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The construction of megaprojects often involves substantial risks. While insurance plays an important role as a traditional risk transfer means, owners and insurance companies may still suffer huge losses during the risk management process. Therefore, considering the strong motivation of insurance companies to participate in the on-site risk management of megaprojects, this study aims to propose a collaborative incentive mechanism involving insurance companies, to optimize the risk management effect and reduce the risk of accidents in megaprojects.

Based on principal-agent theory, the research develops the static and dynamic incentive models for risk management in megaprojects, involving both the owner and insurance company. The study examines the primary factors influencing incentive efficiency. The results are numerically simulated with a validation case. Finally, the impact of parameter changes on the stakeholders' benefits is analyzed.

The results indicate that the dynamic incentive model is available to the achievement of a flexible mechanism to ensure the benefits of contractors while protecting the benefits of the owner and insurance company. Adjusting the incentive coefficients for owners and insurance companies within a specified range promotes the growth of benefits for all parties involved. The management cost and economic benefit allocation coefficients have a positive effect on the adjustment range of the incentive coefficient, which helps implement a more flexible dynamic incentive mechanism to motivate contractors to carry out risk management to reduce risk losses.

This study makes up for the absence of important stakeholders in risk management. Different from traditional megaproject risk management, this model uses insurance companies as bridges to break the island effect of risk management among multiple megaprojects. This study contributes to the body of knowledge by designing appropriate dynamic incentive mechanisms in megaproject risk management through insurance company participation, and provides practical implications to both owner and insurance company on incentive contract making, thus achieving better risk governance of megaprojects.

This study aims to identify and rank the key success factors linked with digitalization in the Indian construction sector. Because the construction firms in India are in the early stages of implementing digitalization in their operations, it provides a framework from which they may focus on the effectiveness of digitalization.

This research study examines 12 success factors related to digitalization in construction, which are derived from various sectors. Furthermore, experts from the construction industry and academia have validated these factors with respect to the Indian construction sector. The multi-criteria decision-making techniques are further used to examine the interrelationship, ranking and weightage of digitalization success. Finally, the success factors are validated through a questionnaire-based empirical study followed by ranking using a t-test. The results from both approaches (company-specific and generalized) are compared and discussed.

This research identifies that selecting appropriate digital methods and techniques is a critical success factor as far as digitalization in the Indian construction scenario is concerned. Besides that, continuous monitoring and control in digital implementation significantly impact other factors.

While similar results are obtained from approaches adopted in the study, a few success factors appear to differ in terms of their ranking position. Further studies can explore the finer details that can explain the behavior pattern. This study can also be extended by assessing the structural relationship among the identified factors that can throw more light on the dynamics of the continuation of digitalization in construction which can further help in formulating policies or digitalization rollout.

The outcome of this study sheds light on construction business knowledge by stressing key success elements connected to digitalization in construction processes in the Indian construction sector. Moreover, this study shows that the success of digitalization in construction is similar to that of transformation in the information technology industry, where adopting suitable digital methods and techniques plays a vital role in the transformation process.

Despite the multiple benefits of construction digitalization, limited research focuses on digitalization success factors, making this study unique. Furthermore, this study demonstrates that integrating Fuzzy decision-making trial and evaluation laboratory and maximum mean de-entropy approaches may be used to successfully prioritize success factors in the nascent stage of construction digitalization.

Evaluating project success within the construction industry presents challenges due to the unique characteristics of the sector, the complexity of projects, and the involvement of diverse stakeholders. Conducting a bibliometric analysis, this paper aims to unravel the major research themes and methodologies utilised by researchers in studying the critical success criteria for construction projects, as well as extracting these success criteria.

The researchers systematically searched and screened 95 papers from Scopus and Web of Science (WoS) databases. This study conducted research focus parallelship network (RFPN) analysis and keywords co-occurrence network (KCON) analysis using BibExcel and Gephi to cluster the papers, illuminate the relationships among keywords within each cluster, and identify the primary research directions.

Using the RFPN analysis, this study classified the papers into three distinct clusters: infrastructure and public projects success, risk and knowledge management, and contractors and procurement management. Statistical techniques such as structural equation modelling (SEM) and multi-criteria decision-making methods such as analytic hierarchy process (AHP) have been used to analyse project success in the construction industry.

Considering the intensified demand for streamlined digital interactions and the increasing emphasis on sustainability and safety performance, construction companies are recommended to allocate greater investments toward the automation and digitisation of their products and processes. Prioritising modular construction and embracing transformative technologies alongside data science is crucial for enabling well-informed decision-making, and enhancing project success.

This study contributes to the existing body of knowledge by conducting a quantitative and systematic evaluation of the literature on project success criteria in the construction industry and uncovering key research areas. It addresses the pressing need to understand the complexities of construction projects amidst evolving industry dynamics and emerging disruptions. Moreover, by highlighting the implications of digital innovations and modular construction, this study urges deeper exploration into their impact on project performance and stakeholder satisfaction. This research sets a comprehensive framework for investigating the interplay between project complexity, technological advancements, and sustainable practices in the construction sector, paving the way for strategic advancements in the field.

The construction industry is complex, human-intensive and driven by monetary values. Hence, disputes are widespread. Initial conflicts among parties may develop into a disastrous dispute that costs the project success and good relationships and affects stakeholders' expectations. There has been a focus on causes of construction-related disputes, and studies over the past three decades have attempted to identify a more comprehensive list of reasons for disputes. Some of these studies' limitations were geographical, project delivery methods and project types. The purpose of this study is to identify the most recent and conclusive list of causes of disputes based on current literature by undertaking a systematic literature review (SLR).

Considering the large number of studies that focused on causes of disputes, this study aims to develop a comprehensive list of causes, using a SLR, as it ensures that all previous articles in multiple databases are reviewed to produce a comprehensive outcome. A six-stage SLR was followed from background study to analysis and reporting.

Not surprisingly, the number of publications has increased over time, most from the Middle East region. The interconnected nature of the causes was widely emphasised. The SLR has produced eight common core causes of disputes. They are: poor contractual arrangements, employer-initiated scope changes, unforeseen site changes, poor contract understanding and administration, contractor’s quality of works, the inability of the contractor to achieve time targets, non- or delayed payments and poor quality of design. The majority of previous authors realised that disputes could be avoided by parties’ involvement during the early stages, avoiding being opportunistic and acting collaboratively.

Even though numerous studies have been carried out to identify the causes of disputes in the construction industry, none did a SLR. This study aggregates all the previous studies that focused on construction-related disputes systematically. Categorising causes based on the party primarily responsible help various stakeholders by providing a distinct list of factors to avoid that contribute to disputes.

This study aims to develop a predictive model using machine learning techniques to forecast construction dispute outcomes, thereby minimizing economic and social losses and promoting amicable settlements between parties.

This study develops a novel conceptual model incorporating project characteristics, root causes, and underlying causes to predict construction dispute outcomes. Utilizing a dataset of arbitration cases in Türkiye, the model was tested using five machine learning algorithms namely Logistic Regression, Support Vector Machines, Decision Trees, K-Nearest Neighbors, and Random Forest in a Python environment. The performance of each algorithm was evaluated to identify the most accurate predictive model.

The analysis revealed that the Support Vector Machine algorithm achieved the highest prediction accuracy at 71.65%. Twelve significant variables were identified for the best model namely, work type, root causes, delays from a contractor, extension of time, different site conditions, poorly written contracts, unit price determination, penalties, price adjustment, acceptances, delay of schedule, and extra payment claims. The study’s results surpass some existing models in the literature, highlighting the model’s robustness and practical applicability in forecasting construction dispute outcomes.

This study is unique in its consideration of various contract, dispute, and project attributes to predict construction dispute outcomes using machine learning techniques. It uses a fact-based dataset of arbitration cases from Türkiye, providing a robust and practical predictive model applicable across different regions and project types. It advances the literature by comparing multiple machine learning algorithms to achieve the highest prediction accuracy and offering a comprehensive tool for proactive dispute management.

This study aims to investigate the most effective enablers and tools for inhabitants’ knowledge acquisition in public policymaking organizations.

Based on a literature review, the study summarized 51 individual, organizational, technological and inhabitant-related enablers of inhabitants’ knowledge acquisition, as well as 36 technological and social tools. The study assessed the effectiveness of these enablers and tools based on two rounds of a Delphi study targeting 31 policymakers in the Gulf Cooperation Council (GCC) countries. Kendall’s coefficient of concordance was assessed to determine consensus among participating policymakers.

The findings indicate that the most effective enablers were inhabitants’ willingness to get involved, information technology (IT) infrastructure, IT staff support and inhabitants’ trust in policymakers. Additionally, the study found that the most effective tools for inhabitants’ knowledge acquisition included meetings, interviews, social media and field visits.

Inhabitants’ knowledge acquisition is an important social and economic force that will drive change in any society by enabling the implementation of innovative ideas and practices. A review of the relevant research shows that despite the usability of inhabitants’ knowledge, the degree to which inhabitants’ knowledge acquisition is integrated into the public policymaking system remains unclear. This study of inhabitants’ knowledge acquisition from the policymaker perspective provides a better understanding of the inhabitants’ knowledge acquisition process in policymaking organizations.

Construction waste reduction (CWR) plays a vital role in achieving sustainability in construction. A good CWR practice can result in optimizing material usage, conserving natural resources, limiting environmental pollution, protecting the environment and enhancing human health. In this regard, the purpose of the current study is to identify the most relevant organizational policies that aid in waste reduction and concurrently explores the congruent measures to be adopted during the construction process in the Indian high-rise building sector.

The research findings were obtained through a mixed- method approach. Content analysis was used to identify waste reduction measures (variables) targeting on the two domains of construction – “waste-efficient execution” and “waste – mitigating organizational policies.” Furthermore, the authors explored and documented the key measures from the identified waste reduction measures using the constraint value of the relative importance index. As the next step, the study listed the theoretical hypothesis based on expert interviews and tested the theory through confirmatory factor analysis.

The results revealed that “waste sensitive construction techniques and strategies” as the most significant category under the domain “Execution” with a path coefficient of 0.85. Concurrently, the study has also determined that “control procedures for budget, quality and resources” as the most effective organizational approach in reducing construction waste in the Indian building industry, with a path coefficient of 0.83.

The current research is context-sensitive to the Indian construction sector. It presents the stakeholder’s perspective on construction waste reduction and the relevant measures to be implemented to reduce construction waste in high-rise building projects in India. It can also act as a concordance for decision-makers to further focus on CWR management and aid in formulating policies suitable for the Indian context.

The increased digital interactions in the B2B industry have enhanced the importance of customer engagement as a measure of firm performance. This study aims to map and analyze temporal and spatial journeys for customer engagement in B2B markets from a bibliometric perspective.

The extant literature on customer engagement research in the B2B context was analyzed using bibliometric analysis. The citation analysis, keyword analysis, cluster analysis, three-field plot and bibliographic coupling were used to map the intellectual structure of customer engagement in B2B markets.

The research on customer engagement in the B2B context was studied more in western countries. The analysis suggests that customer engagement in B2B markets will take centre stage in the coming times as digital channels make it easier to track critical metrics besides other key factors. Issues like digital transformation, the use of artificial intelligence for virtual engagement, personalization, innovation and salesforce management by leveraging technology would be critical for improved B2B customer engagement.

The study provides a comprehensive reference to scholars working in this domain.

The study makes a pioneering effort to comprehensively analyze the vast corpus of literature on customer engagement in B2B markets for business insights.

In the context of an absence of studies examining the interrelationship between Indian construction industry and residential real estate sector, the study aims to develop and test a conceptual framework to stimulate construction industry through optimisation of housing market in India. The developed conceptual framework lays down a blueprint to assess the interaction between construction industry and housing market in other countries.

Means of stimulation of construction industry by residential real estate sector were identified. Housing market was examined to identify factors constituting consumer-centric delivery and consumer-empowered demand. Supply side of housing market was probed to identify underlying factors stifling housing delivery. The identified factors were put together to form the conceptual framework. A questionnaire was developed and administered to the delivery-side stakeholders of housing market.

The study demonstrates significant correlations between real estate investment-led construction industry output stimulation and consumer-centric residential real estate delivery. The deterrents to consumer-centric housing delivery have been ascertained to be having an impact on time, cost and scope of housing projects. Significant correlations have been ascertained between the deterrents. On the demand-side, skills, awareness and engagement of consumers are strongly correlated with each other. Affordability of housing is rightfully correlated with all the three means of stimulation of construction industry output.

Specific to the Indian context, the study presents and validates a novel conceptual framework aimed at stimulation of construction industry output through interventions in housing market.

Many investors have less interest in tendering for public-private partnership (PPP) construction projects as a result of the uncertain risks associated with the project delivery. Moreover, PPP project stakeholders have inadequate information about the probable impacts of time-based delay imposed on PPP projects under the PPP arrangement. This study aims to identify and categorize construction stakeholders’ perceptions of the impact of time-based delays on PPP construction projects.

A purposive sampling technique was adopted where questionnaires were used as the primary instrument for gathering data from PPP experts. Cronbach’s alpha coefficient and Kendall’s concordance were used to measure the reliability of the scale and the respondent’s level of agreement, respectively. One sample t-test, mean score ranking and principal component analysis were used to analyse the identified time-based delay impacts.

The study revealed seven significant impacts of time-based delay on PPP construction project delivery as: “project schedule overrun”, “idling of project resources”, “project cost overrun”, “poor quality of completed works”, “delayed realization of project benefits”, “frequent arbitration/litigation in PPP projects” and “total abandonment of PPP projects”. The study further identified the top five significant impacts of time-based delay on PPP project stakeholders as: “reduction in motivation to attract investment”, “high interest on finance (loans)”, “contractor in financial crisis”, “loss of public confidence in government” and “reduction in parties’ reputation”.

The identified significant impacts of time-based delays will increase stakeholders’ awareness of the repercussions and effects that time-based delays may impose on PPP construction projects if not appropriately managed throughout the project implementation. This awareness will further guide stakeholders to implement targeted risk management strategies to minimize the negative consequences of delays on PPP project performance.

As a pioneering study that provides a better understanding of the impacts of time-based delays on PPP construction projects, this study enhances knowledge of PPP construction project implementation.