What drives corporate carsharing acceptance? A French case study

Fleury, Sylvain; Tom, Ariane; Jamet, Éric; Colas-Maheux, Elsa

doi:10.1016/j.trf.2016.12.004

Cited by 68 publications

(81 citation statements)

References 23 publications

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“…This situation has led developers to ask the question of "can we to change people's attitude toward the EC service?" As stated by Fleury et al [20], to change people's attitudes and perceptions about carsharing service, we need to identify the essential determinants that constitute their intention. Some studies have considered respondents' attitudes and perceptions to classify potential user segments.…”

Section: Introductionmentioning

confidence: 99%

“…They found that not only do the car attributes impact preferences for carsharing services, but also the psychological ownership of potential customers plays an essential role in increasing the preference for a shared car. More recently, Fleury et al [20] considered the use of psychological factors to describe the determinants of users' behavioral intention towards a corporate carsharing service in France. They found that people's intention to use a carsharing service depends on their perception of the services-factors such as perceived usefulness, perceived ease of use, and perceived environmental friendliness.…”

Section: Introductionmentioning

confidence: 99%

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Travelers’ Acceptance of Electric Carsharing Systems in Developing Countries: The Case of China

et al. 2019

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Emerging electric carsharing (EC) systems have demonstrated their advantages and attracted public attention. The number of EC systems is growing throughout the world, especially in metropolitan areas in developing countries. For successful implementation, developers need to understand the public acceptance of EC services. In this study, we sought to determine the factors that affect EC acceptance in the context of developing countries. The study involved 437 individuals, aged between 18 and 65 years, who were randomly sampled from an EC service area in China. The determinants of EC acceptance were investigated based on an extended version of the Unified Theory of Acceptance and Use of Technology (UTAUT) and tested by using Structural Equation Modeling. The results indicated that hedonic motivation (HM) has a powerful effect on behavioral intention (BI) to use the EC service in the future. Performance expectancy (PE), effort expectancy (EE), and familiarity with the carsharing concept (FM) also influenced EC’s acceptance. However, the impact of social influence (SI) did not emerge from this study. The results also revealed that gender moderates the effects of EE and FM on BI. Age moderated the effect of FM on BI and unexpectedly moderated the impact of HM on BI. The present study confirmed the validity of the UTAUT research model in predicting the intention to use an EC system in developing countries. Implications and recommendations for government and EC developers are also discussed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Travelers’ Acceptance of Electric Carsharing Systems in Developing Countries: The Case of China

et al. 2019

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“…Car sharing is a situation where a car is utilized by many or several other people (Fleury et al, 2017). Car sharing is also considered as the shared use of a vehicle fleet for 893697S GOXXX10.1177/2158244019893697SAGE OpenMensah et al…”

Section: Introductionmentioning

confidence: 99%

Determinants of the Continued Intention of College Students in China to Use DiDi Mobile Car-Sharing Services

et al. 2019

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This research paper examined the continued intention of college students to use DiDi mobile car-sharing services in China. The unified theory of acceptance and use of technology (UTAUT) was used as the theoretical framework while the data analysis was completed with SPSS. The results have demonstrated that performance expectancy, reliability, efficiency, and security and privacy were significant predictors of the continued intention to use mobile car-sharing services. Contrary to our expectations, effort expectancy was not a significant determinant of the continued intention to use mobile car-sharing services. In addition to these direct effects, the moderating impact of trust in the internet was also examined. The moderating analysis showed that trust in the internet showed no significant moderating impact on the relationship between performance expectancy, effort expectancy, reliability, efficiency, and security and privacy and the continued intention to use mobile carsharing services. The implications of these findings are discussed.

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“…Previous studies that have examined this problem from the perspective of car-sharing users have focused on two issues, namely, the willingness of users to use car-sharing services and the behavior characteristics of these users. Several studies have focused on the car-sharing willingness and preferences of users by conducting questionnaire surveys and interviews [4][5][6][7][8][9][10][11][12]. A selection model has been proposed to identify those variables that can significantly influence user willingness, such as family economic conditions, travel expenses, vehicle demands, and personal attributes.…”

Section: Literature Reviewmentioning

confidence: 99%

Identifying and Predicting the Expenditure Level Characteristics of Car-Sharing Users Based on the Empirical Data

Sai

Xie

et al. 2019

Sustainability

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Car-sharing plays a positive role in reducing vehicle ownership and greenhouse gas emissions. However, the developmental contradictions between high investment and low revenues hinder the development of the car-sharing industry. Fully understanding car-sharing users can effectively ensure the healthy development of car-sharing companies and promote the development of the entire industry. To this end, this study attempts to develop a user management method that is based on user layering and prediction methods. By using order data from the Lan Zhou car-sharing company in China, this paper develops a clustering method for layering car-sharing users. A multi-layer perceptron model is also developed to categorize these users into different expenditure level categories while considering periodic features. Results show that new users can be divided into three categories according to their expenditures to car-sharing companies within 84 days. After 5 weeks of observation, the 84-day category of new users can be predicted with an accuracy of over 85%. These results provide scientific decision support for the user management and profitability of car-sharing companies.

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What drives corporate carsharing acceptance? A French case study

Cited by 68 publications

References 23 publications

Travelers’ Acceptance of Electric Carsharing Systems in Developing Countries: The Case of China

Travelers’ Acceptance of Electric Carsharing Systems in Developing Countries: The Case of China

Determinants of the Continued Intention of College Students in China to Use DiDi Mobile Car-Sharing Services

Identifying and Predicting the Expenditure Level Characteristics of Car-Sharing Users Based on the Empirical Data

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