AI-based diagnosis of the continuity of research thought: A case study from the perspective of innovation and management accounting

Abstract

Purpose: This study presents an original test of whether generative artificial intelligence (AI) can detect the internal conceptual coherence within a set of papers produced over several years. The analysis is based on five papers on innovation in the insurance sector, evaluated using M365 Copilot (GPT-5 model). Methodology/approach: The full texts of five conference papers, co-authored by the author of this study, were provided to the AI tool, and it was asked to reconstruct the conceptual development and main research direction across them. Findings: The AI tool identified both the chronological order and thematic development of the articles, showing a consistent progression from general reflections on risk toward a more structured, taxonomic model. However, result quality depends strongly on prompt clarity and user experience. Research limitations/implications: The small, specific sample limits generalisability, and the analysis is bound by the content provided and the model's current capabilities. AI can support self-assessment and exploration of research development but should not replace peer review or formal evaluation. Originality/value: A literature check conducted by the tool suggests that this use of AI to analyse long-term conceptual coherence in an author's work is relatively new. The study contributes to the literature on AI in research methodology by applying AI to assess the development of a research agenda over time, rather than single publications. It shows that LLMs can connect dispersed ideas, highlight practical implications, and suggest future research directions, offering a useful tool for researchers reviewing the evolution of their own work.