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<p>This study presents an integrated framework that combines organizational enablers - namely HR flexibility, IT agility, and dynamic capabilities. It also integrates individual-level factors such as attitude, behavioral intention, and cognitive resistance. This approach offers a key multi-level factors analysis of AI-enabled HRM adoption by moving beyond the traditional techno-centric focus. Grounded in the Dynamic Capabilities View (DCV), the Theory of Reasoned Action (TRA), and the Resistance to Organizational Change Model, we developed and tested a structural model using Partial Least Squares Structural Equation Modeling (PLS-SEM). The model is based on data collected from 300 professionals in India. The findings indicate that HR flexibility (β = 0.45, <i>p</i> &lt; 0.01) and IT agility (β = 0.35, <i>p</i> &lt; 0.01) play crucial roles in strengthening dynamic capabilities. These dynamic capabilities, in turn, significantly influence employees’ attitudes (β = 0.21, <i>p</i> &lt; 0.01) and their intention to adopt AI-enabled HRM systems (β = 0.26, <i>p</i> &lt; 0.01). The proposed model accounts for 56% of the variance in adoption intention. Additionally, the analysis reveals that employee attitudes mediate the relationship between dynamic capabilities and adoption intention. Cognitive resistance acts as a moderator, diminishing the strength of the relationship between dynamic capabilities and attitude. These findings highlight the need for organizations seeking to implement AI in HRM to invest not only in flexible HR strategies and agile IT systems but also in developing dynamic capabilities to better manage employee resistance and enhance readiness for adoption.</p>

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How Internal Organizational Enablers Influence AI-Enabled HRM Adoption: A Dynamic Capabilities Perspective

  • 期刊论文

  • 发表日期：

    2026年7月11日

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  • Urmii Himanshu,
  • Jatinder Kumar Jha,
  • Ravi Shekhar Kumar,
  • Shivam Gupta,
  • Prasanta Kumar Pattanaik

摘要

This study presents an integrated framework that combines organizational enablers - namely HR flexibility, IT agility, and dynamic capabilities. It also integrates individual-level factors such as attitude, behavioral intention, and cognitive resistance. This approach offers a key multi-level factors analysis of AI-enabled HRM adoption by moving beyond the traditional techno-centric focus. Grounded in the Dynamic Capabilities View (DCV), the Theory of Reasoned Action (TRA), and the Resistance to Organizational Change Model, we developed and tested a structural model using Partial Least Squares Structural Equation Modeling (PLS-SEM). The model is based on data collected from 300 professionals in India. The findings indicate that HR flexibility (β = 0.45, p < 0.01) and IT agility (β = 0.35, p < 0.01) play crucial roles in strengthening dynamic capabilities. These dynamic capabilities, in turn, significantly influence employees’ attitudes (β = 0.21, p < 0.01) and their intention to adopt AI-enabled HRM systems (β = 0.26, p < 0.01). The proposed model accounts for 56% of the variance in adoption intention. Additionally, the analysis reveals that employee attitudes mediate the relationship between dynamic capabilities and adoption intention. Cognitive resistance acts as a moderator, diminishing the strength of the relationship between dynamic capabilities and attitude. These findings highlight the need for organizations seeking to implement AI in HRM to invest not only in flexible HR strategies and agile IT systems but also in developing dynamic capabilities to better manage employee resistance and enhance readiness for adoption.