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<p>This study conducts a comprehensive examination of the interrelationships among artificial intelligence (AI), trust, entrepreneurship, and decision-making in organizational settings, employing a Multiple Correspondence Analysis (MCA) of 31 peer-reviewed articles published between 2020 and 2025. Building on a systematic literature review and the HOMALS technique, the research identifies four thematic clusters that structure the contemporary conceptual space of AI in business environments: ethical-social governance, strategic-technological core, digital infrastructure, and decision-oriented automation. The findings expose a latent structural tension between technocratic efficiency and ethical legitimacy, highlighting the need for hybrid models that integrate algorithmic capabilities with principles of responsible governance. Within this framework, variables such as user acceptance, transparency, and hybrid cognitive architectures emerge as key dimensions for understanding and designing AI-mediated decision processes in entrepreneurial contexts. The study proposes a conceptual model that articulates these dimensions and provides a theoretical basis for future investigations and practical initiatives aimed at the reflexive, responsible, and sustainable implementation of intelligent technologies in contemporary business ecosystems.</p>

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The AI trust factor: an MCA analysis of automation and decision-making in entrepreneurship

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月7日

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  • María Fernández-Fernández,
  • Álvaro Hernández-Tamurejo,
  • Paula González-Padilla

摘要

This study conducts a comprehensive examination of the interrelationships among artificial intelligence (AI), trust, entrepreneurship, and decision-making in organizational settings, employing a Multiple Correspondence Analysis (MCA) of 31 peer-reviewed articles published between 2020 and 2025. Building on a systematic literature review and the HOMALS technique, the research identifies four thematic clusters that structure the contemporary conceptual space of AI in business environments: ethical-social governance, strategic-technological core, digital infrastructure, and decision-oriented automation. The findings expose a latent structural tension between technocratic efficiency and ethical legitimacy, highlighting the need for hybrid models that integrate algorithmic capabilities with principles of responsible governance. Within this framework, variables such as user acceptance, transparency, and hybrid cognitive architectures emerge as key dimensions for understanding and designing AI-mediated decision processes in entrepreneurial contexts. The study proposes a conceptual model that articulates these dimensions and provides a theoretical basis for future investigations and practical initiatives aimed at the reflexive, responsible, and sustainable implementation of intelligent technologies in contemporary business ecosystems.