Unpacking Boundary-Spanning Search and Green Innovation for Sustainability: The Role of AI Capabilities in the Chinese Manufacturing Industry

Yutong Sun, Meili Zhang (), Jingping Chang and Chenggang Wang
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Yutong Sun: School of Economics and Management, Taiyuan University of Technology, Jinzhong 030600, China
Meili Zhang: School of Economics and Management, Taiyuan University of Technology, Jinzhong 030600, China
Jingping Chang: School of Economics and Management, Taiyuan University of Technology, Jinzhong 030600, China
Chenggang Wang: School of Economics and Business Administration, Heilongjiang University, Harbin 150001, China

Sustainability, 2025, vol. 17, issue 14, 1-26

Abstract: Achieving the dual carbon goal and addressing escalating environmental challenges requires that manufacturing enterprises in China must pursue sustainability via green innovation strategies. A key rationale for green innovation is to overcome boundaries and acquire knowledge through boundary-spanning search. Additionally, leveraging artificial intelligence (AI) capabilities provides technical support throughout the innovation process. Thus, both boundary-spanning search and AI capabilities are crucial for achieving sustainability objectives. Drawing on organizational search and knowledge management theories, this paper aims to analyze how dual boundary-spanning search affects sustainability performance and green innovation. It also examines the moderating role of AI capabilities and constructs a moderated mediation model. We analyzed questionnaire data collected from 171 Chinese manufacturing companies over a 13-month period, employing hierarchical regression and bootstrap sampling methods using SPSS 27.0. Our findings reveal that both prospective and responsive boundary-spanning searches significantly enhance corporate sustainability performance. Furthermore, green innovation acts as a positive partial mediator between dual boundary-spanning search and corporate sustainability performance. Notably, AI capabilities positively moderate the relationship between dual boundary-spanning search and green innovation. They also strengthen the mediating effect of green innovation on the link between dual boundary-spanning search and corporate sustainability performance. Based on these findings, more resources should be allocated to boundary-spanning search while encouraging enterprises to pursue green innovation and develop AI capabilities. These efforts will provide robust support for sustainability performance in the manufacturing sector.

Keywords: prospective boundary-spanning search; responsive boundary-spanning search; sustainability performance; green innovation; AI capabilities; knowledge management (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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