Making the Most of Your Regret: Workers’ Relocation Decisions in On-Demand Platforms

Abstract

Problem definition: We have witnessed a rapid rise of on-demand platforms, such as Uber, in the past few years. Although 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 may influence workers’ relocation decisions and ultimately the system performance. Academic/practical relevance: Studies on on-demand platforms often assume that workers are rational agents who make optimal decisions. Our research investigates workers’ relocation decisions from a behavioral perspective. A deeper understanding of workers’ behavioral biases and their causes will help on-demand platforms design appropriate policies to increase their own profit, worker surplus, and the overall efficiency of matching supply with demand. Methodology: We use a combination of behavioral modeling and controlled laboratory experiments. We develop analytical models that incorporate regret aversion to produce theoretical predictions, which are then tested and verified via a series of controlled laboratory experiments. Results: We find that regret aversion plays an important role in workers’ relocation decisions. Regret-averse workers are more willing to relocate to the supply-shortage zone than rational workers. This increased relocation behavior, however, is not sufficient to translate to a better system performance. Platform interventions, such as demand information sharing and dynamic wage bonus, can help further improve the system. We find that workers’ regret-aversion 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. Managerial implications: Our research emphasizes the importance and necessity of incorporating workers’ behavioral biases such as regret aversion 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.

On-demand service platform operations management: a literature review and research agendas

“…4.1.3 Multiple methods. Multi-method research has been increasingly advocated by scholars in recent years (Jiang et al, 2021). Through an in-depth reading of the existing literature, we find that there are also papers using multiple methods to research on-demand service platform operations management.…”

“…They modeled the driver's decision-making process and platform optimization problem as a Stackelberg game model, and conducted an empirical analysis using comprehensive data obtained from leading ridesharing platforms to calculate optimal bonus rates for different scenarios. Jiang et al (2021) used a combination of behavioral modeling and On-demand service platform operations management controlled laboratory experiments to explore how behavioral biases such as regret aversion affect employees' transfer decisions and system performance between under-supply and over-supply regions. studied the optimal pricing problem for platforms with two service models, customized transit and ridesharing.…”

“…Third, from the perspective of research methods, multi-method research can combine the advantages of quantitative and qualitative research and has been increasingly promoted by academia in recent years (Tong et al, 2020;Chen et al, 2021b;Jiang et al, 2021;. However, there is still a lack of research on multi-method studies on on-demand service platform operations management.…”

Optimal pricing and choice of platform advertising schemes considering across‐side network effect

“…show that a platform can successfully compete by limiting the number of choices available to users while charging a higher price than platforms with unlimited choices. Platform incentive measures, such as wages and bonuses, can improve matching efficiency between demand and supply (Jiang et al, 2020). Zhang et al (2019).…”

From the Classics to New Tunes: A Neoclassical View on Sharing Economy and Innovative Marketplaces

“…(2020),and Jiang et al. (2020) on the ride‐sharing market such as Uber and Lyft, Li et al. (2019), Cui et al.…”