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Digital labor platforms (DLPs) are transforming the nature of the work for an increasing number of workers, especially through extensively employing automated algorithms for performing managerial functions. In this novel working setting – characterized by algorithmic governance, and automatic matching, rewarding and punishing mechanisms – gig-workers play an essential role in providing on-demand services for final customers. Since gig-workers’ continued participation is crucial for sustainable service delivery in platform contexts, this study aims to identify and examine the antecedents of their working outcomes, including burnout and engagement.

We suggested a theoretical framework, grounded in the job demands-resources heuristic model to investigate how the interplay of job demands and resources, resulting from working in DLPs, explains gig-workers’ engagement and burnout. We further empirically tested the proposed model to understand how DLPs' working conditions, in particular their algorithmic management, impact gig-working outcomes.

Our findings indicate that job resources – algorithmic compensation, work autonomy and information sharing– have significant positive effects on gig-workers’ engagement. Furthermore, our results demonstrate that job insecurity, unsupportive algorithmic interaction (UAI) and algorithmic injustice significantly contribute to gig-workers’ burnout. Notably, we found that job resources substantially, but differently, moderate the relationship between job demands and gig-workers’ burnout.

This study contributes a theoretically accurate and empirically grounded understanding of two clusters of conditions – job demands and resources– as a result of algorithmic management practice in DLPs. We developed nuanced insights into how such conditions are evaluated by gig-workers and shape their engagement or burnout in DLP emerging work settings. We further uncovered that in gig-working context, resources do not similarly buffer against the negative effects of job demands.

The chapter presents emerging evidence on the development of the platform economy, paying particular attention to the motivations for entering platform work, the conditions of platform work, and the extent of social protections afforded platform workers. Debate thus far has tended to be highly speculative and lacking in grounded empirical analysis, with policy-makers in particular actively looking to regulate platform work on the basis of its novelty as a form of employment within the wider context of the decline of the “standard employment relationship.” The chapter explores such concerns through an analysis of European Union labor market data and a unique data-set of circa 1,200 online “click workers” across four established platforms. A novel contribution of the analysis is to differentiate between those that only work on platforms (work-dependent platform workers) and those that do such work in addition to another job. The analysis suggests that work-dependent platform workers are more likely to be differentiated by their motivations for doing such work than their experiences of job quality or access to social protections. However, the relationship between platform working and levels of social protection is complex, notably in terms of combined level of social protection and the contractual arrangement of additional job holders. This leaves us to conclude that policy initiatives designed to address gaps in social protections for platform workers would be more appropriately targeted toward problems of insecure work more broadly. Finally, a number of areas for future research are outlined.

The digital platform economy has wide-ranging implications for the nature of work in the twenty-first century. The ease at which suppliers and demanders of labour come together across digital platforms creates a very new kind of labour market characterized by hyper-flexibility and an ambiguous employment relationship. Platform work has been hailed as providing employment opportunities for young people entering the labour market and other groups for whom access to more traditional forms of work is compromised (e.g. women with caring responsibilities or people with chronic health issues), or simply those seeking easily accessible, flexible work (e.g. students). On the other hand, unions and grassroots activist campaigners have highlighted the poor conditions that shape the experience of platform work, such as low pay, lack of choice over working time, tight control over the labour process and a dependency on platforms that belies their self-employed status. These dimensions of decent work are examined in the context of France and the United Kingdom, two countries which represent very different employment contexts (Milner, 2015), and thus provide insights into how specific country contexts may mediate the experience of platform work and the policy response.

This chapter will introduce the concept of the gig economy. It begins with a brief discussion of technological changes and their impact on the workforce and labour market, demonstrating how the trend shifts towards the gig economy. It then examines the trends that are influencing this transformation and discusses various perspectives and the attractiveness of the gig economy for workers and businesses. This chapter will also discuss the gig economy, platform economy, digital platform, and gig worker categories. It concludes with a brief discussion of some of the opportunities, issues, and challenges associated with the gig economy.

The term “crowdwork” describes a new form of digital work that is organized and regulated by internet-based platforms. This chapter examines how crowdwork platforms ensure their virtual workforce’s commitment and control its performance despite its high mobility, anonymity, and dispersion. The findings are based on a case study analysis of 15 microtask and macrotask platforms, encompassing 32 interviews with representatives of crowdwork platforms, and crowdworkers, as well as an analysis of the platforms’ homepages and community spaces. The chapter shows that performance control on crowd platforms relies on a combination of direct control, reputation systems, and community building, which have until now been studied in isolation or entirely ignored. Moreover, the findings suggest that while all three elements can be found on both microtask and macrotask platforms, their functionality and purpose differ. Overall, the findings highlight that platforms are no neutral intermediaries but organizations that adopt an active role in structuring the digital labor process and in shaping working conditions. Their managerial structures are coded and objectified into seemingly neutral technological infrastructures, whereby the underlying power relations between capital and labor become obscured.

This work examines the intersection between traditional human resource management and the novel employment arrangements of the expanding gig economy. While there is a substantial multidisciplinary literature on the digital platform labor phenomenon, it has been largely centered on the experiences of gig workers. As digital labor platforms continue to grow and specialize, more managers, executives, and human resource practitioners will need to make decisions about whether and how to utilize gig workers. Here the authors explore and interrogate the unique features of human resource management (HRM) activities in the context of digital labor platforms. The authors discuss challenges and opportunities regarding (1) HRM in organizations that outsource labor needs to external labor platforms, (2) HRM functions within digital labor platform firms, and (3) HRM policies and practices for organizations that develop their own spin-off digital labor platform. To foster a more nuanced understanding of work in the gig economy, the authors identify common themes across these contexts, highlight knowledge gaps, offer recommendations for future research, and outline pathways for collecting empirical data on HRM in the gig economy.

Working conditions on many digital work platforms often contribute to the grand challenge of establishing decent work. While research has examined the public regulation of platform work and worker resistance, little is known about private regulatory models. In this paper, we document the development of the “Crowdwork Agreement” forged between platforms and a trade union in the relatively young German crowdworking field. We find that existing templates played an important role in the process of negotiating this new institutional infrastructure, despite the radically new work context. While the platforms drew on the corporate social responsibility template of voluntary self-regulation via a code of conduct focusing on procedural aspects of decent platform work (i.e., improving work conditions and processes), the union contributed a traditional social partnership template emphasizing accountability, parity and distributive matters. The trade union’s approach prevailed in terms of accountability and parity mechanisms, while the platforms were able to uphold the mostly procedural character of their template. This compromise is reflected in many formal and informal interactions, themselves characteristic of a social partnership approach. Our study contributes to research on institutional infrastructures in emerging fields and their role in addressing grand challenges.

Drawing on the case of the recent Belgian law on the “sharing economy,” this chapter develops a critique of the dominant discourse of platform-mediated work as fostering the inclusion of individuals belonging to historically underrepresented groups (e.g., women with caring roles, people living in remote areas, individuals with disabilities, etc.) into the labor market. Exempting platform-mediated employment from social contributions and substantially lowering taxation, the law facilitates platform-based crowdsourcing firms’ predatory business model of capital valorization. The author argues that this business model rests precisely on the externalization of the costs of the social reproduction of this “diverse” labor through its precarization. These costs are not only externalized to individual workers, as often held. They are also externalized to the Belgian welfare state, and thus ultimately both to taxpayers and firms operating through classical business models, which fund the welfare state through taxation and social security contributions. For this reason, the debate surrounding platform-based employment might paradoxically provide a historical opportunity for recovering the Belgian tradition of social dialog between employers’ associations and trade unions. The author concludes by identifying key foci for action to ensure a better protection of workers of crowdsourcing firms including classifying them as employees, revising the conditions of access to social security protection, inclusive union strategies, the leveraging of technology to enforce firm compliance, and fostering counter-narratives of firms’ accountability toward society.

The emerging nonstandard employment (i.e. gig work) makes gig workers face a series of forms of labor insecurity. Prior studies focus on the linkage between gig work insecurity and precariousness. However, how gig workers and platforms jointly handle gig work insecurity has been so far overlooked. To this end, this study aims to explore how gig platforms and workers jointly cope with the insecurity of the gig work model.

Building upon the JD-R model, this study used a double-level perspective to hypothesize how gig platforms and workers jointly cope with gig work insecurity. Second, 248 questionnaire data were collected from workers who worked for several gig platforms (e.g. Meituan, Eleme, DidiTax, Zhihu and Credamo) in China. Third, the analysis method based on the partial least squares structural equation model (PLS-SEM) was employed to test the study theoretical model.

Empirical findings show that gig workers can cope with gig work insecurity by crafting their work; gig platforms' formalization governance not only reduces gig work insecurity but also helps gig workers address it by more easily crafting their work.

Gig workers do always have not enough job resources and motivation to work hard. Gig workers merely rely on job crafting to cope with the insecurity of the gig work model, which is insufficient. Gig platforms should also formalize their current governance mechanisms, which can supplement gig workers' job resources and reduce their job demands so as to help them cope with such gig work insecurity.

These results advance the understanding of the joint roles of gig platforms and workers in addressing gig work insecurity and improve governance effectiveness and value of gig platforms.

Algorithms are widely used to manage various activities in the gig economy. Online car-hailing platforms, such as Uber and Lyft, are exemplary embodiments of such algorithmic management, where drivers are managed by algorithms for task allocation, work monitoring and performance evaluation. Despite employing substantially, the platforms face the challenge of maintaining and fostering drivers' work engagement. Thus, this study aims to examine how the algorithmic management of online car-hailing platforms affects drivers' work engagement.

Drawing on the transactional theory of stress, the authors examined the effects of algorithmic monitoring and fairness on online car-hailing drivers' work engagement and revealed the mediation effects of challenge-hindrance appraisals. Based on survey data collected from 364 drivers, the authors' hypotheses were examined using partial least squares structural equation modeling (PLS-SEM). The authors also applied path comparison analyses to further compare the effects of algorithmic monitoring and fairness on the two types of appraisals.

This study finds that online car-hailing drivers' challenge-hindrance appraisals mediate the relationship between algorithmic management characteristics and work engagement. Algorithmic monitoring positively affects both challenge and hindrance appraisals in online car-hailing drivers. However, algorithmic fairness promotes challenge appraisal and reduces hindrance appraisal. Consequently, challenge and hindrance appraisals lead to higher and lower work engagement, respectively. Further, the additional path comparison analysis showed that the hindering effect of algorithmic monitoring exceeds its challenging effect, and the challenge-promoting effect of algorithmic fairness is greater than the algorithm's hindrance-reducing effect.

This paper reveals the underlying mechanisms concerning how algorithmic monitoring and fairness affect online car-hailing drivers' work engagement and fills the gap in the research on algorithmic management in the context of online car-hailing platforms. The authors' findings also provide practical guidance for online car-hailing platforms on how to improve the platforms' algorithmic management systems.