How Dockless Bike Sharing Changes Lives: An Analysis of Chinese Cities

Jiang, Hui; Song, Su; Zou, Xuan; Lu, Lu

doi:10.46830/wrirpt.18.00124

Cited by 7 publications

(8 citation statements)

References 35 publications

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“…Face-to-face questionnaires were randomly distributed to people who were waiting at metro and bus stations and people who were sitting in cafes around commercial areas. The reason for choosing metro and bus stations is that nearly 91% of respondents used DBS to connect to public transport (Jiang et al, 2020).…”

Section: Sample and Data Collectionmentioning

confidence: 99%

“…However, despite the growing convenience of using bikesharing services and environmental benefits of DBS, the report written by Jiang et al (2020) demonstrates that only 61% of users have been using DBS for more than 12 months, which indicates that some registered users are inactive, and that the continued usage rate of the system is not high. Although initial adoption is necessary for DBS operators, successful diffusion of DBS services depends on the continued interest of users (Bhattacherjee, 2001a).…”

Section: Introductionmentioning

confidence: 99%

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Using the Extended Acceptance Model to Understand Continuance Intention of Dockless Bike-Sharing

Lin

2022

Front. Psychol.

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Despite the fact that dockless bike-sharing (DBS) usage first experienced explosive growth, its continuous usage rate remains low. The ultimate success of a DBS service is more dependent on its continued usage rather than its initial adoption. Following the extended technology acceptance model (TAM), this study aims to explore factors that influence the continuance intention of DBS users. The framework of research was validated using a sample of 369 DBS users in China. The results show that perceived usefulness and perceived ease of use positively influence a user’s intention to continue using DBS. Both descriptive social norms and injunctive social norms are positively related to the continuance intention of DBS users. Moreover, environmental concern significantly affects the continuance intention of a user indirectly via perceived usefulness and perceived ease of use. Furthermore, the extended TAM has stronger prediction ability than the original TAM in the context of DBS services.

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Section: Sample and Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using the Extended Acceptance Model to Understand Continuance Intention of Dockless Bike-Sharing

Lin

2022

Front. Psychol.

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“…In the past few decades, with growing concerns over global warming and rapid urban sprawl, numerous efforts have been devoted to promoting bikesharing in cities as a viable, greener, more resilient, and healthier mobility solution towards the transition to sustainable transportation [10,12,13]. As biking can reduce carbon emissions and improve public health due to its usage flexibility, saving of parking space and fossil energy by replacing short-distance motorized trips, increasing fitness, and reducing the stress of riders from cycling activities [14][15][16][17]. During the COVID-19 pandemic, bikesharing systems are proved to be more resilient [18] and safer to move around for essential needs in urban area with a high population density [19], since biking allows for greater social distancing than other means of public transportation [15].…”

Section: Introductionmentioning

confidence: 99%

“…As biking can reduce carbon emissions and improve public health due to its usage flexibility, saving of parking space and fossil energy by replacing short-distance motorized trips, increasing fitness, and reducing the stress of riders from cycling activities [14][15][16][17]. During the COVID-19 pandemic, bikesharing systems are proved to be more resilient [18] and safer to move around for essential needs in urban area with a high population density [19], since biking allows for greater social distancing than other means of public transportation [15]. Biking is also expected to be more frequent in cities after the pandemic [20].…”

Section: Introductionmentioning

confidence: 99%

Quantifying relation between mobility patterns and socioeconomic status of dockless sharing-bike users

Gao¹,

Xu²,

Liu³

et al. 2022

Preprint

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Bikes are among the healthiest, greenest, and most affordable means of transportation for a better future city, but mobility patterns of riders with different income were rarely studied due to limitation on collecting data. Newly emergent dockless bike-sharing platforms that record detailed information regarding each trip provide us a unique opportunity. Attribute to its better usage flexibility and accessibility, dockless bike-sharing platforms are booming over the past a few years worldwide and reviving the riding fashion in cities. In this work, by exploiting massive riding records in two megacities from a dockless bike-sharing platform, we reveal that individual mobility patterns, including radius of gyration and average travel distance, are similar among users with different income, which indicates that human beings all follow similar physical rules. However, collective mobility patterns, including average range and diversity of visitation, and commuting directions, all exhibit different behaviors and spatial patterns across income categories. Hotspot locations that attract more cycling activities are quite different over groups, and locations where users reside are of a low user ratio for both higher and lower income groups. Lower income groups are inclined to visit less flourishing locations, and commute towards the direction to the city center in both cities, and of a smaller mobility diversity in Beijing but a larger diversity in Shanghai. In addition, differences on mobility patterns among socioeconomic categories are more evident in Beijing than in Shanghai. Our findings would be helpful on designing better promotion strategies for dockless bike-sharing platforms and towards the transition to a more sustainable green transportation.

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“…bus and metro) and that these trips replace walking, bus and car trips (i.e. private car, taxi and motorbikes) (Jiang et al, 2020). For the Dutch context -where cycling dominates urban mobility systems -evidence shows that platform enabled bike sharing mainly replaces walking, cycling (with a private bike) and public transport trips Ma et al, 2020;.…”

Section: Implications For Governancementioning

confidence: 99%

Platform innovation in urban mobility transitions

Waes¹

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In the summer of 2017, the city of Amsterdam was overwhelmed by thousands of bikes that were put on its streets. They were not the typical privately owned Dutch bikes you would expect and that the global cycling capital is so fond of. These bikes were different: bright colors, odd designs, catchy brand names, parked all over the city and available to anyone with a smartphone. The strange bikes were distributed by foreign startups and apparently not only in Amsterdam. They were rapidly launching on a large scale in cities across the world, from Beijing to Berlin and from Melbourne to Manchester. This fueled high expectations among urban authorities globally as cities welcome more cycling in their endeavor to create clean, healthy and accessible cities. At the same time they also caused immediate controversy as not everyone welcomed these bikes. Bike sharing could be a sustainable and affordable solution to complex urban challenges. Were we witnessing an urban mobility transition in the making? Three years later, most of these bikes left the streets of Amsterdam and much of the initial excitement is toned down. Leftover bikes are now for sale at local thrift stores. In China, most cities now have colorful bicycle graveyards filled with hundreds of thousands unused two-wheelers. In this dissertation I unravel the journey -the rise and fall -of this seemingly promising urban mobility innovation.

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How Dockless Bike Sharing Changes Lives: An Analysis of Chinese Cities

Cited by 7 publications

References 35 publications

Using the Extended Acceptance Model to Understand Continuance Intention of Dockless Bike-Sharing

Using the Extended Acceptance Model to Understand Continuance Intention of Dockless Bike-Sharing

Quantifying relation between mobility patterns and socioeconomic status of dockless sharing-bike users

Platform innovation in urban mobility transitions

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