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Science and Technology Management and Innovation Management

Research on the Impact of Top Management Team Digital Gen⁃ erational Diversity on Enterprise Artificial Intelligence  Development 

Zhang Ting

(School of International Business, Tianjin Foreign Studies University, Tianjin 300270, China)

Abstract: At the critical stage of accelerated artificial intelligence (AI) development, the  Chinese government attaches great strategic importance to the integrated application of AI in  various industries. A research report released by McKinsey clearly shows that China's AI devel⁃ opment still lags behind some advanced countries in core technology and application depth, thus  leaving considerable room for improvement. As the micro-subject of AI development, the im⁃ provement of the enterprise AI development level is the key to enhancing the overall domestic AI  development level and an important support for enterprises to achieve value enhancement. Thus, how to improve enterprise AI development has become an urgent research issue. The technologi⁃ cal characteristics of AI require enterprises to provide both cognitive and resource support simul⁃ taneously. As the core decision-making body of enterprises, the top management team (TMT)'s  capabilities and traits are crucial to improving enterprise AI development. TMT digital genera⁃ tional diversity is directly related to whether enterprises can obtain the dual support of cognition  and resources required for AI development. Based on this, this paper takes Shanghai and Shen⁃ zhen A-share non-financial listed enterprises as research objects, integrates digital generational  theory, and explores the impact, mechanism, contextual constraints, and economic consequences  of TMT digital generational diversity on enterprise AI development. Empirical results indicate  that TMT digital generational diversity can significantly promote enterprise AI development, with enhanced management ability and alleviated financing constraints playing mechanistic  roles in the relationship between them. Contextual analysis reveals that the positive effect is  more pronounced when enterprises have clear employee voice channels and when the external  technology market is more competitive. Economic consequence tests demonstrate that TMT digi⁃ tal generational diversity can significantly enhance enterprises' stock market valuation by pro⁃ moting the development of enterprise AI. Based on the above findings, this paper proposes rec⁃ ommendations from three aspects. First, enterprises should optimize the digital generational  structure of their TMT to effectively advance enterprise AI development and drive value en⁃ hancement. Second, when promoting AI development, enterprises should fully leverage the dis⁃ tinct cognitive and resource advantages of TMT members across different digital generations. Third, enterprises should align internal governance mechanisms with external market dynamics  to enhance the positive impacts of TMT digital generational diversity on enterprise AI develop⁃ ment. 

Key words: TMT; digital generation; AI; digital economy; TMT's capabilities; financing con⁃ straints; digital immigrant; stock market