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Testing a structural model of young driver willingness to uptake Smartphone Driver Support Systems

Overview of attention for article published in Accident Analysis & Prevention, October 2015
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (74th percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

twitter
7 tweeters

Citations

dimensions_citation
24 Dimensions

Readers on

mendeley
100 Mendeley
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Title
Testing a structural model of young driver willingness to uptake Smartphone Driver Support Systems
Published in
Accident Analysis & Prevention, October 2015
DOI 10.1016/j.aap.2015.07.023
Pubmed ID
Authors

Aoife A. Kervick, Michael J. Hogan, Denis O’Hora, Kiran M. Sarma

Abstract

There is growing interest in the potential value of using phone applications that can monitor driver behaviour (Smartphone Driver Support Systems, 'SDSSs') in mitigating risky driving by young people. However, their value in this regard will only be realised if young people are willing to use this technology. This paper reports the findings of a study in which a novel structural model of willingness to use SDSSs was tested. Grounded in the driver monitoring and Technology Acceptance (TA) research literature, the model incorporates the perceived risks and gains associated with potential SDSS usage and additional social cognitive factors, including perceived usability and social influences. A total of 333 smartphone users, aged 18-24, with full Irish driving licenses completed an online questionnaire examining willingness or Behavioural Intention (BI) to uptake a SDSS. Following exploratory and confirmatory factor analyses, structural equation modelling indicated that perceived gains and social influence factors had significant direct effects on BI. Perceived risks and social influence also had significant indirect effects on BI, as mediated by perceived gains. Overall, this model accounted for 72.5% of the variance in willingness to uptake SDSSs. Multi-group structural models highlighted invariance of effects across gender, high and low risk drivers, and those likely or unlikely to adopt novel phone app technologies. These findings have implications for our understanding of the willingness of young drivers to adopt and use SDSSs, and highlight potential factors that could be targeted in behavioural change interventions seeking to improve usage rates.

Twitter Demographics

The data shown below were collected from the profiles of 7 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 100 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 1%
India 1 1%
United States 1 1%
Switzerland 1 1%
Unknown 96 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 18 18%
Student > Ph. D. Student 17 17%
Researcher 14 14%
Student > Doctoral Student 10 10%
Student > Bachelor 7 7%
Other 22 22%
Unknown 12 12%
Readers by discipline Count As %
Engineering 13 13%
Business, Management and Accounting 11 11%
Social Sciences 11 11%
Psychology 10 10%
Computer Science 9 9%
Other 27 27%
Unknown 19 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 May 2019.
All research outputs
#3,875,119
of 15,055,070 outputs
Outputs from Accident Analysis & Prevention
#909
of 3,243 outputs
Outputs of similar age
#59,118
of 235,991 outputs
Outputs of similar age from Accident Analysis & Prevention
#24
of 84 outputs
Altmetric has tracked 15,055,070 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 3,243 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one has gotten more attention than average, scoring higher than 71% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 235,991 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.
We're also able to compare this research output to 84 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.