Yuanchen Su 苏元晨
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Research Interests​
  • Salesforce, Incentive Design​, Gamification, Behavioral Economics, Consumer Search, Social Interactions

Job Market Paper

The Social Effects of Sales Contest Leaderboards: Theory and Evidence. with Madhu Viswanathan and George John ​
  • Under review
  • IOEA Accessit Best Project Award, 2022
  • 2023 ISBM Doctoral Dissertation Award, Winner and Outstanding Submission
  • AMS 2023 Mary Kay Dissertation Proposal Award, Runner-up

Interim disclosure of performance through leaderboards is a pervasive feature of sales contests, even though standard economic models predict that such disclosure demotivates lower-ranked contestants and reduces total effort. We analyze a large-scale sales contest and document a U-shaped response to disclosure, in which bottom-quintile contestants significantly increase effort. Exploiting a novel page boundary identification strategy that generates exogenous variation in leaderboard pagination, we causally isolate socially motivated responses from economic incentives. We then develop an analytical model that integrates social effects into a standard contest framework with monetary incentives. Taking the model to the data, we identify the conditions under which interim disclosure through leaderboards increases firm performance. Our findings show that properly designed leaderboards can serve as a cost-effective complement to monetary prizes, particularly when firms face constraints on contest payouts.
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Working Papers (Manuscripts Available) 
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Referral, Learning, and Inventory Decisions in Social Networks, with Guangwen Kong and Ankur Mani (Equal Authorship)
  • Reject & Resubmit at Management Science
  • NET Institute Summer Grant, 2018

With the proliferation of digital social networks, businesses increasingly use referral programs to increase market exposure and sales. When customers refer a product to others they naturally disclose their purchase decisions. Thus the referral process introduces a social learning effect. We study the interaction between social learning and referral program structure and examine their impact on a firm's inventory decisions. We find that the presence of customers who lack knowledge of their own preferences introduces demand bias but social learning reduces this bias at the expense of increased demand variance.  We characterize the optimal inventory levels for different numbers of referrals allowed by the firm and find that it is  governed by the combination of market exposure effect and demand substitution effect. In a single referral program, the stock-out of one product can diminish the demand of the other product. In contrast, a multiple referral program allows a firm to achieve full market exposure but meanwhile increases the demand variance.  Hence, the optimal referral program has to balance the trade-off between market exposure and demand variance, and thus allows either one or two referrals per customer.

The Effect of Windfalls on Sales Performance, with Madhu Viswanathan and George John

​Various technological platforms leverage random rewards, incorporated through token “games” that mimic the experience of gambling and introducing an element of surprise and unpredictability, to engage and motivate individuals. Drawing on the long-standing psychology literature on the power of variable reinforcement to predict beneficial effects, such programs have come into widespread use. Nevertheless, using random rewards as a supplementary compensation tool remains under-studied. This paper fills this gap by studying the effect of windfalls coming from the random rewards on subsequent sales performance. Data from a random rewards program aimed at salespeople at a car dealer enable us to examine this question rigorously. We find that larger windfalls paradoxically demotivate salespeople in comparison to smaller windfalls in the short run. We organize these results with a model featuring mis-specified beliefs about uncorrelated draws. Additionally, this effect is quite heterogenous, with a smaller performance reduction among higher productivity salespeople. We close with a discussion of refining random rewards programs to better harness their motivational potential.

Search, Prominence, and Product Design, with Ruitong Wang

Internet search traffic is heavily concentrated on a few selected prominent firms. The public worries that this phenomenon of search prominence can undermine competition, leading to a decline in product quality and thus harming consumers. However, little is known about the impact of search order on the firm’s strategic choice of product quality and pricing. This research provides a game-theoretical framework to unpack the ramification of search prominence on the firm’s choice of product quality and price, market competition, and consumer welfare. Surprisingly, we find that the prominent firm may deliberately downgrade its product quality even when the cost of production is the same for any quality level. In addition, prominence can be a liability rather than an asset such that more search traffic can sometimes reduce a firm’s profit. Finally, we find that search prominence can be welfare-improving to consumers even though it lowers the average quality level in the market.

Working in Progress ​
  • AI-related Biases in B2B, with Shantanu Dutta, Shankar Ganesan, Navid Mojir, Irene Nahm
    • Inaugural B2B Connect Research Symposium


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