Earticle

초록

영어

This study draws on data from the 2025 Survey on the Level of Informatization in Small and Medium-sized Enterprises (SMEs) (N=4,300) to quantitatively classify DX maturity levels among Korean SMEs and empirically examine the effects of firm size characteristics and DX training on ma-turity group membership. Following the Technolo-gy-Organization-Environment (TOE) framework, 23 in-formatization items preprocessed via Multiple Imputation by Chained Equations (MICE) were subjected to Principal Component Analysis (PCA) and K-Means clustering. PCA extracted five principal components (KMO=0.908; cumula-tive variance=62.5%), and k=3 was selected as the optimal cluster solution, yielding three DX maturity profile groups: Executive-Driven DX Will (52.4%), DX Adoption Lagging (17.2%), and DX Implementation Leading (30.4%). Signif-icant inter-cluster differences were confirmed across all five profiling variables (p<.001). Greater firm resource scale (H1) and DX training implementation (H2) were each sig-nificantly associated with higher-maturity cluster member-ship (p<.001), while DX training effects did not vary sig-nificantly by revenue scale (H3 rejected) but differed sig-nificantly by industry sector, particularly manufacturing status (H4 supported). These findings underscore the ur-gency of affordance-based, maturity-differentiated DX support policies as a strategic foundation for the transition toward AI Transformation (AX).

목차

Abstract
1. Introduction
2. Literature Review
2.1 Digital Transformation (DX) in SMEs
2.2 Digital Maturity Models and Typology Approaches
2.3 The TOE Framework
2.4 Absorptive Capacity and DX Training
2.5 Research Gaps and Study Positioning
3. Research Hypotheses
4. Research Methodology
4.1 Data and Sample
4.2 Variable Selection and Definition
4.3 Multi-Stage Analytical Procedure
5. Empirical Results
5.1 PCA Results
5.2 K-Means Clustering Results
5.3 Between-Cluster Profiling Analysis
5.4 MNLR Analysis and Moderation Effects
5.5 Subgroup Analysis: Mfg. vs. Non-Mfg.
5.6 Summary of Findings and Hypotheses Test
6. Discussion
6.1 Theoretical Contributions
6.2 Practical and Policy Implications
6.3 Limitations and Future Research
7. Conclusion
References

키워드

저자

• Sunghyun Kang [ Department of Business Analytics, Ajou University ]
• Juyoung Kang [ Department of Business Intelligence, Ajou University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2026

• 십진분류

  KDC 325 DDC 658