Türk KOBİ'lerinde Teknoloji Benimsemesi İçin Nedensel Merkezli Derin Öğrenme-DEMATEL Çerçevesi

Abstract

Bu çalışma, Türk KOBİ’lerinin karşılaştığı çeşitli operasyonel kısıtları daha doğru biçimde teşhis etmek ve ele almak amacıyla derin öğrenme, Karar Verme Deneme ve Değerlendirme Laboratuvarı ve ajan tabanlı modellemeyi bir araya getiren yenilikçi bir metodolojik çerçeve sunmaktadır. Geleneksel ve statik analizlerden farklı olarak, önerilen yaklaşım önce faktör analizi ve derin sinir ağı kullanarak en kritik performans belirleyicilerini ortaya çıkarır. Ardından DEMATEL, bu belirleyicilerin üretim, pazarlama ve pazar araştırması gibi diğer alanlar üzerindeki yönlü nedensel etkilerini göstererek net etkileyici faktörleri net etkilenenlerden ayırır. Son aşamada ABM, farklı kaynak donanımlarına ve stratejik davranışlara sahip KOBİ’ler arasındaki dinamik etkileşimleri çeşitli senaryolar altında simüle eder. Bu çalışma, önceliklendirmeyi, nedensel haritalamayı ve dinamikleri tek ve şeffaf bir süreç içinde birleştirir ve senaryo temelli SWOT aracılığıyla stratejileri sentezler. Bu entegre süreç, genel performansı artırmaya yönelik yüksek etkili kaldıraç noktalarını ortaya çıkararak teknoloji ve finans alanındaki hedefli müdahalelerin diğer sorun alanlarında da geniş çaplı iyileşmelere yol açabileceğini göstermektedir. Gelişmiş makine öğrenimini sistematik nedensel analiz ve zamansal simülasyonla birleştiren bu çerçeve, stratejik karar verme için daha kapsamlı ve veri odaklı bir temel sunmakta; politika yapıcılar ve yöneticilere KOBİ rekabetçiliğini ve dayanıklılığını güçlendirmeye yönelik daha derin içgörüler sağlamaktadır.

Keywords

• KOBİ Performans Optimizasyonu
• Derin Öğrenme Tabanlı Karar Analizi
• DEMATEL
• KOBİ'ler için Ajan Tabanlı Simülasyon
• Senaryo Tabanlı Stratejik Planlama

Causality-Centred Deep Learning–DEMATEL Framework for Technology Adoption in Turkish SMEs

Abstract

This study advances an innovative methodological framework that unites deep learning, Decision-Making Trial and Evaluation Laboratory (DEMATEL), and agent-based modeling (ABM) to more accurately diagnose and address the diverse operational constraints confronting Turkish SMEs. Unlike conventional, static analyses, the proposed approach first employs factor analysis and a deep neural network to pinpoint the most pivotal performance drivers. Next, DEMATEL reveals how these drivers exert causally directed influences on other domains, such as production, marketing, and market research, thereby distinguishing net “influencer” factors from net “receivers.” Finally, ABM simulates the dynamic interplay among SMEs, each featuring unique resource endowments and strategic behaviors, under varying economic and policy scenarios. We combine prioritization (DL+SHAP), causal mapping, and dynamics into a single, transparent pipeline, and synthesize strategies via scenario-based SWOT. This integrated process uncovers high-impact levers for enhancing overall performance, demonstrating that targeted interventions in technology and finance can yield widespread improvements in other challenge areas. By converging advanced machine learning with systematic causal analysis and temporal simulation, the framework furnishes a more comprehensive, data-driven basis for strategic decision-making, offering policymakers and managers deeper insights into fostering SME competitiveness and resilience.

Keywords

• SME Performance Optimization
• Deep Learning-Based Decision Analysis
• DEMATEL
• Agent-Based Simulation for SMEs
• Scenario-Based Strategic Planning

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