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Xiaoqian (Tracy) Yu

Ph.D. Candidate
Department of Marketing
Marshall School of Business
University of Southern California


Address: ACC 306C
3670 Trousdale Parkway
Los Angeles, CA 90089-0809

Working Papers

  1. Yu, Xiaoqian, Sha Yang, Yi Zhao and Lian Jian, “Modeling Consumer Crowdfunding Behaviors Under Effort-based Incentives.” (Revising for 2nd round review at Marketing Science)

    Effort-based incentives, wherein consumers can complete tasks to earn rewards, have now emerged as a popular promotional tool in marketing. Effort-based incentives possess two unique features. First, since getting a reward is not effortless, consumers face a decision of how many incentive tasks to participate to earn reward. Second, effort-based incentives allow individuals to turn effort into a monetary reward, and such reward can then immediately relax an individual participant’s budget and consequently influence consumer behavior (e.g., contribution in our context). We propose a model to investigate how effort-based incentives affect individual contribution behavior in the context of journalism crowdfunding. In particular, we develop a unified utility maximization framework to simultaneously model two interdependent consumer decisions in the crowdfunding context with effort-based incentives: contribution decision (i.e. how much to contribute) and incentive participation decision (i.e. how many incentives tasks to participate in). We also incorporate dynamics by modeling consumer reward accumulation and budget evolvement over time. Since incentive participation directly affects budget, the optimal contribution and incentive participation amount involve a complex simultaneity. We adopt a two-step estimation approach to cope with the simultaneity issue, and changes-in-variable method to obtain the likelihood and to address the implicitness issue. We use the Bayesian method to make model inference so that we can conveniently account for the multiple-constraint requirement and unobserved heterogeneity. Using data from a pioneering crowdfunding platform for journalism, we find that effort-based incentives tend to increase contribution amount for those individuals with smaller baseline budget, and for those with higher baseline budget, incentives tend to increase their contribution amount if they have lower contribution preference. Our counterfactual analysis leads to important managerial implications on effective use of effort-based incentives. More specifically, we can help the crowdfunding platform improve revenue through targeting and customization of the incentives, and the proposed customization strategy also allows incentives sponsors to increase their return on investment (i.e., creating a win-win scenario for the crowdfunding platform and incentive sponsors).

  1. Yu, Xiaoqian, Sha Yang and Lian Jian, “When and How Monetary Incentives Serve As A Double-Edged Sword.” (In preparation for submission to Journal of Marketing Research)

    This paper exams when and how monetary incentives affect crowdfunding performance. Using a simple yet stylized structural model, we identify the channels through which the monetary incentives influence the funding performance. We construct two measures to quantify the crowdfunding performance: number of contributions attracted within a unit time period, and the total contribution amount. Drawing on both economic and psychology theories, our study shows that (1) monetary incentives have a market expansion effect by attracting a larger number of contributions in a unit of time period, and (2) monetary incentives can increase contribution amount when the expected reward from the monetary incentives is higher than a certain threshold, but can also have a detrimental impact on contribution when the expected reward is too small. Taken together, these findings suggest that monetary incentives can both favorably and adversely affect the overall contribution amount raised for a crowdfunding project, serving as a double-edged sword.

  1. Yu, Xiaoqian, Yi Zhao and Sha Yang, “Crowding-In or Crowding-Out? A Dynamic Model of Donor’s Voluntary Contribution to Public Goods on Crowdfunding Platform.” (Work in Progress)

    Over the past few years, crowdfunding has become an increasingly popular practice of funding a project by raising monetary contributions from a large number of people. In 2015, the crowdfunding industry raised over $34 billion worldwide. In this paper, we collect data from a novel journalistic crowdfunding platform that raises money for public goods (i.e., digital reports with free online access). Typically, a crowdfunding project has a predetermined funding goal and funding deadline, and during the funding drive, people can make a contribution decision based on the observed funding status such as current raised funding amount, funding time left, number of previous contributors or even the detailed individual contribution history. On this crowdfunding platform, one unique feature is that each funding project can receive contributions from both individuals and organizations (/firms). Therefore, a contributor can observe the donation information not only from individual but also from organizations. In contrast to individual contributors, organizations usually make a much larger contribution amount in a single time. This particular dynamic funding setting leads to some interesting research questions: how organizational contribution affects individual crowdfunder’s contribution behavior? Does it crowd in, crowd out, or has no impact on individual contribution? Is it a good idea to disclose organizational contribution on the platform? Can we offer seed money (similar to a large organizational contribution) to improve the performance of a funding project? Drawing on the theory of dynamic provision to public goods, we develop a dynamic structural framework to model crowdfunders contribution behavior, and solve it using numerical dynamic programming techniques. The model is flexible enough to handle funding deadlines and funding result based reward schemes (i.e., the delivery of funding project reward is conditional on whether a project achieves the funding goal or not), all of which are ubiquitous in crowdfunding setting. Our model also explicitly incorporates the dynamics induced by these aspects in contributor behavior. We apply the model to this rich dataset that comprises the complete details of contribution history, funding project and donors’ characteristics. We use the model estimation result to examine the impact of changes to the extant organizational contributions on individual contributions, and also to evaluate the economic benefit of offering seed money from organizations. More generally, our study fits into a growing literature that illustrates that dynamic programming-based solution, when combined with structural empirical specifications of behavior, can help significantly improve decision-making, and potentially crowdfunding platform performance.