An Empirical Investigation of a Digital Platform’s Release Strategy for Serialized Content
with Nitin Mehta and Mengze Shi [Draft]

Using data from an online-book platform, we investigate three release strategies of serialized digital content: simultaneous (releasing the entire content of a book at once), sequential (releasing the content of a book sequentially over time), and hybrid release. These release strategies impact the platform’s revenue through two opposing effects: binge consumption and product exploration. Simultaneous release of a book induces binge consumption, which gets consumers hooked on to it, which then leads to a larger number of its chapters being purchased. Sequential release of a book induces consumers to visit the platform more often to purchase its newly released chapters, which increases their exploration of other books. We estimate a model of consumers’ platform visits and purchases that allows for both these effects. Our counterfactual analysis shows that sequential release yields higher revenue than simultaneous release. Platform revenue is maximized with hybrid release in which first 60% chapters of books are released simultaneously, and the rest are released sequentially. Hybrid release turns binge consumption and product exploration into complementary effects - simultaneous release of the first 60% chapters of a book induces its binge consumption which gets consumers hooked on to it; sequential release of its remaining chapters keeps consumers coming back to the platform, resulting in them exploring other books.

Incentivizing Mass Creativity: An Empirical Study of Online Publishing Market
with Xiaolin Li and Mengze Shi [Draft]

This paper examines the effects of incentive plans on the quantity and quality of creative production. We study a serial publishing platform which switched from a uniform commission plan to a quantity-based incentive plan offering higher commission rates if a writer's production meets higher quantity brackets. Our analysis shows that, for a given book, the chapters published in the time periods when writers reached higher brackets of quantity (hence higher commission rates) had higher quality measured by chapter-to-chapter customer retention rates. Such a positive correlation is not significant in books published when the platform offered a uniform commission plan. We theorize that the quantity-based commission plan can enhance the quantity-quality complementarity in a writer's payoff function. With the enhanced complementarity, the creators who reach a higher bracket of quantity will produce a higher quality under a quantity-based plan than under a uniform commission plan. Further empirical analysis indicates that the degree of enhanced complementarity is weaker for the writers who earn commissions from multiple books. Overall, our results underscore the importance of proper incentive design in improving the platform's performance in managing mass creativity.

A Neuro-Autopilot Theory of Habit: Evidence from Canned Tuna
with Ryan Webb, Matthew Osborne, Peter Landry, and Colin Camerer [Draft]

We integrate a neuroeconomic concept of habit into a structural consumer choice model. We propose that habit represents a distinct decision-making mode, in which past choices are automatically repeated, in contrast with state- dependent utility maximization. Transitions between these decision modes are governed by the reliability of a reinforcement learning algorithm, such that habits arise when the consumption environment is su ciently stable. We estimate and test this model on product choice in the canned tuna category between 2006 and 2010, a period of considerable price and product variation which included a package down-sizing event. Our results suggest that a substantial proportion of choice persistence is due to a habitual automation of consumption, in addition to a degree of state-dependent utility.

How Do People Update Beliefs? Evidence from the Laboratory
with Andrew T. Ching, Tanjim Hossain and Shervin S. Tehrani

The Bayes' rule serves as a cornerstone for learning models when researchers observe consumer choice, but not underlying beliefs. Observational data usually provide information on consumer choices and the environment but lacks information on consumer beliefs. As a result, the vast majority of the existing research assume Bayesian updating process. In this paper, we design a laboratory experiment to directly elicit subjects' posterior beliefs as she receives more information about a random variable in a dynamic setting. We elicit both mean and the uncertainty associated with it and investigate subjects' belief updating rules without assuming strict adherence to the Bayes' rule. We find that subjects are heterogeneous in their belief updating rules. Relative to Bayesian updating, some subjects are too conservative and others are overly reactive to new information signals. Moreover, as subjects receive more signals, their updating rules slowly change over time. Nonetheless, subjects of all types become more reactive to new signals and become more certain about their beliefs as they receive more signals. In addition, subjects assign greater weight to surprising signals and their beliefs become more diffused.