Exploratory factor analysis based on the latent constructs related

TUeTO

The first FA extracted four factors, based on 14 items. The analysis of the Kaiser-Meyer Olkin measure of sampling adequacy suggested that responses given by the sample are acceptable (KMO = 0.745). Bartlett’s test of Sphericity was significant, rejecting the null hypothesis, meaning that correlation matrix was not an identity matrix (p-value = 0.000 < 0.05). The diagonals of the anti-image correlation matrix were all greater than 0.5, except for the variables Eco1 and Eco2; however, their values should not affect the model since they are almost equal to 0.5 (respectively, value = 0.456 and 0.492). Communalities were all greater than 0.3 and presented satisfactory values according to the sample size.

Table 4 presents the items used for the factor analysis as well as their meanings and their communalities.

Table 4 Items used for the factor analysis and their communalities

Table 5 shows the rotated factor matrix as well as the initial eigenvalues, variance explained and the cumulative explained variance of the extracted factors.

Items Communalities

Code _{Code meaning Initial} Extra-

ction

More_CSIfRTInfoStalls I would use more frequently CS system if it provides

RT information about localization of available cars .800 .832 More_CSIfRTInfoTraffic I would use more frequently CS system if it provides

RT information about traffic .819 .897 Tech_LikeTry I like to try new technologies .798 .737 Tech_EnchPoten I am fascinated by the potential of new technologies .789 .839 Tech_IneterestNew I am interested by new technologies .766 .700 Tech_AppsHelp Apps help me in my everyday life .616 .590

Tech_AppsFun Some apps are funny to use .730 .643

Tech_AppsEnjoyNew I like to try new apps .835 .826

TUeTO_Expect_SaveTime Use TUeTO would allow me to save a bit of travel

time .555 .617

TUeTO_Expect_CalmRela x

I would reach my destination with more calm and

more relax using TUeTO .553 .648

TUeTO_Expect_Env Use TUeTO would allow me to reduce the ecological

impact of my travel .470 .503

EcoInfo1 In general, I think I am well informed on

environmental issues .615 .915

EcoInfo2 In general, I think I have all necessary information to

reduce my ecological footprint .593 .577 More_BSIfRTInfo I would use more frequently BS system if it provides

RT information about bike availability and occupation of terminals

Items relevant for each factor are highlighted in blue and were automatically grouped together according to their importance in explaining the factor, thanks to the software SPSS. The four recognized latent constructs account for about 77.6% of the total variance of the original data.

Table 5 Sorted rotated factors’ loads from the EFA

Factor 1 2 3 4 Tech_EnchPoten .917 -.198 Tech_AppsEnjoyNew .899 Tech_LikeTry .820 -.156 Tech_IneterestNew .812 .146 Tech_AppsFun .790 .154 .158 Tech_AppsHelp .750 .119 TUeTO_Expect_SaveTime .801 TUeTO_Expect_CalmRelax .797 TUeTO_Expect_Env .631 -.144 EcoInfo1 .974 EcoInfo2 .739 More_CSIfRTInfoTraffic -.950 More_CSIfRTInfoStalls -.919 More_BSIfRTInfo .193 .221 -.493 Initial Eigenvalues 4.568 2.512 1.636 1.053

Percentage of total variance 32.6% 18% 11.7% 7.5%

Cumulative variance 32.6% 50.6% 62.3% 69.9%

The first factor loaded six items explaining approximately 34.6% of the variance. The six items refer to the general attitudes towards the technology and, more precisely, the pleasure of experimenting new technologies. According to such characteristics and to the construct of the Unified Theory of Acceptance and

Use of Technology 2 (UTAUT2) (Venkatesh et al., 2012) – being “hedonic motivation” defined as the pleasure obtained by using a technology – the factor is called “technologic hedonist #1”.

Representing 20.2% of the explained variance, three items load the second factor; it is called “TUeTO performance expectancy” in reference to the socio- psychological construct of the UTAUT2, “performance expectancy”, measuring the expected benefits provided by the use of a technology. The expected benefits refer to the time saving, the ecological footprint and the serenity gained by using it. This factor shows high loads on items related to the save of time and travel serenity, while a lower load on environmental issues is observed. This second factor is loaded by other four variables, but they have greater loads on other latent constructs.

The third factor “ecological knowledge #1” presents high loads on two items and explains 13.4% of the total variance. The items belonging to this factor refer to participants’ knowledge about ecological issues and the usefulness of this knowledge to reduce their footprint. This factor relates to the psychosocial construct related to the knowledge discussed in the socio-psychological section.

The fourth factor, the smallest one (9.4%), is named “real-time information does not encourage the use of shared transports #1”. It highlights the fact that the deployment of real-time information about sharing modes will not encourage people to use them. While both variables on the CS have strong negative loads, the item related to the BS presents a lower load.

Finally, Conbrach’s alpha value was calculated in order to assess the reliability of the extracted factors. Table 6 shows the alpha of each of the four recognized latent constructs and the used variables. All the extracted factors can be considered as satisfying since they are greater than 0.7.

Table 6 First EFA ouput. Conbrach's alpha calculated for each extracted factor

Cronbach’s

alpha Used variables

Factor 1

Technologic hedonist #1 0.921 Tech_EnchPoten, Tech_AppsEnjoyNew, Tech_LikeTry, Tech_InterestNew, Tech_AppsFun, Tech_AppsHelp

Factor 2

TUeTO expectation 0.799 TUeTO_Expect_SaveTime, TUeTO_Expect_CalmRelax, TUeTO_Expect_Env

Factor 3

Ecological knowledge #1 0.836 EcoInfo1, EcoInfo2

Factor 4

Real-time information expectation to increase sharing modes use #1

0.848 More_CSIfRTInfoTraffic, _{More_CSIfRTInfoStalls, More_BSIfRTInfo}

4.3.2 Exploratory factor analysis based on the latent