Development of the stim-g competency scale for science and technology industry organizational talents under the great changes: an empirical test based on the yangtze river delta science and innovation ecosystem and the ai-link platform
International Journal of Development Research
Development of the stim-g competency scale for science and technology industry organizational talents under the great changes: an empirical test based on the yangtze river delta science and innovation ecosystem and the ai-link platform
Received 12th March, 2026 Received in revised form 24th April, 2026 Accepted 20th May, 2026 Published online 30th June, 2026
Copyright©2026, Xiaokun Guo. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Against the dual backdrop of generative artificial intelligence (GenAI) comprehensively reshaping business forms and highly volatile global geopolitics (e.g., technological decoupling, entity lists), building deep connections between academia and industry relying on digital intelligence infrastructures such as the “AI-Link” platform has become a matter of life and death for Chinese science and technology enterprises. However, the current evaluation system for compound talents is deeply trapped in the path dependence of traditional Western business education, failing to reflect the localized era demands of“digital-intelligence collaboration” and “geopolitical games”. This study aims to break this limitation and explore the construction of an “independent knowledge system” for science and technology business studies with Chinese characteristics. Focusing on the highly scarce organizational talents in the science and technology industry, this study introduces the Upper Echelons Theory and Geopolitical Risk Theory, constructs and validates the STIM-G (Science, Technology, Innovation, Management, Global Geopolitics) competency model containing 5 dimensions and 20 concise items through grounded analysis of multi-source data, including in-depth interviews with 21 core industry practitioners and public speeches by AI-Link platform builders. Based on 304 valid samples of enterprise executives in the Yangtze River Delta region, initial dimension exploration via principal component analysis (N₁=128) and confirmatory factor analysis (N₂=176) were conducted in stages. The empirical results show that the refined scale has excellent reliability and validity (χ²/df = 1.88, CFI = 0.96). Cross-group comparison confirms that the ability to navigate great power game sanctions and the ability to use AI-Link for cross-border integration are the core differentiating competencies distinguishing the new generation of science and technology leaders from traditional managers. This study provides a standardized quantitative tool for the evaluation of science and innovation talents and the reconstruction of new business education under the great changes.