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<p>This article explores how generative AI can augment, rather than replace, traditional theory-building processes in academic research. Drawing from foundational frameworks by Hunt (2010) [<CitationRef CitationID="CR12">12</CitationRef>] and Zeithaml et al. (J Mark 84(1):32–51, 2020), it presents a structured approach to integrating AI into theory development. The authors introduce Delegated Virtue Economics (DVE) as a case study that demonstrates AI’s role in accelerating ideation, identifying cross-disciplinary connections, and structuring conceptual models. They also highlight risks including source hallucination, over-reliance, and cognitive skill decay. The work outlines protocols for responsible human-AI collaboration and argues that the future of theory development lies in disciplined, symbiotic workflows that enhance both rigor and creativity.</p>

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AI-Augmented Theory Building: From Theoretical Foundations to Practical Application

  • 期刊论文

  • 发表日期：

    2025年8月5日

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  • Daniel Finkenstadt

摘要

This article explores how generative AI can augment, rather than replace, traditional theory-building processes in academic research. Drawing from foundational frameworks by Hunt (2010) [12] and Zeithaml et al. (J Mark 84(1):32–51, 2020), it presents a structured approach to integrating AI into theory development. The authors introduce Delegated Virtue Economics (DVE) as a case study that demonstrates AI’s role in accelerating ideation, identifying cross-disciplinary connections, and structuring conceptual models. They also highlight risks including source hallucination, over-reliance, and cognitive skill decay. The work outlines protocols for responsible human-AI collaboration and argues that the future of theory development lies in disciplined, symbiotic workflows that enhance both rigor and creativity.