A Behavioral Investigation of Workers's Relocation in On-Demand Platforms

Topic:

A Behavioral Investigation of Workers's Relocation in On-Demand Platforms

Time&Date: 

10:30-11:45 am, 2019/12/25 (Wednesday)

Venue:

 Room 619, Teaching A

Speaker:

Dr. Yinghao Zhang (University of Cincinnati)

Abstract:

We have witnessed a rapid rise of on-demand platforms, such as Uber, in the past few years. While these platforms allow workers to choose their own working hours, they have limited leverage in maintaining availability of workers within a region. As such, platforms often implement various policies, including offering financial incentives and/or communicating customer demand to workers in order to direct more workers to regions with shortage in supply. This research examines how behavioral biases such as regret aversion and ignorance of suggestion may influence workers’ relocation decisions and ultimately system performance. We use a combination of behavioral modeling and controlled lab experiments. We develop analytical models incorporating behavioral factors to produce theoretical predictions, which are tested and verified via a series of controlled lab experiments. We find that regret aversion and ignorance of suggestion are the two major behavioral factors that influence workers’ relocation decisions. Regret averse workers are more willing to relocate to the supply-shortage zone than rational workers. Sharing demand information is a better way of communicating customer demand compared with providing suggested actions, since workers often ignore such suggestions due to their inability to perform demand update. In addition, the platform may also need to offer extra financial payment to compensate for workers’ relocation cost. Finally, workers’ regret averse behavior may lead to an increased profit for the platform, a higher surplus for the workers, and an improved demand-supply matching efficiency, thus benefiting the entire on-demand system.  Our research emphasizes the importance and necessity of incorporating workers’ behavioral biases into the policy design of on-demand platforms. Policies without considering the behavioral aspect of workers’ decision may lead to lost profit for the platform and reduced welfare for workers and customers, which may ultimately hurt the on-demand business.