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Bike-sharing - Fiji

Fiji

In Fiji, the revenue in the Bike-sharing market is forecasted to reach US$1.74m in 2024.
The revenue is anticipated to demonstrate an annual growth rate (CAGR 2024-2029) of 4.99%, leading to a projected market volume of US$2.22m by 2029.
By 2029, the number of users in the Bike-sharing market is expected to reach 37.54k users.
User penetration is estimated to be 3.3% in 2024 and 3.8% by 2029.
The average revenue per user (ARPU) is projected to be US$55.24.
Approximately 100% of the total revenue in the Bike-sharing market will be generated through online sales by 2029.
When compared globally, the majority of revenue will be generated China (US$5,515m in 2024).
Fiji's bike-sharing market is gaining popularity among tourists, contributing to sustainable transportation initiatives and boosting local economy.

Key regions: South America, Malaysia, India, Indonesia, Saudi Arabia

Definition:

The Bike-sharing market includes short-term bike-sharing services. In bike-sharing services, bicycles are generally owned by a bike-sharing provider and are independently reserved by customers around the clock. Customers are required to open an account with the bike-sharing provider and can then reserve bicycles. This is usually done with a smartphone app, but there are also service providers that allow reservations to be made via the provider's website, by telephone, or at a terminal.

The two most frequently used bike-sharing varieties are the following: station-based (e.g., Stadtrad and Citi Bike New York) and free-floating (such as nextbike and ofo). With station-based bike-sharing, a bicycle is retrieved from a bike-sharing station and returned to either the same station or dropped off at another station. With free-floating bike-sharing, it is possible to find bicycles everywhere within the service provider's business zone and leave the bicycle anywhere in accordance with traffic regulations. Peer-to-peer bike-sharing is not included in the market definition of this market. Moped-sharing services are not available in all countries; thus, only a limited number of countries and regions can be selected.

Additional Information:

The main performance indicators of the Bike-sharing market are revenues, average revenue per user (ARPU), user numbers and user penetration rates. Additionally, online and offline sales channel shares display the distribution of online and offline bookings. The ARPU refers to the average revenue one user generates per year while the revenue represents the total booking volume. Revenues are generated through both online and offline sales channels and include exclusively B2C revenues and users for the mentioned market. User numbers show only those individuals who have made a reservation, independent of the number of travelers on the booking. Each user is only counted once per year.

The booking volume includes all booked rides made by users from the selected region, regardless of where the ride took place.

For further information on the data displayed, refer to the info button right next to each box.

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Analyst Opinion

The Bike-sharing market in Fiji has been experiencing significant growth and development in recent years.

Customer preferences: Customers in Fiji have shown a growing interest in bike-sharing services due to the increasing awareness of environmental issues and the desire for more sustainable transportation options. Additionally, the convenience and cost-effectiveness of bike-sharing services appeal to customers looking for efficient ways to navigate the islands.

Trends in the market: One notable trend in the Bike-sharing market in Fiji is the expansion of bike-sharing services to more remote areas and tourist destinations. This trend is driven by the government's efforts to promote eco-friendly transportation options and enhance the overall tourism experience in Fiji. Furthermore, the integration of technology, such as mobile apps for bike reservations and payments, has improved the accessibility and user experience of bike-sharing services in Fiji.

Local special circumstances: Fiji's unique geography, characterized by a collection of islands with diverse landscapes, presents both opportunities and challenges for the Bike-sharing market. While bike-sharing services are well-suited for urban areas like Suva and Nadi, where traffic congestion is a concern, they may face logistical challenges in more rural and mountainous regions. However, the growing popularity of eco-tourism in Fiji has created a niche market for bike-sharing services in these areas, catering to adventurous travelers seeking sustainable transportation options.

Underlying macroeconomic factors: The growing tourism industry in Fiji, coupled with government initiatives to promote sustainable development, has created a favorable environment for the Bike-sharing market to thrive. Additionally, the increasing urbanization and population density in major cities have fueled the demand for alternative transportation solutions, further driving the growth of bike-sharing services in Fiji.

Methodology

Data coverage:

The data encompasses B2C enterprises. Figures are based on bookings, revenues, and online shares of bike-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.