Driven to succeed, or to leave? The variable impact of self-leadership in rideshare gig work

The nature of gig work – temporary, task-based work that is facilitated by applications and platforms – lacks the structural, interpersonal, and organizational dynamics of leadership. Instead, algorithms and software are the primary source of organizational and informational resources for a gig worker in a process known as 'algorithmic management'. Job performance and career success depends primarily on the individual gig worker's ability to establish objectives, identify strategies, and develop skills for job performance on their own. As such, success in gig work is likely to be connected to self-leadership ability. Drawing on the psychological perspectives of control theory and conservation of resources theory, we find that the agency provided by self-leadership is generally beneficial to rideshare drivers (N = 924) in that it increases both well-being and the intention to seek non-gig work. However, enacting this agency as a leaderless worker is an intensive process, and can either be a resource gain or drain. We find that workers experience better or worse outcomes, respectively, under circumstances of high job satisfaction or high financial stress. Implications and directions for future HRM research and practice among other gig and traditional worker populations are discussed.