or career progression. In addition, platform organisations can operate in ways that avoid the costs of legal employment. Gig workers enter into temporary formal agreements with these on-demand platforms to provide services to the platforms’ clients. The clients are the end users, either an individual consumer or an institution, that make a request for an individual gig worker to perform a task. For these reasons, gig working arrangements, by their very nature, are mostly characterised by poor job security, low pay, reduced commitment and decreased loyalty between parties. However, the structural features of this work should confer substantial autonomy on to the individual gig worker. Yet, unions representing gig workers have been embroiled in a series of long-running disputes with corporate giants of the gig economy for much of the past decade. Similarly, individual stories have been highlighted that raise concern over precarity and exploitation. Typically, such accusations are denied by platform organisations. At the centre of many of these disputes is the ongoing struggle for control (and rights) between workers and labour platforms. The issues at play here are important not just because of the gig economy’s growing prominence but also because the allure of work in the sector is often characterised by the flexibility for individuals to decide when, where and how much they wish to work. Deliveroo, for example, promises “work that fits around your life”, while Uber o ers the chance to “fit driving around your life, not the other way around”. Yet, the reality appears to increasingly tell a di erent story. The prevailing narrative of gig work being flexible is driven by the organisational perspective and not from the real, lived experiences of gig workers. In a recent study, we interviewed gig workers in the food delivery and transportation sectors to understand how much control they exercise in their roles. How users shape the equation The study identifies the widespread use of a new system of organisational control – algorithmic human resource management (algorithmic HRM) control – that regulates the actions of individuals to align them with organisational needs and interests. Unlike common forms of control in more traditional organisations, where the working relationship exists within the boundaries of the organisation, algorithmic HRM control enables non-organisational parties to control workers – directly and indirectly – even though these workers are usually classified as self-employed freelancers rather than employees. The study found that customers in the gig economy exercise a high degree of control over how workers approach tasks by quantitatively rating their performance. Similarly, restaurants influence the earning potential of food delivery workers, as the e ciency in delivery is directly connected to the e ciency in supply. Many restaurants have struggled to rebound since the Covid-19 pandemic and food delivery platforms have provided them with a much-needed lifeline. The research challenges the notion of abundant autonomy in gig work by identifying an intricate and comprehensive system of algorithmic control that substantially shapes the lived experiences of workers. Enabled by self-learning technologies, labour platforms are empowered to manage worker performance and strategically regulate rewards, all in the name of
BIOGRAPHIES
Ronan Carbery is vice-dean for graduate studies and programme director of the executive MBA at Cork University Business School. His research interests include gig work, algorithmic management and learning and development James Duggan is a lecturer in human resource management at Cork University Business School. His research interests include algorithmic HRM, gig work and HRM in the future workplace Ultan Sherman is a senior lecturer in organisational behaviour and human resource management at Cork University Business School, where he is also programme director for the MSc in human resource management Anthony McDonnell is professor of human resource management at Cork University Business School. His research interests include international HRM, talent management and work in the gig economy
16 | Ambition NOVEMBER 2023
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