Journal
JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
Volume 36, Issue 4, Pages 1043-1070Publisher
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/07421222.2019.1661087
Keywords
causal inference; competitive strategies; consumer expectations; Didi Chuxing; ride-hailing; staggered difference-in-differences; synthetic control methodology; Uber; car market; car-riding platforms
Funding
- National Natural Science Foundation of China [71872061, 71702045]
- Humanities and Social Sciences Foundation of the Ministry of Education in China [16YJC630028, 17YJC630047]
- Consumer and Organizational Digital Analytics (CODA) Research Centre at King's College London
- Fundamental Research Funds for the Central Universities [2018B20614, 2017B14414]
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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.
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