A study on Gen-AI technology development trends to enhance small-medium sized enterprise digital competence and management quality
Purpose The rapid advancement of Generative Artificial Intelligence (Gen-AI) offers transformative opportunities for enhancing digital competence and management quality in small and medium-sized enterprises (SMEs). Given the challenges of a Volatile, Uncertain, Complex and Ambiguous (VUCA) business environment, SMEs face risks of losing competitive advantages to larger corporations. This study aims to explore Gen-AI trends and identify strategies that can support SMEs in building digital resilience and operational efficiency. Design/methodology/approach Through text analysis of patent data and using Gen-AI technology, this research examines potential AI applications to address SME challenges, such as labor shortages, productivity declines and operational inefficiencies. The study uses topic modeling and an OS-matrix framework to analyze trends in Gen-AI patents, aligning technological developments with SME-specific needs. Findings The study finds that Gen-AI technologies, such as automated content creation and predictive analytics, provide targeted solutions for key SME challenges. The OS-matrix framework reveals that specific Gen-AI applications can enhance SMEs’ adaptability and competitive positioning in dynamic markets. Research limitations/implications While this research underscores the potential of Gen-AI for SME digital transformation, limitations include a reliance on patent data and a lack of consideration of various industrial features of SMEs. Future research should expand data sources and apply findings across diverse SME sectors. Originality/value This study contributes insights by mapping Gen-AI advancements to SME needs under VUCA conditions. Therefore, integrating topic modeling with OS-matrix for aligning Gen-AI technologies to SME operational challenges, offers a strategic framework for digital adoption.
Authors
- Jung-Sang Yoo (ORCID: https://orcid.org/0000-0002-8835-3809)
- Young-Ju Cho (ORCID: https://orcid.org/0000-0002-0173-8846)
- Soyoung Kim (ORCID: https://orcid.org/0000-0003-3612-1691)
- Junsung Park (ORCID: https://orcid.org/0009-0009-7408-8694)
- Heejun Park (ORCID: https://orcid.org/0000-0002-3663-9258)
Institutions
- Yonsei University (KR)
Publication Details
- Journal
- Journal of Entrepreneurship in Emerging Economies
- Published
- 2025-04-03
- DOI
- https://doi.org/10.1108/jeee-10-2024-0485
- Citations
- 5
- Primary Topic
- Technology and Data Analysis
- Type
- article
- Field-Weighted Citation Impact
- 16.23