author.

Uber differs from Deliveroo in the imposition of its grammar of action. Whereas Deliveroo riders must swipe or tap a button upon completing each step, Uber captures a rider’s live geolocation to automatically determine if they have arrived at the restaurant or the customer’s location. This technical aspect reveals the fourth stage of capture, instrumentation, in which ​ ​ the platforms institute social and technical forces to compel riders into orienting their activities such that they may be captured (Agre 747). Another technical force Uber uses to nudge riders into behaving as desired is withholding pertinent information at certain stages of the delivery process. For example, Uber does not disclose the customer’s location until the rider has collected the order from the restaurant, thus decreasing the chance a rider will decline or cancel an order. Deliveroo also utilizes social and technical forces within the instrumentation phase to compel riders into conforming with its grammar of action. For example, Deliveroo’s geolocation barrier prevents riders from swiping that they have arrived at the restaurant or customer’s location if they are more than fifty meters away. Deliveroo also uses social forces to nudge riders into acting as desired, such as in-app notifications that ask riders ‘Are you sure you want to reject?’ if a rider rejects an incoming order request. Knight 40

By structuring the food delivery activity system in their respective ways, Deliveroo and Uber Eats are able to constantly capture information about riders’ activities, such as their live geolocations and the speed at which they move through the articulated grammars of action. These captured data become economically valuable in the final stage of the capture model, elaboration, in which they are aggregated, statistically analyzed, and used to ​ ​ reorganize and optimize the delivery activity system (Agre 747). In particular, as Agre notes, Deliveroo and Uber Eats can feed these captured data to “concurrent computational processes,” such as algorithms, that allow the platforms to “‘watch’ the ongoing activities for purposes of error detection, advice giving, performance measurement, quality control, and so forth,” (747). In this sense, by capturing data on riders’ activities as they move through each platforms’ structured food delivery activity system, both Deliveroo and Uber Eats can tune their algorithmic systems to make the entire activity system – and riders’ labor – more efficient and profitable. By capturing riders’ activities and utilizing algorithms to manage riders’ labor, Deliveroo and Uber Eats have seemingly designed foolproof food delivery activity systems. In reality, however, these systems are somewhat flexible, and riders retain some ability to negotiate the capture and subsequent algorithmic restructuring of their work. In the following sections, I present my working experiences and those of other riders in relation to Deliveroo’s shift booking algorithm, as well as both platforms’ order assignment and dynamic pricing algorithms.

4.2 Deliveroo’s Shift Booking Algorithm

As my phone’s clock jumps from 10:59 to 11:00, I open my Deliveroo rider app and enter the planner section. This is the first week I’ve gotten into the first priority booking group, and I’m amazed by the number of available shifts for the week ahead. When I was in the 15:00 booking group, I would open the app and there would be one, maybe two hour-long shifts available. Now, it feels like a gold rush. And somehow, it feels more secure than Uber’s ‘just go online’ system, as if reserving a shift guarantees some level of earnings. Even though I know that’s not the case. I think about the full-timers I’ve met – Martijn, Harvey, Rodrigo – who usually book forty to fifty hours worth of sessions a week. Are they earning more than Uber Eats riders who work full time? Knight 41

Deliveroo’s shift booking system serves as a managerial tool that algorithmically ranks riders based on their adherence to the company’s desired rider behavior, and can significantly impact their working lives. As previously mentioned, Deliveroo’s shift booking system categorizes riders based on their captured ‘statistics,’ which include a rider’s shift attendance rate, shift cancellation rate, and super peak participation rate. In turn, these ratings are aggregated and statistically analyzed, and riders are ranked relative to the other riders in their city. Based on this ranking, riders are categorized into three groups: those with the highest ranking statistics who can book shifts for the following week at 11:00, those with mid-ranking statistics who can book shifts at 15:00, and those with the lowest ranking statistics who can book at 17:00. These shifts are available in different geofenced areas, such as Amsterdam Centrum (ACE), Amsterdam Oost (AOO), Amsterdam Noord (ANO) and Amsterdam Nieuw West (ANW). Once riders have reserved a shift, they can select the ‘reserve weekly’ option which gives them priority to automatically reserve that shift for the following week. The algorithmic ranking of riders based on their captured attendance, cancellation and super peak participation rates has had varying effects on their work experiences. On the one hand, Deliveroo says it implemented the shift booking system “in direct response to feedback by riders, who said that they wanted more certainty over their earnings by being able to plan when they work,” (Field and Forsey 7). In this sense, by capping the number of riders who can be online and available for orders at a given time, Deliveroo is attempting to distribute orders more equitably to fewer riders, thereby enabling riders to earn more. Many riders I spoke with experienced this benefit, such as Harvey, who felt that the booking system helped him receive more orders per hour than he would in Uber, which lacks a similar booking system. Additionally, the Deliveroo riders I spoke with liked the structure and daily routine the booking system provided, and felt the system worked fairly to reward riders who worked hard. At the same time, however, the shift booking system serves as an algorithmic management tool that Deliveroo uses to ensure a minimum coverage of riders which can, in turn, make this supposedly flexible form of platform work quite inflexible. In the past few months, Deliveroo has ramped up recruitment efforts in Amsterdam to compete with Uber Eats for market share, which has oversaturated its own labor pool of riders vying for shifts. In turn, even riders with near-perfect statistics have had trouble reserving shifts. Mateo, who has Knight 42 been riding with Deliveroo for seven months, told me that even with first priority access and shifts requested weekly, he has experienced challenges reserving the shifts he would like. “Before it wasn’t like that,” he reported, “but now there’s too many people working for Deliveroo, so there’s barely any shifts.” All the Deliveroo riders I interviewed echoed this sentiment, particularly the full-time riders who have been riding for more than six months. Moreover, the full-time riders felt that their earnings had decreased after Deliveroo’s hiring surges, as Harvey noted: “I’ve seen now from three months they are decreasing the fees every time when there are too many new riders on the road.” While the Deliveroo riders I spoke with did express relative satisfaction with the shift booking system because it provides greater structure and earnings consistency for full-time riders, they were also concerned about the increasingly competitive nature the shift booking system has imparted on their jobs. Shapiro identified a similar concern among food couriers in the United States who saw their hours and earnings cut in the wake of the platforms’ hiring sprees. Indeed, platform companies that onboard workers en masse can create their own “expansive and elastic labor supply that make couriers part of a stream [...] of undifferentiated labor,” (Shapiro 2967). Yet for riders, this oversupply of labor increases the precarity of their work, both in terms of their ability to get shifts and their ability to earn a decent wage. Noting the somewhat detrimental effects of Deliveroo’s algorithmic ranking system does not, however, negate the many ways riders have adapted and developed methods of ‘gaming’ the shift booking system. As Jarrahi and Sutherland remind us, workers who learn to manipulate the inputs for a platform’s various algorithms can “alter, observe, and improve its output” for their specific needs (7). Two riders, Mateo and Rodrigo, mentioned using ‘tricks’ to maintain their perfect statistics by manipulating the captured inputs which inform Deliveroo’s ranking algorithm. To avoid increasing his late cancellation rate if he decides he no longer wants to work a booked shift, Mateo simply goes online and rejects a few orders in a row, which prompts an in-app message that asks him to select whether to stay online or go offline. If he does not select either option, he can remain online but does not receive order requests, which allows him to end his shifts early without penalty. Rodrigo used a similar strategy, in which he learned that riders must only be online in the first fifteen minutes of a booked shift for it to count. Thus, as he told me, he could “stay at home rejecting orders, [...] be online on the fourteen, put it on, wait, and at half past, put it off.” In this sense, the Knight 43 algorithm detected him as attending his booked shift even though he did not leave his house nor deliver a single order. Of course, I had to try these tricks for myself and put them to the test one rainy evening when I had two hours booked. While I was pleased with how easy it was to manipulate the system, I could not help but wonder: Won’t Deliveroo notice this ​ behavior? As I pondered this, I recalled Burawoy’s notion of the organization of consent, or the ability of corporations to compel piecework production or service workers to participate in extractive capitalist labor processes (Burawoy 81). In order to organize consent, according to Burawoy, management cannot hinder the game of ‘making out,’ which encompasses time and effort-saving practices among workers, such as stockpiling or restricting their labor to certain periods of time (80). In this vein, Deliveroo may well be aware of these ‘tricks’ used by Mateo and Rodrigo, but has little reason to prevent riders from making out because doing so would limit riders’ overall cooperation with the food delivery activity system. Moveover, by providing the conditions for riders to develop and use their algorithmic competencies, Deliveroo can ensure it retains a vast labor pool of riders, even if they occasionally ‘make out’ in this way. By instituting an algorithmic process for ranking riders and booking shifts, Deliveroo has been able to optimize its fragmented workforce. While this redistribution of managerial duties to an algorithmic system has had some negative effects on riders in the form of an oversaturated labor supply and marginal decreases in earnings, riders have also retained the ability to exercise their algorithmic competencies and manipulate the system to their advantage. Simultaneously, Deliveroo has organized riders’ consent to its food delivery activity system such that the company’s own profit-maximizing goals are met. In the following section, I explore how both Deliveroo and Uber Eats’ use of order assignment algorithms has impacted riders’ work experiences.

4.3 Order Assignment Algorithms

Sweat drips down my neck as I ring the doorbell of customer number eight of the night; it’s about to storm, and the air is thick with heat. I’m finally buzzed in and begin climbing the ladder-like stairs to the customer’s third floor apartment. My phone pulses in my hand; another order request from Uber Eats. I’ve learned that if I’m close to finishing an order, they’ll send me a new request just before. Well, not ‘they’ – Uber’s order assignment Knight 44 algorithm. It’s so easy to anthropomorphize these systems. My phone pulses angrily; only a few seconds left to accept. Unlike Deliveroo, which tells you up front where the customer is and how much money you’ll make for the delivery, Uber Eats just tells you how many minutes it will take to bike to the restaurant. Will this order be worth it? Will it take forever to deliver? When will the thunderstorm start? I debate internally until the customer opens the door – my thumb reflexively taps the ‘accept’ button. Hello, goedenavond, here’s your McDonald’s. Swipe, complete. This moment illustrates what I have come to imagine as ‘one foot in, one foot out.’ One foot in the physical world, dealing with customers, picking up orders, and the other foot in the world of the app, negotiating the activity system laid out by Uber or Deliveroo. By far the most influential algorithmic system we as riders come into contact with is each platforms’ order assignment algorithm. On the surface, these algorithms computationally determine which orders a rider will deliver. But in my interactions with these algorithmic systems, I have come to learn they exert much more influence over my work than I originally thought. According to the information Uber makes publicly available, riders are assigned orders as long as they are online and close to restaurants that use Uber Eats, and riders have fifteen seconds to accept incoming delivery requests before they are offered to other riders (“How do I receive delivery requests?”). Deliveroo offers slightly more information about the inner workings of its order assignment algorithm and states that riders are assigned orders based on the location of the restaurant, the number of riders on the road, a rider’s live location, and a rider’s vehicle type, as well as other factors (“Meet Frank!”). In my interviews with riders, I was curious to learn what they thought about each platforms’ order assignment algorithms and how they imagined these systems to work. Everyone I spoke with considered their live geolocation and proximity to a restaurant to be the most important factors. As Harvey, who rides for both platforms but prefers Deliveroo, put it:

Harvey: “[The] algorithm is just about convenience for Deliveroo [...] They are giving ​ preference to the nearby riders as compared to the ones far away because they know they will get this rider for less fees as compared [to] giving another rider more fees.”

Knight 45

Yet while all riders I spoke with understood this basic operating principle of the order assignment system, there was a sizeable degree of confusion over other factors that could influence the assignment of orders. For example, Raj, a rider with Uber Eats, thought that the order assignment algorithm also took into account a rider’s order history and allocated orders to ensure riders made at least ten euros per hour. Another Uber Eats rider, Amit, thought the order assignment algorithm prioritized sending orders to riders who worked more hours consecutively. “If there is a disconnection with the app, then there is a low chance of getting orders,” Amit told me. “The system thinks the driver is not working.” This confusion over what specific factors Uber’s order algorithm takes into account reveals a significant information asymmetry that hinders riders’ sensemaking capabilities. While the riders I spoke with understood their order assignment was primarily based on their captured geolocation, their theorizations of other influential factors stemmed from their personal observations and sensemaking. In this vein, as Shapiro notes, a rider’s conception of something as abstract as an order assignment algorithm is largely informed “by a myriad of embodied and contextual cues” received while working in an “ambient informational environment to which they have only limited access,” (2966). While Uber’s order assignment algorithm exerts significant influence on riders by dictating which orders they will be offered, riders’ limited access to information about the algorithms ‘rules’ prevents them from developing a more comprehensive understanding and can result in inaccurate sensemaking. In addition to lacking important information about how the order assignment algorithms operate, riders are impacted by ingrained information asymmetries within the visual and operational design of the order assignment algorithms. Uber Eats riders are especially affected by this because they are not shown the delivery address of an order before accepting it, nor do they know how much they will be paid for delivering an order until after they deliver it. This “deliberately manufactured” information asymmetry serves as a control mechanism for Uber because it forces these supposedly independent contractors to accept uneconomical rides which benefits the platforms’ financial objectives (Veen et al. 11). Deliveroo, on the other hand, shows both the delivery address and fee to riders before they accept an order request, thereby providing riders with sufficient information to make both calculative and qualitative decisions about which orders to accept. These ‘qualculations,’ as Shapiro notes, are “not driven simply by a logic of pure income maximization,” but rather by riders’ own affective reasoning based on previous experiences (2967). In this sense, Knight 46

Deliveroo riders can make informed decisions about accepting or rejecting orders that do not seem worth the fee offered. Without information about customers’ locations, Uber Eats riders must submit to the order algorithms’ determination of where they will be sent, revealing a technical force Uber uses to modulate riders’ behavior as they move through the structured food delivery activity system. In their ethnographic study of Uber ridehailing drivers in the United States, Rosenblat and Stark found this “blind acceptance” requirement had a downward pressure on drivers’ earnings, because drivers had no way of knowing if an incoming ride request would be unprofitable (3762). Moreover, by designing this information asymmetry into the user interface of its order assignment algorithmic system, Uber makes “it difficult for service providers to act on information in a way that could enhance their position on the platform,” (van Doorn, “Platform Labor” 902). Thus, while Uber Eats riders do retain the freedom to reject incoming order requests, these information asymmetries largely prevent riders from exercising this freedom. Uber and Deliveroo also exert managerial control over riders through their order assignment algorithms by sending riders double or ‘stacked’ orders. Stacked orders are two separate customer orders from the same restaurant whose drop-off locations are, in theory, close to one another. However, the drop-off locations for stacked orders are oftentimes not close to one another, which can negatively impact riders who must spend more time cycling for typically lower fees than they would receive for completing two separate orders in the same amount of time. As Raj, who rides for Uber Eats, reported:

Raj: “It’s actually not worth it because if you pick two, then you get paid less basically. Because if you make one delivery it will be four to five [euros] minimum [...] but if you make two deliveries from the same restaurant, you’ll make six, seven [euros]. And then you know the waiting time is also increased for two deliveries to pick up, and then dropping it is also increased.”

Amit, another Uber Eats rider, told me he disliked stacked orders because they often caused longer delivery times for customers who would, in turn, blame him for delays. “The customer is shouting, and he screamed, and then you feel awkward,” Amit told me. “It’s not our fault, we follow the instructions according to Uber Eats.” Knight 47

Uber’s algorithmic assignment of stacked orders represents a tactic the platform uses to pay the lowest fee possible, as well as assign incoming customer orders more quickly. However, this procedural efficiency for Uber is not the reality for riders, who often spend more time cycling to distantly located customer addresses. In turn, as Amit reported, customers can become irate if their delivery is delayed because of the rider’s longer cycling time, and the rider receives the blame even though the delay was caused by the algorithmic determination of delivery priority. Moreover, a disgruntled customer can leave a negative rating for an Uber Eats rider, which unduly penalizes the rider for simply following the instructions he or she receives. For Deliveroo riders, who are shown customers’ addresses on stacked orders, the risk of accepting a stacked order is smaller. However, Deliveroo has increasingly started ‘surprise’ stacking orders, which means riders learn they have been assigned a second order on top of the single order they accepted. Importantly, riders cannot see the delivery address of the second order that has been assigned without their consent. In my experience, surprise stacks were incredibly frustrating because they made me feel forced to deliver orders I did not agree to. On the first surprise stacked order I received, I cycled almost three kilometers in the opposite direction of the first delivery. After getting lost and calling the customer for help finding her, I had spent forty-five minutes delivering the two orders and earned around eight euros, definitely less than I would have earned had I completed two or three shorter deliveries in the same amount of time. I felt duped into delivering an order which, in hindsight, other riders had probably wisely rejected. Rather than offering a rider a higher fee to entice them to deliver a single order to the customer’s remote location, Deliveroo had surreptitiously stacked my order and tricked me into delivering an undesirable order for a lower fee. Indeed, the more experienced riders I interviewed divulged various strategies for coping with the information asymmetries and challenges of each platforms’ order assignment algorithms. More than anything else, Deliveroo riders had developed tactical responses for deciding which orders to accept or reject, which helped them avoid unprofitable or strenuous deliveries. Most Deliveroo riders I spoke with reported they had learned to avoid certain delivery areas and restaurants that prepared orders slowly. In addition, Deliveroo riders reported learning to swipe early at certain stages of the grammaticized delivery process, such as by swiping ‘arrived at restaurant’ before actually Knight 48

doing so and overriding the app’s geolocation barrier. This, in turn, gave riders even more information to support their qualculative decision making, such as finding out the contents of the delivery:

Martijn: “There’s some pizza places where pizzas take long, but when there’s a lasagna on it, it’s ready right away [...] So when it says the item is two pizzas, I’m like, ‘okay maybe I’m gonna not do this one.’ But when I know there are specific items which I think will be ready pretty soon, then okay, I’ll do it.”

Yet even with these competencies, the broader gulf between riders’ working knowledges and the control mechanisms embedded within both platforms’ order assignment algorithms is, for the most part, unbridgeable. Not only do riders have limited information about how the order assignment algorithms work, but Uber riders in particular are harmed by the interfacial information asymmetries that the platform incorporates into its structuring of the food delivery activity system. The lack of transparency into these algorithmic systems, which, as Sun notes, are “claimed to be impartial and value-free,” constitutes one of the many ways Deliveroo and Uber Eats govern a supposedly free market for their own benefit. If Uber Eats riders were truly treated as independent entrepreneurs providing a service via the Uber Eats marketplace, they would be entitled to transparency about the destination of deliveries and the factors which influence how they are assigned orders. Similarly, if Deliveroo riders were truly treated as independent entrepreneurs, they would not be assigned surprise stacked orders. By adopting algorithmic systems to assign orders to riders, Deliveroo and Uber Eats are encoding their profit-maximizing business objectives into allegedly impartial computational systems that fundamentally encumber riders in their work.

4.4 Dynamic Pricing Algorithm

Because of the thunderstorm, Uber Eats is giving riders an extra twenty percent on every order they deliver between 18:00 and 19:30. I arrive at the sushi takeaway place at 19:22 and swipe to navigate to the customers, praying under my breath that the delivery address is closeby. And of course, it’s not. Out near Sloterplas, which will take at least fifteen minutes to get to, if not more. The roads are wet now, too. Wait, if I don’t deliver this before 19:30, does that mean I don’t get the boost? I check the text message I received from Uber Knight 49 earlier that night – all it says is “Enjoy 20% extra on every delivery between 18:00 and 19:30 today.” I grit my teeth as I mount my bike, the rain pelting down on my face like icy pinpricks. When I finally deliver the order, it’s 19:44. At least the customer is sweet, saying thank you as she tips me a euro and bounces a baby on her hip. When I finally swipe ‘complete’ I see I received the boost; it must still count as long as you collect the order within the designated time. Overall, €7.26 for this order, which includes the boost fee of €1.32, for a ​ distance of 3.6 kilometers. Not bad. This instance of uncertainty highlights one of the many ways in which riders are impacted by Deliveroo and Uber’s use of dynamic pricing algorithms. As noted before, both ​ companies utilize distance-based pricing algorithms to determine the fee a rider receives. Under Deliveroo’s dynamic pricing model, riders are paid based on “the distance and effort needed to complete a delivery,” and the company states it sets minimum and maximum market fees, but does not reveal what these minimum or maximum fees are in Amsterdam (“Distance Fees”; Personal communication, April 30, 2019). Uber, on the other hand, states it uses more variables to determine a rider’s fee. Uber Eats riders receive a fixed amount for picking up the order, a fixed amount for dropping off the order, and a fixed fee per kilometer traveled from the restaurant to the customer (“Delivery Partner Payments”). From this total fee, Uber deducts ten percent as a service charge for the privilege of using the platform (“Delivery Partner Payments”). Both platforms make use of incentive pricing by offering riders extra fees for adhering to particular conditions. Uber offers ‘boosts’ in certain zones and during specific hours, meaning riders who meet the boost requirements can earn an additional percentage on top of their delivery fees. For Deliveroo riders, incentive prices are typically offered as fixed bonuses for delivering a certain quantity of orders or as an additional fee per order delivered. It is important to pay attention to incentive pricing trends because the shift from per-gig fees to algorithmically-determined fees typically results in lower earnings for platform service workers (van Doorn, “Thoughts on Deliveroo’s introduction”). In turn, as riders earn less per order on average, platforms can deploy “gamified incentives” to entice riders to work in certain zones at certain times, and work faster and longer, all of which benefit the platform’s own bottom line (van Doorn, “Thoughts on Deliveroo’s introduction”). With the constant fluctuations in earnings, variations in incentives, and limited transparency into the algorithmic determination of fees, riders often consciously and Knight 50 unconsciously attempt to make sense of each platforms’ dynamic pricing systems. One Deliveroo rider, Rodrigo, told me that he thought the contents of an order had an impact on the delivery fee he received. This was because he had received very different fees for deliveries with almost identical travel distances, thus leading him to believe other factors must impact Deliveroo’s pricing algorithm. Raj, who rides for Uber Eats, reported using a mental benchmarking system that helped him estimate the fee he would receive for completing a delivery. He based his estimations off the one piece of information riders receive as they set out to deliver an order, which is the distance in kilometers from the restaurant to the customer:

Raj: “If you’re riding for one kilometer, one and a half kilometer, you will be paid ​ between three to four euros. If you go above two and a half, three kilometer, then you will be paid four, five, six [euros]. If you go four kilometers, five kilometers, then it’s six [euros], sometimes seven, very rare seven [...] It usually ends 6.8, it will go 6.9, but it won't go to seven.”

Thus, even without full transparency into how fees are calculated, riders have developed intuitive understandings of how each platforms’ pricing algorithms function, and used this working knowledge to determine if the fares received were fair. This echoes the findings of Lee et. al, who noted that Uber and Lyft drivers were more likely to cooperate with the platforms’ algorithmic assignment of rides and determination of fees if drivers felt that such algorithmic decisions made sense (1606). Similarly, Shapiro’s findings of the qualculative behavior exhibited by couriers in the United States revealed that in the face of opaque dynamic pricing systems, couriers evaluated the fees offered in terms of their own moral sense of what fees should be offered for that type of delivery ​ (2967). In addition to their sensemaking practices, the riders I spoke with had developed various strategies for maximizing their earnings under each platforms’ dynamic pricing systems. Uber Eats riders, for example, largely reported ‘following the boost’ to increase their earnings. As Amit reported:

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Amit: “Drivers normally change their times to work and go nearby the boost area. Normally they [Uber] provide twenty or ten percent extra for evening, and some days they give a boost to 1.3 or 1.6, so drivers move that to the area where the boost is going up.”

As Uber Eats riders cannot see the fee they will receive before completing a delivery, pursuing the boosts helps riders maintain a sense of security that even if the order fees are low, the boosts will make up for it. At the same time, however, pursuing the boosts supports Uber’s own purposes and can nudge food delivery riders and ridehailing drivers alike into working under much less flexible conditions in pursuit of only marginally higher earnings (Rosenblat and Stark 3763). Deliveroo riders, on the other hand, largely reported taking practical steps to maximize their earnings, such as renting electric bikes that would help them complete more deliveries per hour. One rider, Mateo, informed me of a unique strategy in which he tried to only accept orders that included tips, which were previously shown to riders when they received a new order request. However, in late May 2019, Deliveroo removed this up-front indication as part of a “test with the display of the tip,” (Personal communication, June 11, 2019). When I contacted Mateo to ask his thoughts on this change, he expressed frustration. “I’ve gone from earning fifty euros a week in tips to basically less than ten,” he told me. He believed Deliveroo had removed the up-front display of tips because too many riders were rejecting orders without tips. Indeed, this adjustment in Deliveroo’s interface design reveals the power platforms possess to restructure the food delivery activity system to their benefit. By aggregating captured data on riders’ activities, such as Mateo’s tendency to only accept delivery requests with tip, Deliveroo could adjust its operational design and compel riders to adhere to the imposed grammar of action. While small interface changes such as this may seem insignificant, their impact on riders is substantial because they “constrain workers’ ability to make fully informed decisions,” (Shapiro 2963). Moreover, these changes are typically made without riders’ prior knowledge, thus compounding the limited transparency the platforms offer into their algorithmic systems to begin with. Uber Eats riders also reported experiencing considerable negative effects following changes Uber made to its dynamic pricing models. Since these changes were Knight 52 not communicated beforehand, riders only discovered the modifications by noticing slight decreases in their earnings over time. Raj, an Uber Eats rider, expressed his confusion and subsequent realization that Uber had stopped paying riders for the distance they traveled from their live location to the restaurant. As he told me:

Raj: “I put a complaint on a few deliveries, that this fare is not calculated correctly. I ​ was really new and I did not trust it, so I was trying to understand if I rode for like four kilometers, I am paid 6.8 [euros]. But if I rode for like 3.8 kilometers, then I’m paid five [euros]. So I’m like, it’s just not even two hundred meters less and then you’re paying one euro less? But how does that work? That’s when I started complaining about it [...] when I complained in January, at that time they said that we do not calculate the pickup distance anymore.”

As a result of this change, Raj’s mental benchmarking for order fees relative to the distance traveled was completely thrown off, and he had no choice but to accept a change which decreased his earnings. By reserving the right to modify contractual agreements and algorithmic systems without warning and without workers’ input, platform companies are simultaneously reserving their right to enforce mechanisms of control that dictate the working lives of supposedly independent contractors (van Doorn, “Platform Labor” 902; Shapiro 2960). Moreover, by leveraging riders’ limited knowledge of how these systems operate in the first place, Deliveroo and Uber Eats can dictate the conditions of the activity system such that their profit-maximizing goals are prioritized. In the following section, I explore how Deliveroo and Uber Eats’ structuring of the food delivery activity system currently fails to account for the complexity of this work in the real world, which in turn harms riders.

4.5 Algorithmic Limitations and Automated Errors

For what feels like the millionth time, I check my Deliveroo rider app and glance at the clock; twenty two minutes and fifty one seconds. I let out a frustrated sight and debate my options. Just commit and keep waiting? I’ve already been here this long. Cancel and chalk it up to bad luck? I’ve already asked the restaurant staff twice how much longer it will be, and I send an eager look towards the kitchen, trying to catch the waiter’s eye. Another Deliveroo Knight 53 rider walks in, and we give each other a nod hello. ‘How long have you been waiting?’ he asks me. ‘Way too long,’ I reply. We chat for a while, commiserating about long restaurant wait times. ‘I wish they would time it so you get the order request when the food is already done,’ he tells me. ‘They’ meaning Deliveroo, and I agree. The situation illustrates an all too common occurrence for riders: waiting for your order to be prepared. Yet at the same time, it illustrates the broader reality faced by riders in their work, which is the inability for Deliveroo and Uber Eats’ algorithmic systems to accurately reflect the complexity of the food delivery activity system. Take, for example, long restaurant waiting times. When Uber Eats and Deliveroo’s order assignment algorithms match orders with available riders, they do so by predicting the time at which a restaurant will have an order ready for delivery, and matching the order to a rider who can arrive at the restaurant at this precise moment. However, in my conversations with riders and in my own experience, this prediction was frequently inaccurate and resulted in long periods of unpaid waiting time. From riders’ perspectives, much of this inaccuracy actually stems from the restaurants’ behaviors. Riders like Rodrigo, Julian and Amit expressed exasperation about restaurant staff misleading them about the expected waiting time:

Rodrigo: “It’s my money, it’s my time, and it’s not nice if I arrive to the restaurant and ​ the waiter says to me ‘yes in five minutes it’s ready.’ So seven minutes… ten minutes… fifteen minutes… twenty minutes… and it’s like, come on man, why don’t you say me the truth? Why? My time is also money for me, come on!”

Julian: “If you arrive at the restaurant, you still have to wait like ten or twelve, ​ sometimes twenty minutes, and I’m like… why not give me the order when the food is done? No, I have to wait there, and nobody pays me for that time.”

Amit: “Yesterday I had to wait for forty minutes for one order. And I canceled and ​ stopped working. The guy told me it takes almost ten minutes, and then I wait, wait, wait, wait, and then it took forty minutes and I stopped and cancelled and came home. If I cancel two, three times then they’re gonna stop my account, that happened to another guy.”

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These reflections reveal a systematic issue both Deliveroo and Uber Eats’ order assignment algorithms currently fail to account for: the tendency of restaurant staff to mislead riders by inaccurately communicating waiting times. While riders can cancel orders that take too long, the riders I spoke with felt cancellations put them at risk for waiting even longer to receive another order assignment from the platform. Moreover, Uber Eats riders like Amit felt that cancelling multiple orders in a row would result in punishments, such as deactivation. Riders did report expecting to wait five to ten minutes, especially during the dinner rush. Yet because restaurants aim to send the order out right away, restaurants will often tell riders it will only take a few more minutes, as this ensures a rider is there and ready to go. In turn, even if the restaurant knows the order will take more than five minutes to prepare, they often mislead riders to make sure an assigned rider is waiting at the exact moment the food is ready for delivery. This source of human inaccuracy, and the order assignment algorithms’ inability to model these moments of human nature, subsequently cause riders to suffer because they are not compensated for time spent waiting. Indeed, as Möhlmann and Zalmanson note, on-demand platform companies’ business models depend on all parties involved in the transaction behaving as expected (6). By behaving in ways that support their own objectives, restaurants have disrupted expectations which are incalculable to these platforms’ order assignment algorithms and unrepresented in each platforms’ structuring of the food delivery activity system. In turn, riders are the ones who pay the cost, either in terms of lost time or penalties received for cancelling orders too frequently. The lack of adaptiveness and flexibility in the use of algorithmic systems is also evident when customers behave in ways these algorithmic systems do not expect. Amit, an Uber Eats rider, recalled instances in which the customer entered the wrong delivery address. Thus, the onus is put on riders to recalculate their route to the correct address, which can be kilometers in another direction. Moreover, riders’ fees for these orders are not automatically updated to reflect increases in distance traveled; rather, the rider must contact rider support to request a fare correction. By using indirect communication channels and outsourcing rider support to third-party customer service representatives, platform companies can keep their workforces of independent contractors at arms length and limit their responsibility to pay riders fairly (van Doorn 903). Furthermore, Uber Eats riders in particular must perform emotional labor to appease customers in situations such Knight 55 as these, despite the customers’ causation of the problem to begin with. As Sun notes, the algorithmic design of food delivery applications stipulates a degree of “customer supremacy,” as customers are given greater power to monitor the rider’s real-time location, rate the rider’s performance and make complaints (9). Therefore, Sun argues that “platforms have programmed and engineered workers to be servile laborers” who must perform emotional labor in order to receive positive ratings (10). Technological inaccuracies in each platforms’ use of algorithmic systems also negatively impact riders, as these inaccuracies can lead to the automated enforcement of unfair punitive measures. Each platforms’ algorithmic management of riders depends on the accurate capture of their live geolocations. Yet time and time again, I and many other riders experienced considerable issues and were automatically reprimanded for GPS-related errors. Rodrigo, who rides for Deliveroo, recalled one such instance when he was unable to go online because he was not in the center of the zone. His scheduled shift started at 8:30 in Amsterdam Oost, yet when he arrived, he was unable to go online because the app would not register his geolocation as being in the center of the zone. In turn, Rodrigo spent fifteen minutes cycling around trying to find the exact center of the zone, which was not indicated in his rider app. When he finally was in the right location and could go online at 8:45 AM, he had narrowly avoided having his statistics penalized for missing a shift. While Rodrigo’s story represents a direct punishment riders can receive as a result of Deliveroo’s inaccurate geolocation capture, riders can experience indirect consequences as well. On multiple occasions, I received customer complaints as a result of following Deliveroo’s inaccurate in-app map. The in-app map displays a pin of the customer’s location and a teal navigation route for the rider to follow. Although I knew Deliveroo’s in-app map was not always the most accurate, I used it to find the customer’s general area or street, and from there, I could find the house number myself. However, I frequently arrived at the pinned location only to learn that the pin was incorrect, and that the customer’s true location was kilometers away. As a result, I had to re-navigate, call and apologize to the customer, and upon delivering the food, was often scolded or otherwise received the customer’s disapproval. To be sure, delivering food is a service job, and with any service job comes a degree of emotional labor and unavoidable customer dissatisfaction. Yet when inaccurate Knight 56 algorithmic systems and simplistic food delivery system architectures lead riders astray, are they really to blame? Möhlmann and Zalmanson comment that because platform workers interact with managerial algorithmic systems rather than humans, there is often “no time to discuss or revise decisions arising from special circumstances not wholly captured by the data,” (5). When these circumstances prevent precise capture or deviate from the algorithms’ expectations, workers are left to bear the brunt of customers’ dissatisfaction, as well as seek out for themselves corrections to their miscalculated earnings. Moreover, when customers have the opportunity to act upon their dissatisfaction by leaving platform workers a rating, workers’ ratings and thereby their opportunities on the platform can suffer. As Lee et. al found in their ethnographic study of Uber and Lyft drivers in the United States, “drivers noticed that passengers misattributed system faults and negative experiences that drivers could not control to drivers themselves, which in turn resulted in lower ratings,” (1608). Indeed, as the only face customers see when interacting with a platform, platform service workers bear the burden of blame when any type of algorithmic and automated error occurs. In summary, Deliveroo and Uber’s redistribution of managerial duties to algorithmic systems has had both positive and negative effects on riders working lives, yet the negative effects of such systems largely outweigh the positive. By using algorithms to operationalize their managerial control, the platforms can hide behind the veil of algorithmic neutrality while simultaneously conditioning the market for their benefit. Moreover, the platforms’ use of algorithmic systems unjustifiably punishes riders in moments when these systems fail to accurately model the activity system that is food delivery. While riders do create complex algorithmic competencies for negotiating their working environments, their efforts are mostly negated by the control Deliveroo and Uber retain to modify these algorithms at any time.

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Chapter 5 | Conclusion

Throughout this research, I have been motivated by one question: how has the redistribution of managerial duties to algorithmic systems impacted the experience of work for on-demand food delivery riders in Amsterdam? In my efforts to answer this question, I have sought to center the lived experiences of riders. Indeed, as the field of platform labor studies continues to evolve, this intention must be more widely embraced. As van Doorn expertly critiques, “when discussing platform-mediated labor issues should not we start by asking how these issues impact the everyday lives of people who actually work on/through these platforms?” (“Platform Labor” 908). I strongly agree with this assessment and express my sincere appreciation to the seven food delivery workers who participated in this research. My findings indicate that food delivery riders in Amsterdam experience a variety of effects as a result of Deliveroo and Uber Eats’ adoption of algorithmic systems. On the one hand, riders have developed skillful methods of gaming each platforms’ capture of their activities, which supports their ability to ‘make out’ or otherwise work in ways that suit them. However, the game of making out is predicated on the illusion of choice, and indeed, riders have few real choices within these algorithmically-mediated working environments. Simply put, the redistribution of managerial duties to algorithmic systems has negatively impacted riders because it has conditioned riders’ working environments in ways that support the platforms’ maximization of profits. This conditioning is evident in Deliveroo’s use of its ranking algorithm for its shift booking system, which has allowed the company to oversaturate its own labor supply and exploit the inherent fungibility of riders’ labor. It is further evident in both companies’ use of order assignment algorithms, which operate on the basis of ingrained information asymmetries. The use of dynamic pricing algorithms has also had considerable negative effects on riders’ working experiences because they enable the companies to surreptitiously decrease riders’ earnings over time. Most of all, this redistribution of managerial duties to algorithmic systems has caused riders to experience undue punitive measures that stem from these systems’ technical inability to accurately model the activity system that is food delivery. While this conditioning certainly harms riders, they continue and will continue to develop sophisticated understandings of the algorithmic systems that govern their work. This finding echoes that of Veen et al., who recognized “individual resilience and reworking” Knight 58 practices among Deliveroo and Uber food delivery workers in Australia (15). Yet riders’ construction of algorithmic competencies also supports the “making and remaking of algorithms,” (Sun 4). In this sense, as riders’ algorithmic knowledge and adeptness increases, platforms will use feedback gleaned from riders’ algorithmic competencies to refine their algorithmic systems of managerial control (Sun 4). The platforms’ capture and subsequent restructuring of riders’ labor, as indicated in the elaboration phase of the capture model, is a two-way street: riders will restructure their workarounds and develop new competencies, too. Finally, this thesis has attempted to contribute to the growing conversation around the hyper-capitalist nature of the lean platform business model. As Rosenblat and Stark note, countless on-demand platforms have framed their use of algorithms in managing the labor process as “a natural feature of connectivity rather than an enforced hierarchy or employment power structure,” (3764). In this sense, any mention of ‘algorithms’ symbolizes a rhetorical move on the part of platforms to “encourage the notion that their algorithms operate without any human intervention,” (Sandvig). This framing could not be further from the truth. In their use of algorithms to mediate labor processes, Deliveroo and Uber Eats have encoded their own power and designed algorithmically-controlled activity systems that prioritize their maximization of profit over the equitable treatment and compensation of riders. Platform service workers are typically more vulnerable to capitalist exploitation to begin with, as such labor has traditionally been associated with classist notions of “a lack of value, skill, and dignity,” and largely carried out by people of marginalized socioeconomic backgrounds, such as low-income people, people of color, and people of migrant or transient status (van Doorn “Platform Labor” 907; Veen et al. 15). By centering the use of supposedly impartial or fair algorithmic systems in their business models, platform companies have devised automated means of exacerbating the precarious employment conditions of low-income service workers. While I stand firm in my critique, I am by no means calling for the dissolvement of platform companies like Deliveroo and Uber Eats. Quite the contrary; my experiences working as a rider and my conversations with riders have only confirmed my belief that people want these jobs, and that platform service work can provide important employment opportunities. Multiple riders I met explained that they had moved to the Netherlands in search of better employment opportunities, and working as a rider was the only employment that paid well enough and did not subject them to racial discrimination, which many had experienced in trying to obtain other service jobs in Amsterdam. Instead, I call upon Knight 59

Deliveroo and Uber Eats to do three things: firstly, pay and treat riders fairly. Opaque fares and unclear pricing algorithms are unjust, and riders deserve market-rate compensation for their labor. Uber should end its use of information asymmetries and adopt up-front information provision like that offered by Deliveroo. Secondly, they must involve riders in decisions which impact them. Many of the negative effects that stem from algorithmic systems are due to poor user experiences for riders; Deliveroo and Uber should invest more in rider experience research and actively seek riders’ participation in improving their algorithmic systems. Finally, Deliveroo and Uber, along with riders, should work with regulators to determine what employment protections riders deserve. To be sure, platform companies, like all hyper-capitalist enterprises, are not eager for greater regulation of their business operations. However, riders will continue to be exploited unless legislatures enact substantive employment reform that accounts for the evolving nature of platform service work in the countries where Deliveroo and Uber Eats operate. Further research efforts could qualitatively and quantitatively compare the nature of food delivery work across a greater variety of platforms, including Thuisbezorgd. Such a study could explore the impact of a guaranteed minimum wage, which Thuisbezorgd riders receive, on riders’ work activities. Similarly, multidisciplinary studies of platform-mediated work could qualitatively explore the interfacial designs of on-demand platforms, and analyze how riders’ user experiences with app-based work is influenced by design. Lastly, scholars should continue to prioritize the involvement of platform workers themselves, both in academic and public policy work, and extend their understandings of platform labor by participating in the work themselves. Empathy and talk of solidarity with platform service workers experiencing increased precarity at the hands of platform companies only goes so far; participation in platform service work will enable researchers to more accurately analyze the rapidly evolving dynamics of platform labor.

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References

A note to readers: all web pages referenced in this thesis have been archived using the Internet Archive Wayback Machine, which is accessible at https://archive.org/web/. ​ ​ Unfortunately, most references to web pages under the domain Uber.com could not be archived, as it appears Uber does not permit its site content to be crawled.

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