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<p>In response to evolving technological and societal demands, public sector organizations must adapt their governance and decision-making processes. This study proposes a temporal-causal modeling approach to analyze and predict organizational adaptation dynamics. Unlike static models, this framework integrates network analysis, complex adaptive systems, and AI-driven simulations to capture evolving interactions between governance, leadership, and digital transformation. The findings highlight the interplay between policy changes, technological adoption, and institutional resilience, providing strategic insights for policymakers and administrators navigating digital transformation. This approach not only enhances predictive accuracy but also facilitates data-driven decision-making, improving long-term adaptability and efficiency in governance.</p>

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Advancing temporal-causal modeling for public sector adaptation: an AI-driven approach to organizational strategy and governance

  • 期刊论文

  • 发表日期：

    2025年6月20日

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  • Yusraw O-manee,
  • Nur Syakiran Akmal Ismail,
  • Billel Arbaoui

摘要

In response to evolving technological and societal demands, public sector organizations must adapt their governance and decision-making processes. This study proposes a temporal-causal modeling approach to analyze and predict organizational adaptation dynamics. Unlike static models, this framework integrates network analysis, complex adaptive systems, and AI-driven simulations to capture evolving interactions between governance, leadership, and digital transformation. The findings highlight the interplay between policy changes, technological adoption, and institutional resilience, providing strategic insights for policymakers and administrators navigating digital transformation. This approach not only enhances predictive accuracy but also facilitates data-driven decision-making, improving long-term adaptability and efficiency in governance.