How to realize the sustainable development of the industry-university-research institute (IUR) collaboration innovation ecosystem has become a key factor restricting the independent innovation capability of Chinese enterprises. Knowledge transfer performance is a key consideration in the process of R&D collaboration between companies and research institutes; how to improve the performance of knowledge transfer depends on the matching between the partners of IUR collaboration. This article seeks

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... to explore the influence mechanism of partner differences in the industry-university-research institute collaboration on the performance of knowledge transfer from the perspective of enterprises. Specifically, the study explores the moderating effect of technical knowledge difference and goal difference on the relationship between absorptive capacity, learning willingness, and knowledge transfer performance. The study applied the partial least squares structural equation modeling approach to model these relationships, based on survey data gathered from 211 Chinese firms. The results show that the goal difference of industry-university-research institute collaboration partners has a negative moderating effect on the relationship between learning willingness, absorptive capacity, and knowledge transfer performance. The greater the degree of goal difference, the lower the role of the enterprise's learning willingness and absorptive capacity to promote knowledge transfer performance. Technical knowledge difference has a significant inverted U-shaped effect on the relationship between absorptive capacity and knowledge transfer performance: a high degree of technical knowledge difference weakens the effects of absorptive capacity on knowledge transfer performance, while a low degree of technical knowledge difference will also negatively moderate the effects of absorptive capacity on knowledge transfer performance. The research conclusions provide scientists, government bodies, and decision makers with the necessary information for a better understanding of the effective mechanism of sustainable knowledge transfer in the IUR innovation ecosystem.