4.1 Introduction
Discussions of digital transformation in the workplace are far-reaching and explore a variety of perspectives, although much of this discourse tends to focus on the opportunities and challenges for more conventional forms of employment (Verhoef et al., 2021). Yet, throughout the last decade, a new body of research has emerged, which focuses exclusively on the implications of digitalisation and new technologies for atypical and contingent working arrangements—broadly recognised as causing the inception of the so-called gig economy (Adamson & Roper, 2010).
Conceptualisations of the gig economy have been relatively broad. Some define it as an umbrella term to encapsulate all forms of non-traditional, contingent, and less-structured work (Ashford et al., 2018). Others adopt a more focused perspective by viewing the gig economy as a new type of contingent work that is enabled and mediated via digital platform organisations, connecting on-demand workers with customers to perform fixed-term tasks (Barratt et al., 2020; Kuhn & Maleki, 2017). In this chapter, we subscribe to the latter perspective and consequently, conceptualise gig work as embracing some of the advanced capabilities of new technologies—such as machine learning and artificial intelligence (AI)—in creating service-providing digital infrastructures that thrive on connectivity (Duggan et al., 2020). Millions of gig workers around the world operate and earn a living from this novel form of work, often associated with well-known organisations such as Uber, Lyft, Deliveroo, Amazon Mechanical Turk, and Fiverr.
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Given the breadth of services and sectors found in the gig economy, the proliferation of this labour form holds noteworthy consequences for the world of work. Although exact figures are difficult to source, by 2018 it was estimated that approximately 24 million people were engaged in some form of gig work across the European Union (Mariniello, 2021). The defining characteristic of gig work is that digital platform organisations match supply and demand between workers and users, rather than directly providing the services themselves (Tran & Sokas, 2017). Classified as independent contractors in almost all cases, gig workers are purportedly granted the freedom to work independently by controlling most aspects of the work, from scheduling to selecting tasks (Jabagi et al., 2019). Yet, perhaps the most controversial aspect of this work is the use of advanced algorithmic technologies to manage, monitor, and co-ordinate the activities of workers (Wood et al., 2019). This somewhat contradicts the supposed promise to “be your own boss” as self-employed freelancers. These technologies—collectively known as “algorithmic management”—are commonplace in most types of gig work (Walker et al., 2021).
In this chapter, we begin by tracing the emergence and continuing growth of the gig economy as a disruptive force in the labour market. Next, we examine the role of algorithmic technologies in enabling, shaping, and closely mediating gig working arrangements. Finally, we outline the most significant challenges and controversies associated with algorithmic management, while also exploring the potential to develop timely and effective solutions to these issues.
4.2 The Rise of the Gig Economy
Gig economy research indicates that atypical working arrangements first emerged around the time of the global economic crisis in 2008 (Tran & Sokas, 2017). The intense pressures faced by organisations during this period resulted in a shift away from the protections and security of conventional employment towards working arrangements that were more fragmented and precarious (Fleming, 2017). While some companies that are recognised as part of the gig economy started emerging slightly earlier (Amazon Mechanical Turk, for example, was launched in 2005), the “gig economy” moniker was coined by journalist Tina Brown in 2009 to describe variants of contingent work that are transacted exclusively via digital means, with each piece of work being akin to an individual “gig” (Tran & Sokas, 2017).
Throughout the last decade, the number and diversity of organisations operating in the gig economy has increased significantly (Poon, 2019), as has its popularity amongst users (Dupont et al., 2018). Often, the services available in the gig economy are resemblant of components of formerly full-time roles held by, for example, couriers, taxi drivers, or translators. Yet, in the gig economy, these roles have been fragmented into ad-hoc tasks, available on-demand and offered flexibly via digital technologies available on smart devices (Smith & Leberstein, 2015).
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4.2.1 The Unique Nature of Gig Work
One of the main difficulties in studying gig work is that it encompasses a wide range of job types and working conditions (De Stefano, 2016). For example, many associate the gig economy with on-demand services in local markets, such as transportation with Uber or Lyft, or food delivery with Foodora or UberEats (McDonnell et al., 2021). Yet, gig work also includes remote, cloud-based crowdwork companies such as Fiverr and Amazon Mechanical Turk, who offer services such as transcription, translation, or survey design (Howcroft & Bergvall-Kåreborn, 2019). Some also refer to organisations such as Airbnb and Etsy, where individuals may sell goods or lease assets via platform organisations (Duggan et al., 2022a). However, the labour process involved in these organisations is less transparent, meaning it is more commonly associated with the “sharing economy”, where digital platforms are used by individuals to sell goods or lease assets (Cheng & Foley, 2019).
Across all types of gig work, the intermediation of a digital platform to connect workers with customers or clients is shared (Newlands, 2021), as is the hyper-flexibility of the independent contractor status and the short-term nature of work assignments (Pichault & McKeown, 2019). Despite these shared characteristics, it is vital to recognise that there is substantial heterogeneity in the way gig work is designed, controlled, and organised across different job types and organisations (De Stefano, 2016). For example, some gig workers operate remotely and must actively search and bid for advertised projects (Howcroft & Bergvall-Kåreborn, 2019), while others are assigned specific on-demand tasks in local markets (Duggan et al., 2022b). Therefore, referring to gig work as a monolithic concept is problematic, most notably because this approach may create confusion about the appropriateness of the classification assigned to one type of gig work or the pervasiveness of algorithmic technologies used by certain digital platforms (McDonnell et al., 2021).
4.2.2 The Role of Digital Platforms
For platform organisations, the avoidance of the “employee” label in classifying gig workers is strategically important in removing the need for, among other things, potential overtime payments, union organisation, payroll taxes, and unemployment benefits. Platform organisations claim they “partner” with independent workers who offer their services to customers via digital platforms. A frequently cited example is Uber, which is regarded as the world’s largest transportation company (although it is listed as Uber Technologies), yet the firm owns no vehicles (Duggan et al., 2022a). Similarly, Airbnb is larger than the world’s top five hotel brands combined, with over 4 million listings worldwide, but owns no accommodation properties. While this concept may not seem entirely unusual in today’s market, it nevertheless highlights the rapidly changing connectivity, technological disruption, and innovation that enabled the emergence of this business model.
However, a side-effect of this development is the creation of unique interdependencies and power dynamics between platform organisations and workers. Platform organisations have been accused of utilising questionable and controversial practices in managing gig workers, specifically in satisfying legal criteria to meet the independent contractor status, while simultaneously exerting significant control over workforces (Norlander et al., 2021). Research has consistently addressed and often supported this claim, suggesting that the reality of the working arrangement appears to be one where platform organisations avoid giving direct commands to workers, which may indicate a legal employment relationship, while simultaneously controlling the labour process by using algorithmic technologies (Wu et al., 2019). This context—where human managers are non-existent and potentially fierce competition exists between workers—is arguably not favourable to manifestations of workplace support or work-life balance, possibly yielding damaging outcomes for workers (Meijerink et al., 2021).
4.3 Algorithmic Technologies in Gig Work
A range of new technologies—such as analytics, machine learning, and AI—have become increasingly influential in transforming work and management practices (Schafheitle et al., 2020). Organisations have implemented technologies that can parse through large amounts of data, acquire skills and knowledge, and operate autonomously (Wang et al., 2020). In the context of gig work, we are especially concerned with how this innovation has allowed organisations to develop new, digitally enabled systems of control to comprehensively manage workforces. Ivanova et al. (2018) argue that understanding how strategies of managerial control have evolved with the advent of new technologies is key to understanding the future of workplace relations. The very nature of the algorithmic technologies utilised in gig work represents an intertwining of technology with the management function. In doing so, platform organisations create new, comprehensive forms of monitoring and micro-management that would be difficult to achieve in more conventional roles (Murray et al., 2021).
The algorithmic technologies used in gig work are designed to have agency over the labour process. This agency amounts to a temporally embedded capacity to intentionally constrain, complement, or substitute for humans in the practice of routines (Newlands, 2021). In doing so, these algorithms fundamentally alter our understanding of workplace management by determining protocols, rules, and guidelines for gig workers; by making decisions; and by encouraging specific actions (Kellogg et al., 2020). Research indicates that this is particularly common in local gig work, such as ridesharing and food delivery, but is also found in remote, cloud-based gig work (Duggan et al., 2020). Thus, the flexibility and independence that is seemingly afforded to gig workers is often heavily constrained through control systems that algorithmically structure and monitor workers’ activities.
4.3.1 Algorithms and Managerial Control
Scholarship is increasingly concerned with understanding precisely how algorithms implement control over gig workers’ activities (McDonnell et al., 2021; Schafheitle et al., 2020). The scenario is seemingly one where, at once, various algorithmic processes combine to limit the information that workers can access, place incentives on worker compliance, and pressurise workers to treat prompts as commands (Barratt et al., 2020). Controversially, algorithmic control systems are also characterised by their opaqueness, with research indicating that gig workers experience a lack of transparency over how they are being monitored, ranked, and assessed (Cheng & Foley, 2019; Duggan et al., 2022b).
Thus, despite enabling the efficient organisation and delivery of work, there are concerns about the implications of algorithmic control for individual workers (Kellogg et al., 2020). This is because algorithmic management is primarily focused on the provision of instrumental support, leaving little room for worker engagement or the formation of meaningful social relationships (Ivanova et al., 2018). Accordingly, many of our traditional assumptions about the social aspects of work are considered obsolete, or perhaps even counterproductive, as these would potentially disrupt the monitoring and matching capabilities of the algorithm (Murray et al., 2021).
4.3.2 Algorithmic Management in Practice
Gig economy researchers have drawn on various theories in efforts to effectively understand the role of algorithmic technologies in organising labour (Gandini, 2019; Veen et al., 2020). It is argued that the platform organisation and its architecture, in the form of algorithmic technologies and digital platforms (i.e., smartphone applications or websites), should be understood as a digital-based point of production (Gandini, 2019)—in other words, the novel, unique ‘place’ where the labour process is enacted and where social relations are repurposed. Such perspectives, with the experiences of gig workers at the centre of the analysis, are seen as being valuable in advancing knowledge in this domain.
In seeking to understand the role of algorithms in implementing control, Kellogg et al. (2020) identify the various directional, evaluation, and disciplinary strategies enabled by these technologies. The outcome is that algorithmic control reconfigures working relations in several ways. This includes prompting workers to make specific decisions preferred by the organisation; restricting the availability of information to prevent specific behaviours; evaluating workers’ activities by recording and aggregating performance metrics and data; rating and ranking workers’ performance to guide future behaviour; and disciplining workers by potentially deactivating underperforming workers, while rewarding others with enhanced flexibility and bonus payments (Kellogg et al., 2020). Based on this, algorithmic management appears to be comprehensive, instantaneous, and hyper-efficient when compared to traditional means of control (McDonnell et al., 2021). Yet, long-term effectiveness of this has been called into question, particularly within the strategic context of engaging with and motivating workers (Jabagi et al., 2019).
4.4 Algorithms and Gig Work: Challenges, Controversies, and Uncertainties
The risks of gig work have been well documented, both in scholarly literature and policy reports (Duggan et al., 2022b; Taylor et al., 2017). The scope of these risks is quite extensive and spans an array of disciplinary perspectives. Yet, the core issue underpinning most of the ongoing research in this sphere—regardless of the discipline—is whether gig work is good or bad. Specifically, is gig work exploitative? Is the independent contractor classification unfair? Are algorithmic technologies being used inappropriately? If the answer to any of these questions is “yes”, how can we solve these issues and create a gig economy that works for all stakeholders?
4.4.1 Challenges for Management Practice
For digital platform organisations, the use of algorithmic management undoubtedly increases efficiency, reduces risks, and decreases labour costs—at least in the short term (Walker et al., 2021). Likewise, for gig workers, limited research indicates that algorithmic management grants a degree of autonomy and independence in scheduling and completing work (Duggan et al., 2022b; Meijerink & Bondarouk, 2021). Yet, research predominantly indicates that these technologies create complex challenges for workers via their capacity to dramatically alter the power dynamics between workers and organisations (Schafheitle et al., 2020). The data-dominated approach to managerial responsibility potentially moves gig work into an inhuman form, with algorithms undertaking roles that were traditionally the preserve of supervisors or managers, and with workers being disciplined or penalised in real-time (Norlander et al., 2021).
Management scholars continue to debate the long-term implications of algorithmic technologies, particularly if those practices currently normalised in gig work were to spread to more conventional forms of employment. There are arguments that the monitoring and surveillance capabilities of algorithmic management are contributing to the creation of a contemporary form of scientific management (McGaughey, 2018): jobs are fragmented into simple and repetitive tasks; the labour process is tightly controlled to ensure maximum efficiency; and workers who underperform can be easily identified and replaced (Duggan et al., 2022a). Thus, organisations can guarantee a high degree of consistency and predictability in the services delivered. However, while highly standardised processes may limit the risk of error, such rigidity also tends to increase work arduousness and inhibit professional growth (Jürgens et al., 1993). The “duality” of algorithmic management (Meijerink & Bondarouk, 2021)—both restraining and enabling value for workers—clearly brings the need for a more critical consideration of the implications and consequences for employment relations and the management function.
4.4.2 Challenges for Legislators and Policymakers
The gig economy has altered business models and confounded established people management structures, thereby requiring new ways of thinking about the dynamics that may potentially reshape workplaces. The classification of gig workers as independent contractors is undoubtedly a significant, ongoing challenge for regulatory bodies. To date, proposed solutions are less than straightforward. For example, a commonly cited suggestion is to reclassify all gig workers as employees, thereby providing the protections and benefits that accompany employment. However, this would eradicate the flexibility enjoyed by many gig workers, while also upending the operating model of most gig organisations (Cappelli & Keller, 2013). Others have proposed the creation of a special employment status for gig workers, in recognition that these arrangements do not fully align with either the definition of employees or the self-employed (Taylor et al., 2017). The complexity of this issue is illustrated by the varying rulings of courts on the same issue over the last number of years, perhaps indicating that a universal solution does not exist (Duggan et al., 2022a).
Within the European Union, consultations are currently taking place regarding the development of new instruments to effectively regulate gig work (Hauben et al., 2020). However, regulating digital platform organisations has proven to be a complex task, and policymakers will require an in-depth understanding of how the gig economy is shaping the labour market and working conditions to develop effective measures tailored to different forms of gig work (Duggan et al., 2022a). This calls for a close collaboration between researchers and policymakers, who should combine forces to further our understanding of the advantages and challenges posed by the gig economy.
4.4.3 Uncertainties for Workers
By not belonging to a particular organisation or a continuous, bounded occupational group, gig workers find themselves in what Ibarra and Obodaru (2016) call a ‘liminal space’ between occupations: immersed in hyper-flexibility, completing short-term assignments, and only offered work on a task-by-task basis. The very nature of gig work is that most tasks are performed individually, in isolation, without contact with fellow workers and often in competition with them (McGaughey, 2018). This leads to weak social ties and forms perhaps the largest obstacle for individuals seeking to craft a more meaningful working arrangement (Wang et al., 2020).
Gig work also presents challenges for workers’ abilities to develop career-related skills and competencies. Ostensibly, the autonomy and flexibility promised by gig work seems appealing, where workers can freely move across organisations in developing their professional skills. However, Bérastégui (2021) argues that the apparent fluidity of the gig economy is at best illusory, and at worst like “quicksand”, trapping individuals in a cycle of financial vulnerability and low-skilled work without professional stability. Research indicates that although some may have alternative career options or may enjoy their working arrangement, a longer-term implication is the difficulty for workers to effectively disengage from the gig economy due to a lack of financial security and the inability to develop professional skills (Duggan et al., 2022b).
With some evidence that gig work has started to emerge in wider professional areas such as finance, graphic design, and software coding (Minifie, 2016), these issues highlight the importance of creating a gig economy where decent work can flourish and where arrangements represent a stepping-stone for workers to progress their careers. Yet, caution must be exercised by not assuming that the experiences of all gig workers are homogenous. Instead, organisations and policymakers must work towards achieving a fuller understanding of the individual motivations and experiences of gig workers. If more workers are poised to join the gig economy in some capacity, the current operating model poses significant issues for workers via the lack of opportunities to reskill or upskill through their work.
4.5 Conclusion
As more research becomes available, it seems increasingly likely that the perceived independence from managerial control in gig work does not necessarily result in increased autonomy for workers—at least not to the extent promised by platform organisations (Maffie, 2020). This instigates the need for further studies to determine to what extent different types of gig workers lack various forms of support (e.g., career mentoring, coaching, and collegial or task support) and which aspects of gig work are most detrimental to this. The Covid-19 pandemic has cast new light on the essential nature of many gig workers, while also demonstrating that these workers, in many ways, are the guinea pigs of the new world of work. Likewise, the pandemic also escalated concerns that some aspects of this hyper-flexible, precarious labour form may go mainstream sooner than expected, reinforcing the need for more research and refined policy considerations.
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