Journal of Management in Engineering

Global firms involved in the architecture, engineering, and construction (AEC) domain analyze the risks, benefits, and future market potential of various host countries for business development purposes. Further, appropriate entry modes and business strategies are developed to navigate the complex formal and informal institutions of the host country. Researchers have proposed comprehensive data-driven international construction risk management models to assist global AEC companies in undertaking such investment decisions. However, the reliability and accuracy of these risk assessment models depend on the credibility of the information utilized for analysis. This study presents existing theories describing how humans make credibility judgments and emphasizes the importance of establishing a systematic framework to evaluate the credibility of data sources. Through a literature review, this paper first defines information and source credibility from the perspective of international construction decisions. Second, this paper proposes a framework with (1) five criteria and 20 metrics for information credibility; and (2) three criteria and 18 metrics for source credibility. A survey questionnaire was conducted with construction executives to determine the relevancy and relative importance of the identified criteria and metrics. The implications of this study highlight the critical role of credibility in international construction risk assessment. Stakeholders involved in international construction projects can benefit from recognizing the significance of credible information sources and integrating them into their risk management strategies.

While there is burgeoning research on team resilience, resilience in temporary organizations such as project teams to boost project outcomes has yet to be fully scrutinized. Using a capacity-based conceptualization, this study investigates the predictive mechanism of project team resilience from an adaptive structuration perspective and explores the effect of team resilience on project performance. Survey data from 202 members of construction project teams lend empirical evidence to the proposed model. The results suggest that only project social media use and its interplay with professional identity salience contribute to project team resilience, which in turn improves project performance. Particularly, professional identity salience benefits project team resilience when project social media use is high, while this effect is negative when project social media use is low. These findings add to the burgeoning literature on project team resilience from a systemic and contingent lens, as well as providing informative insights for managing team resilience in engineering project practice.

Need for recovery (NFR) is an early indicator of work-related fatigue that has negative implications for worker health and safety. However, little attention has been paid to NFR in the construction industry. This study aims to investigate NFR patterns across site-based construction practitioners and their relationships with job (i.e., perceived role overload and main role) and employee characteristics (i.e., age, gender, educational level, marital status, and having children or not) in the construction industry. Data collected from 243 site-based construction practitioners were analyzed by latent class analysis (LCA) and multinomial logistic regression. Four NFR classes were identified by LCA and defined as low, moderately low, moderately high, and high. Results revealed that a combined 54% of site-based construction practitioners belonged to the moderately high and high NFR classes. Multinomial logistic regression revealed that site-based construction practitioners who perceived more role overload (versus those who perceived less role overload), held site management positions (versus tradespeople), or were female (versus male) were more likely to be in the high NFR class. The significance of this study lies in enriching the knowledge of NFR in the construction industry.

Megaproject branding on social media is especially needed in this digital era to prompt the success of megaproject delivery. Existing social media–driven branding studies often concentrated on general projects or products, and rarely focused on megaprojects that have substantial societal impacts. This presented study concentrates on megaproject branding on social media. To achieve such a purpose, a conceptual framework is proposed that explores the topic content and diffusion network of megaproject-related information on social media. Megaproject branding insights are provided to examine megaproject brand image and to improve information diffusion. Megaproject brand image is interpreted from the theme aspects of politics, economy, social, environment, national security, and megaproject itself (PESNCM). To illustrate the proposed framework, the information related to China’s major infrastructure projects (CMIPs) on Twitter is studied. Theoretically, this study further proves the necessity of megaproject branding in managing megaproject public stakeholders. Methodologically, the proposed framework could be used to conduct megaproject branding on social media platforms. Practically, the case results could be used to guide branding practices of CMIPs on Twitter.

Abstract

To enhance the performance of learning from past fall-related accidents, this study developed an innovative framework for automatically extracting every individual causal factor from accident investigation reports based upon the modified framework of the human factors analysis and classification system. Multiple techniques including the synthetic minority oversampling technique (SMOTE) algorithm for handling imbalanced data, soft voting with unequal weights for ensemble learning, and hyperparameter optimization were adopted to improve automatic identification of causal factors from unstructured text data. Experimental results denoted there were no classifiers with the best accuracy and $F_1$ score unanimously for any of the 19 subcategories of causal factors. Therefore, one or more specific classifiers were preferred for predicting one specific causal factor with the best performance. Further comparative analyses between seven classifiers demonstrated that the ensemble learning model by the algorithm of soft voting (ELSV) could provide more stable predictions with low variance across different causal factors compared with individual machine learning models. It was suggested that the ELSV ought to be prioritized for collectively identifying all 19 causal factors. These findings are beneficial for substantial learning from past fall-related accidents with high efficiency and reliability, and valuable insights can be discerned and utilized for controlling the risk of fall-from-height at construction sites.

Practical Applications

This study aims to propose an innovative framework based on multiple machine learning models (i.e., support vector machine, naive Bayes, decision tree, $k$-nearest neighbors, random forest, and multilayer perceptron) and one ensemble learning approach. Several techniques (i.e., SMOTE for handling imbalanced data, soft voting with unequal weights for ensemble learning, and hyperparameter optimization) were used for improving automatic identification of causal factors. It was found that there were no best classifiers unanimously for all 19 subcategories of causal factors. Comparative analysis results between seven classifiers demonstrated that the ensemble learning approach was able to provide more stable predictions with low variance across various causal factors compared with individual machine learning models. This innovative framework provides a feasible method of automatic identification of causal factors from fall-from-height postaccident investigation reports at construction workplaces. It decreases the time and subjectivity through a manual process, enhancing the efficiency and reliability in extracting causal factors. It also satisfies the requirement that an investigation process should be implemented as fast as possible after an accident. Safety managers on site will adopt corrective and preventive measures to deal with causal factors immediately, in order to effectively reduce falling risks in the construction industry.

Competitive bidding in design-bid-build contracts holds the potential to lower bid prices, maximize project outcomes, and promote growth in the construction industry. However, these potential benefits of competitive bidding are contingent upon adequate competition among bidders. State highway agencies (SHAs) traditionally evaluate competition levels predominantly based on the number of bidders, neglecting other key elements such as bidders’ competitiveness. Moreover, little research focuses on addressing this limitation. This study proposes a novel data-driven network theory approach to measure competition from the owners’ perspective. Specifically, competition is modeled as a bidirected network of connected bidders using their past winning and losing records. We propose different measures of contractors to provide unique insights into the competitiveness of the bidders. These centrality measures are further correlated with the bidding performance using Spearman’s rank correlation coefficient. Our findings demonstrate that these proposed centrality measures bear stronger correlations with bidding performance than the number of bidders. By employing this novel approach, SHAs can better understand contractor competitiveness and competition levels during the project-letting phase.

The growing interest in the financial materiality of Environmental, Social, and Governance (ESG) ratings has prompted recent investigations into their risk pricing impact in the corporate bond market. The specific implications for the Architecture, Engineering, and Construction (AEC) industry have not been explored, as prior work has primarily focused on broad-based ESG integration. To fill this gap, our study employed an interpretable machine learning technique using a sample universe of U.S. corporate bonds spanning from 2010 to 2021 to estimate the impact of ESG ratings on corporate bond issuance spreads. The results revealed an average ESG benefit of 10 basis points across all sectors. However, it is important to note that the effects of ESG ratings on bond pricing demonstrate variation across sectors and individual ESG constituent ratings. Significantly, our findings show that social and governance ratings emerge as the primary drivers influencing bond issuance costs, whereas the impact of environmental scores is comparatively less significant. Within AEC-related industries, empirical data on the influence of ESG ratings indicate discounted pricing by the market is particularly channeled through environmental and governance scores. These findings emphasize the value-added impact of enhanced ESG performance on the cost of debt financing, presenting a financially material opportunity for operational and management decision-making. By adopting sustainable strategies to improve ESG performance, organizations in the AEC industry can potentially achieve lower costs of debt when issuing bonds to secure financing for construction projects. The managerial implications extend to policymakers, corporate managers, and creditors, as they all stand to benefit from the financial implications of ESG performance.

In the era of big data, the extraction of valuable knowledge and insights becomes feasible through efficient data processing methods. In this research, we establish an analytical framework enabled by big data to aid in the management of construction workers’ unsafe behaviors. To evaluate workers’ behavioral patterns, a multistage data processing model is employed, uncovering previously unknown connections between unsafe act records and situational data. Leveraging the high-level knowledge derived from this analysis, personalized safety management strategies are formulated based on individual workers’ behavioral patterns. The effectiveness of the proposed framework is evaluated by comparing it to conventional management strategies across three construction sites. Results demonstrate that behavioral patterns discovered by the big data framework provide an important decision basis and achieve smart construction unsafe behavior management.

This study introduces the term earned time (ET) for the class of linear methods which estimate the amount of project schedule time earned. Existing methods such as planned value (PV) rate, earned duration, and earned schedule are shown to be specific cases of earned time that use different linear rates to convert schedule variance in units of cost to units of time. An additional PV segment method is proposed that uses the linear rate of only the segment of the PV curve responsible for the schedule variance. The ET methods are examined theoretically and applied to three example projects to clarify their underlying assumptions. Five factors are found to guide the selection of a method for a given application: linearity, adherency, parallelism, stage, and status. The study contributes to theory by generalizing the concept of earned time and providing a new method for its estimation. The study is useful for practitioners seeking to apply ET methods and interpret their results.

The adoption and acceptance of new cutting-edge technologies such as wearable sensing devices (WSDs) have been investigated for personal use in a few industries, but concerns remain about worker postadoption behavior, especially in industries known to be resistant to innovation adoption, such as construction. Information on WSD postadoption behavior is scarce, and, to our best knowledge, no study has investigated this phenomenon for construction applications. The primary purpose of this study is to evaluate the adaptation and infusion of WSDs for construction safety and health monitoring. Using a multiphase mixed-method approach, this study develops and validates a conceptual model to examine the factors that influence the adaptation and infusion of WSDs for construction safety and health monitoring. The findings of the study reveal that construction workers’ behaviors and acceptance antecedents are important factors that significantly influence their adaptation and coping behavior, which in turn predict the extended use of WSDs. The main contribution of this study is in the understanding of the impact of coping behavior on workers’ continuous use of safety and health technology. Theoretically, this study extends the coping theory by incorporating implementation characteristics, providing a novel approach for understanding, explaining, and possibly enhancing the adoption and use of emerging technologies in construction.

This paper presents the development of a comprehensive campus facility management performance assessment model (CFMPM). The model employs structural equation modeling (SEM) and incorporates a thorough list of crucial success factors. The CFMPM tool is intended for use by industry shareholders to evaluate the operational effectiveness of campus facility management (CFM) and analyze the proficiency and constraints of CFM organization across diverse managerial tiers. Key CFM success factors were collected using interviews with experts and relevant literature, validated using two rounds of the Delphi technique with 14 highly qualified experts, and disseminated via an online questionnaire to industry experts who rated them according to their significance. Data were obtained from 402 participants hailing from diverse regions. Then, SEM was used to perform a quantitative analysis of the interconnectedness between the various aspects of CFM performance, with the aim of ascertaining the influence of each construct on overall performance. The model was rigorously assessed for its robustness across several parameters, including goodness-of-fit measurements, multivariate normality tests, and reliability and validity tests, in addition to hypothesis analysis. The gathered data were used to construct a CFMPM comprising 45 key performance indicators of successful CFM grouped into seven different constructs. The application of SEM revealed a noteworthy correlation between the operational indicators of proficient CFM and the construct groups, in addition to the impact of efficacious CFM constructs on CFM performance overall. This research enhances the existing knowledge base by identifying vital indicators for the evaluation of CFM effectiveness and utilizing them to formulate an inclusive global SEM capable of serving as a tool for gauging the efficiency of CFM. This could lend valuable support for organizations, facility managers, and policy makers when making well-informed decisions.

Over the past couple of decades, the US construction sector has experienced a nationwide shortage of skilled craft professionals in several trades. In response to this problem, a multiskilling strategy has emerged as a possible solution to address the rising workforce shortages in the construction industry. Multiskilling is a labor strategy that involves individuals acquiring certification in trades other than their primary trade. While other studies on multiskilling have examined the trade combinations in which multiskilled workers typically become certified, the level of certification in the different trade combinations has not been identified by these studies. Are there patterns where multiskilled workers typically work in a trade as their primary trade and then become multiskilled in typical secondary trades? To reach the intended result, this paper examined more than 640,000 exam completions from the National Center for Construction Education and Research (NCCER) and its National Craft Assessment and Certification Program (NCACP). A hierarchical cluster analysis was performed to identify the certification patterns among multiskilling craft professionals. Additionally, a binary logistic regression was used to identify the potential factors influencing individuals’ exam pass rate. The results of the cluster analysis indicate that current multiskilled craft professionals had limited ability in performing advanced work in both trades. Based on the binary logistic regression, the result indicated that the multiskilling strategy may not be the most practical approach for certain trades, such as electrical and instrumentations trades. Eventually, this study contributes to the body of knowledge by providing a comprehensive assessment of multiskilled craft professionals’ achievement and development across different trades in the construction industry.

Construction projects often face disruptions that can impact project delivery and result in disruption claims. However, dealing with these claims is a complex process due to the challenges involved in proving them, which often leads to disagreements and disputes between contracting parties. This study aims to identify (1) the difficulties encountered in the disruption claims process, (2) their impacts, and (3) the success criteria for handling such claims. To achieve this, 22 disruption claims experts in Australia were interviewed. Difficulties were identified across different stages of disruption claims, including preparation and submission, assessment, and resolution. The findings revealed that nine difficulties were associated with the preparation and submission, seven with the resolution and six with the assessment of disruption claims. According to the experts, the most prevalent difficulty in all stages of disruption claims is the lack of contemporaneous information and records. Moreover, the major impact of these difficulties is the occurrence of disputes between contracting parties, resulting in significant cost and time implications. The experts also emphasized that the availability of relevant information (28%) and achieving commercial success (20%) are crucial factors for a successful disruption claim process. Furthermore, this research proposes a conceptual framework based on information management and the utilization of modern technologies, such as drones, cameras, radio frequency identifiers (RFIDs), building information modeling (BIM), project management software programs, big data and machine learning. The aim is to simplify the disruption claims process and minimize the occurrence of disputes.

Abstract

Institutional environment shapes the success of enterprise digital transformation, but its impact pathways, to our best knowledge, have received relatively little investigation, particularly in the fields of architecture, engineering, and construction (AEC). Grounded in neoinstitutional theory, this study conducted a fuzzy set qualitative comparative analysis of the effects of institutional pressure on digital transformation. The study yielded three results. First, the interaction of coercive, normative, and mimetic pressures can drive the digital transformation of AEC enterprises. For example, facing the digital delivery requirements of stakeholders and the pressure of digital collaboration in the supply chain, digital leaders in an enterprise will quickly apply internal pressure to raise the digitalization level. Also, in a highly competitive industry environment, the pressure to imitate the excellent digital transformation strategies of other enterprises comes with it. In a less competitive environment, however, digital leaders are the main pressure source to promote the digital transformation of enterprises. Second, we find that stakeholder vision, supply chain collaboration needs, and digital leaders are the key sources of digital transformation pressure. Third, high or low digitization levels cannot simply be explained by the opposite cause. Offering a new understanding of coercive, mimetic, and normative pressures on the complex digital transformation pathways of AEC enterprises, this study has implications for enterprises formulating digital transformation strategies.

Practical Applications

The findings point to the importance of corporate strategic change in the current digital environment, identify the institutional factors driving digital transformation, and will help AEC industry enterprises with different main businesses to respond to institutional pressures and improve the effectiveness of digital transformation through more targeted measures. This study can assist engineering enterprises around the world to develop digital transformation strategies. By exploring the complex causal relationships between the institutional environment and the digital transformation of engineering enterprises, this study provides a strong empirical foundation for new initiatives and policies to achieve high digitalization levels.

Competitive bidding is a popular technique State Highway Agencies use for selecting contractors for public construction work. It intends to ensure that construction projects are awarded at the lowest price. However, public owners may award contracts at high prices due to collusive bid arrangements among bidders. The current practices and state-of-the-art methods of bid collusion detection are ineffective because they greatly rely on engineers’ estimates, which can be inaccurate, and the similarity in the cost structure of the bids, which experienced collusive bidders can manipulate. This study contributes to the body of knowledge with a novel data-driven collusion red-flag detection framework (CRFD) that utilizes pattern mining techniques and statistical tests for detecting contractors with a red-flag pattern, which refers to a significant difference in the winning rates of a contractor with and without the co-occurrence of other particular contractors. A mechanism is also proposed to incorporate potential, influential factors into the CRFD to increase the detection power or examine possible collusion between different scenarios. The proposed method is expected to assist project owners in detecting early signs of bid collusion and eventually help them significantly enhance their award decision-making.

The significance of fire inspection has been underscored to prevent building fires, which can lead to property damage and loss of life. While previous studies employed building information modeling (BIM) to evaluate fire safety performance, they primarily focused on a building’s physical characteristics, overlooking environmental factors. To address this limitation, we propose a framework of BIM-based quantitative evaluation, which incorporates both physical and environmental characteristics of buildings to enhance their fire safety performance. We conducted a case study featuring three scenarios to validate the proposed framework. The results demonstrate that Scenario 3, with a score of 60.3 points, outperforms Scenarios 1 (30.9 points) and 2 (38.2 points) in terms of fire safety performance. This is attributed to Scenario 3’s consideration of components, firefighting equipment, and environmental elements in preventing and responding to building fires. The proposed framework contributes to the field by automatically evaluating buildings’ fire safety performance using a comprehensive approach based on BIM.

Work zones are crucial for infrastructure maintenance and improvement. However, ongoing projects within work zones sometimes can place workers and drivers in dangerous situations. Despite safety regulations, work zone accidents persist with notable severity and frequency. Previous research has explored work zone accident causation, but it has not provided a comprehensive understanding of the factors that impact work zone safety. This paper addresses this research gap by following a multistep methodology. First, a systematic literature review (SLR) was conducted to identify a comprehensive list of 37 factors impacting work zone safety. Second, social network analysis (SNA) was employed to analyze the connectivity between these identified factors, based on the theoretical, mathematical, and computational approaches found in the literature. Third, the factors were grouped into clusters using the $k$-means clustering technique, considering the overall discussions present in the literature. Last, association rules were mined from the clustered factors to determine combinations of factors that have not been extensively examined together, thereby identifying gaps and deficiencies in the present body of knowledge. The findings of the SNA indicate that design-related factors have received more extensive attention in the literature, whereas certain driver- and state-related factors have received comparatively less attention. The clustering analysis shows that a significant number of theoretically addressed safety factors in the literature frequently are examined together, which indicates a necessity for additional computational and mathematical investigations to gain a more comprehensive understanding of these factors. Moreover, the association rules highlight the groups of underexamined relationships of factors that affect work zone safety. Ultimately, this research contributes to the body of knowledge by consolidating the studies concerning work zone safety and using them to provide a robust roadmap for future research, thereby enhancing the advancement of this field and ultimately leading to improved work zone safety practices.

Skilled labor plays a crucial role in ensuring that construction projects are completed on time, within budget, and to the required standards of quality and safety. However, the construction industry has been facing a labor shortage in recent years, which poses a significant challenge to the industry’s growth and sustainability. Therefore, it is important to examine the characteristics of the construction skilled labor market to understand the factors that contribute to the shortage of skilled workers and develop strategies to address the issue. This paper fills this knowledge gap. To this end, the authors (1) collected and processed project documentation in relation to 67 construction projects to identify key construction labor-intensive trades, (2) conducted an expert-based survey to collect data in relation to union participation rates and degrees of skilled labor shortages across the identified trades, (3) performed clustering analysis to examine the observed levels of labor shortage across the identified trades, (4) applied a binomial test to analyze the levels of union participation for each of the labor trades, and (5) used a chi-square test of independence to investigate the correlations between workforce location and union participation on the one hand and union participation and labor shortage on the other. As such, the authors identified 10 key labor-intensive trades. It was found that plumbing and electrical trades have the highest degrees of skilled labor shortage, whereas finishing work trades (i.e., plastering and painting, flooring, and waterproofing) had the lowest. Results also showed a significant correlation between high union membership rates and the availability of skilled workers in 3 of the 10 identified trades (i.e., ironworking, flooring, and waterproofing) and that union reach in urban locations is less than that in rural areas where workers are employed. Ultimately, this paper adds to the body of knowledge by offering a closer look into the construction skilled labor market. Such knowledge can be used to mitigate the current labor shortages.

Public construction projects are exposed to corruption vulnerabilities at various stages, which can be manipulated by corrupt actors. Effective management of public resources requires holistic anticipation and mitigation of corruption along the entire project cycle. This study examines the role of the organizational integrity climate in influencing project corruption risks. Twenty-two critical integrity climate measures (ICMs) and 30 major project corruption risks (PCRs) are identified from an extensive literature review and parallel discussions with practitioners. A total of one hundred and ninety-three practitioners in large public organizations in India evaluated the ICMs and PCRs through a questionnaire survey. Exploratory factor analysis extracted four integrity climate factors and five project corruption risk factors which were used to build a hypothesized model of their relationship. Partial least squares structural equation modeling (PLS-SEM) used to test the model shows a significant mitigating effect of integrity climate on project corruption. Key process management, control and oversight, commitment to integrity, and system accessibility and stakeholders’ engagement are the critical integrity climate factors that inhibit corruption. The study underpins the role of the organizational integrity climate in addressing corruption in public construction projects and thus enriches the literature on anti-corruption management. The rich insights offered will enable policymakers and practitioners to develop a control environment in public organizations with a focus on integrity.

Abstract

The fragmented management of traditional construction projects, which mainly lacks the integration of project processes, often results in schedule delays and cost overruns. As a result, claims are most likely to arise between the contracting parties. Numerous studies have investigated the influence of integrating sustainable management practices (SMPs) in construction projects on specific project performance objectives, such as cost and quality; however, none has considered the effect of SMPs on claims resolution. Therefore this research investigated the impact of implementing an integration of SMPs clusters on construction claim mitigation. To achieve this goal, an adaptive neurofuzzy inference system (ANFIS) model was developed to establish 12 optimal approaches for integrating SMPs that promote the reduction of 10 different claim types. This study assists construction project management experts in identifying optimal combinations of a minimum number of sustainable management practices that, if implemented together, can help to significantly reduce targeted types of claims.

Practical Applications

Each construction project is characterized by its uniqueness, complexity, and uncertainty, making it vulnerable to risks, disputes, and severe fluctuation in budget and time of completion. Consequently, it is becoming crucial to adjust the traditional construction management practices into more-sustainable practices to minimize risks and improve the chances of delivering a project with minimum cost and time overruns. Therefore, integrating sustainability concepts into the project management process can support critical planning at very early stages, enhance coordination among key project parties, improve quality of output, increase productivity, and enhance project sustainability performance throughout the useful life of the project. The findings of this study reveal the importance of implementing sustainable management practices in mitigating certain construction claims. This research identified optimal approaches for the implementation of 25 sustainable management practices to avoid the occurrence of 10 frequent claim types. Using the results and models of this study, construction management practitioners can select, considering their project conditions and financial constraints, an ideal combination of critical sustainable management practices that can ensure the effective reduction of major construction claims encountered in their projects.

Large-scale infrastructure projects (LIPs) greatly contribute to human welfare but pose risks to diverse groups. Such imbalance in the distribution of benefits and risks will trigger public perceived justice. Meanwhile, social media make online perceptions of justice more intense and complex. Although existing studies pay close attention to online public opinion on LIPs, there is little research on online perceptions of justice in LIPs. Therefore, this study aims to reveal the temporal patterns and sentiment characteristics of online perceptions of justice. Specifically, this study encoded the data of the South-to-North Water Diversion (SNWD) project on Sina Weibo. Based on the justice theory, six dimensions of online perceptions of justice were identified, and an analytical framework of online perceptions of justice in LIPs was proposed. The results denoted that there were two main lifecycles in the propagation of online perceptions of justice of LIPs. Regional justice appeared in the construction stage of the first lifecycle, while intergenerational justice, environmental justice, and outcome justice arose in the operation stage of the second lifecycle. Among the four dimensions of distributive justice, regional justice accounted for the vast majority of online perceptions of justice in the SNWD project. Moreover, the perception of intergenerational justice increased most sharply with positive emotion. Besides, the sentiment toward environmental justice and outcome justice was negative. This study yields an analytical framework for promoting the conflict management of LIPs from the perspective of online perceptions of justice, and findings further indicate three strategies for authorities to manage online perceptions of justice in LIPs: attaching importance to public participation, strengthening the cooperation with social media platforms, and promoting compensation mechanisms for LIPs.

While the significant role of external environments in shaping the implementation of building information modeling (BIM) as a systemic innovation in construction projects has been increasingly acknowledged, little is known about how heterogeneous projects concretely interact with each other to facilitate or inhibit the advancement of BIM implementation practices in the “conservative” construction industry. Drawing on a social contagion perspective, this study explores how the contagion effects through participant- and attribute-based ties among construction projects differently impact project-level BIM implementation practices via the interproject learning and competition processes. The proposed propositions are tested using an industry-wide longitudinal data set of BIM-based construction projects within the construction industry in Shanghai, China. After controlling for the effects of related organizational and project characteristics, the results show that, compared with the attribute-based contagion effects, which are primarily related to the interproject competition process, the participant-based contagion effects primarily related to the interproject learning process generally play more substantial roles in impacting project-level BIM implementation practices within interproject networks. The results further provide evidence that the participant-based contagion through the ties of project owners is more substantial than that through the ties of project contractors, and that the magnitude of the attribute-based contagion varies in different sizes and types of projects. As an exploratory effort of using longitudinal data to empirically characterize how project-level BIM implementation practices are contagious through different types of interproject ties from a social contagion perspective, this study contributes to a deepened understanding of how heterogeneous construction projects interact with each other through interproject learning and competition processes to facilitate or inhibit the advancement of innovative practices in the industry. The findings reinforce the need to regard BIM implementation practices in construction projects as complexly socialized activities and provide insights for policymakers and practitioners in developing strategies to facilitate the in-depth implementations of BIM and related innovations in the “conservative” project-based construction industry.

Many public–private partnership (PPP) projects around the world suffer from poor risk allocation, questionable technical studies, cost underestimation, and inaccurate demand forecasts. To address these issues, some scholars have suggested public authorities adopt more flexible contracts. However, given their limited use to date, questions about the overall efficacy of flexible contracts persist. To test their suitability for PPPs, this article reassessed the least present value of revenue (LPVR) model—a flexible-term contract that dynamically changes based on project revenues. The capacity of the LPVR model to address variations in both demand and project costs was tested under different scenarios by modeling historical financial data from an operating highway PPP in Peru. The results suggest the LPVR model can effectively mitigate demand risk but cannot adequately address unexpected cost variations commonly found in greenfield projects. This means the LPVR model may be better suited for brownfield projects in emerging markets that have consolidated demand and limited construction risk.