Earticle

초록

영어

This paper proposes how to design and implement data-driven strategies by investigating how a firm can increase its value using data science. Drawing on prior studies on architectural innovation, a behavioral theory of the firm, and the knowledge-based view of the firm as well as the analysis of field observations, the paper shows how data science is abused in dealing with meso-level data while it is underused in using macro-level and alternative data to accomplish machine-human teaming and risk management. The implications help us understand why some firms are better at drawing value from intangibles such as data, data-science capabilities, and routines and how to evaluate such capabilities.

목차

ABSTRACT
Ⅰ. Introduction
Ⅱ. Abusing Meso-Level Data
Ⅲ. Macro-Level Data and Scenario Planning
Ⅳ. The Framework for Data-driven Value-enhancing Strategies
4.1. Architectural Innovation
4.2. A Behavioral Theory of the Firm (BTF)
4.3. The Knowledge-Based View (KBV)
Ⅴ. Discussion and Conclusion
Acknowledgements

저자

• Hyoung-Goo Kang [ Associate Professor, Department of Finance, Hanyang University Business School, Korea ]
• Ga-Young Jang [ Adjunct Professor,. Department of Finance, Hanyang University Business School, Korea ] Corresponding Author
• Moonkyung Choi [ Ph.D. Asset Management Team, The Ministry of Employment and Labor (MOEL) ]

참고문헌