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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.

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.

High-quality employee motivation can contribute to an organization’s long-term success by supporting employees’ well-being and performance. Nevertheless, there is a paucity of research concerning how organizations motivate workers in non-traditional work contexts. In the algocratic context of the gig-economy, the purpose of this paper is to understand the role that technology can play in motivating workers.

Drawing on the self-determination theory, job-characteristic theory and enterprise social media research, this conceptual paper explores how the architecture of the digital labor platforms underlying the gig-economy (and the characteristics of jobs mediated through these IT artifacts) can impact key antecedents of self-motivation.

Combining theory and empirical evidence, this paper develops a mid-range theory demonstrating how organizations can support the self-motivation of gig-workers through the thoughtful design of their digital labor platforms and the integration of two social media tools (namely, social networking and social badging).

This paper answers calls for psychologically-based research exploring the consequences of gig-work as well as research studying the impacts of advanced technologies in interaction with work contexts on motivation. In theorizing around a large set of social-contextual variables operating at different levels of analysis, this paper demonstrates that individual-level motivation can be influenced by both task-based and organizational-level factors, in addition to individual-level factors.

The proposed theory provides novel insight into how gig-organizations can leverage widely accessible social media technology to motivate platform workers in the absence of human supervision and support. Theoretical and practical implications are discussed.

The purpose of this paper is to provide an investigation of how different types of gig workers engage in the gig economy. Specifically, the authors distinguish between workers who view gig work as primary income (or not) and those workers who view it as a job (or not).

In total, 1,190 Mechanical Turk (MTurk) workers completed surveys across two studies examining whether types of workers differ based on demographic characteristics, utilization of MTurk, why they participate in the gig economy on MTurk (i.e. push and pull factors) and how this impacts life satisfaction.

Workers relying on MTurk as a primary income had lower incomes and spent more time completing large numbers of work units. This group of workers also reported fewer pull factors (e.g. enjoyment) as a reason for working in the gig economy and had lower levels of self-reported current and predicted future life satisfaction. Individuals who view MTurk as a job were more likely to treat MTurk like a job – engaging in online communities and having a regular work schedule. These workers were more likely to report pull factors (e.g. enjoyment and challenge) and did not differ on life satisfaction.

The current research contributes to our understanding of MTurk, one of the largest online platforms for gig work, as part of the diverse gig economy and highlights potential areas for future research.

The purpose of this paper is to draw attention to the lack of a clear theoretical framework in management education programs to allow students to understand the world in which gig workers operate. The article asserts the need for practical guidance for pedagogy to prepare students to manage in the gig economy.

This paper provides a rationale for the assertion that, beyond training students to create their own work configurations in the new world of work, management education should acquaint students with the specific challenges faced by managers responsible for a spectrum of workers in various work configurations. This paper explicates gig work configurations and offers theoretical and practical issues related to worker and manager identity to be considered in the development of an educational framework.

Management curricula over time have successfully augmented several competencies to the manager identity. Bolstering the competency of simultaneously managing employees working in multiple employment arrangements is the next natural progression of management education.

Particular challenges for prospective “gig managers” may relate to the management of somewhat elusive processes that enhance their own and their workers' organizational identity and related outcomes.

The authors invoke the literature of holding environments for identity development, along with the distinctness and inclusion facets of Kegan's theory of the evolution of consciousness and tenets of institutional theory, in a call for the examination of methods that may help aspirant managers recognize distinction and inclusion needs in others and cultivate tools to assist in their workers' need fulfillment.

In this paper, the authors explore the relationship that slack resources and technology-mediated human capital investments can have on individuals’ entrepreneurial intentions. Focusing on human capital investments that individuals make through education and work, the authors analyze the relationship among formal online learning opportunities, informal skill development in the gig economy and entrepreneurial intentions.

Leveraging a novel dataset that merges administrative tax data with a survey of over 8,528 low- and moderate income (LMI) households, this study uses machine learning and propensity score weighting to examine the likelihood that individuals who make these technology-mediated human capital investments will have increased odds of entrepreneurial intentions when compared to similar individuals who do not make these investments.

The authors find that both partaking in online learning and working in the gig economy are significantly associated with increased odds of entrepreneurial intentions. Furthermore, through a variety of robustness and mechanism checks, the authors find that technology-mediation is an important factor in these relationships and that informal skill development and career preparation is one way in which gig employment influences entrepreneurial intentions.

As the study’s data come from a cross-sectional survey, the authors cannot make causal inferences about the relationship between online learning, gig employment and entrepreneurial intentions. Thus, future research should explore sources of longitudinal data.

This study has practical implication for individuals and policymakers that seek to increase entrepreneurship among LMI households.

Despite a wealth of research on the relationships among slack resources, technology and innovation at the firm level, there is little of this research at the individual level – especially among LMI individuals. The authors begin to fill this important gap.

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.

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.