3,467
Views
0
CrossRef citations to date
0
Altmetric
MARKETING

Moving towards smart mobility: Factors influencing the intention of consumers to adopt the bus rapid transit (BRT) system

&
Article: 2089393 | Received 22 Dec 2021, Accepted 09 Jun 2022, Published online: 10 Jul 2022

References

  • Aaker, D. A., Kumar, V., Leone, R. P., & Day, G. S. (2013). Marketing research (11th ed.). John Wiley & Sons.
  • Abejide, O., Adedeji, J., & Mostafa Hassan, M. (2018). Intelligent transportation system as an effective remedy to improve the public transportation in South Africa. Papers presented at the 2018 37th Annual Southern African Transport Conference (SATC). Pretoria.
  • Ahmed, W., Hizam, S. M., Sentosa, I., Akter, H., Yafi, E., & Ali, J. (2020). Predicting IoT service adoption towards smart mobility in Malaysia: SEM-neural hybrid pilot study. International Journal of Advanced Computer Science and Applications, 11(1), 524–18. https://doi.org/10.14569/IJACSA.2020.0110165
  • Bansal, P., Kockelman, K. M., & Singh, A. (2016). Assessing public opinions of and interest in new vehicle technologies: An Austin perspective. Transportation Research Part C: Emerging Technologies, 67 (2016) , 1–14. https://doi.org/10.1016/j.trc.2016.01.019
  • Bauerová, R., & Klepek, M. (2018). Technology acceptance as a determinant of online grocery shopping adoption. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 66(3), 737–746. https://doi.org/10.11118/actaun201866030737
  • Belanche, D., Casaló, L., & Flavián, C. (2019). Artificial intelligence in FinTech: Understanding robo-advisors adoption among customers. Industrial Management & Data Systems, 119(7), 1411–1430. https://doi.org/10.1108/IMDS-08-2018-0368
  • Bibri, S. E. (2020). Compact urbanism and the synergic potential of its integration with data-driven smart urbanism: An extensive interdisciplinary literature review. Land Use Policy, 97 (2020) , 104703. https://doi.org/10.1016/j.landusepol.2020.104703
  • Bokolo, A. J., & Petersen, S. A. (2019). A smart city adoption model to improve sustainable living. NOKOBIT - Norsk konferanse for organisasjoners bruk at IT, 27(1). http://hdl.handle.net/11250/2631501
  • Brčić, D., Slavulj, M., Šojat, D., & Jurak, J. (2018). The role of smart mobility in smart cities. Fifth International Conference on Road and Rail Infrastructure (CETRA 2018). 17–19 May 2018. Zadar, Croatia.
  • Bwalya, K. J. (2019). The smart city of Johannesburg, South Africa. In K. J. Mwala (Ed.), Smart city emergence (pp. 407–419). Elsevier.
  • Cheah, I., SHimul, A. S., Liang, J., & Phau, I. (2022). Consumer attitude and intention toward ridesharing. Journal of Strategic Marketing, 30(2), 115–136. https://doi.org/10.1080/0965254X.2020.1733050
  • Chen, C.-D., Fan, Y.-W., & Farn, C.-K. (2007). Predicting electronic toll collection service adoption: An integration of the technology acceptance model and the theory of planned behavior. Transportation Research Part C: Emerging Technologies, 15(5), 300–311. https://doi.org/10.1016/j.trc.2007.04.004
  • Choi, J., & song, C. (2020). Factors explaining why some citizens engage in e-participation, while others do not. Government Information Quarterly, 37(4), 101524. https://doi.org/10.1016/j.giq.2020.101524
  • Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. [Doctoral dissertation]. MIT Sloan School of Management.
  • Davis, F. D. (1987). User acceptance of information systems: The technology acceptance model (TAM). University of Michigan, School of Business Administration.
  • Dečman, M. (2015). Modeling the acceptance of e-learning in mandatory environments of higher education: The influence of previous education and gender. Computers in Human Behavior, 49 (2015) , 272–281. https://doi.org/10.1016/j.chb.2015.03.022
  • Deng, T., & Nelson, J. D. (2013). Bus rapid transit implementation in Beijing: An evaluation of performance and impacts. Research in Transportation Economics, 39(1), 108–113. https://doi.org/10.1016/j.retrec.2012.06.002
  • Feng, B., Ye, Q., & Collins, B. J. (2019). A dynamic model of electric vehicle adoption: The role of social commerce in new transportation. Information & Management, 56(2), 196–212. https://doi.org/10.1016/j.im.2018.05.004
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
  • Francis, J., Eccles, M. P., Johnston, M., Walker, A. E., Grimshaw, J. M., Foy, R., Kaner, E. F. S., Smith, L., & Bonetti, D. (2004). Constructing questionnaires based on the theory of planned behaviour: A manual for health services researchers. Centre for Health Services Research, University of Newcastle-upon-Tyne.
  • Fryszman, F., Carstens, D. D. D. S., & Da Cunha, S. K. (2019). Smart mobility transition: A socio-technical analysis in the city of Curitiba. International Journal of Urban Sustainable Development, 11(2), 141–153. https://doi.org/10.1080/19463138.2019.1630414
  • Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519
  • Haase, D., Güneralp, B., Dahiya, B., Bai, X., & Elmqvist, T. (2018). Urban planet: Knowledge towards sustainable cities. Cambridge University Press.
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (Global edition ed.) Pearson Education.
  • Ho, S. M., Ocasio-Velázquez, M., & Booth, C. (2017). Trust or consequences? Causal effects of perceived risk and subjective norms on cloud technology adoption. Computers & Security, 70 (2017) , 581–595. https://doi.org/10.1016/j.cose.2017.08.004
  • Huang F, Teo T and Zhou M. (2020). Chinese students’ intentions to use the Internet-based technology for learning. Education Tech Research Dev, 68(1), 575–591. 10.1007/s11423-019-09695-y
  • Huang, F., & Teo, T. (2021). Examining the role of technology‐related policy and constructivist teaching belief on English teachers’ technology acceptance: A study in Chinese universities. British Journal of Educational Technology, 52(1), 441–460. https://doi.org/10.1111/bjet.13027
  • Irawan, H., Hendayani, R., & Widyan, D. (2016). Adoption of electronic toll application analysis. International Journal of Economics and Management, 10(1), 211–222 https://d1wqtxts1xzle7.cloudfront.net/51718515/15-VOL_10S2016_Herry_IrawanAdoption_of_Electronic_. .Pages_211-222-with-cover-page-v2.pdf?Expires=1655817932&Signature=OTYCXWtcsPpR7-fGnySF7pD86e7RjJjJRDIRBUL6mqPiUxGO~1Dv0iyzuQqREOcLWS3DZEY8Oy~p6zV-JR5Tzp4KVDUlpMhoXNjC23VjqTGkpqeXkA6y6Gj2uEcQd395~D~BGTGRrEwluyIXonDWfqKyXaI1QkEwGfUXm2oxEYb5Svf~~imVn8rbZUBpU0SaNj3FvnwmbWSkxjhOGaSGzQ8WxQ969JGLFImiZoYyZTrSvca-DpGCRo-mknwtQO-O3ap7Inzoo0xgnJ9DXAddZnA88gzC3nk39QklYLpsgI7ycJD8ZTdqR6rGBGnk8hZwNvOD0yI9Am-Qu4f0u0SwaQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA.
  • Khatibi, H., Wilkinson, S., Baghersad, M., Dianat, H., Ramli, H., Suhatril, M., Javanmardi, A., & Ghaedi, K. (2021). The resilient-smart city development: A literature review and novel frameworks exploration. Built Environment Project and Asset Management May 2021, 11(4), 493–510. https://doi.org/10.1108/BEPAM-03-2020-0049
  • Khumalo, T. N., & Ogra, A. (2018). Effectiveness of Rea Vaya Bus Rapid Transit System (BRTS) in the City of Johannesburg. Proceedings of the International Conference on Industrial Engineering and Operations Management. Pretoria. 29 October - 1 November. pp. 1306–1329.
  • Klopp, J. M., Harber, J., & Quarshie, M. (). A review of BRT as public transport reform in African cities. VREF research synthesis project. Governance of Metropolitan Transport https://doi.org/10.13140/RG.2.2.29342.79686. .
  • T. V. Kumar (Ed). (2018). Smart Economy in Smart Cities: International Collaborative Research: Ottawa, St. Hong Kong. Springer Singapore.
  • Loo, B. P. Y., & Tang, W. S. M. (2019). Mapping smart cities. Journal of Urban Technology, 26(2), 129–146. https://doi.org/10.1080/10630732.2019.1576467
  • Luedtke, A., Sadikova, E., & Kessler, R. C. (2019). Sample size requirements for multivariate models to predict between-patient differences in best treatments of major depressive disorder. Clinical Psychological Science, 7(3), 445–461. https://doi.org/10.1177/2167702618815466
  • Maduku, D. K. (2011). Understanding retail bank customers’ attitude towards and usage of cell phone and internet banking in Gauteng, South Africa. Master’s dissertation. University of Johannesburg.
  • Marfo, P. K. T., & Quansah, E. (2020). Factors influencing the adoption of e-ticketing system in the bus transport sector in Ghana. Journal of Software Engineering and Applications, 13(8), 161–178. https://doi.org/10.4236/jsea.2020.138011
  • Mthimkulu, N. (2017). Southern African solutions to public transport challenges. Master’s dissertation. University of Cape Town.
  • Musakwa, W., & Gumbo, T. (2017). Impact of urban policy on public transportation in Gauteng, South Africa: Smart or dumb city systems is the question. In F. R. Álvarez, S. Zubelzu, & R. Martínez (Eds.), Carbon footprint and the industrial life cycle. Green energy and technology (pp. 339–356). Springer.
  • Olokesusi, F., Aiyegbajeje, F. O., & Arije, I. M. (2019). Smart metropolitan regional development of Abuja and its region. In T. M. V. Kumar (Ed.), Smart metropolitan regional development (pp. 797–825). Springer.
  • Park, E., Kim, H., & Ohm, J. Y. (2015). Understanding driver adoption of car navigation systems using the extended technology acceptance model. Behaviour & Information Technology, 34(7), 741–751. https://doi.org/10.1080/0144929X.2014.963672
  • Rauniar, R., Rawski, G., Yang, J., & Johnson, B. (2014). Technology acceptance model (TAM) and social media usage: An empirical study on Facebook. Journal of Enterprise Information Management, 27(1), 6–30. https://doi.org/10.1108/JEIM-04-2012-0011
  • Sánchez-Prieto, J. C., Hernández-García, Á., García-Peñalvo, F. J., Chaparro-Peláez, J., & Olmos-Migueláñez, S. (2019). Break the walls! Second-order barriers and the acceptance of mLearning by first-year pre-service teachers. Computers in Human Behavior, 95 (2019) , 158–167. https://doi.org/10.1016/j.chb.2019.01.019
  • Saunders, M. K. N., Lewis, P., & Thornhill, A. (2019). Research methods for business students (8th ed.). Pearson.
  • Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90–103. https://doi.org/10.1016/j.im.2006.10.007
  • Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128 (2019) , 13–35 . https://doi.org/10.1016/j.compedu.2018.09.009
  • Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2), 23–74.
  • Shahidehpour, M., Li, Z., & Ganji, M. (2018). Smart cities for a sustainable urbanization: Illuminating the need for establishing smart urban infrastructures. IEEE Electrification Magazine, 6(2), 16–33. https://doi.org/10.1109/MELE.2018.2816840
  • Shroff, R. H., Deneen, C. C., & Ng, E. M. W. (2011). Analysis of the technology acceptance model in examining students’ behavioural intention to use an e-portfolio system. Australasian Journal of Educational Technology, 27(4), 600–618. https://doi.org/10.14742/ajet.940
  • Statista. (2021). Distribution of Facebook users in South Africa as of April 2021, by age group. https://www.statista.com/statistics/1028389/facebook-user-share-in-south-africa-by-age/
  • Stats SA, (2020). SA economy sheds 2,2 million jobs in Q2 but unemployment levels drop Accessed 21 June 2021. http://www.statssa.gov.za/?p=13633
  • Suresh, S., Renukappa, S., Abdul-Aziz, A.-R., Paloo, Y., & Jallow, H. (2021). Developments in the UK road transport from a smart cities perspective. Engineering, Construction and Architectural Management, 28(4), 845–862. https://doi.org/10.1108/ECAM-12-2019-0687
  • Teo, T., Lee, C. B., & Chai, C. S. (2008). Understanding pre-service teachers’ computer attitudes: Applying and extending the technology acceptance model. Journal of Computer Assisted Learning, 24(2), 128–143. https://doi.org/10.1111/j.1365-2729.2007.00247.x
  • Teo, T. (2010). Examining the influence of subjective norm and facilitating conditions on the intention to use technology among pre-service teachers: A structural equation modelling of an extended technology acceptance model. The Asia-Pacific Education Researcher, 11(2), 253–262. https://doi.org/10.1007/s12564-009-9066-4
  • Ursavaş, Ö. F., Yalçın, Y., & Bakır, E. (2019). The effect of subjective norms on preservice and in‐service teachers’ behavioural intentions to use technology: A multigroup multimodel study. British Journal of Educational Technology, 50(5), 2501–2519. https://doi.org/10.1111/bjet.12834
  • Vakula, D., & Raviteja, B. (2017). Smart public transport for smart cities. International Conference on Intelligent Sustainable Systems (ICISS). Palladam, India (IEEE). 7-8 December. pp. 805–810.
  • Van Rensburg, D. (2017). Bus rapid transit bleeding cash Accessed 21 June 2021. https://www.news24.com/fin24/Economy/bus-rapid-transit-bleeding-cash-20170226-2
  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
  • Venter, C., Jennings, G., Hidalgo, D., & Valderrama Pineda, A. F. (2018). The equity impacts of bus rapid transit: A review of the evidence and implications for sustainable transport. International Journal of Sustainable Transportation, 12(2), 140–152. https://doi.org/10.1080/15568318.2017.1340528
  • Yeo V Cheow, Goh S and Rezaei S. (2017). Consumer experiences, attitude and behavioral intention toward online food delivery (OFD) services. Journal of Retailing and Consumer Services, 35 150–162. 10.1016/j.jretconser.2016.12.013
  • Zolnik, E. J., Malik, A., & Irvin-Erickson, Y. (2018). Who benefits from bus rapid transit? Evidence from the Metro Bus System (MBS) in Lahore. Journal of Transport Geography, 71 (2018) , 139–149. https://doi.org/10.1016/j.jtrangeo.2018.06.019