What drives technology-based distractions? A structural equation model on social-psychological factors of technology-based driver distraction engagement
•Data collected from 525 drivers: 30+ years old (n=261) and younger (n=264).•Structural equation models built on tech-based distractions and underlying factors.•Attitudes (most influential), social norms, and personality predict engagement.•Normative influences are significant for younger drivers, b...
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| Veröffentlicht in: | Accident analysis and prevention 2016-06, Vol.91, p.166-174 |
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| creator | Chen, Huei-Yen Winnie Donmez, Birsen |
| description | •Data collected from 525 drivers: 30+ years old (n=261) and younger (n=264).•Structural equation models built on tech-based distractions and underlying factors.•Attitudes (most influential), social norms, and personality predict engagement.•Normative influences are significant for younger drivers, but not 30+ drivers.•Personality is significant for the 30+ group, and marginal for younger drivers.
With the proliferation of new mobile and in-vehicle technologies, understanding the motivations behind a driver's voluntary engagement with such technologies is crucial from a safety perspective, yet is complex. Previous literature either surveyed a large number of distractions that may be diverse, or too focuses on one particular activity, such as cell phone use. Further, earlier studies about social-psychological factors underlying driver distraction tend to focus on one or two factors in-depth, and those that examine a more comprehensive set of factors are often limited in their analyses methods.
The present work considers a wide array of social-psychological factors within a structural equation model to predict their influence on a focused set of technology-based distractions. A better understanding of these facilitators can enhance the design of distraction mitigation strategies.
We analysed survey responses about three technology-based driver distractions: holding phone conversations, manually interacting with cell phones, and adjusting the settings of in-vehicle technology, as well as responses on five social-psychological factors: attitude, descriptive norm, injunctive norm, technology inclination, and a risk/sensation seeking personality. Using data collected from 525 drivers (ages: 18–80), a structural equation model was built to analyse these social-psychological factors as latent variables influencing self-reported engagement in these three technology-based distractions.
Self-reported engagement in technology-based distractions was found to be largely influenced by attitudes about the distractions. Personality and social norms also played a significant role, but technology inclination did not. A closer look at two age groups (18–30 and 30+) showed that the effect of social norms, especially of injunctive norm (i.e., perceived approvals), was less prominent in the 30+ age group, while personality remained a significant predictor for the 30+ age group but marginally significant for the younger group.
Findings from this work provide insights into the soci |
| doi_str_mv | 10.1016/j.aap.2015.08.015 |
| format | Article |
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With the proliferation of new mobile and in-vehicle technologies, understanding the motivations behind a driver's voluntary engagement with such technologies is crucial from a safety perspective, yet is complex. Previous literature either surveyed a large number of distractions that may be diverse, or too focuses on one particular activity, such as cell phone use. Further, earlier studies about social-psychological factors underlying driver distraction tend to focus on one or two factors in-depth, and those that examine a more comprehensive set of factors are often limited in their analyses methods.
The present work considers a wide array of social-psychological factors within a structural equation model to predict their influence on a focused set of technology-based distractions. A better understanding of these facilitators can enhance the design of distraction mitigation strategies.
We analysed survey responses about three technology-based driver distractions: holding phone conversations, manually interacting with cell phones, and adjusting the settings of in-vehicle technology, as well as responses on five social-psychological factors: attitude, descriptive norm, injunctive norm, technology inclination, and a risk/sensation seeking personality. Using data collected from 525 drivers (ages: 18–80), a structural equation model was built to analyse these social-psychological factors as latent variables influencing self-reported engagement in these three technology-based distractions.
Self-reported engagement in technology-based distractions was found to be largely influenced by attitudes about the distractions. Personality and social norms also played a significant role, but technology inclination did not. A closer look at two age groups (18–30 and 30+) showed that the effect of social norms, especially of injunctive norm (i.e., perceived approvals), was less prominent in the 30+ age group, while personality remained a significant predictor for the 30+ age group but marginally significant for the younger group.
Findings from this work provide insights into the social-psychological factors behind intentional engagement in technology-based distractions and in particular suggesting that these factors may be sensitive to demographic differences.</description><identifier>ISSN: 0001-4575</identifier><identifier>EISSN: 1879-2057</identifier><identifier>DOI: 10.1016/j.aap.2015.08.015</identifier><identifier>PMID: 26994371</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Accidents, Traffic - statistics & numerical data ; Adolescent ; Adult ; Age ; Age Factors ; Aged ; Aged, 80 and over ; Attention ; Attitude ; Automobile Driving - psychology ; Cell Phone ; Cell phones ; Distracted driving ; Distracted Driving - psychology ; Female ; Geographic Information Systems - instrumentation ; Humans ; In vehicle ; Inclination ; Male ; Mathematical analysis ; Mathematical models ; Middle Aged ; Models, Statistical ; Norms ; Personality ; Radio ; Safety ; SEM ; Social Norms ; Surveys and Questionnaires ; Text Messaging ; Young Adult</subject><ispartof>Accident analysis and prevention, 2016-06, Vol.91, p.166-174</ispartof><rights>2016</rights><rights>Copyright © 2016. Published by Elsevier Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c447t-d0e1e7d0e0e87b78811fd64df960595f18667eb27c33a65263721d1a106b61293</citedby><cites>FETCH-LOGICAL-c447t-d0e1e7d0e0e87b78811fd64df960595f18667eb27c33a65263721d1a106b61293</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.aap.2015.08.015$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26994371$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Chen, Huei-Yen Winnie</creatorcontrib><creatorcontrib>Donmez, Birsen</creatorcontrib><title>What drives technology-based distractions? A structural equation model on social-psychological factors of technology-based driver distraction engagement</title><title>Accident analysis and prevention</title><addtitle>Accid Anal Prev</addtitle><description>•Data collected from 525 drivers: 30+ years old (n=261) and younger (n=264).•Structural equation models built on tech-based distractions and underlying factors.•Attitudes (most influential), social norms, and personality predict engagement.•Normative influences are significant for younger drivers, but not 30+ drivers.•Personality is significant for the 30+ group, and marginal for younger drivers.
With the proliferation of new mobile and in-vehicle technologies, understanding the motivations behind a driver's voluntary engagement with such technologies is crucial from a safety perspective, yet is complex. Previous literature either surveyed a large number of distractions that may be diverse, or too focuses on one particular activity, such as cell phone use. Further, earlier studies about social-psychological factors underlying driver distraction tend to focus on one or two factors in-depth, and those that examine a more comprehensive set of factors are often limited in their analyses methods.
The present work considers a wide array of social-psychological factors within a structural equation model to predict their influence on a focused set of technology-based distractions. A better understanding of these facilitators can enhance the design of distraction mitigation strategies.
We analysed survey responses about three technology-based driver distractions: holding phone conversations, manually interacting with cell phones, and adjusting the settings of in-vehicle technology, as well as responses on five social-psychological factors: attitude, descriptive norm, injunctive norm, technology inclination, and a risk/sensation seeking personality. Using data collected from 525 drivers (ages: 18–80), a structural equation model was built to analyse these social-psychological factors as latent variables influencing self-reported engagement in these three technology-based distractions.
Self-reported engagement in technology-based distractions was found to be largely influenced by attitudes about the distractions. Personality and social norms also played a significant role, but technology inclination did not. A closer look at two age groups (18–30 and 30+) showed that the effect of social norms, especially of injunctive norm (i.e., perceived approvals), was less prominent in the 30+ age group, while personality remained a significant predictor for the 30+ age group but marginally significant for the younger group.
Findings from this work provide insights into the social-psychological factors behind intentional engagement in technology-based distractions and in particular suggesting that these factors may be sensitive to demographic differences.</description><subject>Accidents, Traffic - statistics & numerical data</subject><subject>Adolescent</subject><subject>Adult</subject><subject>Age</subject><subject>Age Factors</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Attention</subject><subject>Attitude</subject><subject>Automobile Driving - psychology</subject><subject>Cell Phone</subject><subject>Cell phones</subject><subject>Distracted driving</subject><subject>Distracted Driving - psychology</subject><subject>Female</subject><subject>Geographic Information Systems - instrumentation</subject><subject>Humans</subject><subject>In vehicle</subject><subject>Inclination</subject><subject>Male</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Middle Aged</subject><subject>Models, Statistical</subject><subject>Norms</subject><subject>Personality</subject><subject>Radio</subject><subject>Safety</subject><subject>SEM</subject><subject>Social Norms</subject><subject>Surveys and Questionnaires</subject><subject>Text Messaging</subject><subject>Young Adult</subject><issn>0001-4575</issn><issn>1879-2057</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkc2KFDEUhYMoTjv6AG4kSzdV5qYqP4ULGQb_YMCN4jKkklvdaaorPUnVQL-JjzspehQXohLIvTd851zIIeQlsBoYyDf72tpjzRmImum6lEdkA1p1FWdCPSYbxhhUrVDigjzLeV9GpZV4Si647Lq2UbAhP77v7Ex9CneY6YxuN8Uxbk9VbzN66kOek3VziFN-R69omRY3L8mOFG8Xu77TQ_Q40tLk6IIdq2M-ud1qElzBhqKOKdM4_MF93Zp-X0Jx2totHnCan5Mngx0zvniol-Tbh_dfrz9VN18-fr6-uqlc26q58gwBVbkZatUrrQEGL1s_dJKJTgygpVTYc-WaxkrBZaM4eLDAZC-Bd80leX32PaZ4u2CezSFkh-NoJ4xLNqBZOZ1o4N-o0oK3rOH_hTLBOsF5QeGMuhRzTjiYYwoHm04GmFljNntTYjZrzIZpU0rRvHqwX_oD-l-Kn7kW4O0ZwPJ1dwGTyS7g5NCHhG42Poa_2N8DFXC6cg</recordid><startdate>20160601</startdate><enddate>20160601</enddate><creator>Chen, Huei-Yen Winnie</creator><creator>Donmez, Birsen</creator><general>Elsevier Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7T2</scope><scope>7U2</scope><scope>C1K</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope></search><sort><creationdate>20160601</creationdate><title>What drives technology-based distractions? A structural equation model on social-psychological factors of technology-based driver distraction engagement</title><author>Chen, Huei-Yen Winnie ; Donmez, Birsen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c447t-d0e1e7d0e0e87b78811fd64df960595f18667eb27c33a65263721d1a106b61293</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Accidents, Traffic - statistics & numerical data</topic><topic>Adolescent</topic><topic>Adult</topic><topic>Age</topic><topic>Age Factors</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Attention</topic><topic>Attitude</topic><topic>Automobile Driving - psychology</topic><topic>Cell Phone</topic><topic>Cell phones</topic><topic>Distracted driving</topic><topic>Distracted Driving - psychology</topic><topic>Female</topic><topic>Geographic Information Systems - instrumentation</topic><topic>Humans</topic><topic>In vehicle</topic><topic>Inclination</topic><topic>Male</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>Middle Aged</topic><topic>Models, Statistical</topic><topic>Norms</topic><topic>Personality</topic><topic>Radio</topic><topic>Safety</topic><topic>SEM</topic><topic>Social Norms</topic><topic>Surveys and Questionnaires</topic><topic>Text Messaging</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Huei-Yen Winnie</creatorcontrib><creatorcontrib>Donmez, Birsen</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Safety Science and Risk</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>Accident analysis and prevention</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Huei-Yen Winnie</au><au>Donmez, Birsen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>What drives technology-based distractions? A structural equation model on social-psychological factors of technology-based driver distraction engagement</atitle><jtitle>Accident analysis and prevention</jtitle><addtitle>Accid Anal Prev</addtitle><date>2016-06-01</date><risdate>2016</risdate><volume>91</volume><spage>166</spage><epage>174</epage><pages>166-174</pages><issn>0001-4575</issn><eissn>1879-2057</eissn><abstract>•Data collected from 525 drivers: 30+ years old (n=261) and younger (n=264).•Structural equation models built on tech-based distractions and underlying factors.•Attitudes (most influential), social norms, and personality predict engagement.•Normative influences are significant for younger drivers, but not 30+ drivers.•Personality is significant for the 30+ group, and marginal for younger drivers.
With the proliferation of new mobile and in-vehicle technologies, understanding the motivations behind a driver's voluntary engagement with such technologies is crucial from a safety perspective, yet is complex. Previous literature either surveyed a large number of distractions that may be diverse, or too focuses on one particular activity, such as cell phone use. Further, earlier studies about social-psychological factors underlying driver distraction tend to focus on one or two factors in-depth, and those that examine a more comprehensive set of factors are often limited in their analyses methods.
The present work considers a wide array of social-psychological factors within a structural equation model to predict their influence on a focused set of technology-based distractions. A better understanding of these facilitators can enhance the design of distraction mitigation strategies.
We analysed survey responses about three technology-based driver distractions: holding phone conversations, manually interacting with cell phones, and adjusting the settings of in-vehicle technology, as well as responses on five social-psychological factors: attitude, descriptive norm, injunctive norm, technology inclination, and a risk/sensation seeking personality. Using data collected from 525 drivers (ages: 18–80), a structural equation model was built to analyse these social-psychological factors as latent variables influencing self-reported engagement in these three technology-based distractions.
Self-reported engagement in technology-based distractions was found to be largely influenced by attitudes about the distractions. Personality and social norms also played a significant role, but technology inclination did not. A closer look at two age groups (18–30 and 30+) showed that the effect of social norms, especially of injunctive norm (i.e., perceived approvals), was less prominent in the 30+ age group, while personality remained a significant predictor for the 30+ age group but marginally significant for the younger group.
Findings from this work provide insights into the social-psychological factors behind intentional engagement in technology-based distractions and in particular suggesting that these factors may be sensitive to demographic differences.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>26994371</pmid><doi>10.1016/j.aap.2015.08.015</doi><tpages>9</tpages></addata></record> |
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| ispartof | Accident analysis and prevention, 2016-06, Vol.91, p.166-174 |
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| subjects | Accidents, Traffic - statistics & numerical data Adolescent Adult Age Age Factors Aged Aged, 80 and over Attention Attitude Automobile Driving - psychology Cell Phone Cell phones Distracted driving Distracted Driving - psychology Female Geographic Information Systems - instrumentation Humans In vehicle Inclination Male Mathematical analysis Mathematical models Middle Aged Models, Statistical Norms Personality Radio Safety SEM Social Norms Surveys and Questionnaires Text Messaging Young Adult |
| title | What drives technology-based distractions? A structural equation model on social-psychological factors of technology-based driver distraction engagement |
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