Leveraging generative artificial intelligence for sustainable business model innovation in production systems
本研究基于402家中国制造业初创企业数据，发现生成式人工智能通过利用式学习和探索式学习显著促进可持续商业模式创新，并揭示了实现高绩效的四种配置路径。
This study develops an integrated framework, termed the GAI&ABC model, to examine how manufacturing firms can leverage generative artificial intelligence (GAI) to achieve sustainable business model innovation (SBMI) in production systems. While prior research primarily focuses on the technical aspects of AI, we address the challenge of understanding the organisational learning mechanisms that underpin GAI's impact on SBMI. Drawing on the Antecedents-Behaviour-Consequences (ABC) model, we investigate the mediating roles of GAI-powered exploitative and exploratory learning, and consider international entrepreneurship orientation and GAI education as key moderators. Using survey data from 402 manufacturing start-ups in China and employing PLS-SEM analysis, we find that GAI adoption significantly enhances both exploitative learning (β = 0.439, p < 0.001) and exploratory learning (β = 0.444, p < 0.001), which in turn drive SBMI (β = 0.254 and β = 0.353, respectively, p < 0.001). Furthermore, fsQCA reveals four distinct configurations of these factors that lead to high SBMI performance, highlighting the complexity of GAI implementation. This study contributes a novel theoretical framework that integrates GAI adoption with organisational learning and contextual factors to explain SBMI in production systems, offering actionable insights for firms and addressing the challenge of translating GAI's potential into tangible sustainability outcomes.