Mapping Consumer Innovativeness in Technology Adoption: An Integrated Framework and Future Research Agenda

Abstract

Consumer innovativeness (CI) has become central to explaining technology adoption, yet the literature remains fragmented, with prior reviews largely examining innovativeness in a general context. Amidst the low success rate of new product launches (less than 25%), CI offers valuable insights into enhancing the adoption of disruptive technologies. The study offers a comprehensive systematic review encompassing 85 articles spanning over two decades (1998–2024) to investigate the concept of CI in technology adoption and to synthesize the literature using the theories‐contexts‐characteristics‐methodologies (TCCM) framework. The synthesis shows that technology‐related factors (perceived usefulness, technology readiness) and psychological traits (optimism, openness to experience) are the strongest drivers of early CI, while hedonic motivation and novelty‐seeking accelerate adoption of experiential innovations; CI frequently mediates these antecedent‐outcome links, with effects amplified by social influence and market mavenism. In AI‐ and data‐driven contexts, privacy, trust, and ethical concerns moderate CI, and algorithmic failures affect innovative and non‐innovative consumers differently; innovative consumers are also more likely to give feedback and co‐create, while loyalty programs and gamification sustain repeat adoption. CI's effects vary considerably across cultures, and even the most theoretically active recent stream relies almost entirely on cross‐sectional self‐report, limiting causal inference. The study also identifies priming theory, reinforcement theory, consumer ethics theory, and consumer culture theory as novel lenses for explaining trust, ethics, and reinforcement dynamics in AI‐driven adoption. Managerially, firms should segment consumers by CI profile for early‐access/beta‐launch strategies, embed transparent data‐control mechanisms to offset privacy concerns moderating AI‐based adoption, and institutionalize feedback/co‐creation channels for high‐CI users to support iterative improvement.