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Diverse Impacts of AI Investments on Productivity Gains : Effects of Industry and Innovation Characteristics

  • 간행물
    한국경영정보학회 정기 학술대회 바로가기
  • 권호(발행년)
    2024 KMIS International Conference 추계국제학술대회 (2024.11) 바로가기
  • 페이지
    pp.138-172
  • 저자
    Gangmin Park, Sangyoon Yi, Junyoun Kim
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A472504

원문정보

초록

영어
As artificial intelligence (AI) emerges as a key driver of innovation, it is increasingly recognized for its role in enhancing productivity. However, while previous studies have primarily focused on the relationship between AI adoption and productivity at the job or national levels, research at the firm level remains limited. This study aims to broaden our understanding of AI's impact on firm productivity by examining the relationship between AI investment and Total Factor Productivity (TFP) using job-posting data from 2010 to 2021. Our results show that the service sector experiences more substantial productivity gains from AI investment compared to manufacturing. Moreover, firms with a higher exposure to AI-related tasks tend to experience smaller productivity gains. Lastly, leveraging AI to enhance existing operations yields greater productivity improvements than developing new products. Our research contributes to the ongoing dialogue about AI in business by highlighting the need for a nuanced approach to AI investment. It moves beyond the simplistic view of AI as a universal productivity enhancer, emphasizing the importance of tailoring AI deployment to the unique challenges and dynamics of each organization.

목차

ABSTRACT
1. Introduction
2. AI and Firm Productivity
3. Methodology
3.1 Data
3.2 Summary Statistics
4. Results
4.1 AI and Productivity
4.2 Conditions of Productivity Increase
5. Robustness Analysis
6. Conclusion and Discussion
References

저자

  • Gangmin Park [ Software Policy and Research Institute ]
  • Sangyoon Yi [ Korea Advanced Institute of Science and Technology ]
  • Junyoun Kim [ Software Policy and Research Institute ] Corresponding Author

참고문헌

자료제공 : 네이버학술정보

    간행물 정보

    • 간행물
      한국경영정보학회 정기 학술대회 [KMIS Conference]
    • 간기
      반년간
    • 수록기간
      1990~2025
    • 십진분류
      KDC 325 DDC 658