Did Attitudes Interpret and Predict “Better” Choice Behaviour towards Innovative and Greener Automotive Technologies? A Hybrid Choice Modelling Approach
Table 7
Estimation results of HCM.
Attribute
HCM
Install
Not-install
Age
+0.160 (+1.87)
—
Master’s degree
+0.156 (+2.16)
—
ZonRes
+0.0761 (+1.98)
—
Diesel
—
+0.479 (+3.52)
CarAge
+0.0272 (+1.55)
—
By car-shopping
+0.669 (+1.490)
—
By car-personal services
+0.192 (+2.53)
—
Δcost
—
+0.0638 (+8.16)
LVConsumption
+0.548 (+2.55)
—
LVDesign
—
+0.0682 (+2.46)
LVEnvironment
+0.104 (+1.98)
—
δ1
+1.46 (+ 38.90)
—
δ2
+1.34 (+ 43.85)
—
δ3 (the ordinal treatment considers the estimation of three extra parameters in the measurement model)
+1.52 (+ 46.11)
—
Alternative specific constant (ASV)
—
—
STATISTICS
#observations
1364
Init-log-likelihood (only the log-likelihood associated with the discrete choice component is considered)
−944.760
Final log-likelihood
−779.81
Rho-square
0.212
t-Test values are given in parenthesis. δj refers to the thresholds estimation for the ordinal logit model.
