Economic, Functional, and Social Factors Influencing Electric Vehicles’ Adoption: An Empirical Study Based on the Diffusion of Innovation Theory
Abstract
:1. Introduction
2. Literature Review and Theoretical Background
2.1. Literature Review
2.2. Diffusion of Innovation Theory
3. Research Model and Hypotheses
3.1. The Effects of Perceived Innovation Characteristics on the Adoption of EVs
3.2. The Antecedents of Perceived Innovation Characteristics
3.2.1. Economic Aspect
3.2.2. Functional Aspect
3.2.3. Social Aspect
4. Methodology
4.1. Measures
4.2. Data Collection and Samples
4.3. Statistical Analysis
5. Data Analysis and Results
5.1. Measurement Model
5.2. Structural Model
6. Discussion
6.1. Key Findings
6.2. Theoretical Implications
6.3. Practical Implications
6.4. Limitations and Future Research
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Constructs | Items |
---|---|
Monetary subsidy (MS) | MS1: The purchase subsidy for electric vehicles is attractive to me. MS1: The tax exemption policy for electric vehicles is attractive to me. MS1: The larger purchase loan amounts for electric vehicles is attractive to me. MS4: The lower insurance premium for electric vehicles is attractive to me. |
Risk of price reduction (RPR) | RPR1: The price of electric vehicles is likely to fall in the future. RPR2: Buying electric vehicles faces the risk of a price reduction in the future. RPR3: The price of electric vehicles is unstable. |
Intelligent function (IF) | IF1: Electric vehicles are smart. IF2: Electric vehicles are full of intelligence. IF3: Electric vehicles are equipped with a lot of intelligent functions. |
Risk of sustainability (RS) | RS1: The battery of electric vehicles is easy to loss and scrap. RS2: Electric vehicles cannot run for a long duration. RS3: The lifespan of electric vehicles is short. RS4: Overall, electric vehicles are unsustainable. |
Recommendation (REC) | REC1: I will introduce this smart health device to my friends. REC2: I will commend this smart health device to my friends. REC3: I will tell others about the benefits of this smart health device. |
Status symbol (SS) | SS1: Electric vehicles are a symbol of identity and status. SS2: People who drive electric vehicles are brave innovators. SS3: People who drive electric vehicles are practitioners of environmental protection. |
Risk of reputation (RR) | RR1: Driving electric vehicles harms one’s social reputation. RR2: Driving electric vehicles is not gregarious. RR3: Driving electric vehicles will be ostracized by others. |
Perceived compatibility (PCB) | PCB1: Electric vehicles are compatible with my lifestyle. PCB2: Electric vehicles fits well with the way I go out and come home in my daily life. PCB3: Using electric vehicles is completely compatible with my current situation. PCB4: Electric vehicles are a good match for my needs. |
Perceived complexity (PCP) | PCP1: I believe that electric vehicles are cumbersome to use. PCP2: Using electric vehicles raises a lot of concerns. PCP3: Using electric vehicles requires a lot of effort. PCP4: Using electric vehicles is not easy. |
Perceived relative advantage (PRA) | PRA1: Electric vehicles are more convenient than traditional fuel vehicles. PRA2: Electric vehicles are better than traditional fuel vehicles. PRA3: Electric vehicles have more advantages than traditional fuel vehicles. |
Adoption of EVs (AEV) | AEV1: Compared with fuel vehicles, I will give priority to buying electric vehicles. AEV2: I intend to buy an electric vehicle. AEV3: I am willing to buy an electric vehicle in the near future AEV4: I will recommend relatives and friends to buy electric vehicles. |
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Demographic Variable | Types | Frequency | Percentage (%) |
---|---|---|---|
Gender | Male | 198 | 52.80 |
Female | 177 | 47.20 | |
Age | Younger than 18 | 2 | 0.53 |
19–25 | 91 | 24.27 | |
26–30 | 140 | 37.33 | |
31–40 | 118 | 31.47 | |
41–50 | 19 | 5.07 | |
Older than 51 | 5 | 1.33 | |
Educational level | High school and below | 21 | 5.60 |
Junior college | 112 | 29.87 | |
Undergraduate | 216 | 57.60 | |
Master and above | 26 | 6.93 | |
Personal annual income (CNY) | Less than 50,000 | 21 | 5.60 |
50,000 to less than 100,000 | 55 | 14.67 | |
100,000 to less than 150,000 | 128 | 34.13 | |
150,000 to less than 200,000 | 119 | 31.73 | |
More than 200,000 | 52 | 13.87 | |
Number of cars owned | 0 | 54 | 14.40 |
1 | 281 | 74.93 | |
2 | 28 | 7.47 | |
More than 2 | 12 | 3.20 |
Indicator | Substantive Factor Loading (R1) | R12 | Method Factor Loading (R2) | R22 |
---|---|---|---|---|
MS1 | 0.845 *** | 0.714 | −0.017 | 0.000 |
MS2 | 0.881 *** | 0.776 | −0.084 * | 0.007 |
MS3 | 0.572 *** | 0.327 | 0.174 ** | 0.030 |
MS4 | 0.832 *** | 0.692 | −0.044 | 0.002 |
RPR1 | 0.858 *** | 0.736 | −0.049 | 0.002 |
RPR2 | 0.888 *** | 0.789 | 0.055 | 0.003 |
RPR3 | 0.875 *** | 0.766 | −0.006 | 0.000 |
IF1 | 0.841 *** | 0.707 | −0.020 | 0.000 |
IF2 | 0.874 *** | 0.764 | 0.015 | 0.000 |
IF3 | 0.847 *** | 0.717 | 0.004 | 0.000 |
RS1 | 0.804 *** | 0.646 | −0.056 | 0.003 |
RS2 | 0.826 *** | 0.682 | 0.011 | 0.000 |
RS3 | 0.854 *** | 0.729 | −0.026 | 0.001 |
RS4 | 0.817 *** | 0.667 | 0.074 | 0.005 |
SS1 | 0.871 *** | 0.759 | 0.004 | 0.000 |
SS2 | 0.815 *** | 0.664 | 0.066 | 0.004 |
SS3 | 0.885 *** | 0.783 | −0.070 | 0.005 |
RR1 | 0.877 *** | 0.769 | −0.027 | 0.001 |
RR2 | 0.870 *** | 0.757 | 0.053 | 0.003 |
RR3 | 0.878 *** | 0.771 | −0.026 | 0.001 |
PCB1 | 0.667 *** | 0.445 | 0.090 | 0.008 |
PCB2 | 0.913 *** | 0.834 | −0.123 | 0.015 |
PCB3 | 0.784 *** | 0.615 | 0.007 | 0.000 |
PCB4 | 0.768 *** | 0.590 | 0.032 | 0.001 |
PCP1 | 0.845 *** | 0.714 | −0.057 | 0.003 |
PCP2 | 0.789 *** | 0.623 | −0.034 | 0.001 |
PCP3 | 0.843 *** | 0.711 | 0.049 | 0.002 |
PCP4 | 0.858 *** | 0.736 | 0.040 | 0.002 |
PRA1 | 0.852 *** | 0.726 | −0.110 * | 0.012 |
PRA2 | 0.787 *** | 0.619 | 0.087 | 0.008 |
PRA3 | 0.786 *** | 0.618 | 0.010 | 0.000 |
AEV1 | 0.723 *** | 0.523 | 0.088 | 0.008 |
AEV2 | 0.871 *** | 0.759 | −0.038 | 0.001 |
AEV3 | 0.974 *** | 0.949 | −0.118 | 0.014 |
AEV4 | 0.756 *** | 0.572 | 0.074 | 0.005 |
Average | 0.829 | 0.693 | 0.001 | 0.004 |
Constructs | Items | Loadings | CA | CR | AVE |
---|---|---|---|---|---|
Monetary subsidy (MS) | MS1 | 0.846 | 0.796 | 0.868 | 0.623 |
MS2 | 0.826 | ||||
MS3 | 0.685 | ||||
MS4 | 0.791 | ||||
Risk of price reduction (RPR) | RPR1 | 0.874 | 0.845 | 0.906 | 0.763 |
RPR2 | 0.875 | ||||
RPR3 | 0.872 | ||||
Intelligent function (IF) | IF1 | 0.816 | 0.814 | 0.890 | 0.729 |
IF2 | 0.884 | ||||
IF3 | 0.860 | ||||
Risk of sustainability (RS) | RS1 | 0.815 | 0.844 | 0.895 | 0.681 |
RS2 | 0.814 | ||||
RS3 | 0.855 | ||||
RS4 | 0.815 | ||||
Status symbol (SS) | SS1 | 0.879 | 0.819 | 0.892 | 0.734 |
SS2 | 0.843 | ||||
SS3 | 0.849 | ||||
Risk of reputation (RR) | RR1 | 0.881 | 0.847 | 0.907 | 0.766 |
RR2 | 0.873 | ||||
RR3 | 0.870 | ||||
Perceived compatibility (PCB) | PCB1 | 0.746 | 0.791 | 0.965 | 0.615 |
PCB2 | 0.803 | ||||
PCB3 | 0.788 | ||||
PCB4 | 0.799 | ||||
Perceived complexity (PCP) | PCP1 | 0.862 | 0.854 | 0.901 | 0.695 |
PCP2 | 0.778 | ||||
PCP3 | 0.835 | ||||
PCP4 | 0.856 | ||||
Perceived relative advantage (PRA) | PRA1 | 0.755 | 0.729 | 0.847 | 0.649 |
PRA2 | 0.859 | ||||
PRA3 | 0.799 | ||||
Adoption of EVs (AEV) | AEV1 | 0.814 | 0.853 | 0.901 | 0.694 |
AEV2 | 0.832 | ||||
AEV3 | 0.864 | ||||
AEV4 | 0.820 |
Constructs | Mean | S.D | MS | RPR | IF | RS | SS | RR | PC | PP | PRA | AEV |
---|---|---|---|---|---|---|---|---|---|---|---|---|
MS | 5.272 | 1.039 | 0.789 | |||||||||
RPR | 4.427 | 1.217 | −0.010 | 0.873 | ||||||||
IF | 4.752 | 1.230 | 0.304 | −0.037 | 0.854 | |||||||
RS | 4.405 | 1.293 | −0.057 | 0.385 | 0.056 | 0.825 | ||||||
SS | 4.318 | 1.321 | 0.157 | −0.017 | 0.492 | 0.124 | 0.857 | |||||
RR | 3.454 | 1.489 | −0.272 | 0.101 | 0.242 | 0.332 | 0.439 | 0.875 | ||||
PC | 5.205 | 0.967 | 0.471 | −0.128 | 0.412 | −0.109 | 0.393 | 0.037 | 0.784 | |||
PP | 4.099 | 1.337 | −0.220 | 0.301 | 0.093 | 0.436 | 0.222 | 0.489 | −0.102 | 0.834 | ||
PRA | 4.829 | 1.086 | 0.226 | −0.211 | 0.370 | −0.130 | 0.454 | 0.151 | 0.521 | 0.065 | 0.806 | |
AEV | 5.103 | 1.100 | 0.505 | −0.242 | 0.443 | −0.187 | 0.409 | 0.008 | 0.725 | −0.104 | 0.644 | 0.833 |
Hypothesis | Paths | β | t−Statistics | p−Values | Results |
---|---|---|---|---|---|
H1 | Perceived compatibility → Adoption of EVs | 0.514 | 10.752 | 0.000 | Support |
H2 | Perceived complexity → Adoption of EVs | −0.086 | 2.732 | 0.007 | Support |
H3 | Perceived relative advantage → Adoption of EVs | 0.387 | 8.511 | 0.000 | Support |
H4a | Monetary subsidy → Perceived compatibility | 0.387 | 6.783 | 0.000 | Support |
H4b | Monetary subsidy → Perceived complexity | −0.148 | 3.004 | 0.003 | Support |
H4c | Monetary subsidy → Perceived relative advantage | 0.137 | 2.475 | 0.014 | Support |
H5a | Risk of price reduction → Perceived compatibility | −0.078 | 2.115 | 0.035 | Support |
H5b | Risk of price reduction → Perceived complexity | 0.171 | 2.928 | 0.004 | Support |
H5c | Risk of price reduction → Perceived relative advantage | −0.153 | 2.873 | 0.004 | Support |
H6a | Intelligent function → Perceived compatibility | 0.171 | 3.084 | 0.002 | Support |
H6b | Intelligent function → Perceived complexity | 0.020 | 0.373 | 0.709 | Not support |
H6c | Intelligent function → Perceived relative advantage | 0.148 | 2.256 | 0.025 | Support |
H7a | Risk of sustainability → Perceived compatibility | −0.111 | 2.304 | 0.022 | Support |
H7b | Risk of sustainability → Perceived complexity | 0.247 | 3.970 | 0.000 | Support |
H7c | Risk of sustainability → Perceived relative advantage | −0.131 | 2.396 | 0.017 | Support |
H8a | Status symbol → Perceived compatibility | 0.252 | 4.552 | 0.000 | Support |
H8b | Status symbol → Perceived complexity | 0.074 | 1.195 | 0.233 | Not support |
H8c | Status symbol → Perceived relative advantage | 0.351 | 5.846 | 0.000 | Support |
H9a | Risk of reputation → Perceived compatibility | 0.046 | 0.956 | 0.340 | Not support |
H9b | Risk of reputation → Perceived complexity | 0.317 | 4.958 | 0.000 | Support |
H9c | Risk of reputation → Perceived relative advantage | 0.057 | 0.849 | 0.396 | Not support |
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Xia, Z.; Wu, D.; Zhang, L. Economic, Functional, and Social Factors Influencing Electric Vehicles’ Adoption: An Empirical Study Based on the Diffusion of Innovation Theory. Sustainability 2022, 14, 6283. https://doi.org/10.3390/su14106283
Xia Z, Wu D, Zhang L. Economic, Functional, and Social Factors Influencing Electric Vehicles’ Adoption: An Empirical Study Based on the Diffusion of Innovation Theory. Sustainability. 2022; 14(10):6283. https://doi.org/10.3390/su14106283
Chicago/Turabian StyleXia, Zhengwei, Dongming Wu, and Langlang Zhang. 2022. "Economic, Functional, and Social Factors Influencing Electric Vehicles’ Adoption: An Empirical Study Based on the Diffusion of Innovation Theory" Sustainability 14, no. 10: 6283. https://doi.org/10.3390/su14106283