Artificial Intelligence Adoption for Green Innovation: A Systematic Review and Future Research Agenda - Rahman, Asiqur (My University, 2026)
Artificial intelligence (AI) is increasingly recognized as an influential enabler of green innovation through its potential to improve resource efficiency, environmental monitoring, and sustainability-oriented decision-making. Despite growing scholarly interest, research on AI adoption for green innovation remains fragmented and lacks a unified analytical structure integrating technological, organizational, and environmental conditions. This study addresses this gap through a systematic literature review guided by the Technology–Organization–Environment (TOE) framework. Using the Scopus database, 44 peer-reviewed journal articles published between 2020 and 2026 were systematically selected through a rigorous multi-stage screening process. Thematic analysis served as the primary analytical method, supported by bibliometric techniques including keyword co-occurrence analysis and thematic mapping using VOSviewer and Biblioshiny. The findings indicate that AI adoption for green innovation is shaped by interdependent conditions rather than isolated factors. Technological enablers include digital infrastructure, AI capability, cloud computing, and data analytics, while implementation cost, integration complexity, and data security concerns act as key barriers. Organizational factors, including leadership commitment, capability development, knowledge integration, and cross-functional coordination, strongly influence adoption effectiveness. Environmental conditions, including regulatory frameworks, institutional quality, and competitive pressure, shape the likelihood and depth of green innovation outcomes. The synthesis identifies three core arguments: technological readiness is necessary but insufficient; the organizational context is the active translating system converting technological potential into sustainability outcomes; and the environmental context determines adoption quality, distinguishing substantive green innovation from symbolic compliance. Two interaction effects underpin these arguments: the capability-technology complementarity and the institutional amplification effect. The study contributes by advancing the TOE framework through a more dynamic and integrative interpretation of AI adoption for green innovation. It provides managerial and policy implications and proposes a future research agenda emphasizing longitudinal, configurational, and context-sensitive investigation of AI-enabled sustainability transformation.
Keywords: Artificial Intelligence; Green Innovation; AI Adoption; Technology-Organization-Environment Framework; Sustainability