5.3.2 Parameters of the structural model

The coefficients in the structural model are analytically represented by the following equation:

The results are shown in Table 14. In particular, the following attitudes were statistically significant: the attitudes towards the environment (LV1) and the per-

ception of the advantages of EVs (LV2).

\*in parenthesis the t-test values.

Table 13.

36

Measurement model

[def: The vehicle fuel consumption significantly influences my choice in purchasing a new car]

α <sup>10</sup> +0.567

λ <sup>10</sup> +0.725

ν<sup>10</sup> +0.787

[def: I am usually attentive to the special offers of electric

(+3.46)

Transportation Systems Analysis and Assessment

(+11.40)

(+16.37)

Icons0

Icons2

operators]

Fuel consumption Vehicle design Environment

[def: The vehicle design significantly influences my choice in purchasing a new car]

α <sup>20</sup> 1.79

λ <sup>20</sup> +0.148

ν<sup>20</sup> +1.38

[def: When parking I am usually careful to avoid having my car

α <sup>12</sup> 0 α <sup>21</sup> 0 α <sup>33</sup> 1.36

λ <sup>12</sup> 1 λ <sup>21</sup> 1 λ <sup>33</sup> +0.729

ν<sup>12</sup> 1 ν<sup>21</sup> 1 ν<sup>33</sup> +0.983

[def: When furnishing I am willing to buy pieces with modern design features and original

α <sup>23</sup> +2.79

λ <sup>23</sup> +1.60

ν<sup>23</sup> +1.43

[def: I am willing to go to the body shop mechanic not only for

α <sup>24</sup> +2.79

λ <sup>24</sup> +1.84

ν<sup>24</sup> +1.65

(+2.72)

(+5.72)

(+31.24)

(+2.69)

(+6.52)

(+28.17)

(2.77)

(+0.85)

(+33.16)

Ienv0

new car]

Ienv3

Ienv4

Ienv6

pollution]

greenhouse gases]

[def: The evaluation of the environmental impact significantly influences my choice in purchasing a

α <sup>30</sup> 1.89

λ <sup>30</sup> +0.495

ν<sup>30</sup> +1.18

[def: I really enjoy spending my free time in parks and green areas to

[def: How much do you agree with following sentence: We must act and make decisions to reduce emissions of

α <sup>34</sup> 0

λ <sup>34</sup> 1

ν<sup>34</sup> 1

[def: I am not willing to use the car during weekend to protect the environment and then reduce air

α <sup>36</sup> 1.95

λ <sup>36</sup> +0.396

ν<sup>36</sup> +1.13

(26.15)

(+12.18)

(+31.68)

breathe clean area]

(24.48)

(+14.12)

(+31.06)

(19.03)

(+20.82)

(+25.99)

Idesign0

Idesign1

damaged]

Idesign3

details]

Idesign4

Coefficients of the calibrated measurement model. HySolarKit case study.

major damages]


#### Table 16.

Coefficients of the calibrated structural model. EV case study.

$$LV\_p^i = \chi\_p + \sum\_j \beta\_{\rm SE,j} X\_{\rm SE,j}^i + o\_p^i \tag{19}$$

coefficient λp,k associated with the latent variable and the error terms ν<sup>i</sup>

LV 1: Environment LV 2: Perception of EV's advantages

(4.92)

Approaches for Modelling User's Acceptance of Innovative Transportation…

(+5.23)

(+30.15)

α <sup>11</sup> 0 α <sup>21</sup> 0.143

λ <sup>11</sup> 1 λ <sup>21</sup> +0.881

ν<sup>11</sup> 1 ν<sup>21</sup> +1.22

ADRed\_CO2

ADEfficiency

ADRed\_poll

in terms of energy efficiency]

the acoustic pollution in cities]

emissions reduction]

[def: I am interested in EV to contribute to the

α <sup>20</sup> +0.391

λ <sup>20</sup> +1.32

ν<sup>20</sup> +0.742

[def: Compared to a normal car, EV are superior

[def: I believe using EV can significantly reduce

α <sup>22</sup> 0 λ <sup>22</sup> 1 ν<sup>22</sup> 1

(+2.96)

(+8.40)

(+11.55)

(1.37)

(+7.27)

(+27.41)

each perception indicator.

\*in parenthesis the t-test values.

Measurement model

my choice in purchasing a new car]

α <sup>10</sup> 4.62

DOI: http://dx.doi.org/10.5772/intechopen.87088

λ <sup>10</sup> +2.85

ν<sup>10</sup> +1.03

[def: The vehicle fuel consumption significantly influences

[def: I care about the amount of pollution generated by a

Coefficients of the calibrated measurement model. EV case study.

F\_cons

F\_poll

car when it's being used]

6. Conclusions

Table 17.

39

normally distributed with zero mean and σνpk standard deviation are displayed for

Depending on the context, several factors may affect users' choices. In this chapter, the main focus refers to modeling users' propensity to choose/adopt a new/ innovative technology. This is a crucial task in order to increase the attractiveness of strategies that may be employed to achieve sustainable transportation. In particular, two related main issues are still open in the literature: (a) interpreting and modeling users' behaviour towards these new technologies and (b) assessing the potential environmental impacts. It is widely recognized that traditional approaches used to interpret and model users' choice behaviour may lead to neglect the numerous nonquantitative factors that may affect users' behaviors. Indeed, users' choices may be influenced by social and psychological factors, symbolic and affective factors, habits and the conflict between collective and individual interests (e.g. car use as a commons dilemma). These imply that changes in transportation modes may be achieved either by influencing individual motivations and perceptions (psychological

p,k assumed

The estimation results displayed in Table 16 refer only to the two significant latent variables of the model, standing for the attitudes towards the environment (LV1) and the perception of the advantages of EVs (LV2). In particular, for each latent variable, the table displays the results of the intercept value γp, the betacoefficients βSE,j of the socioeconomic attributes X<sup>i</sup> SE,j of the respondents that influence the latent variable and the error terms ω<sup>i</sup> <sup>p</sup> normally distributed with zero mean and σω<sup>p</sup> standard deviations of the error term.

#### 5.3.3 Parameters of the measurement model

Finally, the measurement model depending on the perception indicators is analytically represented by the following equation:

$$I\_{p,k}^{i} = \alpha\_{p,k} + \lambda\_{p,k} L V\_p^i + \nu\_{p,k}^i \tag{20}$$

These parameters were specified in accordance with the preliminary analyses. In particular, the first latent variable about the environment (LV1) is described by two indicators, <F\_cons > and < F\_poll>, while the second latent variable, perception of EV's advantages (LV2), is described by three perception indicators <ADRed\_CO2>, <ADEfficiency > and < ADRed\_poll>. In Table 17, the intercept value αp,k, the

Approaches for Modelling User's Acceptance of Innovative Transportation… DOI: http://dx.doi.org/10.5772/intechopen.87088


#### Table 17.

LV<sup>i</sup>

[def: Age class of individuals (0 = 19 years; 1 = 20 years; 2 = 21 years; 3 = 22 years;

coefficients βSE,j of the socioeconomic attributes X<sup>i</sup>

influence the latent variable and the error terms ω<sup>i</sup>

mean and σω<sup>p</sup> standard deviations of the error term.

5.3.3 Parameters of the measurement model

Structural model

SE\_male

SE\_male

Table 16.

38

SE\_delta\_age

\*in parenthesis the t-test values.

LV1: Attitude towards the environment

Transportation Systems Analysis and Assessment

[def: Interviewee's gender (0 = female; 1 = male)]

[def: Interviewee's gender (0 = female; 1 = male)]

4 = 23 years; 5 = 24 years; 6 = 25 years; 7 = 26 years)]

Coefficients of the calibrated structural model. EV case study.

LV2: Perception of EV's advantages

lytically represented by the following equation:

I i <sup>p</sup> ¼ γ<sup>p</sup> þ ∑<sup>j</sup>

βSE,jX<sup>i</sup>

The estimation results displayed in Table 16 refer only to the two significant latent variables of the model, standing for the attitudes towards the environment (LV1) and the perception of the advantages of EVs (LV2). In particular, for each latent variable, the table displays the results of the intercept value γp, the beta-

Attributes Attribute coefficients

γ<sup>1</sup> +1.83

ω<sup>1</sup> +0.119

γ<sup>2</sup> +0.857

ω<sup>2</sup> +0.584

Finally, the measurement model depending on the perception indicators is ana-

These parameters were specified in accordance with the preliminary analyses. In particular, the first latent variable about the environment (LV1) is described by two indicators, <F\_cons > and < F\_poll>, while the second latent variable, perception of EV's advantages (LV2), is described by three perception indicators <ADRed\_CO2>,

<sup>p</sup> <sup>þ</sup> <sup>ν</sup><sup>i</sup>

p,k <sup>¼</sup> <sup>α</sup>p,k <sup>þ</sup> <sup>λ</sup>p,kLV<sup>i</sup>

<ADEfficiency > and < ADRed\_poll>. In Table 17, the intercept value αp,k, the

SE,j <sup>þ</sup> <sup>ω</sup><sup>i</sup>

<sup>p</sup> (19)

(betas)

(+33.70)

(+3.84)

�0.242 (�5.41)

(+11.57)

(+9.86)

�0.0445 (�1.01)

�0.0246 (�1.39)

SE,j of the respondents that

<sup>p</sup> normally distributed with zero

p,k (20)

Coefficients of the calibrated measurement model. EV case study.

coefficient λp,k associated with the latent variable and the error terms ν<sup>i</sup> p,k assumed normally distributed with zero mean and σνpk standard deviation are displayed for each perception indicator.
