Proceedings of the 51st Hawaii International Conference on System Sciences | 2018

Understanding the Intention to Use Commercial Bike-sharing Systems: An Integration of TAM and TPB

Ying Yu Wenjie Yi Yuanyue Feng College of Management College of Management College of Management Shenzhen University Shenzhen University Shenzhen University [email protected] [email protected] [email protected]

Jia Liu College of Management Shenzhen University [email protected]

Abstract economy (i.e., collaborative finance, peer-to-peer accommodation, peer-to-peer transportation, on- Commercial bike-sharing system is growing demand household services, on-demand professional rapidly as a critical form of the sharing economy. services) could generate a revenue of $335 billion Although past research has discussed the design and worldwide by 2025 [40]. It is noted that the surge of operation of commercial bike-sharing systems, there sharing economy is related to the pursuit of better have been few studies examining the factors value distribution of the supply chain [35], reduction motivating the use of such systems. This study of ecological impacts [57], technology advancement integrates the technology acceptance model (TAM) and finally, users’ changing attitudes towards product and the theory of planned behavior (TPB) to develop ownership and their desire for social connections [12]. a holistic model to explain the intention to use The sharing economy manifests in various forms, commercial bike-sharing systems. The PLS-SEM such as peer-to-peer (or P2P) rental market, results from a survey with 286 users reveal that the accommodation sharing, and the vehicle sharing [40]. intention to use commercial bike-sharing systems is A prominent business model of the sharing economy, positively affected by perceived usefulness of the the commercial bike-sharing systems, has emerged in system, attitude toward bike-sharing and perceived recent years as a popular way of public transportation behavioral control. Further, we find that attitude [18]. For the society, commercial bike-sharing toward the bike-sharing is positively affected by system meets the theme of sustainable development perceived usefulness and perceived ease of use of the because of convenience, lower prices, and system. Beyond our expectation, subjective norm has environmental protection [24, 29]. Consequently, no significant effect on the intention to use. many commercial bike-sharing systems are being Implications and directions for future research are established to satisfy the need. One example of also discussed. commercial bike-sharing system is the CitiBikes, where there are more than 85,000 active users [57]. Keywords: Commercial bike-sharing system; Another example is , a large commercial bike- Intention to use; Technology acceptance model; sharing system in China. In OFO, there are on Theory of planned behavior average 4.4 million active users per day [1]. Past research has noted that the adoption of the bike-sharing systems is crucial for the sustainability of the bike-sharing companies [39]. However, 1. Introduction research has been primarily devoted to the design and operation of the commercial bike-sharing systems [15, Rather than buying their favorite products or 56]. There is little research examining individual’s services, nowadays individuals tend to pay to intention to use such systems. Without a clear temporarily access or share them, which is called the understanding of the drivers for the system usage, “sharing economy” [50]. PricewaterhouseCoopers companies are difficult to attract enough users and has foreseen that five main sectors of sharing will lose in the market competition.

URI: http://hdl.handle.net/10125/49969 Page 646 ISBN: 978-0-9981331-1-9 (CC BY-NC-ND 4.0)

Within the limited studies of the intention to use However, past literature on commercial bike- bike-sharing systems, some researchers borrowed the sharing systems has largely dwelled on topics such as technology acceptance model (TAM) [25] to examine the design and operation of the commercial bike- the effects of two system features (i.e., perceived sharing systems [15, 56], as well as obstacles for the usefulness (PU) and perceived ease of use (PEOU)) sustainability of the system, such as theft and on users’ adoption of bike-sharing systems [41, 49]. sabotage [2], static rebalancing or repositioning Apart from that, other scholars adopted the theory of problem [15, 56]. There have been a limited number planned behavior (TPB) [5] to examine the influence of studies trying to examine the motivators for the of users’ attitude, subjective norms and perceived usage of such systems. For instance, [13] suggested behavioral control on their intention to use bike- that people who use the bike-sharing systems hold sharing systems [46]. Past research has suggested that the beliefs that the sharing of bikes makes them more neither TAM nor TPB alone is able to provide convenient to complete a short-distance travel inside consistently superior explanations or behavioral the city, and help reduce traffic congestion as well as predication [16]. Although TAM has incorporated the environmental pollution. Similarly, [9] suggested that two individual perceptions of system users, it has not convenience and the desire to avoid theft of private accounted for the social influence in the users’ bikes are the key motivators for the system use. adoption of new technology [19]. In the context of Moreover, [59] suggested that mechanisms such as commercial bike-sharing systems, users might not adding stations and real-time bikes, improving bike only be motivated to use the system by their maintenance and locking mechanisms, are needed to perceptions of the values and efforts associated with foster the use of bike-sharing service. As a synthesis, the use, but also being prompted by the desire for [31] argued that people are willing to use sharing social connections with their peers [51]. Therefore, bikes as there is no need to consider the either TAM or TPB alone is unable to provide a responsibilities and costs associated with owning a complete picture for understanding the users’ private bike. However, research on the antecedents intention to use bike-sharing systems. In view of that, for the use of bike-sharing systems remains rare and we purposefully integrate the TAM and TPB to scattered, partly because the bike-sharing system is explore and examine the antecedents of commercial still at its early development stage [64]. bike-sharing system usage. We review prior literature to identify critical psychological factors and other 2.2. Technology acceptance model (TAM) and context related literatures to theorize specific theory of planned behavior (TPB) relationships among the model constructs. In the information systems discipline, the 2. Literature review intention to use different kinds of information systems has been regarded as one of the most important research topics. Two theoretical lenses (i.e., 2.1.Intention to use commercial bike-sharing TAM and TPB) rooted from the classical theory of system reasoned action (TRA) [34] in the social psychology domain are frequently adopted to examine the Commercial bike-sharing system is a new form of intention to use different information systems. the sharing economy. It is a network of bicycles that The TAM model, first introduced by [25], tries to are provided in subway stations, bus stops, campus, explain why individuals choose to use or not to use residential areas, commercial areas and public service specific techniques by the “Perceived Usefulness” areas [28]. According to [47], sharing bikes which do (PU) and “Perceived Ease of Use” (PEOU). not belong to any individual will set aside whether Information systems scholars have relied on the TAM use in a region for short-term communal. For the to understand individual’s intention to use a variety system users, commercial bike-sharing systems of information systems, such as online games [44], enable them to use a healthy, enjoyable, and online learning [3, 22], and social media [55]. relatively inexpensive door-to-door transport mode. Recently, the TAM has also been adopted to examine More importantly, for the companies operating such the intention to use bike-sharing systems. For systems, a large number of active users could example, [49] adopted an extended technology generate supreme market revenues for them. acceptance model (TAM) and found that customer’s Therefore, the frequent use of commercial bike- attitude toward the smart bike-sharing systems and sharing systems by a large amount of individuals is perceived usefulness positively affect their use also crucial for the sustainability of such business intention. [41] also discovered that perceived quality model [39]. (perceived usefulness) and perceived convenience

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(perceived ease of use) foster the adoption of public 3.2. Hypotheses development bicycle sharing systems through perceived value. Apart from TAM, the theory of planned behavior 3.2.1 Hypotheses about TAM. (TPB) suggests that individuals’ technology adoption intention is jointly determined by their attitudes, The first factor in TAM is perceived usefulness subjective norms and perceived behavior control. (PU), which is defined as the degree to which a Similar to the TAM, the TPB has also been adopted person believes that the use of a particular system or to study the intention to use a variety of information technology will improve the performance of a systems [5, 6, 20]. In the area of bike-sharing, TPB particular activity [10, 25]. It has been found in a has been adopted as well. For instance, [61] based on study of bike-sharing system of a university campus TPB to indicate that cycling intentions are related to that the bike-sharing program can help eliminate the positive cycling experience, willingness to accept car need for additional parking, greenhouse emissions restrictions, and negative attitudes towards cars. [46] and traffic congestion, thereby helping reduce applied the TPB and found that the tourists’ intention resource consumption and nurture a greener to use sharing bikes for holiday cycling is positively environment in campus [8]. Furthermore, cycling affected by pro-cycling attitudes, interest in bicycle promotes a healthy lifestyle and improves the health technology, cycling experience, and perceived of person. According to TAM and related literature, cycling ease. attitude and behavioral intention can be fostered by It is noted that TAM or TPB alone could not perceive usefulness [52]. When people feel that the provide consistently superior behavior predictions commercial bike-sharing systems are useful and [16]. And there have been some empirical supports beneficial for them, they will develop a positive view for the better exploratory power with the integration toward the bike-sharing system, and be more willing of TAM and TPB [51]. Nevertheless, in the context to use the system. Hence, we postulate of bike-sharing systems, there is yet to be a study which can integrate these two theoretical lenses into a H1: Perceived usefulness positively influences holistic model. Hence, this study aims to fill this gap the intention to use bike-sharing system. by integrating the TAM and TPB into a holistic H2: Perceived usefulness positively influences research model to predict the intention to use the the attitude toward the bike-sharing system. commercial bike-sharing systems. Another important variable in TAM is the 3. Research model and hypotheses perceived ease of use (PEOU). Perceived ease of use development is regarded as the extent to which a person believes that utilizing the technology could be effortless [25, 53]. In the context of bike-sharing systems, perceived 3.1. Research model ease of use is defined as the level at which users believe that the use of the bike-sharing system is free Drawing on the literature described above, we of effort. Prior works have indicated that perceived develop a model to explain individual’s intention to ease of use has positive effect on users’ attitude [17, use bike-sharing systems as shown in Fig. 1. 43, 45]. Bike-sharing system just requires the users to have smart phones, and the unlocking and payment processes are very simple with just a few clicks. Perceived H1 TAM usefulness Hence, the less mental efforts needed by the system, H2 the more likely users will have a positive attitude H5 towards bike-sharing systems. H4 Attitude Intention Besides, according to TAM, perceived ease of use also affects the perceived usefulness, which Perceived H3 ease of use means that if user feels the system is easy to use, they will feel that the system is useful [7, 26, 54]. Hence, H6 H7 TPB we hypothesize Subjective Perceived norm behavior control H3: Perceived ease of use positively influences the attitude toward the bike-sharing system. H4: Perceived ease of use positively influences Figure 1. Research model the perceived usefulness of the bike-sharing system.

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3.2.2 Hypotheses about TPB users’ intention to use routes with transfers. In an empirical study of the bike-sharing system, the result Attitude, referred to the individual’s positive or showed that students’ intention to use the system is negative feeling to the specific behavior, is a key positively associated with their perceived behavioral antecedent in TPB that has been empirically shown to control [63]. The more the control an individual feel promote intention[48]. In this study, we define about when using bike-sharing system, the more attitude as the user’s preferences when using bike- likely he or she will do so. Hence, we hypothesize sharing system. Many studies show that there is a relationship between attitude and intention [32]. It’s H7: Perceived behavioral control positively believed that if people have a positive attitude toward influences the intention to use bike-sharing system. a specific situation, it will positively affect their intention, otherwise it will negatively affect their We also include gender, age, education level, intention [51, 58]. An empirical survey conducted by and occupation in our model as control variables that [46] suggested that tourists’ intention for might affect the intention to use commercial bike- consumption of bike-sharing system is positively sharing systems. affected by their attitude towards it. When people hold positive feeling toward the adoption of bike- 4. Research methodology sharing systems, they tend to more frequently use the systems. Hence, we hypothesize We use the quantitative survey method to collect empirical data and test the above hypotheses. Both H5: Attitude positively influences the intention online and offline survey questionnaires were sent to use bike- sharing system. out. Details of the online and offline surveys, including the data collection and measurement of Subjective norm is defined as an individual’s variables are provided below. perception of social pressures towards the specific behavior where the pressures come from parents, 4.1. Data collection and sample friends, culture and public institutions [5]. Many studies have showed that subjective norms have a positive influence on a person’s intention. [27] and [4] The online survey questionnaires are distributed both suggested that the subjective norms are positive by posting a microtask on the www.zbj.com, one of with the car use as people would be likely to think the largest crowdsourcing platforms in China. The others expect them to travel by car. Furthermore, [42] microtask contained a link to the online questionnaire stored in a questionnaire service website called thought that subjective norms only influenced the 1 decision to commute by bikes in short distance. In the sojump (https://sojump.com) . For the offline paper- context of bike-sharing system, if using bike-sharing based survey questionnaires, we randomly distributed system is seen as socially desirable behavior, the them to the students in a university of Southern China individual is more likely to use it. That is to say, and to the workers in a high-tech park near the when people perceived a strong subjective norm of university. When we distributed the questionnaires using bike-sharing system from others in the society, offline, we only informed the respondents about the they will be more likely to use it to conform to the purpose of this study. Both the online and offline norm. Hence, we postulate respondents received 5 RMB each for participation. The survey questionnaires consisted of 3 H6: Subjective norm positively influences the sections. First, the participants were asked to answer intention to use bike- sharing system. some cognitive questions about the commercial bike- sharing systems. Specially, we designed a question to Perceived behavioral control (PBC) refers to the ask whether the respondents have used any degree of capability and control that a person commercial bike-sharing systems before. Those perceives over performing a specific behavior [5]. It respondents who haven’t used any commercial bike- depends on the individual’s beliefs about the power sharing systems were excluded from the data analysis. of both situational and internal factors to promote In addition, we designed a multiple-choice question behavior [62]. When people think that they have to ask the participants about the color of the OFO more resources and opportunities and less expected bike-sharing system, one of the largest commercial obstructs, their perceived behavior control is stronger. bike-sharing systems in China, and set the correct As was proposed in [23], perceived behavioral control (PBC) was used to explain public transport 1Sojump.com is one of the largest and well-known online questionnaire service website in China.

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answer in the first option. We set this question to Gender Male 107 37.4 check whether the participants did really know about Female 179 62.6 the commercial bike-sharing systems. Those Age < 18 2 0.7 respondents who were unable to provide the correct 18-24 229 80.1 answer were also excluded from data analysis. This 25-31 40 14 32-38 7 2.4 question of OFO color was used to target more >38 8 2.8 accurately on people who were familiar with the commercial bike-sharing systems and thus enhance Education Below high 1 0.3 level school the validity of our survey data. Next, this section is High school 9 3.1 followed by questions that measure the theoretical /college constructs. The respondents were asked to rate their University 185 64.7 level of agreement with the statements regarding their Master/PhD 91 31.8 perceptions of the commercial bike-sharing system Occupation Students 205 71.7 using a Five-point Likert scale response format. It Company staffs 49 17.1 should be noted that we designed two reverse Government 11 3.8 indicators to check whether the respondents have staffs paid serious attention to the survey questions. The Self-employed 8 2.8 reverse items were used to identify and exclude those Others 13 4.5 respondents who intentionally picked up the same Likert value throughout the survey and thus do not 4.2. Measurement contribute appropriately to the survey. Specifically, when measuring the latent variable Perceived Ease of All measurement scales and items are adapted Use, the statements were set as “I think that using from existing literature as much as possible. In this sharing bikes would be easy.” “Learning how to use paper, perceived usefulness and perceived ease of use the sharing bikes is complicated. (R)”. “For me, using were both measured with five items adapted from the sharing bikes skillfully is very simple.” “For me, [25]. Subjective norm was measured by five items the procedure of using the sharing bikes is very adapted from [5]. For the perceived behavioral clear.” “Overall, using the sharing bikes is control, we used five items adapted from [5, 21] to complicated (R).” The second one and the last one measure it. Besides, the measurements for the attitude were both reverse items. Then, we asked the same toward the bike-sharing system consisted of 5 items question regarding the color of the OFO sharing adapted from [5, 21]. Lastly, for the dependent bikes again. But this time, we set the correct answer variable of intention, five items adapted from [11, 25] to the third option. This question was also used to are used. All items used a five-point Likert-scale of 1 check whether the respondents paid serious attention (strongly disagree) to 5 (strongly agree). to the survey questions. Those respondents who answered these questions wrongly were excluded 5. Data analysis and results from data analysis. The last section was designed to collect respondents’ demographic data, including 5.1. The reliability and validity analyses their age, gender, educational level and their current jobs. We employed the SPSS statistical software 22.0 The online survey, which yielded 315 responses, to conduct the reliability and validity analyses. and the offline survey, which yielded 40 responses, According to Table 2, the Cronbach’s Alpha (CA) of were received in one week. For the online/offline all the constructs are larger than the threshold of 0.7, surveys, we deleted incomplete responses with which shows high reliability of all the constructs. missing data and responses that were finished within After the reliability analysis, we conducted 65seconds.And we also excluded respondents who confirmatory factor analysis to test the validity of the haven’t used the commercial bike-sharing systems constructs. The KMO value is 0.93, which means that and who failed to provide correct answers to the two the survey items are suitable for factor analysis. As questions regarding the color of OFO sharing bikes. shown in Table 2, all the indicators loaded highly on This process results in a final sample size of 286 their respective factor (larger than 0.5). Some items responses. Demographics of the respondents in the (PU1, PU2, PEOU1, PEOU2, SN1, SN4,SN5, PBC1, final sample are depicted in Table 1. PBC2) were deleted due to their low loadings on respective constructs. We also assessed convergent Table 1 Demographic information validity by examining composite reliability (CR) and Measure Item Frequency Percent(%) average extracted variance (AVE). The results in

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Table 2 demonstrate that each construct in the model The path coefficients and their significance levels has values of CR greater than 0.7 and a value of AVE are shown in Fig 2. Most path coefficients had greater than 0.5. Thus, all constructs satisfied the acceptable statistical significance level while the path criteria, demonstrating sufficient convergent validity. from subjective norm to intention is shown to be non- significant. Table 2 Construct reliability and validity Firstly, the effect of perceived usefulness on tests people’s attitude towards commercial bike-sharing Construct Item Factor CA CR AVE systems was 0.66 in the 0.001 level. This showed that loading H2 was fully supported. The p-values of the effects Perceived PU3 0.73 0.93 0.96 0.88 of perceived ease of use on perceived usefulness and usefulness PU4 0.74 the attitude are both less than 0.001. Hence, H3 and PU5 0.62 H4 were supported. When it comes to the effect of Perceived PEOU3 0.57 0.76 0.85 0.67 different variables on intention, as we can see in ease of PEOU4 0.53 Figure 2, the effect of perceived usefulness on use PEOU5 0.87 people’s intention to use the bike-sharing system and Attitude ATT1 0.74 0.93 0.95 0.79 the effect of attitude on the intention to use were both ATT2 0.81 ATT3 0.77 significant in the 0.001 level. Thus, H1 and H5 were ATT4 0.82 fully supported. Perceived behavior control showed ATT5 0.75 lower significance on the intention (p<0.05), but it Subjective SN2 0.89 0.80 0.92 0.85 also can be said that this effect was significant. That norm SN3 0.84 is to say, H7 was supported. Beyond our expectation, Perceived PBC3 0.79 0.82 0.90 0.75 subjective norm showed non-significant effect on behavior PBC4 0.81 people’s intention to use, which means that H6 was control PBC5 0.84 rejected. We would explain this unexpected result in Intention IU1 0.70 0.89 0.92 0.70 the discussion section. to use IU2 0.68 IU3 0.77 5.4. Post-hoc regression analysis IU4 0.72 IU5 0.69 To justify our argument that the TAM in

combination of TPB possess stronger explanatory 5.3. Hypotheses testing power for the use of commercial bike-sharing systems, we conducted two stepwise regression After validating the measurement model as analyses using SPSS 22.0 (see Table 3). The first reported in the previous section, the path (structural) stepwise regression analysis introduced the constructs model was tested using PLS-SEM method with of TAM into the model in the first step, which is SmartPLS 2.0 software. The bootstrapping approach followed by the constructs of TPB in the second step. was used, with cases of 286 and samples of 5000. The second stepwise regression analysis brought in the constructs of TPB into the model in the first step, Perceived 0.43*** TAM then the constructs of TAM were added. The Usefulness regression results showed that the changes of R R2=0.24 0.66*** square in both stepwise regression analyses were 0.34*** 0.49*** Attitude Intention 2 2 significant, indicating that our argument for the R =0.62 R =0.62 integration of TAM and TPB received support. Perceived 0.22*** ease of use Table 3 Results of stepwise regressions 0.04 0.09* TPB Step R Change of Significance of R square R square square change Subjective Perceived Step 1 of 1 0.59 0.03 0.00 norm behavior control Step 2 of 1 0.62 Step 1 of 2 0.52 0.10 0.00 Significant path (p<0.05) Step 2 of 2 0.62 Non-significant path *p<0.05, **p<0.01, ***p0.001 6. Discussion Figure 2. PLS-SEM Results

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Our study presented and validated a multi-facet Findings of this study theoretically contribute to model for the usage of commercial bike-sharing the current literature in several ways. First, this study systems. With empirical analysis, several lessons can enriches the literature of sharing economy by be drawn. unraveling the antecedents (motivators) for the The findings suggest that perceived usefulness is individual’s intention to use a new business model of a significant factor in predicting the intention to use sharing economy, that is, commercial bike-sharing commercial bike-sharing systems. The results systems. Past literature has started to investigate the corroborate with previous research conducted on individual’s intention to adopt different forms of YouBike in Taiwan [19]. Since the costs are sharing economy, such as the crowdsourcing important factors in bike-sharing systems adoption platforms [66], the collaborative buying [38], and the [46], if users find the commercial bike-sharing car-sharing systems [30]. This study contributes to systems are convenient and can save time for them, this line of research by exploring and examining the they are more likely to adopt and utilize the systems, behavioral factors underlying the adoption of and their attitudes towards the systems are more commercial bike-sharing systems. positive. Moreover, riding bikes fits well into a Second, this study enriches the technology healthy and connected lifestyle. And the communities adoption literature by offering empirical supports for can see decreased CO2 emissions which increases the the better explanatory power of integrating the TAM environmental quality of city [36]. All these various with TPB in the context of commercial bike-sharing values could the use of commercial bike- systems. Past research has indicated that the TPB is sharing systems. complementary to the TAM in enhancing the Additionally, results of this study suggest that prediction of new technology adoption [51]. This perceived ease of use positively affect perceived study offers empirical supports for this theoretical usefulness, which is in line with that of [54] studies. argument by showing a greater explanatory power of Besides, our findings have also confirmed the the integration of TAM and TPB in predicting the use argument that if users do perceive commercial bike- of commercial bike-sharing systems. sharing systems as being easy to use, they will Third, this study further develops the TPB in the develop a positive attitude toward the systems. This context of commercial bike-sharing systems by result also conforms to findings of prior works [17, discovering the insignificant role played by 45]. Indeed, an easy-to-use interface could positively subjective norm on use intention. It is found that at influence users’ preferences whereas difficulties and the infant development stage when the commercial obstructs encountered in the adoption process can bike-sharing systems are not widely received by the lead to user’s resistance. crowd, the subjective norms might not be a As shown in Table 5, not all TPB components significant predictor for system usage. This study were significant predictors for the usage intention. contributes to the TPB by identifying the potential Firstly, attitude positively influences the intention to role played by the development stages of new use, which was also proved in previous studies [33]. technology in its adoption, thereby paving the way Secondly, perceived behavior control is found to for future research. have a positive effect on the use intention, which is Practically, the findings of our study have consistent with [37]. Finally, beyond our expectation, important implications for commercial bike-sharing subjective norm has no significant effect on people’s systems providers by offering valuable information intention to use the commercial bike-sharing systems. about how to improve people’s intention to use such The same conclusion also appeared in other contexts a system. First, we found that perceived ease of use in previous studies [14]. The insignificant result positively influences the attitude towards the systems might be due to the insufficient information regarding and the use intention. Therefore, the bike-sharing the commercial bike-sharing systems provided by the operating companies should carefully design the operating companies. Another reason might be that, usage procedures to make it as simple as possible, so sharing bikes has just appeared in recent years. Thus, as to reduce people’s use disorders. For example, the respondents’ family members, friends, and colleagues operating companies should allow their users to might know little about the commercial bike-sharing access the systems and pay for the bill via existing systems. Thus, they would not exert social pressures social platforms like WeChat or Alipay, instead of for the respondents to use the bike-sharing systems. requesting users to download and install an independent APP. Second, perceived usefulness is 7. Implications also found to foster the attitude and use intention. Therefore, the companies should repair the sharing bikes regularly to guarantee their performance.

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Besides, the operating companies should put more efforts on communicating the notion of usefulness of 9. References sharing bikes to the users. Apart from that, the findings showed that perceived behavior control also has a positive effect on people’s use intention. Hence, [1] "The Number of Users' of ofo Accounted for 60%, the operating companies should enhance the users’ Ranking First. The Total Number of Its Users Exceeds autonomy in their use of the commercial bike-sharing for More Than Six Months" systems. One thing the companies could do is to http://www.sohu.com/a/166999034_571168 on 2017.08.24. ensure that sufficient number of sharing bikes is [2] China Daily. "Thefts and Sabotage a Problem for reachable. So that when people need a bike for a China’s Bike-Sharing System." Retrieved from short distance travel, they can find one quickly. In http://www.chinadaily.com.cn/china/2016/12/18/content_2 addition, the companies should delegate more rights 7700047.htm on 2017.06.15. for the users to decide on which bikes they want to [3] Abdullah, F., and Ward, R. "Developing a General use, and how they want to use it. The companies Extended Technology Acceptance Model for E-Learning could also offer more types of bikes as well as more (Getamel) by Analysing Commonly Used External payment/pricing options to enhance the sense of Factors," Computers in Human Behavior (56), 2016, pp. behavioral control of the users. 238-256. [4] Abrahamse, W., Steg, L., Gifford, R., and Vlek, C. 8. Limitation and future research "Factors Influencing Car Use for Commuting and the Intention to Reduce It: A Question of Self-Interest or This research has some limitations that we should Morality?," Transportation Research Part F Traffic Psychology & Behaviour (12:4), 2009, pp. 317-324. acknowledge. First, most of our survey respondents are students in university. Although student sample [5] Ajzen, I. "The Theory of Planned Behavior," might represent an appropriate subject for the Organizational behavior and human decision processes examination of IT usage [65], future research should (50:2), 1991, pp. 179-211. try to collect data from different types of individuals [6] Ajzen, I. "Perceived Behavioral Control, Self-Efficacy, (e.g., workers) to further enhance the generalizability Locus of Control, and the Theory of Planned Behavior," of the findings. Journal of Applied Social Psychology (32:4), 2002, pp. Second, our study was conducted in China, in 665-683. which the intention to adopt commercial bike-sharing [7] Amin, H. "Factors Affecting the Intentions of system might be as well affected by some other Customers in Malaysia to Use Mobile Phone Credit Cards," cultural or institutional factors, such as the face- Management Research News (31:7), 2008, pp. 493-503. saving culture or the green promotion policies by the [8] Ashley, J. "Bike Sharing as Alternative Transportation government. Future research should try to take these at Bridgewater State University," Undergraduate Review aspects into account. (8:1), 2012, pp. 16-25. Finally, our study is based on cross-sectional data. With cross-sectional data, we can only take a [9] Bachand-Marleau, J., Lee, B. H. Y., and El-Geneidy, A. snapshot of this model. A stricter test of our M. "Better Understanding of Factors Influencing Likelihood of Using Shared Bicycle Systems and arguments, however, could be achieved by using a Frequency of Use," Transportation Research Record longitudinal study design. As the bike-sharing Journal of the Transportation Research Board (2314:-1), systems are still at the infancy stage, as technologies 2012, pp. 66-71. become more mature, there may be some different findings. Therefore, by using a longitudinal study in [10] Bandura, A. "Self-Efficacy Mechanism in Human Agency," American Psychologist (37:2), 1982, pp. 122-147. the future, we could investigate new results, thus providing more insight into the phenomenon of using [11] Bhattacherjee, A. "Understanding Information commercial bike-sharing system. Systems Continuance: An Expectation-Confirmation Model," Mis Quarterly (25:3), 2001, pp. 351-370. Acknowledgements [12] Botsman, R., and Rogers, R. "What's Mine Is Yours: How Collaborative Consumption Is Changing the Way We The work described in this paper was financially Live," New York: Harper Collins, 2010. supported by the National Natural Science [13] Castillo-Manzano, J. I., and Sánchez-Braza, A. "Can Foundation of China grant (#71702111, 71402098, Anyone Hate the Bicycle? The Hunt for an Optimal Local 71502114, 71602122, 71701134), and the Natural Transportation Policy to Encourage Bicycle Usage," Science Foundation of Guandong Province grant (# Environmental Politics (22:6), 2013, pp. 1010-1028. 2014A030310314).

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