Factors Influencing Adoption Intention for Electric Vehicles under a Subsidy Deduction: From Different City-Level Perspectives
Abstract
:1. Introduction
2. Literature Review
2.1. The History of EVs
2.2. The Impact of Subsidies on the Adoption of EVs
2.3. The Impact of City Level on the Adoption of EVs
2.4. The Impact of Behavioral Elements on the Adoption of EVs
3. Model Hypotheses
3.1. The Conceptual Model
3.2. The Hypotheses
- (1)
- Basic behavioral elements
- (2)
- Infrastructure
- (3)
- Novelty seeking
- (4)
- Incentive policy
- (5)
- Product cognition
- (6)
- Environmental concern
4. Methodology
4.1. Questionnaire Design
4.2. Demographic Variables
4.3. Data Analysis
4.3.1. Measurement Model
4.3.2. Common Method Variance
4.3.3. Model Fit Test
5. Results and Discussion
5.1. Differences in the Affecting Factors on Adoption Intention
5.2. Differences in the Effects of Demographic Characteristics on Adoption Intention
6. Conclusions and Policy Implications
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Constructs | Item |
---|---|
Attitude (AT) | I think the development of EVs is good for the environment (AT1) |
I support the country to introduce more policies to encourage individuals to buy EVs (AT2) | |
I think buying an EV is a good choice (AT3) | |
Subject Norm (SN) | The opinion of my family members is an important factor in my decision to buy an EV (SN1) |
If someone around me buys an EV, his behavior will motivate me to buy an EV (SN2) | |
Media positive coverage of EVs will motivate me to buy an EV (SN3) | |
Perceived Behavioral Control (PBC) | It is up to me to buy an EV (PBC1) |
I can afford to buy an EV (PBC2) | |
It is easy to buy EVs in my city (PBC3) | |
Infrastructure (IC) | I live in a city with a very good EVs infrastructure (IC1) |
I live or work in or near a place where I can charge an EV (IC2) | |
I would choose to buy an EV if charging facilities were better (IC3) | |
I live in an area where EVs charging facilities are available (IC4) | |
Novelty Seeking (NS) | I am always looking for information about new products and brands (NS1) |
I am always looking for new product experiences (NS2) | |
I consider EVs to be a fashionable and cutting-edge technology (NS3) | |
Subsidy Deduction (SD) | I would not buy an EVs if the EVs purchase subsidy was reduced (SD1) |
I care about EVs subsidies (SD2) | |
A reduction in subsidies would increase the price of EVs, which would affect my adoption intention to purchase them (SD3) | |
Non-Financial Incentive (NFI) | Removal from traffic restrictions for EVs will encourage me to buy one (NFI1) |
The policy of unlimited license plates for EVs would encourage me to buy one (NFI2) | |
The ability to use bus lane will encourage me to buy (NFI3) | |
Exemption from vehicle purchase tax will motivate me to buy an EVs (NFI4) | |
Product cognition (PC) | I understand that EVs are environmentally friendly products (PC1) |
I know that EVs can save energy and protect the environment through fuel substitution (PC2) | |
I am aware of the preferential policies of EVs (PC3) | |
Environmental Concern (EC) | I care about energy saving and environmental protection (EC1) |
I have a sense of mission to protect the environment and save energy (EC2) | |
I think vehicle exhaust is causing a lot of pollution to the environment (EC3) | |
Adoption Intention (AI) | I am willing to buy an EV in the future (AI1) |
I will recommend my friends and relatives to buy EVs in the future (AI2) | |
I will choose an EV when I buy my second car (AI3) |
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Respondents’ Characteristic | FST City (N = 434) | TFT City (N = 424) | ||
---|---|---|---|---|
Frequency | Percentage (%) | Frequency | Percentage (%) | |
Gender | ||||
Female | 181 | 41.70% | 150 | 35.40% |
Male | 253 | 58.30% | 274 | 64.60% |
Age | ||||
Under 18 | 3 | 0.70% | 2 | 0.50% |
18–25 | 126 | 29.00% | 180 | 42.50% |
26–30 | 143 | 32.90% | 86 | 20.30% |
31–40 | 99 | 22.80% | 68 | 16.00% |
41–50 | 44 | 10.10% | 49 | 11.60% |
51–60 | 19 | 4.40% | 38 | 9.00% |
60 above | 0 | 0.00% | 1 | 0.20% |
Education | ||||
Junior high school and below | 2 | 0.50% | 8 | 1.90% |
High school | 20 | 4.60% | 41 | 9.70% |
Associate degree | 62 | 14.30% | 88 | 20.80% |
Bachelor degree | 204 | 47.00% | 215 | 50.70% |
Master degree | 125 | 28.80% | 59 | 13.90% |
Doctor degree | 21 | 4.80% | 13 | 3.10% |
Annual income after tax (RMB) | ||||
50,000 below | 85 | 19.60% | 159 | 37.50% |
50,000–100,000 | 126 | 29.00% | 135 | 31.80% |
110,000–150,000 | 75 | 17.30% | 73 | 17.20% |
160,000–200,000 | 64 | 14.70% | 33 | 7.80% |
210,000–250,000 | 25 | 5.80% | 8 | 1.90% |
250,000 above | 59 | 13.60% | 16 | 3.80% |
Marital status | ||||
Single | 231 | 53.20% | 242 | 57.10% |
Married | 198 | 45.60% | 175 | 41.30% |
Others (Widowed or Divorced) | 5 | 1.20% | 7 | 1.70% |
Number of vehicles in family | ||||
0 | 89 | 20.50% | 92 | 21.70% |
1 | 226 | 52.10% | 212 | 50.00% |
2 | 86 | 19.80% | 101 | 23.80% |
3 | 22 | 5.10% | 15 | 3.50% |
4 | 6 | 1.40% | 2 | 0.50% |
5 | 0 | 0.00% | 1 | 0.20% |
6 or more | 5 | 1.20% | 1 | 0.20% |
Constructs | Indicators | S.td Factor Loading | SMC | Convergent Validity | Ave Square Root | Cronbach’s Alpha (α) | p Value | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
CR | AVE | ||||||||||||||
FST Cities | TFT Cities | FST Cities | TFT Cities | FST Cities | TFT Cities | FST Cities | TFT Cities | FST Cities | TFT Cities | FST Cities | TFT Cities | FST Cities | TFT Cities | ||
AT | AT1 | 0.8663 | 0.8634 | 0.7505 | 0.7455 | 0.8595 | 0.8444 | 0.6721 | 0.6496 | 0.8198 | 0.8060 | 0.848 | 0.815 | *** | *** |
AT2 | 0.8508 | 0.9053 | 0.7239 | 0.8196 | *** | ||||||||||
AT3 | 0.7361 | 0.6195 | 0.5418 | 0.3838 | *** | ||||||||||
SN | SN2 | 0.8428 | 0.7630 | 0.7103 | 0.5822 | 0.8246 | 0.7460 | 0.7015 | 0.5950 | 0.8376 | 0.7714 | 0.818 | 0.733 | *** | *** |
SN3 | 0.8323 | 0.7796 | 0.6927 | 0.6078 | |||||||||||
NFN | NFN1 | 0.9122 | 0.8855 | 0.8321 | 0.7841 | 0.9101 | 0.8682 | 0.7717 | 0.6892 | 0.8785 | 0.8302 | 0.899 | 0.859 | ||
NFN2 | 0.9012 | 0.8809 | 0.8122 | 0.7760 | *** | *** | |||||||||
NFN4 | 0.8191 | 0.7124 | 0.6709 | 0.5075 | *** | *** | |||||||||
SD | SD1 | 0.7493 | 0.6621 | 0.5615 | 0.4384 | 0.8702 | 0.8168 | 0.6919 | 0.6002 | 0.8318 | 0.7747 | 0.869 | 0.814 | *** | *** |
SD2 | 0.8740 | 0.8056 | 0.7639 | 0.6490 | |||||||||||
SD3 | 0.8663 | 0.8446 | 0.7505 | 0.7133 | *** | *** | |||||||||
NS | NS1 | 0.9040 | 0.8065 | 0.8172 | 0.6504 | 0.8566 | 0.7992 | 0.7496 | 0.6656 | 0.8658 | 0.8158 | 0.781 | 0.740 | ||
NS2 | 0.8258 | 0.8251 | 0.6819 | 0.6808 | *** | *** | |||||||||
IC | IC1 | 0.8953 | 0.8229 | 0.8016 | 0.6772 | 0.8322 | 0.8870 | 0.7135 | 0.7978 | 0.8447 | 0.8932 | 0.709 | 0.745 | ||
IC2 | 0.7908 | 0.9584 | 0.6254 | 0.9185 | *** | *** | |||||||||
EC | EC1 | 0.9048 | 0.9199 | 0.8187 | 0.8462 | 0.8872 | 0.8945 | 0.7973 | 0.8092 | 0.8929 | 0.8996 | 0.768 | 0.768 | ||
EC2 | 0.8809 | 0.8787 | 0.7760 | 0.7721 | *** | *** | |||||||||
PC | PC1 | 0.8751 | 0.8279 | 0.7658 | 0.6854 | 0.8375 | 0.7862 | 0.7207 | 0.6479 | 0.8489 | 0.8049 | 0.710 | 0.639 | ||
PC2 | 0.8219 | 0.7813 | 0.6755 | 0.6104 | *** | *** | |||||||||
AI | AI1 | 0.8376 | 0.8050 | 0.7016 | 0.6480 | 0.8602 | 0.8512 | 0.6730 | 0.6564 | 0.8204 | 0.8102 | 0.855 | 0.850 | ||
AI2 | 0.8703 | 0.8490 | 0.7574 | 0.7208 | *** | *** | |||||||||
AI3 | 0.7484 | 0.7748 | 0.5601 | 0.6003 | *** | *** |
Hypothesis | Path | Unstandardized Coefficient | S.E. | p Value | Standardized Coefficient | Test Result | |||||
---|---|---|---|---|---|---|---|---|---|---|---|
FST Cities | TFT Cities | FST Cities | TFT Cities | FST Cities | TFT Cities | FST Cities | TFT Cities | FST Cities | TFT Cities | ||
H1 | AI←AT | 0.1975 | 0.1565 | 0.0433 | 0.0604 | 0.0943 * | 0.0096 *** | 0.2659 | 0.1691 | Supported | Supported |
H2 | AI←SN | 0.1889 | 0.2361 | 0.0399 | 0.055 | *** | *** | 0.2857 | 0.2844 | Supported | Supported |
H3 | AI←PBC | - | - | - | - | 0 | 0 | - | - | NONE | NONE |
H4 | AI←IC | 0.0373 | 0.0573 | 0.0311 | 0.0294 | 0.2306 | 0.0514 * | 0.0578 | 0.0986 | Unsupported | Supported |
H5 | AI←NS | 0.1362 | 0.1886 | 0.0281 | 0.0406 | *** | *** | 0.246 | 0.272 | Supported | Supported |
H6 | AI←SD | −0.1128 | −0.0998 | 0.0352 | 0.0412 | 0.0013 ** | 0.0154 * | −0.165 | −0.1463 | Supported | Supported |
H7 | AI←NFN | 0.2767 | 0.2291 | 0.0501 | 0.0429 | *** | *** | 0.3442 | 0.341 | Supported | Supported |
H8 | AI←PC | 0.3249 | 0.2941 | 0.0514 | 0.0623 | *** | *** | 0.4773 | 0.3598 | Supported | Supported |
H9 | AI←EC | 0.0724 | 0.172 | 0.0433 | 0.051 | 0.0943 * | *** | 0.0816 | 0.1881 | Supported | Supported |
Variable | FST Cities | TFT Cities | ||||||
---|---|---|---|---|---|---|---|---|
Mean | Std. Deviation | F | p Value | Mean | Std. Deviation | F | p Value | |
Gender | ||||||||
Female | 3.8508 | 0.6642 | 2.745 * | 0.098 | 3.8844 | 0.7204 | 7.758 *** | 0.006 |
Male | 3.7154 | 0.9451 | 3.6472 | 0.8966 | ||||
Age | ||||||||
Under 18 | 3.2222 | 0.1925 | 0.406 | 0.845 | 2.6667 | 0.9428 | 3.732 *** | 0.001 |
18–25 | 3.791 | 0.8597 | 3.7889 | 0.7280 | ||||
26–30 | 3.7366 | 0.8096 | 3.8023 | 0.6905 | ||||
31–40 | 3.7879 | 0.9264 | 3.5392 | 1.0042 | ||||
41–50 | 3.7803 | 0.8163 | 3.4218 | 1.1522 | ||||
51–60 | 3.8947 | 0.6091 | 4.0877 | 0.7215 | ||||
60 above | 0 | 4 | ||||||
Education | ||||||||
Junior high school and below | 4 | 0 | 0.268 | 0.931 | 3.1667 | 0.6424 | 2.022 * | 0.075 |
High school | 3.7 | 0.6389 | 3.6748 | 0.9986 | ||||
Associate degree | 3.8226 | 0.9286 | 3.8409 | 0.8562 | ||||
Bachelor degree | 3.799 | 0.8567 | 3.7349 | 0.8175 | ||||
Master degree | 3.7253 | 0.7977 | 3.7797 | 0.7444 | ||||
Doctor degree | 3.6825 | 0.9278 | 3.2308 | 1.0575 | ||||
Annual income after tax (RMB) | ||||||||
50,000 below | 3.9059 | 0.8398 | 1.423 | 0.214 | 3.6813 | 0.8116 | 1.863 * | 0.1 |
50,000–100,000 | 3.7857 | 0.7260 | 3.7531 | 0.8395 | ||||
110,000–150,000 | 3.8133 | 0.7637 | 3.7945 | 0.8474 | ||||
160,000–200,000 | 3.7396 | 0.9804 | 3.596 | 1.0367 | ||||
210,000–250,000 | 3.76 | 0.6125 | 3.3333 | 0.7346 | ||||
250,000 above | 3.5367 | 1.0468 | 4.2292 | 0.6962 | ||||
Marital status | ||||||||
Single | 3.7734 | 0.8393 | 0.192 | 0.826 | 3.7617 | 0.7686 | 0.44 | 0.644 |
Married | 3.7643 | 0.8493 | 3.6857 | 0.9433 | ||||
Others (Widowed or Divorced) | 4 | 0.7071 | 3.8095 | 0.8576 | ||||
Number of vehicles in family | ||||||||
0 | 3.7566 | 0.7650 | 3.459 *** | 0.004 | 3.7138 | 0.8639 | 0.288 | 0.943 |
1 | 3.8083 | 0.8181 | 3.7516 | 0.7950 | ||||
2 | 3.6938 | 0.8559 | 3.7063 | 0.9525 | ||||
3 | 4.0303 | 0.7267 | 3.6444 | 0.8015 | ||||
4 | 3.9444 | 0.8798 | 3.6667 | 0 | ||||
5 | 0 | 4.6667 | ||||||
6 or more | 2.4 | 1.9494 | 4 |
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Zhang, J.; Xu, S.; He, Z.; Li, C.; Meng, X. Factors Influencing Adoption Intention for Electric Vehicles under a Subsidy Deduction: From Different City-Level Perspectives. Sustainability 2022, 14, 5777. https://doi.org/10.3390/su14105777
Zhang J, Xu S, He Z, Li C, Meng X. Factors Influencing Adoption Intention for Electric Vehicles under a Subsidy Deduction: From Different City-Level Perspectives. Sustainability. 2022; 14(10):5777. https://doi.org/10.3390/su14105777
Chicago/Turabian StyleZhang, Jingnan, Shichun Xu, Zhengxia He, Chengze Li, and Xiaona Meng. 2022. "Factors Influencing Adoption Intention for Electric Vehicles under a Subsidy Deduction: From Different City-Level Perspectives" Sustainability 14, no. 10: 5777. https://doi.org/10.3390/su14105777