An Empirical Investigation on Taxi Hailing Mobile App Adoption: A Structural Equation Modelling

Abstract

This empirical investigation aims to determine the factors leading to the adaptation of Taxi Hailing Mobile Apps among Malaysian consumers using the theory of Venkatesh et al. (2003) user theory of acceptance and use of technology (UTAUT). The adopted framework has four independent variables namely performance expectancy, effort expectancy, social influence and facilitating conditions. Dependent variables include behavioural intension and user behaviour. The study follows exploratory research design and quantitative research method. Primary data is collected using online Google survey questionnaire. The target population of this research study is international and local in Malaysia who makes use of taxi hailing app in order to satisfy their transportation needs. Convenience sampling technique is used to collect data from a sample of 202 respondents. Confirmatory Factors Analysis (CFA) and Structural Equation Modelling (SEM) are used in order to answer the research questions. Findings show that Performance expectancy, social influence and behaviour intension has positive influence on user behaviour while effort expectancy and behavioural intension has no influence. For scholars and researchers, understandably this research study opens the gate way of the application of UTAUT on information system (IS) apps that had been recently created.

What drives customers to continue using ride-sharing apps during the COVID-19 pandemic? The case of Uber in Egypt

“…The findings indicated that customers' continuance intention to use the apps are not influenced by their effort expectancy. Our findings are similar to the findings of Haba and Dastane (2018) and Almunawar et al (2020), which indicated that effort expectancy doesn't impact customers' acceptance for ride-hailing apps. On the other hand, our findings are not in line with the findings of Palau-Saumell et al ( 2019) that indicated that effort expectancy is one of the factors that impact customers' acceptance for mobile apps.…”

Investigating the Influential Factors of Shared Travel Behavior: Comparison between App-Based Third Taxi Service and Free-Floating Bike Sharing in Nanjing, China

“…This indicates that compared with TTM, the residents in suburb are more dependent on FFBS and ATTS when they participate in flexible activities [41]. It is probably because the traffic infrastructures in suburb in Nanjing are absent, while shared modes could help to make up for this disadvantage and improve the accessibility in suburb [9,10,14]. Additionally, for FFBS, the coefficient of inflexible travel in suburb is negative compared with that in city center.…”

“…For example, Liu [27] found that perceived usefulness mostly influenced the intentions and attitudes of users to utilize ATTS application, followed by perceived ease of use. Haba and Dastane [10] showed that performance expectancy and social influence positively influenced the use intension of customers, while effort expectancy had no influence.…”

Success Factors for e-Learning Satisfaction during COVID-19 Pandemic Lockdown

“…Primary data was collected using self-administered questionnaire circulated online using google form to university students in Malaysia during the month of March and April 2020 when movement of control order was enforced resulting in lockdown and shifting of university teaching and learning on digital media platform. 470 responses were received which sufficient sample size is considering items per variables [33]. For the questionnaire, a well-structured in a survey format was followed to collect primary data through series of questions.…”

“…The hypotheses were subsequently tested, and the results were discussed. The procedures recommended by [33]; [34]; [35] were followed.…”