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Despite growing interest in the gig economy among academics, policy makers and media commentators, the area is replete with different terminology, definitional constructs and contested claims about the ensuing transformation of work organisation. The aim of this positional piece is to provide a timely review and classification of crowdwork. A typology is developed to map the complexity of this emerging terrain, illuminating range and scope by critically synthesising empirical findings and issues from multidisciplinary literatures. Rather than side-tracking into debates as to what exactly constitutes crowdwork, the purpose of the typology is to highlight commonalities rather than distinctions, enabling connections across areas. The framework serves as a heuristic device for considering the broader implications for work and employment in terms of control and coordination, regulation and classification, and collective agency and representation.

Advancements in Internet and digital technologies have enabled a new work form of open sourcing, which we refer to as the crowdsourcing work environment (CSWE). This new form of work has the potential to disrupt and transform the nature of traditional work. However, our understanding of this new work form is still in its incipient stage. To enhance our understanding, this study captures crowdworkers' perceptions to explore the characteristics of the crowdworkers, crowdsourcing jobs, and the crowdwork environment that collectively drive the crowdworkers to participate in open source work. Guided by the job characteristic theory and work value perspectives, we used the revealed causal mapping method to analyze narratives by 55 crowdworkers registered on Amazon Mechanical Turk (MTurk). Our data analysis uncovered nine main constructs, 22 key concepts, and 815 causal-effect linkages surrounding CSWE that could guide our theoretical understanding of this emerging phenomenon. Individual needs and the crowdwork context emerged as the major factors motivating individuals' initial participation in CSWE, but we found crowdsourcing task characteristics (e.g., job autonomy, task variety, task significance, task instruction, and task compensation) and a digitally enabled environment (e.g., system affordance and MTurk governance) to shape crowdworkers' continued participation in CSWE. The findings suggest several promising research streams, including the psychological factors (i.e., personal growth needs and work values) and social outcomes (i.e., empowerment or exploitation of crowdworkers) for examining the psychology and sociology of crowdsourcing work.

Work and Employment is a critical area that is undergoing major change influenced by the widespread connectivity and utilisation of the Internet and the rise of digital platforms. Crowdwork is an emerging new way of working that is witnessing exponential growth. It is surrounded by a fixed debate between opposite perspectives on its impact on workers. However, both perspectives adopt a static view that does not pay much attention to crowdworkers’ progress in their job over time. In this study, we seek to advance this debate by adopting a dynamic view of crowdwork to explore the trajectory of workers over time based on their own accounts. Through rich qualitative data and inductive analysis, the study unravels that crowdworkers craft what could be conceptualised as a career development path. It identifies five stages in this career path and workers’ efforts to mould their work demands and job-related resources to create a future for themselves. The discussion shows the fruitful insight that this approach brings to theory and practice. Limitations and future avenues for research are then discussed.

This article provides a new typology for understanding human labour integrated into the production of artificial intelligence systems through data preparation and model evaluation. We call these forms of labour ‘AI data work’ and show how they are an important and necessary element of the artificial intelligence production process. We draw on fieldwork with an artificial intelligence data business process outsourcing centre specialising in computer vision data, alongside a decade of fieldwork with microwork platforms, business process outsourcing, and artificial intelligence companies to help dispel confusion around the multiple concepts and frames that encompass artificial intelligence data work including ‘ghost work’, ‘microwork’, ‘crowdwork’ and ‘cloudwork’. We argue that these different frames of reference obscure important differences between how this labour is organised in different contexts. The article provides a conceptual division between the different types of artificial intelligence data work institutions and the different stages of what we call the artificial intelligence data pipeline. This article thus contributes to our understanding of how the practices of workers become a valuable commodity integrated into global artificial intelligence production networks.

The 2007+ crisis led to an increase in occasional and task work, which unleashed the potential of new technological and organizational solutions. The advancements in digital technological platforms stimulated the growth of the segment referred to as gig economy. The article aims to apply a systematic approach to the driving forces behind the emergence of gig economy and its success to date and to assess its development prospects. It argues to confirm the thesis that gig economy has its own inherent logic, while it remains part of the multi-stage process of the evolution of employee-employer relations, from the industrial stage to the digital era to platform-mediated work. Based on the analysis of the current world literature, the article posits that gig economy, as the next stage of development, has significantly reduced the quality of work, but it may also not meet the individual needs of the contemporary consumer. This increases the likelihood of the need for its change. The article also envisages the direction of this change towards post-platform economy based on distributed market spaces and provides the characteristics of its determinants, including social capital and a sense of individual entrepreneurship. The primary methods used in the study involved analysis and critique of the current world literature as well as the method of analysis and logical construction.

Crowdsourcing is not a new practice but it is a concept that has gained substantial attention during recent disasters. Drawing from previous work in the crisis informatics, disaster sociology, and computer-supported cooperative work (CSCW) literature, this paper first explains recent conceptualizations of crowdsourcing and how crowdsourcing is a way of leveraging disaster convergence. The CSCW concept of “articulation work” is introduced as an interpretive frame for extracting the salient dimensions of “crisis crowdsourcing.” Then, a series of vignettes are presented to illustrate the evolution of crisis crowdsourcing that spontaneously emerged after the 2010 Haiti earthquake and evolved to more established forms of public engagement during crises. The best practices extracted from the vignettes clarified the efforts to formalize crisis crowdsourcing through the development of innovative interfaces designed to support the articulation work needed to facilitate spontaneous volunteer efforts. Extracting these best practices led to the development of a conceptual framework that unpacks the key dimensions of crisis crowdsourcing. The Crisis Crowdsourcing Framework is a systematic, problem-driven approach to determining the why, who, what, when, where, and how aspects of a crowdsourcing system. The framework also draws attention to the social, technological, organizational, and policy (STOP) interfaces that need to be designed to manage the articulation work involved with reducing the complexity of coordinating across these key dimensions. An example of how to apply the framework to design a crowdsourcing system is offered with a discussion on the implications for applying this framework as well as the limitations of this framework. Innovation is occurring at the social, technological, organizational, and policy interfaces enabling crowdsourcing to be operationalized and integrated into official products and services.

Crowdworkers, such as Mturk workers, face challenging work conditions, including low pay and unfair treatment. To overcome a lack of means to share information with other workers, they often self-organize in independent online communities, for example, TurkerNation. Although prior research has explored both the crowdwork and online community contexts, it has largely ignored crowdworkers' dual-context roles. This research provides evidence for the dual-context phenomenon. We propose three theory-driven mechanisms-embeddedness, cross-influence, and moderated heuristics-that, together with the conventional model and the sequential-update mechanism, explained up to 72% of key behavioral outcomes in both contexts. Moreover, crowdworkers' active participation in online communities had a persistent mitigating effect on their desires to quit working in the crowdworking environment. These findings add to a richer understanding of crowdworkers' integrated and evolving psychology within the dual-context environment. From a managerial perspective, our findings suggest that crowdwork platforms can better retain their workers by facilitating-and actively engaging with-their discussions in an embedded online community.

Crowdworkers on platforms like Amazon Mechanical Turk face growing competition as a result of the global excess supply of digital labour. As a result, many crowdworkers turn to automated scripts, which help them locate better tasks faster and to boost their earnings. However, to date, it is not clear whether and to what extent the use of such scripts influence the opportunities for those crowdworkers who do not use them. This an important aspect that warrants further exploration because it can have negative implications for the health of crowdwork platforms. In this study, we use Discrete Event Simulation to identify and quantify the unintended consequences of the excessive use of automated scripts. Our findings show that, while the use of scripts allows some crowdworkers to identify and accept far more tasks than others, in the long run, this behaviour results in their competence persistence and reputational persistence and progressively to detrimental impacts for those workers who do not use scripts, and who may ultimately be forced to exit the platform. As a result, automated scripts have negative consequences, whereby their excessive use leads to a tragedy of the commons for all platform stakeholders, including the crowdworkers, the job requesters and the platform itself.

This article focuses on cloudworking in developing countries and the implications of this type of work on development. The analysis draws on two eminent development theories—the capabilities approach and dependency theory. Using these framework, opportunities and risks of cloudwork to developing countries are presented and analyzed, and policy implications discussed.

Resumo A definição de uberização do trabalho se refere a uma nova forma de gestão, organização e controle, compreendida como uma tendência que atravessa o mundo do trabalho globalmente. São analisados os principais elementos da uberização, em especial: a transformação do trabalhador em trabalhador just-in-time; o crowdsourcing; a transformação do trabalho em trabalho amador e as novas formas de controle automatizadas que possibilitam pensar na definição de gerenciamento algorítmico do trabalho. Faz-se uma crítica ao uso da noção de empreendedor para o trabalhador uberizado, propondo-se seu deslocamento para a definição de autogerente subordinado. O artigo apresenta os resultados de pesquisa empírica com motofretistas entre 2014 e 2019, analisando a uberização a partir da própria experiência dos trabalhadores . A partir do uso de dados secundários presenta-se uma análise comparativa com o trabalho dos bike boys. Conclui-se que é necessário estender as análises no tempo e no espaço para que se possam tirar conclusões mais genéricas sobre a uberização e as diferentes formas que assume, ainda, é preciso a produção de dados sobre os trabalhadores de plataforma da América Latina.