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Analyzing factors of delays in construction projects and determining their impact on project performance is necessary to better manage and control projects. Identification of root factors which may lead to project delay and increased cost is vital at the early or planning stage. Better identification of delay factors at the early stage can help the practitioners to reduce their impacts over the long run. Hence, the purpose of this paper is to propose an intelligent method to analyze causal relationships between delay factors in construction projects. The proposed approach is further validated by a real case study of the construction projects in West Azerbaijan province in Iran.

During the first phase, the fuzzy cognitive map (FCM) is drawn to indicate the causal relationships between the delay factors and the evaluation factors. For this purpose, the causal relationships between 20 delay factors and four evaluation factors are considered. Afterward, the effect of each factor on management goals is evaluated by using a hybrid learning algorithm. Delay factors are further prioritized by applying fuzzy data envelopment analysis (FDEA). In the second phase, an interpretive structural modeling (ISM) is employed to determine the root causes of delay factors.

Results of the first phase show that “supervision technical weaknesses for overcoming technical and executive workshop problems” and “Inaccurate estimation of workload, required equipment and project completion time” are the most significant delay factors. In contrary, “non-use of new engineering contracts” has the lowest impact on the management goals. Meanwhile, the results of the second phase conclude that factors like “Inaccurate estimation of workload, required equipment and project completion time” “weakness of laws and regulations related to job responsibilities” and “lack of foreseen of fines and encouragements in the contracts” are the most significant root factors of delay in construction projects.

This paper integrates three methods including FCM method, FDEA and ISM. In the first phase, FCM is drawn according to the experts’ opinions and concerning management goals and delay factors. Later, these factors are prioritized according to the results of running the algorithm and using the FDEA model. The second phase, the seven-step in the ISM methodology, is done to identify the root factors. To ensure that the root factors of the delay are at a lower level of hierarchical structure, delay factors are partitioned by drawing the ISM model.

Purpose – The purpose of this chapter is to show that because the evolutionary roots of many kinds of phenotypic social phenomena can be partly traced to genotypic factors, it would be useful for social sciences to adopt a socio-biological research formula, which combines the impacts of genotypic and environmental explanatory factors.

Design/methodology/approach – The exclusion of evolutionary factors from social sciences and some previous studies using evolutionary factors is first reviewed, after which a socio-biological research formula (y=(a+b)+x) is introduced. It is hypothesized that national IQ as an important genotypic factor explains a significant part of the global variation in all kinds of phenotypic social phenomena. The hypothesis is tested and the use of the socio-biological research formula is illustrated by studies of democratization (ID-10) and human development (HDI-11).

Findings – The results of correlation analysis support the hypothesis on the evolutionary variable’s (national IQ) universal explanatory power. National IQ explains 33 percent of the variation in ID-10 and 68 percent of the variation in HDI-11. Environmental variables increase significantly the explained part of variation in a dependent variable in the case of ID-10 but less in the case of HDI-11.

Practical implications – Because it is evident that national IQ as an evolutionary variable explains a significant part of the variation in all kinds of phenotypic social phenomena, it would be sensible for social sciences to adopt the suggested socio-biological research formula based on the idea that intelligence constitutes an important common explanatory factor.

Originality/value – The suggested socio-biological research formula provides for the social sciences a common theoretical starting point to study many kinds of social problems.

This paper develops a cross-sectionally augmented distributed lag (CS-DL) approach to the estimation of long-run effects in large dynamic heterogeneous panel data models with cross-sectionally dependent errors. The asymptotic distribution of the CS-DL estimator is derived under coefficient heterogeneity in the case where the time dimension (T ) and the cross-section dimension (N ) are both large. The CS-DL approach is compared with more standard panel data estimators that are based on autoregressive distributed lag (ARDL) specifications. It is shown that unlike the ARDL-type estimator, the CS-DL estimator is robust to misspecification of dynamics and error serial correlation. The theoretical results are illustrated with small sample evidence obtained by means of Monte Carlo simulations, which suggest that the performance of the CS-DL approach is often superior to the alternative panel ARDL estimates, particularly when T is not too large and lies in the range of 30–50.

Early termination of public–private partnerships (PPPs) in China is caused by various risk factors, resulting in significant losses. This study aimed to clarify the key factors and identify the causal relationships among these factors.

Social network analysis (SNA) was used to analyze 37 risk factors that were summarized from 97 early terminated PPP cases and to identify the relationships among these key risk factors. Interpretive structural modeling (ISM) was conducted to explore the causal relationships. Data were collected from case documents, questionnaires and interviews.

A total of 17 key risk factors were identified and distributed in a hierarchical structure with six tiers. Among these key risk factors, the root causes affecting the early termination of PPP projects were government oversight in decision-making, local government transition, policy and law changes and force majeure. The direct cause was insufficient returns. Furthermore, local government and private sector defaults were essential mediating factors. Local government transition and the low willingness of the private sector were highlighted as potential key risks.

The cases and experts were all from China, and outcomes in other countries or cultures may differ from those of this study. Therefore, further studies are required.

This research provides knowledge regarding the key risk factors leading to the early termination of PPP projects and guidance on avoiding these factors and blocking the factors' transmission in the project lifecycle.

This study contributes to the knowledge of risk management by emphasizing the importance of local government transition, the low willingness of the private sector and project cooperation and operation, whose significance is ignored in the existing literature. The proposed ISM clarifies the role of risk factors in causing early termination and explains their transmission patterns.

This paper provides a selective survey of the panel macroeconometric techniques that focus on controlling the impact of “unobserved heterogeneity” across individuals and over time to obtain valid inference for “structures” that are common across individuals and over time. We consider issues of (i) estimating vector autoregressive models; (ii) testing of unit root or cointegration; (iii) statistical inference for dynamic simultaneous equations models; (iv) policy evaluation; and (v) aggregation and prediction.

New generation of construction workers (NGCWs) who were born in the 1980s and later have gradually become the main workforce of Chinese construction industry. They may behave differently when dealing with knowledge-related activities due to divergent characteristics caused by generational discrepancy. To provide a theoretical foundation for construction companies and safety managers to improve safety management, this research explores the factors and paths impacting the NGCWs' ability to share their safety knowledge.

Based on literature review, main factors that influence the safety knowledge sharing of the NGCWs were identified. Decision-Making Trial and Evaluation Laboratory and Interpretive Structural Modeling were applied to identify the hierarchical and contextual relations among the factors influencing the safety knowledge sharing of the NGCWs.

The results showed that sharing atmosphere ranked first in centrality and had a high degree of influence and being influenced, indicating itself an extremely important influencing factor of safety knowledge sharing of NGCWs. Six root influencing factors were identified, including individual characteristics, work pressure, sharing platform, incentive mechanism, leadership support and safety management system.

The number of influencing factors of safety knowledge sharing of the NGCWs identified in this study is limited, and the data obtained by the expert scoring method is subjective. In future studies, the model should be further developed and validated by incorporating experts from different fields to improve its integrity and applicability.

The influencing factors identified in this paper can provide a basis for construction companies and safety managers to improve productivity and safety management by taking relevant measures to promote safety knowledge sharing. The research contributes to the understanding knowledge management in the context of the emerging market. It helps to answer the question of how the market can maintain the economic growth success through effective knowledge management.

This paper investigates the influencing factors of NGCWs' safety knowledge sharing from the perspective of intergenerational differences, and the 13 influencing factor index system established expands the scope of research on factors influencing safety knowledge sharing among construction workers and fills the gap in safety knowledge sharing research on young construction workers. Furthermore, this paper establishes a multi-layer recursive structure model to clarify the influence path of the influencing factors and contributes to the understanding of safety knowledge sharing mechanism.

The purpose of this paper is to identify risk factors in building information modeling (BIM) technology application, analyze the correlation between risk factors, and find the most influential risk factors using interpretative structural model (ISM), put forward a response to targeted suggestions, and provide a reference for widespread BIM technology application.

Literature reviews, ISM, a questionnaire survey and expert interviews were used in this paper. First, the risk factors of BIM technology application and their relationships were determined by consulting the relevant BIM literature. Second, to more intuitively reflect the relationships between risk factors, an ISM model based on expert interviews was established to structure the explanatory relationships and hierarchical relationships between factors. Finally, a questionnaire survey was conducted for relevant practitioners, and a scale was designed to evaluate the risk factors of BIM technology application to determine the risk factors that have a greater impact on implementing BIM technology.

The root risk factors of BIM technology consist of unclear data ownership, incomplete standard BIM systems, lack of industry insurance, lack of BIM technicians, lack of BIM project practice experience, BIM infrastructure preparation, inconsistent thinking about BIM from project participants, changes in delivery mode and software function problems.

The risk-sharing of BIM technology and the analysis of responsibilities, rights and benefits of all parties in a BIM project can be used as future research directions to solve higher management problems.

This paper describes the risks of BIM technology application for related researchers and provides another research train of thought for the follow-up studies.

Some papers have investigated the complex factors impacting building information modeling (BIM) application in prefabricated buildings (PBs), but few paid attention to their interaction relationships. Ignoring the fact that different factors are not isolated may lead to some key factors being overlooked without appropriate improvement strategies being proposed. This paper aims to analyze those factors and their inter-relationships, with the view to identify the critical factors and their interaction relationships so as to derive constructive strategies that would effectively facilitate BIM adoption in Chinese prefabrication.

First, factors influencing BIM application in prefabrication are extracted and collated by literature review, expert interview and analysis of PBs characteristics. Thereafter, an evaluation laboratory (decision-making trial and evaluation laboratory) and interpretive structural modeling are used to explore the relationships and hierarchy among the factors. Based on the degree of cause and centrality, critical factors are extracted and the interaction relationship are investigated.

The results show that BIM policies and standards for PBs are the main causal factors. The maturity of BIM software and BIM data interface for PBs, willingness to share data, the strategic goals of the enterprise, BIM law and BIM input and benefit are the main transitional factors while BIM staff and workflow, enterprise attitude, distribution of BIM liability and cooperation of participants are the main direct factors.

Based on the above findings, corresponding improvement strategies are proposed so as to promote BIM application in prefabrication and the rapid development of China’s PBs efficiently.

This study aims to develop a collaborative governance framework (CGF) to systematically investigate the impeding factors (IFs) in terms of the operational sustainability of PPPs. It examines the transmission pattern (i.e. the way in which network members react to each other) of the IFs network.

Literature review and interview were adopted to identify the IFs. Then, with the data collected from the interview in China, the social network analysis and interpretive structure model were synergised to examine the chain reaction, driving and dependent powers, and hierarchical structure of the identified IFs.

The results reveal that the cognition, institutional, financial and participation aspects are key barriers confronted by PPP sustainability, and the government plays a leading role in controlling factors causing sustainability-related problems in PPPs. Weak government leadership and institutional environment were identified as the most fundamental reasons triggering a chain of IFs, while project governance and management activities act as bridge nodes that play an intermediary role in the IFs network.

This research contributes to the literature on PPP governance by (1) bridging the literature gap through the development of CGF for explaining the governance of PPP sustainability with a holistic view that considers both macro environment and operational project processes; and (2) identifying the transmission pattern of IFs network which uncovers the underlying dynamics causing the unsustainable operation of PPPs.

This research provides practitioners with a list of key checkpoints for preventing failure escalation, enables decision-makers to prioritise obstacle-mitigation efforts and develop a feasible process to control PPP operation, and offers management countermeasures to remove the key barriers impeding PPP sustainability.

This study is novel for adopting network-oriented techniques to quantify the relative importance of the IFs and examine the transmission pattern of the IFs system. Therefore, it visualises the complex underlying dynamics causing unsustainable PPP operation, identifies root and direct causes of PPP failures, and provides decision-makers with insights into sustaining PPP sustainability from a network-oriented perspective.

Given the significant role played by valuers and the evidence of a lack of independence in some property valuation industries, particularly in emerging markets, this study analyses the issue of client influence in property valuation by providing a valuer-client perspective and measuring the interrelationships between the clients' influence factors to identify causal factors of prominence, which can assist in developing solutions to address the clients' influence issue.

The study used a mixed-method approach. Firstly, interviews were conducted with ten property valuers and five financial institution staff in Nigeria, and the data were subject to thematic analysis using Nvivo 12 software. A matrix questionnaire survey was administered to the valuers, and the responses were analysed using the fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) method.

The results indicate that institutional clients, loan-seeking customers, property valuers and the perception of corruption within the Nigerian environment fuelled the issue of clients' influence. Based on the measurement of the interrelationship between the 14 identified client influence factors, the type of company, perception the public has of the industry, size of the firm, relationship with the client, type of client and regulatory framework were the factors of prominence.

The findings of this study bear huge implications for Nigeria and other similar structured property markets facing the issue of clients' influence in property valuation. With the prominent factors bearing root in a mix of client, valuer and environmental factors such as the valuation structure, process and public perception, there is a need for solutions that level the playing field between institutional clients and valuers, reinforce transparency and establish excellent regulatory standards to address the issue of clients' influence.

This study is the first to measure the interrelationships between the clients' influence factors to identify the prominent causal factors. Accordingly, considering the multi-factors, the research is novel as it focusses on those factors that would likely lead to other factors, thereby providing opportunities to develop solutions that focus on those factors of prominence. Secondly, the study deviates from the narrative on clients' influence in property valuation, which pits it as solely a client or valuer factor, by showing how the interplay of the stakeholders' interests and the environment promotes the issue in a non-transparent property market.