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    Structural equation modeling of public transport use with COVID-19 precautions: An extension of the norm activation model

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    1-s2.0-S2590198221001792-main.pdf (3.191Mb)
    Date
    2021-10-04
    Author
    Muhammad, Ashraf Javid
    Abdullah, Muhammad
    Ali, Nazam
    Dias, Charitha
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    Abstract
    This study aims to identify the travelers’ attitudes and preferences towards the use of public transport during COVID-19 through the Norm-Activation Model (NAM). A questionnaire survey was designed to assess the traveler’s awareness, sense of responsibility, and moral obligations to comply with safety norms or guidelines while using public transport in Lahore, Pakistan. The Structural Equation Modeling method was applied to analyze the collected 1516 complete responses. The survey results and measurement equations confirmed the association between observed and corresponding latent variables. The results of SEM revealed that the travelers’ Awareness of Consequences (AC) and Ascription of Responsibility (AR) are positively correlated with Personal Norms (PN), whereas attitudes towards public transport are negatively related to the PN. The perceived behavioral control (PBC) and PN have negative structural correlations with public transport use. However, the attitudes towards public transport are strong predictors of travelers’ behavior towards public transport. The current motorcycle users and high-income people have low preferences, whereas current users of public transport modes and middle-income people have high preferences to choose public transport during a pandemic. The travelers’ perceived difficulty of complying with safety guidelines while traveling, such as wearing a facemask, use of sanitizers, and maintaining a social distance, have a significant impact on their public transport use. The travelers’ better awareness, responsibility, and trust in using public transport may have a strong influence on their preferences towards public transport during a pandemic situation.
    URI
    https://www.sciencedirect.com/science/article/pii/S2590198221001792
    DOI/handle
    http://dx.doi.org/10.1016/j.trip.2021.100474
    http://hdl.handle.net/10576/56513
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    • COVID-19 Research [‎848‎ items ]
    • Traffic Safety [‎163‎ items ]

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