Exploring the Role of Attitudinal Factors in Electric Vehicle Timeshare Rentals Adoption
Electric vehicle timeshare rentals (EVTRs) have been recognized as promising solutions to growingly severe problems of traffic congestion, air pollution, and insufficient parking spaces. This study aims to explore the factors that affect the adoption of EVTRs. To achieve the research objective, the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/1/12 |
| _version_ | 1797626353344839680 |
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| author | Shunchao Wang Qinghai Lin Ziyi Zhou Chunting Nie |
| author_facet | Shunchao Wang Qinghai Lin Ziyi Zhou Chunting Nie |
| author_sort | Shunchao Wang |
| collection | DOAJ |
| description | Electric vehicle timeshare rentals (EVTRs) have been recognized as promising solutions to growingly severe problems of traffic congestion, air pollution, and insufficient parking spaces. This study aims to explore the factors that affect the adoption of EVTRs. To achieve the research objective, the household survey is conducted to obtain the travelers’ attitudes towards their travel. Ten latent attitudinal factors are extracted based on the technology acceptance model (TAM) and the theory of planned behavior (TPB). The multi-index and multi-cause (MIMIC) method simultaneously estimates the correlations between the attitudinal factors. Two logit models with attitudinal factors or not are constructed to estimate the quantitative relationship between various factors and EVTR adoption. The results show that the accuracy of the mixed logit model with latent attitude variables is better than the binary logit model without attitude latent variables. This indicates that attitude latent factors could be well matched with the traveler’s travel behavior and could better reflect travelers’ travel demand. Perceived comfort, perceived efficient, subjective evaluation, use preference, and use willingness significantly impact EVTR use frequency. The inconvenience in travelers, rental stations, shared vehicles, and use modes have significant negative impacts. Finally, social pressure has no significant impact. Findings provide valuable insights regarding the efficient planning of the EVTR system and allow decision-makers to develop scientific and practical measures of EVTRs. |
| first_indexed | 2024-03-11T10:09:12Z |
| format | Article |
| id | doaj.art-afa4872136b44666ad954c4250d36c40 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T10:09:12Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-afa4872136b44666ad954c4250d36c402023-11-16T14:49:26ZengMDPI AGApplied Sciences2076-34172022-12-011311210.3390/app13010012Exploring the Role of Attitudinal Factors in Electric Vehicle Timeshare Rentals AdoptionShunchao Wang0Qinghai Lin1Ziyi Zhou2Chunting Nie3School of Transportation, Southeast University, Nanjing 211189, ChinaSchool of Intelligent Systems Engineering, Sun Yat-Sen University, Guangzhou 510006, ChinaSchool of Transportation, Southeast University, Nanjing 211189, ChinaCollege of Transportation Engineering, Tongji University, Shanghai 201804, ChinaElectric vehicle timeshare rentals (EVTRs) have been recognized as promising solutions to growingly severe problems of traffic congestion, air pollution, and insufficient parking spaces. This study aims to explore the factors that affect the adoption of EVTRs. To achieve the research objective, the household survey is conducted to obtain the travelers’ attitudes towards their travel. Ten latent attitudinal factors are extracted based on the technology acceptance model (TAM) and the theory of planned behavior (TPB). The multi-index and multi-cause (MIMIC) method simultaneously estimates the correlations between the attitudinal factors. Two logit models with attitudinal factors or not are constructed to estimate the quantitative relationship between various factors and EVTR adoption. The results show that the accuracy of the mixed logit model with latent attitude variables is better than the binary logit model without attitude latent variables. This indicates that attitude latent factors could be well matched with the traveler’s travel behavior and could better reflect travelers’ travel demand. Perceived comfort, perceived efficient, subjective evaluation, use preference, and use willingness significantly impact EVTR use frequency. The inconvenience in travelers, rental stations, shared vehicles, and use modes have significant negative impacts. Finally, social pressure has no significant impact. Findings provide valuable insights regarding the efficient planning of the EVTR system and allow decision-makers to develop scientific and practical measures of EVTRs.https://www.mdpi.com/2076-3417/13/1/12electric carsharingelectric vehicle timeshare rentalsadoption behaviorattitudinal factorsmulti-index and multi-causemixed logit model |
| spellingShingle | Shunchao Wang Qinghai Lin Ziyi Zhou Chunting Nie Exploring the Role of Attitudinal Factors in Electric Vehicle Timeshare Rentals Adoption Applied Sciences electric carsharing electric vehicle timeshare rentals adoption behavior attitudinal factors multi-index and multi-cause mixed logit model |
| title | Exploring the Role of Attitudinal Factors in Electric Vehicle Timeshare Rentals Adoption |
| title_full | Exploring the Role of Attitudinal Factors in Electric Vehicle Timeshare Rentals Adoption |
| title_fullStr | Exploring the Role of Attitudinal Factors in Electric Vehicle Timeshare Rentals Adoption |
| title_full_unstemmed | Exploring the Role of Attitudinal Factors in Electric Vehicle Timeshare Rentals Adoption |
| title_short | Exploring the Role of Attitudinal Factors in Electric Vehicle Timeshare Rentals Adoption |
| title_sort | exploring the role of attitudinal factors in electric vehicle timeshare rentals adoption |
| topic | electric carsharing electric vehicle timeshare rentals adoption behavior attitudinal factors multi-index and multi-cause mixed logit model |
| url | https://www.mdpi.com/2076-3417/13/1/12 |
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