Purpose: Machine learning (ML) technologies are increasingly being applied in building cost estimation as an advanced method to overcome the challenge of insufficient data and subjective effects of experts. To address the gap of lacking a review of ML applications in building cost estimation, this research aimed to conduct a systematic literature review to provide a robust reference and suggest development pathways for creating novel ML-based building cost prediction models, ultimately enhancing construction project management capabilities. Design/methodology/approach: A systematic literature review according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) was adopted using quantitative bibliographic analysis and qualitative narrative synthesis based on the 70 screened publications from Web of Science (WOS) and Scopus databases. The VOSviewer software was used to prepare the thematic focus from the bibliographic data garnered. Findings: Based on the results of a bibliographic analysis, current research hotspots and future trends in the application of ML to building cost estimation have been identified. Additionally, the mechanisms behind existing ML models and other key points were analyzed using narrative synthesis. Importantly, the weaknesses of current applications were highlighted and recommendations for future development were made. These recommendations included defining the availability of building attributes, increasing the application of emerging ML algorithms and models to various aspects of building cost estimation and addressing the lack of public databases. Research limitations/implications: The findings are instrumental in aiding project management professionals in grasping current trends in ML for cost estimation and in promoting its adoption in real-world industries. The insights and recommendations can be utilized by researchers to refine ML-based cost estimation models, thereby enhancing construction project management. Additionally, policymakers can leverage the findings to advocate for industry standards, which will elevate technical proficiency and ensure consistency. Originality/value: Compared to previous research, the findings revealed research hotspots and future trends in the application of ML cost estimation models in only building projects. Additionally, the analysis of the establishment mechanisms of existing ML models and other key points, along with the developed recommendations, were more beneficial for developing improved ML-based cost estimation models, thereby enhancing project management capabilities.

Traditional villages are the convergence points for rural tourism and cultural heritage, with their uniqueness deriving from their enduring cultural genes. Villagers serve as the continuators of traditional villages, the transmitters of cultural heritage, and active participants in rural tourism, all predisposing villagers to adopt key positions in these areas. However, villager-centered research has not been systematically explored. This gap may be due to the marginalization of villagers in developing rural tourism and cultural heritage, the challenges associated with measuring villager-centered impacts, and the lack of appropriate measurement tools. Therefore, this study aimed to develop a scale to assess the impact of rural tourism on villagers. Using Shangzhuang Village and Guoyu Village in China as case studies, a total of 305 samples were collected. Through construct dimensions, potential item generation, scale purification, Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and reliability and validity testing, a villager-centered tourism impact scale was established, encompassing 13 items across three dimensions: economic, social, and cultural. The findings provide a new tool for village leaders, tourism developers, and policymakers to effectively incorporate villagers’ perspectives into rural tourism and heritage preservation efforts, contributing to the sustainable development of traditional villages and their associated tourism.

Corporate social responsibility (CSR) has become a vital component in addressing environmental, social, and economic challenges. In the context of the construction industry, due to the widespread presence of irresponsible practices, CSR implementation studies have gained significant attention, particularly in developing countries. This study consists of a systematic literature review of 58 articles published between 2018 and 2024, seeking to explore the internal and external factors affecting CSR implementation in emerging construction nations. The analysis employs the PESTEL framework, which refers to external impact factors, including political, economic, social, technological, environmental, and legal aspects. Additionally, the SWOT framework is utilized to identify the strength, weaknesses, opportunities, and threats. The Analytic Hierarchy Process (AHP) is then applied to prioritize the identified factors. The results demonstrate that institutional pressures and economic progress are the most influential external opportunities. Internal weaknesses, including subcontracting and quality challenges and financial constraints of SMEs, are significant barriers. This study contributes to the CSR literature by integrating external macro-environmental factors and internal organizational dynamics, offering a strategic and actionable framework for the advancement of CSR implementation in the construction industries of developing countries.

Purpose: The exponential growth of the construction sector exerts considerable pressure on the environment, and the circular economy (CE) concept has recently gained traction as a means of alleviating such environmental issues. In order to propose solutions to the phenomenon of contractors being hesitant to participate in CE implementation in developing countries, this paper aims to comprehensively explore the enabling factors that motivate contractors to implement CE. Design/methodology/approach: This study is based on the push-pull-mooring (PPM) theory and extends it by introducing information provision (IP) as a trigger on contractors’ intrinsic subjective initiative states on CE implementation. The study considers what and how positive pull, negative push and neutral mooring factors influence the contractors’ CE transition. The framework was validated by questionnaires collected from contractors in China, and the data were analyzed using the structural equation modeling method. Findings: The findings indicate that all factors from the PPM framework exert a positive influence on CE intention, with push factors demonstrating a greater average impact. Furthermore, this study confirms the influence of the IP on contractors’ CE intentions by influencing their intrinsic status. The impact of the IP is most pronounced in terms of contractors’ perceived usefulness and environmental concerns. Originality/value: This study contributes to the existing body of knowledge in the CE transition studies by extending the PPM theory into the construction industry through the lenses of contractors in developing countries. The results highlight the trigger impacts of the IP on contractors’ psychological status regarding CE transition. Furthermore, it offers insights into government management in the CE transition by providing the government with novel approaches to facilitate the CE transition in the construction sector.

Purpose: This research aims to develop a comprehensive guidance framework for the application of artificial intelligence (AI) in construction cost management by investigating the factors affecting and formulating future directions for AI adoption based on the Technology-Organization-Environment (TOE) framework. Design/methodology/approach: A mixed-methods approach was employed, combining quantitative analysis based on survey data from 539 professionals in construction cost management and qualitative insights derived from a focus group discussion with 10 respondents. The quantitative data were analyzed using Structural Equation Modeling (SEM), while thematic analysis was applied to the discussion data to capture industry-specific perspectives. Findings: Quantitative analysis indicates that organization, technology and environment dimensions all significantly influence the performance of AI applications in construction cost management. The environment dimension was found to be the most influential, while qualitative interviews highlighted the key future direction for enhancing the performance of AI applications according to the critical factors tested by SEM. Originality/value: The TOE framework is applied to offer a comprehensive understanding of AI applications in the construction industry. It is one of the first studies to combine both quantitative and qualitative data to explore the critical factors, barriers and enablers of AI in cost management within the construction sector.