4,215 research outputs found

    Understanding the effects of taxi ride-sharing: A case study of Singapore

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    National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

    What Can We Learn from Business Innovation Fail-ure of Uber in Southeast Asia Market?

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    Uber is a global pioneer in the sharing economy platform entitled ride-hailing. It started to enter the Asian market in 2013-2014 with various community responses in each region. In March 2018, Uber withdrew from the competition in Southeast Asia after being acquired by one of the dominant players in the region, Grab. In connection with Uber's failure to operate its business in the region, this paper discusses Uber's business model, business expansion, competition in the market, and the factors that led to Uber's failure in the Southeast Asian market. To comprehensively describe the developing context, we used a qualitative method with a systematic data collection approach from literature reviews in conducting this study. This study emphasizes that large funding supports do not guarantee the success of business operations in a more globalized setting. Different market characteristics require different approaches. The case of Uber's failure in the Southeast Asian market, even though it was supported by large funds to "Uberize the entire world," proves that the characteristics made more "localized" are more likely at a certain point in time to survive. This study also underlines some learning points from the dominant factors causing the failure of Uber's business operations in the region that require immediate adaptation: non-conformity with market preferences, challenges from prevailing policies and infrastructure issues, and strong competition from local competitors

    An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service

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    In this paper, we present machine learning approaches for characterizing and forecasting the short-term demand for on-demand ride-hailing services. We propose the spatio-temporal estimation of the demand that is a function of variable effects related to traffic, pricing and weather conditions. With respect to the methodology, a single decision tree, bootstrap-aggregated (bagged) decision trees, random forest, boosted decision trees, and artificial neural network for regression have been adapted and systematically compared using various statistics, e.g. R-square, Root Mean Square Error (RMSE), and slope. To better assess the quality of the models, they have been tested on a real case study using the data of DiDi Chuxing, the main on-demand ride hailing service provider in China. In the current study, 199,584 time-slots describing the spatio-temporal ride-hailing demand has been extracted with an aggregated-time interval of 10 mins. All the methods are trained and validated on the basis of two independent samples from this dataset. The results revealed that boosted decision trees provide the best prediction accuracy (RMSE=16.41), while avoiding the risk of over-fitting, followed by artificial neural network (20.09), random forest (23.50), bagged decision trees (24.29) and single decision tree (33.55).Comment: Currently under review for journal publicatio

    The Rise of Monopolistic Rideshare Companies in Asia: How Ride Hailing Companies’ Market Control Impacts Drivers

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    Having undergone mergers with Uber China and Uber Southeast Asia in the past few years, DiDi and Grab now hold over 90% and 80% of the market shares in China and Singapore. This market control has allowed them to dominate the rideshare industry in the two countries, and this paper examines the impact of Grab and DiDi’s monopolistic power on rideshare drivers. Specifically, this research considers changes to both licensure requirements, pricing policies, commission rates, insurance coverage, and CSR programs as well as long-term corporate objectives and strategies following the merger. In order to understand the implications of DiDi and Grab’s decrease in competition and greater market control, this research draws from existing scholarly research, DiDi and Grab’s policies and terms of use, news articles, reports, government documents, and most importantly, qualitative interviews with employees at DiDi and Grab. The paper concludes that despite changes in pricing, insurance, and benefits, monopolistic ridesharing companies are able to leverage their resources and market power for diversification, which creates synergies, and greater social impact
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