Infrastructure project financing and public-private partnerships (PPPs) are underpinned by risk allocation between the public and private sectors. Project performance risks are often passed to the private sector as a contractual risk transfer that is often deterministic and inflexible. In complex projects where there are uncertainties in the scope of work and capital costs, performance risks may need more flexibility with active management on the evolution of project uncertainties over the life of the project’s development. Conventional fixed capital structures and project performance measurements cannot capture the evolution of project performance risks and their dynamics. This paper develops a dynamic capital structure approach that explicitly considers the performance risks of complex projects under uncertainty and allows the capital structure to be adjusted proactively to counter their impact on the project value. Unlike most models that focus on the operational phase when revenues are generated, this paper specifically considers the impact of project performance risks that occur during the development phase. This approach identifies the potential threat of default attributable to project performance risks and allows the private-sector-sponsored project company and lenders to make proactive management decisions using a real-options approach. Numerical results show that such a model can add significant value to a PPP project under uncertainty. Furthermore, numerical tests also show that with the flexibility in adjusting the capital structure, the probability of potential default can also be reduced.

In construction projects, contingency is the budget which is made available to cope with uncertainties that would incur schedule and cost overruns. Contingency estimation requires considering project cost, schedule and technology variability. The effect of dynamic project management of the decision maker should also be taken into account. Accordingly a real options approach for estimating contingency from the owner’s point of view is presented. This approach not only explicitly recognizes the uncertainty in the life cycle of a construction project, but also incorporates a dynamic project management mechanism into contingency estimation. The contingency is valued by minimizing the expected project cost via a multi‐stage stochastic model that accounts for the variability of project cost and schedule and the exercise of dynamic project crashing. The numerical results indicate that the value of the decision maker’s dynamic project management makes up an important portion of the contingency.

This paper addresses the integrated planning problem of a power network and the charging infrastructure of electric vehicles (EVs) for enhancing power system resilience under various extreme weather scenarios. The planning methodology determines the optimal joint expansion decisions while modeling the benchmark system operation under the n − k resilience criterion. The proposed coordinated planning framework is a robust two-stage/tri-level mixed-integer optimization model. The proposed robust joint planning model includes the construction plan in the first level, identifying the worst-case scenario in the second level, and optimizing the operation cost and load shedding in the final level. To solve this model, a duality-based column and constraint generation (D-CCG) algorithm is developed. Using case studies, both the robust sole transmission planning and joint planning models are demonstrated on the IEEE 30-bus and IEEE 118-bus power systems. Numerical simulations of the benchmark systems validate the effectiveness of the developed framework and the efficiency of the proposed solution approach. Simulation results show the superiority of the proposed robust integrated planning over the sole transmission planning model.

Problem definition: We study a compliance problem of healthcare workers (HCWs) in hospitals where hand hygiene compliance rates are generally low. We first analyze how peer effects impact compliance decisions of HCWs and then analyze how monitoring can improve compliance. Academic/practical relevance: Inducing process compliance of human operators is an ongoing challenge in operations management with significant implications on the productivity and the quality of the products or services rendered by standardized processes. In healthcare, low hand hygiene compliance of HCWs is the leading cause of hospital-acquired infections, which can be largely prevented by HCWs following standardized hand hygiene processes. Methodology: Using a game-theoretical approach, we model HCWs’ reactions to peers’ (non-)compliance to determine their equilibrium compliance levels. We integrate the model with a disease-transmission model to determine how the compliance affects disease prevalence in a hospital ward. Results: When the process compliance is endogenous, HCWs play a mixed strategy in equilibrium between complying and noncomplying. We establish that a macrolevel hand hygiene compliance rate of HCWs can result from a combination of four different types of microlevel noncompliance: free-riding, safe-playing, self-regarding, and opportunistic behaviors. Finally, we show that the marginal effect of monitoring on reducing disease prevalence depends on clinical factors, HCWs’ interpersonal learning, and other integration factors like goal setting. Managerial implications: The results demonstrate that the monitoring intervention may not effectively prevent disease transmission without understanding the microlevel behaviors of noncompliant HCWs. Our results provide an explanation as to why there is a significant variability in the effectiveness of management intervention as observed in practice.

Problem definition: Social influenced emotions of pride and guilt have been identified by the environmental psychology (EP) literature as crucial drivers impacting recycling behavior, but they have mostly been overlooked in operations management (OM) research. In contrast, EP studies often ignore firms’ operational decisions. We analyze the impacts of both social influence and firms’ operational decisions to provide a comprehensive understanding of consumers’ recycling behaviors, which is essential for realizing remanufacturing’s full potential. Methodology/results: We consider a closed-loop supply chain consisting of a manufacturer selling a single product to a consumer community. Consumers’ recycling behavior depends on both the recycling reward offered by the manufacturer, as well as intrinsic and socially influenced pride (guilt) from recycling (not recycling). We develop an evolutionary game to model consumers’ recycling behavior and characterize the resulting equilibrium recycling rate, which is then integrated into the manufacturer’s decision problem. We characterize the manufacturer’s optimal strategy and the equilibrium recycling rate in four distinct regions defined by both the product’s overall difficulty of remanufacturing and the underlying strengths of consumers’ socially influenced pride and guilt. We show that in settings where the product has a moderately high difficulty of remanufacturing and consumers have stronger socially influenced pride than guilt, the manufacturer optimally induces an interior recycling rate. In such scenarios, there exist win-win pathways in using social influence–based interventions to increase both the manufacturer’s profit and the recycling rate. However, misalignment may occur when consumers substantially care for the product’s recyclability. Managerial implications: This study bridges sustainable OM and EP literature by analyzing how consumers’ socially influenced emotions of pride and guilt affect a manufacturer’s optimal decisions, profits, and the resulting recycling rate. We provide important insights for designing effective and efficient social influence–based interventions to improve recycling rates. Funding: W. Chen was supported by the National Natural Science Foundation of China [Grant 71902017], and C.-L. Tseng was supported by the University of New South Wales UNOVA Knowledge Hub. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.0721 .