Surge Pricing and Short-Term Wage Elasticity of Labor Supply in Real-Time Ridesharing Markets

2022 | Management Information Systems Quarterly | Citations: 0

Authors: He, Shu; Qiu, Liangfei; Cheng, Xusen

Abstract: The prominence of real-time ridesharing services, such as Uber and Lyft, has dra ... Expand

Abstract: The prominence of real-time ridesharing services, such as Uber and Lyft, has dramatically changed the landscape of traditional industries. This study provides a comprehensive analysis of the short-term wage elasticity of labor supply in real-time ridesharing markets using data from a major ridesharing platform in China. By exploiting an exogenous shock from uneven driving restrictions as an instrumental variable, we find a negative labor supply elasticity for ridesharing drivers, suggesting that drivers tend to drive less during days with a higher average hourly wage. Specifically, a percent increase in hourly wage will lead to a 0.931 percent decrease in daily working hours. This surprising finding is consistent with the behavioral income-targeting model based on the theory of reference-dependent preferences: Drivers have heuristic daily targets for total earnings and are more motivated to supply labor when they are below their income target than when they are above it. Therefore, they work less on days when earnings per hour are high and quit the market once their income target is reached. In addition, we find that taxi drivers are more rational and have positive labor supply elasticity, which implies that drivers are more rational when they have repeated opportunities for learning. Estimating labor supply elasticity is critical to understanding the economic efficiency of various surge pricing algorithms and driver subsidization programs for ridesharing platforms and policymakers. Our research suggests that a uniform price surging or driver subsidization approach for all ridesharing drivers may not incentivize the labor supply of drivers effectively. Collapse

Topics: ride sharing platform platform worker mobile application mobile system Uber

Methods: robustness check design artifact autocorrelation analysis k-means clustering linear regression analysis

Effects of Flexibility, Security, and Information Features on Supplier Participation in the Sharing Economy: An Empirical Study

2020 | HICSS | Citations: 0

Authors: Jeong, Soo; Bauer, Johannes M; Lee, Kwangjin; Granados, Nelson F

Abstract: Firms in the sharing economy typically offer higher flexibility but lower securi ... Expand

Abstract: Firms in the sharing economy typically offer higher flexibility but lower security of working conditions. In response to challenges from suppliers and policy makers, several platform companies are reconsidering their approach. This study examines the effects of offering sharing economy suppliers a menu of contract options, differentiated by varying levels of flexibility, security, and information transparency on their willingness to work for a platform. We focus on ridesharing, one of the largest sectors in the sharing economy, but the insights translate to other segments of this emerging sector. Using a discrete choice experiment, we find drivers’ willingness to work for ridesharing generally increases when the platform offers diversified combinations of flexibility, security, and transparency. We also find evidence that suppliers’ preferences to participate in the sharing economy are influenced by the working conditions in their alternative employment options. Collapse

Topics: ride sharing platform sharing platform platform governance logistics management ridesharing company

Methods: experiment regression analysis method discrete choice experiment logistic regression longitudinal research

Disruptive Digital Technology Services: The Case of Uber Car Ridesharing in Ghana

2018 | Americas Conference on Information Systems | Citations: 2

Authors: Simmons, Robert Ohene-Bonsu

Abstract: The focus of this study was to explore and understand how digital technology inf ... Expand

Abstract: The focus of this study was to explore and understand how digital technology influenced Uber car ridesharing and its disrupt impacts on taxi services in Ghana. The study used affordance theory as a theoretical lens to provide an explanation of Uber car ridesharing service as disruptive digital technology in the taxi industry in Ghana. Collapse

Topics: ride sharing platform Uber user experience affordance telephone

Methods: qualitative interview case study field study personal interview literature study

Theories: theory of affordance

Location analytics and decision support: Reflections on recent advancements, a research framework, and the path ahead

2017 | Decision Support Systems | Citations: 11

Authors: Pick, James B.; Turetken, Ozgur; Deokar, Amit V.; Sarkar, Avijit

Abstract: ... Expand

Abstract: Collapse

Topics: decision support decision support system Twitter business model ride sharing platform

Methods: geographic analytics topic model sensitivity analysis longitudinal research literature sample