Paper Number

2087

Paper Type

Complete

Abstract

We examine multihoming drivers in the gig economy during the COVID-19 pandemic. Drawing on the conservation of resources theory, we analyze a comprehensive dataset and develop a theory-driven empirical framework. Our findings show a significant decrease in drivers working for a ride-hailing platform during the lockdown period, which recovered after the reopening. The number of COVID-19 cases increases the propensity of drivers to switch from ride-hailing platforms to delivery ones. Analyses of monetary incentives show that drivers’ switching behaviors exhibit greater sensitivity to the base hourly earnings from ride-hailing platforms, and bonus earnings on delivery platforms effectively retain driver engagement. Additionally, drivers exhibit a higher propensity to switch between services within the same platform company (e.g., Uber Eats to Uber) than between different companies (e.g., Lyft to Uber). Our study uncovers gig workers’ adaptive strategies and informs platform management during disruptive events.

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Dec 15th, 12:00 AM

COVID-19 and Platform Switching of Multihoming Workers in the Gig Economy

We examine multihoming drivers in the gig economy during the COVID-19 pandemic. Drawing on the conservation of resources theory, we analyze a comprehensive dataset and develop a theory-driven empirical framework. Our findings show a significant decrease in drivers working for a ride-hailing platform during the lockdown period, which recovered after the reopening. The number of COVID-19 cases increases the propensity of drivers to switch from ride-hailing platforms to delivery ones. Analyses of monetary incentives show that drivers’ switching behaviors exhibit greater sensitivity to the base hourly earnings from ride-hailing platforms, and bonus earnings on delivery platforms effectively retain driver engagement. Additionally, drivers exhibit a higher propensity to switch between services within the same platform company (e.g., Uber Eats to Uber) than between different companies (e.g., Lyft to Uber). Our study uncovers gig workers’ adaptive strategies and informs platform management during disruptive events.

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