Factors affecting the intention to use an automated car

Exploratory factor analysis EFA with Independent variables.The number of factors analysis with 5 independent variables includes Performance Expectancy, Effort Expectancy, Social Influenc

Social Influence Scale

Cronbach’s Alpha equals 0.869, and the measured measurement scale is accepted

Item-Total Statistics Scale Mean if Item

Scale Variance if Item Deleted

Cronbach's Alpha if Item Deleted

I assume that people whose opinions I value would prefer that

I expect that people who influence my behavior think that I should use a conditionally automated car

I expect that people who are important to me think that I should use a conditionally automated car

I would recommend a conditionally automated car to others

Look at the table’s result, keeping these four items (the Corrected item – Total correlation ≥ 0.3)

Facilitating Conditions

Cronbach’s Alpha equals 0.857, and the measured measurement scale is accepted.

I could acquire the necessary knowledge to use a conditionally automated car

I would expect the use of a conditionally automated car to be compatible with other digital devices

I would expect to have the necessary knowledge to use a conditionally automated car

I would be able to get help from others when I have difficulties using a conditionally automated car

Hedonic Motivation Scale

Item-Total Statistics Scale Mean if

Using a conditionally automated car would be fun 7.80 2.576 724 744

Using a conditionally automated car would be entertaining

Using a conditionally automated car would be enjoyable

Look at the table’s result, keeping these three items (the Corrected item – Total correlation ≥ 0.3)

I intend to use a conditionally automated car in the future 11.58 6.254 709 846

I plan to buy a conditionally automated car once it is available 11.54 6.096 746 832

Assuming that I had access to a conditionally automated car, I predict that I would use it

I would use a conditionally automated car during my everyday trips

Conclusion: After analyzing Cronbach’s Alpha of each scale, we find that no variable, is eliminated and the scales have no changes after reliability testing.

II EXPLORATORY FACTOR ANALYSIS EFA

2.1 Exploratory factor analysis EFA with Independent variables.

The number of factors analysis with 5 independent variables includes Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, and Hedonic Motivation The process has 5 parts:

Part 1: Analysis of the Performance Expectancy Variable

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .832

The factor analysis method has the standard in the KMO that must be greater than 0.5 and the P-value (Sig.) of the significance level must be less than 0.05

Kaiser-Meyer-Olkin Measure of Smapling Adequcy (KMO) value = 0.832 ( >0.5).

-> Acceptable The data used for factor analysis are sufficiently intercorrelated to justify the factors.

P-value (Sig.= 0.000 < 0.05) -> Acceptable Measured variables are sufficiently intercorrelated to justify the factors.

Total Variance Explained Component Initial Eigenvalues Extraction Sums of Squared Loadings

Extraction Method: Principal Component Analysis.

This variable should be kept because it has based on the eigenvalue greater than 1 Explaining the total variance equals 71.886% and acceptable (>50%) in EFA.

I assume that people whose opinions I value would prefer that I use a conditionally automated car 885

I expect that people who influence my behavior think that I should use a conditionally automated car 821

I expect that people who are important to me think that I should use a conditionally automated car 857

I would recommend a conditionally automated car to others 827

Extraction Method: Principal Component Analysis. a 1 component extracted.

4 factors are kept because their overview of principal component analysis is higher than 0.5

Part 4: Analysis EFA of the “Facilitating conditions” variable

Kaiser-Meyer-Olkin Measure of Sampling

Total % of Variance Cumulative % Total % of Variance Cumulative %

I could acquire the necessary knowledge to use a conditionally automated car 860

I would expect the use of a conditionally automated car to be compatible with other digital devices I use 827

I would expect to have the necessary knowledge to use a conditionally automated car 846

I would be able to get help from others when I have difficulties using a conditionally automated car 813

Extraction Method: Principal Component Analysis. a 1 component tracked.

Part 5: Analysis EFA of the “Hedonic Motivation” variable

Kaiser-Meyer-Olkin Measure of Smapling Adequcy (KMO) value = 0.720 > 0.5.

-> Acceptable The data for factor analysis are sufficiently intercorrelated to justify the factors.

Using a conditionally automated car would be fun 883

Using a conditionally automated car would be entertaining 848

Using a conditionally automated car would be enjoyable 870

There are 3 factors keeping because their overview of principal component analysis is higher than 0.5

All independent variables are accepted with having the result of KMO greater than 0.5, and P-value (Sig.) lower than 0.05

All of the explainings of the total variance have equals higher than 50% and are acceptable

All of the factors in each variable have the principal component analysis, which is higher than 0.5

There are no dropped variables

2.2 Exploratory factor analysis EFA with Dependent variable

Factor analysis for dependent variable is Behavior Intention.

-> Acceptable The dada used for factor analysis are sufficiently intercorrelated to justify the factors.

Component Initial Eigenvalues Extraction Sums of Squared Loadings

A factor is based on eigenvalues greater than 1 The total of variance extracted is 72.539% (>50%), explaining the data in the 4 observed variables participating in EFA.

I intend to use a conditionally automated car in the future 838

I plan to buy a conditionally automated car once it is available 863

Assuming that I had access to a conditionally automated car, I predict that I would use it 831

I would use a conditionally automated car during my everyday trips 874

The dependent variables are accepted with having the result of KMO greater than 0.5, and P-value (Sig.) lower than 0.05

Explaining of the total variance have the equals higher than 50% and acceptable

Factors in each variable have the principal component analysis, which is higher than 0.5

III TESTING RESEARCH MODEL AND HYPOTHESIS

PE EE SI FC HM BI

** Correlation is significant at the 0.01 level (2-tailed).

PE (Performance Expectancy) – EE (Effect Expectancy)

H0: rab = 0 (there is no linear relationship between the two variables).

H1: rab ≠ 0 (there is a linear relationship between the two variables)

-> PE (Performance Expectancy) and EE (Effort Expectancy) have a positive relationship.

SI (Social Influence) and FC (Facilitating Conditions)

HI (Hedonic Motivation) and BI (Behavior Intention)

PE, SI, FC b Enter a Dependent Variable: BI b All requested variables entered.

BI = 0 + 1*PE + 2*EE + 3*SI + 4*FC + � � � � � �5*HM

Std Error of the Estimate

1 840 a 705 698 54924377 1.870 a Predictors: (Constant), HM, EE, PE, SI, FC b Dependent Variable: BI

R 2 = 0,705 (70.5%): The model is able to explain 70.5% of the observed data

This linear regression model fits the sample's data set.

Total 221.000 221 a Dependent Variable: BI b Predictors: (Constant), HM, EE, PE, SI, FC

H0: All regression coefficients are equal to zero -> the independent variables do not reliably predict the dependent variable.

H1: The independent variables reliably predict the dependent variable.

From the ANOVA results: Significant = 0.000 < alpha = 0.05 From there reject H0, accept H1: The independent variables reliably predict the dependent variable.

Std Residual -4.233 2.779 000 989 222 a Dependent Variable: BI

CHECK THE VIOLATION OF ASSUMPTIONS

Looking at the scatter plot, we see a uniform dispersion Thus, the assumption of constant variance of the regression model is not violated.

1 840 a 705 698 54924377 1.870 a Predictors: (Constant), HM, EE, Pe, SI, FC b Dependent Variable: BI

Durbin-Watson = 1.870 -> Weak positive autocorrelation: Acceptable

Through the above test results, the assumptions of the linear regression function are not violated and the built regression model is consistent with the overall.

ASSUMPTION 3: NO OR LITTLE MULTICOLLINEARITY: THE INDEPENDENT VARIABLES ARE NOT HIGHLY CORRELATED WITH EACH OTHER [COV(XI, XJ) =0]

Coefficients a Model Unstandardized Coefficients Standardized

B Std Error Beta Tolerance VIF

The variance inflation factor (VIF) reached the maximum value of 4.222 (less than 10), showing that these independent variables have a weak relationship with each other, so there is no multicollinearity phenomenon outside Therefore, the relationship between the independent variables does not significantly affect the explanatory results of the regression model.

The unnormalized regression equation has the form:

Y = 2.612E-016 + 0.103*PE + 0.022*EE – 0.049*SI + 0.298*FC + 0.518*HM

The normalized regression has the form:

Y = 0.103*PE + 0.022*EE – 0.049*SI + 0.298*FC + 0.518*HM

ASSUMPTION 4: RESIDUALS ARE NORMALLY DISTRIBUTED

Observation of the histogram of the normalized residuals shows that the normal distribution of the residuals is approximately standard Mean= -1.57E-16 (mean is close to 0) and standard deviation Std Dev = 0.989 (standard

33 deviation) close to 1) Therefore, it can be concluded that the hypothesis of the normal distribution of the residuals is not violated.

The P-P plot also shows that the points of the residuals are scattered not far but randomly around the diagonal (the expectation line), so the assumption of the normal distribution of the residuals is satisfied.

EXPLORATORY FACTOR ANALYSIS EFA

Exploratory factor analysis EFA with Independent variables