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Informal learning networks are critical to response to calls for practitioners to reskill and upskill in off-site construction projects. With the transition to the coronavirus disease 2019 (COVID-19) pandemic, social media-enabled online knowledge communities play an increasingly important role in acquiring and disseminating off-site construction knowledge. Proximity has been identified as a key factor in facilitating interactive learning, yet which type of proximity is effective in promoting online and offline knowledge exchange remains unclear. This study takes a relational view to explore the proximity-related antecedents of online and offline learning networks in off-site construction projects, while also examining the subtle differences in the networks' structural patterns.

Five types of proximity (physical, organizational, social, cognitive and personal) between projects members are conceptualized in the theoretical model. Drawing on social foci theory and homophily theory, the research hypotheses are proposed. To test these hypotheses, empirical case studies were conducted on two off-site construction projects during the COVID-19 pandemic. Valid relational data provided by 99 and 145 project members were collected using semi-structured interviews and sociometric questionnaires. Subsequently, multivariate exponential random graph models were developed.

The results show a discrepancy arise in the structural patterns between online and offline learning networks. Offline learning is found to be more strongly influenced by proximity factors than online learning. Specifically, physical, organizational and social proximity are found to be significant predictors of offline knowledge exchange. Cognitive proximity has a negative relationship with offline knowledge exchange but is positively related to online knowledge exchange. Regarding personal proximity, the study found that the homophily effect of hierarchical status merely emerges in offline learning networks. Online knowledge communities amplify the receiver effect of tenure. Furthermore, there appears to be a complementary relationship between online and offline learning networks.

Proximity offers a novel relational perspective for understanding the formation of knowledge exchange connections. This study enriches the literature on informal learning within project teams by revealing how different types of proximity shape learning networks across different channels in off-site construction projects.

The Singapore construction sector is promoting performance improvement by implementing collaborative contracting (CC). For CC's successful implementation, there is a need to sieve out the critical success factors (CSFs). Hence, this paper aims to explore factors influencing the adoption of CC and investigate the potential performance improvement by implementing CC.

Based on the literature review, 23 CSFs were identified and packaged to a questionnaire to evaluate the CSF's priority as well as CC's potential impact on the project performance. After prior validation, 165 potential respondents were drawn from survey list by using simple random sampling. Finally, 31 valid responses were received.

The survey result showed that the top five CSFs are mutual trust, all parties to commit fully, openness between parties, commitment to a win–win attitude and resource availability. The majority of respondents hold that CC implementation would improve overall project performance, decision effectiveness, cost, time, quality and risk performance.

Directors, project managers, contractor managers and site engineers have different perceptions of CSFs. Also rather than meeting the same requirements in the implementation of CC, there are differences among projects with different values.

This study provides an in-depth understanding of the CSFs for industry practitioners adopting CC and CC's potential impact on project performance. This study is also helpful for authorities to formulate effective policies to push the implementation of CC.

The purpose of this study is to develop a new hybrid method that combines interpretative structural modeling (ISM) and matrix cross-impact multiplication applied to classification (MICMAC) to investigate the influencing factors of sustainable infrastructure vulnerability (SIV).

(1) Literature review and case study were used to identify the possible influencing factors; (2) a semi-structured interview was conducted to identify representative factors and the interrelationships among influencing factors; (3) ISM was adopted to identify the hierarchical structure of factors; (4) MICMAC was used to analyze the driving power (DRP) and dependence power (DEP) of each factor and (5) Semi-structured interview was used to propose strategies for overcoming SIV.

Results indicate that (1) 18 representative factors related to SIV were identified; (2) the relationship between these factors was divided into a five-layer hierarchical structure. The 18 representative factors were divided into driving factors, dependent factors, linkage factors and independent factors and (3) 12 strategies were presented to address the negative effects of these factors.

The findings illustrate the factors influencing SIV and their hierarchical structures, which can benefit the stakeholders and practitioners of an infrastructure project by encouraging them to take effective countermeasures to deal with related SIVs.

Although previous research has recognized the pivotal role of mobile social media in knowledge sharing among project members, little is known about what factors affect knowledge sharing in mobile social media groups (MSMGs). Against this background, using normative social influence theory, this study attempts to explore factors influencing knowledge sharing in MSMGs.

Data from 205 Chinese construction project members are collected and used for analysis. Ordinary least squares regression by Stata 16 is used to test the proposed hypotheses.

Concerning role norms, gender difference in knowledge sharing behavior (KSB) is found, while it is not the case for knowledge quality (KQ). Work experience is found to positively affect KQ, but shows no influence on KSB. As for group norms, the inverted-U relationship between number of members and KSB is partially supported. In addition, organizational norms generally exhibit the greatest influence on both KSB and KQ among the three forms of norms.

This study deepens the understanding of knowledge sharing factors in mobile social media environments and affords practical implications for how to make full use of social media for knowledge management within construction project teams.

Accurate and timely cost prediction is critical to the success of construction projects which is still facing challenges especially at the early stage. In the context of rapid development of machine learning technology and the massive cost data from historical projects, this paper aims to propose a novel cost prediction model based on historical data with improved performance when only limited information about the new project is available.

The proposed approach combines regression analysis (RA) and artificial neural network (ANN) to build a novel hybrid cost prediction model with the former as front-end prediction and the latter as back-end correction. Firstly, the main factors influencing the cost of building projects are identified through literature research and subsequently screened by principal component analysis (PCA). Secondly the optimal RA model is determined through multi-model comparison and used for front-end prediction. Finally, ANN is applied to construct the error correction model. The hybrid RA-ANN model was trained and tested with cost data from 128 completed construction projects in China.

The results show that the hybrid cost prediction model has the advantages of both RA and ANN whose prediction accuracy is higher than that of RA and ANN only with the information such as total floor area, height and number of floors.

(1) The most critical influencing factors of the buildings’ cost are found out by means of PCA on the historical data. (2) A novel hybrid RA-ANN model is proposed which proved to have the advantages of both RA and ANN with higher accuracy. (3) The comparison among different models has been carried out which is helpful to future model selection.

The purpose of this article is to investigate the influence that firm-specific characteristics, such as organisational capabilities, risk management methods and stakeholder relationships, have on political risks (PRs) that are associated with multinational construction projects in Pakistan.

The methodology employed in this investigation involved the acquisition of data through the use of questionnaires administered to experts in the construction industry. The research applied a quantitative method, and the sources of the data are from the Pakistani stakeholders. One hundred questionnaires were used for the data collection during field visits. Based on the data, it has been ensured that the valid questionnaires were utilised, and the data were tested for validity and reliability. The analysis tool utilised was SPSS software. For the questionnaire, a total of 15 firm-specific factors were considered in order to design the survey, which specifically targeted the identified features. The factors identified as risks were investigated using quantitative method to determine firm-specific risks.

It was found that when stakeholders have a better grasp of these dynamics, they are better able to strengthen their resilience and efficacy in managing PRs, which ultimately increases the likelihood that the project will be successful.

International construction projects (ICPs) in emerging countries are substantially impacted by PRs, which can have a considerable impact on their success and sustainability. The study is localised and not generic as it is limited to Pakistan, and the risk factors considered are firm-specific but related to PRs.

By identifying key risk factors, these firms can develop targeted risk management strategies, leading to enhanced decision-making and more efficient resource allocation. Effective strategies include diversification, local partnerships and comprehensive risk assessments tailored to the unique challenges faced by international contracting firms in Pakistan.

ICPs in emerging countries like Pakistan face critical problems, which include the presence of PRs. Although the larger political environment plays a significant part, the manner in which businesses navigate and mitigate PRs is also influenced by firm-specific elements.

The study is novel in terms of the factors looked at, the data, the conceptual framework and the findings of the study. The dynamic political scene, which is characterised by instability, policy changes, corruption and geopolitical conflicts, poses significant dangers to the timeliness of projects, the expenses of such projects and the investments that are made in those projects.