The Consumption of Serial Media Products and Optimal Release Strategy
with Nitin Mehta and Mengze Shi (Draft available upon request)
We study the impact of a digital platform’s product release strategy of serialized media content on its consumption and the ensuing profits made by the platform. Specifically, we study in the context of a digital book platform, whether a platform should release all the chapters of book simultaneously to its consumers or should it release it sequentially over time, or should it follow a hybrid strategy where it releases a fraction of the chapters of a book simultaneously and the rest sequentially. We show that the release speed of chapters impacts consumption through two opposite forces: binge consumption and product exploration. A slower release speed of chapters, while limiting binge consumption, leads to an increased exploration of other books through a combination of two mechanisms - consumers visit the platform more often and the explore other books due to the constrained availability of the focal book. We estimate our structural model on consumers’ purchases of chapters on an online book platform in China, which currently uses a sequential release strategy. Our counterfactual analysis shows that the platform will be worse off with a simultaneous release of chapters but it can improve its sales revenue by implementing an optimized hybrid release strategy.
Incentivizing Mass Creativity: An Empirical Study of Online Publishing Market
with Xiaolin Li and Mengze Shi [Draft]
Recipient of 2019 China Research Grant
This paper examines the effects of quantity-based incentive plans on the quality of creative production. We study a publishing platform which switched from a uniform commission rate to a quantity-based plan offering higher commission rates if a writer's production meets higher quantity brackets. Theoretical analysis indicates that a quantity-based commission plan can enhance the quantity-quality complementarity: the creators who reach a higher bracket of quantity should also produce a higher quality. This theoretical result is confirmed consistently in multiple empirical tests. First, for a given book, the chapters published in the months when writers reached higher brackets of quantity 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. Second, when writers produced higher quantity in the early days of a month but failed later, there was a greater quality drop under the quantity-based commission plan than under the uniform commission plan. Finally, in response to positive demand shocks, writers reached higher quantity brackets and increased quality. Overall, our results underscore the importance of proper incentive design in managing mass creativity.
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.
A Neuro-Autopilot Theory of Habit: Evidence from Canned Tuna
with Ryan Webb, Matthew Osborne, Peter Landry, and Colin Camerer [Draft]
Recipient of 2019 SSHRC Insight Development Grant
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 sufficiently 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.