This study examines the incentive dilemmas faced by food-delivery riders in the digital economy, focusing on how algorithmic management shapes workers psychology and behavior. Drawing on Self-Determination Theory and the crowding-out effect, it explores, via a literature review of journals, industry reports, how platform algorithms as forms of extrinsic motivation interact with riders intrinsic motivation. Through a labor-quota mechanism and the dispatch logic, platforms systematically suppress riders autonomy, alienate their sense of competence, and erode their relatedness, leading to a crowding-out effect in which extrinsic motivation undermine intrinsic motivation and manifest as professional burnout and identity loss. The study proposes a three-fold improvement path: reconstructing algorithmic transparency mechanisms, establishing career-development systems, and building multi-stakeholder collaborative networks. Platform employment models must move beyond a purely extrinsic framework and, through institutional design, better coordinate intrinsic and extrinsic motivation to achieve both decent work and sustainable development for riders. These findings offer a theoretical foundation and practical insights for breaking the incentive trap of algorithmic management and address a key gap in understanding how intrinsic and extrinsic motivations interact in digital labor.
Jingyi Yan (Wed,) studied this question.