Advancing Systematic Literature Reviews With Artificial Intelligence: A TCCM‐Based Synthesis of Over 50 Years of Double Jeopardy Research

ABSTRACT Combining artificial intelligence (AI) with a human‐in‐the‐loop (HITL) approach, this study advances framework‐based systematic literature reviews, showcasing how to harness advanced technologies for high‐quality integrations of marketing knowledge. The proposed approach leverages the Theory–Context–Characteristics–Methodology (TCCM) framework for classifying and validating scientific knowledge, and combines AI and HITL iterations for a rigorous, speedy, and scalable review method. AI can substantially reduce manual coding time and costs, achieving coding performance with inter‐coder reliability scores ranging from 96% (ChatGPT‐4o) to 83% (Gemini Advanced). The highest‐performing AI platform, with Cohen's Kappa ( κ = 0.95), is used to undertake the initial steps of the systematic literature review process, followed by HITL review to identify and resolve discrepancies. To demonstrate the value and results of this approach, this study presents a systematic synthesis of 179 academic, peer‐reviewed publications and 50 years of marketing research on Double Jeopardy—a widely established, empirically‐derived theory. The proposed review method surpasses software‐based alternatives while meeting the requirements of impactful, replicable, and transparent bibliometric research. This research makes substantial contributions to contemporary scholarly discourses on the value of systematic knowledge syntheses for both academia and practice, as well as the scope of technological advancements, or “smart bibliometrics.”