Analyzing Drivers’ Distractions due to Smartphone Usage: Evidence from AutoLog Dataset

Table 6

Ordinal Logistic Regression Model for the perception about avoiding lengthy messages automatically.

Avoiding lengthy messages	Coefficient	Standard error	Z-alue
Exp	0.0450562	0.1648833	0.95	0.344
Sex	−2.720471	1.855887	−0.99	0.324
Age	−.858536	1.097658	−0.78	0.434
Qualification	2.16993	1.426154	1.52	0.128
Driving-mode	0.1544981	.9879131	0.16	0.876
Traveling mode	5.649868	2.11949	2.67	0.008
Purpose of travel	2.612008	.9911119	2.64	0.008
Valid license	−9.216121	3.564899	−2.59	0.010
SN1Txt	1.230921	1.199268	1.03	0.305
SN2Txt	−0.8629046	.7964669	−1.08	0.279
SN3Txt	−0.1809369	.8859942	−0.20	0.838
SN4Txt	−2.107618	1.672393	−1.26	0.208
SN5Txt	0.0972826	1.644342	0.06	0.953
SN6Txt	0.4282987	1.053603	0.41	0.684
SN7Txt	3.022207	1.158804	2.61	0.009
SN8PCall	0.0371694	1.116448	0.03	0.973
SN9PCall	1.053618	0.9636646	1.09	0.274
SN10PCall	−2.579661	1.381452	−1.87	0.062
SN11PCall	2.130834	0.9951324	2.14	0.032
SN1Mail	−0.3561978	1.167004	−0.31	0.760
SN2Mail	−0.1387213	1.004044	−0.14	0.890
SN3Mail	0.6601901	1.316659	0.50	0.616
SN4Mail	0.478997	1.206689	0.40	0.691
SN5Mail	1.257184	.7716569	1.63	0.103
SN6Mail	2.536931	.9134279	2.78	0.005
Visual interface-1	3.161218	1.353173	2.34	0.019
Visual interface-2	−3.132817	1.248298	−2.51	0.012
Visual interface-3	0.5081588	1.088503	0.47	0.641
Visual interface-4	0.7872293	1.161302	0.68	0.498
Visual interface-5	0.6762423	1.361247	0.50	0.619
Visual interface-6	−0.5132211	0.4750714	−1.08	0.280
Visual interface-7	1.142862	0.9429931	1.21	0.226
/cut1	35.82099	10.84208
/cut2	40.25627	10.84055
/cut3	47.22419	11.72782
No of observations = 59
Wald chi^2(32) = 102.30
Pseudo R^2 = 0.5730
Log pseudolikelihood = −26.674877