Hybrid Marketing Science: A Recursive, Empirically Testable S-O-R-E Framework for AI-Augmented Consumer Interactions

Hybrid Marketing Science proposes a recursive S-O-R-E framework that fuses real-time AI analytics, human judgment, contextual awareness, and endogenous ethical safeguards. Five operationalized propositions address sustained attention, cognitive load reduction, amplified engagement, ethical trust formation, and self-learning adaptation. Metrics include gaze duration, NASA-TLX scores, conversion rates, intrusion perceptions, and weekly performance gains. Unlike prior linear models, this is the first to position ethics as an integral core layer within a recursive cycle, enabling dynamic hybrid human-AI systems. Supported by meta-analytic evidence (Li & Wang, 2024: 38% trust variance explained by ethics), we project +12% additional variance via context integration. This blueprint targets high-impact empirical testing in retail, transit, metaverse, and emerging service ecosystems.