Riding personal mobility vehicles on the road: an analysis of the intentions of Chinese users | Cognition, Technology & Work
Skip to main content

Advertisement

Springer Nature Link
Log in

Riding personal mobility vehicles on the road: an analysis of the intentions of Chinese users

  • Original Article
  • Published:
Cognition, Technology & Work Aims and scope Submit manuscript

Abstract

In China, there are no road rights for personal mobility vehicles (PMVs), but sales of PMVs in China are rapidly increasing, and at present, a large group of PMV users has been formed. Research on the demand and intentions of PMV users holds great significance for road safety and policy formulation. This research adopts an online questionnaire based on the unified theory of acceptance and use of technology model and introduces perceived risk, product satisfaction, and policy measures to construct a model of the impact mechanism of user intentions to ride PMVs on the road. First, a questionnaire survey was conducted among 418 PMV users in China, and the factors affecting their intentions to ride PMVs on the road were analyzed through structural equation modeling (SEM). The results from analyzing the questionnaires show that performance expectancy, effort expectancy, social influence, perceived risk and policy measures have a significant positive impact on user intentions to ride PMVs on the road. However, product satisfaction has no significant impact on user intentions to ride PMVs on the road, but through risk perception, it has a mediating effect on user intentions. In addition, there are four adjustment variables: gender, age, educational level and experience witnessing others riding PMVs on the road. The results of multigroup SEM analysis show that there are significant differences in the path of user intentions to ride PMVs on the road. Second, compared with other surveys obtained from the Internet among 307 non-users and non-owners of PMVs, there are significant differences in perceived risk, policy measures and intentions to ride PMVs on the road. Implications for policy-making and suggestions for future research are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Ajisaka S, Kubota T, Hashimoto H (2013) Human balance control ability for affinitive personal vehicle. In: IEEE 10th international conference on ubiquitous robots and ambient intelligence (URAI). pp 503–508

  • Ando R, Li A (2012) An analysis on users’ evaluation for self-balancing two-wheeled personal mobility vehicles. In: 15th international IEEE conference on intelligent transportation systems. pp 1525–1530

  • Ando R, Li A, Nishihori Y, Kachi N (2013) Acceptability of personal mobility vehicles to public in Japan: Results of social trial in Toyota city. In: Spatial planning and sustainable development. Springer, Dordrecht, pp 213–225

  • Campbell AA, Cherry CR, Ryerson MS, Yang X (2016) Factors influencing the choice of shared bicycles and shared electric bikes in Beijing. Transport Res Part C Emerg Technol 67:399–414

    Google Scholar 

  • Chantala K, Suchindran C, Blanchette D (2006) Adjusting for unequal selection probability in multilevel models: a comparison of software packages. In: Proceedings of the American Statistical Association. American Statistical Association, Seattle, pp 2815–2824

    Google Scholar 

  • Cicchetti DV (1994) Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychol Assess 6(4):284

    Google Scholar 

  • Dias C, Iryo M, Nishiuchi H (2017) Evaluation of safe avoidance distance for pedestrians in personal mobility vehicles and pedestrian mixed traffic: a simulation based study. J East Asia Soc Transp Stud 12:1994–2005

    Google Scholar 

  • Dias C, Nishiuchi H, Hyoudo S, Todoroki T (2018) Simulating interactions between pedestrians, Segway riders and cyclists in shared spaces using social force model. Transp Res Procedia 34:91–98

    Google Scholar 

  • Diekmann M, Theuvsen L (2019) Non-participants interest in CSA-insights from Germany. J Rural Stud 69:1–10

    Google Scholar 

  • Du H, Liu D, Sovacool BK, Wang Y, Ma S, Li RYM (2018) Who buys new energy vehicles in China? Assessing social-psychological predictors of purchasing awareness, intention, and policy. Transport Res Part F Traffic Psychol Behav 58:56–69

    Google Scholar 

  • Fang K, Handy S (2019) Skateboarding for transportation: exploring the factors behind an unconventional mode choice among university skateboard commuters. Transportation 46(1):263–283

    Google Scholar 

  • Farah H, Koutsopoulos HN, Saifuzzaman M, Kölbl R, Fuchs S, Bankosegger D (2012) Evaluation of the effect of cooperative infrastructure-to-vehicle systems on driver behavior. Transport Res Part C Emerg Technol 21(1):42–56

    Google Scholar 

  • General Administration of Quality Supervision (2017) National Standard of PMV. http://www.aqsiq.gov.cn/zjxw/dfzjxw/dfftpxw/201712/t20171215_508954.htm. Accessed 15 Dec 2017

  • Guo Y, Li Z, Wu Y, Xu C (2018) Evaluating factors affecting electric bike users’ registration of license plate in China using Bayesian approach. Transport Res Part F Traffic Psychol Behav 59:212–221

    Google Scholar 

  • Guo Y, Sayed T, Zaki MH (2019) Evaluating the safety impacts of powered two wheelers on a shared roadway in China using automated video analysis. J Transp Saf Secur 11(4):414–429

    Google Scholar 

  • Hasegawa Y, Dias C, Iryo-Asano M, Nishiuchi H (2018) Modeling pedestrians’ subjective danger perception toward personal mobility vehicles. Transport Res Part F Traffic Psychol Behav 56:256–267

    Google Scholar 

  • Huang GQ, Chen X (2017) A study on the intention of bike-sharing usage based on context awareness theory an UTAUT model. Consum Econ 33(3):62–68

    Google Scholar 

  • Huang X, Ge J (2019) Electric vehicle development in Beijing: an analysis of consumer purchase intention. J Clean Prod 216:361–372

    Google Scholar 

  • Hulse LM, Xie H, Galea ER (2018) Perceptions of autonomous vehicles: relationships with road users, risk, gender and age. Saf Sci 102:1–13

    Google Scholar 

  • Im I, Hong S, Kang MS (2011) An international comparison of technology adoption: testing the UTAUT model. Inf Manag 48(1):1–8

    Google Scholar 

  • Iryo T, Kusakabe T, Yamanaka I, Asakura Y (2013) Effect on travelers’ activities and environmental impacts by introducing a next-generation personal transport system in a city. Int J Sustain Transp 7(3):226–237

    Google Scholar 

  • Khazaei H, Khazaei A (2016) Electric vehicles and factors that influencing their adoption moderating effects of driving experience and voluntariness of use (conceptual framework). J Bus Manag 18(12):60–65

    Google Scholar 

  • Lai WT, Chen CF (2011) Behavioral intentions of public transit passengers—the roles of service quality, perceived value, satisfaction and involvement. Transp Policy 18(2):318–325

    MathSciNet  Google Scholar 

  • Li A, Ando R (2013) Measuring the acceptability of self-balancing two-wheeled personal mobility vehicles. J East Asia Soc Transport Stud 10:444–453

    Google Scholar 

  • Liu K, Asakura Y (2009) Willingness-to-pay for the low speed private transportation mode (LSPTM) system: a web-based stated preference survey. In: 12th International Conference on Travel Behaviour Research, Jaipur, Rajasthan, India

  • Madigan R, Louw T, Dziennus M, Graindorge T, Ortega E, Graindorge M, Merat N (2016) Acceptance of automated road transport systems (ARTS): an adaptation of the UTAUT model. Transport Res Procedia 14:2217–2226

    Google Scholar 

  • Madigan R, Louw T, Wilbrink M, Schieben A, Merat N (2017) What influences the decision to use automated public transport? Using UTAUT to understand public acceptance of automated road transport systems. Transport Res Part F Traffic Psychol Behav 50:55–64

    Google Scholar 

  • Martin EW, Shaheen SA (2014) Evaluating public transit modal shift dynamics in response to bikesharing: a tale of two US cities. J Transp Geogr 41:315–324

    Google Scholar 

  • Nakagawa C, Nakano K, Suda Y, Nabeshima K (2009) Proposal for personal mobility vehicle. Seisan-Kenkyu 61(1):71–74

    Google Scholar 

  • National Bureau of Statistics (2019) Interpretation of May industrial production data by the industrial department of the national bureau of statistics. http://www.stats.gov.cn/tjsj/sjjd/201906/t20190614_1670402.html. Accessed 15 June 2019 (in Chinese)

  • Nishihori Y, Kawai M, Kachi N, Inagaki T, Ando R (2010) A discussion for introducing personal mobility vehicles into real society. In: Proceedings of 2010 JSAE Annual Congress (Autumn), vol 142, no 10, pp 23–26

  • Oye ND, Iahad NA, Rahim NA (2014) The history of UTAUT model and its impact on ICT acceptance and usage by academicians. Educ Inf Technol 19(1):251–270

    Google Scholar 

  • Poulter DR, Chapman P, Bibby PA, Clarke DD, Crundall D (2008) An application of the theory of planned behaviour to truck driving behaviour and compliance with regulations. Accid Anal Prev 40(6):2058–2064

    Google Scholar 

  • Rahman MM, Lesch MF, Horrey WJ, Strawderman L (2017) Assessing the utility of TAM, TPB, and UTAUT for advanced driver assistance systems. Accid Anal Prev 108:361–373

    Google Scholar 

  • Sarstedt M, Ringle CM, Smith D, Reams R, Hair JF Jr (2014) Partial least squares structural equation modeling (PLS-SEM): a useful tool for family business researchers. J Fam Bus Strategy 5(1):105–115

    Google Scholar 

  • Shaheen S, Chan N (2016) Mobility and the sharing economy: potential to facilitate the first-and last-mile public transit connections. Built Environ 42(4):573–588

    Google Scholar 

  • Shaheen S, Rodier CJ (2008) Easyconnect: Low-speed modes linked to transit planning project. https://escholarship.org/uc/item/4w92s6nj. Accessed 1 Oct 2008

  • Shaheen S, Rodier C, Seelig J (2005) Easyconnect ii: integrating transportation, information, and energy technologies at transit oriented developments. https://escholarship.org/uc/item/9mc515hg. Accessed 14 May 2005

  • Sovacool BK, Kester J, Noel L, de Rubens GZ (2019) Are electric vehicles masculinized? Gender, identity, and environmental values in Nordic transport practices and vehicle-to-grid (V2G) preferences. Transport Res Part D Transp Environ 72:187–202

    Google Scholar 

  • TaiHe Electric (2018) Solving the last kilometer problem: an analysis of the current situation of PMV in China, http://m.sohu.com/a/279156084_764272. Accessed 2 Dec 2018 (in Chinese)

  • Talukder MS, Shen L, Talukder MFH, Bao Y (2019) Determinants of user acceptance and use of open government data (OGD): an empirical investigation in Bangladesh. Technol Soc 56:147–156

    Google Scholar 

  • Ulrich KT (2005) Estimating the technology frontier for personal electric vehicles. Transport Res Part C Emerg Technol 13(5–6):448–462

    Google Scholar 

  • Vanderhaegen F (2014) Dissonance engineering: a new challenge to analyse risky knowledge when using a system. Int J Comput Commun Control 9(6):750–759

    Google Scholar 

  • Vanderhaegen F (2016) Toward a petri net based model to control conflicts of autonomy between cyber-physical & human-systems. IFAC-PapersOnLine 49(32):36–41

    Google Scholar 

  • Vanderhaegen F (2017) Towards increased systems resilience: new challenges based on dissonance control for human reliability in cyber-physical and human systems. Ann Rev Control 44:316–322

    Google Scholar 

  • Vanderhaegen F, Carsten O (2017) Can dissonance engineering improve risk analysis of human–machine systems? Cogn Technol Work 19(1):1–12

    Google Scholar 

  • Vanderhaegen F, Zieba S (2014) Reinforced learning systems based on merged and cumulative knowledge to predict human actions. Inf Sci 276(20):146–159

    Google Scholar 

  • Venkatesh V, Morris MG, Ackerman PL (2000) A longitudinal field investigation of gender differences in individual technology adoption decision-making processes. Organ Behav Hum Decis Process 83(1):33–60

    Google Scholar 

  • Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 27(3):425–478

    Google Scholar 

  • Wang YS, Shih YW (2009) Why do people use information kiosks? A validation of the unified theory of acceptance and use of technology. Gov Inf Q 26(1):158–165

    Google Scholar 

  • Xu J, Shang S, Yu G, Qi H, Wang Y, Xu S (2016) Are electric self-balancing scooters safe in vehicle crash accidents? Accid Anal Prev 87:102–116

    Google Scholar 

  • Zhou T, Lu Y, Wang B (2010) Integrating TTF and UTAUT to explain mobile banking user adoption. Comput Hum Behav 26(4):760–767

    Google Scholar 

  • Zieba S, Polet P, Vanderhaegen F, Debernard S (2010) Principles of adjustable autonomy: a framework for resilient human–machine cooperation. Cogn Technol Work 12(3):193–203

    Google Scholar 

  • Zmud JP, Sener IN (2017) Towards an understanding of the travel behavior impact of autonomous vehicles. Transport Res Procedia 25:2500–2519

    Google Scholar 

Download references

Acknowledgements

This paper was supported by the National Natural Science Foundation of China, Young Scientists Fund (no. 51905142) and the Fundamental Research Funds for the Central Universities (no. JZ2017HGTB0209).

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Hefei University of Technology, Hefei, 230009, Anhui, People’s Republic of China

    Zhenhua Yu

  2. School of Automotive and Traffic Engineering, Hefei University of Technology, Hefei, 230009, Anhui, People’s Republic of China

    Zhongxiang Feng, Kang Jiang, Zhipeng Huang & Zhiwei Yang

Authors
  1. Zhenhua Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhongxiang Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kang Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhipeng Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhiwei Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kang Jiang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, Z., Feng, Z., Jiang, K. et al. Riding personal mobility vehicles on the road: an analysis of the intentions of Chinese users. Cogn Tech Work 22, 801–814 (2020). https://doi.org/10.1007/s10111-019-00617-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10111-019-00617-9

Keywords

Search

Navigation