Abstarct: The present study was conducted with the aim of designing and testing a structural model to examine the effect of perceived procedural fairness in algorithmic human resource decisions on job satisfaction, with perceived organizational support serving as a mediating variable. More specifically, this research investigated the effects of perceived procedural fairness of algorithmic matching decisions and algorithmic evaluation decisions on perceived organizational support and job satisfaction among active drivers of the Snapp internet taxi platform. In terms of purpose, this research is applied, and regarding methodology, it is a descxriptive-survey study of a correlational nature. The statistical population of the study consisted of active drivers of the Snapp internet taxi platform. Due to the lack of precise access to the full list of active drivers, the statistical population was considered unlimited. Using Cochran’s formula, the required sample size was estimated at 384 respondents. Data were collected through purposive convenience sampling using a standardized questionnaire baxsed on Likert-type scales. The validity of the measurement instrument was confirmed through Confirmatory Factor Analysis (CFA), while its reliability was verified using Cronbach’s alpha coefficients exceeding the acceptable threshold of 0.70. The collected data were analyzed using SPSS and SmartPLS software within the frxamework of Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that perceived procedural fairness of algorithmic matching decisions had a positive and significant effect on perceived organizational support. Moreover, perceived procedural fairness of algorithmic evaluation decisions also had a positive and significant effect on perceived organizational support. The results further showed that both perceived fairness of algorithmic matching decisions and perceived fairness of algorithmic evaluation decisions had positive and significant direct effects on job satisfaction. In addition, perceived organizational support had a positive and significant effect on job satisfaction. The mediation analysis confirmed that perceived organizational support significantly mediated the relationship between perceived procedural fairness of algorithmic matching decisions and job satisfaction. Similarly, perceived organizational support also mediated the relationship between perceived procedural fairness of algorithmic evaluation decisions and job satisfaction. Therefore, it can be concluded that fair algorithmic procedures in ride allocation and performance evaluation not only directly improve drivers’ job satisfaction but also enhance it indirectly by strengthening their perception of organizational support.