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<p>This study investigates artificial intelligence (AI) adoption in human resource management (HRM) through a two-stage research design that combines bibliometric review analysis and quantitative empirical testing. In stage one, bibliometric analysis is used to map the intellectual structure, publication trends, influential sources, leading authors, country collaboration patterns, and keyword co-occurrence themes in AI-enabled HRM research. This stage provides a systematic foundation for identifying dominant research streams and theoretical gaps. In stage two, the study empirically examines the determinants of AI adoption in HRM by applying the Technology-Organization-Environment (TOE) framework to survey data collected from 421&#xa0;h professionals in China. The quantitative model includes technological factors (AI availability and AI compatibility), organizational factors (HR capability and HR readiness), and environmental factors (regulatory environment and leadership support), with AI adoption positioned as a mediating mechanism linking contextual conditions to HRM performance. Structural equation modelling (SEM) is employed to test the measurement and structural models. The results show that all TOE dimensions significantly influence AI adoption, and AI adoption positively enhances HRM performance. The mediation analysis further confirms that AI adoption translates technological, organizational, and environmental enablers into HRM performance gains. By integrating bibliometric evidence with SEM-based empirical validation, this study contributes to AI-HRM literature by connecting the knowledge structure of the field with a theoretically grounded adoption-performance model.</p>

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Mapping and testing AI adoption in human resource management: a bibliometric review and TOE-based quantitative analysis

  • 期刊论文

  • 发表日期：

    2026年6月22日

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  • Zongwen Xia

摘要

This study investigates artificial intelligence (AI) adoption in human resource management (HRM) through a two-stage research design that combines bibliometric review analysis and quantitative empirical testing. In stage one, bibliometric analysis is used to map the intellectual structure, publication trends, influential sources, leading authors, country collaboration patterns, and keyword co-occurrence themes in AI-enabled HRM research. This stage provides a systematic foundation for identifying dominant research streams and theoretical gaps. In stage two, the study empirically examines the determinants of AI adoption in HRM by applying the Technology-Organization-Environment (TOE) framework to survey data collected from 421 h professionals in China. The quantitative model includes technological factors (AI availability and AI compatibility), organizational factors (HR capability and HR readiness), and environmental factors (regulatory environment and leadership support), with AI adoption positioned as a mediating mechanism linking contextual conditions to HRM performance. Structural equation modelling (SEM) is employed to test the measurement and structural models. The results show that all TOE dimensions significantly influence AI adoption, and AI adoption positively enhances HRM performance. The mediation analysis further confirms that AI adoption translates technological, organizational, and environmental enablers into HRM performance gains. By integrating bibliometric evidence with SEM-based empirical validation, this study contributes to AI-HRM literature by connecting the knowledge structure of the field with a theoretically grounded adoption-performance model.