The objective of this research is to design a model for the implementation of a data-driven human resource (HR) management system utilizing digital and intelligent tools within the Tehran Blood Transfusion Organization. In terms of its goal, this study is classified as fundamental research, and in terms of its execution, it employs a mixed-methods approach (qualitative and quantitative).



The statistical population for the qualitative phase included 15 academic experts, faculty members, and managers of the Blood Transfusion Organization, selected through purposive and theoretical (judgmental) sampling. The quantitative population consisted of 700 active employees of the Tehran Blood Transfusion Organization, from which a sample size of 196 individuals was selected using simple random sampling. Data collection tools involved semi-structured interviews for the qualitative phase and questionnaires for the quantitative phase. Data analysis was performed using MAXQDA 2020 for the qualitative findings, while SPSS and SmartPLS were utilized for the quantitative analysis.



Findings from the qualitative phase revealed that through axial coding, 44 primary codes were categorized into 22 axial codes, representing the key concepts and structural components of a data-driven HR system. The quantitative analysis confirmed that causal, contextual, intervening, and strategic factors significantly impact the implementation of the data-driven human resource system. Furthermore, alignment between organizational structure, technology, and policies was identified as a prerequisite for successful data-driven decision-making and enhanced human resource effectiveness. By presenting an integrated and data-driven model, this study highlights the critical role of digital and intelligent tools in improving HR processes and strategic decision-making, providing a valuable practical and theoretical guide for organizations navigating the path of digital transformation.