Objective: The present study aims to construct a model of data-driven culture in human capital management.

Method: This research is of a developmental-applied nature and follows a descriptive-causal approach. Data were collected using both library and field methods. A mixed-methods strategy, including thematic analysis and fuzzy DEMATEL (implemented in Python 3.11), was employed for data analysis. The study population included all academic literature and resources that address data-driven culture and data-oriented human capital management. Additionally, the statistical population for completing the questionnaire consisted of expert managers and scholars in the fields of artificial intelligence, data science, and human resource management. A snowball sampling method was used to select 16 participants. The content validity of the questionnaire was confirmed by subject matter experts, and its reliability was verified through the consistency ratio.

Results: The findings indicate that the model of data-driven culture in human capital management comprises three levels: 1) Requirements and Tools of a Data-Driven Culture (including components such as transformational leadership and innovation in organizational culture, data and analytical literacy of human capital, data protection and adherence to ethical-legal principles, and the use of modern technologies and tools); 2) Processes of Data-Driven Culture (including components such as the collection of valid and transparent data, data quality and integration, continuous data monitoring and evaluation, and data-driven decision-making); and 3) Outcomes of Data-Driven Culture (including components such as data-driven recruitment and selection of human capital, data-driven development and advancement of human capital, and data-driven performance planning and management).

Conclusions: The results of this study provide a roadmap for human resource managers to establish data-driven culture within their organizations.