The rapid expansion of the gig economy has transformed conventional understandings of employment, work autonomy, and organizational control. Digital labour platforms increasingly connect workers with customers through short-term, task-based and project-based arrangements. Flexibility, autonomy, and the ability to determine when and where work is undertaken are among the defining characteristics of gig work. However, the same autonomy that provides workers with freedom may also increase the requirement for self-regulation. In the absence of fixed working hours, continuous supervisory oversight, and conventional organizational routines, gig workers must independently initiate, prioritize, monitor, and complete work activities. This creates a potentially important behavioural challenge: procrastination. Procrastination is generally understood as the unnecessary delay of an intended action despite anticipating that the delay may have negative consequences. Although procrastination has been extensively examined in educational and individual contexts, its relevance to contemporary flexible work and the gig economy remains comparatively underexplored. Recent research on workplace procrastination emphasizes the importance of self-regulation, motivation, task characteristics, and contextual conditions, while contemporary gig-economy research highlights autonomy, algorithmic management, work uncertainty, and the changing nature of organizational control. This chapter integrates these streams of literature to develop a conceptual framework explaining how characteristics of gig work may influence procrastination through self-regulation, task motivation, digital distraction, and perceived work pressure. The chapter proposes that job autonomy and flexibility are not inherently associated with procrastination; rather, their consequences depend on the worker's capacity for self-regulation and the structure of the platform environment. Algorithmic management may simultaneously reduce and increase procrastination by creating external performance cues while also introducing monitoring, uncertainty, and digital interruptions. Eight hypotheses are proposed for future empirical testing. The chapter concludes with implications for gig workers, platform organizations, human resource professionals, and policymakers and identifies directions for future research.