PhD Oral Exam - Nura Jabagi, Supply Chain and Business Technology Management
Four Essays on the Impacts of Platform-Mediated Work in the Gig-Economy
This event is free
School of Graduate Studies
When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.
Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.
This dissertation consists of four essays which address the fast-growing gig-economy – a global phenomenon which has been of concern for scholars, policymakers, and practitioners over the last decade. The gig-economy is a labour-market characterized by the prevalence of part-time and/or temporary positions in which organizations engage independent workers for short-term contracts by connecting workers to customers via a digital marketplace. The technological foundations underlying these digital marketplaces are known as digital labour platforms. Despite being lauded as the future of work (Shapiro, 2018), a deeper understanding of digital labour platform architectures is needed to provide “a more realistic picture of how platforms are shaping the future of work in the online gig-economy” (Corporaal & Lehdonvirta, 2017, p. 2). Moreover, our understanding of the impacts of advanced technologies in interaction with work contexts on gig-workers’ attitudes and behaviours remains under-developed (Deci, et al., 2017; Kuhn, 2016; Kuhn & Maleki, 2017; Pichault, et al., 2017; de Reuver, et al., 2018; Spreitzer, et al., 2017; Sutherland & Jarrahi, 2018). This collection of essays aims to bridge these research gaps.
The first essay in this dissertation explores how the architecture of the digital labour platforms underlying the gig-economy, and the characteristics of jobs mediated through these IT artifacts, can impact key antecedents of self-motivation. Drawing on self-determination theory (Deci & Ryan, 2000), job-characteristic theory (e.g. Hackman & Oldham, 1980), and enterprise social media research, this essay develops a mid-range theory demonstrating how organizations can engender gig-workers’ intrinsic motivation through the thoughtful design of their digital labor platforms and the integration of two social media tools, namely: social networking and social badging. In doing so, it advances the notion that the operational choices embodied in a gig-organization’s digital labour platform will act as critical determining factors of a gig-worker’s intrinsic motivation.
The second essay in this dissertation explores how gig-workers perceive organizational support in the context of algorithmically-managed platform work. 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; the fairness of organizational procedures; and organizational rewards and job conditions. In this conceptual 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 conceptual model that centres 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) – two newly proposed theoretical constructs.
The third essay in this dissertation is an instrument development piece which describes the process of developing and validating a theoretically-based measure of perceived algorithmic autonomy support (PAAS). To develop our instrument, we adopt Mackenzie et al.’s (2011) well-cited scale development process. As part of the scale development process, interviews were conducted with Uber drivers to support item generation. This was followed by content-validation with subject matter experts to develop and validate our instrument, and finally statistical and nomological validation were conducted using data collected from a total sample of 435 Uber drivers. The results of the survey confirm that: (i) PAAS is a second-order formative measure with four first-order reflective constructs; (ii) our resulting 13-item scale demonstrates adequate psychometric properties; and finally (iii) PAAS is positively, and significantly, related to perceived organizational support (POS) and job satisfaction.
The fourth essay in this dissertation aims to advance our understanding of how workers perceive managerial algorithms. On digital labour platforms, algorithms are responsible for making a wide range of decisions including performance evaluation, matching, and reward assignments, to name a few. Although algorithmic decision-making is a central feature of digital labour platforms, our understanding of how people perceive decisions made by algorithms, particularly in terms of fairness, remains underdeveloped. Drawing on the Theory of Organizational Justice (Colquitt, 2001; Greenberg, 1987), this paper explores the impacts of workers’ perceived fairness of managerial algorithms on perceived organizational support (POS) and job satisfaction. Through a survey of 435 Uber drivers, this paper finds that workers’ perceptions of the fairness of performance evaluation decisions taken by the Uber app’s algorithm play a central and significant role in promoting perceptions of organizational support and job satisfaction. On the other hand, workers’ perceptions of the fairness of matching (work allocation) decisions were found to play a less important, albeit positive, role. Moreover, our analyses also find POS to be a mediator between workers’ perceptions of fairness and job satisfaction.