논문 상세보기
pp.69-81
The adoption of generative artificial intelligence (AI) has attracted growing attention across industries due to its potential to transform organizational processes and value creation. Despite its high applicability, however, the diffusion of generative AI in the telecommunications industry remains limited. Existing studies have largely focused on identifying individual barriers to AI adoption, providing insufficient understanding of how these barriers interact and form a complex hierarchy of constraints. Addressing this gap, this study investigates the structural interrelationships among barriers to generative AI adoption in the telecommunications industry. Based on a comprehensive literature review and expert validation, fifteen key barriers were identified. Using a Delphi-based Interpretive Structural Modeling (ISM) approach, this study examined the hierarchical influence structure among the barriers. Subsequently, the Matrix Impact Cross-reference Multiplication Applied to Classification (MICMAC) technique was employed to classify the barriers according to their driving power and dependence. The results reveal a four-level hierarchical structure in which environmental barriers play a foundational role. In particular, the absence of alignment in institutional frameworks and technical standards emerges as a root-level barrier exerting strong influence on higher-level constraints. Regulatory uncertainty and concerns about job displacement function as independent drivers linking foundational environmental conditions to execution- level constraints. Most technical, organizational, and economic barriers are concentrated at the intermediate level, forming a highly interdependent execution layer. At the top level, delays and uncertainties in decision-making regarding generative AI adoption appear as outcome-oriented barriers resulting from the cumulative effects of lower-level constraints. By highlighting that barriers to generative AI adoption in the telecommunications industry operate as a structurally connected system rather than isolated factors, this study extends existing adoption research through a structural perspective. The findings provide practical insights for telecommunications firms in prioritizing adoption strategies and offer implications for addressing institutional and regulatory conditions that shape the diffusion of generative AI.
2. 배 경
2.1 생성형 인공지능 도입 장애요인 관련 연구 동향
2.2 통신 산업 범위 및 생성형 인공지능 도입 현황
3. 연구 방법
3.1 생성형 인공지능 도입 장애요인의 도출 및정제
3.2 생성형 인공지능 도입 장애요인 간 구조적관계 도출
3.3 생성형 인공지능 도입 장애요인의 구조적 역할및 상호의존성 분석
4. 연구 결과
4.1 생성형 인공지능 도입 장애요인의 계층적 구조
4.2 생성형 인공지능 도입 장애요인의 구조적 역할분류
5. 논의 및 시사점
6. 결 론
Acknowledgements
References
