제조업의 생성형 AI 적용을 위한 핵심 성공 요인 도출에 관한 연구

The accelerating adoption and transformation of artificial intelligence (AX) in manufacturing has highlighted the growing importance of generative AI. In manufacturing, generative AI can be applied throughout the product lifecycle, including design, production, and usage, and it offers benefits such as new product development, productivity improvement, quality enhancement, cost reduction, and data-driven decision-making. Although technical research on the development of generative AI algorithms has advanced rapidly, there remains a lack of studies that provide strategic insights for the successful application of generative AI in manufacturing. This study develops a research model for the intention to adopt generative AI in the manufacturing sector based on the Extended Theory of Planned Behavior(ETPB) and identifies Critical Success Factor(CSF) through multiple regression analysis. The results indicate that “Attitude toward the behavior, Perceived usefulness, and Technology compatibility” have significant positive effects on adoption intention. Among these factors, “Attitude toward the behavior” has the strongest influence. In contrast, “Subjective norm, Perceived behavioral control, and Security” are not statistically significant. This study provides academic and practical implications by identifying CSF that support manufacturing firms in establishing strategic foundations for adopting new technologies such as generative AI, reducing technological resistance, and formulating effective strategies and response plans.