Conceptualizing algorithmic management characteristics and exploring their effects on Gig workers in online labor platforms

Abstract

The widespread application of digital technologies boosts the rapid growth of the gig economy. In this context, online labor platforms (OLPs), as a new form of organization embodying the duality nature of market and organization, widely adopt algorithmic management to control and coordinate gig workers. However, due to the absence of traditional employment relationships and the transition of management agents from managers to algorithmic technologies, how to effectively manage gig workers has become a severe challenge for OLPs. Existing algorithmic management literature has mainly portrayed algorithmic management as an escalated form of labor control and limited studies have been conducted to comprehensively explore the effects of algorithmic management on gig workers. To address this limitation, this dissertation firstly completes two studies (Studies 1A and 1B) to conceptualize and operationalize the characteristics of algorithmic management respectively. Based on grounded theory, the results of interview data analysis (n = 23) in Study 1A reveal the concept and dimensions of platform workers’ perceived characteristics of algorithmic management. In Study 1B, following the six-step procedure, I develop the scale and examine the structure and validation in two different samples of food delivery workers (n=300) and gig drivers (n=300). Based on the results of Study 1A and Study 1B, I adopt the perspective of job crafting to explore the potential double-edged sword effects of algorithmic management. Specifically, I propose that promotion-focused job crafting will mediate the positive relationships between gig workers’ perceived algorithmic management characteristics and their platform commitment. Differently, prevention-focused job crafting will mediate the positive relationships between gig workers’ perceived algorithmic management characteristics and their job insecurity. Moreover, I also investigate the boundary conditions for mitigating the negative effects and amplifying the positive effects of algorithmic management in Study2. Specifically, I propose personal resilience will strengthen the positive relationships between gig workers’ perceived algorithmic management characteristics and their promotion-focused job crafting. However, personal resilience will weaken the positive relationships between gig workers’ perceived algorithmic management characteristics and their prevention-focused job crafting. In Study 3A and Study 3B, dialoguing with employment-organization relationship (EOR) literature, I explore how the relationships between gig workers and OLPs construct and evolve, and how different relationship types will influence the effects of algorithmic management characteristics on gig workers. Specifically, in Study 3A, based on a case study, I first develop a process model of EOR evolution in OLPs to illustrate how external environment characteristics determine the switching of OLPs’ two functions (i.e., organization and market) and further lead to different types of EOR. Based on Study 3A, Study 3B proposes and examines the moderating role of relationship types on the effects of algorithmic management characteristics on gig workers. The findings from this dissertation primarily suggest the positive effects of algorithmic management in OLPs on gig workers, which will be moderated by gig workers’ personal resilience and their relationship types with OLPs. The implications of this dissertation for theory and practice are also discussed.