Predicting consumers’ adoption of electric vehicles during the city smog crisis: An application of the protective action decision model

Liu, Y; Ouyang, Z; Cheng, P, 2019, in Journal of Environmental Psychology

doi:10.1016/j.jenvp.2019.04.013
Location Beijing, China
Population Other (specify)
Sample size 482
Factor analysis type exploratory factor analysis, unknown rotation
Stepwise regression no
Removal of insignificant variables no
Reviewed by LCM

Abstract

Encouraging citizens to adopt Electric Vehicles (EVs)is an effective approach to mitigate the city smog risk caused by motor vehicles. The protection action decision model (PADM)provides a valuable framework to explain adaptive risk behaviors, doing so by employing a wide set of predictors such as the protective action perception, risk perception, and stakeholder perception. In this study, data was collected via questionnaire from 482 participants drawn from EV customers in Beijing. The results show that hazard-related attributes have positive effects on the societal acceptability of EVs in the long run. Resource-related attributes have negative effects on their long-run acceptability and purchase intention. The study revealed that attribute importance moderates the effect of hazard and resource-related attributes on adoption indicators. Additionally, risk perception has positive effects on consumers' adoption indicators, and trustworthiness and the government's responsibility to protect the environment also influence consumer behavior intention. © 2019 Elsevier Ltd

Factors

Variable Pattern loading
It is important that driving EV instead of fossil fuel cars will reduce the city smog risk (Environmentalism) 0.919
It is important that driving EV will be useful to protect me from city smog (Environmentalism) 0.919
Variable Pattern loading
Local government, provincial, or national television station, local news media, and local environmental protection department officials are knowledgeable about the city smog (Environmentalism) 0.8
Variable Pattern loading
Adoption of electric vehicles would help me reduce the city smog risk (Environmentalism) 0.935
Adoption of electric vehicles would be useful for protect me from city smog (Environmentalism) 0.935
Variable Pattern loading
I will buy an electric vehicle in a long run (Other) 0.934
It is acceptable that electric vehicles should replace fossil fuel vehicles in the long run (Environmentalism) 0.934
Variable Pattern loading
Local government, provincial, or national television station, local news media, and local environmental protection department officials are responsible for protecting me from city smog (Environmentalism) 0.8
Variable Pattern loading
I am interested in buying an electric vehicle (Other) 0.88
I will consider an electric vehicle when purchasing a next car (Other) 0.873
I intend to purchase an electric vehicle (Other) 0.87
Variable Pattern loading
It is important that buying and using EVs are cheap (Price) 0.849
It is important that buying and using EVs take little time and effort (Convenience) 0.823
It is important that buying and using EVs require little knowledge and skills (Convenience) 0.813
It is important that buying and using EVs require little help and cooperation from others (Convenience) 0.808
Variable Pattern loading
Adoption of electric vehicles would cost a lot of money (Price) 0.873
Adoption of electric vehicles would require a lot of effort or time (Convenience) 0.864
Adoption of electric vehicles would require specialized knowledge (Convenience) 0.863
Adoption of electric vehicles would require a lot of cooperation from others (Convenience) 0.857
Variable Pattern loading
City smog threatens personal physical health (Environmentalism) 0.882
City smog threatens personal psychological health (Environmentalism) 0.867
City smog threatens my work efficiency and family daily life (Environmentalism) 0.866
Variable Pattern loading
Local government, provincial, or national television station, local news media, and local environmental protection department officials are willing to provide me with accurate information on the city smog (Environmentalism) 0.8

Models

Dependent variable Purchase intention
Model type Linear regression
Sample size 482.0
R2 nan
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence nan
Variable Coefficient p-value
Constant -0.007 0.876
Hazard-related attribute 0.174 0.0
Efficacy importance 0.054 0.318
Hazard-related attribute*Efficacy importance 0.048 0.318
Dependent variable Long-run acceptability
Model type Linear regression
Sample size 482.0
R2 nan
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence nan
Variable Coefficient p-value
Constant -0.046 0.274
Hazard-related attribute 0.302 0.0
Efficacy importance 0.067 0.111
Hazard-related attribute*Efficacy importance 0.309 0.0
Dependent variable Purchase intention
Model type Linear regression
Sample size 482.0
R2 nan
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence nan
Variable Coefficient p-value
Constant -0.007 0.876
Resource-related attribute -0.25 0.0
Resource importance -0.037 0.401
Resource-related attribute*Resource importance -0.136 0.001
Dependent variable Long-run acceptability
Model type Linear regression
Sample size 482.0
R2 nan
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence nan
Variable Coefficient p-value
Constant -0.024 0.577
Resource-related attribute -0.187 0.0
Resource importance 0.031 0.471
Resource-related attribute*Resource importance -0.25 0.0
Dependent variable Purchase intention
Model type Linear regression
Sample size 482.0
R2 nan
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence nan
Variable Coefficient p-value
Constant 2.867 0.0
Age -0.056 0.0
Gender 0.008 0.93
Income 0.016 0.657
Education level 0.155 0.0
Hazard-related attribute 0.004 0.946
Resource-related attribute -0.278 0.0
Risk perception 0.145 0.014
Trustworthiness 0.29 0.0
Expertise -0.043 0.521
Protect Responsibility -0.204 0.002
Dependent variable Long run acceptability
Model type Linear regression
Sample size 482.0
R2 nan
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence nan
Variable Coefficient p-value
Constant 1.404 0.005
Age -0.091 0.0
Gender 0.005 0.947
Income -0.026 0.387
Education level -0.021 0.525
Hazard-related attribute 0.24 0.0
Resource-related attribute -0.148 0.003
Risk perception 0.318 0.0
Trustworthiness 0.319 0.0
Expertise 0.029 0.598
Protect Responsibility 0.016 0.767

The Attitudes and Travel Database is produced with support from the Center for Teaching Old Models New Tricks at Arizona State University, a University Transportation Center sponsored by the US Department of Transportation through Grant No. 69A3551747116.

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