{"title":"The optimal timing and conditions for the digital transformation of traditional enterprises","authors":"Zhuming Chen, Wanhua Liang","doi":"10.1016/j.najef.2025.102443","DOIUrl":null,"url":null,"abstract":"<div><div>By taking data as the core factor of production, reconstructing the corporate value function after digital transformation and using in real option game theory, this paper provides an analytical formula for the optimal timing of the digital transformation of traditional corporations, the value functions of leader corporations and follower corporations and the optimal timing of digital transformation. This study identifies at least 11 factors that influence the optimal timing of transition. The study shows that the greater the cost of transformation investment, the higher the expected return of the enterprise is, the higher the marginal cost of production of the enterprise, the slower the digital transformation, and the longer the interval between the digital transformation of the follower after the transformation of the leader. Usually, the optimal time for the digital transformation of large enterprises is earlier than that of small and medium-sized enterprises. The more data elements an enterprise generates, the better the growth of the industry and the data element market, the higher the volatility, the faster the transformation, and the shorter the transition time between leaders and followers.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"79 ","pages":"Article 102443"},"PeriodicalIF":3.9000,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"North American Journal of Economics and Finance","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S106294082500083X","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
引用次数: 0
Abstract
By taking data as the core factor of production, reconstructing the corporate value function after digital transformation and using in real option game theory, this paper provides an analytical formula for the optimal timing of the digital transformation of traditional corporations, the value functions of leader corporations and follower corporations and the optimal timing of digital transformation. This study identifies at least 11 factors that influence the optimal timing of transition. The study shows that the greater the cost of transformation investment, the higher the expected return of the enterprise is, the higher the marginal cost of production of the enterprise, the slower the digital transformation, and the longer the interval between the digital transformation of the follower after the transformation of the leader. Usually, the optimal time for the digital transformation of large enterprises is earlier than that of small and medium-sized enterprises. The more data elements an enterprise generates, the better the growth of the industry and the data element market, the higher the volatility, the faster the transformation, and the shorter the transition time between leaders and followers.
期刊介绍:
The focus of the North-American Journal of Economics and Finance is on the economics of integration of goods, services, financial markets, at both regional and global levels with the role of economic policy in that process playing an important role. Both theoretical and empirical papers are welcome. Empirical and policy-related papers that rely on data and the experiences of countries outside North America are also welcome. Papers should offer concrete lessons about the ongoing process of globalization, or policy implications about how governments, domestic or international institutions, can improve the coordination of their activities. Empirical analysis should be capable of replication. Authors of accepted papers will be encouraged to supply data and computer programs.