Psychosocial Factors That Contribute to Problematic Phone Use While Driving

Kyle Hickerson, M.A. – George Mason University

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Yi-Ching Lee, Ph.D. – George Mason University

Background

• Despite a decrease of 13.6% of miles driven during the COVID-19 pandemic. Traffic fatalities increased by 7.2% (38,680 total) [1].
• In addition, Information Communication Technology (ICT) use increased, which refers to devices that transmit and store information, such as computers and smartphones [2].
• Fields where ICT use increased: employment, education, & elderly care to name a few [3], [4], [5].
• Research has also shown that individuals use email and social media to manage their anxiety and stress during the pandemic [6].
• A large portion of the personality and driving research has focused on how personality constructs predict phone use while driving.
• Common constructs include Boredom Proneness, Habitual Smartphone Use, Self-Regulation, and Neuroticism [7], [8], [9], [10].
• Our goal was to model the relationship between Psychosocial factors, ICT Use, and distracted driving behavior.

Key Measures

• Boredom Proneness [7]
• Need to Belong [11]
• Fear of Missing Out [12]
• Perceived Attachment to Phone [13]
• Habitual Smartphone Use [8]
• Self-Regulation [14]
• Neuroticism [15]
• Distracted driving was measured by examining the number of technologies used while driving (binary endorsement)
• Audio/Visual Calling, Texting, Navigation, Music, Email, Social Media, News or radio

Hypotheses

Fear of Missing Out

Need to Belong

Self-Regulation

Boredom Proneness

Phone Attachment

Habitual Use

Neuroticism

General ICT Difference

App Use While Driving

H_2a

H_2b

H_2c

H_2d

H_2e

H_2f

H_2g

H_4a

H₃

H_4b

H_4c

H_4d

H_4e

H_4g

H_4f

Method

• Data was collected on MTURK during mandatory stay-at-home orders (April 20, 2020)
• Requirements to participant were as follows: at least 18 years old, able to drive, and reside in the US.
• 402 participants were collected and compensated $7. After cleaning, the final sample was 364 participants.
• Demographics were as follows:
• Women = 203, Men = 161
• Mean age = 40.89, SD = 11.36
• 74% identified as White or European American
• 14% as Asian or Asian American
• 4% as Black or African American
• 1.65% as Native American or Alaskan Native
• 1.65% as Hispanic or Latinx
• 9.31% as two or more ethnicities

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Results – ICT Use Before & During the Pandemic

• To ascertain if there was an increase in ICT usage during the pandemic, participants were asked to rate their daily ICT use before and during the pandemic.
• t(363) = 17.04, p < 0.001, μ_diff = 2.05 [1.81, 2.28], d = 0.893 [0.79, 1.02]
• Participants reported a significant difference of ICT hours between before and during the pandemic, also with a large effect size.
• Participants used more ICT during the pandemic than before

Daily ICT Use (Hours)

Results – Confirmatory Factor Analysis

• Exploratory Factor Analysis was used to reduce the number of constructs.
• The Final 4 constructs were:
• Self-Regulation, Need to Belong, Habitual Use and Perceived Attachment to Phone
• Other constructs were not distinct enough to be identified by the model
• The 4-factor solution was assessed using dynamic fit indices, and was satisfactory, χ² (224) = 380.69, p < 0.001, CFI = 0.966, RMSEA = 0.047 [0.037, 0.051], SRMR = 0.048.
• The dynamic fit indices were as follows: RMSEA = 0.074, CFI = 0.925, SRMR = 0.073.
• Local fit was examined via the residual correlation matrix and no substantial sources of misfit were found.
• Dynamic fit indices are better attuned to identifying model misspecification, which provides more robust support for the model.

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Results - CFA

BD 1

SR 1

SR 2

SR 4

SR 6

SR 8

SR 10

NTB2

NTB5

NTB8

NTB9

NTB10

FM

9

Self Reg

0.83

0.46

0.60

0.49

0.53

0.66

0.61

NTB

0.80

0.69

0.87

0.95

0.97

0.72

Hab Use

HB 1

HB 3

HB 5

HB 7

HB 9

HB 11

PA 1

PA 2

PA 3

PA 4

0.94

1.07

1.03

1.10

0.93

0.84

Perc Attch

1.08

1.23

1.04

1.10

-0.20

-0.056

-0.071

0.21

0.31

0.632

Results – a-path

• Linear Regression was used to examine the a-path, from constructs of Self-Regulation, Need to Belong, Habitual Use, and Perceived Attachment to Phone to the ICT Use Difference. No significant differences were detected for Psychosocial factors predicting ICT Use difference.

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Results – b-path

• Logistic Regressions were used to examine the b-path between the hypothesized mediator (ICT Difference) and the various ICT uses while driving (Audio/Visual Calling, Texting, Navigation, Music, Email, Social Media, News or radio)

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Results – c-path

• Logistic Regressions were used to examine the direct effects between the hypothesized Psychosocial Factors and the various ICT uses while driving (Audio/Visual Calling, Texting, Navigation, Music, Email, Social Media, News or radio). For the sake of brevity, only a selection of the most interesting results will be displayed.

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Conclusion

• When individuals were isolated during mandatory stay-at-home orders during the COVID-19 pandemic, individuals engaged in more ICT Use, which supports previous work regarding the use of ICT to mitigate anxiety and loneliness [6].
• In addition, a host new and old psychosocial factors were evaluated, and reduced to 4 dimensions: Self-Regulation, Need to Belong, Habitual Use, and Perceived Attachment to Phone.
• The disappearance of Fear of Missing Out, Boredom Proneness, and Neuroticism may be due to a lack of practical differences between the four found factors.
• Of note is the prevalence of Need to Belong and Perceived Attachment to Phone on predicting whether participants would email or use social media while driving.
• Similar patterns again emerged when ICT is used to combat feelings of stress and loneliness [6].

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References

[1] National Center for Statistics and Analysis. (2021, May). Early estimate of motor vehicle traffic fatalities in 2020 (Crash•Stats Brief Statistical Summary. Report No. DOT HS 813 115). National Highway Traffic Safety Administration.

[2] Ziemba, E. (2019). The Contribution of ICT Adoption to the Sustainable Information Society. Journal of Computer Information Systems, 59(2), 116–126. https://doi.org/10.1080/08874417.2017.1312635

[3] Jalagat, R. C., & Aquino, P. G. (2022). The Usefulness of Information Communication Technology (ICT) in the Recruitment and Selection of Employees During Covid 19. In P. G. Aquino Jr. & R. C. Jalagat Jr. (Eds.), Effective Public Administration Strategies for Global “New Normal” (pp. 19–34). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-3116-1_2

[4] Espino-Díaz, L., Fernandez-Caminero, G., Hernandez-Lloret, C.-M., Gonzalez-Gonzalez, H., & Alvarez-Castillo, J.-L. (2020). Analyzing the Impact of COVID-19 on Education Professionals. Toward a Paradigm Shift: ICT and Neuroeducation as a Binomial of Action. Sustainability, 12(14), 5646. https://doi.org/10.3390/su12145646�

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[5] Robič M, Rotar Pavlič D. COVID-19 and Care for the Elderly in Long-Term Care Facilities: The Role of Information Communication Technology. Acta Med Acad. 2021 Dec;50(3):414-422. doi: 10.5644/ama2006-124.363. PMID: 35164520.

[6] Lee, Y.-C., Malcein, L. A., & Kim, S. C. (2021). Information and Communications Technology (ICT) Usage during COVID-19: Motivating Factors and Implications. International Journal of Environmental Research and Public Health, 18(7). https://doi.org/10.3390/ijerph18073571

[7] Vodanovich, S. J., Wallace, J. C., & Kass, S. J. (2005). A Confirmatory Approach to the Factor Structure of the Boredom Proneness Scale: Evidence for a Two-Factor Short Form. Journal of Personality Assessment, 85(3), 295–303. https://doi.org/10.1207/s15327752jpa8503_05

[8] van Deursen, A. J. A. M., Bolle, C. L., Hegner, S. M., & Kommers, P. A. M. (2015). Modeling habitual and addictive smartphone behavior: The role of smartphone usage types, emotional intelligence, social stress, self-regulation, age, and gender. Computers in Human Behavior, 45, 411–420. https://doi.org/10.1016/j.chb.2014.12.039

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[9] Bayer, J. B., Campbell, S. W., & Ling, R. (2016). Connection Cues: Activating the Norms and Habits of Social Connectedness. Communication Theory, 26(2), 128–149. https://doi.org/10.1111/comt.12090

�[10] Caci, B., Miceli, S., Scrima, F., & Cardaci, M. (2020). Neuroticism and Fear of COVID-19. The Interplay Between Boredom, Fantasy Engagement, and Perceived Control Over Time. Frontiers in Psychology, 11. https://www.frontiersin.org/articles/10.3389/fpsyg.2020.574393

[11] Leary, M. R., Kelly, K. M., Cottrell, C. A., & Schreindorfer, L. S. (2013). Construct Validity of the Need to Belong Scale: Mapping the Nomological Network. Journal of Personality Assessment, 95(6), 610–624. https://doi.org/10.1080/00223891.2013.819511

[12] Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in Human Behavior, 29(4), 1841–1848. https://doi.org/10.1016/j.chb.2013.02.014

References

[13] Weller, J. A., Shackleford, C., Dieckmann, N., & Slovic, P. (2013). Possession attachment predicts cell phone use while driving. Health Psychol, 32(4), 379–387. PubMed. https://doi.org/10.1037/a0029265

[14] Diehl, M., Semegon, A. B., & Schwarzer, R. (2006). Assessing Attention Control in Goal Pursuit: A Component of Dispositional Self-Regulation. Journal of Personality Assessment, 86, 306–317. https://doi.org/10.1207/s15327752jpa8603_06

[15] Rammstedt, B., & John, O. P. (2007). Measuring personality in one minute or less: A 10-item short version of the Big Five Inventory in English and German. Journal of Research in Personality, 41, 203–212. https://doi.org/10.1016/j.jrp.2006.02.001