2.1. Ride-sourcing services
Ride-sourcing service can be considered as “a service that takes advantage of digital technologies (smartphone applications, web applications, Global Positioning System (GPS), network techniques, cashless payment system, dynamic routing algorithm, and data analytics) to connect and match dedicated drivers (that use their private cars for commercial purposes in exchange for money) with riders requesting a ride in real-time” (Elnadi and Gheith 2022). Several terminologies, including “ride-sourcing, app-based ride services, e-hailing apps, ride-booking, on-demand ride services, call-a-taxi applications, and taxi-hailing” (Elnadi and Gheith 2022; Li et al. 2023; Li et al. 2022; Nguyen-Phuoc et al. 2022; Nguyen and Ha 2022), have been used to characterise these services in past studies.
In light of the continued widespread use of ride-sourcing services around the world, numerous researchers have attempted to examine factors affecting users’ behaviour towards ride-sourcing services using a variety of theories and models (Elnadi and Gheith 2022; Inan et al. 2022; Pandita et al. 2021).
A literature review revealed that previous studies conducted to explore users’ behaviour towards ride-sourcing services can be categorised into two groups. The first group focused on investigating potential users’ pre-adoption behaviour. They have examined the initial intention of prospective users to accept or reject these services (e.g., Acheampong et al. 2020; Almunawar et al. 2021; Arumugam et al. 2020; Goel and Haldar 2020; Hu et al. 2022; Huynh et al. 2020; Inan et al. 2022; Javid et al. 2022; Karim et al. 2020; Kim et al. 2019a; Lee and Wong 2021; Lee et al. 2021; Lee and Chan 2018; Min et al. 2019; Pandita et al. 2021; Peng et al. 2014; Soares et al. 2020; Suhud et al. 2019). Although investigating potential users' intentions regarding ride-sourcing services is important, another group of researchers has turned their interest into exploring existing users’ post-adoption behaviour. They have shifted their focus to understanding factors motivating existing users to continue using ride-sourcing services rather than the initial intention. According to the second group of researchers, exploring the continuance intention towards ride-sourcing services is a critical issue because some individuals who initially accept ride-sourcing services may not translate their intentions to actual behaviour and use the services, or they may discontinue using these services at a later stage as they cannot experience the benefits of utilising such services (Elnadi and Gheith 2022; Fauzi and Sheng 2021; Nguyen and Ha 2022; Weng et al. 2017). Additionally, they argued that examining motivational factors that encourage the prolonged usage of ride-sourcing services is vital for service providers to enhance and improve their services to retain their existing users.
Previous studies have used a variety of theories and models, including TAM, TRA, UTAUT, IDT, Means-end chain theory, and ECM, to examine consumers' reuse intentions towards ride-sourcing services. For example, Joia and Altieri (2018) developed a model that integrates TRA, TPB, and TAM to examine the users’ continuous intentions towards e-hailing apps. They found that subjective norms and user satisfaction form the reuse intention of e-hailing apps. By combining TAM and IDT, Sedighi et al. (2021) have identified trust in the ride-sourcing platform, social norms, and perceived usefulness (PU) as key antecedents of users’ reuse intentions. Similarly, Elnadi and Gheith (2022) have integrated TAM and IDT, as well as innovativeness and environmental consciousness to examine the desire to continue using ride-sourcing services. They discovered that consumers' favourable perceptions about app-based ride-sourcing services significantly influence their propensity to reuse these services.
Jing et al. (2021) applied TAM and TPB to explore the intention to reuse ride-sourcing apps. They found that consumers’ reuse of ride-sourcing services is positively correlated with their attitudes towards these services, PU, perceived behaviour control, subjective norms, and perceived security. Additionally, they have confirmed the negative association between reuse intention and security risk.
Based on UTAUT, “performance expectancy, social influence, facilitating conditions, and economic benefits” were determined as critical antecedents of users’ continued usage intention as demonstrated by Gaber and Elsamadicy (2021). Also, Siyal et al. (2021) clarified that the influence of performance expectation and effort expectation on the continuous intention is mediated by hedonic motivation. Moreover, they have validated the direct positive influence of hedonic motivation on the continuous intention. By applying the perceived value theory, users’ continued usage intentions was formed by their satisfaction level, hedonic and economic value (Ofori et al. 2022). Additionally, ride-sourcing services’ reuse intention was significantly influenced by services’ monetary value, relative attractiveness, reassurance, and interactivity (Lee et al. 2019).
Malik and Rao (2019) and Weng et al. (2017) assessed consumers' intentions to keep using app-based ride-sourcing services utilising the ECM and the TAM. It has been asserted that the continued usage intention can be predicted by users’ satisfaction and self-efficacy, as well as their perception of ride-sourcing apps’ usefulness, value, and perceived ease of use (PEOU) (Malik and Rao 2019). Also, users’ satisfaction, attitudes, and PU have been evidenced by Weng et al. (2017) as motivational factors that affect users’ intentions to continue using ride-sourcing apps. Moreover, Fauzi and Sheng (2021) and Nguyen and Ha (2022) extended the information technology continuous model to examine users’ continuous intention. Fauzi and Sheng (2021) claimed that the continuance use intention is associated with perceived utilitarian value and perceived hedonic value. According to Nguyen and Ha (2022), users' level of satisfaction, behavioural adaptation, and self-efficacy greatly influence their intention to keep using ride-sourcing services platforms.
Other theories such as means-end chain theory, social exchange theory, and justice theory have been applied by Aw et al. (2019), Boateng et al. (2019), and Shao et al. (2022) respectively. Aw et al. (2019) suggested that perceived value significantly affects the continuance use intention. While, Boateng et al. (2019) argued that trust, consumer return on investment, and search convenience are the main determinants that drive riders to start and continue to use ride-sourcing services. Shao et al. (2022) claimed that users’ continued usage intention is formed by distributive, procedural, and interactional justice. Finally, by utilising the Risk to Trust Unidirectional Model, Ma et al. (2019) suggested the inhibitors that lead to the discontinuance use of ride-sourcing services. They concluded that the discontinuance usage intention has a direct negative relationship with both trust and attitude towards the platform.
The literature review reveals the following. First, most ride-sourcing previous studies have examined potential users’ pre-usage behaviour. Studies assessing users’ post-usage behaviour are few, and studies examining users’ continued intent to keep using ride-sourcing services are still rare (Boateng et al. 2019; Elnadi and Gheith 2022; Malik and Rao 2019; Nguyen and Ha 2022; Ofori et al. 2022). Second, although robust theories and models such as TAM, TPB, TRA, UTAUT, and DIT, along with other theories, have been applied by many researchers (e.g., Gaber and Elsamadicy 2021; Jing et al. 2021; Joia and Altieri 2018; Ofori et al. 2022; Sedighi et al. 2021; Siyal et al. 2021) to examine users’ continued usage intention, other researchers in different IS contexts (e.g., Bhattacherjee and Barfar 2011; Chiu et al. 2020; Franque et al. 2021; Khan et al. 2023; Park 2020; Prakash et al. 2021; Rekha et al. 2023; Selim et al. 2022; Sreelakshmi and Prathap 2020) argued that these theories and models suffer from some limitations. As suggested by these researchers, applying the ECM to investigate the continuance use intention is superior to other models. Third, most recent research investigating the continuance use intention of ride-sourcing apps was conducted in Asia such as Malaysia (Aw et al. 2019; Weng et al. 2017), Indonesia (Fauzi and Sheng 2021), China (Jing et al. 2021; Ma et al. 2019), Korea (Lee et al. 2019), India (Malik and Rao 2019), Vietnam (Nguyen-Phuoc et al. 2020; Nguyen and Ha 2022), and Pakistan (Siyal et al. 2021). Few studies have been established to explore consumers’ reuse intentions towards ride-sourcing apps in Africa and the Middle East, particularly in Egypt. For example, Boateng et al. (2019) and Ofori et al. (2022) have investigated users’ continued intention to use ride-sourcing services in Ghana, and Sedighi et al. (2021) have explored the continuance use intention in Iran. Only two studies were conducted in Egypt by Gaber and Elsamadicy (2021) and Elnadi and Gheith (2022) to examine the continuance use intention of ride-sourcing services.
Finally, the TRM has not been employed in previous studies to examine how personality dimensions may affect consumers' intentions to keep using ride-sourcing services. Thus, the present study aims to determine the significant factors influencing existing users’ continuance intentions to reuse ride-sourcing apps in Egypt via integrating three robust models, namely ECM, TAM, and TRM as the theoretical foundation of this study.
2.2. Theoretical background
2.2.1. The Expectation-Confirmation Model (ECM)
The ECM is one of the generally acknowledged frameworks that has been implemented in IS research to investigate consumers’ satisfaction and continued usage intention of IT products/services. The ECM accurately captures the process that IT products/services users go through to decide whether to continue to use IT products/services or not (Franque et al. 2021; Hsu and Chen 2021; Jumaan et al. 2020; Pal et al. 2020; Selim et al. 2022). The ECM developed by Bhattacherjee (2001) integrates the PU construct from the TAM (Davis et al. 1989) into the expectation-confirmation theory (ECT) (Oliver 1980). According to the ECM, before using new technology, individuals form initial expectations about the performance of that technology. Individuals develop post-expectations about a technology's performance (perceived performance) after accepting it and using it for a while, based on their usage experience. Then, they compare their post-expectations (perceived performance) against their initial expectations to assess whether their initial expectations are confirmed or not. If their initial expectations are confirmed, then they will be satisfied with the new technology, leading to the continued usage of this technology and vice versa (Jumaan et al. 2020; Loh et al. 2022; Pal et al. 2020; Soria-barreto et al. 2021).
The four fundamental constructs of the ECM are the continuance use intention, user’s satisfaction level, PU, and user’s confirmation of expectations level. According to Bhattacherjee (2001), users’ continuous intention to use IT products/services is determined by their satisfaction levels followed by their PU of using IT products/services. Furthermore, users’ satisfaction level is connected with their PU of IT products/services and their confirmation of expectations level. Finally, users’ confirmation of expectations level is a key determinant of their perception of the usefulness of IT products/services (Al-Sharafi et al. 2022; Chiu et al. 2020; Prakash et al. 2021; Tam et al. 2020; Yousaf et al. 2021).
The ECM is a well-established framework that has been used by several researchers for exploring users’ IT products/services post-usage behaviour in various settings, such as mobile and online shopping (Ashraf et al. 2020; Cuong 2023; Maduku and Thusi 2023; Shang and Wu 2017; Tam et al. 2022; Wu et al. 2020; Yu et al. 2023), e-learning (Alsadoon 2022; Al Amin et al. 2023b; Cheng 2021a; Chibisa and Mutambara 2022; Dai et al. 2020; Huang 2019; Rafique et al. 2021; Rekha et al. 2023; Youssef and Issam 2022), mobile payment (Al-Sharafi et al. 2022; Franque et al. 2023; Jaiswal et al. 2022; Kumari and Biswas 2023; Loh et al. 2022; Sreelakshmi and Prathap 2020), internet banking (Rahi et al. 2021), E-health (Chiu et al. 2020; Leung and Chen 2019; Nie et al. 2023; Shen et al. 2022; Wu et al. 2022), smart wearable devices (Pal et al. 2020; Park 2020; Rabaa’i et al. 2022; Shen et al. 2018), travel and tourist (Dhiman and Jamwal 2023; Leou and Wang 2023; Liu et al. 2023; Tiwari and Mishra 2023), and ride-sourcing and ride-sharing (Arteaga-Sánchez et al. 2020; Jia et al. 2020; Malik and Rao 2019; Nguyen and Ha 2022; Si et al. 2022; Weng et al. 2017).
2.2.2. Technology readiness model (TRM)
Technology readiness (TR) reflects “people’s propensity to embrace and use new technologies to accomplish goals in home life and at work” (Parasuraman 2000). TR indicates the set of beliefs and the state of mind that individuals hold about technology. TR determines how individuals are inclined to accept and employ new technologies (Chen and Lin 2018; Jin 2020; Kim et al. 2019b). Individuals’ TR can be evaluated using the Technology Readiness Index (TRI) based on four main personality traits, namely optimism (positive belief about technology), innovativeness (propensity to experiment with new technology), discomfort (feeling of not having control over technology), and insecurity (belief in the possibility of negative outcomes from technology) (Parasuraman 2000; Parasuraman and Colby 2015). Optimism and innovativeness, which are the driving forces behind technological readiness, inspire individuals to accept new technologies. On the other side, the barriers that prevent people from accepting new technologies are discomfort and insecurity.
Each of the TR personality dimensions is distinct and independent. Individuals have varying levels of each personality trait (Chen and Lin 2018; Mishra et al. 2018). Additionally, TRI does not reflect an individual’s capability in using technology but indicates an individual’s beliefs and mental state towards technology (Parasuraman and Colby 2015). The TR varies among individuals according to the varying combination of the four dimensions. Usually, an individual with high scores of “optimism and innovativeness”, and low scores of “discomfort and insecurity”, will have a high overall TR and is more receptive to new technologies and willing to employ these technologies (Parasuraman 2000). However, individuals that have a high TR score do not necessarily embrace new technology (Jin 2020; Parasuraman 2000). Therefore, to explore how individuals’ personality traits are correlated with their technology acceptance, the “Technology readiness and acceptance model (TRAM)” was introduced by Lin et al. (2007) by combining TR's four dimensions with TAM's PU and PEOU.
Previous research suggests that by combining TR and TAM, the two models' predictive power will be increased. TR and TAM have been used together in various research settings such as mobile payment and digital banking (Balakrishnan and Shuib 2021; Caldeira et al. 2021; Humbani and Wiese 2019; Musyaffi et al. 2022; Rafdinal and Senalasari 2021), education and m-learning (Alhasan et al. 2023; Amron et al. 2022; Kampa 2023), health, fitness apps, and wearable devices (Aboelmaged et al. 2022; Chen and Lin 2018; Chiu and Cho 2021; Dash and Mohanty 2023; Raman and Aashish 2022), self-service technologies (Huy et al. 2019; Kaushik and Rahman 2017; Smit et al. 2018), virtual reality (Jeong and Kim 2023; Seong and Hong 2022; Wibisono et al. 2023), brand apps (Jin 2020), and smart/e-shops (Chang and Chen 2021; Mukerjee et al. 2019).