| ISO 56000 관점의 혁신 이니셔티브에 관한 체계적 문헌 분석: 토픽모델링(LDA)과 생성형 AI 기반 해석을 중심으로 |
| 박세훈1, 홍일성1, 옥영석1 |
1부경대학교 기술경영전문대학원
2부경대학교 기술경영전문대학원 |
| A Systematic Literature Analysis of Innovation Initiatives from an ISO 56000 Perspective: Focusing on Topic Modelling(LDA) and Generative AI-based Interpretation |
| Se-Hoon Park1, Il-Seong Hong1, Young-Seok Ock1 |
1Adjunct Professor, Graduate School of Management of Technology, Pukyong National University
2Full Professor, Graduate School of Management of Technology, Pukyong National University |
|
|
|
Received: August 29, 2024; Revised: September 27, 2024 Accepted: November 21, 2024. Published online: December 31, 2024. |
|
|
|
| ABSTRACT |
Purpose:
This study examines the utilization of 'innovation initiatives' in management and business literature, aiming to systematically classify its various types.
Methods:
: Employing the Latent Dirichlet Allocation (LDA) topic modeling technique, we analyzed 690 SSCI-listed scholarly articles published between 2000 and 2023 related to innovation initiatives. We extracted and categorized the main themes to understand the evolution and classification of innovation initiatives. To enhance the reliability of topic interpretation, generative AI models were used to verify and complement the researchers' subjective analyses by comparing AI-generated insights with human interpretations.
Results:
: The analysis identified four primary types of innovation initiatives: digital technology utilization, collaborative process innovation, knowledge-based process innovation, and product innovation. This classification illustrates that companies adopt diverse approaches to promote innovation. The topic interpretations showed high consistency between the researchers' assessments and the generative AI models, reinforcing the reliability of the findings.
Conclusion:
This study offers insights into the classification and evolution of innovation initiatives, providing a robust framework for businesses to develop and manage their strategies effectively. Integrating generative AI in topic interpretation enhances the objectivity and reliability of the analysis. However, the research is limited by its focus on English-language publications and the ethical considerations of AI usage. Future research should incorporate multilingual data and explore AI’s ethical implications. Additionally, expanding to diverse organizational structures and industry-specific innovations will further enrich the understanding of innovation initiatives. |
| Key Words:
Innovation Management System, ISO 56000, Innovation Initiatives, LDA, Generative AI |
|
|
|
PDF Links
Full text via DOI
Download Citation 
