What influences travelers to use Uber? Exploring the factors affecting the adoption of on-demand ride services in California

Alemi, Farzad; Circella, Giovanni; Handy, Susan; Mokhtarian, Patricia L.

doi:10.1016/j.tbs.2018.06.002

Cited by 416 publications

(295 citation statements)

References 10 publications

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“…These findings are consistent with the literature. For instance, Alemi et al (2018) find that early adopters of TNC services are likely to be "technology-oriented" and thus they tend to adopt such services in bundle as a part of their modern lifestyle. Similarly, Hall and Krueger (2018) and Alemi et al (2018) also observe higher inclination of metropolitan residents toward using these services.…”

Section: Model 1 -Preference Of Being a Tnc Rider Or A Drivermentioning

confidence: 99%

“…Proponents of TNC services emphasize positive impacts such as making travel easier by providing more travel options, especially in high-demand areas with uncertain and inefficient transit services (Alemi et al, 2018). There is also a possibility of partnerships between transit agencies and TNCs to improve the first and the last mile connectivity.…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies have focused on understanding travelers' preferences to use or not use ridehailing services. These studies have shown that early adopters of TNC services are primarily welleducated young individuals who come from affluent families (Rayle et al, 2014;Clewlow and Mishra, 2017;Alemi et al, 2018). In the context of TNC drivers, some studies have touched upon driver safety (Feeney, 2015), driver wages (Berger and Frey, 2017), and sociodemographic characteristics (Kooti, 2017;Hall and Krueger, 2018), as well as personal attitude of individuals who are willing to become TNC drivers (Berliner and Tal, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Eliciting preferences of TNC users and drivers: Evidence from the United States

Bansal

Sinha

Dua

et al. 2020

Travel Behaviour and Society

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Transportation Network Companies (TNCs) are changing the transportation ecosystem, but microdecisions of drivers and users need to be better understood to assess the system-level impacts of TNCs. In this regard, we contribute to the literature by estimating a) individuals' preferences of being a rider, a driver, or a non-user of TNC services; b) preferences of ridehailing users for ridepooling; c) TNC drivers' choice to switch to vehicles with better fuel economy, and also d) the drivers' decision to buy, rent or lease new vehicles with driving for TNCs being a major consideration. We use a unique sample (N= 11,902) of the U.S. population residing in TNC-served areas. Elicitation of drivers' preferences using a large sample is the key feature of this study. The population-weighted statistical analysis indicates that ridehailing services are mainly attracting personal vehicle users as riders, without substantially affecting demand for transit. Moreover, around 10% of ridehailing users reported postponing the purchase of a new car due to the availability of TNC services. Using a multinomial logistic regression, we find that that the likelihood of being a TNC user increases with the increase in age for someone younger than 44 years, but the pattern is reversed post 44 years. This change in direction of the marginal effect of age is insightful as the previous studies have reported a linear negative association. Moreover, older ridehailing users with higher household vehicle ownership who live in suburban areas are less likely to pool rides. On the supply side, 65% of TNC drivers who work daily indicated that driving for TNCs was a consideration in vehicle purchase decisions. We also find that households with postgraduate drivers who drive daily and live in metropolitan regions are more likely to switch to fuel-efficient vehicles. These findings can inform transportation planners and TNCs in developing policies to encourage ridepooling and to improve the average fuel economy of the TNC fleet.

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Section: Model 1 -Preference Of Being a Tnc Rider Or A Drivermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Eliciting preferences of TNC users and drivers: Evidence from the United States

Bansal

Sinha

Dua

et al. 2020

Travel Behaviour and Society

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“…The Stated Preference approach offering the modal choice at once to the respondent, then the respondent make a sort based on their choice that shows the level of the preference of their choice. [9] Three types of questionnaires that can be used in stated preference studies namely ranking, choice, and rating. In a ranking questionnaire, respondents must order the hypothetical situations to state their preference, meanwhile, in the choice questionnaire the respondents simply choose the hypothetical combination of attributes that is more favourable according to them and the researcher has an actual prediction of the respondent's choice in a hypothetical situation.…”

Section: Literature Reviewmentioning

confidence: 99%

Online-Taxi Choice Model Based on Passenger Perception in Indonesia

Sitinjak¹,

Djakfar²,

Wicaksono³

2020

Proceedings of the 2nd International Symposium on Transportation Studies in Developing Countries (ISTSDC 2019)

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Malang, and Surabaya are two cities in East Java Province, Indonesia with day-population more than 1 million and have experienced daily traffic congestions because of the over usage of private vehicle. This study aim is to find the modal choice model for shifting the passenger from conventional-taxi to online-taxi. To develop the modal choice model, a Stated Preference (SP) questionnaire were developed to obtain a preference of respondent, as many as 400 questionnaires were collected. Binary Logit Model was used to develop a modal choice model, based on price and waiting time, the result shows the difference in trip costs is Rp. 4.348 and waiting time is 10 minutes. If the trip cost more than Rp. 4.348 and waiting time more than 10 minutes, the passenger rather using the conventional-taxi.

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“…The latter states that women are more reluctant to use them due to a fear of being scammed. Other studies conclude that the age of users is mostly between 25 and 34 years, so they are typically millennials who are more likely to be single and childless [5,27,34,35,[42][43][44].…”

Section: Theory and Hypothesesmentioning

confidence: 99%

Repeat Consumer Behavior on Smart P2P Tourism Platforms

et al. 2019

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Despite the key role played by frequent consumers interacting on smart P2P (peer to peer) tourism platforms, there are hardly any studies on the explanatory variables of their frequency of use. This paper aims to understand the motivational and sociodemographic factors that bring about repeat consumers in collaborative accommodation and transport services. In order to test various assumptions, a set of logistic regressions were made where the dependent variable is the frequency of use and the independent variables are sociodemographic and motivational factors. The results suggest that many consumer attributes recognized as being typical of collaborative platforms, such as young people traveling with friends for leisure who are interested in low prices, have changed. We found that, due to the consolidation of these smart business models, the frequency of use increases with age and for those who travel for work reasons. In addition, it is worth noting the existence of a positive relationship between consumers who provide reviews on these platforms and their frequency of use.Sustainability 2019, 11, 7082 2 of 13 company in the "Global 500 Rankings 2019" [7] with the highest brand value, ranking above Marriott International, the world's hotel leader. In addition, it has managed to overtake Booking.com, one of the main online hosting booking platforms [8]. Uber's fleet in New York is now three-fold larger than the number of yellow taxis [2].This sustained growth contradicts initial perceptions that these were temporary business models, aimed at a young audience, which were hardly going to be able to compete with traditional tourist services. The contextual factors that contributed to the rapid development of this business model, such as the globalization of society caused by the Internet [9-11], the boost from millennials [2,12,13], and the global economic crisis that favored the reuse of resources [14] are less influential now. Therefore, no one doubts that collaborative tourism is here to stay and will continue to grow [5,15].The consolidation of these smart tourism business models has been supported by the highest degree of satisfaction and recommendation of these services versus the traditional ones [16,17]. Both domestic [18] and European studies [5] show the greatest tendency for users to recommend collaborative platforms.Smart P2P tourism platforms are adapting their products to better fit the needs of consumers than traditional services, which creates the perception of quality and thus increases its frequency of use [12,[19][20][21][22]. This increased frequency of use causes greater confidence in the product and service [23][24][25], which is particularly relevant, since consumer insecurity and mistrust have been established as some of the most important reasons for the non-use of collaborative platforms [22,26,27]. In addition, the frequency of use is usually related to the service recommendation (e-wom) [28][29][30][31].Despite the success of these smart models, there are few studies focused on P2P accommod...

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What influences travelers to use Uber? Exploring the factors affecting the adoption of on-demand ride services in California

Cited by 416 publications

References 10 publications

Eliciting preferences of TNC users and drivers: Evidence from the United States

Eliciting preferences of TNC users and drivers: Evidence from the United States

Online-Taxi Choice Model Based on Passenger Perception in Indonesia

Repeat Consumer Behavior on Smart P2P Tourism Platforms

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