Observational Learning in a Competitive Two-Sided Crowdsourcing Market: A Bayesian Inferential Approach: IIT Dept.

Friday, March 31, 2017 - 1:30pm to 2:30pm
Faculty Lounge

IIT Faculty Candidate Seminar
Sponsored by Department of Integrated Information Technology

Yoris Au
Department of Information Systems and Cyber Security
College of Business
The University of Texas at San Antonio

This study investigates the effect of observational learning in the crowdsourcing market as an attempt to identify appropriate mechanism(s) for sustaining this increasingly popular business model. Observational learning occurs when crowdsourcing participating agents obtain knowledge from signals they observe in the marketplace and incorporate such knowledge into subsequent actions to improve their participation outcomes. This form of learning is examined in the context of the two-sided crowdsourcing platform in which participating customers’ and professionals’ decisions interact with and influence each other.
Two structural models are constructed to capture customer and professional’s probability of success in the presence of various constantly changing market signals. A third model is developed to capture factors that influence market outcomes such as level of participation by professionals. These models will be estimated using the Bayesian approach on a longitudinal dataset that consists of seven years of transaction data in four product categories from a leading crowdsourcing site.
We expect to observe learning effect in this crowdsourcing market and to identify various factors that influence the probability of a professional (agent) submitting a bid to a crowdsourcing project and the probability of a customer (principal) selecting a winner through observational learning.