Development of a Framework for the Integration of Artificial Intelligence in Human Resource Management through Qualitative Mixed Methods

Subject Areas : human resource management

Mohammadreza Zolghadr ^{1 *} , Ali Naeimi Khondabi ² , مجید رمضان ³

1 - Master's Student, Department of Management, Management and Industrial Engineering Complex, Malek Ashtar University of Technology, Tehran, Iran
2 - 3. Master's Student, Department of Management, Management and Industrial Engineering Complex, Malek Ashtar University of Technology, Tehran, Iran.
3 - عضو هیات علمی دانشگاه صنعتی مالک اشتر

Received: 2025-05-01 Accepted : 2025-06-03 Published : 2025-08-06

Keywords: Artificial Intelligence, Human Resource Management, A Meta-synthesis Method,

Abstract :

Background and Purpose: In the contemporary, dynamic, and competitive organizational landscape, conventional human resource management practices encounter several challenges, including diminished efficiency, heightened susceptibility to human errors, and an inadequate reliance on data-driven decision-making processes. The objective of the current study is to establish a comprehensive framework for the integration of artificial intelligence into human resource management.

Methodology: This qualitative study employed a meta-synthesis methodology and was based on the analysis of ninety-five scholarly articles published between 2000 and 2025. Data analysis was conducted utilizing the seven-step approach developed by Sandelowski and Barroso, with the resulting model subsequently validated through the Delphi technique, incorporating the insights of ten experts. Additionally, interpretive structural modeling (ISM) and MICMAC analysis were utilized to elucidate the causal relationships among the components.

Findings: The findings indicated that four primary dimensions—namely, provision, development, maintenance, and exploitation—comprising sixteen key components, are integral to the digital transformation of human resources.

Conclusion: The integration of artificial intelligence with human methodologies has the potential to enhance decision-making efficacy, organizational agility, and employee job satisfaction. The proposed model, serving as a strategic framework, will facilitate a transition toward a data-driven approach to human resource management.

References:

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