Abstarct: The present study aims to investigate the impact of algorithmic monitoring methods on drivers' self-efficacy, considering the mediating role of technological stressors and the moderating role of algorithmic steering control, among the community of active drivers in two major online transportation platforms. This research is applied in terms of purpose and descxriptive-survey correlational in terms of data collection method. The statistical population includes all drivers actively providing services on the aforementioned platforms. Given the vastness of the population, which exceeds one million two hundred thousand individuals, the sample size was determined as 384 individuals baxsed on Morgan's table. The sampling method was multi-stage random sampling. Standard questionnaires were utilized for data collection, and the electronic lixnk was made available to users through the official page of the platforms on social networks, asking them to respond to the questions if they met the criteria. Structural equation modeling was employed for data analysis and hypothesis testing. The findings from hypothesis testing present a complex and intriguing picture of the relationships among variables. The results indicated that observational monitoring, while negatively and significantly reducing challenge stressors, significantly increases threat stressors. In contrast, interactive monitoring was able to positively and significantly increase challenge stressors while simultaneously significantly reducing the intensity of threat stressors. This finding suggests that the manner in which monitoring is implemented determines the quality of the driver's psychological experience of that monitoring.