Jie Wu

The rapidly evolving domain of artificial intelligence (AI) is significantly influencing the green building (GB) sector, acting as a catalyst for green building technology innovation (GBTI). Notably, unlike AI applications in green buildings (AI-in-GB), AI-driven GBTI positions AI as the central force, promoting and leading novel technological breakthroughs. Although research has been conducted in AI-in-GB, there remains a lack of in-depth analysis on AI-driven GBTI advancements. To address this gap, this study comprehensively reviews the existing research in AI-driven GBTI, systematically organizing and analyzing the knowledge structure, thematic evolution, research paradigms, and potential future research directions. This study conducts bibliometric analyses on 151 research publications sourced from Scopus using VOSviewer and CiteSpace, capturing the temporal characteristics, research hotspots, and frontiers of research in this area. Additionally, based on dynamic topic modeling, this study analyzes 86 representative articles, identifying three key research themes and their evolution trends, systematically elucidating the knowledge framework within the field. Through further discussion, this study reveals four core research paradigms and proposes three potential future research directions, providing theoretical support and guidance for its continued development. This study is the first to focus on AI-driven GBTI, contributing to a comprehensive understanding and expanding the knowledge domain of GBTI.

In order to strengthen the cooperation ability between the owner and the consultant and improve the project management performance of the whole process engineering consulting, this study firstly introduces knowledge sharing as a mediator variable and constructs a theoretical model between trust and contract governance—knowledge sharing—project management performance. A structural equation model was then used to empirically test the questionnaire data. The results show that contract governance indirectly promotes project performance through the intermediary of explicit knowledge sharing, and trust indirectly promotes project performance through the intermediary of knowledge sharing (explicit knowledge sharing and tacit knowledge sharing). Based on the above, for further analysis of the complex antecedent configuration and improvement path that affect management performance, fuzzy set qualitative comparative analysis was used for group analysis with contract governance, trust, explicit knowledge sharing, and tacit knowledge sharing as conditional variables and project management performance as the outcome variable. The results show that there are two parallel paths that can improve the whole process engineering consulting project management performance: contract governance*explicit knowledge sharing*tacit knowledge sharing→project management performance and contract governance*trust*explicit knowledge sharing→project management performance. Finally, through analysis of the research results, it is suggested that in whole process engineering consulting projects, the owner and the consultant should actively promote the willingness and behavior of the consultant to share knowledge based on therational use of trust and contract governance, to better improve project management performance.