1. Introduction
The United Nations adopted Sustainable Development Goal (SDG) 13: Climate Action in response to climate change in September 2015, emphasizing the need to adopt urgent measures to address climate change and its impacts. In December 2015, the United Nations Framework Convention on Climate Change (UNFCCC) adopted the Paris Agreement, which clearly defines the goal of enhancing adaptation, reducing vulnerability, and increasing resilience in response to climate change globally in Article 7. This is the first climate agreement in the history of the United Nations that mentions fossil fuels. To achieve the temperature-rise limit of 1.5 degrees Celsius, a target was set to reduce global carbon dioxide emissions by half by 2030 and to reach net zero by 2050. For example, many European countries have announced that they will ban the sale of fuel-powered cars in the future for that sake of saving energy, reducing carbon emissions, and lowering pollution. The U.S. President Joe Biden introduced many green policies, such as reaching zero-carbon power generation, vehicle electrification, and reentering the Paris Agreement by 2035, as soon as he took office in 2021. These conventions and agreements signify that climate change has become a focus of global concern [1]. To cope with climate change, Taiwan has set a long-term national greenhouse gas (GHG) reduction target of less than 50% of 2005 GHG emissions by 2050 and has formulated a climate change response strategy based on the National Climate Change Adaptation Action Plan (2018–2022) approved in 2019 to improve its adaptation capacity, strengthen its resilience, and reduce the vulnerability caused by climate change impacts, thereby ensuring the country’s sustainable development.
Since the global transport sector contributes nearly 30% of greenhouse gas emissions from economic activities a year, and road transport itself accounts for 10% of the total transport sector emissions [2].Therefore, sustainable mobility with the goal of reducing the use of private fuel vehicles has become the main policy of urban transportation policies in many countries, including the construction and use of public transportation, the creation of active transportation (walking and bicycling) environments, the development of green energy transportation technology, and its promotion, etc. [3,4,5,6,7]. With the rising awareness of sustainable mobility development, new energy vehicles have become hotly discussed issues. Electric motorcycles are considered to be low-carbon road vehicles that can replace traditional combustion engines. Vehicle electrification has many advantages and can bring multi-faceted benefits to society, including improving the energy efficiency of road transportation [8], reducing the country’s oil imports to improve national energy security, combating global climate change (for example, with the low-carbon power system, it can better play its carbon reduction benefits), improving urban air quality, developing new industries, and promoting economic growth.
In addition, with the rise of environmental protection and the sharing economy, the trend of “micromobility” shared transportation, which is mainly based on short-distance movement within the city, has gradually emerged. More and more developed countries have adopted the shared leasing method to build an electric scooter business model [9,10,11,12,13]. In Paris, Cityscoot launched 100 electric motorcycles in the summer of 2016; in August 2016, Bosch, Germany launched an electric motorcycle sharing program with a fleet of 200 Gogoro Smartscooters; In Milan, Enjoy has 150 motorcycles in operation. In Cologne and Munich, Scoome leases 50 electric motorcycles to users; In Berlin, eMil has a fleet of 150 electric motorcycles; In Taiwan, WeMo launched in Taipei in the fall of 2016. During the COVID-19 pandemic, the rapid growth of shared electric motorcycles was driven. In August 2020, the global shared electric motorcycles will have been distributed in 22 countries and 122 cities, with a total of 104,000 motorcycles launched, an increase in 38,000 motorcycles over 2019, an increase in nearly 58%. The number of registered members reached 9 million around the world, an increase in 4 million over 2019, an increase in nearly 80%. Among them, Taiwan has become the third largest shared electric motorcycle market in the world [14].
Taiwan has the highest density of motorcycles in the world, and motorcycles serve as the main means of transportation in Taiwan. As of October 2022, the number of motorcycles in Taiwan had reached 14,375,666; almost every household owned one or more motorcycles. Because fuel vehicles rely on petroleum products for most of their energy consumption, Taiwan’s transportation sector produces the second highest carbon dioxide emissions, preceded only by the industrial sector. To effectively reduce the pollution generated by fuel motorcycles, the government encourages people to purchase electric motorcycles through measures such as purchase subsidies, fuel tax exemptions, and license tax reductions, considering the relatively high unit price of electric motorcycles and the resulting low intention of acceptance among the public. In July 2009, the Electric Motorcycle Industry Development Promotion Plan was adopted, stipulating that, in accordance with the current Implementation Guidelines for Subsidies to Promote the Electric Vehicle Industry by the Ministry of Economic Affairs, subsidies of NT$7000 per heavy- and light-duty electric motorcycle and NT$5100 per small light-duty electric motorcycle are provided for new purchases. The Industrial Development Bureau also provides subsidies to electric motorcycle manufacturers for setting up charging facilities and offers incentives to motorcycle manufacturers. The above subsidy policies for electric motorcycles are mainly developed to echo the Taiwan 2050 Net Zero Emission Path, which aims to achieve the following goals: 35% of the annual sales of motorcycles to be new electric motorcycles by 2030, 70% by 2035, and 100% by 2040.
In recent years, the issue of green energy has attracted much attention, and people have developed an increasingly heightened awareness of environmental protection. There is also a growing acceptance of green products. Riding on the Gogoro boom, coupled with the government’s policy of replacing old vehicles with new ones and extending subsidy programs to stimulate purchases, sales of electric motorcycles reached a record high of 173,033 units by 2019. In 2020, with the COVID-19 pandemic ravaging the world, oil prices dropped to stimulate fuel-powered motorcycle sales, and sales of electric motorcycles decreased sharply. In 2022, many external factors (e.g., the Russia–Ukraine war) caused oil prices to soar, and electric motorcycle sales were expected to rebound. The aforementioned sales figures indicate that the acceptance of electric motorcycles in Taiwan has increased. However, while the sales of electric motorcycles are increasing, there are some problems that need to be solved. Lin and Wu [15] pointed out that the high unit price of electric motorcycles is a barrier that keeps consumers from making purchases. People would continue using fuel-powered motorcycles unless they voluntarily replaced them with electric motorcycles. Do other problems such as mileage anxiety and the fact that electric motorcycle charging stations are not as prevalent as gas stations affect people’s willingness to buy electric motorcycles? Furthermore, over the years that the government has encouraged people to purchase electric motorcycles through the provision of incentives, how have the users perceived the ease of use and convenience of electric motorcycles? These are the questions that this study intends to investigate.
Consumer acceptance often affects the future development of new products. Among the studies on user acceptance and behavioral intentions toward technology, based on the theory of reasoned action (TRA) developed by Davis et al. [16], the Technology Acceptance Model (TAM) is the most popular; it has become one of the theories most commonly used to explore IT acceptance behavior today [17]. TAM asserts that human beings’ intention to accept technology will be affected by two dimensions: perceived usefulness and perceived ease of use, and based on this framework, it can evaluate and predict user acceptance of new IT systems [16]. With technological innovations and environmental changes, scholars use TAM as a framework to combine different backgrounds, theories, and methods to expand the model in order to make it more explanatory. For example, Venkatesh et al. [18] proposed the unified theory of acceptance and use of technology (UTAUT1) based on important aspects such as planned behavior theory, innovation diffusion, and the social cognitive model, and in 2012, they proposed an extended unified theory of acceptance and use of technology (UTAUT2) [19]. Venkatesh et al. [18] identify the key factors affecting user acceptance in the new technology environment, analyze the advantages and disadvantages of different theoretical models, integrate and expand the existing theoretical models, and improve the overall explanatory power.
Prior researches revealed that perceived values such as economic benefits and the environmental concern of using e-vehicles is found to influence the purchase intention of the e-vehicles [20]. He et al. [21] used personal innovativeness to understand the purchase intention of e-vehicles along with environmental concern. However, value propositions and government policies have been understudied by prior researchers. As a result, the study has attempted to develop a model based on TAM for determining the factors that influence the intention to use electric motorcycles. Furthermore, the present study utilizes Partial Least Squares Structural Equation Modeling (PLS-SEM) and examines the relationships between the variables in more depth by utilizing mediation analysis.
With the changes in consumer behavior resulting from the rise of environmental awareness, there is a growing concern about personal responsibility for environmental protection and personal behavior toward the environment [22]. Consumers’ willingness to purchase products is not judged solely on the basis of their preferences; environmental awareness changes consumer behavior. Studies indicate that individuals’ perceptions of environmental concerns influence their intention to purchase green products [23,24]. Examples include consumers switching from fuel motorcycles to electric ones in China [25]; purchasing environmentally friendly grape pomace powder in Brazil considering environmental concerns [26]; changing their intention to purchase organic food depending on the level of environmental concerns in Pakistan [27]; and purchasing biodegradable plastics in Malaysia being positively influenced by environmental concerns [28]. Therefore, in this study, environmental concerns were included as a variable to investigate whether people’s intentions to purchase electric motorcycles are influenced by environmental concerns.
Furthermore, check the brand positioning of Gogoro’s electric motorcycle on Gogoro’s official website. In addition to emphasizing it as a “transportation vehicle”, it also uses slogans such as “Riding this way is smarter”, and “Evolution gives you new technology”, and highlights its design. New technologies such as big data and smart technology can create a new energy experience, intending to establish a value proposition that can create a better life for users through technology [29]). The product positioning of the company has gone beyond transportation itself, and it is also a technological product at the same time. In addition, in the design of Gogoro as a “transportation vehicle”, it also appeals to its friendliness to the environment, including the characteristics of reducing energy consumption and producing lower noise, which are environmentally friendly. However, due to its positioning as a technology product, factors such as its relatively unfamiliar operation and its higher pricing (compared with cheaper fuel locomotives) may also affect the public’s intention to use it. Therefore, focusing on this research question, this research aims to integrate the pro-environmental motivation and the value proposition motivation to explore the psychological process of pro-environmental behavior and its influencing factors.
Studies reveal that public policy can influence or even change consumer behavior [30,31]. For example, German consumers’ pro-environmental behavior is influenced by policy support [32]; Chinese consumers’ willingness to purchase electric vehicles is influenced by government subsidy policies [33]; and Taiwanese consumers’ willingness to recycle and reuse resources is influenced by government subsidies [34]. Li et al. [35] and Wang et al. [36] have noted that tax incentives veritably have a positive influence on people’s purchases of electric cars. Therefore, public policy is included as a variable in this study.
Based on the above discussion, this study will take the Gogoro as an example to explore the behavioral intentions of Taiwan’s Generation Z young people towards electric motorcycles. In terms of research methods, based on the TAM, we analyze the influencing factors and effects of electric motorcycle use behavioral intentions from the perspectives of environmental concerns, value proposition, and government policies. Through the above discussion, this research will be able to break through the previous limitation of only using the psychological process of environmental concern to explain pro-environmental intentions and behaviors and explore the role of pro-environmental factors and self-interested factors of value propositions in the generation of pro-environmental behavior intentions, which in turn helps to develop and formulate strategies to promote pro-environmental tourism behavior with self-interested factors driving the value proposition.
In order to achieve the aforementioned research objectives, this study first identified research gaps. In Section 2, we carried out a review of relevant theories corresponding to the research gaps and developed research hypotheses. In Section 3, since the influencing variables analyzed in this study are psychological factors, psychological measurement tools will also be developed, and the analysis methods and data collection process will be introduced. In Section 4, we refer to the psychological dimension check and correction before confirming the sample characteristics of this study and model calibration. After the aforementioned checks. In Section 5, we test the influence of the psychological factors in the hypothesis and its influencing mechanisms, in order to respond to the purpose of this study. In Section 6, the author accordingly proposes research contributions and policy implications, discusses the limitations of this study, and makes recommendations for follow-up research.
2. Literature Review and Hypotheses
2.1. Technology Acceptance Model
The TAM was proposed by Davis in 1989, based on Fishbein and Ajzen’s [37] theory of reasoned action (TRA). Considering the characteristics of information technology usage, he predicted the user’s behavior toward new information technology in order to explain and understand the user’s acceptance of the new technology system [16].
In the TAM theory, the influences of external factors on users’ internal beliefs, attitudes, and intentions affect the use of technology; it is believed that perceived usefulness and perceived ease of use influence attitude toward using technology and thus behavioral intention to use. Additionally, the TAM also emphasizes the influence of external variables on technology use behavior, such as the users’ own characteristics and usage environment.
Brandon-Jones and Kauppi [38] and Wolff and Madlener [39] have pointed out that people’s perceived usefulness positively influence their attitudes. Dutot et al. [40] found that the perceived usefulness and perceived ease of use of wearable devices influence people’s attitudes and further positively influence purchase intentions. Based on the above, this study proposes that people’s perceived usefulness and perceived ease of use of electric scooters positively influence their attitudes toward using them, and their attitudes toward using electric motorcycles positively influences their purchase intentions. Accordingly, hypotheses 1–3 were developed.
People’s perceived usefulness of electric motorcycles positively influences their attitudes toward using electric motorcycles.
People’s perceived ease of use of electric motorcycles positively influences their attitudes toward using electric motorcycles.
People’s attitudes toward using electric motorcycles positively influence their behavioral intentions toward electric motorcycles.
2.2. Environmental Concern
Environmental concern is defined as awareness about environmental problems and a person’s willingness to be part of the solution [41]. Dunlap and Van Liere [42] proposed the New Environmental Paradigm scale (NEP) to assess underlying environmental values and environmental ethics [43]. Lee [44] indicated that environmental concern is an important indicator for predicting green purchasing behavior (GPB). Individuals’ perceptions of environmental concern influence their willingness to purchase green products [24,25,45].
Wasaya et al. [46] stated that when consumers have high environmental awareness, their trust in and intention to buy green products increase. Yadav and Pathak [47] pointed out that environmental concern has a significant influence on the behavioral intention of young consumers in developing countries to purchase green products. Chen et al. [48] pointed out that individuals’ environmental concerns can reduce the possibility of rejecting innovative vehicles. Kuhn et al. [49] showed that environmental concern and trust tend to have greater effects on usage intention for station-based carsharing. Xue et al. [50] and Xiao et al. [51] found that higher education levels positively influence people’s attitudes toward environmental protection. Accordingly, hypothesis 4 was developed.
People’s environmental concern positively influences their attitudes toward using electric motorcycles.
2.3. Value Proposition
The value proposition is the value that a company provides to its customers through its products and services. A properly constructed and communicated customer value proposition can make a significant contribution to a company’s strategy and performance [52]. Osterwalder et al. [53] contend that the purpose of a value proposition is to identify what really drives customers, to uncover what they want to accomplish, their deeper motivations and goals, and what they are deterred from doing. According to Bowman and Ambrosini [54], when customers understand the value proposition of a product or service, a perceived value is formed in their mind, which is the upper limit of the price range of the transaction. Thus, the more customers agree with the value proposition, the higher the price ceiling will be, and the more attractive the product or service offered by the company will be, the greater the competitive advantage it brings to the company. In different product categories and market segments, there are differences in the value proposition that customers attach importance to. Companies need to examine the real value from the customer’s viewpoint to establish a competitive value proposition. Customers often do not simply consider the economic value related to price. Therefore, it is necessary to clarify what features customers really care about [55]. The value proposition is divided into four main dimensions: (1) economic: determined by price; (2) functional: solutions that make customers feel more convenient and gain utility from functionality and practicality; (3) emotional: evoking positive emotional states through the experience of the product or service; and (4) symbolic: values that reflect self-expression in the process of consumption or experience. In this regard, hypothesis 5 was developed.
People’s value propositions positively influence their behavioral intention to use electric motorcycles.
2.4. Government Policy
Tummers [30] pointed out that public policies can change consumer behavior. Public policies can be divided into four major categories, namely, incentive, bans and mandates, communication, and nudge, with the aim of reducing environmental pollution. Studies on the intention of policies to influence consumer behavior, such as incentives and restrictions, have positively influenced the increased use of electric scooters in China [56,57]; and the support of the Indian government has led to an increase in consumer willingness to use electric scooters [58]. Studies reveal that the preferential policies implemented by the government significantly and positively influence people’s purchase of electric scooters [35,36,59]. Accordingly, hypothesis 6 was developed.
Government policy positively influences people’s behavioral intention to use electric motorcycles.
3. Materials and Methods
3.1. Research Framework
This study investigates the influence of people’s perceived usefulness and perceived ease of use of electric motorcycles on their attitudes toward electric motorcycle use, as well as the influence of people’s environmental concern on their attitudes toward use. Further, the study investigates whether people’s attitudes, value propositions, and government policies influence their behavioral intentions to use electric motorcycles. This study’s framework is summarized in Figure 1.
3.2. Research Questionnaire Design
The questionnaire’s design covers eight parts. The first part is behavioral intention, containing four items developed based on Wu et al. [60] and Sepasgozar et al. [61]. The second part is an attitude toward using, containing four items, developed by modifying the concepts of Davis et al. [16], Mercedes et al. [62], and Wolff and Madlener [39] to meet the needs of this study. The third part is perceived usefulness, containing four items developed based on Davis et al. [16] and Wu et al. [60]. The fourth part is perceived ease of use, containing four items developed based on Davis et al. [16] and Wu et al. [60]. The fifth part is environmental concern, containing 15 items based on the NEP as modified by Dunlap et al. [63]. The sixth part is the value proposition, which contains four items based on Rintamäki et al. [55]. The seventh part is government policy, containing four items. In addition to referring to relevant works by Du et al. [64], Rhodes et al. [65], Wang et al. [36], and Qian et al. [59], we have also considered the subsidy measures currently available in Taiwan to understand whether Taiwanese people are influenced by government policies when buying electric motorcycles. The eighth part contains the basic demographic information of the respondents, including gender, age, education level, and average monthly income. Except for the last part, all questions were answered on a 7-point Likert scale according to the individual’s perceptions or actual situations, with a score of 1 representing strongly disagree and 7 representing strongly agree.
3.3. Sample and Data Collection
Nguyen et al. [66] found that in Vietnam, Generation Z, which is defined as the generation born after 1997, has a positive intention of purchasing green products, having grown up in an environment subject to climate change and environmental pollution. Khan [67] found that in Pakistan, Generation Z, defined as the generation born between 1995 and 2010, in Pakistan has a very high intention to purchase green products. In line with the above findings, this study will target an appropriate sample of young people in Taiwan, aged 18 years and above and below 30 years old, to understand the opinions of Taiwanese people on the issues addressed.
This study used statistical analysis methods to obtain the data through a questionnaire survey, and the SPSS 25.0 and Smart-PLS 3.3.9. The study adopted convenience sampling and conducted an online questionnaire survey (Google Forms). A total of 468 samples were collected, and after excluding the unanswered and incomplete ones, 391 valid samples were obtained. In terms of gender, there were 233 females, constituting the largest number of respondents (59.6%), and 158 males (40.4%). The majority of respondents were aged 18–24 (37.2%) and 25–30 (40.4%) years and were university graduates (68.4%), with a monthly income of less than NT$35,000 (68.3%).
4. Analysis and Results
4.1. Test Results of Measurement Model Evaluation
This study first used validation factor analysis to examine the reliability and validity of the questionnaire and to see if the variables correctly measured their latent variables. To test the degree of correlation among the latent variables and indicators, factor loading was measured. Hulland [68] suggested that the factor loading should be greater than 0.50 to confirm the reliability between the latent variables and the measurement indicators. After confirming good reliability between latent variables and measurement indicators, it is necessary to check the internal consistency of the dimensions, that is, to check the composite reliability (CR). Fornell and Larcker [69] have suggested that the reliability indicator of potential variables should be greater than 0.70. After confirming the consistency of the measures, it is necessary to test whether the scale can effectively measure latent variables. In this case, an average variance extracted (AVE) of 0.50 or higher is used as a criterion to determine whether the scale has convergent and discriminant validity [70]. The Cronbach’s alpha values, CR, and AVE analysis results of the constructs are listed in Table 1. The contents of the table indicate that all dimensions of the questionnaire have met the requirements of convergent validity and composite reliability; therefore, the intrinsic quality of the measurement model is good.
Table 2 compares the correlation coefficient between two variables and the square root of the AVE of each variable. The square root was greater than the correlation coefficient, which is in accordance with the criteria suggested by Hair et al. [70].
4.2. Structural Model Analysis
In this study, the PLS algorithm was further applied to obtain the path coefficients of the variables in the model to understand whether the causal relationships among the variables were valid. Table 3 presents the path coefficients and hypothesis verification of this model. The perceived usefulness and perceived ease of use positively influence attitude toward using. The coefficients of perceived usefulness and perceived ease of use are 0.724 and 0.136, respectively, and the t-values are 17.624 and 2.737, respectively. Thus, both H1 and H2 are supported, and the attitude toward using these positively influences behavioral intention (β = 0.684; t = 14.158). Thus, H3 is supported; value proposition positively influences behavioral intention (β = 0.121; t = 3.654). Thus, H5 is supported; government policy positively influences behavioral intention (β = 0.112; t = 2.836). Thus, H6 is supported, while the correlation coefficient of environmental concern toward attitude toward using is 0.021 (t = 0.843). Thus, the hypothesis is not verified, and H4 is not supported. This indicates that the respondents’ environmental concern does not significantly influence their attitude toward using electric scooters.
Second, this study used PLS-SEM to detect R2 with the aim of discerning the explanatory power of the overall model antecedent variables for the dependent variables. R2 values close to 0.25 are considered low explanatory power; R2 values close to 0.50 are considered moderate explanatory power; and R2 values close to 0.75 are considered significant for model explanatory power [70]. Regarding the overall model set up in this study, in terms of explanatory power, R2 perceived usefulness, perceived ease of use, and environmental concern had 78% effective explanatory power for attitudes toward use and 82% effective explanatory power for behavioral intentions, both of which were greater than 0.75. It follows that the respondents’ predictive power for their attitude toward using electric motorcycles was moderate to high, while government policy and value proposition had 82% effective predictive power for behavioral intention, indicating that government policy and value proposition were the key factors influencing the respondents’ use of electric scooters.
The goodness of fit (GOF) value was used as the basis for testing the fitness of the model; the higher the fitness, the higher the usability of the model. According to Memon and Rahman [71], a GOF value close to 0.10 is considered a weak fit, a GOF value close to 0.25 is considered a moderate fit, and a GOF value close to 0.36 is considered a significant fit. The GOF value of the model is 0.783, indicating that the model has a good fit and is appropriate to be used to measure people’s behavioral intentions toward using electric motorcycles.
This study converged the path coefficients between the variables and summarized the direct and indirect effects as shown in Table 4. The indirect effects of perceived usefulness, perceived ease of use, and environmental concern on behavioral intentions were 0.516, 0.107, and 0.015, respectively. Only the indirect effect of environmental concern on behavioral intentions was insignificant while the other two variables had indirect effects on behavioral intention.
5. Discussion
5.1. Technology Acceptance Model Positively Influences Behavioral Intention
Hypotheses H1, H2, and H3 are supported, which are consistent with the findings of Brandon-Jones and Kauppi [38] and Wolff and Madlener [39], among others. The respondents’ perceived usefulness and perceived ease of use of electric motorcycles positively influenced their attitudes toward using them. The results suggest that to attract customers and ensure their positive intention to use electric motorcycles, it is necessary to make them feel the usefulness and ease of use of electric motorcycles; that is, the quality of electric motorcycles must be effectively improved to meet customers’ daily commuting needs to improve their attitudes toward using electric motorcycles. Further, respondents’ attitudes toward using electric motorcycles positively influenced their behavioral intentions to use them. This is consistent with Dutot et al.’s [40] findings, indicating there is consistency between attitudes and behavioral intentions. From the perspective of environmental protection, electric motorcycles are likely to become a mainstream mode in the future, and if consumers can maintain positive attitudes toward electric motorcycles, they should eventually influence their behavioral intentions toward electric motorcycles.
5.2. Environmental Concerns Do Not Positively Influence Attitudes toward Using Electric Motorcycles
H4 is not supported. The result is inconsistent with the findings of Chen and Zhang [4], Jain et al. [24] and Tong et al. [45]. Based on the results of the study, the respondents’ environmental concerns did not significantly influence their attitudes toward using electric motorcycles. It follows that, presently, the respondents’ perceptions and attitudes toward the environment do not play a significant role in evaluating electric motorcycles or even considering whether to purchase electric motorcycles. Although the respondents agree that electric motorcycles are helpful for environmental protection, they still maintain their existing behavioral intentions. This phenomenon can be understood from the perspective of a social dilemma; each individual in a social dilemma has the tendency to choose self-interested behavior (maintain the existing fuel carrier use behavior without adaptation costs and lower usage costs). This result implies that people’s attitude toward the environment still needs to be improved. Environmental knowledge and attitudes should be instilled in Generation Z consumers.
5.3. Value Proposition Positively Influences Behavioral Intention
H5 is supported, which is in line with the findings of Calza et al. [72] and Kaur et al. [73]. The first requirement for electric motorcycles to impress the public is a reduction in the frequency of energy replenishment and quick, easy recharge; second, in terms of driving and battery safety, motorcycle stability during the ride and leak-proof batteries are imperative requirements for the provision of a fast, convenient, and safe riding experience, which will in turn increase people’s behavioral intentions toward electric motorcycles.
5.4. Government Policy Positively Influences Behavioral Intention
H6 is supported. This result is consistent with the findings of Li et al. [35], Wang et al. [36], and Qian et al. [59]. According to the aforementioned results, government subsidy policy for electric motorcycles positively influences people’s behavioral intentions toward electric motorcycles. This result implies that government subsidies play a significant role in influencing consumers’ purchases of electric motorcycles. In the future, if government subsidies are reduced or even canceled, people’s intention to buy electric motorcycles may be greatly reduced; subsequently, the government will need to design other incentives to increase their purchase intention again. Therefore, at this stage, government subsidies may still be a key factor in increasing people’s intention to purchase electric motorcycles.
6. Conclusions
6.1. Management Implications
Among the overall behavioral patterns of respondents’ behavioral intentions toward electric motorcycles, their perceived usefulness and perceived ease of use have been found to significantly positively influence their attitude toward using electric motorcycles, among which perceived usefulness exerted a more significant influence, whereas environmental concerns did not exert a significant positive influence. The above results imply that people’s overall attitude toward electric motorcycles is mainly based on the practicality of electric motorcycles, that is, whether they can carry things and ride around similar to fuel motorcycles. Further, the ease of riding is also an important consideration for people when evaluating electric motorcycles; meanwhile, the impact of electric motorcycles on the environment may not be a critical factor for Generation Z consumers. It is suggested that future motorcycle manufacturers should strengthen the perceived usefulness and perceived ease of use of electric motorcycles to create values other than daily commuting, including adding digital meters to electric motorcycles so that users can enter the destinations they wish to visit and navigate through the meters and adding a speed camera warning function so that users can reduce the chance of speeding. Furthermore, storage compartments in electric motorcycles should not be neglected because most consumers in Taiwan take their motorcycles with them when they go shopping.
The research results show that the respondents’ environmental concern is difficult to influence their behavioral intentions toward electric motorcycles; that is, even if the respondents have a strong environmental attitude and believe that electric motorcycles can reduce energy consumption and environmental pollution, their behavioral intentions toward electric motorcycles will not be enhanced. Therefore, if an individual’s environmental concern can influence their behavioral intentions toward electric scooters, it is possible to enhance the individual’s social dilemma awareness through environmental education and social consensus construction. The impact of its travel behavior on the environment, so as to achieve the development goal of sustainable transportation.
Additionally, the respondents’ attitude toward using electric motorcycles, value proposition, and government policy have significant positive influences on their behavioral intention toward electric motorcycles, with attitude toward using having the greatest influence. This indicates that if electric motorcycles can meet people’s riding habits and make people consider that riding electric motorcycles is delightful, wise, and even a good choice, then people will be highly likely to make purchases. As for the value proposition, people are extremely likely to care about quick and easy charging and battery safety when choosing electric motorcycles. Therefore, it is recommended that the industry improve the goodness of their products to increase the public’s evaluation of electric motorcycles, create a good reputation, and influence potential consumers to make a purchase. Finally, in terms of government policies, the research results reveal that government subsidies are truly effective in encouraging people’s electric motorcycle purchases. As such, to continue promoting electric motorcycles in the future, subsidies will remain a crucial support measure.
6.2. Research Limitations and Future Research Directions
There are some limitations in the process of this study. The research framework would be more complete if we could expand the research scope in the future. First, this study takes the Gogoro electric motorcycle as an example, which reflects behavioral intentions toward electric motorcycles. The similarities and differences with other environmentally friendly vehicles such as the use of electric scooters, electric vehicles, and hybrid vehicles, as well as the choice and transfer behavior between brands, are still to be studied and clarified in the future. Second, this study did not collect and analyze multi-time tracking data, so it is impossible to explore how the actual behavioral intentions toward electric motorcycles are affected by the “changes” of various motivational factors. Therefore, subsequent research should further expand this psychological model framework to actual behavior, the test of the causal relationship between various psychological factors and behaviors can be used as a scientific basis for electric motorcycle promotion policies or management measures. Next, the participants were Taiwan’s Generation Z; therefore, the extrapolation of the findings is limited. To improve the accuracy of the research results and obtain complete information, subsequent researchers can use this study as a basis and collect data from different countries, regions, and ages to understand the behavioral intentions of Taiwan’s Generation Z toward using electric motorcycles across societies and cultures.
Methodology, X.Z.; Data curation, M.C.; Writing—original draft, M.C.; Writing—review & editing, X.Z. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
The data that support the findings of this study are available from the corresponding author, M.C., upon reasonable request.
The authors declare no conflict of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Overview of the measurements for the reliability test.
| Dimensions | Item | Factor Loadings | Cronbach’s α | CR | AVE |
|---|---|---|---|---|---|
| Behavioral |
1. I think electric motorcycle is worth buying. | 0.817 | 0.928 | 0.962 | 0.883 |
| 2. I intend to ride an electric motorcycle in the future. | 0.932 | ||||
| 3. I would recommend riding electric motorcycle to my friends and relatives. | 0.948 | ||||
| 4. I would like to buy an electric motorcycle in the future. | 0.925 | ||||
| Attitude |
1. I think it feels good to ride an electric motorcycle. | 0.916 | 0.907 | 0.938 | 0.854 |
| 2. I think riding an electric motorcycle is a wise decision. | 0.907 | ||||
| 3. I think riding an electric motorcycle will be enjoyable. | 0.935 | ||||
| 4. I think riding an electric motorcycle is a good choice. | 0.914 | ||||
| Perceived |
1. I think riding an electric motorcycle can improve my quality of life. | 0.829 | 0.912 | 0.926 | 0.875 |
| 2. I think riding an electric motorcycle can solve my daily needs. | 0.817 | ||||
| 3. I think riding an electric motorcycle makes my commute more comfortable. | 0.902 | ||||
| 4. Overall, I think electric motorcycle is practical. | 0.901 | ||||
| Perceived ease |
1. I think it should be easy for me to ride an electric motorcycle. | 0.856 | 0.906 | 0.933 | 0.785 |
| 2. I think riding an electric motorcycle does not require much effort. | 0.876 | ||||
| 3. I think the functions of electric s motorcycle is not very complicated. | 0.869 | ||||
| 4. Overall, it is easy to ride electric motorcycle. | 0.905 | ||||
| Environmental |
1. The total population of the world is approaching the limit of what the earth can support. | 0.801 | 0.782 | 0.774 | 0.764 |
| 2. Humans have the right to change the natural environment to suit their needs. | 0.812 | ||||
| 3. When humans try to interfere with nature, it usually has catastrophic consequences. | 0.719 | ||||
| 4. Human intelligence will ensure that the earth does not become uninhabitable. | 0.842 | ||||
| 5. Humans are currently overexploiting natural resources and the environment. | 0.764 | ||||
| 6. The earth is in fact rich in natural resources if we know how to tap into it. | 0.736 | ||||
| 7. Plants and animals exist primarily to meet human needs. | 0.762 | ||||
| 8. The balance of nature is quite sufficient, and modern industrialization will not destroy such a balance. | 0.749 | ||||
| 9. Despite our power, humans are still subject to the laws of nature. | 0.757 | ||||
| 10. The so-called “great environmental crisis” that humankind is currently facing has in fact been overstated. | 0.792 | ||||
| 11. The earth has very limited resources and carrying space. | 0.793 | ||||
| 12. Humans should dominate nature. | 0.763 | ||||
| 13. The balance of nature is very fragile and can be easily disrupted. | 0.795 | ||||
| 14. Humans can eventually master the workings of nature and control nature. | 0.784 | ||||
| 15. If the present situation continues, we will soon encounter a massive natural disaster. | 0.837 | ||||
| Value |
1. In the case of electric motorcycle, I think swift battery swap is important. | 0.908 | 0.841 | 0.982 | 0.784 |
| 2. In the case of electric motorcycle, I think efficient and durable batteries are important. | 0.925 | ||||
| 3. In the case of electric motorcycle, I think an excellent riding range is important. | 0.752 | ||||
| 4. In the case of electric motorcycle, I think safety is important. | 0.903 | ||||
| Government |
1. I think the government’s offer of free parking for electric motorcycle in public parking spaces will lead to an increase in my intention to buy electric motorcycle. | 0.891 | 0.917 | 0.936 | 0.867 |
| 2. I think the government’s exemption of fuel and license tax on electric motorcycle will increase will lead to an increase in my intention to buy electric scooters. | 0.917 | ||||
| 3. I think if the government can increase the amount of subsidy, I will want to buy an electric motorcycle. | 0.929 | ||||
| 4. I think if the government can provide subsidies for the battery cost of electric motorcycle (such as Gogoro), I will want to buy electric motorcycle. | 0.892 |
Discriminant validity test of all variables.
| BI | AT | PU | PEU | EC | VP | GP | |
|---|---|---|---|---|---|---|---|
| BI | 0.916 | ||||||
| AT | 0.893 | 0.946 | |||||
| PU | 0.837 | 0.876 | 0.928 | ||||
| PEU | 0.752 | 0.752 | 0.743 | 0.886 | |||
| EC | 0.743 | 0.735 | 0.731 | 0.696 | 0.772 | ||
| VP | 0.727 | 0.719 | 0.787 | 0.645 | 0.682 | 0.865 | |
| GP | 0.705 | 0.728 | 0.742 | 0.703 | 0.714 | 0.773 | 0.891 |
Note 1: BI = behavioral intention; AT = attitude toward using; PU = perceived usefulness; PEU = perceived ease of use; EC = environmental concern; VP = value proposition; GP = government policy. Note 2: The diagonal line is the root of the AVE of each dimension, and the non-diagonal line is the correlation coefficient between each dimension.
Verification results of research hypotheses.
| Hypothesis | Standardized Coefficient | Standard Error | t-Values | p-Values | Result | |
|---|---|---|---|---|---|---|
| H1 | Perceived usefulness → attitude toward using | 0.724 | 0.036 | 17.624 | 0.000 | Supported |
| H2 | Perceived ease of use → attitude toward using | 0.136 | 0.040 | 2.737 | 0.001 | Supported |
| H3 | Attitude toward using → behavioral intention | 0.684 | 0.042 | 14.158 | 0.000 | Supported |
| H4 | Environmental concern → attitude toward using | 0.021 | 0.029 | 0.843 | 0.291 | Unsupported |
| H5 | Value proposition → behavioral intention | 0.121 | 0.041 | 3.654 | 0.000 | Supported |
| H6 | Government policy → behavioral intention | 0.112 | 0.035 | 2.836 | 0.002 | Supported |
Direct and indirect effects of variables.
| Latent Variable | Attitude toward Using | Behavioral Intention | ||
|---|---|---|---|---|
| Direct Effect | Indirect Effect | Direct Effect | Total | |
| Perceived usefulness (PU) | 0.724 | 0.516 | - | 1.240 |
| Perceived ease of use (PEU) | 0.135 | 0.107 | - | 0.242 |
| Environmental concern (EC) | 0.024 | 0.015 | - | 0.039 |
| Government policy (GP) | - | - | 0.118 | 0.118 |
| Value proposition (VP) | - | - | 0.121 | 0.121 |
| Explanatory power (R2) | 0.783 | 0.816 | ||
References
1. Woo, J.; Chung, S.; Lee, C.Y.; Huh, S.Y. Willingness to participate in community-based renewable energy projects: A contingent valuation study in South Korea. Renew. Sustain. Energy Rev.; 2019; 112, pp. 643-652. [DOI: https://dx.doi.org/10.1016/j.rser.2019.06.010]
2. The Intergovernmental Panel on Climate Change (IPCC). Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; 1st ed. Edenhofer, O.R.; Pichs-Madruga, Y.; Sokona, E.; Farahani, S.; Kadner, K.; Seyboth, A.; Adler, I.; Baum, S.; Brunner, P.; Eickemeier, B. et al. Cambridge University Press: New York, NY, USA, 2014; pp. 1-1523.
3. Hertzke, P.; Muller, N.; Schaufuss, P.; Schenk, S.; Wu, T. Expanding Electric-Vehicle Adoption Despite Early Growing Pains; McKinsey & Company: New York, NY, USA, 2019.
4. Saghapour, T.; Moridpour, S.; Thompson, R. Sustainable transport in neighbourhoods: Effect of accessibility on walking and bicycling. Transp. A Transp. Sci.; 2019; 15, pp. 849-871. [DOI: https://dx.doi.org/10.1080/23249935.2018.1540502]
5. Schlüter, J.; Weyer, J. Car sharing as a means to raise acceptance of electric vehicles: An empirical study on regime change in automobility. Transp. Res. Part F Traffic Psychol. Behav.; 2019; 60, pp. 185-201. [DOI: https://dx.doi.org/10.1016/j.trf.2018.09.005]
6. Shaukat, N.; Khan, B.; Ali, S.M.; Mehmood, C.A.; Khan, J.; Farid, U.; Majid, M.; Anwar, S.M.; Jawad, M.; Ullah, Z. A survey on electric vehicle transportation within smart grid system. Renew. Sustain. Energy Rev.; 2018; 81, pp. 1329-1349. [DOI: https://dx.doi.org/10.1016/j.rser.2017.05.092]
7. Shaheen, S.; Wong, S. Public Transit and Shared Mobility COVID-19 Recovery: Policy Options and Research Needs; University of California Institute of Transportation Studies: Berkeley, CA, USA, 2021.
8. U.S. Department of Energy. All-Electric Vehicles. 2020; Available online: http://www.fueleconomy.gov/feg/evtech.shtml (accessed on 5 February 2023).
9. Carrese, S.; D’Andreagiovanni, F.; Nardin, A.; Giacchetti, T.; Zamberlan, L. Seek Beautify: Integrating UAVs in the optimal beautification of e-scooter sharing fleets. Proceedings of the 2021 7th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (IEEE MT-ITS); Heraklion, Greece, 16–17 June 2021; [DOI: https://dx.doi.org/10.1109/MT-ITS49943.2021.9529312]
10. Echeverría-Su, M.; Huamanraime-Maquin, E.; Cabrera, F.I.; Vázquez-Rowe, I. Transitioning to sustainable mobility in Lima, Peru. Are e-scooter sharing initiatives part of the problem or the solution?. Sci. Total Environ.; 2023; 866, 161130. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2022.161130] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36566856]
11. Gössling, S. Integrating e-scooters in urban transportation: Problems, policies, and the prospect of system change. Transp. Res. Part D Transp. Environ.; 2020; 79, 102230. [DOI: https://dx.doi.org/10.1016/j.trd.2020.102230]
12. Laa, B.; Leth, U. Survey of E-scooter users in Vienna: Who they are and how they ride. J. Transp. Geogr.; 2020; 89, 102874. [DOI: https://dx.doi.org/10.1016/j.jtrangeo.2020.102874]
13. Kopplin, C.S.; Brand, B.M.; Reichenberger, Y. Consumer acceptance of shared e-scooters for urban and short-distance mobility. Transp. Res. Part D Transp. Environ.; 2021; 91, 102680. [DOI: https://dx.doi.org/10.1016/j.trd.2020.102680]
14. UNU. Global Moped Sharing Market Report 2020. Available online: https://share.unumotors.com/globalmobility-sharing-market-report (accessed on 5 February 2023).
15. Lin, B.; Wu, W. Why people want to buy electric vehicle: An empirical study in first-tier cities of China. Energy Policy; 2018; 112, pp. 233-241. [DOI: https://dx.doi.org/10.1016/j.enpol.2017.10.026]
16. Davis, F.D.; Bagozzi, R.P.; Warshaw, P.R. User acceptance of computer technology: A comparison of two theoretical models. Manag. Sci.; 1989; 35, pp. 985-1003.
17. Marangunić, N.; Granić, A. Technology acceptance model: A literature review from 1986 to 2013. Univers. Access Inf. Soc.; 2015; 14, pp. 81-95. [DOI: https://dx.doi.org/10.1007/s10209-014-0348-1]
18. Venkatesh, V.; Morris, M.G.; Davis, G.B.; Davis, F.D. User acceptance of information technology: Toward a unified view. MIS Q.; 2003; 27, pp. 425-478. [DOI: https://dx.doi.org/10.2307/30036540]
19. Venkatesh, V.; Thong, J.Y.L.; Xu, X. Consumer acceptance and use of information technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Q.; 2012; 36, pp. 157-178. [DOI: https://dx.doi.org/10.2307/41410412]
20. Kim, M.K.; Oh, J.; Park, J.H.; Joo, C. Perceived value and adoption intention for electric vehicles in Korea: Moderating effects of environmental traits and government supports. Energy; 2017; 159, pp. 799-809. [DOI: https://dx.doi.org/10.1016/j.energy.2018.06.064]
21. He, X.; Zhan, W.; Hu, Y. Consumer Purchase Intention of Electric Vehicles in China: The Roles of Perception and Personality. J. Clean. Prod.; 2018; 204, pp. 1060-1069. [DOI: https://dx.doi.org/10.1016/j.jclepro.2018.08.260]
22. Zhang, L.; Chen, L.; Wu, Z.; Zhang, S.; Song, H. Investigating young consumers’ purchasing intention of green housing in China. Sustainability; 2018; 10, 1044. [DOI: https://dx.doi.org/10.3390/su10041044]
23. Junior, S.S.B.; da Silva, D.; Gabriel, M.L.D.; de Oliveira Braga, W.R. The effects of environmental concern on purchase of green products in retail. Procedia Soc. Behav. Sci.; 2015; 170, pp. 99-108. [DOI: https://dx.doi.org/10.1016/j.sbspro.2015.01.019]
24. Jain, V.K.; Dahiya, A.; Tyagi, V.; Dube, P. Predicting factors affecting green purchase intentions towards green products. World Rev. Sci. Technol. Sustain.; 2021; 17, pp. 360-374. [DOI: https://dx.doi.org/10.1504/WRSTSD.2021.117893]
25. Chen, M.; Zhang, W.H. Purchase intention for hydrogen automobile among Chinese citizens: The influence of environmental concern and perceived social value. Int. J. Hydrogen Energy; 2021; 46, pp. 18000-18010. [DOI: https://dx.doi.org/10.1016/j.ijhydene.2020.11.099]
26. Baldissera, C.; Hoppe, A.; Carlini, N.R.B.S.; Sant’Anna, V. Factors influencing consumers’ attitudes towards the consumption of grape pomace powder. Appl. Food Res.; 2022; 2, 100103. [DOI: https://dx.doi.org/10.1016/j.afres.2022.100103]
27. Suki, N.M.; Majeed, A.; Suki, N.M. Impact of consumption values on consumers’ purchase of organic food and green environmental concerns. Soc. Responsib. J.; 2022; 18, pp. 1128-1141. [DOI: https://dx.doi.org/10.1108/SRJ-01-2021-0026]
28. Moshood, T.D.; Nawanir, G.; Mahmud, F.; Mohamad, F.; Ahmad, M.H.; AbdulGhani, A. Why do consumers purchase biodegradable plastic? The impact of hedonics and environmental motivations on switching intention from synthetic to biodegradable plastic among the young consumers. J. Retail. Consum. Serv.; 2022; 64, 102807. [DOI: https://dx.doi.org/10.1016/j.jretconser.2021.102807]
29. Gogoro. Available online: https://www.gogoro.com/tw/ (accessed on 7 February 2023).
30. Tummers, L. Public Policy and Behavior Change. Public Adm. Rev.; 2019; 79, pp. 925-930. [DOI: https://dx.doi.org/10.1111/puar.13109]
31. Tian, J.; Gong, Y.; Li, Y.; Chen, X.; Zhang, L.; Sun, Y. Can policy implementation increase public waste sorting behavior? The comparison between regions with and without waste sorting policy implementation in China. J. Clean. Prod.; 2022; 363, 132401. [DOI: https://dx.doi.org/10.1016/j.jclepro.2022.132401]
32. Sharpe, E.J.; Perlaviciute, G.; Steg, L. Pro-environmental behaviour and support for environmental policy as expressions of pro-environmental motivation. J. Environ. Psychol.; 2021; 76, 101650. [DOI: https://dx.doi.org/10.1016/j.jenvp.2021.101650]
33. Lin, B.; Shi, L. Identify and bridge the intention-behavior gap in new energy vehicles consumption: Based on a new measurement method. Sustain. Prod. Consum.; 2022; 31, pp. 432-447. [DOI: https://dx.doi.org/10.1016/j.spc.2022.03.015]
34. Tsai, C.H.; Shen, Y.H.; Tsai, W.T. Sustainable Material Management of Industrial Hazardous Waste in Taiwan: Case Studies in Circular Economy. Sustainability; 2021; 13, 9410. [DOI: https://dx.doi.org/10.3390/su13169410]
35. Li, W.; Long, R.; Chen, H. Consumers’ evaluation of national new energy vehicle policy in China: An analysis based on a four paradigm model. Energy Policy; 2016; 99, pp. 33-41. [DOI: https://dx.doi.org/10.1016/j.enpol.2016.09.050]
36. Wang, S.; Li, J.; Zhao, D. The impact of policy measures on consumer intention to adopt electric scooters: Evidence from China. Transp. Res. Part A; 2017; 105, pp. 14-26.
37. Fishbein, M.; Ajzen, I. Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research; Addison-Wesley: Reading, MA, USA, 1975.
38. Brandon-Jones, A.; Kauppi, K. Examining the antecedents of the technology acceptance model within e-procurement. Int. J. Oper. Prod. Manag.; 2018; 38, pp. 22-42. [DOI: https://dx.doi.org/10.1108/IJOPM-06-2015-0346]
39. Wolff, S.; Madlener, R. Driven by change: Commercial drivers’ acceptance and efficiency perceptions of light-duty electric vehicle usage in Germany. Transp. Res. Part C; 2019; 105, pp. 262-282. [DOI: https://dx.doi.org/10.1016/j.trc.2019.05.017]
40. Dutot, V.; Bhatiasevi, V.; Bellallahom, N. Applying the technology acceptance model in a three-countries study of smartwatch adoption. J. High Technol. Manag. Res.; 2019; 30, pp. 1-14. [DOI: https://dx.doi.org/10.1016/j.hitech.2019.02.001]
41. Hoang, H.C.; Hoang, T.Q.H.; Chovancová, M.; Jibril, A.B. The theory of planned behavior toward organic food in Vietnam: The moderation of environmental concern. Proceedings of the DOKBAT 2019—15th International Bata Conference for Ph.D. Students and Young Researchers, Tomas Bata University; Zlín, Czech Republic, 6–7 November 2019; Available online: http://dokbat.utb.cz/wp-content/uploads/2020/01/DOKBAT_2019_Conference_Proceedings.pdf (accessed on 15 September 2020). ISBN 978-80-7454-893-2
42. Dunlap, R.E.; Van Liere, K.D. The “new environmental paradigm”. J. Environ. Educ.; 1978; 9, pp. 10-19. [DOI: https://dx.doi.org/10.1080/00958964.1978.10801875]
43. Dunlap, R.E. The new environmental paradigm scale: From marginality to worldwide use. J. Environ. Educ.; 2008; 40, pp. 3-18. [DOI: https://dx.doi.org/10.3200/JOEE.40.1.3-18]
44. Lee, K. Opportunities for green marketing: Young consumers. Mark. Intell. Plan.; 2008; 26, pp. 573-586. [DOI: https://dx.doi.org/10.1108/02634500810902839]
45. Tong, Q.; Anders, S.; Zhang, J.; Zhang, L. The roles of pollution concerns and environmental knowledge in making green food choices: Evidence from Chinese consumers. Food Res. Int.; 2020; 130, 108881. [DOI: https://dx.doi.org/10.1016/j.foodres.2019.108881] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32156344]
46. Wasaya, A.; Saleem, M.A.; Ahmad, J.; Nazam, M.; Khan, M.M.A.; Ishfaq, M. Impact of Green trust and green perceived quality on green purchase intentions: A moderation study. Environ. Dev. Sustain.; 2021; 23, pp. 13418-13435. [DOI: https://dx.doi.org/10.1007/s10668-020-01219-6]
47. Yadav, R.; Pathak, G.S. Young consumers’ intention towards buying green products in a developing nation: Extending the theory of planned behavior. J. Clean. Prod.; 2016; 135, pp. 732-739. [DOI: https://dx.doi.org/10.1016/j.jclepro.2016.06.120]
48. Chen, H.-S.; Tsai, B.-K.; Hsieh, C.-M. The Effects of Perceived Barriers on Innovation Resistance of Hydrogen-Electric Motorcycles. Sustainability; 2018; 10, 1933. [DOI: https://dx.doi.org/10.3390/su10061933]
49. Kuhn, M.; Marquardt, V.; Selinka, S. “Is Sharing Really Caring?”: The Role of Environmental Concern and Trust Reflecting Usage Intention of “Station-Based” and “Free-Floating”—Carsharing Business Models. Sustainability; 2021; 13, 7414. [DOI: https://dx.doi.org/10.3390/su13137414]
50. Xue, W.; Hine, D.W.; Marks, A.D.G.; Phillips, W.J.; Zhao, S. Cultural worldviews and climate change: A view from China. Asian J. Soc. Psychol.; 2016; 19, pp. 134-144. [DOI: https://dx.doi.org/10.1111/ajsp.12116]
51. Xiao, C.; Dunlap, R.E.; Hong, D. Ecological Worldview as the Central Component of Environmental Concern: Clarifying the Role of the NEP. Soc. Nat. Resour.; 2019; 32, pp. 53-72. [DOI: https://dx.doi.org/10.1080/08941920.2018.1501529]
52. Bettencourt, L.; Ulwick, A.W. The Customer-Centered Innovation Map. Harv. Bus. Rev.; 2008; 86, pp. 109-114.
53. Osterwalder, A.; Pigneur, Y.; Bernarda, G.; Smith, A. Value Proposition Design: How to Create Products and Services Customers Want; John Wiley and Sons: Hoboken, NJ, USA, 2014.
54. Bowman, C.; Ambrosini, V. Value creation versus value capture: Towards a coherent definition of value in strategy. Br. J. Manag.; 2000; 11, pp. 1-15. [DOI: https://dx.doi.org/10.1111/1467-8551.00147]
55. Rintamäki, T.; Kuusela, H.; Mitronen, L. Identifying competitive customer value propositions in retailing. Manag. Serv. Qual.; 2007; 17, pp. 621-634. [DOI: https://dx.doi.org/10.1108/09604520710834975]
56. Li, W.; Long, R.; Chen, H.; Chen, F.; Zheng, X.; Yang, M. Effect of policy incentives on the uptake of electric scooters in China. Sustainability; 2019; 11, 3323. [DOI: https://dx.doi.org/10.3390/su11123323]
57. Ye, F.; Kang, W.; Li, L.; Wang, Z. Why do consumers choose to buy electric scooters? A paired data analysis of purchase intention configurations. Transp. Res. Part A Policy Pract.; 2021; 147, pp. 14-27.
58. Jain, N.K.; Bhaskar, K.; Jain, S. What drives adoption intention of electric scooters in India? An integrated UTAUT model with environmental concerns, perceived risk and government support. Res. Transp. Bus. Manag.; 2022; 42, 100730. [DOI: https://dx.doi.org/10.1016/j.rtbm.2021.100730]
59. Qian, L.; Grisolía, J.M.; Soopramanien, D. The impact of service and government-policy attributes on consumer preferences for electric scooters in China. Transp. Res. Part A Policy Pract.; 2019; 122, pp. 70-84.
60. Wu, J.; Liao, H.; Wang, J.W.; Chen, T. The role of environmental concern in the public acceptance of autonomous electric scooters: A survey from China. Transp. Res. Part F; 2019; 60, pp. 37-46. [DOI: https://dx.doi.org/10.1016/j.trf.2018.09.029]
61. Sepasgozar, S.; Hawken, S.; Sargolzaei, S.; Foroozanfa, M. Implementing citizen centric technology in developing smart cities: A model for predicting the acceptance of urban technologies. Technol. Forecast. Soc. Chang.; 2019; 142, pp. 105-116. [DOI: https://dx.doi.org/10.1016/j.techfore.2018.09.012]
62. Mercedes, S.; Natalia, L.M.; Fernando, L.L.; Javier, F. An Extended Planned Behavior Model to Explain the Willingness to Pay to Reduce Noise Pollution in Road Transportation. J. Clean. Prod.; 2018; 177, pp. 144-154.
63. Dunlap, R.E.; Van Liere, K.D.; Mertig, A.G.; Jones, R.E. Measuring Endorsement of the New Ecological Paradigm: A Revised NEP Scale. J. Soc. Issues; 2000; 56, pp. 425-442. [DOI: https://dx.doi.org/10.1111/0022-4537.00176]
64. Du, H.; Liu, D.; Sovacool, B.K.; Wang, Y.; Ma, S.; Li, R.Y.M. Who buys New Energy Vehicles in China? Assessing social psychological predictors of purchasing awareness. Intent. Policy Transp. Res. Part F; 2018; 58, pp. 56-69. [DOI: https://dx.doi.org/10.1016/j.trf.2018.05.008]
65. Rhodes, E.; Axsen, J.; Jaccard, M. Exploring Citizen Support for Different Types of Climate Policy. Ecol. Econ.; 2017; 137, pp. 56-69. [DOI: https://dx.doi.org/10.1016/j.ecolecon.2017.02.027]
66. Nguyen, T.; Nuynh, M.; Le, H.; Tran, N.; Doan, N. Factors Affecting Environmental Consciousness on Green Purchase Intention: An Empirical Study of Generation Z in Vietnam. J. Asian Financ. Econ. Bus.; 2022; 9, pp. 333-343.
67. Khan, K. Factors Influencing Green Buying Behavior of “Generation Z” in Pakistan. Ph.D. Thesis; Capital University: Bexley, OH, USA, 2022.
68. Hulland, J. Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strateg. Manag. J.; 1999; 20, pp. 195-204. [DOI: https://dx.doi.org/10.1002/(SICI)1097-0266(199902)20:2<195::AID-SMJ13>3.0.CO;2-7]
69. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res.; 1981; 18, pp. 39-50. [DOI: https://dx.doi.org/10.1177/002224378101800104]
70. Hair, J.F.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM); 3rd ed. Sage: Thousand Oaks, CA, USA, 2022.
71. Memon, A.H.; Rahman, I.A. Analysis of Cost Overrun Factors for Small Scale Construction Projects in Malaysia Using PLS -SEM Method7. Mod. Appl. Sci.; 2013; 7, pp. 78-88. [DOI: https://dx.doi.org/10.5539/mas.v7n8p78]
72. Calza, F.; Pagliuca, M.; Risitano, M.; Sorrentino, A. Testing moderating effects on the relationships among on-board cruise environment, satisfaction, perceived value and behavioral intentions. Int. J. Contemp. Hosp. Manag.; 2020; 32, pp. 934-962. [DOI: https://dx.doi.org/10.1108/IJCHM-09-2019-0773]
73. Kaur, P.; Dhir, A.; Talwar, S.; Ghuman, K. The value proposition of food delivery apps from the perspective of theory of consumption value. Int. J. Contemp. Hosp. Manag.; 2021; 33, pp. 1129-1159. [DOI: https://dx.doi.org/10.1108/IJCHM-05-2020-0477]
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
1 Department of Accounting, School of Business, Nanjing University, Nanjing 210093, China
2 Department of Accounting, Jiaxing University, Jiaxing 314001, China