Behavioral Modeling of Urban Freight Transport - Sustainable Urban Logistics: Concepts, Methods and Information Systems - page 230

Table 4 Econometric results based on M2

Variable Coefficient St.Err. T-Stat

LUB3 0.215 0.046 4.68

PLUBF2 0.246 0.059 4.15

PLUBF3 0.262 0.068 3.86

EF1 1.113 0.104 10.65

EF2 0.937 0.087 10.67

EF4 -0.761 0.099 -7.68

EF5 -1.589 0.126 -12.54

ASC_Alt1 1.085 0.166 6.51

ASC_Alt2 0.814 0.143 5.66

M2, thanks to the effects coding of the variables, provides more detailed
information and is characterized by a statistically significant better fit 11 with
respect to M1 (adj. Rho 2 = 0.154; 9 Coeff.). All reported coefficients are statis-
tically significant. In fact, the LUB2 (e.g. the second level of the variable LUB, i.e.
800) coefficient, not reported in the table, was not statistically significant thus
suggesting agents' utility is not influenced by a variation of only 400 LUB from
the SQ situation (i.e. 400). 12
As it is for the PLUBF one can notice that there is an evidently non-linear effect
of the variable. In fact, going from a 10 Probability Base Points (PBP) for PLUBF
(i.e. SQ level) to 20 PBP we have a much greater impact on retailers' utility
[BetaPLUBF2-1 = BetaPLUBF2 (0.246)—BetaPLUBF1 (-0.509) = 0.756] than
going from 20 PBP to 30 PBP [BetaPLUBF3-2 = BetaPLUBF3 (0.262)—
BetaPLUBF2 (0.246) = 0.016]. EF is the variable that benefited the most from the
adoption of effects coding in detecting non-linearities. This is both due to the
presence of five levels compared to the three levels for the other variables as well
as to their symmetricity with respect to the SQ (i.e. 600€). The analysis of ASCs
leads us to the same conclusions reported for M1.
With reference to Fig. 1 , and in line with prospect theory (Kahneman and
Tversky 1979 ), one can observe that reductions in EF produce positive effects on
utility compared to negative effects induced by opposite variations of similar
amount. Initial variations, in both directions, from the SQ (EF3 = 600€) have
bigger effects [Beta EF3-4 = Beta EF3 (0.300)-Beta EF4 (-0.762) = 1.062 and
Beta EF2-3 = Beta EF2 (0.937)-Beta EF3 (0.300) = 0.637] with respect to subsequent
ones [Beta EF4-5 = Beta EF4 (-0.762)-Beta EF5 (-1.589) = 0.828 and Beta EF1-2 =
Beta EF1 (1.114)-Beta EF2 (0.937) = 0.176]. In fact, for positive variations (EF
increases; EF4 = 800€ and EF5 = 1.000€) we have Beta EF3-4 = 1.062 [ -
Beta EF4-5 = 0.828 and for negative variations (EF reductions, EF2 = 400€ and
EF1 = 200€) we have Beta EF2-3 = 0.637 [ Beta EF1-2 = 0.176. Furthermore, still

11 We checked this by performing a log-likelihood ratio test.
12 Therefore, we recoded this variable so that LUB3 = 1 when LUB = 1,200 and -1 otherwise
(according to the effects coding of the variables).