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This study investigates the determinants influencing the adoption of artificial intelligence (AI) in organizational strategies, drawing on a robust empirical framework. Using a sample of 250 participants from diverse sectors and organizational sizes, the research employs exploratory and confirmatory factor analyses to identify four latent dimensions: organizational, technological, environmental, and individual. Logistic regression results reveal significant predictors of AI adoption, such as top management support, digital maturity, system compatibility, and trust in AI. External pressures, including competitive dynamics and regulatory support, are also highlighted as critical enablers. The findings offer both theoretical and practical contributions, enriching existing frameworks like the Technology Acceptance Model (TAM) and the Technology-Organization-Environment (TOE) framework while providing actionable insights for decision-makers. Despite its contributions, the study acknowledges limitations in sample size and methodology, proposing future research avenues to explore the long-term impacts of AI on organizational performance and sustainability.

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Determinants of the Adoption of Artificial Intelligence in Managerial Strategies: An Empirical Analysis Based on Logistic Regression

  • 会议论文

  • 发表日期：

    2026年

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  • El Broumi Soufiane,
  • Assaad Idrissi Maha,
  • Eddahmouny Hicham

摘要

This study investigates the determinants influencing the adoption of artificial intelligence (AI) in organizational strategies, drawing on a robust empirical framework. Using a sample of 250 participants from diverse sectors and organizational sizes, the research employs exploratory and confirmatory factor analyses to identify four latent dimensions: organizational, technological, environmental, and individual. Logistic regression results reveal significant predictors of AI adoption, such as top management support, digital maturity, system compatibility, and trust in AI. External pressures, including competitive dynamics and regulatory support, are also highlighted as critical enablers. The findings offer both theoretical and practical contributions, enriching existing frameworks like the Technology Acceptance Model (TAM) and the Technology-Organization-Environment (TOE) framework while providing actionable insights for decision-makers. Despite its contributions, the study acknowledges limitations in sample size and methodology, proposing future research avenues to explore the long-term impacts of AI on organizational performance and sustainability.