The emergence of communication technology and mobile devices has contributed to rapid developments in mobile applications (apps). Advances in network communication and mobile technology have considerably changed the lives and learning methods of people (Nikou and Economides, 2017; Chao, 2019; Liu et al., 2021). Researchers (Chao, 2019; Alshurideh et al., 2020; Hoi, 2020) have indicated that online learning combined with mobile technology is becoming an essential educational practice for teachers and students. Because of the portability of mobile devices such as smartphones and tablets, their usage by learners has increased considerably. A growing number of mobile learning (m-learning) apps have been developed (Chao, 2019; Alshurideh et al., 2020). Unlike computer-aided learning, m-learning allows users to learn without time and space limitations, which increases their learning intention and learning effectiveness (Chao, 2019; Quesada-Pallares et al., 2019; Wang et al., 2019; Liu et al., 2021).

Research that has examined mobile technology usage behaviors from the perspective of expectation confirmation theory (ECT) (Hsu and Lin, 2015; Li and Fang, 2019; Alshurideh et al., 2020) has indicated that expectation confirmation positively affects the satisfaction of users with and perceived usefulness of mobile technology; thus, expectation confirmation facilitates the continuance usage of such technology. However, ECT emphasizes the exploration of intention to continue using information technology from a utility perspective (e.g., perceived usefulness) rather than a hedonic perspective (e.g., perceived pleasure) (Bhattacherjee, 2001; Dağhan and Akkoyunlu, 2016; Alshurideh et al., 2020). Some studies (Cheng and Yuen, 2018; Fang et al., 2019; Rodríguez-Ardura and Meseguer-Artola, 2019; Yen and Lin, 2020) have suggested that both the usefulness of information systems and hedonic factors should be considered when information technology is applied in a learning context.

Most studies have explored the hedonic experiences of users based on flow theory (Kim and Hall, 2019; Lee et al., 2020; Yen and Lin, 2020), according to which flow is a pleasant experience of complete absorption in a task. The study of Alraimi et al. (2015) argued that the pleasant feelings of users help to strengthen their motivation to use and continue to use information technology for learning. Researchers (Stavrou et al., 2015; Huang et al., 2017; Rodríguez-Ardura and Meseguer-Artola, 2019; Yen and Lin, 2020) have found that flow experience originates from a balance between challenge and the ability of an individual to overcome the challenge. Thus, flow occurs when an individual has confidence in their ability to overcome a challenge.

The influence of habit on the usage behavior of individuals toward information systems has been widely investigated (Lin et al., 2017; Nascimento et al., 2018; Bolen, 2020). Researchers have argued that users might be reluctant to use information systems simply because of their habits (Lin et al., 2017). Thus, the effect of habits on the behavioral intention of users toward information technology should not be ignored when examining the continuance usage of new technology.

Based on the above-mentioned studies, this study adopted the utility viewpoint and considered positive experiences to examine the adoption of users of information technology for learning. This study also investigated the effect of habit on the intention of learners to continue using such technology. Based on the literature, this study constructed an integrated research model by adopting the utility viewpoint from ECT, the hedonic viewpoint from flow theory, and habit to identify the factors that affect the continuance intention of users to use m-learning apps.

Figure 1 displays the research framework for examining continuance intention to use m-learning apps. In this model, expectation confirmation factors (i.e., confirmation, perceived usefulness, and satisfaction), flow characteristics (i.e., perceived skill and perceived challenge), and habit were considered to influence the continuance intention of users to use m-learning apps. The definitions of the constructs used in this study and the research hypotheses were presented in the following text.

This study explored the intention to continue using m-learning apps by using an integrated research model based on ECT, flow theory, and habit. Figure 2 displays the research results of full samples. The findings obtained for intention to continue usage are consistent with those obtained in other studies (Cheng and Yuen, 2018; Li and Fang, 2019; Alshurideh et al., 2020) that indicate that user behavioral intention is positively affected by the satisfaction of users with information technology. In addition, the results of this study suggest that if users perceive that their learning efficiency has increased through the use of an m-learning app, the perceived usefulness of the app would increase, which would affect user behavior. The findings of this study also echoed those of studies that have suggested that flow experience plays a critical role in learning with information systems (Fang et al., 2019; Rodríguez-Ardura and Meseguer-Artola, 2019; Yen and Lin, 2020). If developers improve the design of their m-learning apps to maximize enjoyability, the apps could generate positive feels for users, which may encourage them to continue using the apps.

The results of this study also revealed that the assessment of users of m-learning apps by directly comparing pre-use expectation and post-use performance affects perceived usefulness, which influences user satisfaction. In addition, the experience of the flow of users when using an m-learning app would positively affect their satisfaction. As reported in many studies, the use of information technology is usually accompanied by a flow experience, which affects user satisfaction and usage behavior (Lee et al., 2018; Kim and Hall, 2019). Thus, when users achieve a state of flow in an activity, they become satisfied because their hedonic needs are met.

A central issue in this study was the extent to which other variables influence flow experience. The results indicate that expectation confirmation, perceived skill, and perceived challenge positively affect flow experience, among which perceived skill and perceived challenge are the predominant factors inducing flow experience. Flow experience originates from a balance between challenges and capability. When individuals overcome challenges, their flow experience is positively affected (Zhou, 2013; Huang et al., 2017; Lee et al., 2018; Rodríguez-Ardura and Meseguer-Artola, 2019; Yen and Lin, 2020). Accordingly, a feeling of flow emerges when users are sufficiently skilled and can fulfill the challenge of using teaching content during the learning process.

The results of this study also suggested that habit moderated the relationships of perceived usefulness, satisfaction, and flow experience with continuance intention to use m-learning apps. First, the influence of habit on perceived usefulness was higher for the low-habit users than for the high-habit users. This finding indicated that low-habit users are more aware of the usefulness of m-learning apps (e.g., convenience and learning efficiency) than high-habit ones. Therefore, continuance intention to use m-learning apps was stronger among the low-habit users than among the high-habit users. In addition, the influences of habit on user satisfaction and flow experience were stronger for the high-habit users than for the low-habit users. Compared with low-habit users, high-habit users were more concerned about whether systems could provide satisfaction and flow experience because of their strong habit of using the systems. Overall, the results of this study indicated that habit exerted a moderating effect on the continuance intention of users to use the m-learning apps.

The results of this study had several academic and practical implications related to the use of m-learning apps. First, studies have adopted ECT to investigate only the effect of m-learning systems on user behaviors (Lin and Wang, 2012; Dağhan and Akkoyunlu, 2016); however, researchers have recommended that in the context of using information systems for learning, it is necessary to examine the influence on continuance usage intention from the hedonic perspective apart from verifying whether the use of systems can bring about benefits (Cheng and Yuen, 2018; Fang et al., 2019; Rodríguez-Ardura and Meseguer-Artola, 2019; Yen and Lin, 2020). Moreover, when used as a moderating variable for the use of information systems, habit influences user behavioral intention (Hsu et al., 2015; Lin et al., 2017). Therefore, this study considered flow experience and habit to accurately determine the continuance usage intention of users for m-learning systems.

Second, in the context of learning-related information systems, ECT factors (confirmation, perceived usefulness, and user satisfaction), flow factors (perceived skill and perceived challenge), and habit had critical effects on the intention of users to continue using m-learning apps. Moreover, the extent of confirmation and degree of perceived usefulness are both the main antecedents affecting user satisfaction with m-learning apps. These findings provided crucial information for mobile app service providers and indicate that in the context of learning-related information systems, users have high satisfaction when their use experience exceeds their expectations before use. Furthermore, user perceptions of the usefulness of m-learning apps are enhanced if they perceive that the apps are convenient, increase their learning efficiency, and enable them to acquire knowledge and learning opportunities. To retain users, developers of digital teaching materials should focus on the perceptions of users when it comes to the usefulness during the use of m-learning systems. The higher the perception of usefulness among users is, the higher their satisfaction is. High satisfaction may cause users to continue to use a system or service for learning, which would give service providers opportunities to earn a high profit.

Third, this study validated the perspective of Fang et al. (2019) that flow experience was a crucial factor affecting user satisfaction. App designers should consider the joy of learning when designing materials for m-learning apps so that users could experience happiness during the learning process. Such a feeling might enhance the intention of users to continue using m-learning apps.

Fourth, skills and challenges were two key factors in flow theory. Flow occurs only when the skills of users match the challenges they face (Stavrou et al., 2015; Rodríguez-Ardura and Meseguer-Artola, 2019; Yen and Lin, 2020). System designers should understand that users could achieve high learning efficiency when an online learning system has clear goals and can increase their learning motivation. The challenges in m-learning apps could be designed to intrigue learners and increase motivation (positive feelings), which in turn could drive their usage behaviors.

Finally, numerous researchers (Lin et al., 2017; Nascimento et al., 2018) have argued that habit has a moderating influence on the relationships between various factors and the use of information systems. The results of this study indicated that satisfaction has a crucial influence on the intention to continue using a system among low-habit and high-habit users. Moreover, perceived usefulness and flow experience positively affected the intention to continue using m-learning apps among low-habit and high-habit users, respectively. These results were crucial for m-learning system providers and suggested that different levels of habit could have different effects on the intention to use information systems for learning.

This study has several limitations. First, the data were collected from users of m-learning apps in Taiwan. The respondents were Chinese-speaking learners of English. Future research should examine and compare users from different countries to test the generalizability of the findings of this study. Second, the cross-sectional investigation method adopted in this study could only be used to examine and explain the situations of participants at a certain time; thus, the investigation was limited to the conditions of a certain period. Consequently, researchers should conduct longitudinal investigations to identify possible changes in perception with experience. Third, this study adopted an online questionnaire and a quantitative statistical research model. The online survey was advertised on several popular websites. To prevent self-selection bias in online surveys, subsequent studies could use an experimental method to support research results. Third, this study adopted an online questionnaire and a quantitative statistical research model.

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

The studies involving human participants were reviewed and approved by the Ling Tung University. The patients/participants provided their written informed consent to participate in this study.

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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