I am an Assistant Professor of Analytics, Information and Operations Management at the University of Kansas, School of Business. I am interested in strategic problems in operations management. Specifically, my research focuses on the impact of customer behavior on operational decisions, platform operations, operations of new business models, operations of start-up firms, and problems at the interface of operations and marketing.

Media Mention: How to Succeed at Crowdfunding, Fuqua Insights, January, 2019.

Research Interests:

• Business Model Innovation
• Operations of Online Platforms
• Operations of Start-up Firms
• Implications of Consumer Behavior on Operations
• Interface of Operations, Marketing, and Finance

Academic Position

Education

Working Paper

• • Persuading Skeptics and Fans: Information Design for New Experience Goods - with H. Gurkan.
  • Abstract

  • The advent of social media platforms has enabled firms to disseminate information to their customers via new channels. A prominent example of this phenomenon is firms using online reviews from expert opinion leaders to inform customers about the quality of new experience goods. As customers rely heavily on these reviews before making a purchase decision, some firms selectively offer their new products to experts and solicit their reviews before launching the products in the market. By choosing which experts to solicit reviews from, these firms can control how to disseminate product quality information publicly to a market of regular customers who might have differing initial opinions about the quality. Beyond reading these solicited reviews, customers can acquire additional information about quality from sources outside the firm's reach. Motivated by this phenomenon, we consider the information design problem of a firm launching a product of unknown quality to a polarized market consisting of customers who have heterogeneous prior beliefs about quality and can acquire additional information at a cost. We construct a game-theoretic model of Bayesian persuasion between firm and customers. We characterize the firm's optimal information policy and show that the firm might exaggerate the quality or release incomplete information depending on the market characteristics. Furthermore, we analyze the effect of customers acquiring additional information on the informativeness of the firm's policy, the firm's expected demand, and the customers' expected utility. Our results highlight that customers' acquisition of additional information can make both firm and customers worse off.

Journal Publications

• • Designing Rewards-Based Crowdfunding Campaigns for Strategic (but Distracted) Contributors - with A. Ma and R. Swinney. Forthcoming in Naval Research Logistics.
  • Abstract

  • In rewards-based crowdfunding, entrepreneurs solicit donations from a population of potential contributors. If total donations exceed a prespecified funding target, the entrepreneur distributes non-monetary rewards to contributors; otherwise, their donations are refunded. We study how to design such campaigns when contributors choose not just whether to contribute, but also when to contribute. We show that strategic contribution behavior—when contributors intentionally delay until campaign success is likely—can arise from the combination of non-refundable (potentially very small) hassle costs and the risk of campaign failure, and can explain pledging patterns commonly observed in crowdfunding. Furthermore, such delays do not hurt the entrepreneur if contributors are perfectly rational, but they do if contributors are distracted, i.e., if they may fail to return to the campaign after an intentional delay. To mitigate this, an entrepreneur can use a simple menu of two rewards with a fixed number of units sold at a low price, and an unlimited number sold at a higher price; this segments contributors over time based on the information they observe upon arrival. Despite its simplicity, such a menu performs well compared to a theoretically optimal menu consisting of an infinite number of different rewards and price levels under many conditions. We experimentally validate our theoretical insights by designing crowdfunding campaign simulations and studying pledging behavior of participants recruited from Mechanical Turk.

• • Intertemporal Content Variation with Customer Learning - with F. Bernstein and R. Swinney. Manufacturing & Service Operations Management, Vol. 24, No. 3, May-June 2022.
  • Abstract

  • We analyze a firm that sells repeatedly to a customer population over multiple periods. While this setting has been studied extensively in the context of dynamic pricing—selling the same product in each period at a varying price—we consider intertemporal content variation, wherein the price is the same in every period, but the firm varies the content available over time. Customers learn their utility on purchasing and decide whether to purchase again in subsequent periods. The firm faces a budget for the total amount of content available during a finite planning horizon, and allocates content to maximize revenue. Using a lower bound approximation to the problem for a horizon of general length T, we show that while the optimal allocation policy is not, in general, constant over time, it is monotone: content value increases over time if customer heterogeneity is low and decrease otherwise. We demonstrate that the optimal policy for this lower bound problem is either optimal or very close to optimal for the general T period problem. Furthermore, for the case of T = 2 periods, we show how two critical factors—the fraction of "new" versus "repeat" customers in the population, and the size of the content budget—affect the optimal allocation policy and the importance of varying content value over time.

• • Signaling to the Crowd: Private Quality Information and Rewards-Based Crowdfunding - with R. Swinney. Manufacturing & Service Operations Management, Vol. 23, No. 1, January-February 2021.
  • Accepted for presentation at the iFORM SIG Conference, 2018.
  • Abstract

  • Crowdfunding is a new and popular way of funding innovative products. Despite numerous advantages, there are challenges to this model, chief among them credibly signaling information about product quality to a pool of small, uninformed investors. We explore how an entrepreneur might accomplish this and overcome a key obstacle to crowdfunding. Specifically, we analyze a game theoretic model of signaling between an entrepreneur and campaign backers in which the entrepreneur designs a fixed funding rewards-based crowdfunding campaign for an innovative product. Product quality is known to the entrepreneur but unknown to some backers, and we analyze how the entrepreneur can signal quality to backers via the design of the crowdfunding campaign, including the price of the reward and the funding target. We find that the entrepreneur should signal high quality by setting a high target that is distorted above the full information optimal level. While a separating equilibrium always exists, a pooling equilibrium can only occur under very specific circumstances. We show that the high target affects the quality choice of entrepreneurs and may deter high quality, innovative projects. In addition, we discuss how the entrepreneur should modify the signaling strategy when a high target potentially deters backers from pledging due to the cost of participating in a failed campaign.

Other Research

• • The Cyclical Scheduling Problem - with B. Bhattacharya, E. Iranmanesh and R. Krishnamurti. Theoretical Computer Science (2014), Vol. 555, October 2014.
  • Abstract

  • We consider the (n−2,n) cyclical scheduling problem which assigns a shift of n−2 consecutive periods among a total of n periods to workers. We solve this problem by solving a series of b-matching problems on a cycle of n vertices. Each vertex has a capacity, and edges have costs associated with them. The objective is to maximize the total cost of the matching. The best known algorithm for this problem uses network flow, which runs in O(n²log ⁡n) on a cycle. We provide an O (nlog⁡ n) algorithm for this problem. Using this, we provide an O (nlog⁡ n log⁡ n  b_max) algorithm for the (n−2,n) cyclical scheduling problem, where b_max is the maximum capacity on a vertex.