Ride or Not to Ride: Does the Customer Deviate toward Ridesharing?

Azra Shamim, Awais Ali Khan, Muhammad Ahsan Qureshi, Hamaad Rafique and Adnan Akhunzada ()
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Azra Shamim: College of Computing and Information Technology at Khulais, University of Jeddah, Jeddah 23218, Saudi Arabia
Awais Ali Khan: Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
Muhammad Ahsan Qureshi: College of Computing and Information Technology at Khulais, University of Jeddah, Jeddah 23218, Saudi Arabia
Hamaad Rafique: School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China

IJERPH, 2021, vol. 18, issue 19, 1-18

Abstract: Traditional taxi services have now been transformed into e-hailing applications (EHA) such as Uber, Careem, Hailo, and Grab Car globally due to the proliferation of smartphone technology. On the one hand, these applications provide transport facilities. On the other hand, users are facing multiple issues in the adoption of EHAs. Despite problems, EHAs are still widely adopted globally. However, a sparse amount of research has been conducted related to EHAs, particular in regards to exploring the significant factors of intention behind using EHAs Therefore, there is a need to identify influencing factors that have a great impact on the adoption and acceptance of these applications. Hence, this research aims to present an empirical study on the factors influencing customers’ intentions towards EHAs. The Technology Acceptance Model (TAM) was extended with four external factors: perceived mobility value, effort expectancy, perceived locational accuracy, and perceived price. A questionnaire was developed for the measurement of these factors. A survey was conducted with 211 users of EHAs to collect data. Structural equation modeling (SEM) was used to analyze the collected data. The results of this study exposed that perceived usefulness, perceived price, and perceived ease of use affect behavior intention to use EHAs. Furthermore, perceived ease of use was impacted by effort expectancy, perceived locational accuracy, and perceived mobility. The findings of the study provide a foundation to develop new guidelines for such applications that will be beneficial for developers and designers of these applications.

Keywords: e-hailing applications (EHA); smart phones; technology acceptance model (TAM) (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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