Literature DB >> 22608268

Speeding by young novice drivers: What can personal characteristics and psychosocial theory add to our understanding?

Bridie Scott-Parker1, Melissa K Hyde, Barry Watson, Mark J King.   

Abstract

PURPOSE: Young novice drivers continue to be overrepresented in fatalities and injuries arising from crashes even with the introduction of countermeasures such as graduated driver licensing (GDL). Enhancing countermeasures requires a better understanding of the variables influencing risky driving. One of the most common risky behaviours performed by drivers of all ages is speeding, which is particularly risky for young novice drivers who, due to their driving inexperience, have difficulty in identifying and responding appropriately to road hazards. Psychosocial theory can improve our understanding of contributors to speeding, thereby informing countermeasure development and evaluation. This paper reports an application of Akers' social learning theory (SLT), augmented by Gerrard and Gibbons' prototype/willingness model (PWM), in addition to personal characteristics of age, gender, car ownership, and psychological traits/states of anxiety, depression, sensation seeking propensity and reward sensitivity, to examine the influences on self-reported speeding of young novice drivers with a Provisional (intermediate) licence in Queensland, Australia.
METHOD: Young drivers (n=378) recruited in 2010 for longitudinal research completed two surveys containing the Behaviour of Young Novice Drivers Scale, and reported their attitudes and behaviours as pre-Licence/Learner (Survey 1) and Provisional (Survey 2) drivers and their sociodemographic characteristics.
RESULTS: An Akers' measurement model was created. Hierarchical multiple regressions revealed that (1) personal characteristics (PC) explained 20.3%; (2) the combination of PC and SLT explained 41.1%; (3) the combination of PC, SLT and PWM explained 53.7% of variance in self-reported speeding. Whilst there appeared to be considerable shared variance, the significant predictors in the final model included gender, car ownership, reward sensitivity, depression, personal attitudes, and Learner speeding.
CONCLUSIONS: These results highlight the capacity for psychosocial theory to improve our understanding of speeding by young novice drivers, revealing relationships between previous behaviour, attitudes, psychosocial characteristics and speeding. The findings suggest multi-faceted countermeasures should target the risky behaviour of Learners, and Learner supervisors should be encouraged to monitor their Learners' driving speed. Novice drivers should be discouraged from developing risky attitudes towards speeding.
Copyright © 2012 Elsevier Ltd. All rights reserved.

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Year:  2012        PMID: 22608268     DOI: 10.1016/j.aap.2012.04.010

Source DB:  PubMed          Journal:  Accid Anal Prev        ISSN: 0001-4575


  9 in total

1.  Predictors of non- hookah smoking among high-school students based on prototype/willingness model.

Authors:  Sedigheh Abedini; MohammadAli MorowatiSharifabad; Mosharafeh Chaleshgar Kordasiabi; Amin Ghanbarnejad
Journal:  Health Promot Perspect       Date:  2014-07-12

2.  Novel use of a virtual driving assessment to classify driver skill at the time of licensure.

Authors:  Elizabeth A Walshe; Michael R Elliott; Daniel Romer; Shukai Cheng; Allison E Curry; Tom Seacrist; Natalie Oppenheimer; Abraham J Wyner; David Grethlein; Alexander K Gonzalez; Flaura K Winston
Journal:  Transp Res Part F Traffic Psychol Behav       Date:  2022-04-29

3.  Peer influence predicts speeding prevalence among teenage drivers.

Authors:  Bruce G Simons-Morton; Marie Claude Ouimet; Rusan Chen; Sheila G Klauer; Suzanne E Lee; Jing Wang; Thomas A Dingus
Journal:  J Safety Res       Date:  2012-10-12

4.  The Moderating Effects of Emotions on the Relationship Between Self-Reported Individual Traits and Actual Risky Driving Behaviors.

Authors:  Yaqi Liu; Xiaoyuan Wang; Yongqing Guo
Journal:  Psychol Res Behav Manag       Date:  2021-04-09

Review 5.  Determinants of risky driving behavior: a narrative review.

Authors:  Saba Jafarpour; Vafa Rahimi-Movaghar
Journal:  Med J Islam Repub Iran       Date:  2014-12-06

6.  Investigating in-vehicle distracting activities and crash risks for young drivers using structural equation modeling.

Authors:  Khaled Shaaban; Sherif Gaweesh; Mohamed M Ahmed
Journal:  PLoS One       Date:  2020-07-02       Impact factor: 3.240

7.  Understanding Traffic Accidents among Young Drivers in Qatar.

Authors:  Faris Tarlochan; Mohamed Izham Mohamed Ibrahim; Batool Gaben
Journal:  Int J Environ Res Public Health       Date:  2022-01-04       Impact factor: 3.390

Review 8.  Identifying Interactive Factors That May Increase Crash Risk between Young Drivers and Trucks: A Narrative Review.

Authors:  Melissa R Freire; Cassandra Gauld; Angus McKerral; Kristen Pammer
Journal:  Int J Environ Res Public Health       Date:  2021-06-16       Impact factor: 3.390

9.  Factors determining speed management during distracted driving (WhatsApp messaging).

Authors:  Sonia Ortiz-Peregrina; Oscar Oviedo-Trespalacios; Carolina Ortiz; Miriam Casares-López; Carlos Salas; Rosario G Anera
Journal:  Sci Rep       Date:  2020-08-06       Impact factor: 4.379

  9 in total

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