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- ItemMulti-party working relationships in the gig economy: examining the experiences of app-workers and the role of algorithmic management(University College Cork, 2021-03) Duggan, James; Sherman, Ultan; Carbery, Ronan; McDonnell, Anthony; Irish Research CouncilThe gig economy has emerged as a significant theme of debate in the world of work. Gig work is non-standard in nature and facilitated by digital platform organisations who use algorithmic technologies to intermediate between gig workers and customers. The aim of this research is to examine the nature of multi-party working relationships in the gig economy, with a specific focus on understanding app-based gig workers’ experiences with algorithmic management. As a publication-based thesis, this research draws on multiple theoretical perspectives from employment relations and human resource management (HRM) to examine the unique features of gig work. Adopting a constructivist approach, this study draws on semi-structured interviews with 56 gig workers to examine their role in the working relationship, their perceptions of algorithmic management, and career-related issues in this form of labour. Findings illustrate that the pervasiveness of algorithmic management results in significant fragmentation of the working relationship. While some workers are content with the arrangement, most report concerns over the intense labour process control enabled by algorithms. Findings also indicate that algorithmic management hinders workers’ abilities to develop transferable competencies useful in seeking more secure arrangements, potentially trapping individuals in the hyper-flexible work forms found in the gig economy. This research articulates several contributions. Conceptually, this study proposes a complete typology of gig work, thereby departing from monolithic conceptualisations and identifying the most significant HRM and employment relations implications of algorithmic management. Empirically, this thesis adopts labour process and boundaryless career perspectives to contribute novel insights on the role of algorithmic management in shaping workers’ experiences in an especially insecure work form. Practically, this research provides insights on the most complex and problematic aspects of app-work, which hold particular relevance given ongoing efforts to develop effective policy responses.