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

Abstract

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.

Keywords

• Low-code AI
• Supply Chain Risk Management
• Technology Management
• Bibliometric Analysis
• Digital Transformation

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

Abstract

Tedarik zinciri risk yönetimi, küreselleşme ve dijital dönüşüm çağında giderek daha karmaşık hale gelmiştir. Yapay zekâ (YZ) uygulamaları, özellikle low-code ve no-code platformlar, bu zorlukların üstesinden gelmek için yeni fırsatlar sunmaktadır. Bu çalışma, YZ’nin tedarik zinciri risk yönetimine entegrasyonunu bibliyometrik analiz ve konu modelleme yöntemleriyle incelemektedir. Web of Science veri tabanından elde edilen 62 çalışmadan oluşan bir corpus kullanılmış, analiz için 45 yayın seçilmiştir. Bulgular, YZ’nin operasyonel dayanıklılığı artırma, risk öngörülerini geliştirme ve sürdürülebilirlik hedeflerini destekleme açısından kritik bir rol oynadığını göstermektedir. Ayrıca çalışma, teknoloji yönetimi perspektifinden low-code YZ’nin benimsenmesine ilişkin teorik bir model önermektedir. Çalışma, hem akademik literatüre katkı sağlamakta hem de politika yapıcılar ve uygulayıcılar için yol gösterici çıkarımlar sunmaktadır.

Keywords

• low-code Yapay Zeka
• Tedarik Zinciri Risk Yönetimi
• Teknoloji Yönetimi
• Bibliyometrik Analiz
• Dijital Dönüşüm

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