Evaluating Observational Learning in a Competitive Two-Sided Crowdsourcing Market: A Bayesian Inferential Approach
This dissertation investigates the effect of observational learning in crowdsourcing markets as a lens 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 their subsequent decision making process 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 and to examine the existence of network effects in the market. These models are 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. The results of the study confirm the presence of learning effect in this crowdsourcing market and 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 observation learning. The findings also show that the effect of such learning leads to a more accurate prediction of market outcome and stronger network effects.
The study contributes to literature by extending the Bayesian estimation framework to an emergent domain characterized by dynamic learning and two-sided competition where both sides of the market experience high uncertainty. By investigating a market where both sides of the market (customers and professionals) hold private information, the dissertation also extends the principal-agency theory to a domain where information signaling can reduce uncertainty for participating agents in the absence of bonds and guarantees. The results of the study provide important guidance on how to utilize the various information signals to facilitate learning and maximize the benefits of a two-sided crowdsourcing platform.