경영정보학연구 (Information Systems Review)

  • 제26권3호
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  • Pages.279-298
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  • 2024
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  • 2982-6551(pISSN)
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  • 2982-6837(eISSN)
한국경영정보학회 (The Korea Society of Management Information Systems)
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AI를 활용한 최적의 비즈니스 혁신 성과 예측

How does AI Select the Best Model of Business Innovation Prediction?

  • 곽영 (한국과학기술정보연구원, 정책연구센터) ;
  • 양우령 (한양대학교, 비즈니스인포메틱스학과)
  • Young Kwak (Korea Institute of Science and Technology Information, Policy Research Center) ;
  • Wooryeong Yang (Department of Business Informatics, Hanyang University)
  • 투고 : 2024.05.13
  • 심사 : 2024.08.03
  • 발행 : 2024.08.31
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초록

글로벌 경기침체를 극복하기 위해 많은 기업들이 혁신역량 강화를 모색하고 있다. 학계에서는 전통적인 정량적 방법론을 활용하여 혁신역량을 결정하는 요인을 규명하는 것에 초점을 맞추고 있지만, 실제 문제와 밀접한 고차원적 구조를 반영한 전략 마련의 필요성이 대두되고 있다. 본 연구는 비즈니스 혁신성과 예측을 위한 AI 알고리즘을 규명하고, 중요한 요인을 중심으로 현시점에서 필요한 전략을 제안하였다. 본 연구는 서비스 부문 2022년 한국혁신조사(KIS) 자료를 활용하였으며, 최적 트리 앙상블 기반 머신러닝의 성능을 비교하였다. 분석 결과 단일 알고리즘보다 가중치가 부여된 소프트보팅(Soft Voting)의 예측 성능이 가장 우수한 것으로 나타났다. 또한, 기업의 내부 자금조달, 내부 R&D, 고객 맞춤형 포커스 전략과 관련된 중요한 요인을 도출하였다. 본 연구는 혁신성과의 전반적인 메커니즘을 이해하고 예측하는 알고리즘 선정을 위한 학문적 시사점과 더불어 기업의 자금조달 규제 완화 및 내부 전략을 제안한다는 점에서 실무적 시사점을 갖는다. 또한, 본 접근법은 경제성장을 위한 혁신전략을 마련하는 기초자료가 될 것이다.

To overcome the global economic recession, many companies are seeking to strengthen their innovation capabilities. Although the academic world focuses on identifying factors that determine the innovation capabilities using traditional quantitative methodologies, the need to prepare strategies that reflect high-level structures closely related to real-world problems is emerging. This study identified the AI algorithm for business innovation performance prediction, and proposed strategies required at the present time, focusing on important factors. This study utilized data from the 2022 Korean Innovation Survey (KIS) in the Service Sector, and the performance of optimal tree ensemble-based machine learning was compared. As a result of the analysis, the prediction performance of Soft Voting with weighted XGBoost was the best than single algorithms. Also, important factors related to the company's internal funding, internal R&D, and customer-tailored focus strategies were derived. This study has practical implications in that it proposes corporate funding deregulation and internal strategies as well as academic implications for selecting an algorithm that understands and predicts the overall mechanism of innovation performance. Also, this approach would serve as basic data to prepare innovation strategies for economic growth.

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