PurposeThis study aims to investigate the literature related to the use of digital technologies for promoting circular economy (CE) in the construction industry.Design/methodology/approachA comprehensive approach was adopted, involving bibliometric analysis, text-mining analysis and content analysis to meet three objectives (1) to unveil the evolutionary progress of the field, (2) to identify the key research themes in the field and (3) to identify challenges hindering the implementation of digital technologies for CE.FindingsA total of 365 publications was analysed. The results revealed eight key digital technologies categorised into two main clusters including “digitalisation and advanced technologies” and “sustainable construction technologies”. The former involved technologies, namely machine learning, artificial intelligence, deep learning, big data analytics and object detection and computer vision that were used for (1) forecasting construction and demolition (C&D) waste generation, (2) waste identification and classification and (3) computer vision for waste management. The latter included technologies such as Internet of Things (IoT), blockchain and building information modelling (BIM) that help optimise resource use, enhance transparency and sustainability practices in the industry. Overall, these technologies show great potential for improving waste management and enabling CE in construction.Originality/valueThis research employs a holistic approach to provide a status-quo understanding of the digital technologies that can be utilised to support the implementation of CE in construction. Further, this study underlines the key challenges associated with adopting digital technologies, whilst also offering opportunities for future improvement of the field.

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The relational behavior of project participants is crucial to the success of a 4 megaproject. Although project governance has been widely studied with the aim of improving 5 participants' relational behavior, limited research examines the distinct effectiveness of various 6 governance mechanisms on influencing relational behavior, especially in megaprojects. 7Through examining three varieties of governance mechanisms, including contract, trust and 8 institutional support, a hierarchical moderated regression analysis has been used to explore the 9 impact of each of the governance mechanisms in facilitating the relational behavior of 10 megaproject participants and further team performance. The analysis is based on data collected 11 from 202 contractors and consultants working at megaprojects in China. Results unveiled that 12 both contractual term specificity and its interaction with trust can facilitate relational behavior. 13 Project uncertainty moderates the relationship between governance mechanisms and relational 14 behavior in affecting project team performance. The findings offer both theoretical and 15 managerial implications for megaproject participants to cultivate beneficial relational behavior 16 so as to improve team performance in megaprojects. 17

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