Mapping human augmentation technologies for societal impact: A multilevel framework for classification and innovation potential

While human augmentation (HA) technologies offer significant potential for addressing societal challenges, their specific application towards social goals like sustainability, quality of life, and well-being remains understudied. This study addresses this gap by investigating the central research question: How do current HA research landscapes align with global social targets, and what are the comparative advantages of these technologies across individual, organizational and societal levels? To answer this, we employ a comprehensive bibliometric analysis of 6,284 papers across 60 specific research topics. Using citation network analysis and text similarity mapping, we reveal that although HA literature is smaller in volume, it exhibits high topical diversity and distinct specialization. Semantic mapping identifies selective but significant intersections between HA research and social domains, particularly through three influential subclusters: Industrial Worker Augmentation Systems, Human-Building Interface Assessment Technologies, and Assistive Augmentation Technologies. Based on these findings, we developed a novel nine-dimensional performance framework for multi-level evaluation of HA's societal value. The practical applicability of this framework is validated through real-world case studies, including industrial exoskeleton deployments at Ford and VR training at Walmart, demonstrating tangible gains in productivity, safety, and learning acceleration. This framework offers actionable insights for assessing HA's social impact, guiding responsible innovation, and identifying commercialization opportunities aligned with well-being, productivity, and sustainability.