The results indicate that the overall effect of corporate AI transformation on corporate risk-taking capacity is positive.
Authors
Yuhuan Sun, School of Statistics, Dongbei University of Finance and Economics, Dalian, China.
Lu Wan, School of Statistics, Dongbei University of Finance and Economics, Dalian, China; School of Mathematics and Physics, Hulunbuir University, Hulunbuir, China.
Sachin K. Mangla, Professor, Jindal Global Business School, O. P. Jindal Global University, Sonipat, Haryana, India; Plymouth Business School, University of Plymouth, Plymouth, U.K.
Xiaofeng Xu, School of Economics and Management, China University of Petroleum, Qingdao, China.
Malin Song, Collaborative Innovation Center for Ecological Economics and Management, Anhui University of Finance and Economics, Bengbu, China; Adnan Kassar School of Business, Lebanese American University, Beirut, Lebanon.
Summary
Amidst recent supply chain disruptions triggered by pandemics and crises, the imperative of bolstering supply chain security and resilience is escalating. Central to this endeavor is the augmentation of risk-taking capabilities among enterprises at supply chain nodes. Rapid advances in artificial intelligence (AI), coupled with the burgeoning concentration within supply chains present promising avenues for enhancing corporate risk-taking capacity (CRTC ).
However, a conspicuous gap exists in the relationship between enterprise AI transformation, supply chain concentration, and CRTC . This study constructs a panel simultaneous equation model to contrast the direct impact of corporate AI transformation on CRTC with its indirect influence, facilitated by the reduction of supply chain concentration (Scii) .
The results indicate that the overall effect of corporate AI transformation on CRTC is positive. In the indirect path, an increase in supply chain concentration effectively enhances CRTC , and firms exhibiting higher CRTC also show a preference for supply chains with centralized configurations. All of these interactions involve heterogeneity in property rights and firm life cycle stages.
This study broadens the understanding of factors influencing CRTC at the supply chain level and sheds light on the policy implications of enterprise AI transformation. It offers valuable insights for corporates in shaping their risk management strategies and contributes to the discourse on the advancement of emerging technologies and supply chain practices.
Published in: IEEE Transactions on Engineering Management
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