Purpose 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. Design/methodology/approach 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. Findings 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). Research limitations/implications 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. Originality/value 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.

Organizational support theory proposes that employees develop global beliefs concerning the degree to which an organization values their contributions and cares about their well-being. These beliefs, known as perceived organizational support (POS), are related to a number of positive employee outcomes, including: job satisfaction, work effort, performance, etc. Three categories of POS antecedents have been recognized in the literature: perceived supervisor support; fairness of organizational procedures; and organizational rewards and job conditions. In this paper, we explore these antecedent categories in the gig-work context where organizations replace human managers with algorithmic management practices and data-driven procedures. In doing so, we develop a new conceptual model that centers on the role that a gig-organization's algorithm plays in engendering POS by promoting perceptions of algorithmic fairness (PAF) and perceptions of autonomy support (PAAS). Contributions and future research avenues are discussed.

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Platform workers' autonomy and agency are recurring themes in the study of the gig-economy where narratives purporting workers' autonomy and empowerment conflict with those alleging the control and marginalization of workers. While it has been said that promoting workers' agency can threaten the valuation of platform-based companies, the benefits of supporting workers' autonomy in traditional organizations are well-established. To understand such inconsistencies, it is necessary to measure perceptions of autonomysupport; yet, no validated instruments exist that can be used to measure workers' perceptions of algorithmic autonomy-support. To address this gap, we draw on the Theory of Self-Determination to reconceptualize the notion of autonomy-support for the technoorganizational phenomenon of algorithmically managed platform work. In doing so, we introduce a new construct, namely: Perceived Algorithmic Autonomy Support (PAAS). In this work-in-progress paper, we describe our current work in developing and validating a theoretically-based measure for PAAS. Preliminary results are provided.