Next Article in Journal
Integration of VIS–NIR Spectroscopy and Multivariate Technique for Soils Discrimination Under Different Land Management
Previous Article in Journal
Deep Learning and Remote Sensing for Restoring Abandoned Agricultural Lands in the Middle Volga (Russia)
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Land
Volume 13
Issue 12
10.3390/land13122055
land-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Determinants of Dockless Bicycle-Sharing Adoption and Usage Intensity for Commuting and Errands: Evidence from Disadvantaged Neighborhoods

by
Hongyu Wang
,
Yu Dong
and
Liang Ma
*
College of Urban and Environmental Sciences, Peking University, No. 100, Zhongguancun North Street, Haidian District, Beijing 100871, China
*
Author to whom correspondence should be addressed.
Land 2024, 13(12), 2055; https://doi.org/10.3390/land13122055
Submission received: 24 October 2024 / Revised: 17 November 2024 / Accepted: 19 November 2024 / Published: 30 November 2024
Browse Figures
Review Reports Versions Notes

Abstract

:
The emergence of dockless bicycle sharing has transformed urban transportation, particularly in China, by offering a flexible and accessible travel option. However, understanding the factors driving its adoption and usage in disadvantaged neighborhoods is crucial, as these areas often face unique mobility challenges. This study explores these determinants, providing a more comprehensive analysis than prior research by focusing specifically on disadvantaged communities. Using survey data from four such neighborhoods in Xi’an, China, we apply Structural Equation Modeling to investigate the factors influencing individuals’ decisions to adopt and intensively use dockless bicycle sharing for commuting and errands. The results reveal key determinants, including psychological factors, demographic characteristics, and spatial and social contexts, and their interaction mechanisms. Attitudes are found to have a substantial impact on bicycle-sharing behavior for both commuting and errands, while social norms and perceived behavioral control (PBC) mainly influence usage for errands. Interestingly, PBC affects adoption but not usage frequency. The findings also highlight that proximity to schools, subways, and neighborhood aesthetics positively correlate with bicycle-sharing adoption for errands, whereas bicycling infrastructure significantly influences usage intensity. However, none of the neighborhood environment factors were found to significantly affect adoption for commuting purposes. These insights are especially valuable for developing targeted strategies to promote bicycle sharing as a sustainable transportation solution in disadvantaged neighborhoods, where improved access can significantly enhance mobility and quality of life.

1. Introduction

Shared bicycles offer a sustainable solution to urban transportation challenges, including alleviating traffic congestion, reducing air pollution, and decreasing dependence on fossil fuels [1]. They contribute to transportation equity [2] and public health improvements [3], while also lowering personal transportation expenses and supporting the development of multimodal transportation systems [4]. Operating on a membership or pay-per-use basis, bicycle sharing embodies the principles of being economical, efficient, and eco-friendly [5]. Its user-friendly approach, which eliminates concerns such as maintenance, storage, and theft risk, attracts a diverse user base, including individuals transitioning from other active travel modes [6,7,8]. Notably, shared bicycles offer a cost-effective alternative for many low-income individuals deterred by the expenses associated with bicycle ownership and maintenance [9]. It can be seen that the shared bicycle system is vital for improving transportation equity, public health, and environmental sustainability.
China, as a global leader in dockless bicycle-sharing adoption, presents a unique and relevant context for this research. The localization of dockless bicycle sharing in China presents significant advantages stemming from the country’s unique context, characterized by a high land mix rate, population density, and cultural acceptance. This environment fosters the ideal conditions for developing bicycle-sharing systems that address the “first and last mile” of mobility, serving as an alternative transportation mode [10]. Particularly noteworthy is the emergence of a “cycling–transit–cycling” travel chain model, effectively resolving the challenge of active travel distance [4]. This advancement plays a vital role in enhancing transportation equity in China, notably by improving access for disadvantaged groups [11]. Furthermore, the superior convenience, enhanced integration with public transport, and extensive urban coverage of dockless shared bicycles have propelled their widespread adoption, surpassing docked counterparts and reshaping the Chinese market dynamics [12,13]. This trend is gaining greater prominence in China, both now and in the foreseeable future, which is why we have focused our research on dockless bicycle sharing.
Over the past decade, there has been a growing interest in studying bicycle-sharing systems to reduce motorized traffic and promote sustainable transportation policies. Despite numerous studies examining bicycle-sharing usage, they frequently present mixed results regarding the determinants influencing usage patterns. Furthermore, there remains a limited academic understanding of the multifaceted aspects of bicycle-sharing travel behavior, particularly in terms of adoption and usage intensity for various travel purposes [14,15,16]. We use an extended version of the Theory of Planned Behavior (TPB), integrating neighborhood environmental characteristics to offer a more comprehensive model of bicycle-sharing behavior. This study promotes a nuanced understanding of how environmental and personal characteristics interact through psychological mechanisms to influence bicycle-sharing choices. The combination of endogenous psychological variables with exogenous neighborhood characteristics provides a dual-perspective analysis that enhances our understanding of the factors that either enable or inhibit bicycle-sharing adoption and usage intensity for commuting and errands.
Since much of the research has focused on general urban areas [17,18,19], this leaves a significant gap in understanding how these factors operate in disadvantaged neighborhoods, where unique socio-economic and spatial constraints may influence adoption and usage behaviors differently. Disadvantaged neighborhoods often face mobility challenges such as lower access to infrastructure, safety concerns, and economic limitations [20,21], yet little attention has been paid to how these factors shape the effectiveness of bicycle-sharing systems in such contexts. Additionally, the impact of differing built environments and cultural contexts across countries and regions on bicycle-sharing usage patterns also represents a significant area of disparity [12], further underscoring the need for targeted research in underrepresented areas, including disadvantaged neighborhoods. To address these gaps, this study focuses specifically on four disadvantaged communities in Xi’an, China. By examining these underrepresented communities, our research provides critical insights into how environmental and psychological factors influence bicycle-sharing choices, thus advancing the discourse on transportation equity. By shedding light on these factors, this research provides crucial insights for promoting active travel, optimizing transportation policies, and advancing transport equity in disadvantaged neighborhoods.

2. Literature Review

In this review, we conducted an in-depth examination of bicycle-sharing behavior, analyzing a comprehensive range of factors that significantly influence this transportation mode, particularly for commuting and other purposes. We scrutinized the variations in how diverse factors impact bicycle-sharing adoption and usage frequency. Moreover, we explored psychological theories that underpin the travel decision-making process, elucidating the intricate motivations and cognitive mechanisms driving individuals to choose bicycle sharing as a transportation option.

2.1. The General Factors Regarding Bicycle-Sharing Behavior

The contribution of environmental factors, both natural and anthropogenic, to bicycle-sharing behavior is substantial, encompassing aspects such as geographical distribution, natural landscapes, and the built environment. High-density areas with mixed land use typically feature shorter travel distances, enhancing the convenience of bicycle sharing as a transportation option [18,22]. Moreover, scenic landscapes and attractive surroundings may bolster the appeal of cycling, motivating individuals to opt for bicycles for both transportation and recreation [23]. Conversely, natural obstacles such as steep inclines, uneven terrain, or water bodies can impede cycling and influence bicycle-sharing utilization patterns [4]. Safe and convenient cycling and parking facilities increase the likelihood of bicycle-sharing usage [24,25], and a dense network of bicycle-sharing systems promotes frequent use [26]. Strategically positioned shared bicycle stations near public transit hubs facilitate the integration of cycling into multimodal travel routes [27]. Well-lit, traffic-calmed areas can heighten perceptions of safety, encouraging greater bicycle-sharing adoption [28].
Individual factors also play a considerable role, such as demographic characteristics, personal attitudes, and habitual practices [5,29,30,31]. For instance, younger individuals, due to heightened environmental awareness and ease with emerging technologies, are more inclined to use bicycle-sharing systems compared to older individuals [32,33]. Additionally, positive attitudes toward cycling and sustainability can motivate people to adopt bicycle sharing [15]. Perceived barriers, such as lack of safe cycling infrastructure or concerns about theft, discourage usage [34,35]. Habitually, those with a background in cycling or active transportation are likelier to engage in bicycle sharing [36], as familiarity with cycling and recognition of active travel’s advantages enhance the propensity to participate in bicycle-sharing programs [37].
However, these factors may differ significantly in disadvantaged neighborhoods, where access to resources, infrastructure, and safety can be lower [38,39]. Research in disadvantaged communities remains sparse, despite their residents often exhibiting distinct travel behaviors and a greater dependence on affordable, flexible transportation modes like bicycle sharing. Understanding how environmental and individual factors intersect in these contexts is critical for promoting transport equity and addressing the unique barriers these populations face.

2.2. The Determinants Influencing Various Bicycle-Sharing Behaviors

2.2.1. Different Travel Purposes

Recent studies have increasingly focused on the use of bicycle-sharing systems for diverse travel needs, including commuting, leisure, and errands. In the context of commuting, land use is a significant factor influencing demand, with the density of job and employment centers playing a key role [40]. Higher density and mixed land use in communities often lead to greater reliance on bicycles for commuting [41]. Shorter distances encourage users to choose bicycle sharing, while longer distances typically result in users opting for other transportation modes [12].
In contrast, bicycle sharing for errands or recreational activities tends to be more flexible and is strongly linked to Points of Interest (POIs) and nearby amenities [42]. Dockless bicycle-sharing systems, which allow for fewer restrictions on starting and ending points, are particularly effective for errands, providing greater convenience compared to commuting [43]. However, the existing literature has often overlooked the role of disadvantaged neighborhoods in this dynamic, where bicycle sharing can be an essential solution for residents with limited access to other transportation options [44,45]. More attention is needed to examine how these communities utilize bicycle sharing for both commuting and utilitarian purposes, particularly in light of the socio-economic barriers they face.

2.2.2. Distinctions and Linkages for Adoption and Frequency of Bicycle Sharing

Both mode access and usage frequency are critical in assessing travel demand [46]. Adoption of bicycle sharing refers to the initial decision to use the service, whereas the frequency of use indicates how regularly or intensively it is utilized by adopters [47]. Environmental factors such as proximity to shared bicycle stations, key destinations, and transit hubs have been shown to facilitate both adoption and frequent use [27,48]. However, climate and other natural environmental factors can also discourage bicycle-sharing usage [6,49,50]. While disadvantaged groups may initially exhibit lower adoption rates due to economic and infrastructural challenges, studies have shown that once these groups begin using bicycle-sharing systems, their frequency of use can be higher than that of other demographic groups [51]. This suggests that these communities could greatly benefit from improved bicycle-sharing infrastructure and supportive policies.

2.3. The Psychological Theory of Bicycle Sharing

Previous studies have often employed utility maximization theory to explore the relationship between the built environment and bicycle-sharing travel behavior. However, this approach has limitations, and more recent research has shifted toward incorporating psychological theories to gain a deeper understanding of individual motivations and attitudes. As emphasized by Zhu et al. [52], successful implementation of bicycle sharing cannot be achieved without considering factors such as user attitudes, experiences, and preferences. The Theory of Planned Behavior (TPB) remains one of the most frequently used frameworks, positing that an individual’s intention to perform a behavior is shaped by their attitudes, subjective norms, and perceived behavioral control (PBC) [53]. Within the bicycle-sharing framework, these elements collectively influence an individual’s intent to utilize bicycle sharing, subsequently affecting their actual usage [36,54]. Attitudes encompass beliefs about the advantages and challenges of bicycle sharing and the willingness to embrace this transport mode [55]. For instance, viewing bicycle sharing as eco-friendly and convenient can heighten the inclination to use it [15]. Subjective norms involve the perceived expectations of significant others, like family, friends, or colleagues, regarding bicycle-sharing usage. Endorsement from one’s social circle can bolster the intent to engage in bicycle sharing [56,57]. PBC relates to an individual’s perception of how easy or challenging it is to use bicycle sharing, factoring in aspects like availability, cost-effectiveness, accessibility, and cycling proficiency [58]. A strong sense of control and adequate resources can positively sway the decision to adopt bicycle sharing [59,60].
In disadvantaged neighborhoods, psychological factors may play an even more critical role, as residents often face additional barriers such as economic constraints and concerns about personal safety [61]. Understanding how attitudes, social norms, and PBC influence adoption and usage in these communities is essential for designing interventions that effectively promote bicycle sharing and improve transportation equity. Integrating psychological and environmental perspectives in such contexts can provide a more comprehensive understanding of the factors driving bicycle-sharing behavior across diverse socio-economic groups.

2.4. Review Summary

Existing studies on bicycle-sharing systems highlight the pivotal roles of environmental factors (e.g., land use, infrastructure, and accessibility), demographic characteristics, and psychological factors in shaping adoption and usage behavior. However, the influence of these factors varies significantly across contexts, and targeted research is needed, especially for disadvantaged communities, which face unique barriers such as financial constraints and lower infrastructure availability. Despite the wealth of literature on bicycle-sharing systems, several critical gaps remain: There is limited research on disadvantaged communities, where travel may have its own characteristics; a lack of integration of psychological constructs and environmental factors in explaining adoption and use intensity; an insufficient distinction between the determinants of commuting and errands; and few studies explicitly investigate the difference between adoption and use frequency. By focusing on disadvantaged communities in Xi’an, China, this study will address these gaps.

3. Research Methodology

3.1. Study Design

We conducted a survey in Xi’an, China, between 20 August and 4 November 2018, through face-to-face interviews in four disadvantaged neighborhoods with diverse characteristics. Xi’an is one of the oldest cities in China, with a population of over 13 million. It is characterized by rapid urbanization, a high population density, and a combination of historical and modern infrastructure. Despite these advantages, it has many disadvantaged communities due to historical reasons. These characteristics make Xi’an an ideal setting for this study.
In the implementation of the survey, disadvantaged neighborhoods were selected based on a combination of the socioeconomic level of the majority of the population and the type of neighborhood itself. Specifically, we considered neighborhoods with lower average household incomes, higher unemployment rates, and less infrastructure and services. Four neighborhoods were selected for this study to ensure that the characteristics of the built environment varied sufficiently, including (1) Bajia, a redeveloped shantytown; (2) Sanyin, an old-style workplace community; (3) Lougetai, a typical traditional urban village; and (4) Changfengyuan, a redeveloped urban village. A common feature is that most of the residents are unemployed workers, migrant farmers, and urban migrants, who are generally older, less educated, and have poor incomes.
Face-to-face interviews were particularly beneficial given the characteristics of our target population—mainly disadvantaged groups with low levels of education. The survey was facilitated by the authors and trained graduate students. Before the formal survey, we conducted a pilot test with volunteer residents to refine the questions. Interviewers provided necessary clarifications, enhancing data accuracy. The formal data collection occurred at central community locations over two weeks per neighborhood, from 9 am to 9 pm, including weekends. Participants in the survey were voluntary and could receive a small thank-you gift for their time. We collected socio-economic data, including age, gender, marital status, and education level, to ensure the accuracy of the survey. To ensure the representativeness of the sample, we employed a stratified sampling strategy. We aimed for 300 responses per neighborhood, ultimately collecting 1263 responses, of which 921 were valid after quality control, which consisted of excluding incomplete or invalid answers, filtering by time (must be over 18 min), and a “trap” question to ensure attention. Table 1 presents the sample characteristics: the sample was predominantly female (69.8%), with 64.2% of respondents over the age of 45. Education levels were generally low, with less than 16% holding a bachelor’s degree or higher. The sample also had a high unemployment and retirement rate (60%), and the majority of respondents were married. Additionally, 74.2% of participants did not possess a driver’s license.

3.2. Modeling Method

This study hypothesized three basic elements of TPB related to bicycle sharing, including attitudes, social norms, and PBC, as potential endogenous variables affecting the three outcome variables, while socio-demographic factors, along with built and social environmental characteristics, were hypothesized as exogenous variables that could directly or indirectly impact bicycle-sharing behavior. This research introduces more detailed measures of bicycle-sharing behavior than previous studies, specifically “bicycle-sharing adoption for commuting” (BSAC), “bicycle-sharing adoption for daily errands” (BSAE), and “bicycle-sharing frequency for daily errands” (BSFE). Notably, the BSAC analysis underwent a filtering process, selecting 368 employed individuals based on their formal employment status. This step aimed to eliminate the potential bias from those without regular commuting patterns, thereby refining the accuracy of the findings. The entire study’s theoretical framework, grounded in TPB, is illustrated in Figure 1.
We employed a structural equation model (SEM) to operationalize the pathways outlined in our TPB-based conceptual framework. This approach allowed us to examine the direct effects of socio-demographic and neighborhood environmental factors on bicycle-sharing behavior change, while also evaluating the mediating role of individual psychological factors. Since our two dependent variables, BSAC and BSAD, are categorical dummy variables, we used the Weighted Least-Squares Mean and Variance Adjusted (WLSMV) estimation method in our analysis, implemented through Mplus 8.11 software.

3.3. Research Variables

Table 2 outlines all the variables used in this study. We employed an extended version of the Theory of Planned Behavior (TPB) to investigate bicycle-sharing behaviors, focusing on attitudes, social norms, and PBC as core variables. These variables were measured using validated survey questions that are well-established in the existing literature.
Bicycle-sharing adoption is categorized into two main purposes: commuting and daily errands. Respondents were asked to report their bicycle-sharing trips from home to work, as well as to six types of destinations: civic buildings, service providers, stores, restaurants or cafes, entertainment/recreational venues, and exercise venues. Adoption was measured as a binary variable (1 for use as a primary mode of transport, 0 for not used as a primary mode). For daily errands, the study further examined usage frequency, using a 6-point scale ranging from 1 (never) to 6 (twice or more per week). The total score across the six categories represents the overall frequency of bicycle-sharing usage. Figure 2 illustrates the distribution of respondents’ bicycle-sharing usage for various trip purposes. The data show that 82.34% of respondents were unlikely to adopt bicycle sharing for commuting, while 46.8% considered using it for daily errands, though only a minority did so frequently.
We assessed environmental factors using the Neighborhood Environmental Walkability Scale (NEWS), which employs a four-point Likert scale ranging from “strongly disagree” to “strongly agree” to capture respondents’ perceptions of their surroundings. The scores were summarized to reflect environmental features such as building density, street connectivity, aesthetics, and safety [62]. Environmental safety was further divided into two indicators—traffic hazards (including accidents and congestion) and crime rates—as suggested by [63]. Service accessibility was measured by the walking time to 14 local destinations, including stores, supermarkets, post offices, schools, coffee shops, restaurants, hospitals, bus stops, subway stations, and parks. To address variable inconsistencies, we conducted a Principal Component Analysis (PCA), which identified three primary accessibility components: amenities, schools, and medical facilities. Additionally, walking times to bus stops, subway stations, and parks were treated as separate variables. Other factors, such as the presence of bicycle lanes, safety, bicycle-sharing infrastructure, and user satisfaction, were also measured using a four-point Likert scale.
For the neighborhood social environment, we used an adapted version of the Neighborhood Cohesion Scale, which includes measures of “mutual support”, “connectedness”, “trust”, “friendliness”, and “shared values” among neighbors [64]. These statements were rated on a four-point Likert scale, ranging from “strongly disagree” (1) to “strongly agree” (4). The mean scores of these five items were calculated to represent overall measures of social cohesion and trust.

4. Results and Discussion

4.1. Factors Associated with Bicycle-Sharing Adoption for Commuting (BSAC)

The SEM model results for BSAC are presented in Table 3. The model fit indices, including CFI (0.900) and RMSEA (0.057), suggest a good fit based on the criteria set by [65,66]. Furthermore, the standardized loadings for the three latent constructs of the Theory of Planned Behavior (TPB) exhibit substantial magnitudes: 0.810 to 0.826 for attitudes, 0.455 to 0.808 for social norms, and 0.581 to 0.767 for PBC. All the coefficients in the tables are standardized. Our primary focus is to interpret the coefficients that are statistically significant at the 5% and 1% levels; we have also reported those that are significant at the 10% level for informational purposes.
According to the model results, of the three TPB constructs, only attitude (p < 0.01) has a positive impact on the adoption of using bicycle sharing for commuting. This suggests that the more positive a commuter’s attitude toward using bicycle sharing, the more likely he or she is to use bicycle sharing, which is consistent with the findings of related studies [37,56,61,67]. Unexpectedly, neither social norms nor PBC are statistically associated with the adoption of using bicycle sharing for commuting. While social norms can significantly influence behavior in various contexts, their impact may be attenuated when predicting a behavior as specific and routine as daily commuting. In Chinese cities, where bicycle sharing may be primarily perceived as a practical mode of transportation rather than a social activity, social norms related to bicycle-sharing behavior may carry less weight in influencing adoption decisions for daily commuting. The predictive power of PBC may also be limited in the context of daily commuting. Individuals’ beliefs about their ability to perform the behavior (i.e., using bicycle sharing for daily commuting) may be less variable or influential compared to behaviors that allow for greater flexibility and choice. In situations where the behavior is perceived as routine and obligatory, perceived control may be less salient in shaping adoption decisions.
Regarding the role of the perceived neighborhood environment, most variables were not directly associated with the likelihood of adopting bicycle sharing for commuting, except for accessibility to bus stops. This is probably because many commuters prefer to use bicycle sharing for the initial or final leg of their bus journey [68], facilitating a smooth transition between transport modes [52,69]. This pattern is particularly pronounced in areas with limited access to transit [70,71,72]; a more complementary and synergistic relationship between the two modes of transport, bicycle sharing and bus, will emerge in the long term [73,74]. Similarly, none of the perceived neighborhood environment variables have significant total effects on using bicycle sharing for daily commuting, although some are directly associated with three psychological constructs. These findings emphasize the lack of significant total effects from home-located community context on bicycle sharing for commuting, indicating that psychological factors and personal attitudes might play a more substantial role in the decision to use bicycle-share programs. This implies that interventions aimed at promoting bicycle sharing for commuting may benefit more from focusing on enhancing the perceived convenience and reliability of bicycle-sharing systems rather than solely improving the physical attributes of the neighborhood environment.

4.2. Factors Associated with Bicycle-Sharing Adoption for Daily Errands (BSAE)

The results of the SEM model concerning bicycle sharing for errands (BSAE) are presented in Table 4. The model fit indices, including CFI (0.937) and RMSEA (0.051), suggest a good fit. The higher R2 value for BSAE compared to bicycle sharing for commuting (BSAC) indicates that the model has greater explanatory power for errand-related bicycle sharing. This suggests that the factors considered in the model are more effective in explaining the use of bicycle sharing for errands than for commuting. Moreover, more variables show significant direct and total effects on BSAE than on BSAC. This disparity likely arises because commuting trips are generally more restrictive in terms of bicycling use compared to daily errands. Commuting often involves fixed schedules, longer distances, and less flexibility, which may limit the influence of neighborhood environment and psychological factors. In contrast, errand trips typically offer more flexibility in terms of timing and routes, making them more susceptible to the influence of these factors.
All three psychological factors, attitudes towards bicycling, social norms, and PBC, are positively associated with BSAE. This finding implies that individuals who perceive cycling as enjoyable, convenient, and beneficial are more inclined to use bicycle-sharing services. This finding also suggests that when cycling is viewed as a socially accepted and encouraged behavior, individuals are more likely to adopt it. This highlights the role of community and societal influences in promoting bicycle sharing. PBC, which reflects individuals’ confidence in their ability to cycle under various circumstances, further enhances the likelihood of adopting bicycle sharing. This indicates that individuals who feel they have the necessary skills, resources, and opportunities to cycle are more likely to use bicycle-sharing services. This finding also implies that improving infrastructure, providing accessible and well-maintained bicycles, and ensuring safe cycling environments can empower individuals in low-income neighborhoods and reduce barriers to cycling.
Among the perceived neighborhood environment variables, several have significant total effects on BSAE. Specifically, accessibility to schools, accessibility to subways, and neighborhood aesthetics are positively associated with BSAE. These factors indicate that a supportive and appealing neighborhood environment can encourage the adoption of bicycle sharing for daily errands. For example, the proximity of schools can make bicycle sharing a convenient option for parents and students running school-related errands, aligning partially with findings from Guo et al. [12]. Similarly, aesthetically pleasing and well-maintained neighborhoods make cycling more enjoyable for non-commuting purposes [23]. Further, the positive association between accessibility to subways and BSAE underscores the importance of integrating different modes of transport. Subways and bicycle-sharing systems can complement each other, providing seamless multimodal travel options that enhance overall mobility. Similar findings suggest that the presence of better subway availability in a neighborhood stimulates the use of bicycle sharing [40] and that such increases are higher for non-commuting than for commuting purposes [75]. These findings suggest that urban planners and policymakers should consider these environmental factors when designing and implementing bicycle-sharing programs. Enhancing accessibility to key destinations like schools and subways and improving neighborhood aesthetics can significantly boost the adoption of bicycle-sharing services by people (especially disadvantaged populations) for daily errands.
Unexpectedly, proximity to amenities is negatively associated with BSAE. This finding suggests that when amenities are closely located, individuals may prefer walking to cycling for short trips, as walking is often perceived as more convenient and requires no additional equipment or preparation [18,76]. Moreover, the presence of nearby amenities might reduce the perceived need for a bicycle-sharing service, as the distance is easily walkable. Walking frequency and amenity accessibility are validated in the relevant evidence [77]. This highlights a potential trade-off in urban design, where overly convenient access to amenities might reduce the use of active transport options like bicycle sharing.
Compared to the model results for BSAC, more neighborhood environment variables are significantly associated with BSAE. This suggests that the neighborhood environment may play a more important role in the adoption of bicycle sharing for daily errands than for daily commuting. This could be because running errands often involves shorter, more frequent trips that are more sensitive to local environmental factors, such as the availability of bike lanes, proximity to destinations, and perceived safety. In contrast, commuting typically involves longer, more routine journeys that may be less influenced by immediate neighborhood characteristics.

4.3. Factors Associated with Bicycle-Sharing Frequency for Daily Errands (BSFE)

Unlike the BSAE model, the BSFE model examines factors associated with the frequency of using bicycle sharing for daily errands. The model results presented in Table 5 (CFI = 0.933, RMSEA = 0.044) suggest a good fit. Among the three psychological constructs examined, both attitudes towards bicycle sharing and social norms are significantly and positively associated with BSFE. This finding implies that individuals who have a favorable view of bicycle sharing and perceive it as a socially endorsed behavior are more likely to use these services frequently. Notably, based on previous knowledge, the factors that motivate travelers to use bicycle sharing do not necessarily increase the frequency of use [27,47]. For instance, PBC significantly affects the adoption of bicycle sharing for daily errands but does not influence the frequency of its use. This differentiation suggests that while feeling capable and in control is crucial for initially deciding to adopt bicycle sharing, it does not necessarily translate to frequent use. Once individuals overcome initial barriers and adopt bicycle sharing, their continued and frequent use depends more on their attitudes towards the service and the social norms surrounding it.
Among the neighborhood environment variables, the presence of bicycling infrastructure is positively associated with BSFE. This indicates that well-developed bicycling infrastructure plays a crucial role in promoting the frequent use of bicycle-sharing services for daily errands. Conversely, this variable is insignificant in the BSAE model, highlighting that while bicycling infrastructure is critical for frequent use, it is not as influential in the initial adoption of bicycle sharing for daily errands. This suggests that once individuals in disadvantaged communities decide to use bicycle-sharing services, the availability and quality of bicycling infrastructure significantly impact how often they use these services.
Unexpectedly, the results of this paper differ from the findings of previous related studies on park accessibility which concluded that parks attract substantial recreational trips, thereby promoting frequent bicycle-sharing use [12,78]. While parks are generally seen as positive urban features, their presence might not directly facilitate or encourage the use of bicycle sharing for errands. This counterintuitive finding might be explained by several factors. Parks are often destinations for leisure rather than for running errands. In disadvantaged communities that are closer to neighborhood recreation sites such as parks, individuals may prefer walking due to its cost-effectiveness and alignment with recreational purposes. For another, some Chinese parks may prohibit the use of bicycle sharing. Street connectivity is also negatively associated with BSFE. High street connectivity can sometimes lead to increased traffic and congestion, making bicycling less safe and appealing [79]. This could discourage frequent use of bicycle-sharing services, even if the overall connectivity of the area is high. Interestingly, street connectivity is positively associated with the three psychological constructs (attitudes towards bicycle sharing, social norms, and PBC), while it is negatively associated with BSFE. The mixed effects of street connectivity on bicycle-sharing use are particularly noteworthy. On one hand, high street connectivity reduces travel distances and makes bicycling easier, positively influencing attitudes towards bicycle sharing, social norms, and PBC. On the other hand, increased traffic and congestion in highly connected streets may make bicycling less safe and appealing, thereby reducing the frequency of bicycle-sharing use.

5. Conclusions and Policy Implications

5.1. Conclusions

This study provides a comprehensive analysis of the factors influencing the adoption and frequency of bicycle sharing for commuting and daily errands in disadvantaged neighborhoods. Using Structural Equation Modeling (SEM) and the Theory of Planned Behavior (TPB), we examined the complex relationships between psychological constructs, perceived neighborhood environment variables, and bicycle-sharing behavior in these underrepresented areas. The focus on disadvantaged neighborhoods, where residents face unique mobility and infrastructure challenges, adds a crucial dimension to our findings, underscoring the importance of equitable transportation solutions.
The key findings, summarized in Figure 3, reveal distinct differences between the factors influencing bicycle sharing for commuting and those affecting its use for daily errands. For commuting, only attitudes towards bicycling significantly impact adoption. In contrast, for daily errands, all three psychological constructs—attitudes, social norms, and PBC—positively influence bicycle-sharing adoption. This highlights the importance of individual perceptions, societal influences, and confidence in using bicycle sharing for errands, which are more critical in disadvantaged neighborhoods, where transportation options are often limited.
Neighborhood environment variables also showed varying impacts on bicycle-sharing behavior. Accessibility to schools, subways, and neighborhood aesthetics positively influence the adoption of bicycle sharing for errands, highlighting the importance of well-integrated urban infrastructure in these areas. However, negative associations were found between proximity to amenities, accessibility to parks, and street connectivity and frequent use of bicycle sharing. These counterintuitive results suggest that while certain environmental features are generally beneficial, their influence on bicycle-sharing behavior can be complex and context-dependent, particularly in disadvantaged neighborhoods. A plausible explanation for these negative associations is the competition between walking and bicycling as modes of transport for daily errands. Proximity to amenities, accessibility to parks, and street connectivity are well-established measures of walkability, and in areas where destinations are easily accessible by foot, walking may be preferred over bicycling for short trips. This finding underscores the nuanced differences between walkability and bikeability.

5.2. Policy Implications

The unique contributions of this study offer valuable insights for urban planners and policymakers, particularly with respect to promoting bicycle-sharing programs in disadvantaged neighborhoods. Initiatives to improve public attitudes towards bicycle sharing, strengthen positive social norms, and foster a bike-friendly culture can significantly boost adoption rates. Educational campaigns, community engagement activities, and social incentives should be targeted specifically to underrepresented communities to increase awareness and address barriers related to safety and accessibility. In these areas, efforts to improve PBC—by ensuring the availability of well-maintained bicycles, enhancing safety measures, and providing user-friendly infrastructure—are particularly crucial for increasing bicycle-sharing adoption, especially for daily errands.
The positive association between public transit accessibility and bicycle sharing underscores the need for integrated transportation solutions, particularly in neighborhoods where access to transportation is limited. Policies should focus on creating seamless connectivity between bicycle-sharing stations and other public transport modes, such as subways and buses, to facilitate efficient travel. This is especially relevant in disadvantaged neighborhoods, where access to affordable, multimodal transportation options can greatly enhance mobility and quality of life.
Furthermore, the role of bicycling infrastructure in promoting frequent use for errands highlights the importance of continuous investment in dedicated bike lanes, secure parking, and supportive facilities. In disadvantaged neighborhoods, where safety and infrastructure gaps are often more pronounced, such investments can address key barriers to bicycle-sharing usage. Urban planners should also consider how features like street connectivity, traffic patterns, and safety concerns interact with bicycle-sharing behavior, adjusting land use and urban design to maximize positive outcomes and mitigate potential drawbacks, such as traffic congestion and safety risks.
In conclusion, the findings of this study offer valuable insights for advancing transport equity through targeted bicycle-sharing policies and infrastructure improvements in disadvantaged neighborhoods. By addressing the unique challenges faced by these communities, policymakers can help ensure that bicycle sharing serves as an inclusive, sustainable transportation solution that enhances mobility and quality of life for all residents. The emphasis on differentiated policy approaches based on specific trip purposes and neighborhood characteristics can lead to more effective interventions that enhance transportation equity and sustainability.

5.3. Future Work

Although this study includes valuable research, certain limitations remain.
First, females accounted for a large proportion of the respondents in our sample, which may be considered a limitation. Future studies could use alternative data collection methods, such as stratified sampling or conducting interviews at different times of the day, to ensure a more gender-balanced sample. Second, future research could further explore differences between disadvantaged and general neighborhoods, providing a clearer understanding of how socio-economic and environmental factors uniquely impact bicycle-sharing behaviors and informing more targeted policy recommendations. Third, the study focuses on a specific location (Xi’an, China), which may limit the generalizability of the findings to other regions or countries with different socio-economic and cultural contexts. Broader studies across diverse geographic areas could provide a more comprehensive understanding of bicycle-sharing dynamics. Fourth, the research employed a cross-sectional survey design, capturing data at a single point in time. This approach limits the ability to assess changes in bicycle-sharing behavior over time or to establish causal relationships between variables. Longitudinal studies would be beneficial to track changes and trends. Fifth, the reliance on self-reported data may have introduced response bias, as the participants might have provided socially desirable answers rather than reporting their true behaviors or attitudes. This could have affected the accuracy of the findings related to psychological constructs and usage patterns. Sixth, the study may not fully account for external factors that could influence bicycle-sharing adoption, such as local policies, economic conditions, or seasonal variations in weather. These factors can significantly impact transportation choices and should be considered in future research.

Author Contributions

Conceptualization, H.W. and L.M.; Methodology, H.W. and L.M.; Formal analysis, H.W. and Y.D.; Data curation, H.W. and Y.D.; Writing—original draft, H.W. and L.M.; Writing—review and editing, L.M.; Supervision, L.M.; Funding acquisition, L.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (Nos. 42171190 and 42371199).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Peking University (protocol code: E20231023, 15 January 2023).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Zhang, Y.P.; Mi, Z.F. Environmental benefits of bike sharing: A big data-based analysis. Appl. Energy 2018, 220, 296–301. [Google Scholar] [CrossRef]
  2. Chen, Z.; van Lierop, D.; Ettema, D. Dockless bike-sharing systems: What are the implications? Transp. Rev. 2020, 40, 333–353. [Google Scholar] [CrossRef]
  3. Chen, Y.; He, K.; Deveci, M.; Coffman, D.M. Health impacts of bike sharing system—A case study of Shanghai. J. Transp. Health 2023, 30, 101611. [Google Scholar] [CrossRef]
  4. Shaheen, S.A.; Guzman, S.; Zhang, H. Bikesharing in Europe, the Americas, and Asia: Past, present, and future. Transp. Res. Rec. 2010, 2143, 159–167. [Google Scholar] [CrossRef]
  5. Fishman, E.; Washington, S.; Haworth, N. Bike Share: A Synthesis of the Literature. Transp. Rev. 2013, 33, 148–165. [Google Scholar] [CrossRef]
  6. Campbell, A.A.; Cherry, C.R.; Ryerson, M.S.; Yang, X. Factors influencing the choice of shared bicycles and shared electric bikes in Beijing. Transp. Res. Part C Emerg. Technol. 2016, 67, 399–414. [Google Scholar] [CrossRef]
  7. Ma, X.W.; Yuan, Y.F.; Van Oort, N.; Hoogendoorn, S. Bike-sharing systems’ impact on modal shift: A case study in Delft, the Netherlands. J. Clean. Prod. 2020, 259, 120846. [Google Scholar] [CrossRef]
  8. Shaheen, S.A.; Zhang, H.; Martin, E.; Guzman, S. China’s Hangzhou Public Bicycle: Understanding Early Adoption and Behavioral Response to Bikesharing. Transp. Res. Rec. 2011, 2247, 33–41. [Google Scholar] [CrossRef]
  9. Leister, E.H.; Vairo, N.; Sims, D.; Bopp, M. Understanding bike share reach, use, access and function: An exploratory study. Sustain. Cities Soc. 2018, 43, 191–196. [Google Scholar] [CrossRef]
  10. Yang, Y.; Li, T.Z.; Zhang, T.; Yang, W.Y. Understanding the Utilization Characteristics of Bicycle-Sharing Systems in Underdeveloped Cities A Case Study in Xuchang City, China. Transp. Res. Rec. 2017, 2634, 78–85. [Google Scholar] [CrossRef]
  11. Barbour, N.; Zhang, Y.; Mannering, F. A statistical analysis of bike sharing usage and its potential as an auto-trip substitute. J. Transp. Health 2019, 12, 253–262. [Google Scholar] [CrossRef]
  12. Guo, Y.Y.; Yang, L.C.; Chen, Y. Bike Share Usage and the Built Environment: A Review. Front. Public Health 2022, 10, 848169. [Google Scholar] [CrossRef] [PubMed]
  13. Chen, M.; Wang, D.; Sun, Y.; Waygood, E.O.D.; Yang, W. A comparison of users’ characteristics between station-based bikesharing system and free-floating bikesharing system: Case study in Hangzhou, China. Transportation 2020, 47, 689–704. [Google Scholar] [CrossRef]
  14. Zhu, M.; Hu, X.F.; Lin, Z.Z.; Li, J.; Wang, S.Y.; Wang, C.Y. Intention to adopt bicycle-sharing in China: Introducing environmental concern into the theory of planned behavior model. Environ. Sci. Pollut. Res. 2020, 27, 41740–41750. [Google Scholar] [CrossRef] [PubMed]
  15. Wang, Y.; Douglas, M.A.; Hazen, B.T.; Dresner, M. Be green and clearly be seen: How consumer values and attitudes affect adoption of bicycle sharing. Transp. Res. Part F-Traffic Psychol. Behav. 2018, 58, 730–742. [Google Scholar] [CrossRef]
  16. Maas, S.; Nikolaou, P.; Attard, M.; Dimitriou, L. Spatial and temporal analysis of shared bicycle use in Limassol, Cyprus. J. Transp. Geogr. 2021, 93, 103049. [Google Scholar] [CrossRef]
  17. Bai, Q.; Yu, Z.L.; Ma, S.H.; Wang, Y.Q.; Agbelie, B. Examining influencing factors of bicycle usage for dock-based public bicycle sharing system: A case study of Xi’an, China. J. Clean. Prod. 2022, 362, 132332. [Google Scholar] [CrossRef]
  18. Meng, S.a.; Zacharias, J. Street morphology and travel by dockless shared bicycles in Beijing, China. Int. J. Sustain. Transp. 2021, 15, 788–798. [Google Scholar] [CrossRef]
  19. Zheng, L.L.; Meng, F.Y.; Ding, T.Q.; Yang, Q.F.; Xie, Z.F.; Jiang, Z.T. The effect of traffic status on dockless bicycle-sharing: Evidence from Shanghai, China. J. Clean. Prod. 2022, 381, 135207. [Google Scholar] [CrossRef]
  20. Loraamm, R.; Mustain, M. Social Deprivation and the Performance of Pedestrian Infrastructure for School Children: Identifying Need in the Putnam City School District, Oklahoma City, Oklahoma. Prof. Geogr. 2022, 74, 231–245. [Google Scholar] [CrossRef]
  21. Su, S.; Pi, J.; Xie, H.; Cai, Z.; Weng, M. Community deprivation, walkability, and public health: Highlighting the social inequalities in land use planning for health promotion. Land Use Policy 2017, 67, 315–326. [Google Scholar] [CrossRef]
  22. Tian, D.; Wen, Z.; Sun, Y. Analyzing the Spatial Interaction Characteristics of Urban Area Shared Bicycle Systems: A Case Study of Beijing’s Central Area. Buildings 2023, 13, 2646. [Google Scholar] [CrossRef]
  23. Jia, Y.N.; Fu, H. Association between innovative dockless bicycle sharing programs and adopting cycling in commuting and non-commuting trips. Transp. Res. Part A-Policy Pract. 2019, 121, 12–21. [Google Scholar] [CrossRef]
  24. Wang, X.; Lindsey, G.; Schoner, J.E.; Harrison, A. Modeling Bike Share Station Activity: Effects of Nearby Businesses and Jobs on Trips to and from Stations. J. Urban Plan. Dev. 2016, 142, 04015001. [Google Scholar] [CrossRef]
  25. Li, W.; Wang, S.; Zhang, X.; Jia, Q.; Tian, Y. Understanding intra-urban human mobility through an exploratory spatiotemporal analysis of bike-sharing trajectories. Int. J. Geogr. Inf. Sci. 2020, 34, 2451–2474. [Google Scholar] [CrossRef]
  26. Conrow, L.; Murray, A.T.; Fischer, H.A. An optimization approach for equitable bicycle share station siting. J. Transp. Geogr. 2018, 69, 163–170. [Google Scholar] [CrossRef]
  27. Bachand-Marleau, J.; Lee, B.H.Y.; El-Geneidy, A.M. Better Understanding of Factors Influencing Likelihood of Using Shared Bicycle Systems and Frequency of Use. Transp. Res. Rec. 2012, 2314, 66–71. [Google Scholar] [CrossRef]
  28. DiGioia, J.; Watkins, K.E.; Xu, Y.; Rodgers, M.; Guensler, R. Safety impacts of bicycle infrastructure: A critical review. J. Saf. Res. 2017, 61, 105–119. [Google Scholar] [CrossRef]
  29. Fishman, E. Bikeshare: A Review of Recent Literature. Transp. Rev. 2016, 36, 92–113. [Google Scholar] [CrossRef]
  30. Pucher, J.; Buehler, R. Making cycling irresistible: Lessons from the Netherlands, Denmark and Germany. Transp. Rev. 2008, 28, 495–528. [Google Scholar] [CrossRef]
  31. Guo, Y.Y.; Zhou, J.B.; Wu, Y.; Li, Z.B. Identifying the factors affecting bike-sharing usage and degree of satisfaction in Ningbo, China. PLoS ONE 2017, 12, e0185100. [Google Scholar] [CrossRef] [PubMed]
  32. Reck, D.J.; Axhausen, K.W. Who uses shared micro-mobility services? Empirical evidence from Zurich, Switzerland. Transp. Res. Part D Transp. Environ. 2021, 94, 102803. [Google Scholar] [CrossRef]
  33. Chevalier, A.; Charlemagne, M.; Xu, L. Bicycle acceptance on campus: Influence of the built environment and shared bikes. Transp. Res. Part D Transp. Environ. 2019, 76, 211–235. [Google Scholar] [CrossRef]
  34. Xin, F.; Chen, Y.; Wang, X.; Chen, X. Cyclist Satisfaction Evaluation Model for Free-Floating Bike-Sharing System: A Case Study of Shanghai. Transp. Res. Rec. 2018, 2672, 21–32. [Google Scholar] [CrossRef]
  35. Fishman, E.; Washington, S.; Haworth, N. Barriers and facilitators to public bicycle scheme use: A qualitative approach. Transp. Res. Part F Traffic Psychol. Behav. 2012, 15, 686–698. [Google Scholar] [CrossRef]
  36. Zhang, X.; Ma, L.; Wang, Z.; Xing, H. Psychosocial factors influencing shared bicycle travel choices among Chinese: An application of theory planned behavior. PLoS ONE 2019, 14, e0210964. [Google Scholar]
  37. Chen, X. Predicting College Students’ Bike-Sharing Intentions Based on the Theory of Planned Behavior. Front. Psychol. 2022, 13, 836983. [Google Scholar] [CrossRef] [PubMed]
  38. Desjardins, E.; Higgins, C.D.; Páez, A. Examining equity in accessibility to bike share: A balanced floating catchment area approach. Transp. Res. Part D Transp. Environ. 2022, 102, 103091. [Google Scholar] [CrossRef]
  39. Hosford, K.; Yanagawa, C.; Lore, M.; Winters, M. Effects of Mobi’s equity initiatives on public bike share access and use. Transp. Res. Part D Transp. Environ. 2024, 131, 104223. [Google Scholar] [CrossRef]
  40. Faghih-Imani, A.; Eluru, N.; El-Geneidy, A.M.; Rabbat, M.; Haq, U. How land-use and urban form impact bicycle flows: Evidence from the bicycle-sharing system (BIXI) in Montreal. J. Transp. Geogr. 2014, 41, 306–314. [Google Scholar] [CrossRef]
  41. Rybarczyk, G.; Ozbil, A.; Andresen, E.; Hayes, Z. Physiological responses to urban design during bicycling: A naturalistic investigation. Transp. Res. Part F Traffic Psychol. Behav. 2020, 68, 79–93. [Google Scholar] [CrossRef]
  42. Faghih-Imani, A.; Hampshire, R.; Marla, L.; Eluru, N. An empirical analysis of bike sharing usage and rebalancing: Evidence from Barcelona and Seville. Transp. Res. Part A Policy Pract. 2017, 97, 177–191. [Google Scholar] [CrossRef]
  43. Ma, X.W.; Ji, Y.J.; Yuan, Y.F.; Van Oort, N.; Jin, Y.C.; Hoogendoorn, S. A comparison in travel patterns and determinants of user demand between docked and dockless bike-sharing systems using multi-sourced data. Transp. Res. Part A-Policy Pract. 2020, 139, 148–173. [Google Scholar] [CrossRef]
  44. Wang, J.; Lindsey, G. Neighborhood socio-demographic characteristics and bike share member patterns of use. J. Transp. Geogr. 2019, 79, 102475. [Google Scholar] [CrossRef]
  45. Goodman, A.; Cheshire, J. Inequalities in the London bicycle sharing system revisited: Impacts of extending the scheme to poorer areas but then doubling prices. J. Transp. Geogr. 2014, 41, 272–279. [Google Scholar] [CrossRef]
  46. Zhan, G.; Yan, X.; Zhu, S.; Wang, Y. Using hierarchical tree-based regression model to examine university student travel frequency and mode choice patterns in China. Transp. Policy 2016, 45, 55–65. [Google Scholar] [CrossRef]
  47. Blazanin, G.; Mondal, A.; Asmussen, K.E.; Bhat, C.R. E-scooter sharing and bikesharing systems: An individual-level analysis of factors affecting first-use and use frequency. Transp. Res. Part C Emerg. Technol. 2022, 135, 103515. [Google Scholar] [CrossRef]
  48. El-Assi, W.; Salah Mahmoud, M.; Nurul Habib, K. Effects of built environment and weather on bike sharing demand: A station level analysis of commercial bike sharing in Toronto. Transportation 2017, 44, 589–613. [Google Scholar] [CrossRef]
  49. Médard de Chardon, C.; Caruso, G.; Thomas, I. Bicycle sharing system ‘success’ determinants. Transp. Res. Part A Policy Pract. 2017, 100, 202–214. [Google Scholar] [CrossRef]
  50. Gebhart, K.; Noland, R.B. The impact of weather conditions on bikeshare trips in Washington, DC. Transportation 2014, 41, 1205–1225. [Google Scholar] [CrossRef]
  51. Mohiuddin, H.; Fitch-Polse, D.T.; Handy, S.L. Does bike-share enhance transport equity? Evidence from the Sacramento, California region. J. Transp. Geogr. 2023, 109, 103588. [Google Scholar] [CrossRef]
  52. Zhu, Y.; Diao, W.; Zhao, H. Understanding Users’ Perceptions of Bicycle-Sharing Systems in Chinese Cities: Evidence from Beijing and Guangzhou. Urban Sci. 2023, 7, 95. [Google Scholar] [CrossRef]
  53. Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 1991, 50, 179–211. [Google Scholar] [CrossRef]
  54. Wei, W.; Gu, C.; Yang, C. Examining the Influence of Moral Norms on Dockless Shared Bicycle Users’ Parking Behavior-An Exploratory Study Based on the Theory of Planned Behavior. Systems 2022, 10, 11. [Google Scholar] [CrossRef]
  55. Alimo, P.K.; Agyeman, S.; Danesh, A.; Yu, C.; Ma, W. Is public bike-sharing feasible in Ghana? Road users’ perceptions and policy interventions. J. Transp. Geogr. 2023, 106, 103509. [Google Scholar] [CrossRef]
  56. Ge, Y.; Qu, W.; Qi, H.; Cui, X.; Sun, X. Why people like using bikesharing: Factors influencing bikeshare use in a Chinese sample. Transp. Res. Part D Transp. Environ. 2020, 87, 102520. [Google Scholar] [CrossRef]
  57. Li, R.; Krishna Sinniah, G.; Li, X. The Factors Influencing Resident’s Intentions on E-Bike Sharing Usage in China. Sustainability 2022, 14, 5013. [Google Scholar] [CrossRef]
  58. Kaplan, S.; Manca, F.; Nielsen, T.A.S.; Prato, C.G. Intentions to use bike-sharing for holiday cycling: An application of the Theory of Planned Behavior. Tour. Manag. 2015, 47, 34–46. [Google Scholar] [CrossRef]
  59. Xu, D.; Bian, Y.; Shu, S. Research on the Psychological Model of Free-floating Bike-Sharing Using Behavior: A Case Study of Beijing. Sustainability 2020, 12, 2977. [Google Scholar] [CrossRef]
  60. Chen, S.-Y. Using the sustainable modified TAM and TPB to analyze the effects of perceived green value on loyalty to a public bike system. Transp. Res. Part A Policy Pract. 2016, 88, 58–72. [Google Scholar] [CrossRef]
  61. Dill, J.; Ma, J.; McNeil, N.; Broach, J.; MacArthur, J. Factors influencing bike share among underserved populations: Evidence from three U.S. cities. Transp. Res. Part D Transp. Environ. 2022, 112, 103471. [Google Scholar] [CrossRef]
  62. Saelens, B.E.; Sallis, J.F.; Black, J.B.; Chen, D. Neighborhood-based differences in physical activity: An environment scale evaluation. Am. J. Public Health 2003, 93, 1552–1558. [Google Scholar] [CrossRef] [PubMed]
  63. Sun, Y.R.; Mobasheri, A.; Hu, X.K.; Wang, W.K. Investigating Impacts of Environmental Factors on the Cycling Behavior of Bicycle-Sharing Users. Sustainability 2017, 9, 1060. [Google Scholar] [CrossRef]
  64. Sampson, R.J.; Raudenbush, S.W.; Earls, F. Neighborhoods and violent crime: A multilevel study of collective efficacy. Science 1997, 277, 918–924. [Google Scholar] [CrossRef] [PubMed]
  65. Hu, L.t.; Bentler, P.M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equ. Model. A Multidiscip. J. 1999, 6, 1–55. [Google Scholar] [CrossRef]
  66. MacCallum, R.C.; Browne, M.W.; Sugawara, H.M. Power analysis and determination of sample size for covariance structure modeling. Psychol. Methods 1996, 1, 130. [Google Scholar] [CrossRef]
  67. Si, H.; Shi, J.-g.; Tang, D.; Wu, G.; Lan, J. Understanding intention and behavior toward sustainable usage of bike sharing by extending the theory of planned behavior. Resour. Conserv. Recycl. 2020, 152, 104513. [Google Scholar] [CrossRef]
  68. Qiu, W.; Chang, H. The interplay between dockless bikeshare and bus for small-size cities in the US: A case study of Ithaca. J. Transp. Geogr. 2021, 96, 103175. [Google Scholar] [CrossRef]
  69. Mateo-Babiano, I.; Bean, R.; Corcoran, J.; Pojani, D. How does our natural and built environment affect the use of bicycle sharing? Transp. Res. Part A Policy Pract. 2016, 94, 295–307. [Google Scholar] [CrossRef]
  70. Huang, G.; Zhang, W.; Xu, D. How do dockless bikesharing services affect public transit and taxi use? Evidence from 36 central cities in China. Res. Transp. Bus. Manag. 2023, 50, 101030. [Google Scholar] [CrossRef]
  71. Martin, E.W.; Shaheen, S.A. Evaluating public transit modal shift dynamics in response to bikesharing: A tale of two U.S. cities. J. Transp. Geogr. 2014, 41, 315–324. [Google Scholar] [CrossRef]
  72. Kwiatkowski, M.A. Regional bicycle-sharing system in the context of the expectations of small and medium-sized towns. Case Stud. Transp. Policy 2021, 9, 663–673. [Google Scholar] [CrossRef]
  73. Campbell, K.B.; Brakewood, C. Sharing riders: How bikesharing impacts bus ridership in New York City. Transp. Res. Part A Policy Pract. 2017, 100, 264–282. [Google Scholar] [CrossRef]
  74. Radzimski, A.; Dzięcielski, M. Exploring the relationship between bike-sharing and public transport in Poznań, Poland. Transp. Res. Part A Policy Pract. 2021, 145, 189–202. [Google Scholar] [CrossRef]
  75. Gu, T.; Kim, I.; Currie, G. Measuring immediate impacts of a new mass transit system on an existing bike-share system in China. Transp. Res. Part A Policy Pract. 2019, 124, 20–39. [Google Scholar] [CrossRef]
  76. Ma, L.; Dill, J. Associations between the objective and perceived built environment and bicycling for transportation. J. Transp. Health 2015, 2, 248–255. [Google Scholar] [CrossRef]
  77. Ma, L.; Ettema, D.; Ye, R. Determinants of bicycling for transportation in disadvantaged neighbourhoods: Evidence from Xi’an, China. Transp. Res. Part A Policy Pract. 2021, 145, 103–117. [Google Scholar] [CrossRef]
  78. Tu, Y.; Chen, P.; Gao, X.; Yang, J.; Chen, X. How to Make Dockless Bikeshare Good for Cities: Curbing Oversupplied Bikes. Transp. Res. Rec. 2019, 2673, 618–627. [Google Scholar] [CrossRef]
  79. Marshall, W.E.; Garrick, N.W. Does street network design affect traffic safety? Accid. Anal. Prev. 2011, 43, 769–781. [Google Scholar] [CrossRef]
Land 13 02055 g001
Figure 1. Conceptual framework.
Figure 1. Conceptual framework.
Land 13 02055 g001
Land 13 02055 g002
Figure 2. Different bicycle-sharing usage behaviors of the same respondents. Notes: True = bicycle sharing is the primary mode; False = bicycle sharing is not the primary mode; Always = “more than twice a week”; Usually = “once a week”; Often = “about once every two weeks”; Sometimes = “once or twice a month”; Rarely = “less than once a month”.
Figure 2. Different bicycle-sharing usage behaviors of the same respondents. Notes: True = bicycle sharing is the primary mode; False = bicycle sharing is not the primary mode; Always = “more than twice a week”; Usually = “once a week”; Often = “about once every two weeks”; Sometimes = “once or twice a month”; Rarely = “less than once a month”.
Land 13 02055 g002
Land 13 02055 g003
Figure 3. Summary of model results.
Figure 3. Summary of model results.
Land 13 02055 g003
Table 1. Sample characteristics.
Table 1. Sample characteristics.
NumberPercent
Age groups
Below 3012213.2
30–4520019.0
45–6023411.6
60–7023416.6
70 and over13022.0
Gender
Female64369.8
Male27830.2
Married
Married/cohabiting77884.5
Single/divorced/widowed14315.5
Education level
Did not go to school161.7
Finished primary school647.0
Completed secondary school qualification52056.5
Completed technical school758.1
Completed junior college12813.9
Completed bachelor’s degree qualification10411.3
Completed post-graduate qualification144.5
Annual household income
CNY 1–19,999 per year (CNY 1–1666 per month)9615.5
CNY 20,000–39,999 (CNY 1667–3333)15424.8
CNY 40,000–59,999 (CNY 3334–5000)11017.7
CNY 60,000–79,999 (CNY 5001–6666)8413.5
CNY 80,000–99,999 (CNY 6667–8333)6210.0
CNY 100,000–149,999 (CNY 8334–12,499)7211.6
CNY 150,000–199,999 (CNY 12,500–16,666)213.4
CNY 200,000 and above (CNY 16,667 and above)223.5
No. of cars in household
032034.7
147051.0
29510.3
>2363.9
No. of bikes in household
052056.5
128430.8
2879.4
>2303.3
No. of e-bikes in household
047451.5
134737.7
2748.0
>2262.8
Driving license
Yes23825.8
No68374.2
Employment status
Employed (including full-time and part-time)36840.0
Unemployed
(including unemployment and retirement)		55360.0
Table 2. Description of variables.
Table 2. Description of variables.
VariableDescriptionCode or Unit
Behavior
Bicycle-sharing adoption for commuting (BSAC)Use bicycle sharing as the main mode of transportation for commute0 = False; 1 = True
Bicycle-sharing adoption for daily errands (BSAE)Use bicycle sharing as the main mode of transportation for daily errands (from home to the destinations nearby: civic buildings, service providers, shops, restaurants or cafes, places for entertainment/recreation, and places to exercise)0 = False; 1 = True
Bicycle-sharing frequency for daily errands (BSFE)If BSAE = 1, how often has the respondent used bicycle sharing for daily errands in the past month?Count
Socio-Demographics
Age Continuous variable
Gender 0 = Male; 1 = Female
MarriedMarital status0 = False; 1 = True
Education levelEducation level1–8 = From “illiterate” to “postgraduate or above”
Annual household incomeAnnual household income1–8 = From “none” to “200,000 + CNY/per year”
No. of carsNo. of cars in householdCount
No. of bikesNo. of bikes in householdCount
No. of e-bikesNo. of e-bikes in householdCount
Holding a driving licenseHolding a driving license0 = False; 1 = True
EmployedCurrent working status0 = Unemployed; 1 = Employed
Attitudes
PreferenceI like riding a shared bicycle.1 = Strongly disagree; 2 = Somewhat Disagree; 3 = Neither agree nor disagree; 4 = Somewhat agree; 5 = Strongly agree
Riding betterIt is easier for me to ride a shared bicycle than to ride my own bicycle.
ConvenienceUsing a shared bicycle has greatly facilitated my daily travel.
Social Norms
SN1Most people who are important to me, for example, my family and friends, think I should ride a (shared) bicycle more.1 = Strongly disagree; 2 = Somewhat disagree; 3 = Neither agree nor disagree; 4 = Somewhat agree; 5 = Strongly agree
SN2Most people who are important to me, for example, my family and friends, would support me riding a (shared) bicycle more.
SN3Many of my family, friends, and co-workers ride a (shared) bicycle to get to places, such as errands, shopping, and work.
Perceived Behavior Control
PBC1For me to ride a (shared) bicycle for daily travel would be easy.1 = Strongly disagree; 2 = Somewhat disagree; 3 = Neither agree nor disagree; 4 = Somewhat agree; 5 = Strongly agree
PBC2I know where safe bike routes are in my neighborhood.
PBC3Many of the places I need to get to regularly are within bicycling distance of my home.
Neighborhood Environment
DensityHow would you describe the type of housing unit where you currently live?Weighted sum
Accessibility to amenitiesWalking time to shop, supermarket, restaurant, etc.Factor score
Accessibility to schoolsWalking time to primary/middle schoolFactor score
Accessibility to bus stopWalking time to bus stop5 = Under 5 min; 4 = 6–10 min; 3 = 11–20 min; 2 = 21–30 min; 1 = More than 30 min
Accessibility to subwayWalking time to subway station5 = Under 5 min; 4 = 6–10 min; 3 = 11–20 min; 2 = 21–30 min; 1 = More than 30 min
Accessibility to parkWalking time to park5 = Under 5 min; 4 = 6–10 min; 3 = 11–20 min; 2 = 21–30 min; 1 = More than 30 min
Street connectivity(1) The distance between street intersections in my neighborhood is generally short; (2) There are many alternative routes on the streets in my neighborhood; (3) The streets in my neighborhood do not have many cul-de-sacs.Mean
Aesthetics(1) There are trees along the streets in my neighborhood; (2) There are many streets with greenery in my neighborhood; (3) There are many interesting things in my neighborhood; (4) There are many attractive natural sights in my neighborhood; (5) There are many attractive buildings and shops in my neighborhood.Mean
Traffic hazards(1) There is so much traffic along the streets in my neighborhood that it feels bad to walk; (2) There is so much traffic along the streets in my neighborhood that it feels bad to ride a (shared) bicycle; (3) Most drivers exceed the speed limit along the streets in my neighborhood; (4) The speed of traffic is slow (40 km/h or less) on most streets in my neighborhood (reverse indicator, RI); (5) It is common to see/smell exhaust fumes from motor vehicles when walking in my neighborhood (RI).Mean
Crime rate(1) There is a high crime rate in my neighborhood; (2) I feel unsafe walking during the day; (3) I feel unsafe walking at night; (4) My neighborhood is unsafe enough to not allow a 10-year-old boy to walk along the streets.Mean
Social cohesion and trust(1) People around here are willing to help their neighbors; (2) This is a close-knit neighborhood; (3) People in this neighborhood can be trusted; (4) People in this neighborhood generally don’t get along with each other (RI); (5) People in this neighborhood do not share the same values (RI).Mean
Bicycle infrastructure(1) There are off-street bike trails or paved paths in or near my neighborhood that are easy to get to; (2) There are bike lanes that are easy to get to; (3) There are well-maintained and safe bike lanes; (4) There are quiet streets, without bike lanes, that are easy to get to on a bike.Mean
Availability of bicycle-sharing servicesIt’s easy to find shared bicycles in my neighborhood.1 = Strongly disagree; 2 = Somewhat Disagree; 3 = Neither Agree nor Disagree; 4 = Somewhat agree; 5 = Strongly agree
Table 3. BSAC model results (n = 355).
Table 3. BSAC model results (n = 355).
AttitudesSocial NormsPBCBSAC
R2 = 0.072R2 = 0.149R2 = 0.167R2 = 0.121
Direct effects Direct effects Direct effects Direct effects Total Effects
Psychological factor
Attitudes0.250***0.250***
Social Norms0.109 0.109
Perceived Behavior Control0.096 0.096
Neighborhood environment
Accessibility to amenities0.102 −0.104 0.073 −0.081 −0.06
Accessibility to schools−0.057 0.033 0.117 **0.040 0.040
Accessibility to bus stop0.000 −0.146**−0.097 0.115 **0.089
Accessibility to subway−0.076 0.090 −0.024 0.028 0.017
Accessibility to park0.032 −0.051 0.015 0.041 0.045
Street connectivity0.018 0.121 **0.230 ***−0.077 −0.038
Aesthetics0.091 0.022 −0.071 −0.021 −0.002
Traffic hazards0.020 −0.113*−0.084 −0.001 −0.017
Crime rate−0.117**0.006 −0.062 −0.012 −0.046
Social environment−0.032 −0.017 0.053 0.044 0.039
Bicycle infrastructure−0.015 0.079 0.086 0.001 0.014
Availability to bicycle-sharing0.081 0.072 0.017 −0.042 −0.012
Socio-demographics
Age −0.01 −0.011 0.073 0.007 0.010
Female−0.07 −0.044 −0.058 0.103 *0.075
Married0.006 0.051 −0.042 −0.051 −0.048
Education level−0.07 −0.093 −0.084 0.087 0.051
No. of bikes0.116 **0.159 ***0.139 **0.039 0.099 *
Driving license0.055 −0.122*0.047 −0.131**−0.126**
Notes: * p < 0.1; ** p < 0.05; *** p < 0.01.
Table 4. BSAE model results (n = 885).
Table 4. BSAE model results (n = 885).
AttitudesSocial NormsPBCBSAE
R2 = 0.097R2 = 0.256R2 = 0.124R2 = 0.396
Direct effects Direct effects Direct effects Direct effects Total Effects
Psychological factor
Attitudes0.207 ***0.218 ***
Social Norms0.155 ***0.171 ***
Perceived Behavior Control0.150 ***0.016 ***
Neighborhood environment
Accessibility to amenities−0.033 −0.044 0.077 *−0.072**−0.074**
Accessibility to schools−0.011 −0.041 −0.021 0.095 ***0.083 **
Accessibility to bus stop0.011 −0.061 −0.058 −0.003 −0.019
Accessibility to subway0.005 −0.019 −0.016 0.077 ***0.072 **
Accessibility to park0.040 0.063 0.067 *−0.018 0.010
Street connectivity−0.091**−0.007 0.088 **−0.042 −0.049
Aesthetics0.074 *0.037 −0.011 0.083 ***0.102 ***
Traffic hazards0.059 −0.003 0.006 0.027 0.039
Crime rate−0.071*−0.006 −0.024 0.022 0.003
Social environment−0.006 −0.011 0.035 −0.012 −0.01
Bicycle infrastructure0.040 0.078 **0.086 **−0.006 0.027
Availability to bicycle-sharing0.115 ***0.085 **0.057 −0.066**−0.02
Socio-demographics
Age −0.171***−0.348***−0.233***−0.331***−0.455***
Female−0.022 −0.071**−0.086**−0.099***−0.127***
Married0.009 0.081 **0.090 ***0.002 0.030
Education level0.018 −0.052 −0.035 0.038 0.028
No. of bikes0.006 0.111 ***0.152 ***0.044 0.085 ***
Driving license 0.010 −0.102***−0.005 0.001 −0.013
Employment Status 0.083 *0.079 *−0.007 0.024 0.052
Notes: * p < 0.1; ** p < 0.05; *** p < 0.01.
Table 5. BSFE model results (n = 407).
Table 5. BSFE model results (n = 407).
AttitudesSocial NormsPBCBSFE
R2 = 0.150R2 = 0.190R2 = 0.192R2 = 0.202
Direct effects Direct effects Direct effects Direct effects Total Effects
Psychological factor
Attitudes 0.151 ***0.151 ***
Social Norms 0.148 **0.148 **
Perceived Behavior Control −0.001 −0.001
Neighborhood environment
Accessibility to amenities0.094 −0.085 0.116 *−0.017 −0.015
Accessibility to schools0.072 0.037 0.055 −0.017 0.000
Accessibility to bus stop−0.078 −0.138**−0.149**−0.035 −0.068
Accessibility to subway−0.118**−0.045 −0.072 0.102 *0.078
Accessibility to park−0.033 −0.101 0.003 −0.128**−0.148***
Street connectivity0.142 **0.207 ***0.223 ***−0.153***−0.101**
Aesthetics0.065 0.028 0.015 0.068 0.082
Traffic hazards0.144 ***−0.124**0.005 0.084 *0.088 *
Crime rate−0.168***0.035 −0.005 −0.032 −0.052
Social environment−0.081 −0.007 0.040 −0.014 −0.027
Bicycle infrastructure0.037 0.103 0.019 0.175 ***0.195 ***
Availability to bicycle−sharing0.148 ***0.123 **0.112 **−0.087*−0.046
Socio−demographics
Age 0.168 **−0.008 0.207 ***−0.139**−0.115*
Female0.006 0.016 0.016 0.026 0.029
Married−0.037 −0.018 −0.085 −0.002 −0.011
Education level−0.021 −0.123**−0.068 0.032 0.011
No. of bikes0.043 0.086 0.121 **0.077 0.097 **
Driving license 0.059 −0.158**0.001 −0.038 −0.052
Employment Status 0.097 0.083 −0.013 0.061 0.088
Notes: * p < 0.1; ** p < 0.05; *** p < 0.01.
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.

Share and Cite

MDPI and ACS Style

Wang, H.; Dong, Y.; Ma, L. Determinants of Dockless Bicycle-Sharing Adoption and Usage Intensity for Commuting and Errands: Evidence from Disadvantaged Neighborhoods. Land 2024, 13, 2055. https://doi.org/10.3390/land13122055

AMA Style

Wang H, Dong Y, Ma L. Determinants of Dockless Bicycle-Sharing Adoption and Usage Intensity for Commuting and Errands: Evidence from Disadvantaged Neighborhoods. Land. 2024; 13(12):2055. https://doi.org/10.3390/land13122055

Chicago/Turabian Style

Wang, Hongyu, Yu Dong, and Liang Ma. 2024. "Determinants of Dockless Bicycle-Sharing Adoption and Usage Intensity for Commuting and Errands: Evidence from Disadvantaged Neighborhoods" Land 13, no. 12: 2055. https://doi.org/10.3390/land13122055

APA Style

Wang, H., Dong, Y., & Ma, L. (2024). Determinants of Dockless Bicycle-Sharing Adoption and Usage Intensity for Commuting and Errands: Evidence from Disadvantaged Neighborhoods. Land, 13(12), 2055. https://doi.org/10.3390/land13122055

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop