DISC Research Seminar Series

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This paper studies a long-term power purchase agreement (PPA) between a firm and a new renewable energy generator. The firm must dynamically satisfy uncertain electricity demand beyond its existing energy sources, while wholesale electricity prices evolve stochastically over time. Upon signing a PPA, a new renewable facility becomes operational, and the firm owns its output for the contract duration. The new facility’s capacity is determined based on PPA terms. The firm dynamically chooses when to initiate the PPA and how much to pay to maximize its expected total discounted benefit. We show that the firm’s optimal timing follows a (time-dependent) threshold policy. Our results offer key insights for policymakers and renewable energy developers. We find that, contrary to common wisdom, reducing investment costs for renewable technologies can lead to smaller renewable capacity, output, and emissions savings when projects are developed under PPAs. This finding calls for caution in applying investment tax credits in such contexts. We also show that total renewable energy generation and emissions savings may decrease with higher site productivity. Therefore, restricting renewable facility development to most productive sites might be counterproductive under PPAs. We establish the robustness of our findings across a broad range of practical scenarios.

This paper examines how firms design their global supply networks in response to perceived trade policy effect uncertainty (TPEU). Drawing on organizational information processing theory and analyzing 33,807 firm-year observations from 2007 to 2022, we document a U-shaped relationship between TPEU and supply network interconnectedness (SNI). This pattern suggests that firms adopt distinct supply network strategies depending on the degree of perceived uncertainty. Specifically, under low TPEU, firms reduce SNI by relying on less connected but more trusted suppliers. In contrast, under high TPEU, firms increase SNI by establishing redundant ties to enhance flexibility. Furthermore, we find that this U-shaped relationship is attenuated for firms with greater digital capability or higher levels of organizational slack. Our results remain robust across a range of tests addressing potential endogeneity concerns.

Artificial Intelligence has seen tremendous successes over the years and is expected to lead to an era of augmented intelligence systems that can leverage both human and AI capabilities in a seamless manner. Yet, this is not without challenges and getting there requires a focus on intentional design. This talk presents an augmented intelligence principle regarding the effectiveness of such systems and uses this principle to highlight key issues including the search for optimal designs and how to do so. Drawing on this perspective we discuss complex systems simulations as a possible alternative and illustrate its value through examples.

Technology sharing to rivals and new-product introductions enabled by those technologies have often been observed across different industries. We develop a gametheoretic model to examine why a firm would voluntarily share its technology and help its rival develop a new product. We find that the cannibalization consideration in the rival’s multiproduct pricing imposes externality on the focal firm, which largely gives rise to its incentive to share technology, in addition to the potential benefit from the change in demand elasticity. Surprisingly, the rival does not always embrace the shared technology. In equilibrium, as long as the existing product valuation is not too high, the new product would be introduced when the new product’s valuation is neither low nor high. When the new product’s valuation is high, the excessive additional competition against the focal firm discourages it from sharing. When the new product’s valuation is low, either cannibalization does not emerge in the rival’s pricing, providing no incentive for the focal firm to share, or the rival could be harmed by a new product with mediocre valuation. We show that social welfare increases with the new-product introduction to a large extent except when the existing product’s valuation is high but the new product’s valuation is relatively low. The new-product introduction increases consumer surplus only when the existing product valuation is low. Compared to technology sharing to an independent third party, the focal firm is more likely to share its technology to the rival.

Keywords:
technology sharing, new-product development, cannibalization externality, spokes model

Puffery advertising raises concerns about misleading consumers. This study explores how generative artificial intelligence (GAI) can correct potentially deceptive puffery so as to comply with media platforms’ restrictions for launching targeted advertisements while maintaining ad attractiveness. Two field studies were conducted. In the first study, due to varying puffery tolerance policies, the same advertisement was revised for some media platforms but not others. Utilizing this quasi-experimental setting and analyzing 251,694 real advertising impressions, we found that by using legitimate content in puffery through guided prompts, GAI’s revisions can significantly increase the likelihood of advertisement clicks by 19.6%. We further conducted a field experiment, optimizing prompt engineering to guide GAI in revising each of twelve individual linguistic and emotional features of the advertisements. The results of 32,200 advertising impressions reveal that enhancing linguistic readability when correcting puffery is the most effective approach for transforming potentially misleading puffery advertisements into attractive and legitimate ones. The study generates important implications on how GAI can be used to effectively address “unacceptable” puffery advertising. It also illustrates that GAI can not only resolve ethical concerns in advertising but also enhance advertising effectiveness.

Despite being one of the most cost-effective and sustainable modes for transporting freight, railways globally have been rapidly losing market share in the inland freight transportation sector. One of the salient reasons for this is the slow speed of freight trains in many parts of the world. For example, in Indian Railways, the world’s fourth-largest in size, the average freight train speed is only around 25 kmph and has remained constant for the past few decades. The slow pace of freight trains is because passenger trains, which share the same railway infrastructure, get prioritized in dispatch by railway traffic managers (also known as section controllers). In this paper, we empirically study freight delays in the Indian railway setting by analyzing how section controllers make freight train stop and hold decisions while dispatching freight trains. Subsequently, we propose policies to reduce freight delays and, thus, increase trains’ speeds through the network. We use detailed high-frequency network congestion data and estimate a structural model to estimate the key parameters underlying the controllers’ decisions. The estimated parameters provide empirical evidence for (i) the priority accorded to passenger trains over freight trains, (ii) the push effects in the freight train queue, and (iii) the strategic behavior of section controllers in holding trains at larger stations. Using the estimated model, we conduct a set of counterfactual analyses to address the problem of slow freight train speeds. First, we evaluate the impact of constructing freight-only corridors (FOCs), which are high-capacity corridors reserved for freight transport. We find that the FOCs lead to a 29% reduction in freight train delays and a 12% improvement in train speeds. Then, we also evaluate non-capacity-investment-based alternatives to FOCs, like (i) threshold-based releases for freight trains dwelling longer than a specified time limit and (ii) freight capacity consolidation by using vertically stacked trains. Interestingly, we find that our non-capacity interventions can provide benefits similar to those of FOCs while being considerably cheaper. Specifically, a 45-minute threshold release policy leads to around 31% reduction in dwell times and 9% increase in speeds. Similarly, vertically consolidating freight capacity by about 25% leads to around a 10% increase in speed, comparable to the improvement achievable with the FOC. Our policy recommendations for improving freight speeds could enhance the overall efficiency of India’s transportation infrastructure, benefiting the country’s economic and social development.

Little is known about how people-centric factors affect the shape of service time distributions, despite distributional statistics (variance or quantiles) being key drivers of system performance in many service industries. We investigate the impact of three people-centric factors—worker experience, fatigue, and rest—on the average, variance, and quantiles of service times in paramedic operations. Our analysis uses data on the performance of 368,634 paramedic teams in the London Ambulance Service over 10 years.