Optimal dynamic regulatory architectures for riders' non-compliant deliveries: A reputation-triggered game analysis

Quality issues in delivery services due to non-compliant behaviors by riders have infringed upon consumer rights and hindered the development of the instant delivery industry. This study develops a reputation-triggered evolutionary game between instant delivery platforms and riders, in which riders' reputation values determine the intensity of the platform's incentives and sanctions. Focusing on three governance elements commonly used in practice, commissions, penalties, and subsidies, we identify seven dynamic regulatory architectures based on whether the platform adopts a single element, a partial combination, or a full combination. The model is used to evaluate regulatory effectiveness and to determine the optimal architecture under different conditions. Findings reveal that relying solely on a subsidy-based regulatory architecture is unsustainable due to high costs, potentially leading platforms to adopt negative regulation. By reducing excess returns from non-compliant deliveries, cutting active regulatory costs, and adjusting riders' reputation values, the effectiveness of single-element and partially mixed architectures can be enhanced. A completely mixed regulatory architecture employing commissions, penalties, and subsidies concurrently is the most effective. However, fluctuations in platforms' active regulation and riders' compliance over time highlight the need for a continuous mixed regulatory approach. These findings provide decision-making insights for optimizing regulatory architectures against delivery violations from a reputation-based perspective.