Yaqi Zhao

Limited understanding of how external regulations influence construction firms’ environmental, social, and governance (ESG) integration constrains policy effectiveness and industry sustainability performance. Drawing on neo-institutional theory and managerial cognition (MC), we examine how regulations influence construction firms’ ESG integration by leveraging managerial cognition of the ESG-firm performance link. Data from Chinese A-share listed construction firms is analyzed through ordinary least squares (OLS) regression and qualitative comparative analysis. Results show that government procurement projects, subsidies, and the ESG Best Practice Case Collection directly foster ESG integration, while ESG disclosure regulations and government penalties exert little impact. Peer ESG rating gaps also discourage ESG integration. Five regulatory configurations are identified, with normative pressures strengthening coercive effects. Managerial cognition mediates the influence of penalties, procurement projects, and best-practice collection on ESG integration. The findings highlight that project-based operational models influence the relationship between external regulations and ESG integration in construction firms. By revealing institutional and cognitive pathways, this study advances neo-institutional theory and ESG literature and offers actionable insights for policy makers and firms to integrate ESG.

Purpose: Previous studies have focused on the financial effects of environmental, social and governance (ESG) practices but have overlooked non-financial aspects. This study aims to address this gap for the construction industry, with its resource and labour dependence, diverse stakeholders and complex governance. It examines ESG effects on Chinese construction enterprises' competitive advantage (CA) from financial and non-financial perspectives and explores innovation's mediating role under environmental uncertainty (EU). Design/methodology/approach: Through structural equation modelling and regression analysis, this study examines ESG effects on CA and their mediators. Data are collected through a survey of Chinese construction enterprises. Findings: (1) The environmental dimension and governance dimension enhance CA, whereas the social dimension has no significant effect. (2) Enterprise innovation mediates this relationship, with business model innovation (BI) and technological innovation (TI) playing distinct roles. (3) EU negatively moderates innovation's effect on CA. Originality/value: This study expands the theoretical framework by integrating non-financial aspects into ESG's effect on CA. The findings offer insights into how ESG fosters CA through innovation, with distinct roles for the ESG dimensions and emphasise the need for adaptive innovation strategies in uncertain environments.

Purpose: Limited use of Internet of Things (IoT) technology on construction sites has restricted its value in the construction industry. To propel its widespread application, this paper explores the influencing factors and action paths of construction companies' IoT technology adoption behavior.Design/methodology/approach: First, literature research, technology adoption theories, and semi-structured expert interviews were employed to build the adoption model. Second, a questionnaire survey was conducted among Chinese construction contractors to collect empirical data. Third, the structural equation model method and regression analysis were used to test the adoption model. Finally, the findings were further validated with interviews, case studies, and field observations.Findings: External environmental pressure (EEP), perceived benefit (PB), top management support (TMS), company resource readiness (CRR), adoption intention (AI), and perceived compatibility (PCA) have a direct positive impact on adoption behavior (AB). In contrast, perceived cost (PC) and perceived complexity (PCL) exert a direct negative impact on AB. The EEP, PB, and PC are critical factors affecting AB, whereas AI is strongly affected by CRR and TMS. Besides, AI plays a part mediating role in the relationship between seven factors and AB. Company size and nature positively moderate AI's positive effect on AB.Originality/value: This paper contributes to the knowledge of IoT technology adoption behavior in the construction sector by applying the technology adoption theories. Exploring the implementation barriers and drivers of IoT technology in construction sites from the perspective of organizational technology adoption behavior and introducing moderating variables to explain adoption behavior are innovations of this paper. The findings can help professionals better understand the IoT technology adoption barriers and enhance construction companies' adoption awareness, demand, and ability. This work also provides a reference for understanding the impact mechanism of the adoption behavior of other innovative technologies in construction.

Purpose: Human–robot collaboration (HRC) is an emerging research field for the construction industry along with construction robot adoption, but its implementation remains limited in construction sites. This paper aims to identify critical risk factors and their interactions of HRC implementation during engineering project construction.Design/methodology/approach: Literature research, expert interviews, a questionnaire survey and a social network analysis (SNA) method were used. First, literature research and expert interviews were employed to identify risk factors of HRC implementation and preliminarily understand factor interactions. Second, a questionnaire survey was conducted to determine the degree of interactions between risk factors. Third, based on the data collected from the questionnaire survey, SNA metrics were used to find critical risk factors and critical interactions.Findings: The critical risk factors consist of robot technology reliability, robot-perceived level, conflict between designers and users of construction robots, organisational culture, organisational strength, project cost requirements, changeability of project construction, project quality requirements and project safety requirements. The interactions between risk factors are strong and complex. Robot technology risk factors were relatively fundamental risk factors, and project risk factors had a direct influence on the risk of HRC implementation. The implementation cost of HRC was not identified as a critical risk factor. Individual risk factors could be mitigated by improving technical and organisational factors.Originality/value: This paper contributes to the body of knowledge in the field of both HRC behaviours and its risk management in construction project management. Identifying the critical risk factors and their interactions of HRC implementation in the construction industry and introducing social network theory to the research on critical risk factors are the innovations of this paper. The findings and proposed suggestions could help construction professionals to better understand the HRC risk factors and to manage the risk of HRC implementation more effectively.

The Sports and Leisure Characteristic Town (SLCT) has become a reasonable strategy for enhancing the sustainability of new urbanization processes in rural China. Although the Chinese government has issued a series of policies to support the development of SLCTs, limited financial resources are a major obstacle. Thus, public-private partnerships (PPPs) have been increasingly encouraged for establishing SLCT projects. However, the factors that significantly influence private partners’ willingness to participate in SLCT PPP projects remain unclear. The authors of this paper conducted expert interviews and questionnaire surveys concerning the SLCT PPP projects that have been implemented in China, identifying 23 factors that can be grouped into five major components using principal component factor analysis. Multiple linear regression was then performed to evaluate the relationships between factors and private partners’ willingness to participate. The results indicated that the factors that critically influence private partners’ willingness to participate include stakeholder factors, the internal factors of private partners, external environmental risk factors, the supporting measure factors of SLCTs, and the locational factors of SLCTs. This work also offers suggestions for encouraging private partners to participate in SLCT PPP projects. This study can provide a theoretical basis and practical guidance for government and private partners in order to help them implement sustainable SLCT PPP projects.