It's the Algorithm, It Decides: an Autoethnographic Exploration Of
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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 Knight 2 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 Knight 3 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 Knight 4 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. Knight 5 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