[458 Pages Report] The global e-bike market is projected to grow from USD 48.7 billion in to USD 71.5 billion by at a CAGR of 6.6%. The worldwide demand for e-bikes is growing rapidly as customers see them as an eco-friendly solution for commutes, with rising fuel prices supporting this inclination. Government bodies in various cities worldwide have undertaken initiatives to build bicycle highway lanes for e-bikes.
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European e-bike sales declined in due to wars, inflations, overstocks of e-bike components, and a global economic slowdown. Furthermore, due to wars, European consumers do not want to spend more money buying e-bikes. According to data from the European Cycling Industry Federation (ECF), European e-bike sales declined by 15.3% in . This is a significant drop from the 11.2% growth the market saw in . In , the e-bike market saw a decline of 3.1% and is expected to increase from due to the growing popularity of e-bikes for commuting and leisure; governments worldwide are investing in infrastructure and incentives to promote e-bike use.
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The rising demand for micro-mobility as a service (MaaS) significantly boosts the eBike market in several ways. MaaS platforms offer a convenient and accessible way to try eBikes without the commitment of buying one. This can lead to increased interest and potential future purchases by riders who enjoy the experience. MaaS companies often invest heavily in marketing and promotion, raising public awareness of eBikes and their potential as a transportation option. This benefits the entire eBike market, not just the rental companies. Micro-mobility service companies like Lime, Bird, and Dott offer eBikes alongside scooters as part of their rental fleets. This exposes a broader audience to eBikes, especially in urban areas, and allows them to experience the benefits firsthand. In January , Tier Mobility (Germany), Europe&#;s leading provider of micro-mobility, partnered with the Netherlands&#; primary rail operator, Nederlandse Spoorwegen (NS), to promote sustainable mobility. This integration represents a significant advancement in the realm of MaaS within the Netherlands.
The rise of micromobility services acts as a springboard for the e-bike market by increasing awareness, providing trial experiences, and catering to different transportation needs. Countries like the US, China, Taiwan, The Netherlands, Germany, India, the UK, France, Paris, etc., are seeing extreme demand for micromobility services, contributing to the ebike market growth.
Government laws and terminologies are diverse regarding eBikes. Some countries have national regulations, but states and provinces decide the authority and legal road use regulations. Therefore, any change/update in the regulation of eBikes impacts the market demand. For example, China, which is one of the largest eBike markets in the world, announced a regulation on eBikes, wherein bicycles are classified as eBikes only if they can pedal assist, have a maximum speed of up to 25 km/h, and have a motor power of up to 400W. Any eBike exceeding 25 km/h is considered a moped.
Similarly, Class-II and Class-III e-bikes face restrictions in some European and Asia Oceania regions due to their high speeds and use of throttles, which can lead to traffic issues. India is an exception, as it permits Class-II bikes. Most European countries, excluding Italy and the UK, have banned Class-II e-bikes by European Union regulations. Class-III e-bikes are not widely available in Europe. In the United States, California does not allow Class 3 e-bikes with throttles, and Colorado and Washington restrict e-bikes with motors exceeding 750 watts.
One of the other restraining factors for the growth of the eBike market is the need for proper infrastructure, such as bike paths and bike lanes, in emerging economies of Asia, Oceania, and North America, such as India and Mexico. Some cities in North America are making efforts to overcome these restraints. Austin (US) has finished constructing 115 miles of new bike lanes, Pittsburgh (US) has completed an additional 50 miles of network miles, Providence (US) has developed 43 miles of fresh bikeways, and Denver (US) has built 100 miles of new bikeways Therefore, the regulations and lack of infrastructure restrain the growth of e-bikes, especially in the emerging markets of North America and Asia Oceanic.
Connected eBikes are intelligent and omniscient bikes capable of communicating critical data on their condition and use. In some cases, connected bikes can also receive information and commands remotely. Connected eBike systems allow both data gathering and data mining. Bosch&#;s SmartphoneHub can connect eBikes to a smartphone and support eBike riders before and after their journey. The Lock premium function offered by Bosch transforms the Kiox-connected onboard computer into the key to greater security. This feature provides more security when eBiking and makes the pedelec less attractive to thieves. Intelligent onboard computers &#; such as the Nyon from Bosch &#; determine the pedaling force and frequency during the journey and use this information to calculate the cyclist&#;s performance and energy consumption.
Urban Arrow (The Netherlands) partnered with Comodule (Estonia). It launched a cargo bike equipped with Comodule's connectivity solution, offering advanced security features like location tracking, a digital lock, and activated movement alerts. Riders can conveniently access these features through the Urban Arrow app, ensuring constant connectivity with their bike. Alongside the security functions, the app provides essential information such as the bike's battery status, range, and ride data.
Furthermore, companies like Specialized Bicycle Components Inc. and Shimano Inc. are developing connected solutions such as mission control features and e-tube ride functionalities, expanding the technological capabilities available to e-bike users. With urbanization, digitalization, and sustainability driving global trends, the demand for connected e-bikes is expected to rise significantly. This trend aligns with projections indicating that 70% of the world's population will reside in cities by , increasing traffic congestion. Consequently, the e-bike market will grow during this forecasted period.
The price of traditional bicycles, on average, is nearly USD 400, whereas eBikes are expensive. Their price ranges between USD 1,500 and USD 1,700 and can go up to USD 5,000, with some premium models almost double that. The price of an eBike varies across regions. For example, Europe and North America are technologically advanced, resulting in the high cost of eBikes. In the Asia Oceania region, the market for eBikes is price-sensitive, which can be a potential challenge for buyers.
Earlier, lead-acid-equipped eBikes were prevalent in China. Their price ranged between USD 400 and USD 500. However, according to the new regulations announced in China in , all eBikes must be equipped with lithium-ion batteries, thereby increasing the price of eBikes to USD 700-800. Therefore, with increased prices, consumers would prefer electric two-wheelers or scooters, which fall under the same price range. This might result in a decline in eBike sales. According to a survey by Shimano, almost 40% of the respondents cited this issue. Therefore, manufacturers need to reduce the price of eBikes without declining the build quality and enhancing the performance. However, offering pricing incentives for purchasing eBikes may effectively promote eBikes and tackle the price challenges.
The analysis of the ebike ecosystem sheds light on various market participants, including raw material suppliers, component manufacturers, ebike producers, and end users. Two primary motordrive technologies dominate the bike industry. The first is the hub motor drive, which can be situated either in the front or rear of the bike and integrated into the wheel hub. The second technology is the mid-drive motor, which is incorporated into the bicycle's bottom bracket, offering superior balance to the rider due to its frame-specific design. Lithium-ion batteries are the predominant choice for e-bikes, offering widespread adoption. Lead acid batteries, once popular in Asia Oceania due to their affordability and accessibility, have been phased out over time.
Ebikes equipped with lithium-ion batteries are projected to hold the largest market share, attributed to the numerous advantages these batteries offer, including superior lifecycle, high energy density, wide operating temperatures, and a higher power-to-weight ratio compared to other battery types. Lithium-ion batteries have become the preferred choice for electric bikes over lead-acid batteries due to their superior performance and long-term benefits. One of the critical advantages of lithium-ion batteries is their higher energy density, which allows them to store more energy in a smaller and lighter package compared to lead-acid batteries. This translates to more extended range and reduced overall vehicle weight, making lithium-ion batteries ideal for electric bikes. Another significant advantage of lithium-ion batteries is their longer lifespan. They can handle more charge cycles than lead-acid batteries, with lithium-ion batteries lasting - charge cycles, while lead-acid batteries have a shorter lifespan. Lithium-ion batteries require less frequent replacement, making them a more cost-effective option in the long run. Furthermore, the prices of lithium-ion batteries have witnessed a significant decline in recent years, plummeting by approximately 90%. Intensifying competition within the market has compelled manufacturers to slash costs to maintain competitiveness.
The high demand for lithium-ion batteries in the US and Europe can be attributed to several key factors. In the US, the government has been implementing various incentives and rebate programs to promote the adoption of electric vehicles (EVs) and reduce greenhouse gas emissions. For instance, in the year , The Electric Bicycle Incentive Kickstart for the Environment (E-BIKE) Act in the US proposes providing individual consumers with a refundable tax credit of 30% for purchasing an electric bicycle, with a maximum credit of $1,500 for new bicycles priced under $8,000.
As the Ebike industry continues to expand, the lithium-ion battery market is poised for growth, driven by the exploration of this innovative technology. The focus of manufacturers on crafting lightweight Ebikes has further fueled the widespread adoption of lithium-ion batteries. Major players providing lithium-ion batteries include Robert Bosch Gmbh (Germany), Bafang Electric (Suzhou) Co., Ltd. (China), Samsung SDI (Japan), and Panasonic Corporation (Japan).
The mid-drive motor is forecasted to be the fastest-growing segment in the bike market. Mid-drive motors are positioned near the pedals, where a traditional bike's crankset is. This placement allows them to engage the drivetrain directly, resulting in a more natural and responsive riding experience than hub motors on the wheels. Mid-drive motors can be more powerful than hub motors, offering excellent assistance when climbing hills or accelerating. Additionally, they deliver higher torque, which translates to better pulling power, which is especially beneficial for tackling challenging terrain or carrying heavy loads. Mid-drive motors suit various e-bikes, including mountain, commuter, and gravel. Mid motors manage batteries efficiently while consistently delivering power. North America and Europe shifted towards mid-motor motors compared to hub motors because mid-drive motors place the weight near the center of the bike, which can improve handling and balance compared to hub motors concentrated in the wheels. This is a significant advantage for performance-oriented riders and those tackling technical terrain, and these regions are facing high demand for mountain/trekking bikes. Europe has the largest market for mountain/trekking e-bikes, with a market share of 63% in value. Meanwhile, mountain/trekking e-bikes are North America's second fastest-growing e-bike type after cargo e-bikes.
Mid-drive motors are considered more efficient than the hub motors, providing more extended range per charge. According to industry experts, mid-drive motors showed higher efficiency, especially during pedal assist mode, while consuming less battery than the hub motor. Some of the major companies which provide mid-drive motors include Robert Bosch GmbH (Germany), Bafang Electric (Suzhou) Co., Ltd. (China), and Brose Fahrzeugteile SE & Co. KG (Germany).
In North America, cargo ebikes are expected to grow at the highest CAGR due to the increased adoption of electric bikes for transporting goods from one place to another: traffic congestion and rising fuel prices in North American cities. Cargo e-bikes offer a faster and greener alternative to traditional delivery vans or trucks, especially for last-mile deliveries. Ebikes require minimal maintenance and have lower electricity costs compared to gas-powered vehicles. Some regions offer subsidies or tax breaks for businesses switching to e-bikes, further boosting their adoption. This is boosting up the demand for cargo e-bikes.
After the severity of economic uncertainties, the US and Canada witnessed a decline in e-bike sales by 10% and 6.8%, respectively, in compared to .
The US was the largest market for e-bikes in in North America. After , both the US and Canada are experiencing a rise in e-bike sales. Industry experts say the US market is rising due to the consumer shift towards eBike aftermarket kit. E-bikes offer a convenient and sustainable alternative to cars, appealing to environmentally conscious consumers. Micromobility as a Service is also rising in the US. Many micro-mobility service providers are establishing their market in North America, including Lime (California), Bird (Florida), and Lyft (New York).
Ebikes represent a sustainable mode of commuting that plays a vital role in preserving the environment. The urgent need to safeguard the ecosystem and minimize the emission of harmful substances into the atmosphere, thus reducing the carbon footprint, underscores the swift adoption of e-bikes in North America. Government participation in promoting sustainable Ebikes has further accelerated market demand for these vehicles across the region. In , the federal government in Canada started offering a 15% tax credit for the purchase of eligible bikes, with a maximum credit of $1,000. Authorities in numerous cities globally have initiated efforts to construct dedicated bicycle lanes for e-bikes, with a strong focus on enhancing biking infrastructure. Major bike players include Trek Bicycle Corporation (US), Specialized Bicycle Components (California), and Pedego Electric Bikes (California).
>Class I eBikes are popular in North America due to their compliance with regulations and the increasing demand for sustainable transportation options. These eBikes are pedal-assist only, assisting while the rider pedals without a throttle. As more people seek eco-friendly modes of transportation, Class I eBikes offer a convenient and environmentally conscious way to commute, contributing to the decarbonization of transportation systems.
City/Urban e-bikes hold the largest market share in terms of value in the US and Canada, as American cities increasingly seek ways to reduce traffic congestion and pollution. High fuel costs and dense traffic conditions in urban areas have driven commuters to seek alternative modes of transportation, such as eBikes, which offer a cost-effective and efficient solution for navigating congested city streets.
The key players in the Ebike market are Accell Group N.V. (Netherlands), Giant Manufacturing Co., Ltd. (Taiwan), Yadea Group Holdings., Ltd. (China), Yamaha Motor Company (Japan), and Pedego Electric Bikes (US). Major companies&#; key strategies to maintain their position in the global e-bike market are vital global networking, mergers and acquisitions, partnerships, and technological advancement.
Report Attribute
Details
The base year for estimation
Forecast period
-
Market Growth forecast
USD 71.48 billion by from USD 48.72 billion in at 6.6% CAGR
Top Players
Accell Group NV (Netherlands), Giant Manufacturing Co., Ltd. (Taiwan), Yadea Group Holdings., Ltd. (China), Yamaha Motor Company (Japan) and Pedego (US)
Segments Covered
By Class
Class-I, Class-II, Class-III
By Speed
Up to 25 Km/h, 25-45 Km/h
By Motor type
Mid, Hub
By Mode
Pedal Assist, Throttle
By Component
Batteries, Electric Motors, Frames with Forks, Crank Gears, Wheels, Motor Controllers, and Brake Systems
By Usage
Mountain/Trekking, City/Urban, Cargo, and Others
By Battery type
Lithium-ion, Lithium-ion Polymer, Lead Acid, and Others
By Battery Capacity
<250W, >250 & <450W, >450 & <650W, and >650W
By Ownership
Shared and Personal
By Motor Weight
<2 kg, >2 kg & <2.4 kg, >2.4 kg
By Motor Power
<40nm, >40nm-<70nm, >70nm
By Region
North America, Europe, Asia Oceania
The global e-bike market is projected to reach USD 71.5 billion by , up from USD 48.7 billion in , with Asia Oceania dominating the market.
Class I is leading the class-type segment of the electric bike market. Class I e-bikes, with their pedal-assist only functionality and speed limit (typically 25 km/h or 15 mph), often need more regulations than higher-powered Class II or III e-bikes.
The mobility as a service market is dominated by major players such as PON.bikes (Netherlands), Accell Group NV (Netherlands), Giant Manufacturing Co., Ltd. (Taiwan), Merida Industries (Taiwan), Yamaha Motor Company (Japan), and Specialized Bicycle Components (California). Major companies' key strategies to maintain their position in the global e-bike market are vital global networking, mergers and acquisitions, partnerships, and technological advancement.
Asia Oceania is the largest ebike market in terms of manufacturing and selling. In , China was the largest market for e-bikes in Asia Oceania, followed by Japan, India, South Korea, Taiwan, and Australia.
The growth in the EBike industry is expected mainly due to driving factors for the market, such as growing traffic congestion in the city, the growing popularity of electric mountain bikes (e-MTB), and government support for increasing e-bike sales in support of lower-emission transportation commutes. .
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Sustainability think tanks such as the United Nations Organization have a strong focus on achieving economic and environmental sustainability goals globally. On the road to sustainable development, electric bike (E-bike) adoption is crucial. Nevertheless, research on the factors associated with E-Bike use, especially the psychological, financial, and capacity factors, has remained unexplored. This paper extends the theory of planned behavior with six novel factors related to individual choices to analyze E-bike adoption behavior. A sample of 507 Chinese bike riders is collected through the snowball sampling technique. The sample is estimated through structural equation modeling. The key findings are as follows: first, speed capacity, mileage capacity, and real-time camera positively drove E-bike adoption intention. Second, price differentiation negatively affected E-bike adoption intention. Third, the theory of planned behavior factors, including perceived relative advantage, cost savings, subjective norms, perceived behavioral control, and attitudes toward E-bike adoption, proved to be drivers of E-bike adoption intention. Finally, cost savings are the most critical factor of E-bike adoption intention, whereas perceived behavior control is the least critical factor. These results will help green transportation companies and emerging economies promote E-bike adoption to reach the environmental sustainability goals of the United Nations.
Climate change mitigation and environmental sustainability are hotly debated concerns of modern economies (Ahmad et al., b; Satrovic et al., ). The clean and renewable energy technologies have provided major breakthroughs to cope with the environmental hazards and climatic adversities (Irfan et al., ). In this regard, the transportation sector is renowned for heavily contributing to environmental pollution and climatic adversities (Irfan and Ahmad, ). To cope with such situations, green transportation has been considered an efficient way of reducing environmental degradation and improving human health. Most research has focused on electric vehicles for achieving environmental sustainability. To this end, ride selection is an emergent lump in transportation research. One of the most environmentally friendly ride alternatives may be electronic bikes (E-bikes). In the 20th century, E-bikes in China improved rapidly. In 20 years, from to , E-bike use increased at a rate of 64.8% per year. In addition to E-bikes, recent research of Luo et al. () concerning a different type of environmentally friendly riding&#;bike-sharing&#;has been extensively oversupplied in Xiamen, China. Such a scenario is imperative for environmental sustainability because transportation generates one-quarter of the gas emissions in global energy utilization (McCollum et al., ).
Compared to E-bikes, other types of bikes may not be superior options for meeting the environmental sustainability goals. For example, Pal and Zhang () found that even shared bikes might generate harmful externalities. This kind is still a sustainable transportation approach. However, bike delivery is performed by trucks/vans, and fossil fuels create gas emissions. In addition to speeding behavior, Truong et al. () reported that 16% of motorcycle drivers were involved in accidents, which is higher than E-bike riders or other drivers. Undoubtedly, several studies have been performed on E-bike speed and risk behavior. However, most studies excluded personal rides. For example, in China, although E-bike riding is high, most accidents take place among bike delivery riders. Although the United Nations Organization (UNO) and China value E-bike use, in other countries such as Bangladesh, motorcycle riders have increased by 7.45% for different reasons (Wadud, ). Green energy adoption depends on cost, emotional dimensions, societal perception, and conditional dimensions (Jabeen et al., a). Studies focusing on bike riders&#; adoption attitudes would be a hot issue for different countries to increase green transport energy consumption.
The above literature represents the comparative aspects of E-bike adoption. Our study has unique points. The psychological factors related to the comparison of E-bike and motorbike adoption have not previously been explored. Furthermore, the comparison of riders&#; feelings about capacity factors provides great insight into the decisions underlying the individual&#;s psychology about economic factors.
Relying on one type of transportation for populated cities may not be sustainable (Ahmad and Khattak, ; Adedoyin et al., ; Ahmad et al., b). China, a developed and populated country, has no energy crisis. Therefore, E-bike companies and users could increase their role in achieving the goals of sustainability. In China and 50 other countries up until October , 2,080 schemes of bike-sharing and 360 further plans were in operation. Comparing E-bikes with other kinds of riding options, riders might make psychological distinctions. Riders decide on the basis of psychological factors; in China, the factors that influence their behavior would be different from those in the last 3 or 5 years. After the purchase of E-bikes, riders&#; psychology changes to ask if the battery is safe from thieves. The most expensive thing in an E-bike is the battery. The second adoption factor, financial issues, is designed on the basis of price differentiation and cost savings. Financial factors, after and during the purchase process, involve which kind of bike would provide short-term financial benefits. The reasons involve rider decisions based on price differentiation. Third, the rider&#;s adoption behavior is also due to E-bike capacity compared with that of motorbikes. On a motorbike, drivers can meet petrol needs at every step. Therefore, their ride behavior may not be related to their fuel demand in normal situations. However, E-bike riders know that low or normal speed and battery capacity can increase their mileage capacity.
China leads the world in various kinds of E-bike transport (Fishman and Cherry, ). For transport sustainability, different kinds of E-bikes and transportation would help to accomplish the country&#;s objectives. However, research studies on the adoption of E-bikes are lacking, particularly in emerging populated countries where there are no current research studies. The most important call for E-bike adoption is the comparative study of behavior at the individual level. Research on sustainable transportation in different emerging countries has mainly investigated adoption decisions in the following directions: 1) non-adoption of eco-friendly bikes in Johannesburg (Wood, ); 2) E-bike adoption boosted environmental sustainability in the Netherlands (Sun et al., ); 3) E-bike ownership significantly minimizes the use of other transport facilities (Kroesen, ); 4) recently, Simsekoglu and Klöckner () suggested quantifying e-bike rider comparisons and their adoption to identify the impact on further transportation approaches; and 5) E-bike riders were more careful about safety measures than different road users (Wang et al., ). Comparatively, new research on the sparkling vicinity of E-bike adoption has basically continued unchanged. Similarly, because of lack of existing knowledge about Chinese users&#; E-bike adoption, many countries may seek to understand the motives for E-bike adoption at the personnel level and governments seek to accomplish and adopt the strategies used by the Chinese government. There is interest in achieving the United Nations sustainability goals through changing individual psychology and developing comparisons of transportation opportunities to promote bike riders for responding to the demand for environmentally friendly transportation techniques.
This paper scientifically establishes and investigates the role of rider&#;s intention to adopt E-bikes with selective attention on 1) a comparison of bike adoption on the basis of price differentiation, 2) a comparison of the cost savings of using the bike for 3 or 5 years of personnel use, 3) the perceived relative advantage of E-bikes and their effect on adoption behavior, 4) the reasons behind the thinking about E-bike safety and real-time camera effects to overcome the psychological factors, and 5) individual thoughts about speed capacity differences and its role in adopting the E-bike. Last, mileage capacity impacts the adoption of E-bikes. In addition, Ru et al. () examined attitudes and experiential attitudes with the help of the theory of planned behavior (TPB). However, compared with earlier researchers, our current research has measured newly emerging areas of research. Comparatively, no studies have been conducted on new technology differences in E-bike adoption, proficient compensation of E-bikes, and effects on E-bike adoption. Hence, this study added interesting new knowledge by filling these prominent gaps. Therefore, to check the theoretical model, primary data of 507 E-bike Chinese riders from different cities were used. We developed the original conclusion formed on the smart PLS structural equation modeling (PLS-SEM) approach by applying an online survey questionnaire. The core result of the analysis proved that bike adoption needs to improve in four different categories. The user response to the conditions to obtain individual intentions was the promising effect of transforming their behavior from petrol bikes to E-bikes. The research findings provide the empirical basis to develop a strategy proposition for companies and governments to boost sustainable planning, especially in areas of less E-bike use.
In addition, our study has new comprehensive findings compared with previous research. For example, individual adoption aspects and transportation choices or preferences have not been previously studied. Coupled with this, personal decisions on the basis of individual psychology are deficient. Similarly, emerging matters related to financial paybacks at the individual level in the behavioral structure of the TPB in the energy sector have remained sparse. In short, the present research produces innovative findings compared with previous research.
Regarding the application of our results, the main conclusions are found through the survey of Chinese riders, but the explanations of rider intentions and those factors that influence adoption behavior of the E-bike sustainable transportation choice can be provided as lesson learned strategies. According to this view, the Chinese government can also implement it in their future planning division to improve the levels of sustainable transport. In addition, emerging countries can implement these interesting results to improve adoption behavior. Importantly, psychological factors provide relevant directions for undeveloped countries with energy problems and could be implemented on a short-term basis or just in capital cities (Abul and Satrovic, ; Ahmad et al., c). Dual factors related to financial aspects could be implemented in educational institutes by countries experiencing energy crises (Ahmad et al., ; Ahmad and Zheng, ). Here, accelerating the comparison of our study will also help to implement sustainable policies. Moreover, the PLS-SEM approach in this essential case is the most appropriate methodology because it can help measure attitude preferences and compare unobserved research variables.
The remainder of the research work is organized as follows: Theoretical Framework and Hypothesis Development describes the theoretical framework and hypothesis development; Data and Analyses presents the data and analyses; Results and Discussions explains the results and discussions; finally, Conclusion and Policy Implications presents the conclusions, research limitations, and future research directions.
Prominent researchers are paying attention to the need to understand the user decision process to adopt emerging sustainable selections at different levels. The user decision process has various comprehensive features. The TPB (Ajzen, ) highlights social, psychological, and socioeconomic factors. Various theoretical models are proposed in the decision-making process. However, TPB is better developed compared with other models because it is an improved form of the theory of reasoned action (TRA). At present, several researchers have compared TPB with other theories. Recently, comparative research by Hollett et al. () and Jabeen et al. (a) found the TPB to be a suitable research model to explain intentions and behavior. In addition, Irfan et al. (a) applied it to assess the face mask adoption intention of consumers. In addition, in his book (Ajzen, ), Ajzen revealed that &#;Individual available information mediates the effects of biological and environmental factors on behavior.&#; It shows that TPB can facilitate the prediction of user intentions toward a form of particular transportation. We adopted the TPB approach considering these important aspects. First, the comparative TPB points include the best measurement through accepted behavior of the alternatives in shaping the selection, which means that psychological factors need to be considered (Ajzen, ). Second, we identified three major points of the model: 1) &#;attitude&#; that a person observes and believes, 2) &#;subjective norms&#; of what will be the social impact if a person follows the points or adopts the opportunity, and 3) &#;perceived behavioral control&#; means how individuals feel about ease of use or difficulty in adopting. In the energy sector, Jabeen et al. (a) mentioned the adoption of sustainable resources from a different perspective, recommending the TPB (Neto et al., ) as an approach to understand the selection of different transportation options. Fourth, to the best of our knowledge, no studies have extended the TPB to predict E-bike riders&#; attitudes and their role in achieving the UNO sustainability goals. Our modified theoretical framework is presented in Figure 1.
FIGURE 1
FIGURE 1. Extending the theory of planned behavior for factors affecting E-bike adoption intention. Source: Modified from Jabeen et al. (b).
&#;The degree to which an innovation is perceived as being better than the idea it supersedes,&#; defined by Rogers (), is simply the degree of improvement from the previous level of a product or technology (Moore, ). This is important because, currently, the results by Edge et al. () proved that more research is needed to better understand e-bikes and to develop clarity on acceptable use at city scales. E-bikes might be the first and final significant achievement due to the perceived relative advantage. On the basis of research opinions and to answer the abovementioned needs, the first hypothesis is formulated as follows:
H1: Perceived relative advantage is expected to positively affect E-bike adoption intentions.
In the Netherlands, van den Berg et al. () verified that the safety perception and the social environment have an effect on satisfaction. In Singapore, a significant increase in cycling behavior was observed due to improving the cycling network (Zhou et al., ). Rider psychology and battery safety might have a large role in adoption. Therefore, the gap between the actual and perceived safety and security issues by bikers needs to be addressed. Perhaps some riders do not select bikes if they feel unsafe about batteries and bikes. However, recently, 3 years of improvement or safety changes in China might have influenced massive E-bike adoption. The camera structure for bike safety includes social structure improvement, and Hawley et al. () found that social influence has a significant psychological effect on a structural approach for future adoption. The government should focus on public acceptance and safety concerns (Roh and Kim, ). Therefore, these interesting findings helped us to formulate the second unique hypothesis:
H2: Real-time camera features are expected to positively affect E-bike adoption intentions.
Price differentiation refers to the purchase cost at present choice. First, we aimed to relate the factors of financial pull and E-bike adoption psychology. Riders might choose something best compared with the price of other alternatives. This interesting concept related to the relationship between costs and cognitions has been discussed in the research of Kurzban et al. (). They projected that an individual&#;s efforts could inspire the selection by giving importance to the purchasing cost (Stavrakas et al., ); green adoption and financial planning have shown that retail price and cost affect solar power adoption.
Updated research has focused on E-bike pricing and new opportunities. Currently, Fyhri et al. () mentioned that a price strategy could increase e-bike user trends, for example, tax reductions. However, research is lacking about the benefit of price differentiation and the cost benefits. Research about price differentiation is needed, as Eccarius and Lu () pointed out about cost awareness knowledge. What kind of awareness improves adoption should be better understood; awareness of the purchasing cost can be a solid reason for decision-making. The authors investigate a costing mock-up and how it affects the rider&#;s financial intentions through E-bike adoption psychology.
H3: Price differentiation is expected to affect E-bike adoption intentions negatively or positively.
For a comparative study, business research shows a significant difference at the management and innovation levels (Roth Cardoso et al., ). Therefore, companies or government plans for E-bike promotion are core decisions. The daily marginal cost or daily cost savings might play a role in adoption psychology. During purchase decisions, cost savings might be a key plan for E-bike adoption, as well as proof that customer satisfaction increases cost-saving psychology (Van Poucke et al., ). In addition, the achievement of cost savings is a short-term goal of the customer (Schiele, ). Therefore, the fourth hypothesis is formulated as follows:
H4: Cost savings are expected to positively affect E-bike adoption intentions.
Mileage capacity is important for traveling. König and Grippenkoven () verified that long travel times are a huge usage barrier. Similarly, 70% of the respondents had a 12-km range from the university on e-bike trips (Nematchoua et al., ). As studies confirmed, the first selection for short distances might be E-bikes. Mostly, E-bike use depends on perceived usefulness (Wolf and Seebauer, ). Mileage capacity is an important factor in adoption psychology. Advancing the 100-km capacity of E-bikes will impact purchase decisions and, thus, is very important to know. A total of 72.0% confirmed that E-bikes generally substituted conventional bikes (Van Cauwenberg et al., ). However, mileage capacity should not be a barrier in E-bike adoption because the threshold mileage capacity was 5.1 km in Spanish bike users (Chillón et al., ). The reason for this may be that battery capacity is much better than it was a few years ago. With these findings, comparative research contributes to the development of the fifth hypothesis as follows:
H5: Mileage capacity is expected to affect E-bike adoption intentions.
Interestingly, Bai and Sze () compared two kinds of rides and proved the difference during the red light crossing tendency. There might be different perceptions about speed for E-bikes and motorbikes. Ellison and Greaves () explored whether drivers would like to increase their speed to save time. However, transportation in China has recently improved considerably. Therefore, there might be psychological changes. Speed capacity as a research variable tries to answer these changes and their effects on adoption behavior. Importantly, quick accelerations cause travelers to slide (Schau and Masory, ). Recent research has mainly investigated these matters: for E-bikes, the average speed is approximately 16 km/h; and the maximum riding speed cannot exceed 30 km/h (Cherry and Cervero, ). Our research incorporates E-bikes and motorbikes. Different kinds of transportation opportunities have been developed in recent years. Therefore, to understand the relationship of speed capacity and adoption behavior, the sixth hypothesis is formulated as follows:
H6: Speed capacity is expected to positively or negatively affect E-bike adoption intentions.
Attitude refers to a person&#;s positive thinking about behavior. Currently, Jamšek and Culiberg () found the relationship between &#;perceived sustainable usefulness&#; and the technology acceptance model. Bike quality influences perceived sustainable usefulness, and use loyalty advances their proposed idea. We try to forward this research by improving the level of an individual&#;s attitude by linking it with E-bike green sustainable transport. E-bike users&#; findings by Zhang et al. (b) confirm that attitude followed by innovativeness is the most imperative predictor. However, adoption psychology and attitude studies still do not exist. Importantly, Ajzen and Fishbein () conduct a comparison of the theory of value&#;belief&#;norms and TPB for environmental change and adaptation behavior, but the E-bike adoption model is lacking. In particular, China is leading in electric vehicle improvements. Important new findings by Zhang et al. (a) relating to E-bikes suggest that they offer maximum satisfaction compared with other transportation modes. Attitude can be improved with different strategies. This mentioned importance of attitude and its improvements encourage us to formulate the seventh hypothesis as follows:
H7: Attitude toward E-bike adoption is expected to positively or negatively affect E-bike adoption intentions.
Individuals&#; confidence in their personal capacity to engage in behaviors is called perceived behavioral control (PBC) (Ajzen and Fishbein, ). PBC directly affects behavior. The required intentions could not be shaped if individuals had no confidence in performing any behavior. At the country level, Chinese users might think more about PBC because the most successful way to decrease carbon emissions in transport is fuel switching behavior (Zhang et al., a).
H8: Perceived behavioral control is expected to positively impact E-bike adoption intentions.
Subjective norms (SNs) are clarified by Ajzen () as the perception of the society about adopting an alternative or selection for personnel use. In the society, family, friends, or colleagues may be inspired to adopt the E-bike. These inspiration sources were also mentioned by Jabeen et al. (a) regarding the adoption of new green technologies, which depends on the energy sector reforms and energy efficiency plans (Demirbas et al., ) to impress the public. In Chinese culture, Yang et al. () verified that situational factors impact green behavioral awareness and intention. On these bases, to address the research gap, the ninth hypothesis was formulated as follows:
H9: Subjective norms are expected to impact E-bike adoption intentions.
The questionnaire items for the perceived relative advantage were taken with a minor modification from Moore () and Wang et al. (a). The perception of the role of the real-time camera was adopted from Klobas et al. () with a minor modification. Price differentiation was adopted from Carter and Jennings () and Wang et al. (b); cost savings from Meuter et al. (); speed capacity from Saleem et al. (); PBC from Halder et al. (); SNs from Kardooni et al. () and Turel (); attitudes from Yang et al. (); and E-Bike adoption intention from Ahmad et al. (), Asadi et al. (), and Paul et al. (). Overall, five questions were deleted in the analysis due to less overloading. The detailed questionnaire items are presented in Supplementary Appendix Table SA1 (see Supplementary Materials). The demographic data are reported in Table 1.
TABLE 1
TABLE 1. Demographic data.
Smart PLS-SEM has played a superior role since . Unfortunately, PLS-SEM has insufficient use in green energy transportation adoption to explore the benefits through this novel approach. Traditional studies used the covariance-based SEM (Jöreskog, ). However, we apply PLS-SEM because it is suitable for a small sample size (Wong, ); we used PLS to explain the research objectives, as it shows a higher power of statistical explanation of the variables than CB-SEM. PLS-SEM includes advanced bootstrapping techniques and is not an alternative to CB-SEM but a &#;complementary modeling approach&#; toward the SEM technique (Hair Jr et al., ), and prediction power is significantly better (Sarstedt et al., ). The past studies also considered other probability methods such as Probit and Propensity Score Matching (Jabeen et al., ); however, because of flexibility of application, we have used CB-SEM technique. This technique is used in energy adoption and acceptance, e.g., acceptance and renewable energy utilization (Irfan et al., ; Jabeen et al., b; Fatima et al., ) and willingness to use solar energy (Irfan et al., b).
Primarily, we verified the data analysis through convergent validity and discriminant validity to attain the measurement model basics. Actually, the convergent validity of the measure demonstrates the strength or power level of the items with theoretical relevance of the factors. Following the work of Anderson and Gerbing (), a composite reliability (CR) assessment was used to investigate the internal consistency of the variables.
Previous research regarding CR, rho indices = 0.7 (Dijkstra and Henseler, ) and AVE > 0.5 (Chin, ); importantly, the AVE square root value of every construct was higher than the value of the construct correlation (Fornell and Larcker, ). Our analysis shows values of factor loading >0.707 (Hair et al., ). Figure 2 and Table 2 also show outer loading. Values were more than the compulsory standard requirements or standards (Schuberth et al., ). In Tables 2 and 3, Cronbach&#;s alpha was sometimes not used for the reliability test because it is not suitable for the PLS-SEM, as suggested by Gadermann et al. (), but our results were accurate, indicating that the response was good in the questionnaire. In addition, as important criteria, the correlation values between the constructs were less than the self-correlation of each construct, which is consistent with the proposition of Kline () and Hair Jr et al. ().
FIGURE 2
FIGURE 2. Structured model results (PLS extracted).
TABLE 2
TABLE 2. Reliability and validity of measurement scales.
TABLE 3
TABLE 3. Discriminant validity.
Bootstrapping is an algorithm technique to apply small sample analysis on a large-scale sample. The bootstrap replication number can fluctuate from 500 to 5,000. The hypothesized relations linking the constructs of the planned, structured model were checked by bootstrapping (3,000 resamples) to obtain the confidence intervals and variable t-values (Table 4).
TABLE 4
TABLE 4. Structured model and variables direct effects.
The bootstrapping technique is suitable for small sample volumes because it does not depend on the normality conjecture (Sardianou and Genoudi, ). To conclude, the bootstrapping technique was used to calculate the estimated accuracy of the measurement model, as suggested by Hair Jr et al. ().
As mentioned by Stone (), the prediction power for the structure model was confirmed by investigating the coefficient of determination (R2), which indicates the collective effect of exogenous (independent) variables on endogenous variables (dependent variable). The R2 can measure the level of variation in the dependent variable highlighted in the explanatory (independent) variables in the model. R2 = 0.25, 0.50, and 0.75 refer to weak, moderate, and strong predictive power, respectively. Our R2 was 0.922 (Figure 2). Comparatively, for further software, the R2 value (0.75) entails significant understanding while exceeding 0.35, the threshold value suggested by Ketchen ().
All the independent variables indicated a significant positive effect on the dependent variable, except for the (H3) price differentiation on the intentions. For p values, only H8 PBC has **p < 0.05, indicating that it is not highly significant but has a sufficient significance level (Table 4 and Figure 2).
The next compulsory step, the value of Q2, defined as a measure of cross-validated redundancy, is estimated to determine all planned constructs; the analysis proved that our structural model has a significant predictive level. Following the suggestion of Stone () and Geisser (), the values of Q2 were used to confirm the predictive relevance and validity of the model. Q2 can estimate the predictive validity of the large and multipart PLS model through the blindfolding technique. We calculated it through the blindfold bootstrapping technique (Figure 2).
Cohen () proposed f2 to verify the degree of input by independent or exogenous variables in terms of f2 to explain the independent or endogenous variable. Criteria of 0.02, 0.15, and 0.35 are referred to as weak, moderate, and strong effect sizes of the research constructs, respectively. In particular, the f2 value was large (CS&#;EBAI, f2 = 0.572, exceeds 0.35), which showed a large effect between cost savings and E-bike adoption intention. There was a moderate effect between mileage capacity and E-bike adoption intention (MC&#;EBAI, f2 = 0.230, exceeds 0.15) and between perceived relative advantage and E-bike adoption intention (RD&#;EBAI, f2 = 0.188, exceeds 0.15). The f2 values met the base level criteria of 0.02 in the case of the remaining relationships (Table 4).
The standardized root mean square residual (SRMR) as per Henseler et al. () can be applied as a goodness of fit in PLS-SEM to avoid misspecification in the research model. The value of SRMR was acceptable (Hu and Bentler, ). This shows how that the set of variables is a good fit for the model.
Importantly, Ringle and Sarstedt () and Hair Jr et al. () suggested the use of importance&#;performance map analysis (IPMA). According to them, IPMA is a valuable analysis technique in PLS-SEM to broaden the typical results covering the path coefficient estimates through the addition of dimensions that consider the average rate of the latent variable. Figure 3 illustrates the results of the IPMA, showing that speed capacity demonstrated the highest performance, whereas mileage capacity depicted the lowest performance. Most importantly, cost savings proved to be the most important factor, whereas PBC exhibited the least importance (Figure 3).
FIGURE 3
FIGURE 3. Importance&#;performance map analysis (IMAP).
Chinese data were analyzed about significant adoption factors for E-bikes. Current research based on the TPB theoretical framework presented an overall theoretical extended framework to successfully clarify the role of the human psychological role in a deep sense of financial, capacity, and conditional psychological factors. Consequently, our study correlates at an advanced level with Rankavat and Tiwari () and Ahmad et al. (a); as questioned, planners and researchers must consider e-bike risk observations. Finally, two psychological factors, i.e., perceived relative advantage (H1) and the role of the real-time camera (H2), were included in our study.
A real-time camera is a solution to the barrier to E-bike adoption. Thus, our model responds to the advanced level for the mentioned barrier to adoption.
Concerning the financial perception (H3 and H4) and capacity factors (H5 and H6) of the bike, Lehr et al. () confirmed that user involvement could increase intentions, but how? Therefore, parallel to their suggestion of using information integration theory, our study also supports that Chinese policies should try to offer the maximum unintended trial by explaining our research insights. Green energy adoption in developing countries is important for anti-poverty policies (Rahman et al., ) and for achieving extra savings in the case of emerging countries (Gelani et al., ).
Concerning financial perceptions, an important factor regarding cost savings has an impact on E-bike adoption. It is a targeted effort by an E-bike purchaser. Bike adoption and experiences can improve an individual&#;s daily basis cost savings. With the mentality of improvement, these experiences can be enhanced with increasing sustainable transportation.
Cost savings and PBC are parallel forward solutions for previous research by Gao et al. (), in which monetary rewards can motivate citizens to engage in bike-sharing. Predefined energy subsidies helped minimize costs (Matosović and Tomšić, ). Therefore, riders are more likely to adopt E-bikes with the help of cost differentiation, cost savings, and PBC. For Chinese users, the price at the time of purchase was not an explanatory factor of E-bike adoption. This may be because the E-bikes had almost the same price as petrol bikes or, perhaps, due to being one of the richest countries in the world.
Importantly, for capacity comparisons, our study tries to provide a solution for road safety. As Mao et al. () mentioned through big data, long drives and peak-hour driving are the main reasons for accidents. Therefore, we proved that the E-bike was a good solution compared with motorbikes because E-bike users need bikes for a maximum of 100 km in 2 days&#; usage and speed capacity from 20 to 30 km/h. In China, long drives, subways, and other sources are good enough for our sampling areas, similar to findings in the Canary Islands in Spain in terms of the factors of distance and cost (Maas et al., ).
Capacity comparisons (H5 and H6) are advancing the research on E-bikes by Makarova et al. () as &#;fun and different to drive on.&#; Therefore, adoption psychology for companies could use our model to follow in practical research to advance E-bike transportation. We advanced their model; an important reason is that these kinds of studies have proven that E-bikes are better than other bikes in driving modes.
Speed capacity is a different scenario. Wager et al. () confirmed that, if an electric vehicle is driven for a long distance at a high speed, then it consumes more energy. In parallel, perceived relative advantage, attitude toward adoption, mileage capacity, and speed capacity are also considered relevant factors. Because a small improvement in vehicles&#; weight or speed greatly impacts the mileage capacity, comparatively, electric cars have a larger energy storage capacity or a larger fuel tank. Very importantly, in China, E-bikes have shown yearly improvements in relative advantages compared with other riding alternatives.
Our sample-based model is extended and has been supported by Nematchoua et al. (); their results on E-bikes show they produce maximum satisfaction compared with other transport modes. In their sample, 70% of riders living in the 12-km range agree to ride e-bikes. Mileage capacity and attitude (H7) were entirely supported by their concepts and confirmed the research. However, little is known about the relationship between adoption psychologies. By following the multimediation approach of Yasir et al. () about awareness, the government and companies should advertise based on our model. First-ever benefits, e.g., improvement in perceived relative advantage, will increasingly convince users to adopt E-bikes. The findings are comparable to those of Michas et al. (), who suggest the implementation of active adaptive policies by focusing on short-term and long-term perspectives to support adoption behavior.
Environmental sustainability is the prime concern of global economies to avoid climatic adversities in the future. In this regard, enhancing sustainable transportation, such as E-bikes, would significantly contribute to environmental protection. Our insights provide a significant addition to E-bike research by ride selection psychology in China and the appropriate TPB extensions for an alternative ride category. We concluded that adoption behavior would positively depend on an individual&#;s perception of financial benefits through cost savings; however, it negatively relied on cost differentiation. In addition, the significance of individual psychology through the role of perceived relative advantage shows the improvement of E-bikes compared with other rides. Moreover, speed capacity and mileage capacity were addressed for the first time and proved to be positive E-bike adoption factors. Further focus on driving factors and the elimination of impediments to E-bike adoption will not only contribute to sustainable transportation but will also enhance its contributions to environmental sustainability globally, with the extreme need for other counties to follow China&#;s green investment policies (Ahmad et al., a).
Our proposed idea with theoretical support will provide a new direction for research on the adoption of E-bikes. However, there are some potential limitations to be addressed by future studies. First, this research examined the different determinants, such as psychological factors, in the safety of E-bikes and their batteries and perceived relative advantages over other kinds of bikes. In addition, it included financial factors, capacity factors, and TPB factors. It was challenging to add more adoption factors due to statistical suggestions and to complete the study&#;s analysis without bias. Therefore, future studies should include other factors, such as different kinds of E-bikes (e.g., pedal bikes and without pedal bikes), transiting factors, and other bike capacity factors. Second, we collected data from cities of China that can be extended to the whole of China and can also be extended by comparing e-bike adoption in different countries because the supply of E-bikes is very different and less in other countries. Third, we used snowball sampling, which can be improved by using different sampling and data collection techniques. Fourth, we focused on different factors that lead to the adoption of E-bikes, so our research model did not include any mediating variables. Future research can be improved by extending our study research model and by using one or two mediating variables. Fifth, because we used the TPB, this study encourages further research by using different theories, e.g., personality theory and social influence, as mediation factors.
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.
Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.
AY: Conceptualization, writing-original draft, variable construction, and formal analysis. XH: Supervision, funding acquisition, writing&#;review, and editing. MA: Overall quality improvement, structure enhancement, writing&#;review and editing, and variable construction. RA: Writing review and editing. MKA: Writing review and editing. CI: Writing review and editing. AC: Writing review and editing. AA: Data curation, writing review and editing, and language improvement. IK: Data curation, writing review and editing, and language improvement.
The Ministry of Education Humanities and Social Sciences Research Project of China (No. 16YJA).
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10./fenrg../full#supplementary-material
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