Ride-hailing services provide not only alternative transportation for passengers but also job opportunities for potential drivers, resulting in both negative and positive effects on new car purchases. Our study assesses the impact of ride-hailing platforms’ market entry on new car purchases in the presence of platform competition. Our data is a monthly panel data on new car registration plates from 2013 to 2015, during which two leading ride-hailing platforms (Didi Chuxing and Uber) rolled out their services across select cities in China. We find that, while the entry of a single ride-hailing platform led to a decline in new car purchases, platform competition mitigated the negative impacts of platform entries. Our explanation is that the two competing platforms may have provided subsidies to drivers such that more people purchased new cars in order to sign up as drivers. By leveraging brand heterogeneity, our analysis finds supporting evidence that platform competition has resulted in increased sales of those car brands that are commonly adopted by ride-hailing drivers. Our study contributes to the literature on pricing strategies and subsidy allocation for two-sided markets by providing empirical evidence from the ride-hailing market. It suggests that companies’ competitive strategies need to account for consumer expectations and learning in the presence of strong network effects.

Major public health incidents such as COVID-19 typically have characteristics of being sudden, uncertain, and hazardous. If a government can effectively accumulate big data from various sources and use appropriate analytical methods, it may quickly respond to achieve optimal public health decisions, thereby ameliorating negative impacts from a public health incident and more quickly restoring normality. Although there are many reports and studies examining how to use big data for epidemic prevention, there is still a lack of an effective review and framework of the application of big data in the fight against major public health incidents such as COVID-19, which would be a helpful reference for governments. This paper provides clear information on the characteristics of COVID-19, as well as key big data resources, big data for the visualization of pandemic prevention and control, close contact screening, online public opinion monitoring, virus host analysis, and pandemic forecast evaluation. A framework is provided as a multidimensional reference for the effective use of big data analytics technology to prevent and control epidemics (or pandemics). The challenges and suggestions with respect to applying big data for fighting COVID-19 are also discussed.

This paper studies the link between the diffusion of the sharing economy and traditional mature industries by empirically examining the economic impacts of sharing economy entrants. This study adds to the ongoing debate over whether and how ride-hailing platforms influence new car sales in USA and China. Our results suggest that the short-term impact of Didi Chuxing’s entry on new car sales is positive. Unlike the effect of Didi Chuxing on new car sales in China, Uber’s entry negatively influences new car sales in USA. The entry of Didi Chuxing is related to a 9.24% increase in new car sales in China and the entry of Uber is related to an 8.1% decrease in new car sales in USA. We further empirically confirm that the impact of ride-hailing companies is trivial in small cities.