Abstract
The provision of on-demand door-to-door trips within the hailing sector has experienced significant disruptions in the last years due to the advent of ride-hailing services. The evolution of the sector is ongoing and one of the latest novelties is the introduction of e-hailing, which allows the booking of conventional regulated taxis through digital applications. The regulatory and technological dimensions of taxi and ride-hailing seem to play a key role when trying to explain users’ choices of hailing services. For this reason, providing travelers with an option that combines traditional regulated services with the advantages of digital applications promises significant changes in users’ preferences. This paper aims to understand the preferences of hailing users taking Spain as case study because of the controversy after the advent of competition in the hailing market. To that end, a Generalized Structural Equation Model (GSEM) has been built to simultaneously study the influence of socioeconomic characteristics, latent psychological constructs, and mobility habits on individuals’ frequency of use of hailing services and their probability to belong to a specific hailing user profile. The results show that the adoption of ride-hailing and e-hailing is mainly influenced by psychological constructs. Individuals who wish to enjoy a high quality of service, but are not so liberal from the economic perspective, are more likely to have used e-hailing. Furthermore, the frequency of use of hailing services is affected by psychological constructs as much as by socio-economic variables and mobility habits. Finally, the effect of the openness to competition in the hailing market is discussed.
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Notes
In some cities, Uber has introduced the possibility of booking a taxi through their mobile-app. However, this option was not available in Spanish cities by the time the research was carried out.
As the survey was carried out online and disseminated via social networks or instant messaging applications, 354 individuals accessed the questionnaire but answered only the first few questions and left the survey unfinished. In other cases, respondents fully completed the questionnaire but spent an amount of time significantly lower than what could be considered reasonable (48 individuals). Finally, 252 observations were removed because some indications suggested that the answers had been totally random (e.g., respondents giving the same answer for all questions capturing attitudinal indicators).
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Acknowledgements
The authors wish to thank the the Comunidad de Madrid, which has funded the Project S-2020/L3-736 // PM210430C-066A (SHAPE). The project has also been co-funded by the European FSE funds
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MV: Conceptualization, Methodology, Software, Formal analysis, Writing – Original Draft, Writing – Review & Editing. ÁAG: Conceptualization, Data Curation, Formal analysis, Writing – Original Draft, Methodology, Writing – Review & Editing. JG: Conceptualization, Methodology, Formal analysis, Writing – Original Draft, Writing – Review & Editing. JM Vassallo: Resources, Writing – Original Draft, Writing – Review & Editing, Supervision.
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Appendices
Appendix 1
Graphic representation of the cross-tabulation between socio-economic attributes and outcome variables
See Figs.
Distribution of residential location across socio-economic groups
3,
Distribution of the mode of urban transport most frequently used on weekdays across socio-economic groups
4,
Distribution of hailing trip purposes across socio-economic groups
5,
Distribution of ride-hailing adoption across socio-economic groups
6,
Distribution of e-hailing adoption across socio-economic groups
7 and
Distribution of hailing usage profiles across socio-economic groups
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Vega-Gonzalo, M., Aguilera-García, Á., Gomez, J. et al. Traditional taxi, e-hailing or ride-hailing? A GSEM approach to exploring service adoption patterns. Transportation (2023). https://doi.org/10.1007/s11116-022-10356-y
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DOI: https://doi.org/10.1007/s11116-022-10356-y