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The prevalence of independent contractors in the US workforce is growing. This research examines the social environment and career outcomes of labor and employment arbitrators, a unique profession of high-skilled and high-status independent contractors who play a significant role in facilitating organizational justice. Previous research has focused on the employment relationships that independent contractors have with hiring organizations and the characteristics of individuals who become independent contractors; however, little attention has been given to how relational factors influence the career outcomes of high-skilled independent contractors or how such influences differ by gender. Building upon theories of social networks and unequal network returns (UNR), our study investigates the informal social relationships among arbitrators, the association between interpersonal relationship patterns and arbitrators’ career success, and how these associations vary based on gender.

A social network survey is used to collect the social networks, attitudes and fee information of 407 labor and employment arbitrators working in North America. A multi-level regression analysis was used to examine the proposed relationships among social networks, gender and career outcomes of the arbitrators.

We discovered that occupying a central position within advice networks is positively associated with occupational satisfaction. On the other hand, having strong ties is associated with achieving high employment arbitration fees. Notably, we found that the advantages of strong ties for arbitration fees are comparatively weak for female arbitrators relative to their male counterparts.

This research examines the relationship between social networks and career outcomes for independent contractors in the unique context of arbitrators. It further highlights inequalities experienced by female arbitrators in a male-dominated profession where their social networks offer fewer rewards relative to their male counterparts.

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.

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.