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Social Learning and Distracted Driving among Young Adults

Abstract

This paper examines the relevance of Akers’ social learning theory (Akers and Jensen 2006; Akers and Jennings 2009) for the problem of distracted driving. Distracted driving is widespread and dangerous, with many drivers, particularly youthful ones, continuing to engage in such driving despite knowledge of its risks (see, e.g., Atchley, Atwood, Boulton, 2011, Accident Analysis and Prevention, 43, 134–142; Prat, Gras, Planes, Font-Mayolas, Sullman, 2017, Transportation Research Part F, 37, 119–128). Much of the research to date is limited to texting; however, in recent years, cell phones have become major tools for entertainment and information, especially among younger people, resulting in the emergence of newer forms of distracted driving. Explanations of distracted driving from a criminological approach are limited (Quisenberry 2015 is an exception), yet criminological theory can contribute to our understanding of this relatively new and expanding form of deviance. Drawing on social learning theory, we explore the attitudes and behaviors of 935 college students regarding ‘traditional’ and newer types of phone-related distracted driving, as well as their perceptions of self and others’ cell phone use. Multivariate analysis indicates support for some of the social learning concepts, with definitions and one of the differentialreinforcement measures standing out in particular (perceived benefits of cell phone use while driving). We consider the implications of these findings for theory and policy.

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Notes

  1. 1.

    Bingham, Zakrajsek, Almani, Shope, and Sayer (2015); Fleiter et al. (2006); Scott-Parker, Watson, and King (2009); Scott-Parker, Hyde, Watson, and King (2013); Taubman-Ben-Ari, Mikulincer, and Gillath (2005).

  2. 2.

    Studies utilizing college students include Atchley et al. (2011), Beck and Watters (2016), Gauld et al. (2014), George et al. (2018), Nemme and White (2010), O’Connor et al. (2017), Panek et al. (2015), Pearson et al. (2013), Prat et al. (2015), Quisenberry (2015), and Scott-Parker et al. (2009). Further, other studies focus on samples of teens and young adults (Scott-Parker et al. 2012; Scott-Parker et al. 2013; Taubman-Ben Ari et al. 2005; Tucker et al. 2015; Weller, Shackleford, Dieckmann, and Slovic 2013).

  3. 3.

    After removing “speeders,” those who completed the survey in less than 47% of the median duration of 10 min (129 cases), there were 1092 cases. A review of the frequency distributions for all variables to be included in our statistical models revealed missing cases on seven of the eleven variables (missing values ranged from 17 to 149). No data were missing on either of the outcome variables (count of cell phone use while moving and while stopped). We employed Complete Case Analysis (CCA) for our outcomes and theoretical variables of interest (only). This resulted in a sample size of 935 cases for all variables, except for texting frequency (n = 925) and sex (n = 865), which, along with age, serve as controls in our statistical models. We compared the complete cases (n = 935) to cases with incomplete theoretical data (n = 157) on sex, age, and texting frequency. There were no significant differences by sex (X2(1, N = 943) = .001, p = .979) or age (t(1,090) = 0.06, p = .951), but there was a significant difference based on number of texts sent last month (t(1,073) = −3.13, p = .002). Subjects with complete data sent significantly more texts last month (M = 4.85, SD = 1.81) than subjects without complete data (M = 4.34, SD = 1.88). The final sample for analysis was 935.

  4. 4.

    We chose linear regression after careful consideration. We concluded that OLS Regression was the appropriate technique for the following reasons. Poisson regression is not appropriate for the data since we are not trying to estimate a rate of occurrence and we do not take into account exposure to the activity. In addition, an individual occurrence of cell phone use is not assumed to be independent from additional counts/types of cell phone use. Furthermore, our analyses indicate that there are no significant issues with overdispersion or underdispersion (Orme and Combs-Orme 2009). Skewness and kurtosis statistics indicate that our data are normally distributed (Mertler and Vannatta 2010). For both variables, skewness and kurtosis statistics are within −1 to +1. In addition, there is no indication that scores on the dependent variables are zero-inflated. Finally, there is no evidence of problematic outliers at the bivariate level. In other words, there are linear relationships between our independent and dependent variables.

References

  1. Akers, R. L. (2011). A social learning theory of crime. In F. T. Cullen & R. Agnew (Eds.), Criminological theory: Past to present (pp. 130–142). New York: Oxford University.

  2. Akers, R. L., & Jennings, W. G. (2009). The social learning theory of crime and deviance. In M. D. Krohn, A. J. Lizotte, & G. P. Hall (Eds.), Handbook on crime and deviance (Handbooks of sociology and social research) (pp. 103–120). New York: Springer.

  3. Akers, R. L., & Jensen, G. F. (2006). The empirical status of social learning theory of crime and deviance: The past, present, and future. In F. T. Cullen, J. P. Wright, & K. R. Blevins (Eds.), Taking stock: The status of criminological theory, volume 15 (pp. 37–76). New Brunswick: Transaction.

  4. AltPress (2017). New study finds ‘Pokemon Go’ led to an increase in traffic accidents. https://www.altpress.com/news/pokemon_go_traffic_accidents_study/ . Accessed 17 December 2018.

  5. Atchley, P., Atwood, S., & Boulton, A. (2011). The choice to text and drive in younger drivers: Behavior may shape attitude. Accident Analysis and Prevention, 43, 134–142.

  6. Barr, G. C., Kane, K. F., Barraco, R. D., Rayburg, T., Demers, L., Kraus, C. K., Greenberg, M. R., Rupp, V. A., Hamilton, K. M., & Kane, B. G. (2015). Gender differences in perceptions and self-reported driving behaviors among teenagers. The Journal of Emergency Medicine, 48(3), 366–370.

  7. Beck, K. H., & Watters, S. (2016). Characteristics of college students who text while driving: Do their perceptions of a significant other influence their decisions? Transportation Research Part F, 37, 119–128.

  8. Bingham, C. R., Zakrajsek, J. S., Almani, F., Shope, J. T., & Sayer, T. B. (2015). Do as I say, not as I do: Distracted driving behavior of teens and their parents. Journal of Safety Research, 55, 21–29.

  9. Braitman, K. A., & McCartt, A. T. (2010). National reported patterns of driver cell phone use in the United States. Traffic Injury Prevention, 11, 543–548.

  10. Carter, P. M., Bingham, C. R., Zakrajsek, J. S., Shope, J. T., & Sayer, T. B. (2014). Social norms and risk perception: Predictors of distracted driving behavior among novice adolescent drivers. Journal of Adolescent Health, 54, 532–541.

  11. Chappell, B. (2017). Honolulu’s ‘Distracted Walking’ Law Takes Effect, Targeting Phone Users. https://www.npr.org/sections/thetwo-way/2017/10/25/559980080/honolulus-distracted-walking-law-takes-effect-targeting-phone-users. Accessed 17 December 2018.

  12. Faccio, M., McConnell, J.J. (2018). Death by Pokémon Go: The economic and human cost of using apps while driving. NBER working paper no. w24308. https://www.nber.org/papers/w24308. Accessed 17 December 2018.

  13. Fleiter, J., Watson, B., Lennon, A., Lewis, I. (2006). Significant others, who are they?-Examining normative influences on speeding. In Proceedings 2006Australasian road safety research policing education conference, Gold Coast, Australia.

  14. Gardner, G. (2018). Driving while distracted. Consumer Reports, January, 48–57.

  15. Gauld, C. S., Lewis, I., & White, K. M. (2014). Concealing their communication: Exploring psychosocial predictors of young drivers’ intentions and engagement in concealed texting. Accident Analysis and Prevention, 62, 285–293.

  16. George, A. M., Brown, P. M., Scholz, B., Scott-Parker, B., & Rickwood, D. (2018). ‘I need to skip a song because it sucks’: Exploring mobile phone use while driving among young adults. Transportation Research Part F, 58, 382–391.

  17. Gliklich, E., Guo, R., & Bergmark, R. W. (2016). Texting while driving: A study of 1211 U.S. adults with the Distracted Driving Survey. Preventive Medicine Reports, 4, 486–489.

  18. Governor’s Highway Safety Association (2019). Distracted Driving. http://www.ghsa.org/state-laws/issues/Distracted-Driving . Accessed 3 December 2019.

  19. Hayashi, Y., Russo, C. T., & Wirth, O. (2015). Texting while driving as impulsive choice: A behavioral economic analysis. Accident Analysis and Prevention, 83, 182–189.

  20. Mertler, C. A., & Vannatta, R. A. (2010). Advanced and multivariate statistical methods, 4 thedition. Glendale, CA: Pyrczak.

  21. Mohn, T. (2017). Reading this while walking? In Honolulu, It Could Cost You. https://www.nytimes.com/2017/10/23/business/honolulu-walking-and-texting-fine.html . Accessed 17 December 2018.

  22. National Center for Statistics and Analysis (NCSA). (2016). Distracted Driving 2014. Traffic Safety Facts Research Note. Report No. DOT HS 812 260. https://crashstats.nhtsa.dot.gov/Api/Public/Publication/812260 . Accessed 17 December 2018.

  23. National Highway Traffic Safety Administration (NHTSA). (2018a). U.S. DOT and NHTSA Kick Off 5th Annual ‘U Drive. U Text. U Pay.’ Campaign. U.S. Department of Transportation. https://www.nhtsa.gov/press-releases/us-dot-and-nhtsa-kick-5th-annual-u-drive-u-text-u-pay-campaign . Accessed 17 December 2018.

  24. National Highway Traffic Safety Administration (NHTSA). (2018b). Distracted Driving. https://www.nhtsa.gov/risky-driving/distracted-driving / . Accessed 17 December 2018.

  25. Nemme, H. E., & White, K. M. (2010). Texting while driving: Psychosocial influences on young people’s texting intentions and behavior. Accident Analysis and Prevention, 42, 1257–1265.

  26. O’Connor, S. S., Shain, L. M., Whitehill, J. M., & Ebel, B. E. (2017). Measuring a conceptual model of the relationship between compulsive cell phone use, in-vehicle cell phone use, and motor vehicle crash. Accident Analysis and Prevention, 99, 372–378.

  27. Olsen, E. O., Shults, R. A., & Eaton, D. K. (2013). Texting while driving and other risky motor vehicle behaviors among US high school students. Pediatrics, 131(6), e1708–e1715.

  28. Orme, J. G., & Combs-Orme, T. (2009). Multiple regression with discrete dependent variables. New York: Oxford University Press.

  29. Panek, E. T., Bayer, J. B., Dal Cin, S., & Campbell, S. W. (2015). Automaticity, mindfulness, and self-control as predictors of dangerous texting behavior. Mobile Media and Communication, 3, 383–400.

  30. Paternoster, R., Jaynes, C. M., & Wilson, T. (2017). Rational choice theory and interest in the ‘fortune of others’. Journal of Research in Crime and Delinquency, 54(6), 847–868.

  31. Pearson, M. R., Murphy, E. M., & Doane, A. N. (2013). Impulsivity-like traits and risky driving behaviors among college students. Accident Analysis and Prevention, 3, 142–148.

  32. Pelzer, J., (2018). Distracted drivers could be hit with $100 fines under new Ohio law. https://www.cleveland.com/open/index.ssf/2018/07/new_ohio_law_looks_to_crack_do.html . Accessed 17 December 2018.

  33. Pickrell, T. M., Li, R., KC, S. (2016). Driver electronic device use in 2015. Traffic Safety Facts Research Notes. Report no. DOT HS 812 326. Washington, DC: National Highway Traffic Safety Administration.

  34. Prat, F., Gras, M.-E., Planes, M., Gonzalez-Iglesias, B., & Sullman, M. J. M. (2015). Psychological predictors of texting while driving among university students. Transportation Research Part F, 34, 76–85.

  35. Prat, F., Gras, M.-E., Planes, M., Font-Mayolas, S., & Sullman, M. J. M. (2017). Driving distractions: An insight gained from roadside interviews on their prevalence and factors associated with driver distraction. Transportation Research Part F, 45, 194–207.

  36. Pratt, T., Cullen, F. T., Sellers, C. S., Winfree Jr., T., Madensen, T. D., Daigle, L. E., Fearn, N. E., & Gau, J. M. (2010). The empirical status of social learning theory: A meta-analysis. Justice Quarterly, 27, 765–802.

  37. Quisenberry, P. N. (2015). Texting and driving: Can it be explained by the general theory of crime? American Journal of Criminal Justice, 40, 303–316.

  38. Scott-Parker, B., Hyde, M. K., Watson, B., & King, M. J. (2013). Speeding by young novice drivers: What can personal characteristics and psychosocial theory add to our understanding? Accident Analysis and Prevention, 50, 242–250.

  39. Scott-Parker, B., Watson, B., & King, M. J. (2009). Understanding the psychosocial factors influencing the risky behaviour of young drivers. Transportation Research Part F. https://doi.org/10.1016/j.trf.2009.08.003.

  40. Scott-Parker, B., Watson, B., King, M. J., & Hyde, M. K. (2012). ‘They’re lunatics on the road’: Exploring the normative influences of parents, friends, and police on young novices’ risky driving decisions. Safety Science, 50, 1917–1928.

  41. Taubman-Ben-Ari, O., Mikulincer, M., & Gillath, O. (2005). From parents to children—Similarity in parents and offspring driving styles. Transportation Research Part F, 8, 19–29.

  42. Tucker, S., Pek, S., Morrish, J., & Ruf, M. (2015). Prevalence of texting while driving and other risky driving behaviors among young people in Ontario, Canada: Evidence from 2012 and 2014. Accident Analysis and Prevention, 84, 144–152.

  43. Weller, J., Shackleford, C., Dieckmann, N., & Slovic, P. (2013). Possession attachment predicts cell phone use while driving. Health Psychology, 32, 379–387.

  44. White, K. M., Hyde, M. K., Walsh, S. P., & Watson, B. (2010). Mobile phone use while driving: An investigation of the beliefs influencing drivers’ hands-free and hand-held mobile phone use. Transportation Research Part F, 13, 9–20.

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Correspondence to Pamela Tontodonato.

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All procedures performed in this study involving human participants were in accordance with the ethical standards of the Institutional Review Board (Protocol # 18–413) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Tontodonato, P., Drinkard, A. Social Learning and Distracted Driving among Young Adults. Am J Crim Just (2020) doi:10.1007/s12103-020-09516-6

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Keywords

  • Distracted driving
  • Texting while driving
  • Cell phone use while driving
  • Social learning theory