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This study designs and evaluates an AI-driven system using GPT-4 and Retrieval-Augmented Generation (RAG) to enhance HR and talent management in Iraq’s healthcare sector. Addressing challenges like resource limitations and low trust in new technologies, the study applies the Unified Theory of Acceptance and Use of Technology (UTAUT) to examine adoption intention. A cross-sectional survey of 435 HR managers in public and private hospitals achieved a 96.7% response rate. Using Partial Least Squares Structural Equation Modelling (PLS-SEM) with SmartPLS 3.9, findings reveal that social influence is the strongest determinant of adoption, followed by facilitating conditions, performance expectancy, and effort expectancy. The system’s simplicity, low cost, and minimal infrastructure needs support its adoption. The study validates the model’s effectiveness and contributes to AI adoption research in developing healthcare contexts, offering a practical tool to automate HR processes, enhance decision-making, and improve efficiency, particularly in Arabic-speaking, resource-limited environments.

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Large Language Models and Talent Management: Developing RAG Model in Healthcare

  • 章节

  • 发表日期：

    2025年

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  • Osama Mohammed Ahmed AL Atraqchi,
  • Amir A. Abdulmuhsin,
  • Saad G. Yaseen,
  • Abeer F. Alkhwaldi,
  • Shafique Ur Rehman

摘要

This study designs and evaluates an AI-driven system using GPT-4 and Retrieval-Augmented Generation (RAG) to enhance HR and talent management in Iraq’s healthcare sector. Addressing challenges like resource limitations and low trust in new technologies, the study applies the Unified Theory of Acceptance and Use of Technology (UTAUT) to examine adoption intention. A cross-sectional survey of 435 HR managers in public and private hospitals achieved a 96.7% response rate. Using Partial Least Squares Structural Equation Modelling (PLS-SEM) with SmartPLS 3.9, findings reveal that social influence is the strongest determinant of adoption, followed by facilitating conditions, performance expectancy, and effort expectancy. The system’s simplicity, low cost, and minimal infrastructure needs support its adoption. The study validates the model’s effectiveness and contributes to AI adoption research in developing healthcare contexts, offering a practical tool to automate HR processes, enhance decision-making, and improve efficiency, particularly in Arabic-speaking, resource-limited environments.