Did Attitudes Interpret and Predict “Better” Choice Behaviour towards Innovative and Greener Automotive Technologies? A Hybrid Choice Modelling Approach
Table 11
Comparison between BNL model and HCM (only significant attributes are listed).
Attribute
BNL
HCM
Age
+0.352 (+2.86)
+0.160 (+1.87)
Master’s degree
+0.360 (+2.86)
+0.156 (+2.16)
ZonRes
+0.157 (+2.04)
+0.0761 (+1.98)
Diesel
+0.356 (+2.72)
+0.479 (+3.52)
CarAge
+0.0358 (+2.11)
+0.0272 (+1.55)
By car-shopping
+0.867 (+2.34)
+0.669 (+1.490)
By car-personal services
+0.678 (+1.80)
+0.192 (+2.53)
Δcost
+0.0641 (+8.55)
+0.0638 (+8.16)
LVConsumption
—
+0.548 (+2.55)
LVDesign
—
+0.0682 (+2.46)
LVEnvironment
—
+0.104 (+1.98)
ASC
+1.53 (+7.67)
—
STATISTICS
#observations
1364
1364
Init-log-likelihood (only the log-likelihood associated with the discrete choice component is considered)
−944.760
−944.760
Final log-likelihood
−780.962
−779.81
Rho-square
0.184
0.212
t-Test values are given in parenthesis. δj refers to the thresholds estimation for the ordinal logit model.
