ADOPTION OF LOW-CODE AI IN SUPPLY CHAIN RISK MANAGEMENT: A TECHNOLOGY MANAGEMENT PERSPECTIVE WITH BIBLIOMETRIC EVIDENCE

Supply chain risk management (SCRM) has become a strategic priority as global networks face increasing turbulence from pandemics, geopolitical conflicts, economic volatility, and climate-related disruptions. Artificial intelligence (AI) is widely recognized as a transformative enabler for predictive and prescriptive risk analytics, yet its practical adoption is often constrained by technical and organizational barriers. Low-code and no-code AI platforms have recently emerged as democratizing tools that lower entry barriers, enabling non-programmers to design, deploy, and scale intelligent workflows with greater accessibility. Despite this promise, scholarly research explicitly focusing on low-code AI in the context of SCRM remains scarce. This study addresses this gap by integrating bibliometric and text-mining approaches with a technology management perspective. A dataset of 62 publications retrieved from the Web of Science Core Collection was analyzed through bibliometric mapping to identify influential works, collaboration structures, and thematic clusters. Complementing this, Latent Dirichlet Allocation (LDA) topic modeling of 45 abstracts uncovered four distinct thematic groups. While the dominant clusters revolve around AI-driven resilience, digital transformation, and cybersecurity, a marginal but emerging theme reflects low-code and no-code adoption, highlighting its nascent role in SCRM research. Building on these findings, the paper proposes a conceptual model that synthesizes Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), Diffusion of Innovation (DOI), and the Technology–Organization–Environment (TOE) framework. The model introduces accessibility-driven resilience as a capability linking low-code AI adoption to organizational outcomes. The study contributes by (i) mapping the intellectual landscape of AI-enabled SCRM, (ii) theorizing low-code AI adoption as a managerial decision in technology management, and (iii) outlining implications for practitioners, particularly SMEs, seeking resilience through accessible AI solutions. The findings further indicate that low-code and no-code adoption, though marginal in the current literature, is emerging as a distinct research stream, underscoring the concept of accessibility-driven resilience.