Support for legislative, technological, and organizational strategies to reduce cellphone use while driving: Psychological predictors and influences of language

Figures & data

Table 1. Descriptive statistics.

	Mean (SD)	Range	Cronbach’s α
Support for distraction reduction (17 items)	4.31 (0.83)	1.65–6.00	.91
Support for handsfree CUWD (2 items)	4.34 (1.05)	1.00–6.00
Self-reported distracted driving	17.30 (15.42)	0.00–73.33	.94
CUWD relative ability percentile	46.88 (23.68)	0.00–100.00
Verbal benefit perceptions	2.95 (1.13)	1.00–7.00	.64
Verbal risk perceptions	5.17 (1.24)	1.00–7.00	.75
Anti-CUWD belief scale	5.89 (0.82)	2.44–7.00	.91
Reactance to warnings	2.02 (1.03)	1.00–6.33	.91
Driving amount (hours/week)	6.80 (6.06)	0.20–50.00

Means with standard deviations, ranges, and Cronbach’s α are reported where relevant. Additional variables are reported in Table A3. Correlations appear in Table A4.

Table 2. Average support for programs, policies, and technology to reduce distracted driving.

Are you in favor of or opposed to __________?	M (SD)	In favor
1. Insurance discounts or plans for safe drivers	5.31 (0.89)	96%
2. Educational programs targeted at teen drivers	5.21 (0.90)	96%
3. Technology that assists parents in teaching teen drivers	4.96 (1.02)	94%
4. Organizational (e.g., school or workplace) pledges to not drive distracted	4.68 (1.16)	87%
5. Organizational (e.g., school or workplace) bans on cell phone use	4.55 (1.32)	80%
6. Eye tracking technology in your car that warns you when you’re distracted	3.82 (1.47)	63%
7. Technology to read messages aloud but not respond	3.79 (1.41)	62%
8. Steering wheel sensors that warn you to keep both hands on the steering wheel	3.39 (1.56)	48%
9. Technology that pulls your car over if it detects you are distracted	3.21 (1.57)	43%

Participants rated their support on a scale from 1 (strongly opposed) to 6 (strongly in favor). Strategies are ordered from most to least supported. Items used in the within-participants experiment are included in Table 4; the index of support for distraction reduction included all the items here and in Table 4. The full wording of item 7 appears in Table A2. Two items that measured support for strategies that facilitated handsfree use of devices while driving (i.e., Bluetooth and apps that allow people to receive and respond to messages using voice-to-text) were included in a separate index of handsfree use (Appendix B).

Table 3. Regression model predicting support for CUWD reduction strategies.

	B (se)	β	p	VIF
(Constant)	2.28 (0.39)		<.001
CUWD relative ability percentile	0.00 (0.00)	−.03	.442	1.34
Self-reported distracted driving	0.01 (0.00)	.11	<.001	1.45
Anti-CUWD beliefs	0.29 (0.05)	.29	<.001	2.16
Verbal risk perceptions	0.11 (0.03)	.17	<.001	1.62
Verbal benefit perceptions	−0.01 (0.03)	−.01	.760	1.75
Reactance to warnings	−0.14 (0.04)	−.17	<.001	1.76

Unstandardized B-coefficients with standard errors in parentheses and standardized β’s are both reported, along with variance inflation factors (VIF). For gender, 0 = male, 1 = female. For race, 1 = non-Hispanic white, 0 = nonwhite. All other variables were continuous. Model fit is F(10,633) = 22.89, p < .001, adjusted R² = .26. Age, gender, race, and driving amount were included as covariates but omitted from this table (none were significant predictors).

Table 4. Support for programs, policies, and technology to reduce distracted driving depending on language used to describe strategy.

Are you in favor of or opposed to __________?	M (SD)	In favor
1a. Legislation and laws that punish people who use their cell phones while driving with fines	4.59 (1.25)	83%
1b. Legislation and laws that ban cell phone use while driving	4.39 (1.36)	76%
2a. Apps or settings that help you drive without using your phone	4.58 (1.10)	86%
2b. Apps or settings that prevent you from using your phone while driving	4.15 (1.42)	70%
3a. Technology that monitors driving ability and evaluates performance	4.12 (1.33)	73%
3b. Technology that measures driver behavior patterns and coaches driving ability	4.07 (1.27)	73%
4a. Insurance programs that … charge good drivers less	4.33 (1.41)	77%
4b. Insurance programs that … charge poor drivers more	4.06 (1.50)	69%

Participants rated their support on a scale from 1 (strongly opposed) to 6 (strongly in favor). Pairs of strategies are indicated with a and b and are ordered from most to least supported by the most supported of the pair.

Data availability

Data used in this paper are available at https://osf.io/3ydzx