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<p>Green innovation has recently emerged as a viable approach to sustainable development, as environmental concerns and resource depletions have become more severe globally. In the context of Chinese SMEs, artificial intelligence and green transformational leadership improve green innovation and environmental performance. This study aims to address this gap by investigating Chinese SMEs using a hybrid structural equation modeling-artificial neural network (SEM-ANN), integrating dynamic capabilities and ability-motivation-opportunity theories. A survey of 762 managers from Chinese manufacturing SMEs was used to analyze our conceptual model. The results indicate that artificial intelligence uses (AIU) and green transformational leadership (GTL) have a strong impact on green innovation (GI) and environmental performance (EP). GI mediate the relationships between AIU, GTL, and EP, while regulatory pressure (RP) moderate between GI and EP, enabling the adoption of green practices and establishing a business environment which promote sustainability. The findings contribute substantially to the growing body of literature on AIU, GTL, GI, ENP and RP by providing a new proto-type or blueprint to understand these relationships that integrates dynamic capabilities theory (DCT) and ability–motivation–opportunity (AMO) theory. The study by integrating these factors into a single model also offers new directions to foster sustainability in resource-challenged context with implications for business practitioners and policy makers alike.</p>

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Integrating AI Usability and Transformational Leadership for Green Innovation and Environmental Sustainability in Chinese SMEs: A Hybrid SEM-ANN Analysis

  • 期刊论文

  • 发表日期：

    2026年3月17日

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  • Rahat Ullah,
  • Ning Zhang,
  • Shiraz Hussain,
  • He Lifeng,
  • Cheng Xin

摘要

Green innovation has recently emerged as a viable approach to sustainable development, as environmental concerns and resource depletions have become more severe globally. In the context of Chinese SMEs, artificial intelligence and green transformational leadership improve green innovation and environmental performance. This study aims to address this gap by investigating Chinese SMEs using a hybrid structural equation modeling-artificial neural network (SEM-ANN), integrating dynamic capabilities and ability-motivation-opportunity theories. A survey of 762 managers from Chinese manufacturing SMEs was used to analyze our conceptual model. The results indicate that artificial intelligence uses (AIU) and green transformational leadership (GTL) have a strong impact on green innovation (GI) and environmental performance (EP). GI mediate the relationships between AIU, GTL, and EP, while regulatory pressure (RP) moderate between GI and EP, enabling the adoption of green practices and establishing a business environment which promote sustainability. The findings contribute substantially to the growing body of literature on AIU, GTL, GI, ENP and RP by providing a new proto-type or blueprint to understand these relationships that integrates dynamic capabilities theory (DCT) and ability–motivation–opportunity (AMO) theory. The study by integrating these factors into a single model also offers new directions to foster sustainability in resource-challenged context with implications for business practitioners and policy makers alike.