Entrepreneurs often use crowdfunding to solicit feedback from customers to improve their products, and may therefore prefer to launch crowdfunding campaigns for a basic version of their products. Yet, customers may not be persuaded by a campaign if a product appears too basic. Analyzing a game-theoretical model and testing its predictions empirically, we study how a product’s level of enhancement at campaign launch influences product improvements during campaign and campaign success.

We consider two ways that blockchain can enhance supply chain transparency: (1) making the manufacturer’s sourcing cost transparent to the buyers (vertical cost transparency), and (2) making the ordering status of buyers transparent to each other (horizontal order transparency). We develop prescriptions to supply chain practitioners with regard to when blockchain should be adopted, who should be the initiator, and how to design the blockchain’s access control layer for the logistics data.

Accounts receivable can be turned into digital assets that program ownership and the flow of cash into transferable tokens that can either be sold on at a discount via factoring or be passed on to the upstream of the supply chain as a payment instrument. This paper investigates how accounts receivable tokenization impacts the multitier supply chain's decisions and profits under different configurations and contractual forms.

Problem Definition: A large segment of cryptoassets consists of tokens that serve as a payment or governance mechanism for a digital platform, usually a peer-to-peer marketplace of various services. An ongoing debate about the viability of the token market is centered on whether tokens are used purely for speculation, or have actual utility on their underlying platforms. The objective of this study is to create and validate a set of granular measures of token utility and speculation.

We study the role of financing in establishing supply chain (SC) sustainability. We consider an SC where a buyer sources from a financially constrained supplier, who borrows from either a bank or the buyer. We show that both bank financing and buyer financing cannot simultaneously manage SC sustainability and profitability. We thus propose an alternative, in which the supplier borrows from a bank but the buyer offers a reward if the supplier passes the audit, and demonstrates its effectiveness.

We study the contract design and the effectiveness of the retailer initiated inventory-based financing (IBF) scheme. Using a game-theoretical model, we derive the small retailer’s optimal inventory ordering and pledging decisions during the stockpiling phase and characterize the optimal interest rate for the large retailer. We also empirically study the small retailer’s borrowing pattern and estimate the impact of the interest rate on the small retailers’ planning horizon and the loan amount.

Service platforms connecting consumers to independent service providers are now ubiquitous in industries such as ride-hailing, food delivery, and accommodation. We study how a platform may expand capacity through either financing new providers' assets (e.g., cars), or investing in assets directly and employing the providers. We use a game-theoretic model to show when the platform prefers to expand by either scheme compared to conventional bank financing, and how this affects provider profits.

Recent development in the U.S. tariff policies has forced companies to rethink their global operational strategies, particularly whether to add a domestic production location that is immune to tariffs. This paper formulates a three-stage model to analyze the global firm's reshoring capacity, output quantity, and production decisions. We examine how reshoring capacity investment is affected by domestic competition and by tariff uncertainty at both the raw-material and finished-goods level.

We present our collaboration with OCP, the world’s largest producers of phosphate and phosphate-based products, in support of a green initiative designed to reduce the company’s greenhouse gas emissions. The proposed robust optimization-based methodology guides the company’s investment in solar panels and batteries, which accounts to over one billion US dollars, as well as their day-to-day operations, and is expected to significantly reduce both the company’s emissions and energy bill.

Electrification is as a critical enabler of the decarbonization. It is imperative to study the growth in electric vehicles adoption to plan for this impending transformation. Existing EV adoption studies typically ignore the influence of other green technologies. In this paper, we bridge these critical gaps. We employ a dynamic discrete choice model to study these technologies' diffusion. Our work projects adoption and evaluates counterfactual scenarios.

Firms that already invest in more sustainable technologies as a proactive measure against changing market dynamics, are likely to gain a significant competitive advantage. We analyze a large focal firm's optimal green investment strategy, accounting for the uncertainty in the competitors' actions and the future green market size. Optimal policy is composed of `Retreat’, `Defend’, and `Attack’ strategies, one of which is optimal based on the problem parameters. We provide managerial insights.

We study the energy commitment, generation, and storage problem for a wind farm co-located with a battery. Modeling this problem as a Markov decision process under wind and price uncertainties, we prove the optimality of a state-dependent threshold policy. Our numerical study with data-calibrated instances has revealed the high efficiency and scalability of our solution method constructed with structural knowledge.

We study the energy generation and storage problem for a pumped hydro energy storage facility integrated with a wind farm. The operator decides in real-time how much water to pump/release, how much wind energy to generate, and how much energy to buy/sell. Modeling the problem as a Markov decision process, we prove the optimality of a state-dependent threshold policy for the upper reservoir water level. Leveraging this result, we develop an efficient solution method for data-calibrated instances.

In this paper, we consider the question of how to better manage turnover among independent drivers who transport parcels for last-mile delivery platforms. We collaborate with a last-mile delivery platform to build a structural model that enables us to estimate the effects of key predictors of drivers' decisions to continue or leave the platform. For this estimation, we apply a dynamic discrete-choice framework in a two-step procedure that accounts for unobserved heterogeneity among drivers.

We introduce the `Driver-Aide (DA) Problem', a new mode of service operations in last-mile delivery. The use of a DA can shorten route durations, allowing larger delivery volumes without the need for additional vehicles. However, it is challenging to determine the best way to use a DA (as there are two different ways to use a DA) and evaluate the tradeoffs involved. We develop an optimization-based solution framework and conduct an economic analysis using data provided by an industrial partner.

In this work, we build a structural model to study customers’ preferences on prices and service features when choosing between private taxis and a scheduled shuttle service.

Disputes can be a common occurrence in online labor platforms due to users' gaming behavior and disagreement over contracting terms. While traditional platforms resolve disputes using a centralized approach, there are emerging platforms that relegate dispute resolution to independent platform users through a voting mechanism. We study when and why this decentralized approach can be better for the platform and the social welfare and how the platform should adjust the dispute fee when adopting it.

We consider the optimal operating policies of a free-to-play game. Accounting for social comparisons between free and premium players, we model the game attracting or losing players. We characterize optimal dynamic pricing and advertising policies and show that the developer should prioritize initial growth through aggressive advertising while postponing the introduction of premium subscription. Surprisingly, the optimal subscription price may start high and gradually decrease.

In many service encounters front-line workers have the discretion to attempt to resolve a customer request or to transfer the customer to an expert service provider. We experimentally examine this decision. Our experiments show that transfers are too low under some incentive systems. However, transfer behavior responds correctly to congestion information. Taken together, these results advance our understanding of cognitive capabilities and rationality limits on human server behavior.

We study the problem of designing experiments that are conducted on a set of units, such as users in an online marketplace, for multiple time periods. We first study the optimal design of experiments, to most precisely estimate the instantaneous and lagged effects, post-experiment, when treatment decisions are made before the experiment starts. Next, we study the design of sequential experiments, where adaptive decisions are allowed, and the experiments can potentially be stopped early.

While observational data from queueing system is of great interest for statistical inference of arrival and service processes, the queueing dynamics and the absence of distributional information render queue estimation remarkably challenging. To this end, we propose a robust optimization based framework for inferring service times from waiting time observations. We provide conditions under which our framework produces statistically consistent estimators and present its managerial insights.

A residential construction firm's product mix and land investment decisions are highly complex due to the need for long-term planning. We study these decisions using a 3-stage capacity-constrained stochastic optimization model with a heterogeneous consumer base under willingness-to-pay distribution uncertainty. Among others, we find that the land investment increases with uncertainty. We also discuss the impact of competition and housing affordability regulations and find non-intuitive results.

We empirically analyze price bargaining for houses between the company and individual buyers. In each bargain, the seller and buyer take turns to make concessions until one of them terminates the bargain by accepting the other’s offer or by exiting it. We relate concessions to compromises via reservation prices and then measure bargaining powers through compromises. We identify house and buyer types and obtain type-dependent estimates of reservation prices and bargaining powers.

Visitor experience in museums is complex, where the utility procured by an artwork depends on multiple artistic, layout-related, and environmental factors. In this paper, we build a framework that analyses sequential choices made by the incumbent when the options are made available in a physical space. By applying it to the context of museums, we are able to study the effect of the curatorial decisions on visitor engagement and build counterfactuals for identifying better layout configurations.

A driver of service value, relevant in practice but understudied in the literature, is the experience of the customer while the service is performed. The design of the service experience is nontrivial given that it needs to tie in pricing and service delivery, and requires carefully designed incentives for the service workers to deliver the experience promised to customers. In this paper we use a novel framework that helps firms understand the essential tradeoffs underlying these choices.

This paper investigates how service evaluations, often collected as star ratings and comments, are biased and how this bias can be mitigated through the ordering of questions. Our findings suggest that writing comments first provides the time and space for the participants to reflect on their entire experience and allows the subsequent star ratings to capture a more holistic assessment of server quality.

Service providers are challenged by the demand to deliver near-customized services without noteworthy wait times, known as the “customization-responsiveness (CR) squeeze”. The approaches so far to manage the CR squeeze are mostly conceptual, or focused on a single dimension. Using choice-based offer selection and queuing theory, we develop a mathematical model to address the CR squeeze from a formalized perspective, and derive managerial insights and recommendations for specific applications.