Paper Type
Complete
Paper Number
PACIS2025-1881
Description
Small and medium-sized enterprises (SMEs) form the backbone of Europe’s largest economy, yet they face significant challenges in adopting artificial intelligence (AI). This study investigates factors influencing AI adoption in German manufacturing SMEs through the lens of the technology-organization-environment (TOE) framework. We identify key factors of German SMEs within the manufacturing sector across the TOE dimensions by combining a systematic literature review (n = 29) and qualitative interviews (n = 15 experts). The results show that SMEs favor cost-effective AI solutions with a clear ROI, are confronted with general data protection regulation (GDPR)-related data restrictions, and benefit significantly from public subsidies—compared to larger companies. Extending the TOE framework by integrating SME-specific contextual factors (e.g., research network and public subsidies), offering actionable insights for German SMEs to bridge the AI adoption gap. Results underscore the need for userfriendly AI tools and collaborative industry-academia initiatives to enhance SME competitiveness in manufacturing.
Recommended Citation
Weiss, Lukas; Möhring, Michael; and Dahal, Keshav, "Advancing AI Adoption in Germany's Manufacturing SMEs: A TOE Framework Analysis" (2025). PACIS 2025 Proceedings. 26.
https://aisel.aisnet.org/pacis2025/aiandml/aiandml/26
Advancing AI Adoption in Germany's Manufacturing SMEs: A TOE Framework Analysis
Small and medium-sized enterprises (SMEs) form the backbone of Europe’s largest economy, yet they face significant challenges in adopting artificial intelligence (AI). This study investigates factors influencing AI adoption in German manufacturing SMEs through the lens of the technology-organization-environment (TOE) framework. We identify key factors of German SMEs within the manufacturing sector across the TOE dimensions by combining a systematic literature review (n = 29) and qualitative interviews (n = 15 experts). The results show that SMEs favor cost-effective AI solutions with a clear ROI, are confronted with general data protection regulation (GDPR)-related data restrictions, and benefit significantly from public subsidies—compared to larger companies. Extending the TOE framework by integrating SME-specific contextual factors (e.g., research network and public subsidies), offering actionable insights for German SMEs to bridge the AI adoption gap. Results underscore the need for userfriendly AI tools and collaborative industry-academia initiatives to enhance SME competitiveness in manufacturing.
Comments
AI ML