Modeling Acceptance of Electric Vehicle Sharing Based on Theory of Planned Behavior
Abstract
:1. Introduction
2. Methodology
2.1. Theory of Planned Behavior
2.2. Model Construction and Hypothesis
- Attitude towards the behavior has a significant positive effect (+) on the acceptance of EV sharing.
- Subjective norm has a significant positive effect (+) on the acceptance of EV sharing.
- Perceived behavioral control has a significant positive effect (+) on the acceptance of EV sharing.
- Policy support has a significant positive effect (+) on the attitude towards the behavior.
- Policy support has a significant positive effect (+) on the subjective norm.
2.3. Structural Equation Modeling of EV Sharing Acceptance
3. Scale Development and Data Validation
3.1. Scale Development
3.2. Sample Distribution
3.3. Data Validation
4. Model Verification and Hypothesis Test
4.1. Model Verification
4.2. Path Analysis and Hypothesis Test
5. Discussion
5.1. Research Findings
5.1.1. The Acceptance of EV Sharing Varies on Different Demographic Features
5.1.2. The Attitude towards Behavior Insignificantly Influences Sharing Intention
5.1.3. Subjective Norm and Perceived Behavior Control Both Have Significant Positive Effects on Sharing Intention
5.1.4. Policy Support Affects Sharing Intention via Mediating Effect
5.2. Recommedations to Promote EV Sharing
5.2.1. Providing More Accessible Resources of EV Sharing, Highlights on Improving the Perceived Behavioral Control
5.2.2. Improving Social Pressure, Keeping Focus on Enhancing Subjective Norm
5.2.3. Under Current Condition, Propaganda Aiming to Promote Behavioral Attitude Is Not an Effective Way
5.2.4. Policy Works Better When It Focuses on Improving Social Pressure
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Latent Variable | Item Number | Observation Item | Reference |
---|---|---|---|
The Attitude towards Behavior (AB) | 4 | 1. EV sharing can satisfy my travel demand by automobile if I can’t afford a private one. | [14,15] |
2. EV sharing can meet my need of temporary automobile usage. | |||
3. EV sharing contributes to environmental conservation. | |||
4. EV sharing provides an available automobile usage method under the policy of vehicle registration via lottery system and the vehicle usage restriction according to the license plate. | |||
Subjective Norm (SN) | 3 | 1. Family members and friends support my participation in EV sharing. | [14,15] |
2. Family members and friends encourage me to participate in EV sharing. | |||
3. Family members and friends expect me to choose EV sharing to meet part of my travel demand. | |||
Perceived Behavioral Control (PBC) | 2 | 1. I am confident that I could attend EV sharing. | [14,15] |
2. If I want to, it would be easy for me to attend EV sharing. | |||
Sharing Intention (SI) | 3 | 1. After the survey, I will try to participate in EV sharing. | [14,15] |
2. After the survey, I will encourage my friends to participate in EV sharing | |||
3. I plan to adopt EV sharing as an available travel mode in the future. | |||
Policy Support (PS) | 2 | 1. Government’s vehicle restriction policies, such as vehicle registration via lottery system and traffic restriction according to license number, lead me to participate in EV sharing. * | Authors designed |
2. Government’s stringent emission regulations lead me to choose “zero emission” EV sharing. |
Observed Variable | Component | ||||
---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | |
SI1 | 0.262 | 0.839 | 0.044 | 0.245 | 0.191 |
SI2 | 0.028 | 0.770 | −0.018 | 0.108 | 0.097 |
SI3 | 0.250 | 0.844 | 0.181 | 0.069 | 0.039 |
AB1 | 0.076 | 0.102 | 0.701 | −0.224 | 0.143 |
AB2 | 0.100 | −0.093 | 0.704 | 0.272 | 0.380 |
AB3 | −0.111 | 0.071 | 0.600 | 0.458 | −0.076 |
AB4 | 0.215 | 0.106 | 0.866 | 0.096 | −0.106 |
SN1 | 0.839 | 0.115 | 0.042 | 0.245 | 0.191 |
SN2 | 0.912 | 0.185 | 0.136 | 0.108 | 0.097 |
SN3 | 0.879 | 0.195 | 0.103 | 0.069 | 0.039 |
PBC1 | 0.294 | 0.336 | 0.072 | −0.148 | 0.689 |
PBC2 | 0.055 | 0.183 | 0.097 | 0.254 | 0.816 |
PS1 | 0.179 | 0.159 | −0.003 | 0.743 | 0.238 |
PS2 | 0.223 | 0.179 | 0.126 | 0.803 | −0.026 |
Eigenvalue | 2.685 | 2.333 | 2.193 | 1.769 | 1.610 |
Contribution of Variance (%) | 19.178 | 16.663 | 15.666 | 12.638 | 11.498 |
Accumulated Contribution of Variance (%) | 19.178 | 15.666 | 51.507 | 64.145 | 75.643 |
Variable | Factor Loading | t-Value | Construct Reliability | Variance Extraction |
---|---|---|---|---|
Attitude towards the Behavior | — | — | 0.762 | 0.459 |
AB1 | 0.532 | — | — | — |
AB2 | 0.704 | 4.106 *** | — | — |
AB3 | 0.491 | 3.121 ** | — | — |
AB4 | 0.904 | 4.271 *** | — | — |
Subjective Norm | — | — | 0.914 | 0.781 |
SN1 | 0.870 | — | — | — |
SN2 | 0.943 | 10.736 *** | — | — |
SN3 | 0.835 | 8.930 *** | — | — |
Perceived Behavioral Control | — | — | 0.634 | 0.469 |
PBC1 | 0.773 | 3.401 *** | — | — |
PBC2 | 0.583 | — | — | — |
Sharing Intention | — | — | 0.861 | 0.677 |
SI1 | 0.920 | 7.893 *** | — | — |
SI2 | 0.715 | 6.251 *** | — | — |
SI3 | 0.820 | — | — | — |
Policy Support | — | — | 0.694 | 0.532 |
PS1 | 0.744 | 3.239 ** | — | — |
PS2 | 0.714 | — | — | — |
Latent Variable | AB | SN | PBC | SI | PS |
---|---|---|---|---|---|
AB | 0.459 | — | — | — | — |
SN | 0.327 * | 0.781 | — | — | — |
PBC | 0.234 | 0.402 * | 0.469 | — | — |
SI | 0.253 ** | 0.462 ** | 0.316 ** | 0.677 | — |
PS | 0.267 ** | 0.471 ** | 0.215 | 0.471 ** | 0.532 |
χ2 | df | χ2/df | RMSEA | CFI | GFI | TLI | |
---|---|---|---|---|---|---|---|
Recommended Standard | — | — | 1~3, the hypothetical model can well fit the sample data. | <0.05, good fit; 0.05~0.08, reasonable fit. | >0.9 | >0.9 | >0.9 |
Experimental Data | 75.748 | 65 | 1.165 | 0.052 | 0.972 | 0.963 | 0.961 |
Path | Standardized Path Coefficient | t-Value |
---|---|---|
AB→SI | 0.069 | 0.571 |
SN→SI | 0.347 | 2.630 * |
PBC→SI | 0.530 | 2.823 * |
PS→AB | 0.375 | 2.023 ** |
PS→SN | 0.559 | 3.138 * |
AB1→AB | 0.500 | 3.008 * |
AB2→AB | 0.711 | 3.799 *** |
AB3→AB | 0.496 | — |
AB4→AB | 0.919 | 3.841 *** |
SN1→SN | 0.852 | — |
SN2→SN | 0.955 | 10.2999 *** |
SN3→SN | 0.835 | 8.495 *** |
PBC1→PBC | 0.608 | 3.075 * |
PBC2→PBC | 0.665 | — |
SI1→SI | 0.898 | 7.971 *** |
SI2→SI | 0.716 | 6.202 *** |
SI3→SI | 0.836 | — |
PS1→PS | 0.674 | 3.576 *** |
PS2→PS | 0.738 | — |
Path | Direction | Result |
---|---|---|
Hypothesis 1: AB→SI | + | Not supported |
Hypothesis 2: SN→SI | + | Supported |
Hypothesis 3: PBC→SI | + | Supported |
Hypothesis 4: PS→AB | + | Supported |
Hypothesis 5: PS→SN | + | Supported |
AB | SN | PBC | PS | |
---|---|---|---|---|
AB | — | — | — | 0.375 |
SN | — | — | — | 0.559 |
SI | 0.069 | 0.347 | 0.530 | 0.219 * |
Variables | Familiar to the EV-Sharing Knowledge | Intention to Participate in EV Sharing | |||||
---|---|---|---|---|---|---|---|
Mean | SD | t-Test | Mean | SD | t-Test | ||
Gender | male | 3.21 | 1.18 | t(60) = 1.966, p = 0.008 | 3.33 | 0.64 | t(60) = −0.454, p = 0.014 |
female | 2.84 | 1.21 | 3.42 | 0.66 | |||
Vehicle number | none | 3.43 | 0.7 | t(60) = −1.345, p = 0.039 | 3.27 | 0.48 | t(60) = −0.807, p = 0.542 |
at least 1 | 3.51 | 0.42 | 3.42 | 0.51 | |||
Travel mode | public transit | 3.05 | 0.52 | t(60) = 0.045, p = 0.977 | 3.45 | 0.57 | t(60) = −0961, p = 0.354 |
private car | 3.04 | 0.56 | 3.27 | 0.39 |
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Zhang, K.; Guo, H.; Yao, G.; Li, C.; Zhang, Y.; Wang, W. Modeling Acceptance of Electric Vehicle Sharing Based on Theory of Planned Behavior. Sustainability 2018, 10, 4686. https://doi.org/10.3390/su10124686
Zhang K, Guo H, Yao G, Li C, Zhang Y, Wang W. Modeling Acceptance of Electric Vehicle Sharing Based on Theory of Planned Behavior. Sustainability. 2018; 10(12):4686. https://doi.org/10.3390/su10124686
Chicago/Turabian StyleZhang, Kai, Hongwei Guo, Guangzheng Yao, Chenggang Li, Yujie Zhang, and Wuhong Wang. 2018. "Modeling Acceptance of Electric Vehicle Sharing Based on Theory of Planned Behavior" Sustainability 10, no. 12: 4686. https://doi.org/10.3390/su10124686