Key regions: China, Germany, Thailand, Saudi Arabia, India
The E-Scooter-sharing market in Dominican Republic has experienced significant growth in recent years, driven by changing customer preferences, emerging trends in the market, local special circumstances, and underlying macroeconomic factors. Customer preferences in the E-Scooter-sharing market in Dominican Republic have shifted towards more sustainable and convenient modes of transportation. With increasing concerns about climate change and the need for eco-friendly transportation options, customers are opting for electric scooters as a greener alternative to traditional modes of transportation. Additionally, the convenience of E-Scooter-sharing services, which allow users to easily rent and drop off scooters at various locations, has also contributed to the growing popularity of this market. Trends in the E-Scooter-sharing market in Dominican Republic reflect global and regional developments. One major trend is the rise of micro-mobility solutions, which provide short-distance transportation options for urban areas. E-Scooter-sharing services align with this trend by offering a cost-effective and efficient mode of transportation for short trips. Furthermore, the integration of E-Scooter-sharing services with smartphone applications has made it easier for customers to locate and rent scooters, further driving the growth of the market. Local special circumstances in Dominican Republic have also played a role in the development of the E-Scooter-sharing market. The country's urban areas, such as Santo Domingo and Santiago, face challenges related to traffic congestion and limited parking spaces. E-Scooter-sharing services provide a solution to these issues by offering a flexible and convenient mode of transportation that can navigate through congested areas and be easily parked. This has made E-Scooter-sharing particularly attractive to urban residents and commuters. Underlying macroeconomic factors have also contributed to the growth of the E-Scooter-sharing market in Dominican Republic. The country has experienced steady economic growth in recent years, leading to an increase in disposable income and consumer spending. This has created a favorable environment for the adoption of new transportation services, including E-Scooter-sharing. Additionally, the government has shown support for sustainable transportation initiatives, which has further encouraged the growth of the market. In conclusion, the E-Scooter-sharing market in Dominican Republic is developing rapidly due to changing customer preferences, emerging trends in the market, local special circumstances, and underlying macroeconomic factors. As customers increasingly prioritize sustainability and convenience, E-Scooter-sharing services provide an attractive solution for short-distance transportation needs. The integration of smartphone applications and the support of the government have also contributed to the growth of this market.
Data coverage:
The data encompasses B2C enterprises. Figures are based on bookings and revenues of e-scooter-sharing services.Modeling approach:
Market sizes are determined through a bottom-up approach, building on a specific rationale for each market. As a basis for evaluating markets, we use financial reports, third-party studies and reports, federal statistical offices, industry associations, and price data. To estimate the number of users and bookings, we furthermore use data from the Statista Consumer Insigths Global survey. In addition, we use relevant key market indicators and data from country-specific associations, such as demographic data, GDP, consumer spending, internet penetration, and device usage. This data helps us estimate the market size for each country individually.Forecasts:
In our forecasts, we apply diverse forecasting techniques. The selection of forecasting techniques is based on the behavior of the relevant market. For example, ARIMA, which allows time series forecasts, accounting for stationarity of data and enabling short-term estimates. Additionally, simple linear regression, Holt-Winters forecast, the S-curve function and exponential trend smoothing methods are applied.Additional notes:
The data is modeled using current exchange rates. The market is updated twice a year in case market dynamics change.
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