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<p>The rapid advancement of Artificial Intelligence (AI) has introduced transformative changes in Higher Education Institutions (HEIs), particularly in the deployment of Management Information Systems (MIS). The primary aim of this study is to examine how managerial support, technical capability, and other organizational factors interact with technological drivers and government involvement to shape AI adoption within HEIs. Guided by the “Technology–Organization–Environment (TOE) and Diffusion of Innovation (DOI) frameworks”, the study employs a quantitative, survey-based methodology. Data were collected from 334 respondents across Saudi Arabian HEIs actively engaged in AI initiatives. Structural Equation Modeling (SEM) was applied to test hypothesized relationships among constructs, including Managerial Capability (MC), Technological Drivers (TD), Government Involvement (GI), and AI Adoption (AIA). The results reveal that technological drivers, particularly digital platforms, big data utilization, and trust, security, are significant enablers of managerial capability, which subsequently exerts a strong positive influence on both AI adoption and government involvement. In contrast, traditional enablers such as managerial support, technical capability, organizational readiness, and change management did not demonstrate direct effects, suggesting their role may be mediated through integrated technological infrastructures. Furthermore, government involvement emerged as a critical external facilitator, significantly enhancing AI adoption outcomes. This study enriches literature by clarifying the interdependent pathways through which technology, management, and institutional frameworks coalesce to enable AI adoption in HEIs. These findings also highlight the importance of aligning technological investments with managerial competencies while leveraging supportive government policies, offering actionable insights for policymakers, administrators, and institutional leaders aiming to accelerate AI integration in higher education.</p>

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Exploring success factors that impact artificial intelligence adoption on technological drivers and managerial capabilities

  • 期刊论文

  • 发表日期：

    2026年5月31日

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  • Waleed Mugahed Al-Rahmi

摘要

The rapid advancement of Artificial Intelligence (AI) has introduced transformative changes in Higher Education Institutions (HEIs), particularly in the deployment of Management Information Systems (MIS). The primary aim of this study is to examine how managerial support, technical capability, and other organizational factors interact with technological drivers and government involvement to shape AI adoption within HEIs. Guided by the “Technology–Organization–Environment (TOE) and Diffusion of Innovation (DOI) frameworks”, the study employs a quantitative, survey-based methodology. Data were collected from 334 respondents across Saudi Arabian HEIs actively engaged in AI initiatives. Structural Equation Modeling (SEM) was applied to test hypothesized relationships among constructs, including Managerial Capability (MC), Technological Drivers (TD), Government Involvement (GI), and AI Adoption (AIA). The results reveal that technological drivers, particularly digital platforms, big data utilization, and trust, security, are significant enablers of managerial capability, which subsequently exerts a strong positive influence on both AI adoption and government involvement. In contrast, traditional enablers such as managerial support, technical capability, organizational readiness, and change management did not demonstrate direct effects, suggesting their role may be mediated through integrated technological infrastructures. Furthermore, government involvement emerged as a critical external facilitator, significantly enhancing AI adoption outcomes. This study enriches literature by clarifying the interdependent pathways through which technology, management, and institutional frameworks coalesce to enable AI adoption in HEIs. These findings also highlight the importance of aligning technological investments with managerial competencies while leveraging supportive government policies, offering actionable insights for policymakers, administrators, and institutional leaders aiming to accelerate AI integration in higher education.