Understanding bike sharing use over time by employing extended technology continuance theory

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Abstract

The wide acceptance of bike sharing services depends on the consumers’ continuing use of bike sharing services. Facilitating users’ continuance intentions and retaining consumers are important to bike sharing service providers and governments. Following extended technology continuance theory and incorporating perceived risk, we aim to identify factors that affect bike sharing services’ continuance intentions in this study. We use a questionnaire survey involving 559 respondents to conduct data analysis with structural equation modeling. Our empirical results demonstrate that the extended technology continuance theory could provide a strong rationale in the investigation of continuance intention to adopt bike sharing services. Perceived usefulness, satisfaction, and attitude are positively associated with continuance intention. Perceived usefulness also positively impacts satisfaction and attitude. Perceived risk tends to be negatively related to satisfaction. Additionally, confirmation can positively impact perceived usefulness and perceived ease of use. Perceived ease of use is positively associated with perceived usefulness and attitude.

Introduction

Bike sharing is one of the fastest growing modes of public transportation in the world. It has an average expanding rate of 37% per annum since 2009. The largest fraction of growth in bike sharing is in China, where the number of bike sharing users will be more than 18,860,000 by the end of 2016 and will reach 50,000,000 at the end of 2018 (Big Data Research, 2017). Compared with traditional public transportation models, bike sharing schemes possess advantages in providing the best transportation model for short length trips and reducing environmental problems derived from transportation emissions (Shaheen and Guzman, 2011). Moreover, shared bikes require little space for parking and movement compared with other vehicles, thereby conserving resources, e.g., urban land (Altaf, 2017, Claude, 2014, Shaheen et al., 2013). Given the enormous advantages of bike sharing schemes in relation to environment protection and energy conservation, the persistent initiation of policies to stimulate the introduction and adoption of bike sharing systems is among the basic local services in many major cities of China (Ma et al., 2018). For example, in Shanghai, more than 1.5 million shared bicycles have been circulating in the city since November 2017; hence, approximately 1 shared bike is available for every 16 citizens.1

The current generation of bike sharing systems has been fully equipped with advanced features, e.g., app-based registration system, multi-modal access, mobile payment, global positioning system (GPS) tracking, and real-time transit integration (Altaf, 2017). Compared with traditional public bike sharing services, the primary advantage of the current bike sharing systems is ubiquity. Ubiquity means that people can access bike sharing systems anytime anywhere with the popularization of mobile networks and terminals, thereby ultimately facilitating users’ adoption of bike sharing systems. Nonetheless, bike sharing systems have their limitations, such as safety concerns, privacy leakage, inconvenient operating, and deposit refund concerns. These constraints can negatively affect people’s attitude toward the shared bikes and impede continuance adoption. In addition, Chinese bike sharing companies are aggressively competing for investment and territory. Among these bike sharing service providers, Mobike and Ofo are currently the two largest operators with a combined market share of 90%. Bike sharing operators are spending money excessively to provide bike sharing services and finance their business. Retaining users and facilitating their continuance usage are important.

Motivated by the innovative characteristics of bike sharing, existing research has commonly employed theory of planned behavior (TPB) and the technology acceptance model (TAM) to investigate initial adoption and usage of bike sharing services (Du and Cheng, 2018, Li et al., 2018, Schneider, 2013). Positive factors, e.g., perceived ease of use, perceived usefulness, and subjective normal, are identified to influence initial adoption (Liao et al., 2009). However, the post-adoption usage of bike sharing systems has received little attention. In consideration of the great importance of retaining users, factors that affect continuance usage should be identified. Furthermore, negative factors, such as perceived risk associated with the adoption of bike sharing systems, should also be addressed as a critical factor influencing the continuance usage. In this study, we aim to explore the determinants of continuance usage in the context of bike sharing systems. We use the extended technology continuance theory (TCT) with incorporating perceived risk as the theoretical model.

The paper is organized as follows. In Section 2, we construct a theoretical framework. In Section 3, we present the hypotheses. In Section 4, we describe the data and explain the methodology. In Section 5, we report the empirical results. In 6 Discussion and conclusions, 7 Limitations and future research, we conclude this study and present discussions, respectively.

Section snippets

Bike sharing scheme as a step toward active mobility

Human society is subjected to intense environmental and health problems. Thus, less dependence on fossil fuels in traditional vehicles has become critical to political agenda. Governments and transport agencies, especially in developing countries and major cities, dedicate to making the transportation systems sustainable and green to alleviate the environmental impact of traditional transport emissions (Shaheen and Guzman, 2011). Various solutions, e.g., a widely employment of active

Hypotheses

To propose our hypotheses, we consider consumers’ continuance pro-bike sharing behavioral intentions from the view of technology acceptance, expectation confirmation and cognitive model, and risk perception of the TCT framework. We elucidate the hypotheses from the following perspectives.

Sample and data collection

To investigate the extended TCT model and its hypotheses empirically, we conducted an offline questionnaire survey. We designed and adopted a three-page questionnaire to collect data. We composed this questionnaire with three parts: (1) a brief introduction to express appreciation to the participants; (2) specific items designed to reflect multiple scales of constructs; and (3) several extra questions capturing the demographics of the respondents.

Nanjing is far ahead of other cities in the

Reliability and validity assessment

We first test the reliability and validity of the constructs. The factor loading of all the items are higher than 0.7. Thus, it is neglected. The values of composite reliability range from 0.867 to 0.907, and the Cronbach’s alpha values of the constructs range from 0.790 to 0.845, all of which are greater than 0.70. Thus, the construct reliability is acceptable.

Then, we test the construct validity using average variance extracted (AVE). The AVE scores range from 0.620 to 0.764, which are all

Contributions and implications

According to research, promoting bike sharing services has prominent benefits in solidifying a climate-smart and health-benefiting transport culture (Shaheen and Guzman, 2011). A primary question in facilitating the use of bike sharing services is: What motivates the continuance usage intention of the bike sharing services? The extended TCT model functions as a proper theoretical framework to examine what governs peoples’ continuance intentions toward bike sharing. The fundamental idea of the

Limitations and future research

This study has several limitations that need to be addressed in future research. The extended TCT model appears to be a proper framework to predict bike sharing continuance intentions. However, future research needs to examine whether this extended TCT model can be used to advance other ride-sharing services related to such sharing economy as car-sharing systems, e.g., blue-bike sharing scheme. Derived from multiple theoretical frameworks (e.g., TAM, ECM, and COG) adopted in modeling new

Acknowledgements

This research was funded by the Humanity and Social Science Youth foundation of Ministry of Education of China (18YJC630106), China Postdoctoral Science Foundation (2018M642546), the National Natural Science Foundation of China (71702180), and the Social Science Foundation of Jiangsu (18GLC001).

References (57)

  • M.C. Lee

    Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model

    Comput. Educ.

    (2010)
  • Y. Lee et al.

    Intimacy, familiarity and continuance intention: An extended expectation–confirmation model in web-based services

    Electron. Commer. Res. Appl.

    (2011)
  • H. Li et al.

    Understanding post-adoption behaviors of e-service users in the context of online travel services

    Inf. Manage.

    (2014)
  • C. Liao et al.

    Information technology adoption behavior life cycle: Toward a technology continuance theory (TCT)

    Int. J. Inf. Manage.

    (2009)
  • Z. Lin et al.

    Airline passengers’ continuance intention towards online check-in services: The role of personal innovativeness and subjective knowledge

    Transportation Res. Part E: Logistics Transportation Rev.

    (2015)
  • L. Ma et al.

    Bike sharing and users’ subjective well-being: An empirical study in China

    Transportation Res. Part A: Policy Practice

    (2018)
  • S. Mouakket

    Factors influencing continuance intention to use social network sites: The Facebook case

    Comput. Hum. Behav.

    (2015)
  • C.K. Park et al.

    A study of factors enhancing smart grid consumer engagement

    Energy Policy

    (2014)
  • R.J. Schneider

    Theory of routine mode choice decisions: An operational framework to increase sustainable transportation

    Transp. Policy

    (2013)
  • W.L. Shiau et al.

    Understanding behavioral intention to use a cloud computing classroom: A multiple model comparison approach

    Inf. Manage.

    (2016)
  • J.Y.L. Thong et al.

    The effects of post-adoption beliefs on the expectation–confirmation model for information technology continuance

    Int. J. Hum Comput Stud.

    (2006)
  • G.S. Weng et al.

    Mobile taxi booking application service’s continuance usage intention by users

    Transportation Res. Part D: Transport Environ.

    (2017)
  • H.J. Ye et al.

    Solvers’ participation in crowdsourcing platforms: Examining the impacts of trust, and benefit and cost factors

    J. Strateg. Inf. Syst.

    (2017)
  • Altaf, S., 2017. Investigating the factors influencing the use of public bike sharing schemes for the last mile travel...
  • S.J. Barnes et al.

    Modeling user continuance behavior in microblogging services: The case of Twitter

    J. Comput. Inf. Syst.

    (2011)
  • A. Bhattacherjee

    Managerial influences on intraorganizational information technology use: A principal–agent model

    Decision Sci.

    (1998)
  • A. Bhattacherjee

    Understanding information systems continuance: an expectation-confirmation model

    MIS Quarterly

    (2001)
  • Big Data Research, 2017. 2016 China share cycling market research report. Retrieved from...
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