earticle

영어

In platform work environments, algorithmic dispatch systems function as a central mechanism for both efficiency and control. However, prior research has primarily focused on the technical features of algorithms or workers' static perceptions, lacking empirical analysis of how platform workers' responses evolve over time. Therefore, this study aimed to investigate how delivery riders' perceptions and reactions to algorithmic dispatch evolve. A mixed-methods approach was employed in this study: grounded theory analysis of interviews revealed three core themes, including AI dispatch and operational systems, delivery income and work intensity, and rider skills and work practices. These qualitative insights provided a deeper understanding of riders' experience-based strategies and the factors influencing their algorithmic dependence. OLS regression using three waves of survey data identified key factors influencing algorithmic dependence. Results show that algorithmic dependence significantly decreases as operational load increases and over time. In particular, the study found that riders with higher operational load demonstrate a preference for autonomous decision-making over algorithmic control. Additionally, over the three survey waves, riders' reliance on algorithmic dispatch diminished as they became more familiar with the system. These findings highlight that acceptance of algorithmic control is not static but dynamic and adaptive. This study deepens the understanding of algorithm-user interaction and offers meaningful implications for platform algorithm design and labor policy, suggesting ways to enhance user engagement and mitigate resistance.