From Physical to Virtual: Understanding User Intention to Use Online Study Rooms Through Technology Acceptance Model (TAM) and Flow Theory

From Physical to Virtual: Understanding User Intention to Use Online Study Rooms Through Technology Acceptance Model (TAM) and Flow Theory | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article From Physical to Virtual: Understanding User Intention to Use Online Study Rooms Through Technology Acceptance Model (TAM) and Flow Theory Jun Wang, Shang-jie Yuan, Zhong Zheng, Ji-ping Zhang, Qian-ting Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7846720/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Though online study rooms have gained popularity among users for their flexibility and interactivity, there remains a lack of in-depth analysis of factors that influence user behaviors in these virtual learning environments. This research delves into the determinants of user behaviors in the context of online study rooms by integrating the Technology Acceptance Model (TAM) and the Flow Theory. Structural Equation Modeling (SEM) was performed to analyze 389 valid responses. The research findings manifest that: (a) external variables (i.e., learning convenience, interactivity, and the incentive and constraint mechanism) significantly influence users’ perceived ease of use and perceived usefulness of such platforms; (b) perceived ease of use has a direct and positive influence on the intention to use, while perceived usefulness has no direct influence; (c) flow experience is a key determinant of intention to use; (d) gender and habituation significantly moderate the relationships between perceived ease of use, perceived usefulness, flow experience, and intention to use. By integrating the flow theory and extending the application boundary of TAM, this research reveals that flow experience plays a critical role in shaping user behaviors. Our research results offer data support for optimizing the design of online study rooms, theoretical evidence for understanding user behaviors in novel digital learning environments, and practical implications for developers of such platforms and personnel working in this domain. Business and commerce/Business and management Social science/Business and management Business and commerce/Information systems and information technology Biological sciences/Psychology Social science/Psychology Online Study Room Technology Acceptance Model (TAM) Flow Theory Intention to Use User Behavior Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction With the increasing prevalence of the Internet and the growing demand for flexible learning, online study rooms, a novel digital learning environment, are mushrooming quickly. This innovative learning environment highlights the flexibility and efficiency of independent learning, where users can concentrate on learning tasks on online platforms, such as Zoom or YouTube, and users can engage in discussions and share insights with other users regarding their learning progress(Wang et al., 2024 ). Since the emergence of online live streaming “study with me” on YouTube in 2017, online study rooms have gained popularity quickly on Chinese platforms such as BiliBili and Douyin. Statistics revealed by BiliBili display that in 2018, the number of participants in online learning live streams surged to 18.27 million, the cumulative live streams started hit 1.03 million, and the total hours dedicated to such live streams exceeded 1.46 million hours, positioning this type of live streaming as the predominant genre in terms of streaming hours (BiliBili official statistics, 2018). Since July 2021, the number of participants in “Learning Companions” live streaming has exceeded 327 million, manifesting the massive potential of online study rooms (BiliBili official statistics, 2021). The intricate relationship between education and information technology has made online learning an integral part of education reform. Especially during the COVID-19 pandemic, online learning emerged as the principal pedagogical method adopted by numerous educational institutions, which greatly facilitated the alteration of learning methods. Backed by live streaming technologies, online study rooms refer to virtual learning environments accessible via devices such as smartphones and computers, where users can conduct real-time interaction with peers across diverse geographical locations who share a synchronous viewing experience(Y. Zhang, 2024 ).This learning mode not only transcends the temporal and spatial limits of traditional learning but also enhances social presence and interactivity, and cultivates a collective learning atmosphere(Lim, 2023 ). Despite the rapid growth of research on online study rooms in China, particularly in the wake of the COVID-19 pandemic, the predominant methodologies employed in existing studies have been qualitatively oriented, such as participatory observation and in-depth interviews, and there is a lack of quantitative studies.The existing literature on online study rooms can be categorized into three types: first, studies focusing on the operational dynamics and user demographics of online study rooms (Xu et al., 2021 ). second, investigations on user motivations and the intrinsic factors that encourage participation in such online study rooms(Lim, 2023 ),third, studies evaluating the effects of online study rooms on learning outcomes and their efficacy(M. Kim, 2022 ).Nevertheless, a research gap persists regarding factors that influence the behavioral intention to use online study rooms, especially in the aspects of technology integration and emotional drivers((Venkatesh, 2022 ). Technology Acceptance Model (TAM) serves as a classical theory for understanding user behavior in accepting technologies. Its key variables, perceived usefulness and perceived ease of use, have been widely applied to elucidate users’behavioral intentions(Davis, 1989 ).In recent years, TAM has been extensively employed in the context of digital learning(Lisana, 2023 ) (Afacan Adanır & Muhametjanova, 2021 )However, TAM pays little attention to emotionally driven factors, which makes it inadequate to explain users’ continuous participation in online learning environments (Venkatesh, 2022 ), and fails to investigate the moderating effects of individual differences such as gender and habituation. In recent years, the flow theory has garnered attention in studying online learning environments, which unveils the significant influence of emotional factors on user behavior (Hsu et al.,2012). By integrating TAM and the flow theory into the theoretical framework, this research aims to systematically elucidate users’ behavioral intention to use online study rooms. Specifically, this research not only seeks to explore the roles of perceived usefulness and perceived ease of use as rational driving forces of user behaviors but also introduces flow experience as a mediating variable that highlights the critical role of emotional factors in making decisions. Furthermore, this research examines the moderating effects of gender and habituation on user behavior, which addresses the existing gap in the literature concerning emotionally driven factors and the moderating effects of individual differences. Structural equation modeling (SEM) was employed to analyze the 389 valid responses. This research not only enriches the application scenario of TAM but also offers practical insights for the design and optimization of online study rooms (Qashou, 2021 ) (Afacan Adanır & Muhametjanova, 2021 ).The research results can help platform developers better understand user demands and design features to improve user engagement and satisfaction (Lim, 2023 ). The structure of this study is organized as follows: the first part is the introduction; the second section constitutes a literature review that examines the current status of research on online study rooms and TAM; the third part outlines the research framework and hypotheses; the fourth part introduces the research methods, including questionnaire design and data collection processes; the fifth part presents the research results; the sixth part discusses research findings and implications; the last part centers around the conclusion, limitations, and future research directions. 2. Literature Review 2.1 Online Study Rooms As a novel learning environment, online study rooms have garnered much attention in recent years. Established by digital technologies, these spaces support coordinated, independent, and immersive learning experiences (Sage et al.,2021).From the perspectives of Situated Learning Theory and Distributed Cognition Theory, online study rooms offer learners cognitive opportunities for embodied cognition by simulating authentic learning scenarios, which can enhance their contextual understanding of knowledge (Chou & Liu, 2005 ). Take China’s BiliBili online study rooms as an example. This platform leverages live streaming technologies to enable individual streamers or groups of users to create a virtual learning environment using their electronic devices, such as smartphones or computers. Learners can showcase diverse learning activities, ranging from note-taking and problem-solving to recitation and typing, via the real-time “lives streaming” feature. The audience can participate by watching these activities in real time, fostering a collective learning experience where participants can engage with content simultaneously despite geographical dispersion. Features of bullet comments and virtual avatars enable online study rooms to enhance social presence and simulate a co-present experience, which further boosts interaction and exchange among learners. To cultivate a good learning atmosphere, the streamer usually pinpoints clear learning objectives, timelines, and motivational slogans. Users can send bullet comments to document their engagement, encourage each other, and share insights to foster an environment where supervision, self-motivation, and companionship are available. Previous studies in this field have mainly focused on the foundational aspects of online study rooms, such as platform stability and user interface design. As research deepens, scholars shift toward examining the relationship between such platforms and user demands, and their influence on learning outcomes. (Liu et al., 2022 ) pointed out that effective emotional support in online teaching environments can offer empathy, understanding, motivation, and encouragement to learners. While prior research has offered valuable insights, most of them are qualitative studies that lack accurate quantitative analysis. Moreover, existing literature fails to delve into how online study rooms, a novel learning medium, trigger and influence intention to use, and the endeavors to investigate the positive influences of emotional factors on intention to use remain insufficient, leading to gaps in explaining user behaviors within this context (Fathali & Okada, 2018 ). 2.2 Theoretical Frameworks 2.2.1 TAM First proposed by Davis ( 1989 ), TAM is an essential theoretical framework for examining user acceptance behaviors toward information technologies. Its application spans multiple fields, including information systems, informatics, and library science. Perceived usefulness and perceived ease of use are two primary variables in TAM used to explain technology acceptance behaviors. Davis(1989)posited that perceived usefulness refers to users’ cognition of whether a given technology is useful, and perceived ease of use pertains to the perception regarding the simplicity of using a particular information system. Subsequent studies have sought to refine this model by including the influences of external variables on perceived usefulness and perceived ease of use. Notably, Venkatesh ( 2003 ) extended the TAM2 framework by including factors that influence perceived ease of use. In the context of online learning environments, the effectiveness of TAM has been extensively validated. Motaghian (2013)employed a comprehensive model integrating information systems, and psychological and behavioral factors to evaluate teachers’ intention to adopt a learning system. Their research results show that perceived usefulness and perceived ease of use notably improved teachers’ behavioral intention to use online study systems, and PU was found to be the most influential factor.Arteaga Sánchez (2014) investigated students’ intention to use Facebook to aid learning, and the results showed that perceived usefulness, perceived ease of use, and facilitating conditions have significant positive influences on the adoption of Facebook. To this end, TAM is deemed an effective framework for investigating the adoption and behavioral intention to use online study rooms. 2.2.2 Flow Theory The flow theory was initially conceptualized by Csikszentmihalyi ( 2014 )which delineates a psychological state when an individual is completely immersed in enjoyable activities and temporarily ignores other stimuli in the surroundings. This state of intense concentration is marked by several key features: the individual’s complete concentration, a distorted perception of time, and the intrinsic satisfaction derived from the activity. As Internet technologies advance, the flow theory is widely applied in a host of areas, such as social media usage behaviors (Hyun et al., 2022 ), information technology(S. Kim et al., 2019 ), and online gaming(Erhel & Jamet, 2019 ), It is particularly valuable in explaining individual behavioral motivations and flow experiences. Additionally, the flow theory has been applied in learning contexts that require a higher degree of individualism.Hoffman & Novak(1996),first introduced the flow theory into online environments, and they asserted that flow could boost increased learning, perceived behavioral control, exploratory mindset, and positive subjective experiences. Hoffman & Novak ( 2009 ) described the flow state as a cognitive state and elucidated the seamless response, interactivity, intrinsic enjoyment, the loss of self-consciousness, and self-reinforcement in online environments.Research by Cheng & Jiang(2020)regarding reading and learning behaviors illuminated that the learning efficiency and outcomes in an immersive environment outperform that of a normal environment. 2.3 Research Hypotheses 2.3.1 Perceived Usefulness and Perceived Ease of Use Based on the Theory of Reasoned Action (TRA) proposed by Fishbein and Ajzen, Davis(1989)developed TAM. Currently, TAM has been recognized as the best framework to understand acceptance behaviors associated with information technologies(Venkatesh, 2022 ). (Y. Wang et al., 2020 ).Perceived usefulness and perceived ease of use are two critical constructs for elucidating users’ behavioral intentions. Specifically speaking, perceived usefulness refers to the degree to which users believe that using an application can improve their work or learning outcomes.perceived ease of use refers to the degree to which users believe that using an application or equipment requires few efforts (Alturki & Aldraiweesh, 2023 ).For example, in the learning context of the metaverse, perceived ease of use is deemed a crucial determinant of technology acceptance (Al-Adwan, 2023). In this research, perceived ease of use is defined as the degree to which users of online study rooms do not encounter complex technological operations or excessive cognitive burdens. To be more specific, a higher level of perceived ease of use correlates positively with the behavioral intention to use online study rooms. Previous research on TAM has substantiated that perceived ease of use serves as a prerequisite for perceived usefulness (Nguyen et al., 2024 ; Türker et al., 2022 ).In this study, perceived ease of use can enhance users’ belief of users’ belief in the potential benefits of online study rooms, such as improved learning efficiency and heightened concentration. Given the above analysis, we propose the following hypotheses: H1 Perceived ease of use of online study rooms positively influences perceived usefulness. H2 Perceived ease of use of online study rooms positively influences their intention to use such platforms. In the current research, perceived usefulness is defined as users’ perception of learning benefits derived from using online study rooms, which is mainly presented in two aspects. First, the improvement of learning efficiency. With real-time discussion and sharing of learning progress, online study rooms allow users to access information and resolve inquiries more efficiently. For example, users can interact with streamers via bullet comments to address questions occurring in the learning process in real time, thus improving their learning outcomes (Q. Zhang et al., 2023 ).Second, the cultivation of learning habits. Through the check-in feature, objective setting, and the incentive and constraint mechanism, users can better establish and maintain beneficial learning habits, which not only enhances users’ motivations to study but also improves their intention to use by fostering a sense of achievement gained from goal fulfillment.Moreover, higher perceived usefulness is likely to be associated with a greater intention to use online study rooms, as users recognize the tangible benefits of improved concentration, optimized learning strategies, and enhanced learning outcomes (M. Kim, 2022 ).To this end, this research hypothesizes that: H3 Perceived usefulness of online study rooms positively influences their intention to use such platforms. 2.3.2 Learning Convenience Learning convenience is defined as students’ capacity to engage in learning activities without temporal and spatial constraints(Lisana, 2023 ). Pramana ( 2018 ) indicated that the inconvenience of attending physical classrooms is a key factor affecting university students’ intention to use Mobile Learning platforms. Several researchers have also highlighted the significance of convenience in fostering students’ willingness to adopt Mobile Learning in higher education institutions (Qashou, 2021 ; Saroia & Gao, 2019 ). In this research, learning convenience is defined as the ease of access to online study rooms, which are no longer hindered by the temporal and spatial limits of conventional learning environments(Lisana, 2023 ).Compatible with various devices, online study rooms allow users to switch seamlessly between mobile phones and computers, which alleviates the cognitive burden of adapting to different technological interfaces. Users can customize their learning content and pace to meet their individual needs, which enhances the flexibility and autonomy of learning(Pramana, 2018 ).Streamlined operational procedures contribute to a perception of convenience and further enhance users’ perceived ease of use. For example, the multi-device compatibility of online study rooms can mitigate the cognitive burden of adapting to different technological interfaces. The flexible learning time options, such as 24-hour live streaming, enable users to engage in learning activities without changing their schedules, which can lower the psychological barrier to using such technology(Saroia & Gao, 2019 ).When autonomy is enabled, for example, when users can choose their learning content, they will perceive less external control and pressure and master the technology with greater ease (Ryan & Deci, 2000 ) .Based on the above analysis, we propose the following hypothesis: H4 Learning convenience positively influences perceived ease of use. The learning convenience offered by online study rooms improves users’ recognition of the system’s practicability through the optimized allocation of learning resources. Without spatial constraints and the necessity of commuting, online study rooms save users’ time and energy,and enable users to dedicate more time to achieving their learning objectives, which can lead to better learning outcomes, such as increased concentration duration.Hussein & Hilmi(2021)highlighted the significance of convenience in online learning environments and pointed out that convenience greatly contributes to improving user engagement and satisfaction. Furthermore, the autonomy enabled by customizing learning plans that cater to user demands strengthens users’ perception of the alignment between system functions and individual objectives, which can enhance their evaluation of usefulness (Collis & Moonen, 2011 ).The empirical study conducted by Lee ( 2010 ) further corroborates that flexible learning procedures (such as the effective use of fragmented time), facilitated by convenience, can significantly improve users’ recognition of the effectiveness of online learning. Users deem the system as a practical tool for achieving their goals. According to Q. Zhang et al., ( 2023 ),convenience significantly influences users’ perceived usefulness of online educational platforms. Given the above analysis, it is hypothesized that: H5 Learning convenience positively influences perceived usefulness. 2.3.3 Interactivity Interactivity refers to the extent of two-way communication experienced by users when interacting with the system, content, or other users. Prior research has highlighted the critical impact of interaction quality and frequency on online learning satisfaction (She et al., 2021 ).Burgoon et al., (2000) posit that interactivity can be understood through the qualitative aspects of users’ experiences during interactions, such as their level of engagement, mutual participation, and personalization. Kamoyo et al., ( 2025 ) research shows that e-learning platforms with high interactivity can enhance users’ perceived ease of use and usefulness and foster a positive attitude among students. In the context of online study rooms, interactivity manifests through bullet comments, live chats, and the sharing of learning progress. By minimizing operational barriers, interactivity can improve users’ perceived ease of use of such platforms. Specifically, features like bullet comments and real-time feedback can streamline the interactive process between users and the system, and the ability to engage in discussions with a single click and the auto-synchronization of learning progress diminish users’ cognitive burden of using such systems(Song & Zinkhan, 2008 ). Moreover, high-quality information exchange, supported by well-refined bullet commenting rules and prompt responses to inquiries, optimizes the user experience and simplifies the mastery of such systems(Roy Dholakia & Zhao, 2009 ).To this end, this research hypothesizes that: H6 The interactivity of online study rooms positively influences perceived ease of use. Lee ( 2010 ) demonstrated that human-to-human interaction significantly enhances users' perceived usefulness of ACG (animation, comic, and game) social media sites.In this study, interactivity is posited to improve perceived usefulness by enhancing learning support functions. For example, real-time discussion and coordinated problem-solving efforts enable users to quickly access learning resources and tackle complicated problems, which directly improves learning efficiency. Meanwhile, personalized interactions, such as streamers’ content adjustments in live streaming based on user demands, can reinforce users’ recognition of the system’s value and their belief that online study rooms are effective tools for achieving their learning objectives. Therefore, this research proposes the following hypothesis: H7 The interactivity of online study rooms positively influences perceived usefulness. In examining the relationship between interactivity and immersion, scholars have posited that interactivity can effectively elicit consumers' sense of immersion within digital environments (Klingenberg et al., 2024 ). This is attributed to the fact that engaging with interactive elements significantly enhances psychological engagement among consumers(Roy et al., 2023 ). Furthermore, Kowalczuk et al., (2021) have demonstrated that interactivity not only positively influences immersion but also contributes to user enjoyment of augmented reality (AR) and their intention to reuse it in e-commerce contexts. Based on the above analysis, this research proposes that high levels of interactivity can significantly influence users’ flow experience. Thus, it is hypothesized that: H8 The interactivity of online study rooms positively influences the flow experience. 2.3.4 Incentive and Constraint Mechanism As a fundamental component of behavioral monitoring mechanisms, the incentive and constraint mechanism functions as an objective-oriented feedback system in educational psychology and information technology. Khaldi et al.,(2023) identified that PBL elements (points, badges, and leaderboards), levels, and feedback are the most frequently adopted gamification elements in e-learning systems within higher education. These elements facilitate social comparisons among learners through leaderboards and drive them to ascertain their positions within peer groups. It is also a competitive mechanism can significantly improve learning engagement.Meanwhile, Kluger & DeNisi ( 1996 ) posit that instant feedback on learning progress and outcome evaluation can help learners dynamically adjust their strategies and enhance their self-efficacy. In online learning scenarios, such as the BiliBili online study rooms, the incentive and constraint mechanism manifests through learning time leaderboards and instant feedback on concentration. Leaderboards can clearly display learning outcomes and assist users in setting specific objectives. The change in rankings indicates individual progress and can foster a sense of competitiveness. Moreover, dynamic data also help users to refine their learning strategies and improve their efficiency. The functions of leaderboards and concentration supervision of online study rooms can streamline the interactive processes between users and systems. Rapid rank checks and instant feedback on learning progress can alleviate users’ psychological burden of using technologies. Besides, the instant feedback mechanism can optimize user experience by offering real-time evaluations of learning progress and outcomes. To this end, the system is suitable for analysis through the lenses of TAM and the flow theory. As the external variable, the incentive and constraint mechanism can influence users’ perceived ease of use and usefulness of the system and their flow experience, thereby indirectly promoting their intention to use such systems. To this end, this research hypothesizes that: H9 The incentive and constraint mechanism positively influences users’ perceived ease of use of online study rooms. Wiyono(2021)discovered that the incentive and constraint mechanism has a significant influence on performance-based learning. In the scenario of online study rooms, this mechanism can improve perceived usefulness by providing robust learning support. For example, learning time leaderboards and concentration supervision enable users to quickly access learning resources and address their problems, thereby directly improving their learning efficiency. By delivering learning support and personalized services, users’ recognition of the system’s value will be significantly increased, which can further improve perceived usefulness. In essence, if the constraint mechanism successfully enhances learning motivation and performance, the goal of supervision is realized. To this end, this research hypothesizes that: H10 The incentive and constraint mechanism positively influences users’ perceived usefulness of online study rooms. According to the flow theory proposed byCsikszentmihalyi ( 2014 ), in this research, supervision and incentives create a sense of belonging and enjoyment by stimulating emotional engagement (such as bullet comment encouragement and virtual avatar coordination) and offering continuous external stimulus (such as learning progress leaderboard and bullet comment check-in). These elements promote prolonged concentration and enhance the flow state. Real-time interactions and objective-oriented feedback mechanisms can significantly improve the flow experience. Therefore, it is hypothesized that: H11 The incentive and constraint mechanism positively influences users’ flow experience in online study rooms. 2.3.5 Flow Experience The concept of flow experience was first proposed by Csikszentmihalyi(1975), which pertains to the mental state of concentration and a distorted sense of time perception when an individual is deeply engaged in an activity. In information technology,Hoffman & Novak ( 2009 ) introduced this concept to online environments and highlighted that flow experience can enhance users’ sense of control and exploratory behavior via technological features such as interactivity and remote display capabilities. Extant research has indicated that flow experience can significantly improve the intention to use (Ozkaraet al.,2017), especially in the context of online learning, where flow experience can enhance the enjoyment of learning (Guo & Zhang, 2024 ), boost cognitive absorption and ultimately lead to behavioral intentions(Agarwal & Karahanna, 2000 ). In this study, online study rooms leverage virtual simulation technologies to simulate physical learning environments, where users can enjoy a quiet learning atmosphere without interruption. Therefore, users’ concentration and learning motivation can be improved. Besides, the design of online study rooms can harness customizable settings and interactive functions (such as real-time discussions and the sharing of learning progress) to further improve users’ flow state. Given the above analysis, this research proposes the following hypothesis: H12 The flow experience of online study rooms positively influences the usage intention. 2.3.6 The Moderating Effects of Gender and Habits Gender differences in this research refer to the different preferences and behavioral patterns exhibited by men and women when engaging with e-learning platforms. Previous research has indicated that men and women could have different behavioral patterns and psychological mechanisms when accepting and using technologies. Female users tend to prioritize interactivity and user experience, while male users are inclined to emphasize practicability and functional design(Hoffman & Novak, 2021).These gender differences could moderate the influence of perceived usefulness and perceived ease of use on the intention to use. Specifically speaking, female users may tend to enhance learning experiences through interactive features, such as bullet comments and real-time discussions, while male users may be more concerned about whether the platform can directly improve learning efficiency and outcomes. Consequently, it is posited that gender can moderate the influences of perceived ease of use and perceived usefulness on intention to use, and we propose the following hypothesis: H3a Gender moderates the relationship between perceived ease of use and intention to use. H3b Gender moderates the relationship between perceived usefulness and intention to use. Habituation refers to the automatic influence of users’ prior experiences on their current behaviors(Ryan, 2022 ), which can influence user acceptance and the intention to use a new platform. For example, users familiar with similar learning tools as online study rooms can adapt to the functions more quickly, thereby increasing their intention to use such platforms. Habituation can also influence perceived usefulness and perceived ease of use by lowering the cognitive barriers of technology acceptance and enhancing user recognition of the system’s value. Specifically, users acclimated to highly interactive learning platforms may find it easier to accept the interactive features of online study rooms, which improves their perceived ease of use of such platforms. Meanwhile, users accustomed to efficient learning tools may show more recognition for the functional design of online study rooms, which improves perceived usefulness. Furthermore, habituation can directly influence the intention to use as individuals exhibiting higher levels of habituation are more likely to view using online study rooms as a natural behavioral option. To this end, this research hypothesizes that: H4a Habituation negatively moderates the relationship between perceived ease of use and intention to use. H4b Habituation negatively moderates the relationship between perceived usefulness and intention to use. Flow experience refers to the state of complete immersion and enjoyment of using a technology. Though flow experience itself exerts a direct influence on the intention to use, gender and habituation can also moderate the relationship between flow experience and the intention to use. Therefore, it is hypothesized that: H12a Gender moderates the relationship between flow experience and the intention to use. H12b Habituation moderates the relationship between flow experience and the intention to use. 3. Methods 3.1 Participants Wenjuanxing ( https://www.wjx.cn/ ), an online questionnaire platform, was employed to collect data from February 4 to 21, 2025. The questionnaire was distributed to potential participants through WeChat and QQ, two widely used social platforms in China with 1.327 billion and 558 million monthly active users (MAUs), respectively. Participation in the survey was voluntary and anonymous, ensuring the confidentiality and integrity of the responses. A total of 408 questionnaires were completed, with 19 invalid responses excluded due to incomplete or inconsistent answers. Consequently, 389 valid responses were utilized for analysis, representing an effective response rate of 95.3%. In terms of gender, female (N = 272, 69.92%), male (N = 117, 30.08% ). The demographic analysis of the participants reveals that the predominant representation of individuals aged between 18–25 years old, accounting for 75.06% of the sample (N = 292), followed by the age groups of 26–35 (20.31%, N = 79), 36–45 (3.86%, N = 15), and 46–55 (0.77%, N = 3). There is no participant aged above 56. In terms of educational background, a significant majority of participants possess junior college or bachelor’s degrees (70.95%, N = 276), followed by participants with master’s degrees or higher (19.79%. N = 77). 3.34% of the participants have a high school degree or below (N = 13). These findings coincide with the research findings of Lu (2022), which suggest that online learning platforms mainly attract individuals with educational credentials. Participants engage with these platforms for various purposes, including exam preparations (76.86%, N = 299), skill improvement (64.01%, N = 249), personal growth (50.64%, N = 197), and daily learning (45.50%, N = 177). Additional reported motivations include the pursuit of topics of interest (31.10%, N = 121), language learning (16.97%, N = 66), and vocational training (9.25%, N = 36). The emphasis on exam preparation and skill improvement is consistent with the educational background distribution, which reflects a pronounced inclination toward self-learning among well-educated individuals. Overall, the demographic information and usage patterns of the collected samples correspond closely to the typical characteristics of e-learning users, which means the research results are representative.Participants’ detailed demographic information is presented in Table 1 . Table 1 Demographic information of the participants Characteristics Frequency (n = 389) Percentage (%) Gender Male 117 30.08 Female 272 69.92 Age 18 − 25 years old 292 75.06 26 − 35 years old 79 20.31 36 − 45 years old 15 3.86 46 − 55 years old 3 0.77 Over 56 years old 0 0 Educational Background Senior middle school or below 13 3.34 Junior college and bachelor’s degrees 276 70.95 Master’s degrees or higher 77 19.79 Others 23 5.91 Purpose of Use (Multiple Choices) Exam preparation 299 76.86 Skills improvement 249 64.01 Personal growth 197 50.64 Hobby learning 121 31.10 Daily learning 177 45.50 Language learning 66 16.97 Vocational training 36 9.25 Others 19 4.88 3.2 Instrument development The questionnaire design of this study rigorously adhered to established academic research standards. A five-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree), was employed to quantify the subjective experiences of respondents. The questionnaire is structured into four sections: Technology Acceptance Model (TAM), Flow Experience (FE), Intention to use (IU), and Habituation (HAB). All items in the questionnaire were adapted from previously validated scales to ensure the scientific rigor and validity of the measurement tools. Specifically, the TAM section was used to measure perceived usefulness (PU) and perceived ease of use (PEOU), with learning convenience (LC), interactivity (INT), and the incentive and constraint mechanism (ICM) as external variables. All items in the TAM section were adapted from previous studies(Davis, 1989 ;Venkatesh et al.,2003;Cheung & Lee, 2011 ).In the FE section, adaptations from Erhel & Jamet ( 2019 ) were utilized to evaluate participants’ flow experience when using the online learning platform. Both the IU and HAB sections were adapted from Saroia & Gao ( 2019 ), Tang et al.,(2021), The IU section aimed to evaluate participants’ intention to use such platforms, and the HAB section targeted at evaluating participants' habituation to these platforms. Moreover, the questionnaire includes a demographic section for collecting data on participants’ gender, age, educational background, and usage purposes. Prior to the formal distribution, the questionnaire was pre-tested by experts and researchers, and the wording and structure of the questionnaire were refined to ensure clarity and relevance. The final questionnaire has passed the reliability and validity analyses, confirming its overall reliability and validity. 3.3 Data analysis Data were analyzed using SPSS 27.0 and AMOS 24.0. First, a confirmatory factor analysis (CFA) was conducted to evaluate the validity of the latent variables. Second, Cronbach’s alpha (α) was calculated to assess the internal consistency of all subscales in the instrument. Third, structural equation modeling (SEM) was performed to examine the relationships among the eight latent variables. According to Byrne ( 2010 ), the recommended sample size for conducting CFA is 5 to 10 times the total number of items in the survey scales. In this study, there are 24 items in total, and we used 389 samples for both CFA and SEM analyses. In the analysis process, this study employed the PROCESS macro in SPSS 27.0 and set the number of bootstrap resamples to 5,000 to ensure the robustness and reliability of the results and to minimize the impact of sampling error on the analysis outcomes. The confidence interval was set at 95% to assess the significance of the moderation effects. PROCESS Model 1 was used to systematically examine the moderating roles of gender and habituation. Through these analytical procedures and parameter settings, the study can comprehensively and systematically evaluate the moderating effects of gender and habituation on the relationships between perceived ease of use, perceived usefulness, flow experience, and intention to use. 4. Results In this study, the factor loadings of all 24 items ranged from 0.700 to 0.905. Cronbach’s alpha coefficients for the subscales of LC, INT, ICM, PU, PEOU, FE, HAB, and IU were 0.717, 0.789, 0.700, 0.765, 0.778, 0.709, 0.813, and 0.905, respectively. These values indicate good internal consistency across the subscales. The factor loadings for each item and the Cronbach’s alpha values for each subscale are presented in Table 2 . Table 2 Results of construct validity and reliability analysis. Latent Variable Measurement Variable Mean Std.Dev Factor Loadings Alpha CR AVE LC LC1 4.414 0.591 .688 .717 0.719 0.461 LC2 .631 LC2 .683 INT INT1 4.116 0.759 .737 .789 0.79 0.557 INT2 .732 INT3 .729 ICM ICM1 4.205 0.672 .641 .700 0.701 0.439 ICM2 .658 ICM3 .668 PU PU1 4.237 0.656 .717 .765 0.766 0.522 PU2 .713 PU3 .727 PEOU PEOU1 4.239 0.640 .716 .778 0.779 0.54 PEOU2 .634 PEOU3 .630 FE FE1 4.144 0.736 .726 .709 0.709 0.449 FE2 .716 FE3 .712 IU IU1 4.302 0.740 .719 .813 0.814 0.593 IU2 .781 IU3 .795 HAB HAB 3.111 1.242 .874 .905 0.905 0.76 HAB .850 HAB .895 4.1 Assessment of the model fit As demonstrated in Table 3 , the fit indices of the measurement and structural models confirmed the validity of the employed constructs. Although the model fit indices (AGFI = 0.814, TLI = 0.826, CFI = 0.853) were slightly below the recommended thresholds, they are considered acceptable in exploratory studies (Hu & Bentler, 1999 . While the RMR (0.160) exceeds the conventional 0.05 cutoff, this metric’s sensitivity to parameter scales makes it less reliable than RMSEA in complex models (Bentler, 2006). Our RMSEA value (0.089) falls within the ‘mediocre fit’ range (0.08–0.10), as defined by MacCallum et al. (1996), which remains acceptable when: a) Sample size smaller than 500 (N = 389 in this study); b) Model includes over 20 observed variables (24 items here). The slightly elevated RMSEA may stem from our integration of TAM and flow theory, an understudied combination requiring cross-paradigm adjustments. As emphasized by Barrett (2007), model evaluation should prioritize theoretical coherence over mechanical cutoff adherence. Table 3 The goodness of fit indices for the measurement model and the research model. Model χ²/df AGFI TLI CFI RMR RMSEA Measurement model 1.659 .905 .960 .967 .032 .041 Research model 4.078 .814 .826 .853 .160 .089 Recommended value references .90 > .90 > .90 < .05 < .08 4.2 Hypotheses testing The significant correlations among LC, INT, ICM, PU, PEOU, FE, and IU indicate that these variables are highly interconnected. To examine the structural relationships among these variables, Structural Equation Modeling (SEM) was conducted to test the significance of each hypothesized path. The analysis calculated non-standardized coefficients (B), standardized coefficients (β), standard errors (SE), t-value, and R2 (explanatory power of independent variables) were calculated to test the hypotheses. The results indicated that ten out of twelve hypotheses were supported (Table 4 and Fig. 2 ). Learning Convenience was found to have significant positive direct effects on Perceived Ease of Use (β = 0.504, p = 0.000), and Perceived usefulness (β = 0.461, p = 0.000), supporting Hypothesis 4 and Hypothesis 5. Interactivity was found to have significant positive direct effects on Perceived Ease of Use (β = 0.357, p = 0.0.000), Perceived Usefulness (β = 0.622, p = 0.000), and Flow Experience (β = 0.747, p = 0.000), supporting Hypothesis 6, Hypothesis 7 and Hypothesis 8. The Incentive and Constraint Mechanism was found to have significant positive direct effects on Perceived Ease of Use (β = 0.489, p = 0.0.000), Perceived Usefulness (β = 0.691, p = 0.000), and Flow Experience (β = 0.659, p = 0.000), supporting Hypothesis 9, Hypothesis 10 and Hypothesis 11. Perceived Ease of Use was found to have significant positive direct effects on Intention to Use (β = 0.262, p = 0.000), supporting Hypothesis 3. Flow Experience was found to have significant positive direct effects on Intention to Use (β = 0.395, p = 0.000), supporting Hypothesis 12. However, Perceived Ease of Use was not found to have significant direct effects on Perceived Usefulness (β =-.101, p = − .728), rejecting Hypothesis 1. Also, Perceived Usefulness was not found to have significant direct effects on Intention to Use (β = − .017, p = − .095), rejecting Hypothesis 2. Table 4 The results of hypothesis testing. Hypotheses Hypothesized path B β SE CR Supported H1 Perceived Ease of Use->Perceived Usefulness − .101 − .101 .139 − .728 Not supported H2 Perceived Usefulness–>Intention to Use − .022 − .017 .231 − .095 Not supported H3 Perceived Ease of Use–>Intention to Use .329 .262 .149 2.209 Supported H4 Learning Convenience—> Perceived Ease of Use .447 .504 .067 6.628*** Supported H5 Learning Convenience—>Perceived Usefulness .489 .461 .091 4.469*** Supported H6 Interactivity—> Perceived Ease of Use .240 .357 .044 5.430*** Supported H7 Interactivity—>Perceived usefulness .461 .622 .059 7.130*** Supported H8 Interactivity—>Flow Experience .599 .747 .057 10.478*** Supported H9 Incentive and Constraint Mechanism—> Perceived Ease of Use .437 .489 .071 6.180*** Supported H10 Incentive and Constraint Mechanism—>Perceived Usefulness .615 .691 .103 5.984*** Supported H11 Incentive and Constraint Mechanism–>Flow Experience .701 .659 .085 8.257*** Supported H12 Flow Experience–>Intention to Use .416 .395 .147 2.830 Supported ***p < 0.001; **p < 0.01; *p < 0.05. 4.3 Results of Moderating Effects Table 5 presents the analysis of the moderating effects of gender (GEN) and habituation (HAB) on PEOU and IU. As shown in the table, the interaction term between PEOU and HAB (PEOU*HAB) has a significant influence on IU (β = -0.115, p = 0.013), indicating the moderation of HAB on the relationship between PEOU and IU. The moderating effect is negative, which suggests that as the level of HAB increases, the positive influence of PEOU on IU diminishes. In contrast, though the interaction term between PEOU and GEN (PEOU*GEN) shows a negative trend (β = -0.092), the effect is only marginally significant (p = 0.061), which did not reach the conventional significance threshold (p < 0.05). In summary, PEOU has a robust positive predictive influence on IU and HAB significantly and negatively moderates this relationship. However, the moderating effect of GEN is relatively weak. Table 5 Analysis of the moderating effects of GEN and HAB on the relationships between PEOU and IU Unstandardized Coefficient (B) Standard Error Standardized coefficient Beta t Significance (Constant) 2.026 0.222 9.117 0 PEOU 0.59 0.056 0.51 10.443 0 PEOU*GEN -0.031 0.017 -0.092 -1.88 0.061 PEOU*HAB -0.015 0.006 -0.115 -2.504 0.013 Dependent Variable:IU Table 6 investigates the moderating effects of GEN and HAB on the relationship between PU and IU. The results of regression analysis demonstrate that GEN has a significant negative moderating effect (β = -0.108, p = 0.026), which suggests that compared with female users, the positive influence of PU on IU is diminished among male users. The moderating effect of HAB is also negative (β = -0.087, t = -1.948) but it only reaches marginal significance (p = 0.052), which indicates that habituation may weaken PU’s positive influence on IU. Table 6 Analysis of the moderating effects of GEN and HAB on the relationships between PU and IU Unstandardized Coefficient (B) Standard Error Standardized coefficient Beta t Significance (Constant) 1.89 0.211 8.979 0 PEOU 0.631 0.054 0.559 11.58 0 PEOU*GEN -0.036 0.016 -0.108 -2.232 0.026 PEOU*HAB -0.011 0.006 -0.087 -1.948 0.052 Dependent Variable:IU Table 7 elucidates that the interaction term between GEN and FE (FE*GEN) exhibits a negative trend (β = -0.08), but it does not reach statistical significance (p = 0.105). Similarly, the interaction term between HAB and FE (FE*HAB) also shows a negative but non-significant moderating effect (β = -0.06, p = 0.179). These findings suggest that, within the context of this study, the moderating effects of gender and habituation on the relationship between flow experience and intention to use may be relatively weak. Table 7 Analysis of the moderating effects of GEN and HAB on the relationships between FE and IU Unstandardized Coefficient (B) Standard Error Standardized coefficient Beta t Significance (Constant) 2.16 0.184 11.734 0 PEOU 0.562 0.049 0.559 11.358 0 FE*GEN -0.027 0.016 -0.08 -1.625 0.105 FE*HAB -0.008 0.006 -0.06 -1.348 0.179 Dependent variable: IU 5. Discussion 5.1 Integration of TAM and the Flow Theory This study substantiates the combined influences of rational cognition and emotional engagement on the behavioral intention to use online study rooms, aligning with the opinions of Hsu et al.,(2012),which emphasizes that user behavior is driven by both instrumental benefits and emotional rewards. Notably, our research results display that flow experience, as an emotional participation mechanism, mediates the relationships between perceived usefulness, perceived ease of use, and intention to use. This finding broadens the applicability of TAM and demonstrates that while choosing online study rooms, users weigh both the instrumental benefits offered by such platforms and the emotional rewards experienced through flow. It is worth noting that the direct influence of perceived usefulness on intention to use is not significant, which diverges from traditional TAM hypotheses. This result suggests that in the context of online study rooms, users may prioritize the enjoyment brought by using such platforms over mere instrumental benefits. 5.2 Extending TAM with External Variables This research unveils the significant positive influences of external variables (i.e., learning convenience, interactivity, incentive and constraint mechanism) on perceived usefulness and perceived ease of use, which coincides with the research results of Lisana( 2023) and Qashou( 2021)and suggests that these external variables can significantly improve user acceptance of and intention to use online learning platforms. Different from established TAM studies, the direct influence of perceived usefulness on intention to use is not significant in our research, which contrasts with previous emphasis on its central role in prior research (Davis, 1989 ).Through our analysis, it is posited that this shift may stem from the widespread adoption of technology and users’growing adaptability. With the popularization of Internet technologies, contemporary users, especially the young generation, have become adept at navigating complex multitasking interfaces, which may render them less sensitive to perceived ease of use. Moreover, the objectives of using online study rooms among users exhibit considerable diversification. Apart from improving learning efficiency, some users emphasize the emotional support and the sense of belonging derived from interaction and socializing. This diversification can lead to a corresponding variation in evaluation criteria, which can diminish the direct influence of perceived usefulness. According to the Self-Determination Theory, in emotionally driven learning contexts, user demands for autonomy and a sense of control may outweigh the pursuit of instrumental benefits. Therefore, these users are more inclined to favor platforms that can satisfy their emotional needs instead of solely seeking those that provide instrumental benefits. 5.3 Analysis of the Moderating Effects of Gender and Habituation This research further discusses the moderating effects of GEN and HAB on the relationships between PEOU, PU, FE, and IU. The discovery of the moderating effects enhances our understanding of the mechanisms underlying users’ behavioral intentions and uncovers the significant role of individual differences in decision-making processes. 5.3.1 The Moderating Effect of Gender (GEN) Our research results exhibit that gender has a significant negative moderating effect on the relationship between PU and UI (β= -0.108, p = 0.026). Specifically, the positive influence of PU on UI is weaker among male users compared to that among female users. This finding indicates that gender differences can influence the behavioral intention to use online study rooms. Previous literature has also revealed that male users tend to prioritize instrumental benefits and functional values of technologies while female users place greater emphasis on emotional experiences and social interactions (Eagly & Wood, 1999). Consequently, in the context of online study rooms, female users’ intention to use may be enhanced more through flow experiences and emotional rewards, whereas male users may prioritize instrumental benefits. This finding offers new evidence for the influence of gender differences on user behavior and suggests that product designers and marketers should consider gender-specific strategies to better cater to diverse user needs. 5.3.2 The Moderating Effect of Habituation (HAB) Habituation has a significant negative moderating effect on the relationship between PEOU and UI (β= -0.115, p = 0.013), which indicates that as users’ level of habituation level increases, PEOU’s positive influence on UI diminishes. This result demonstrates that habituation can weaken users’ sensitivity to PEOU. According to (Verplanken & Orbell, 2022 ), habituation is characterized as an automated behavioral mode. Once developed, decision-making processes could be more driven by habituation rather than through rational evaluation. Within the context of online study rooms, users may develop usage habits over time and their attention to PEOU can be reduced. This finding highlights the critical role of habituation in decision-making processes and suggests that product designers can improve user satisfaction and intention to use by designing functions that suit users’ habituated behaviors. 6. Contribution&Implication 6.1 Theoretical Contribution 6.1.1 Integration of TAM and Flow Theory This research establishes a cross-theory framework by integrating TAM and the flow theory to elucidate the behavioral intention to use online study rooms. This framework integrates emotional experiences (such as flow experience) with rational cognitive processes, which addresses gaps in extant literature. Traditional TAM studies have predominantly focused on the cognitive dimension of perceived usefulness and perceived ease of use. By introducing the flow experience as an emotional driver, this study unveils a more comprehensive perspective for understanding user behavior in online learning environments. This innovation offers a novel analytical tool for online learning scenarios that involve emotional and cognitive interactions. 6.1.2 Expand the external variable of TAM This research validates the moderating effects of external variables (i.e., learning convenience, interactivity, and the incentive and constraint mechanism) in TAM, which expands the applicability of TAM. By introducing the socializing features and the behavioral supervision mechanism, this research reveals how these external variables influence user behaviors through dual pathways of emotion and cognition. This finding aligns with the research results of (Cheng & Jiang, 2020 ), which highlights the enhancement of concentration via real-time interactions. Furthermore, this study offers new theoretical insights for the application of TAM in the domain of online education. 6.1.3 Revelation of the Moderating Effects of Gender and Habituation This research broadens the theoretical scope of TAM by investigating how gender and habitual behaviors moderate the technology acceptance trajectory of engaging with online learning platforms. First, the incorporation of gender and habituation offers a more nuanced framework for understanding the mechanisms underlying behavioral intention formation and unveils the essential role of individual differences in decision-making processes. Second, the identification of these moderators provides new theoretical support for the application of TAM in online learning scenarios. It is highlighted that user behaviors are not only influenced by instrumental benefits and emotional rewards but also influenced by individual differences, such as gender and habituation. By revealing these moderating effects, this research not only enriches the theoretical connotation of TAM but also offers a new theoretical lens and analytical framework for studying user behaviors in the context of online learning environments. 6.2 Practical Implications 6.2.1 Optimization of Online Study Room Design In the design of online study rooms, platform operators are encouraged to optimize functionalities in response to gender differences. For female users, more interest-based learning groups can be established for them to socialize and enhance emotional exchanges and mutual support; while for male users, the emphasis should be placed on enhancing learning efficiency through the provision of tools such as intelligent learning plan generators and learning data analysis. For the habituation part, it is essential to leverage users’ historical data to provide personalized study paths. In this way, users can better adapt to the platforms and improve their learning outcomes. At the same time, the interactive features should be continuously optimized–such as improving bullet comment rules, real-time Q&A mechanisms, and users’ flow experience in learning. 6.2.2 Improvement of Promotional Strategies to Boost the Intention to Use Our research results offer valuable insights for online learning platforms aiming to develop more effective promotional strategies. For users who prioritize emotional experiences, platforms should highlight the interactive features and social atmosphere; for users who seek to improve learning efficiency, a focus on practical functionalities that facilitate efficient learning should be emphasized; for novice users, such platforms should shed light on the ease of use, basic functionalities, and advantages; while for experienced users, emphasis should be placed on the advanced features and personalized services. Moreover, online learning activities, such as check-in challenges and group competitions, can be held to attract users of different types and enhance user stickiness and frequency of use. Furthermore, differentiated features and services tailored to the distinct needs of users of different genders should be provided. Meanwhile, it is also recommended to encourage users to cultivate positive learning habits to improve their reliance on and loyalty to the platform. 6.2.3 Offering Analysis Results for Online Education The integrated model in our study serves as a systematic instrument for user behavior analysis in online education. By quantifying the influences of learning convenience, interactivity, and the incentive and constraint mechanism on user behavior, online learning platforms can identify user demands more accurately, optimize their function designs accordingly, and formulate corresponding data-driven operational strategies. Diversified online learning scenarios applicable to multi-task interfaces and social interaction in this research offer scientific evidence for the application of educational technologies. 7. Limitations and further research This research integrates TAM and the flow theory to examine the driving forces of users’ behavioral intention to use online study rooms. Despite its contributions, there exist some limitations. First, the research data are predominantly from China, which may impose cultural limitations. Future research can further explore the behavioral differences among users of online study rooms against different cultural backgrounds, thereby offering more robust theoretical support for the internationalization of such platforms. Second, this study relies on cross-sectional data and views the flow experience as a static outcome, which limits the ability to capture the dynamic change of flow thresholds. To this end, future research can conduct multi-modal data analyses, such as integrating eye-tracking and log data, to conceptualize flow experience as a dynamic variable to understand user behavior and psychological changes. Declarations Author Contribution Conceptualization: Wang Jun; Methodology:Jun Wang ; Formal analysis and investigation: Shang-jie Yuan ; Writing - original draft preparation: Jun Wang ; Writing - review and editing:Jun Wang ; Supervision: Zhong Zheng , Ji-ping Zhang ，Qian-ting Zhang . Data Availability The datasets generated and/or analysed during the current study are available in the Mendeley Data repository. The datasets can be accessed using the following link: [https://doi.org/10.17632/gcpptfds6y.1 Funding Sources This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Data Availability Statement The datasets generated and/or analysed during the current study are available in the Mendeley Data repository, https://doi.org/10.17632/gcpptfds6y.1. Ethics Statement All methods were carried out in accordance with relevant guidelines and regulations. The experimental protocols were approved by the Seal of Ethics Committee at Hunan Institute of Science and Technology. Informed consent was obtained from all subjects and/or their legal guardian(s). References Afacan Adanır, G. & Muhametjanova, G. University students’ acceptance of mobile learning: A comparative study in Turkey and Kyrgyzstan. Educ. Inform. Technol. 26 (5), 6163–6181. https://doi.org/10.1007/s10639-021-10620-1 (2021). Agarwal, R. & Karahanna, E. Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Q. 24 (4), 665. https://doi.org/10.2307/3250951 (2000). Al-Adwan, A. S. et al. Extending the technology acceptance model (TAM) to predict university students’ intentions to use metaverse-based learning platforms. Educ. Inform. Technol. 28 (11), 15381–15413. https://doi.org/10.1007/s10639-023-11816-3 (2023). Alturki, U. & Aldraiweesh, A. An empirical investigation into students’ actual use of MOOCs in Saudi Arabia higher education. Sustainability 15 (8), 6918. https://doi.org/10.3390/su15086918 (2023). Arteaga Sánchez, R., Cortijo, V. & Javed, U. Students’ perceptions of facebook for academic purposes. Comput. Educ. 70 , 138–149. https://doi.org/10.1016/j.compedu.2013.08.012 (2014). Byrne, B. M. & Van De Vijver, F. J. R. Testing for measurement and structural equivalence in large-scale cross-cultural studies: Addressing the issue of nonequivalence. Int. J. Test. 10 (2), 107–132. https://doi.org/10.1080/15305051003637306 (2010). Cheng, Y. & Jiang, H. How do AI-driven chatbots impact user experience? Examining gratifications, perceived privacy risk, satisfaction, loyalty, and continued use. J. Broadcast. Electron. Media . 64 (4), 592–614. https://doi.org/10.1080/08838151.2020.1834296 (2020). Cheung, C. M. K. & Lee, M. K. O. Antecedents and consequences of user satisfaction with an e-learning portal. Int. J. Digit. Soc. 2 (1), 373–380. https://doi.org/10.20533/ijds.2040.2570.2011.0045 (2011). Chou, S. & Liu, C. Learning effectiveness in a web-based virtual learning environment: A learner control perspective. J. Comput. Assist. Learn. 21 (1), 65–76. https://doi.org/10.1111/j.1365-2729.2005.00114.x (2005). Collis, B. & Moonen, J. Flexibility in Higher Education: Revisiting Expectations. Comunicar 19 (37), 15–25. https://doi.org/10.3916/C37-2011-02-01 (2011). Csikszentmihalyi, M. Play and intrinsic rewards. 收入 M. Csikszentmihalyi, Flow and the Foundations of Positive Psychology (135–153). Springer Netherlands. (2014). https://doi.org/10.1007/978-94-017-9088-8_10 Davis, F. D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13 (3), 319. https://doi.org/10.2307/249008 (1989). Eagly, A. H. & Wood, W. The origins of sex differences in human behavior . (2016). Erhel, S. & Jamet, E. Improving instructions in educational computer games: Exploring the relations between goal specificity, flow experience and learning outcomes. Comput. Hum. Behav. 91 , 106–114. https://doi.org/10.1016/j.chb.2018.09.020 (2019). Fathali, S. & Okada, T. Technology acceptance model in technology-enhanced OCLL contexts: A self-determination theory approach. Australasian J. Educational Technol. 34 (4). https://doi.org/10.14742/ajet.3629 (2018). Guo, C. & Zhang, X. The impact of AR online shopping experience on customer purchase intention: An empirical study based on the TAM model. PLOS One . 19 (8), e0309468. https://doi.org/10.1371/journal.pone.0309468 (2024). Hoffman, D. L. & Novak, T. P. Marketing in hypermedia computer-mediated environments: Conceptual foundations . (1996). Hoffman, D. L. & Novak, T. P. Flow online: Lessons learned and future prospects. J. Interact. Mark. 23 (1), 23–34. https://doi.org/10.1016/j.intmar.2008.10.003 (2009). Hsu, C., Chang, K. & Chen, M. Flow experience and internet shopping behavior: Investigating the moderating effect of consumer characteristics. Syst. Res. Behav. Sci. 29 (3), 317–332. https://doi.org/10.1002/sres.1101 (2012). Hu, L. & Bentler, P. M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equation Modeling-a Multidisciplinary J. 6 (1), 1–55. https://doi.org/10.1080/10705519909540118 (1999). Hussein, L. A. & Hilmi, M. F. The influence of convenience on the usage of learning management system. Electron. J. E-Learning . 19 (6), 504–515. https://doi.org/10.34190/ejel.19.6.2493 (2021). Hyun, H., Thavisay, T. & Lee, S. H. Enhancing the role of flow experience in social media usage and its impact on shopping. J. Retailing Consumer Serv. 65 , 102492. https://doi.org/10.1016/j.jretconser.2021.102492 (2022). Kamoyo, M., Masamha, T. & Chikazhe, L. Remote e-learning model in the post COVID-19 era; building a resilient higher education strategy. Cogent Educ. 12 (1), 2498854. https://doi.org/10.1080/2331186X.2025.2498854 (2025). Khaldi, A., Bouzidi, R. & Nader, F. Gamification of e-learning in higher education: A systematic literature review. Smart Learn. Environ. 10 (1), 10. https://doi.org/10.1186/s40561-023-00227-z (2023). Kim, M. How can I Be as attractive as a fitness YouTuber in the era of COVID-19? The impact of digital attributes on flow experience, satisfaction, and behavioral intention. J. Retailing Consumer Serv. 64 , 102778. https://doi.org/10.1016/j.jretconser.2021.102778 (2022). Kim, S., Choi, M. J. & Choi, J. S. Empirical study on the factors affecting individuals’ switching intention to augmented/virtual reality content services based on push-pull-mooring theory. Information 11 (1), 25. https://doi.org/10.3390/info11010025 (2019). Klingenberg, S., Bosse, R., Mayer, R. E. & Makransky, G. Does embodiment in virtual reality boost learning transfer? Testing an immersion-interactivity framework. Educational Psychol. Rev. 36 (4), 116. https://doi.org/10.1007/s10648-024-09956-0 (2024). Kluger, A. N. & DeNisi, A. The effects of feedback interventions on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychol. Bull. 119 (2), 254–284. https://doi.org/10.1037/0033-2909.119.2.254 (1996). Kowalczuk, P. & Adler, J. Siepmann (Née Scheiben), C., Cognitive, affective, and behavioral consumer responses to augmented reality in e-commerce: A comparative study. Journal of Business Research , 124 , 357–373. (2021). https://doi.org/10.1016/j.jbusres.2020.10.050 Lee, M. C. Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Comput. Educ. 54 (2), 506–516. https://doi.org/10.1016/j.compedu.2009.09.002 (2010). Lim, W. M. Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The Int. J. Manage. Education (2023). Lisana, L. Factors affecting university students switching intention to mobile learning: A push-pull-mooring theory perspective. Educ. Inform. Technol. 28 (5), 5341–5361. https://doi.org/10.1007/s10639-022-11410-z (2023). Liu, P., Zhang, Y. & Liu, D. Flow experience in foreign language writing: Its effect on students’ writing process and writing performance. Front. Psychol. 13 , 952044. https://doi.org/10.3389/fpsyg.2022.952044 (2022). MacCallum, R. C. & Hong, S. Power analysis in covariance structure modeling using GFI and AGFI. Multivar. Behav. Res. 32 (2), 193–210. https://doi.org/10.1207/s15327906mbr3202_5 (1997). Maydeu-Olivares, A. Assessing the size of model misfit in structural equation models. Psychometrika 82 (3), 533–558. https://doi.org/10.1007/s11336-016-9552-7 (2017). McIntosh, C. N. Rethinking fit assessment in structural equation modelling: A commentary and elaboration on barrett (2007). Personality and Individual Differences , 42 (5), 859–867. (2007). https://doi.org/10.1016/j.paid.2006.09.020 Motaghian, H., Hassanzadeh, A. & Moghadam, D. K. Factors affecting university instructors’ adoption of web-based learning systems: Case study of Iran. Comput. Educ. 61 , 158–167. https://doi.org/10.1016/j.compedu.2012.09.016 (2013). Nguyen, T. T. et al. Fintech literacy and digital entrepreneurial intention: Mediator and moderator effect. Int. J. Inform. Manage. Data Insights . 4 (1), 100222. https://doi.org/10.1016/j.jjimei.2024.100222 (2024). Ozkara, B. Y., Ozmen, M. & Kim, J. W. Examining the effect of flow experience on online purchase: A novel approach to the flow theory based on hedonic and utilitarian value. J. Retailing Consumer Serv. 37 , 119–131. https://doi.org/10.1016/j.jretconser.2017.04.001 (2017). Pramana, E. Determinants of the adoption of mobile learning systems among university students in Indonesia. J. Inform. Technol. Education: Res. 17 , 365–398. https://doi.org/10.28945/4119 (2018). Qashou, A. Influencing factors in M-learning adoption in higher education. Educ. Inform. Technol. 26 (2), 1755–1785. https://doi.org/10.1007/s10639-020-10323-z (2021). Roy Dholakia, R. & Zhao, M. Retail web site interactivity: How does it influence customer satisfaction and behavioral intentions? Int. J. Retail Distribution Manage. 37 (10), 821–838. https://doi.org/10.1108/09590550910988011 (2009). Roy, S. K., Singh, G., Sadeque, S., Harrigan, P. & Coussement, K. Customer engagement with digitalized interactive platforms in retailing. J. Bus. Res. 164 , 114001. https://doi.org/10.1016/j.jbusres.2023.114001 (2023). Ryan, R. M. Meta-analytic findings within self-determination theory 2 . (2022). Ryan, R. M. & Deci, E. L. Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemp. Educ. Psychol. 25 (1), 54–67. https://doi.org/10.1006/ceps.1999.1020 (2000). Sage, K., Jackson, S., Fox, E. & Mauer, L. The virtual COVID-19 classroom: Surveying outcomes, individual differences, and technology use in college students. Smart Learn. Environ. 8 (1), 27. https://doi.org/10.1186/s40561-021-00174-7 (2021). Saroia, A. I. & Gao, S. Investigating university students’ intention to use mobile learning management systems in Sweden. Innovations Educ. Teach. Int. 56 (5), 569–580. https://doi.org/10.1080/14703297.2018.1557068 (2019). She, L., Ma, L., Jan, A., Nia, S., Rahmatpour, P. & H., & Online learning satisfaction during COVID-19 pandemic among chinese university students: The serial mediation model. Front. Psychol. 12 , 743936. https://doi.org/10.3389/fpsyg.2021.743936 (2021). Song, J. H. & Zinkhan, G. M. Determinants of perceived web site interactivity. J. Mark. 72 (2), 99–113. https://doi.org/10.1509/jmkg.72.2.99 (2008). Tang, Y. M. et al. Comparative analysis of student’s live online learning readiness during the coronavirus (COVID-19) pandemic in the higher education sector. Comput. Educ. 168 , 104211. https://doi.org/10.1016/j.compedu.2021.104211 (2021). Türker, C., Altay, B. C. & Okumuş, A. Understanding user acceptance of QR code mobile payment systems in Turkey: An extended TAM. Technol. Forecast. Soc. Chang. 184 , 121968. https://doi.org/10.1016/j.techfore.2022.121968 (2022). Venkatesh, M., Davis & Davis, & User acceptance of information technology: Toward a unified view. MIS Q. 27 (3), 425. https://doi.org/10.2307/30036540 (2003). Venkatesh, V. Adoption and use of AI tools: A research agenda grounded in UTAUT. Ann. Oper. Res. 308 (1–2), 641–652. https://doi.org/10.1007/s10479-020-03918-9 (2022). Verplanken, B. & Orbell, S. Attitudes, habits, and behavior change. Ann. Rev. Psychol. 73 (1), 327–352. https://doi.org/10.1146/annurev-psych-020821-011744 (2022). Wang, H. et al. Framework effect and achievement motivation on college students’ online learning intention–based on technology acceptance model (TAM) and theory of planned behaviour (TPB) model. Educ. Inform. Technol. https://doi.org/10.1007/s10639-024-13254-1 (2024). Wang, Y., Dong, C. & Zhang, X. Improving MOOC learning performance in China: An analysis of factors from the TAM and TPB. Comput. Appl. Eng. Educ. 28 (6), 1421–1433. https://doi.org/10.1002/cae.22310 (2020). Wiyono, B. B., Rasyad, A. & Maisyaroh The Effect of Collaborative Supervision Approaches and Collegial Supervision Techniques on Teacher Intensity Using Performance-Based Learning. Sage Open. 11 (2), 21582440211013779. https://doi.org/10.1177/21582440211013779 (2021). Xu, A. et al. A study of young Chinese intentions to purchase online paid knowledge: An extended technological acceptance model. Front. Psychol. 12 , 695600. https://doi.org/10.3389/fpsyg.2021.695600 (2021). Zhang, Q., Ariffin, S. K., Richardson, C. & Wang, Y. Influencing factors of customer loyalty in mobile payment: A consumption value perspective and the role of alternative attractiveness. J. Retailing Consumer Serv. 73 , 103302. https://doi.org/10.1016/j.jretconser.2023.103302 (2023). Zhang, Y. Effect of interactive immediacy on online learning satisfaction of international students in chinese universities: The chain mediating role of learning interest and academic engagement. Acta Psychologica (2024). Additional Declarations No competing interests reported. Supplementary Files QuestionnaireItemsandTheirSourceofAdoption.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 19 Feb, 2026 Editor assigned by journal 17 Feb, 2026 Editor invited by journal 31 Oct, 2025 Submission checks completed at journal 23 Oct, 2025 First submitted to journal 23 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7846720","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":593789641,"identity":"71486d4b-6566-4859-bae0-723e8885623e","order_by":0,"name":"Jun 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Technology","correspondingAuthor":false,"prefix":"","firstName":"Shang-jie","middleName":"","lastName":"Yuan","suffix":""},{"id":593789643,"identity":"6c7163f2-9643-4694-8474-59d9b257b2c6","order_by":2,"name":"Zhong Zheng","email":"","orcid":"","institution":"Hunan Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhong","middleName":"","lastName":"Zheng","suffix":""},{"id":593789644,"identity":"38aa7c82-15dc-4421-aca2-057f3f7d6633","order_by":3,"name":"Ji-ping Zhang","email":"","orcid":"","institution":"Hunan Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ji-ping","middleName":"","lastName":"Zhang","suffix":""},{"id":593789645,"identity":"472d1f2f-670a-4c26-a7be-fbbf9ed24a8a","order_by":4,"name":"Qian-ting Zhang","email":"","orcid":"","institution":"Hunan Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Qian-ting","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-10-13 08:53:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7846720/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7846720/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103239047,"identity":"f0cbdd0f-8af7-4491-951e-0aaa4334e8d6","added_by":"auto","created_at":"2026-02-23 13:44:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":214384,"visible":true,"origin":"","legend":"\u003cp\u003eTheoretical framework.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7846720/v1/62e016cad3924a88b2edccca.png"},{"id":103506300,"identity":"9bf5f553-aa96-48b8-8ba2-d32bb765f097","added_by":"auto","created_at":"2026-02-26 13:34:59","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":71989,"visible":true,"origin":"","legend":"\u003cp\u003eThe simple slope plot of the moderation analysis of GEN and HAB on the relationship between PEOU and IU\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7846720/v1/e2997c85fce57b0178f3fb38.jpg"},{"id":103505497,"identity":"a1837239-aff3-4928-8ffa-ff9428f49671","added_by":"auto","created_at":"2026-02-26 13:31:30","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":57999,"visible":true,"origin":"","legend":"\u003cp\u003eThe moderating effects of GEN and HAB on the relationship between PEOU and IU\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7846720/v1/0a5b05e3cab0e41c246c588b.jpg"},{"id":103239049,"identity":"cb11a5bd-82ed-45bb-ad00-1e9c8540920a","added_by":"auto","created_at":"2026-02-23 13:44:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":61518,"visible":true,"origin":"","legend":"\u003cp\u003eThe moderating effects of GEN and HAB on the relationship between FE and IU\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7846720/v1/80f5b3df3159393038fc2f97.png"},{"id":103239050,"identity":"4d3ce5d7-7a4c-4f48-9a53-1f77c0e597d7","added_by":"auto","created_at":"2026-02-23 13:44:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2650267,"visible":true,"origin":"","legend":"\u003cp\u003eHypotheses test results.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7846720/v1/7c3f52b780fa960af2448c59.png"},{"id":103509622,"identity":"624192ba-e419-48dc-bafd-12d3e254e7cc","added_by":"auto","created_at":"2026-02-26 14:00:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2281424,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7846720/v1/a2a870ce-585d-4b5c-948c-8b4051f7ac8e.pdf"},{"id":103239052,"identity":"b1988d4e-4dea-4ac8-a7d4-1a23d051de42","added_by":"auto","created_at":"2026-02-23 13:44:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17978,"visible":true,"origin":"","legend":"","description":"","filename":"QuestionnaireItemsandTheirSourceofAdoption.docx","url":"https://assets-eu.researchsquare.com/files/rs-7846720/v1/cc7b500df8d5e615b645c7ed.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"From Physical to Virtual: Understanding User Intention to Use Online Study Rooms Through Technology Acceptance Model (TAM) and Flow Theory","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWith the increasing prevalence of the Internet and the growing demand for flexible learning, online study rooms, a novel digital learning environment, are mushrooming quickly. This innovative learning environment highlights the flexibility and efficiency of independent learning, where users can concentrate on learning tasks on online platforms, such as Zoom or YouTube, and users can engage in discussions and share insights with other users regarding their learning progress(Wang et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSince the emergence of online live streaming \u0026ldquo;study with me\u0026rdquo; on YouTube in 2017, online study rooms have gained popularity quickly on Chinese platforms such as BiliBili and Douyin. Statistics revealed by BiliBili display that in 2018, the number of participants in online learning live streams surged to 18.27\u0026nbsp;million, the cumulative live streams started hit 1.03\u0026nbsp;million, and the total hours dedicated to such live streams exceeded 1.46\u0026nbsp;million hours, positioning this type of live streaming as the predominant genre in terms of streaming hours (BiliBili official statistics, 2018). Since July 2021, the number of participants in \u0026ldquo;Learning Companions\u0026rdquo; live streaming has exceeded 327\u0026nbsp;million, manifesting the massive potential of online study rooms (BiliBili official statistics, 2021).\u003c/p\u003e \u003cp\u003eThe intricate relationship between education and information technology has made online learning an integral part of education reform. Especially during the COVID-19 pandemic, online learning emerged as the principal pedagogical method adopted by numerous educational institutions, which greatly facilitated the alteration of learning methods. Backed by live streaming technologies, online study rooms refer to virtual learning environments accessible via devices such as smartphones and computers, where users can conduct real-time interaction with peers across diverse geographical locations who share a synchronous viewing experience(Y. Zhang, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).This learning mode not only transcends the temporal and spatial limits of traditional learning but also enhances social presence and interactivity, and cultivates a collective learning atmosphere(Lim, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the rapid growth of research on online study rooms in China, particularly in the wake of the COVID-19 pandemic, the predominant methodologies employed in existing studies have been qualitatively oriented, such as participatory observation and in-depth interviews, and there is a lack of quantitative studies.The existing literature on online study rooms can be categorized into three types: first, studies focusing on the operational dynamics and user demographics of online study rooms (Xu et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). second, investigations on user motivations and the intrinsic factors that encourage participation in such online study rooms(Lim, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e),third, studies evaluating the effects of online study rooms on learning outcomes and their efficacy(M. Kim, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).Nevertheless, a research gap persists regarding factors that influence the behavioral intention to use online study rooms, especially in the aspects of technology integration and emotional drivers((Venkatesh, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTechnology Acceptance Model (TAM) serves as a classical theory for understanding user behavior in accepting technologies. Its key variables, perceived usefulness and perceived ease of use, have been widely applied to elucidate users\u0026rsquo;behavioral intentions(Davis, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1989\u003c/span\u003e).In recent years, TAM has been extensively employed in the context of digital learning(Lisana, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) (Afacan Adanır \u0026amp; Muhametjanova, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)However, TAM pays little attention to emotionally driven factors, which makes it inadequate to explain users\u0026rsquo; continuous participation in online learning environments (Venkatesh, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and fails to investigate the moderating effects of individual differences such as gender and habituation. In recent years, the flow theory has garnered attention in studying online learning environments, which unveils the significant influence of emotional factors on user behavior (Hsu et al.,2012).\u003c/p\u003e \u003cp\u003eBy integrating TAM and the flow theory into the theoretical framework, this research aims to systematically elucidate users\u0026rsquo; behavioral intention to use online study rooms. Specifically, this research not only seeks to explore the roles of perceived usefulness and perceived ease of use as rational driving forces of user behaviors but also introduces flow experience as a mediating variable that highlights the critical role of emotional factors in making decisions. Furthermore, this research examines the moderating effects of gender and habituation on user behavior, which addresses the existing gap in the literature concerning emotionally driven factors and the moderating effects of individual differences. Structural equation modeling (SEM) was employed to analyze the 389 valid responses. This research not only enriches the application scenario of TAM but also offers practical insights for the design and optimization of online study rooms (Qashou, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) (Afacan Adanır \u0026amp; Muhametjanova, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).The research results can help platform developers better understand user demands and design features to improve user engagement and satisfaction (Lim, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe structure of this study is organized as follows: the first part is the introduction; the second section constitutes a literature review that examines the current status of research on online study rooms and TAM; the third part outlines the research framework and hypotheses; the fourth part introduces the research methods, including questionnaire design and data collection processes; the fifth part presents the research results; the sixth part discusses research findings and implications; the last part centers around the conclusion, limitations, and future research directions.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Online Study Rooms\u003c/h2\u003e \u003cp\u003eAs a novel learning environment, online study rooms have garnered much attention in recent years. Established by digital technologies, these spaces support coordinated, independent, and immersive learning experiences (Sage et al.,2021).From the perspectives of Situated Learning Theory and Distributed Cognition Theory, online study rooms offer learners cognitive opportunities for embodied cognition by simulating authentic learning scenarios, which can enhance their contextual understanding of knowledge (Chou \u0026amp; Liu, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTake China\u0026rsquo;s BiliBili online study rooms as an example. This platform leverages live streaming technologies to enable individual streamers or groups of users to create a virtual learning environment using their electronic devices, such as smartphones or computers. Learners can showcase diverse learning activities, ranging from note-taking and problem-solving to recitation and typing, via the real-time \u0026ldquo;lives streaming\u0026rdquo; feature. The audience can participate by watching these activities in real time, fostering a collective learning experience where participants can engage with content simultaneously despite geographical dispersion. Features of bullet comments and virtual avatars enable online study rooms to enhance social presence and simulate a co-present experience, which further boosts interaction and exchange among learners. To cultivate a good learning atmosphere, the streamer usually pinpoints clear learning objectives, timelines, and motivational slogans. Users can send bullet comments to document their engagement, encourage each other, and share insights to foster an environment where supervision, self-motivation, and companionship are available.\u003c/p\u003e \u003cp\u003ePrevious studies in this field have mainly focused on the foundational aspects of online study rooms, such as platform stability and user interface design. As research deepens, scholars shift toward examining the relationship between such platforms and user demands, and their influence on learning outcomes. (Liu et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) pointed out that effective emotional support in online teaching environments can offer empathy, understanding, motivation, and encouragement to learners. While prior research has offered valuable insights, most of them are qualitative studies that lack accurate quantitative analysis. Moreover, existing literature fails to delve into how online study rooms, a novel learning medium, trigger and influence intention to use, and the endeavors to investigate the positive influences of emotional factors on intention to use remain insufficient, leading to gaps in explaining user behaviors within this context (Fathali \u0026amp; Okada, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Theoretical Frameworks\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 TAM\u003c/h2\u003e \u003cp\u003eFirst proposed by Davis (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1989\u003c/span\u003e), TAM is an essential theoretical framework for examining user acceptance behaviors toward information technologies. Its application spans multiple fields, including information systems, informatics, and library science. Perceived usefulness and perceived ease of use are two primary variables in TAM used to explain technology acceptance behaviors. Davis(1989)posited that perceived usefulness refers to users\u0026rsquo; cognition of whether a given technology is useful, and perceived ease of use pertains to the perception regarding the simplicity of using a particular information system. Subsequent studies have sought to refine this model by including the influences of external variables on perceived usefulness and perceived ease of use. Notably, Venkatesh (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) extended the TAM2 framework by including factors that influence perceived ease of use.\u003c/p\u003e \u003cp\u003eIn the context of online learning environments, the effectiveness of TAM has been extensively validated. Motaghian (2013)employed a comprehensive model integrating information systems, and psychological and behavioral factors to evaluate teachers\u0026rsquo; intention to adopt a learning system. Their research results show that perceived usefulness and perceived ease of use notably improved teachers\u0026rsquo; behavioral intention to use online study systems, and PU was found to be the most influential factor.Arteaga S\u0026aacute;nchez (2014) investigated students\u0026rsquo; intention to use Facebook to aid learning, and the results showed that perceived usefulness, perceived ease of use, and facilitating conditions have significant positive influences on the adoption of Facebook. To this end, TAM is deemed an effective framework for investigating the adoption and behavioral intention to use online study rooms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Flow Theory\u003c/h2\u003e \u003cp\u003eThe flow theory was initially conceptualized by Csikszentmihalyi (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)which delineates a psychological state when an individual is completely immersed in enjoyable activities and temporarily ignores other stimuli in the surroundings. This state of intense concentration is marked by several key features: the individual\u0026rsquo;s complete concentration, a distorted perception of time, and the intrinsic satisfaction derived from the activity. As Internet technologies advance, the flow theory is widely applied in a host of areas, such as social media usage behaviors (Hyun et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), information technology(S. Kim et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and online gaming(Erhel \u0026amp; Jamet, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), It is particularly valuable in explaining individual behavioral motivations and flow experiences.\u003c/p\u003e \u003cp\u003eAdditionally, the flow theory has been applied in learning contexts that require a higher degree of individualism.Hoffman \u0026amp; Novak(1996),first introduced the flow theory into online environments, and they asserted that flow could boost increased learning, perceived behavioral control, exploratory mindset, and positive subjective experiences. Hoffman \u0026amp; Novak (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) described the flow state as a cognitive state and elucidated the seamless response, interactivity, intrinsic enjoyment, the loss of self-consciousness, and self-reinforcement in online environments.Research by Cheng \u0026amp; Jiang(2020)regarding reading and learning behaviors illuminated that the learning efficiency and outcomes in an immersive environment outperform that of a normal environment.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Research Hypotheses\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Perceived Usefulness and Perceived Ease of Use\u003c/h2\u003e \u003cp\u003eBased on the Theory of Reasoned Action (TRA) proposed by Fishbein and Ajzen, Davis(1989)developed TAM. Currently, TAM has been recognized as the best framework to understand acceptance behaviors associated with information technologies(Venkatesh, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). (Y. Wang et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).Perceived usefulness and perceived ease of use are two critical constructs for elucidating users\u0026rsquo; behavioral intentions. Specifically speaking, perceived usefulness refers to the degree to which users believe that using an application can improve their work or learning outcomes.perceived ease of use refers to the degree to which users believe that using an application or equipment requires few efforts (Alturki \u0026amp; Aldraiweesh, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).For example, in the learning context of the metaverse, perceived ease of use is deemed a crucial determinant of technology acceptance (Al-Adwan, 2023).\u003c/p\u003e \u003cp\u003eIn this research, perceived ease of use is defined as the degree to which users of online study rooms do not encounter complex technological operations or excessive cognitive burdens. To be more specific, a higher level of perceived ease of use correlates positively with the behavioral intention to use online study rooms. Previous research on TAM has substantiated that perceived ease of use serves as a prerequisite for perceived usefulness (Nguyen et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; T\u0026uuml;rker et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).In this study, perceived ease of use can enhance users\u0026rsquo; belief of users\u0026rsquo; belief in the potential benefits of online study rooms, such as improved learning efficiency and heightened concentration. Given the above analysis, we propose the following hypotheses:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH1\u003c/strong\u003e \u003cp\u003ePerceived ease of use of online study rooms positively influences perceived usefulness.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH2\u003c/strong\u003e \u003cp\u003ePerceived ease of use of online study rooms positively influences their intention to use such platforms.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eIn the current research, perceived usefulness is defined as users\u0026rsquo; perception of learning benefits derived from using online study rooms, which is mainly presented in two aspects. First, the improvement of learning efficiency. With real-time discussion and sharing of learning progress, online study rooms allow users to access information and resolve inquiries more efficiently. For example, users can interact with streamers via bullet comments to address questions occurring in the learning process in real time, thus improving their learning outcomes (Q. Zhang et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).Second, the cultivation of learning habits. Through the check-in feature, objective setting, and the incentive and constraint mechanism, users can better establish and maintain beneficial learning habits, which not only enhances users\u0026rsquo; motivations to study but also improves their intention to use by fostering a sense of achievement gained from goal fulfillment.Moreover, higher perceived usefulness is likely to be associated with a greater intention to use online study rooms, as users recognize the tangible benefits of improved concentration, optimized learning strategies, and enhanced learning outcomes (M. Kim, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).To this end, this research hypothesizes that:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH3\u003c/strong\u003e \u003cp\u003ePerceived usefulness of online study rooms positively influences their intention to use such platforms.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Learning Convenience\u003c/h2\u003e \u003cp\u003eLearning convenience is defined as students\u0026rsquo; capacity to engage in learning activities without temporal and spatial constraints(Lisana, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Pramana (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) indicated that the inconvenience of attending physical classrooms is a key factor affecting university students\u0026rsquo; intention to use Mobile Learning platforms. Several researchers have also highlighted the significance of convenience in fostering students\u0026rsquo; willingness to adopt Mobile Learning in higher education institutions (Qashou, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Saroia \u0026amp; Gao, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this research, learning convenience is defined as the ease of access to online study rooms, which are no longer hindered by the temporal and spatial limits of conventional learning environments(Lisana, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).Compatible with various devices, online study rooms allow users to switch seamlessly between mobile phones and computers, which alleviates the cognitive burden of adapting to different technological interfaces. Users can customize their learning content and pace to meet their individual needs, which enhances the flexibility and autonomy of learning(Pramana, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).Streamlined operational procedures contribute to a perception of convenience and further enhance users\u0026rsquo; perceived ease of use. For example, the multi-device compatibility of online study rooms can mitigate the cognitive burden of adapting to different technological interfaces. The flexible learning time options, such as 24-hour live streaming, enable users to engage in learning activities without changing their schedules, which can lower the psychological barrier to using such technology(Saroia \u0026amp; Gao, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).When autonomy is enabled, for example, when users can choose their learning content, they will perceive less external control and pressure and master the technology with greater ease (Ryan \u0026amp; Deci, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) .Based on the above analysis, we propose the following hypothesis:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH4\u003c/strong\u003e \u003cp\u003eLearning convenience positively influences perceived ease of use.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe learning convenience offered by online study rooms improves users\u0026rsquo; recognition of the system\u0026rsquo;s practicability through the optimized allocation of learning resources. Without spatial constraints and the necessity of commuting, online study rooms save users\u0026rsquo; time and energy,and enable users to dedicate more time to achieving their learning objectives, which can lead to better learning outcomes, such as increased concentration duration.Hussein \u0026amp; Hilmi(2021)highlighted the significance of convenience in online learning environments and pointed out that convenience greatly contributes to improving user engagement and satisfaction. Furthermore, the autonomy enabled by customizing learning plans that cater to user demands strengthens users\u0026rsquo; perception of the alignment between system functions and individual objectives, which can enhance their evaluation of usefulness (Collis \u0026amp; Moonen, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).The empirical study conducted by Lee (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) further corroborates that flexible learning procedures (such as the effective use of fragmented time), facilitated by convenience, can significantly improve users\u0026rsquo; recognition of the effectiveness of online learning. Users deem the system as a practical tool for achieving their goals. According to Q. Zhang et al., (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e),convenience significantly influences users\u0026rsquo; perceived usefulness of online educational platforms. Given the above analysis, it is hypothesized that:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH5\u003c/strong\u003e \u003cp\u003eLearning convenience positively influences perceived usefulness.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Interactivity\u003c/h2\u003e \u003cp\u003eInteractivity refers to the extent of two-way communication experienced by users when interacting with the system, content, or other users. Prior research has highlighted the critical impact of interaction quality and frequency on online learning satisfaction (She et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).Burgoon et al., (2000) posit that interactivity can be understood through the qualitative aspects of users\u0026rsquo; experiences during interactions, such as their level of engagement, mutual participation, and personalization. Kamoyo et al., (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) research shows that e-learning platforms with high interactivity can enhance users\u0026rsquo; perceived ease of use and usefulness and foster a positive attitude among students.\u003c/p\u003e \u003cp\u003eIn the context of online study rooms, interactivity manifests through bullet comments, live chats, and the sharing of learning progress. By minimizing operational barriers, interactivity can improve users\u0026rsquo; perceived ease of use of such platforms. Specifically, features like bullet comments and real-time feedback can streamline the interactive process between users and the system, and the ability to engage in discussions with a single click and the auto-synchronization of learning progress diminish users\u0026rsquo; cognitive burden of using such systems(Song \u0026amp; Zinkhan, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Moreover, high-quality information exchange, supported by well-refined bullet commenting rules and prompt responses to inquiries, optimizes the user experience and simplifies the mastery of such systems(Roy Dholakia \u0026amp; Zhao, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).To this end, this research hypothesizes that:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH6\u003c/strong\u003e \u003cp\u003eThe interactivity of online study rooms positively influences perceived ease of use.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eLee (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) demonstrated that human-to-human interaction significantly enhances users' perceived usefulness of ACG (animation, comic, and game) social media sites.In this study, interactivity is posited to improve perceived usefulness by enhancing learning support functions. For example, real-time discussion and coordinated problem-solving efforts enable users to quickly access learning resources and tackle complicated problems, which directly improves learning efficiency. Meanwhile, personalized interactions, such as streamers\u0026rsquo; content adjustments in live streaming based on user demands, can reinforce users\u0026rsquo; recognition of the system\u0026rsquo;s value and their belief that online study rooms are effective tools for achieving their learning objectives. Therefore, this research proposes the following hypothesis:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH7\u003c/strong\u003e \u003cp\u003eThe interactivity of online study rooms positively influences perceived usefulness.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eIn examining the relationship between interactivity and immersion, scholars have posited that interactivity can effectively elicit consumers' sense of immersion within digital environments (Klingenberg et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This is attributed to the fact that engaging with interactive elements significantly enhances psychological engagement among consumers(Roy et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, Kowalczuk et al., (2021) have demonstrated that interactivity not only positively influences immersion but also contributes to user enjoyment of augmented reality (AR) and their intention to reuse it in e-commerce contexts. Based on the above analysis, this research proposes that high levels of interactivity can significantly influence users\u0026rsquo; flow experience. Thus, it is hypothesized that:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH8\u003c/strong\u003e \u003cp\u003eThe interactivity of online study rooms positively influences the flow experience.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Incentive and Constraint Mechanism\u003c/h2\u003e \u003cp\u003eAs a fundamental component of behavioral monitoring mechanisms, the incentive and constraint mechanism functions as an objective-oriented feedback system in educational psychology and information technology. Khaldi et al.,(2023) identified that PBL elements (points, badges, and leaderboards), levels, and feedback are the most frequently adopted gamification elements in e-learning systems within higher education. These elements facilitate social comparisons among learners through leaderboards and drive them to ascertain their positions within peer groups. It is also a competitive mechanism can significantly improve learning engagement.Meanwhile, Kluger \u0026amp; DeNisi (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) posit that instant feedback on learning progress and outcome evaluation can help learners dynamically adjust their strategies and enhance their self-efficacy.\u003c/p\u003e \u003cp\u003eIn online learning scenarios, such as the BiliBili online study rooms, the incentive and constraint mechanism manifests through learning time leaderboards and instant feedback on concentration. Leaderboards can clearly display learning outcomes and assist users in setting specific objectives. The change in rankings indicates individual progress and can foster a sense of competitiveness. Moreover, dynamic data also help users to refine their learning strategies and improve their efficiency.\u003c/p\u003e \u003cp\u003eThe functions of leaderboards and concentration supervision of online study rooms can streamline the interactive processes between users and systems. Rapid rank checks and instant feedback on learning progress can alleviate users\u0026rsquo; psychological burden of using technologies. Besides, the instant feedback mechanism can optimize user experience by offering real-time evaluations of learning progress and outcomes. To this end, the system is suitable for analysis through the lenses of TAM and the flow theory. As the external variable, the incentive and constraint mechanism can influence users\u0026rsquo; perceived ease of use and usefulness of the system and their flow experience, thereby indirectly promoting their intention to use such systems. To this end, this research hypothesizes that:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH9\u003c/strong\u003e \u003cp\u003eThe incentive and constraint mechanism positively influences users\u0026rsquo; perceived ease of use of online study rooms.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eWiyono(2021)discovered that the incentive and constraint mechanism has a significant influence on performance-based learning. In the scenario of online study rooms, this mechanism can improve perceived usefulness by providing robust learning support. For example, learning time leaderboards and concentration supervision enable users to quickly access learning resources and address their problems, thereby directly improving their learning efficiency. By delivering learning support and personalized services, users\u0026rsquo; recognition of the system\u0026rsquo;s value will be significantly increased, which can further improve perceived usefulness. In essence, if the constraint mechanism successfully enhances learning motivation and performance, the goal of supervision is realized. To this end, this research hypothesizes that:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH10\u003c/strong\u003e \u003cp\u003eThe incentive and constraint mechanism positively influences users\u0026rsquo; perceived usefulness of online study rooms.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eAccording to the flow theory proposed byCsikszentmihalyi (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), in this research, supervision and incentives create a sense of belonging and enjoyment by stimulating emotional engagement (such as bullet comment encouragement and virtual avatar coordination) and offering continuous external stimulus (such as learning progress leaderboard and bullet comment check-in). These elements promote prolonged concentration and enhance the flow state. Real-time interactions and objective-oriented feedback mechanisms can significantly improve the flow experience. Therefore, it is hypothesized that:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH11\u003c/strong\u003e \u003cp\u003eThe incentive and constraint mechanism positively influences users\u0026rsquo; flow experience in online study rooms.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.3.5 Flow Experience\u003c/h2\u003e \u003cp\u003eThe concept of flow experience was first proposed by Csikszentmihalyi(1975), which pertains to the mental state of concentration and a distorted sense of time perception when an individual is deeply engaged in an activity. In information technology,Hoffman \u0026amp; Novak (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) introduced this concept to online environments and highlighted that flow experience can enhance users\u0026rsquo; sense of control and exploratory behavior via technological features such as interactivity and remote display capabilities. Extant research has indicated that flow experience can significantly improve the intention to use (Ozkaraet al.,2017), especially in the context of online learning, where flow experience can enhance the enjoyment of learning (Guo \u0026amp; Zhang, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), boost cognitive absorption and ultimately lead to behavioral intentions(Agarwal \u0026amp; Karahanna, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, online study rooms leverage virtual simulation technologies to simulate physical learning environments, where users can enjoy a quiet learning atmosphere without interruption. Therefore, users\u0026rsquo; concentration and learning motivation can be improved. Besides, the design of online study rooms can harness customizable settings and interactive functions (such as real-time discussions and the sharing of learning progress) to further improve users\u0026rsquo; flow state. Given the above analysis, this research proposes the following hypothesis:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH12\u003c/strong\u003e \u003cp\u003eThe flow experience of online study rooms positively influences the usage intention.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.3.6 The Moderating Effects of Gender and Habits\u003c/h2\u003e \u003cp\u003eGender differences in this research refer to the different preferences and behavioral patterns exhibited by men and women when engaging with e-learning platforms. Previous research has indicated that men and women could have different behavioral patterns and psychological mechanisms when accepting and using technologies. Female users tend to prioritize interactivity and user experience, while male users are inclined to emphasize practicability and functional design(Hoffman \u0026amp; Novak, 2021).These gender differences could moderate the influence of perceived usefulness and perceived ease of use on the intention to use. Specifically speaking, female users may tend to enhance learning experiences through interactive features, such as bullet comments and real-time discussions, while male users may be more concerned about whether the platform can directly improve learning efficiency and outcomes. Consequently, it is posited that gender can moderate the influences of perceived ease of use and perceived usefulness on intention to use, and we propose the following hypothesis:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH3a\u003c/strong\u003e \u003cp\u003eGender moderates the relationship between perceived ease of use and intention to use.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH3b\u003c/strong\u003e \u003cp\u003eGender moderates the relationship between perceived usefulness and intention to use.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eHabituation refers to the automatic influence of users\u0026rsquo; prior experiences on their current behaviors(Ryan, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which can influence user acceptance and the intention to use a new platform. For example, users familiar with similar learning tools as online study rooms can adapt to the functions more quickly, thereby increasing their intention to use such platforms. Habituation can also influence perceived usefulness and perceived ease of use by lowering the cognitive barriers of technology acceptance and enhancing user recognition of the system\u0026rsquo;s value. Specifically, users acclimated to highly interactive learning platforms may find it easier to accept the interactive features of online study rooms, which improves their perceived ease of use of such platforms. Meanwhile, users accustomed to efficient learning tools may show more recognition for the functional design of online study rooms, which improves perceived usefulness. Furthermore, habituation can directly influence the intention to use as individuals exhibiting higher levels of habituation are more likely to view using online study rooms as a natural behavioral option. To this end, this research hypothesizes that:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH4a\u003c/strong\u003e \u003cp\u003eHabituation negatively moderates the relationship between perceived ease of use and intention to use.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH4b\u003c/strong\u003e \u003cp\u003eHabituation negatively moderates the relationship between perceived usefulness and intention to use.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eFlow experience refers to the state of complete immersion and enjoyment of using a technology. Though flow experience itself exerts a direct influence on the intention to use, gender and habituation can also moderate the relationship between flow experience and the intention to use. Therefore, it is hypothesized that:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH12a\u003c/strong\u003e \u003cp\u003eGender moderates the relationship between flow experience and the intention to use.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH12b\u003c/strong\u003e \u003cp\u003eHabituation moderates the relationship between flow experience and the intention to use.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Methods","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Participants\u003c/h2\u003e \u003cp\u003eWenjuanxing (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wjx.cn/\u003c/span\u003e\u003cspan address=\"https://www.wjx.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), an online questionnaire platform, was employed to collect data from February 4 to 21, 2025. The questionnaire was distributed to potential participants through WeChat and QQ, two widely used social platforms in China with 1.327\u0026nbsp;billion and 558\u0026nbsp;million monthly active users (MAUs), respectively. Participation in the survey was voluntary and anonymous, ensuring the confidentiality and integrity of the responses. A total of 408 questionnaires were completed, with 19 invalid responses excluded due to incomplete or inconsistent answers. Consequently, 389 valid responses were utilized for analysis, representing an effective response rate of 95.3%. In terms of gender, female (N\u0026thinsp;=\u0026thinsp;272, 69.92%), male (N\u0026thinsp;=\u0026thinsp;117, 30.08% ).\u003c/p\u003e \u003cp\u003eThe demographic analysis of the participants reveals that the predominant representation of individuals aged between 18\u0026ndash;25 years old, accounting for 75.06% of the sample (N\u0026thinsp;=\u0026thinsp;292), followed by the age groups of 26\u0026ndash;35 (20.31%, N\u0026thinsp;=\u0026thinsp;79), 36\u0026ndash;45 (3.86%, N\u0026thinsp;=\u0026thinsp;15), and 46\u0026ndash;55 (0.77%, N\u0026thinsp;=\u0026thinsp;3). There is no participant aged above 56. In terms of educational background, a significant majority of participants possess junior college or bachelor\u0026rsquo;s degrees (70.95%, N\u0026thinsp;=\u0026thinsp;276), followed by participants with master\u0026rsquo;s degrees or higher (19.79%. N\u0026thinsp;=\u0026thinsp;77). 3.34% of the participants have a high school degree or below (N\u0026thinsp;=\u0026thinsp;13). These findings coincide with the research findings of Lu (2022), which suggest that online learning platforms mainly attract individuals with educational credentials. Participants engage with these platforms for various purposes, including exam preparations (76.86%, N\u0026thinsp;=\u0026thinsp;299), skill improvement (64.01%, N\u0026thinsp;=\u0026thinsp;249), personal growth (50.64%, N\u0026thinsp;=\u0026thinsp;197), and daily learning (45.50%, N\u0026thinsp;=\u0026thinsp;177). Additional reported motivations include the pursuit of topics of interest (31.10%, N\u0026thinsp;=\u0026thinsp;121), language learning (16.97%, N\u0026thinsp;=\u0026thinsp;66), and vocational training (9.25%, N\u0026thinsp;=\u0026thinsp;36). The emphasis on exam preparation and skill improvement is consistent with the educational background distribution, which reflects a pronounced inclination toward self-learning among well-educated individuals. Overall, the demographic information and usage patterns of the collected samples correspond closely to the typical characteristics of e-learning users, which means the research results are representative.Participants\u0026rsquo; detailed demographic information is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic information of the participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency (n\u0026thinsp;=\u0026thinsp;389)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026thinsp;\u0026minus;\u0026thinsp;25 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u0026thinsp;\u0026minus;\u0026thinsp;35 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u0026thinsp;\u0026minus;\u0026thinsp;45 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u0026thinsp;\u0026minus;\u0026thinsp;55 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOver 56 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational Background\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior middle school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior college and bachelor\u0026rsquo;s degrees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaster\u0026rsquo;s degrees or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePurpose of Use (Multiple Choices)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExam preparation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkills improvement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonal growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHobby learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLanguage learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVocational training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Instrument development\u003c/h2\u003e \u003cp\u003eThe questionnaire design of this study rigorously adhered to established academic research standards. A five-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree), was employed to quantify the subjective experiences of respondents. The questionnaire is structured into four sections: Technology Acceptance Model (TAM), Flow Experience (FE), Intention to use (IU), and Habituation (HAB). All items in the questionnaire were adapted from previously validated scales to ensure the scientific rigor and validity of the measurement tools. Specifically, the TAM section was used to measure perceived usefulness (PU) and perceived ease of use (PEOU), with learning convenience (LC), interactivity (INT), and the incentive and constraint mechanism (ICM) as external variables. All items in the TAM section were adapted from previous studies(Davis, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1989\u003c/span\u003e;Venkatesh et al.,2003;Cheung \u0026amp; Lee, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).In the FE section, adaptations from Erhel \u0026amp; Jamet (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) were utilized to evaluate participants\u0026rsquo; flow experience when using the online learning platform. Both the IU and HAB sections were adapted from Saroia \u0026amp; Gao (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Tang et al.,(2021), The IU section aimed to evaluate participants\u0026rsquo; intention to use such platforms, and the HAB section targeted at evaluating participants' habituation to these platforms. Moreover, the questionnaire includes a demographic section for collecting data on participants\u0026rsquo; gender, age, educational background, and usage purposes. Prior to the formal distribution, the questionnaire was pre-tested by experts and researchers, and the wording and structure of the questionnaire were refined to ensure clarity and relevance. The final questionnaire has passed the reliability and validity analyses, confirming its overall reliability and validity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Data analysis\u003c/h2\u003e \u003cp\u003eData were analyzed using SPSS 27.0 and AMOS 24.0. First, a confirmatory factor analysis (CFA) was conducted to evaluate the validity of the latent variables. Second, Cronbach\u0026rsquo;s alpha (α) was calculated to assess the internal consistency of all subscales in the instrument. Third, structural equation modeling (SEM) was performed to examine the relationships among the eight latent variables. According to Byrne (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), the recommended sample size for conducting CFA is 5 to 10 times the total number of items in the survey scales. In this study, there are 24 items in total, and we used 389 samples for both CFA and SEM analyses.\u003c/p\u003e \u003cp\u003eIn the analysis process, this study employed the PROCESS macro in SPSS 27.0 and set the number of bootstrap resamples to 5,000 to ensure the robustness and reliability of the results and to minimize the impact of sampling error on the analysis outcomes. The confidence interval was set at 95% to assess the significance of the moderation effects. PROCESS Model 1 was used to systematically examine the moderating roles of gender and habituation. Through these analytical procedures and parameter settings, the study can comprehensively and systematically evaluate the moderating effects of gender and habituation on the relationships between perceived ease of use, perceived usefulness, flow experience, and intention to use.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eIn this study, the factor loadings of all 24 items ranged from 0.700 to 0.905. Cronbach\u0026rsquo;s alpha coefficients for the subscales of LC, INT, ICM, PU, PEOU, FE, HAB, and IU were 0.717, 0.789, 0.700, 0.765, 0.778, 0.709, 0.813, and 0.905, respectively. These values indicate good internal consistency across the subscales. The factor loadings for each item and the Cronbach\u0026rsquo;s alpha values for each subscale are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of construct validity and reliability analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatent Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeasurement Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd.Dev\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor Loadings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAlpha\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.672\u003c/p\u003e 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align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e 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align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEOU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEOU2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEOU3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIU2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIU3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Assessment of the model fit\u003c/h2\u003e \u003cp\u003eAs demonstrated in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the fit indices of the measurement and structural models confirmed the validity of the employed constructs. Although the model fit indices (AGFI\u0026thinsp;=\u0026thinsp;0.814, TLI\u0026thinsp;=\u0026thinsp;0.826, CFI\u0026thinsp;=\u0026thinsp;0.853) were slightly below the recommended thresholds, they are considered acceptable in exploratory studies (Hu \u0026amp; Bentler, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1999\u003c/span\u003e. While the RMR (0.160) exceeds the conventional 0.05 cutoff, this metric\u0026rsquo;s sensitivity to parameter scales makes it less reliable than RMSEA in complex models (Bentler, 2006). Our RMSEA value (0.089) falls within the \u0026lsquo;mediocre fit\u0026rsquo; range (0.08\u0026ndash;0.10), as defined by MacCallum et al. (1996), which remains acceptable when: a) Sample size smaller than 500 (N\u0026thinsp;=\u0026thinsp;389 in this study); b) Model includes over 20 observed variables (24 items here). The slightly elevated RMSEA may stem from our integration of TAM and flow theory, an understudied combination requiring cross-paradigm adjustments. As emphasized by Barrett (2007), model evaluation should prioritize theoretical coherence over mechanical cutoff adherence.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe goodness of fit indices for the measurement model and the research model.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eχ\u0026sup2;/df\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAGFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasurement model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResearch model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecommended value references\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Hypotheses testing\u003c/h2\u003e \u003cp\u003eThe significant correlations among LC, INT, ICM, PU, PEOU, FE, and IU indicate that these variables are highly interconnected. To examine the structural relationships among these variables, Structural Equation Modeling (SEM) was conducted to test the significance of each hypothesized path. The analysis calculated non-standardized coefficients (B), standardized coefficients (β), standard errors (SE), t-value, and R2 (explanatory power of independent variables) were calculated to test the hypotheses. The results indicated that ten out of twelve hypotheses were supported (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLearning Convenience was found to have significant positive direct effects on Perceived Ease of Use (β\u0026thinsp;=\u0026thinsp;0.504, p\u0026thinsp;=\u0026thinsp;0.000), and Perceived usefulness (β\u0026thinsp;=\u0026thinsp;0.461, p\u0026thinsp;=\u0026thinsp;0.000), supporting Hypothesis 4 and Hypothesis 5. Interactivity was found to have significant positive direct effects on Perceived Ease of Use (β\u0026thinsp;=\u0026thinsp;0.357, p\u0026thinsp;=\u0026thinsp;0.0.000), Perceived Usefulness (β\u0026thinsp;=\u0026thinsp;0.622, p\u0026thinsp;=\u0026thinsp;0.000), and Flow Experience (β\u0026thinsp;=\u0026thinsp;0.747, p\u0026thinsp;=\u0026thinsp;0.000), supporting Hypothesis 6, Hypothesis 7 and Hypothesis 8. The Incentive and Constraint Mechanism was found to have significant positive direct effects on Perceived Ease of Use (β\u0026thinsp;=\u0026thinsp;0.489, p\u0026thinsp;=\u0026thinsp;0.0.000), Perceived Usefulness (β\u0026thinsp;=\u0026thinsp;0.691, p\u0026thinsp;=\u0026thinsp;0.000), and Flow Experience (β\u0026thinsp;=\u0026thinsp;0.659, p\u0026thinsp;=\u0026thinsp;0.000), supporting Hypothesis 9, Hypothesis 10 and Hypothesis 11. Perceived Ease of Use was found to have significant positive direct effects on Intention to Use (β\u0026thinsp;=\u0026thinsp;0.262, p\u0026thinsp;=\u0026thinsp;0.000), supporting Hypothesis 3. Flow Experience was found to have significant positive direct effects on Intention to Use (β\u0026thinsp;=\u0026thinsp;0.395, p\u0026thinsp;=\u0026thinsp;0.000), supporting Hypothesis 12.\u003c/p\u003e \u003cp\u003eHowever, Perceived Ease of Use was not found to have significant direct effects on Perceived Usefulness (β =-.101, p\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.728), rejecting Hypothesis 1. Also, Perceived Usefulness was not found to have significant direct effects on Intention to Use (β = \u0026minus;\u0026thinsp;.017, p\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.095), rejecting Hypothesis 2.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of hypothesis testing.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypotheses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypothesized path\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerceived Ease of Use-\u0026gt;Perceived Usefulness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerceived Usefulness\u0026ndash;\u0026gt;Intention to Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerceived Ease of Use\u0026ndash;\u0026gt;Intention to Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLearning Convenience\u0026mdash;\u0026gt; Perceived Ease of Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.628***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLearning Convenience\u0026mdash;\u0026gt;Perceived Usefulness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.469***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInteractivity\u0026mdash;\u0026gt; Perceived Ease of Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.430***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInteractivity\u0026mdash;\u0026gt;Perceived usefulness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.130***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInteractivity\u0026mdash;\u0026gt;Flow Experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.478***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncentive and Constraint Mechanism\u0026mdash;\u0026gt; Perceived Ease of Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.180***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncentive and Constraint Mechanism\u0026mdash;\u0026gt;Perceived Usefulness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.984***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncentive and Constraint Mechanism\u0026ndash;\u0026gt;Flow Experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.257***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlow Experience\u0026ndash;\u0026gt;Intention to Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Results of Moderating Effects\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the analysis of the moderating effects of gender (GEN) and habituation (HAB) on PEOU and IU. As shown in the table, the interaction term between PEOU and HAB (PEOU*HAB) has a significant influence on IU (β = -0.115, p\u0026thinsp;=\u0026thinsp;0.013), indicating the moderation of HAB on the relationship between PEOU and IU. The moderating effect is negative, which suggests that as the level of HAB increases, the positive influence of PEOU on IU diminishes. In contrast, though the interaction term between PEOU and GEN (PEOU*GEN) shows a negative trend (β = -0.092), the effect is only marginally significant (p\u0026thinsp;=\u0026thinsp;0.061), which did not reach the conventional significance threshold (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In summary, PEOU has a robust positive predictive influence on IU and HAB significantly and negatively moderates this relationship. However, the moderating effect of GEN is relatively weak.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of the moderating effects of GEN and HAB on the relationships between PEOU and IU\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnstandardized Coefficient (B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandardized coefficient Beta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU*GEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU*HAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eDependent Variable:IU\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e investigates the moderating effects of GEN and HAB on the relationship between PU and IU. The results of regression analysis demonstrate that GEN has a significant negative moderating effect (β = -0.108, p\u0026thinsp;=\u0026thinsp;0.026), which suggests that compared with female users, the positive influence of PU on IU is diminished among male users. The moderating effect of HAB is also negative (β = -0.087, t = -1.948) but it only reaches marginal significance (p\u0026thinsp;=\u0026thinsp;0.052), which indicates that habituation may weaken PU\u0026rsquo;s positive influence on IU.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of the moderating effects of GEN and HAB on the relationships between PU and IU\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnstandardized Coefficient (B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandardized coefficient Beta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU*GEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU*HAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eDependent Variable:IU\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e elucidates that the interaction term between GEN and FE (FE*GEN) exhibits a negative trend (β = -0.08), but it does not reach statistical significance (p\u0026thinsp;=\u0026thinsp;0.105). Similarly, the interaction term between HAB and FE (FE*HAB) also shows a negative but non-significant moderating effect (β = -0.06, p\u0026thinsp;=\u0026thinsp;0.179). These findings suggest that, within the context of this study, the moderating effects of gender and habituation on the relationship between flow experience and intention to use may be relatively weak.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of the moderating effects of GEN and HAB on the relationships between FE and IU\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnstandardized Coefficient (B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandardized coefficient Beta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFE*GEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFE*HAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eDependent variable: IU\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Integration of TAM and the Flow Theory\u003c/h2\u003e \u003cp\u003eThis study substantiates the combined influences of rational cognition and emotional engagement on the behavioral intention to use online study rooms, aligning with the opinions of Hsu et al.,(2012),which emphasizes that user behavior is driven by both instrumental benefits and emotional rewards. Notably, our research results display that flow experience, as an emotional participation mechanism, mediates the relationships between perceived usefulness, perceived ease of use, and intention to use. This finding broadens the applicability of TAM and demonstrates that while choosing online study rooms, users weigh both the instrumental benefits offered by such platforms and the emotional rewards experienced through flow. It is worth noting that the direct influence of perceived usefulness on intention to use is not significant, which diverges from traditional TAM hypotheses. This result suggests that in the context of online study rooms, users may prioritize the enjoyment brought by using such platforms over mere instrumental benefits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Extending TAM with External Variables\u003c/h2\u003e \u003cp\u003eThis research unveils the significant positive influences of external variables (i.e., learning convenience, interactivity, incentive and constraint mechanism) on perceived usefulness and perceived ease of use, which coincides with the research results of Lisana( 2023) and Qashou( 2021)and suggests that these external variables can significantly improve user acceptance of and intention to use online learning platforms. Different from established TAM studies, the direct influence of perceived usefulness on intention to use is not significant in our research, which contrasts with previous emphasis on its central role in prior research (Davis, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1989\u003c/span\u003e).Through our analysis, it is posited that this shift may stem from the widespread adoption of technology and users\u0026rsquo;growing adaptability. With the popularization of Internet technologies, contemporary users, especially the young generation, have become adept at navigating complex multitasking interfaces, which may render them less sensitive to perceived ease of use. Moreover, the objectives of using online study rooms among users exhibit considerable diversification. Apart from improving learning efficiency, some users emphasize the emotional support and the sense of belonging derived from interaction and socializing. This diversification can lead to a corresponding variation in evaluation criteria, which can diminish the direct influence of perceived usefulness. According to the Self-Determination Theory, in emotionally driven learning contexts, user demands for autonomy and a sense of control may outweigh the pursuit of instrumental benefits. Therefore, these users are more inclined to favor platforms that can satisfy their emotional needs instead of solely seeking those that provide instrumental benefits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Analysis of the Moderating Effects of Gender and Habituation\u003c/h2\u003e \u003cp\u003eThis research further discusses the moderating effects of GEN and HAB on the relationships between PEOU, PU, FE, and IU. The discovery of the moderating effects enhances our understanding of the mechanisms underlying users\u0026rsquo; behavioral intentions and uncovers the significant role of individual differences in decision-making processes.\u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e5.3.1 The Moderating Effect of Gender (GEN)\u003c/h2\u003e \u003cp\u003eOur research results exhibit that gender has a significant negative moderating effect on the relationship between PU and UI (β= -0.108, p\u0026thinsp;=\u0026thinsp;0.026). Specifically, the positive influence of PU on UI is weaker among male users compared to that among female users. This finding indicates that gender differences can influence the behavioral intention to use online study rooms. Previous literature has also revealed that male users tend to prioritize instrumental benefits and functional values of technologies while female users place greater emphasis on emotional experiences and social interactions (Eagly \u0026amp; Wood, 1999). Consequently, in the context of online study rooms, female users\u0026rsquo; intention to use may be enhanced more through flow experiences and emotional rewards, whereas male users may prioritize instrumental benefits. This finding offers new evidence for the influence of gender differences on user behavior and suggests that product designers and marketers should consider gender-specific strategies to better cater to diverse user needs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e5.3.2 The Moderating Effect of Habituation (HAB)\u003c/h2\u003e \u003cp\u003eHabituation has a significant negative moderating effect on the relationship between PEOU and UI (β= -0.115, p\u0026thinsp;=\u0026thinsp;0.013), which indicates that as users\u0026rsquo; level of habituation level increases, PEOU\u0026rsquo;s positive influence on UI diminishes. This result demonstrates that habituation can weaken users\u0026rsquo; sensitivity to PEOU. According to (Verplanken \u0026amp; Orbell, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), habituation is characterized as an automated behavioral mode. Once developed, decision-making processes could be more driven by habituation rather than through rational evaluation. Within the context of online study rooms, users may develop usage habits over time and their attention to PEOU can be reduced. This finding highlights the critical role of habituation in decision-making processes and suggests that product designers can improve user satisfaction and intention to use by designing functions that suit users\u0026rsquo; habituated behaviors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"6. Contribution\u0026Implication","content":"\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Theoretical Contribution\u003c/h2\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e6.1.1 Integration of TAM and Flow Theory\u003c/h2\u003e \u003cp\u003eThis research establishes a cross-theory framework by integrating TAM and the flow theory to elucidate the behavioral intention to use online study rooms. This framework integrates emotional experiences (such as flow experience) with rational cognitive processes, which addresses gaps in extant literature. Traditional TAM studies have predominantly focused on the cognitive dimension of perceived usefulness and perceived ease of use. By introducing the flow experience as an emotional driver, this study unveils a more comprehensive perspective for understanding user behavior in online learning environments. This innovation offers a novel analytical tool for online learning scenarios that involve emotional and cognitive interactions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section3\"\u003e \u003ch2\u003e6.1.2 Expand the external variable of TAM\u003c/h2\u003e \u003cp\u003eThis research validates the moderating effects of external variables (i.e., learning convenience, interactivity, and the incentive and constraint mechanism) in TAM, which expands the applicability of TAM. By introducing the socializing features and the behavioral supervision mechanism, this research reveals how these external variables influence user behaviors through dual pathways of emotion and cognition. This finding aligns with the research results of (Cheng \u0026amp; Jiang, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which highlights the enhancement of concentration via real-time interactions. Furthermore, this study offers new theoretical insights for the application of TAM in the domain of online education.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003e6.1.3 Revelation of the Moderating Effects of Gender and Habituation\u003c/h2\u003e \u003cp\u003eThis research broadens the theoretical scope of TAM by investigating how gender and habitual behaviors moderate the technology acceptance trajectory of engaging with online learning platforms. First, the incorporation of gender and habituation offers a more nuanced framework for understanding the mechanisms underlying behavioral intention formation and unveils the essential role of individual differences in decision-making processes. Second, the identification of these moderators provides new theoretical support for the application of TAM in online learning scenarios. It is highlighted that user behaviors are not only influenced by instrumental benefits and emotional rewards but also influenced by individual differences, such as gender and habituation. By revealing these moderating effects, this research not only enriches the theoretical connotation of TAM but also offers a new theoretical lens and analytical framework for studying user behaviors in the context of online learning environments.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Practical Implications\u003c/h2\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003e6.2.1 Optimization of Online Study Room Design\u003c/h2\u003e \u003cp\u003eIn the design of online study rooms, platform operators are encouraged to optimize functionalities in response to gender differences. For female users, more interest-based learning groups can be established for them to socialize and enhance emotional exchanges and mutual support; while for male users, the emphasis should be placed on enhancing learning efficiency through the provision of tools such as intelligent learning plan generators and learning data analysis. For the habituation part, it is essential to leverage users\u0026rsquo; historical data to provide personalized study paths. In this way, users can better adapt to the platforms and improve their learning outcomes. At the same time, the interactive features should be continuously optimized\u0026ndash;such as improving bullet comment rules, real-time Q\u0026amp;A mechanisms, and users\u0026rsquo; flow experience in learning.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section3\"\u003e \u003ch2\u003e6.2.2 Improvement of Promotional Strategies to Boost the Intention to Use\u003c/h2\u003e \u003cp\u003eOur research results offer valuable insights for online learning platforms aiming to develop more effective promotional strategies. For users who prioritize emotional experiences, platforms should highlight the interactive features and social atmosphere; for users who seek to improve learning efficiency, a focus on practical functionalities that facilitate efficient learning should be emphasized; for novice users, such platforms should shed light on the ease of use, basic functionalities, and advantages; while for experienced users, emphasis should be placed on the advanced features and personalized services. Moreover, online learning activities, such as check-in challenges and group competitions, can be held to attract users of different types and enhance user stickiness and frequency of use. Furthermore, differentiated features and services tailored to the distinct needs of users of different genders should be provided. Meanwhile, it is also recommended to encourage users to cultivate positive learning habits to improve their reliance on and loyalty to the platform.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section3\"\u003e \u003ch2\u003e6.2.3 Offering Analysis Results for Online Education\u003c/h2\u003e \u003cp\u003eThe integrated model in our study serves as a systematic instrument for user behavior analysis in online education. By quantifying the influences of learning convenience, interactivity, and the incentive and constraint mechanism on user behavior, online learning platforms can identify user demands more accurately, optimize their function designs accordingly, and formulate corresponding data-driven operational strategies. Diversified online learning scenarios applicable to multi-task interfaces and social interaction in this research offer scientific evidence for the application of educational technologies.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"7. Limitations and further research","content":"\u003cp\u003eThis research integrates TAM and the flow theory to examine the driving forces of users\u0026rsquo; behavioral intention to use online study rooms. Despite its contributions, there exist some limitations. First, the research data are predominantly from China, which may impose cultural limitations. Future research can further explore the behavioral differences among users of online study rooms against different cultural backgrounds, thereby offering more robust theoretical support for the internationalization of such platforms. Second, this study relies on cross-sectional data and views the flow experience as a static outcome, which limits the ability to capture the dynamic change of flow thresholds. To this end, future research can conduct multi-modal data analyses, such as integrating eye-tracking and log data, to conceptualize flow experience as a dynamic variable to understand user behavior and psychological changes.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: Wang Jun; Methodology:Jun Wang ; Formal analysis and investigation: Shang-jie Yuan ; Writing - original draft preparation: Jun Wang ; Writing - review and editing:Jun Wang ; Supervision: Zhong Zheng , Ji-ping Zhang ，Qian-ting Zhang .\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analysed during the current study are available in the Mendeley Data repository. The datasets can be accessed using the following link: [https://doi.org/10.17632/gcpptfds6y.1\u003c/p\u003e\u003cp\u003e \u003cb\u003eFunding Sources\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available in the Mendeley Data repository, https://doi.org/10.17632/gcpptfds6y.1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll methods were carried out in accordance with relevant guidelines and regulations. The experimental protocols were approved by the Seal of Ethics Committee at Hunan Institute of Science and Technology. Informed consent was obtained from all subjects and/or their legal guardian(s).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAfacan Adanır, G. \u0026amp; Muhametjanova, G. University students\u0026rsquo; acceptance of mobile learning: A comparative study in Turkey and Kyrgyzstan. \u003cem\u003eEduc. Inform. Technol.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e (5), 6163\u0026ndash;6181. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10639-021-10620-1\u003c/span\u003e\u003cspan address=\"10.1007/s10639-021-10620-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgarwal, R. \u0026amp; Karahanna, E. Time flies when you\u0026rsquo;re having fun: Cognitive absorption and beliefs about information technology usage. \u003cem\u003eMIS Q.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e (4), 665. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/3250951\u003c/span\u003e\u003cspan address=\"10.2307/3250951\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Adwan, A. S. et al. Extending the technology acceptance model (TAM) to predict university students\u0026rsquo; intentions to use metaverse-based learning platforms. \u003cem\u003eEduc. Inform. Technol.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e (11), 15381\u0026ndash;15413. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10639-023-11816-3\u003c/span\u003e\u003cspan address=\"10.1007/s10639-023-11816-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlturki, U. \u0026amp; Aldraiweesh, A. An empirical investigation into students\u0026rsquo; actual use of MOOCs in Saudi Arabia higher education. \u003cem\u003eSustainability\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e (8), 6918. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su15086918\u003c/span\u003e\u003cspan address=\"10.3390/su15086918\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArteaga S\u0026aacute;nchez, R., Cortijo, V. \u0026amp; Javed, U. Students\u0026rsquo; perceptions of facebook for academic purposes. \u003cem\u003eComput. Educ.\u003c/em\u003e \u003cb\u003e70\u003c/b\u003e, 138\u0026ndash;149. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.compedu.2013.08.012\u003c/span\u003e\u003cspan address=\"10.1016/j.compedu.2013.08.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eByrne, B. M. \u0026amp; Van De Vijver, F. J. R. Testing for measurement and structural equivalence in large-scale cross-cultural studies: Addressing the issue of nonequivalence. \u003cem\u003eInt. J. Test.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e (2), 107\u0026ndash;132. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/15305051003637306\u003c/span\u003e\u003cspan address=\"10.1080/15305051003637306\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng, Y. \u0026amp; Jiang, H. How do AI-driven chatbots impact user experience? Examining gratifications, perceived privacy risk, satisfaction, loyalty, and continued use. \u003cem\u003eJ. Broadcast. Electron. Media\u003c/em\u003e. \u003cb\u003e64\u003c/b\u003e (4), 592\u0026ndash;614. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/08838151.2020.1834296\u003c/span\u003e\u003cspan address=\"10.1080/08838151.2020.1834296\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheung, C. M. K. \u0026amp; Lee, M. K. O. Antecedents and consequences of user satisfaction with an e-learning portal. \u003cem\u003eInt. J. Digit. Soc.\u003c/em\u003e \u003cb\u003e2\u003c/b\u003e (1), 373\u0026ndash;380. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.20533/ijds.2040.2570.2011.0045\u003c/span\u003e\u003cspan address=\"10.20533/ijds.2040.2570.2011.0045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChou, S. \u0026amp; Liu, C. Learning effectiveness in a web-based virtual learning environment: A learner control perspective. \u003cem\u003eJ. Comput. Assist. Learn.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e (1), 65\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2729.2005.00114.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2729.2005.00114.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollis, B. \u0026amp; Moonen, J. Flexibility in Higher Education: Revisiting Expectations. \u003cem\u003eComunicar\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e (37), 15\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3916/C37-2011-02-01\u003c/span\u003e\u003cspan address=\"10.3916/C37-2011-02-01\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCsikszentmihalyi, M. Play and intrinsic rewards. 收入 M. Csikszentmihalyi, \u003cem\u003eFlow and the Foundations of Positive Psychology\u003c/em\u003e (135\u0026ndash;153). Springer Netherlands. (2014). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-94-017-9088-8_10\u003c/span\u003e\u003cspan address=\"10.1007/978-94-017-9088-8_10\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis, F. D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. \u003cem\u003eMIS Q.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (3), 319. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/249008\u003c/span\u003e\u003cspan address=\"10.2307/249008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1989).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEagly, A. H. \u0026amp; Wood, W. \u003cem\u003eThe origins of sex differences in human behavior\u003c/em\u003e. (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErhel, S. \u0026amp; Jamet, E. Improving instructions in educational computer games: Exploring the relations between goal specificity, flow experience and learning outcomes. \u003cem\u003eComput. Hum. Behav.\u003c/em\u003e \u003cb\u003e91\u003c/b\u003e, 106\u0026ndash;114. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chb.2018.09.020\u003c/span\u003e\u003cspan address=\"10.1016/j.chb.2018.09.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFathali, S. \u0026amp; Okada, T. Technology acceptance model in technology-enhanced OCLL contexts: A self-determination theory approach. \u003cem\u003eAustralasian J. Educational Technol.\u003c/em\u003e \u003cb\u003e34\u003c/b\u003e (4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.14742/ajet.3629\u003c/span\u003e\u003cspan address=\"10.14742/ajet.3629\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo, C. \u0026amp; Zhang, X. The impact of AR online shopping experience on customer purchase intention: An empirical study based on the TAM model. \u003cem\u003ePLOS One\u003c/em\u003e. \u003cb\u003e19\u003c/b\u003e (8), e0309468. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0309468\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0309468\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoffman, D. L. \u0026amp; Novak, T. P. \u003cem\u003eMarketing in hypermedia computer-mediated environments: Conceptual foundations\u003c/em\u003e. (1996).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoffman, D. L. \u0026amp; Novak, T. P. Flow online: Lessons learned and future prospects. \u003cem\u003eJ. Interact. Mark.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e (1), 23\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.intmar.2008.10.003\u003c/span\u003e\u003cspan address=\"10.1016/j.intmar.2008.10.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHsu, C., Chang, K. \u0026amp; Chen, M. Flow experience and internet shopping behavior: Investigating the moderating effect of consumer characteristics. \u003cem\u003eSyst. Res. Behav. Sci.\u003c/em\u003e \u003cb\u003e29\u003c/b\u003e (3), 317\u0026ndash;332. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/sres.1101\u003c/span\u003e\u003cspan address=\"10.1002/sres.1101\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu, L. \u0026amp; Bentler, P. M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. \u003cem\u003eStruct. Equation Modeling-a Multidisciplinary J.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e (1), 1\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10705519909540118\u003c/span\u003e\u003cspan address=\"10.1080/10705519909540118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1999).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHussein, L. A. \u0026amp; Hilmi, M. F. The influence of convenience on the usage of learning management system. \u003cem\u003eElectron. J. E-Learning\u003c/em\u003e. \u003cb\u003e19\u003c/b\u003e (6), 504\u0026ndash;515. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.34190/ejel.19.6.2493\u003c/span\u003e\u003cspan address=\"10.34190/ejel.19.6.2493\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHyun, H., Thavisay, T. \u0026amp; Lee, S. H. Enhancing the role of flow experience in social media usage and its impact on shopping. \u003cem\u003eJ. Retailing Consumer Serv.\u003c/em\u003e \u003cb\u003e65\u003c/b\u003e, 102492. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jretconser.2021.102492\u003c/span\u003e\u003cspan address=\"10.1016/j.jretconser.2021.102492\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKamoyo, M., Masamha, T. \u0026amp; Chikazhe, L. Remote e-learning model in the post COVID-19 era; building a resilient higher education strategy. \u003cem\u003eCogent Educ.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (1), 2498854. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/2331186X.2025.2498854\u003c/span\u003e\u003cspan address=\"10.1080/2331186X.2025.2498854\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhaldi, A., Bouzidi, R. \u0026amp; Nader, F. Gamification of e-learning in higher education: A systematic literature review. \u003cem\u003eSmart Learn. Environ.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e (1), 10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40561-023-00227-z\u003c/span\u003e\u003cspan address=\"10.1186/s40561-023-00227-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, M. How can I Be as attractive as a fitness YouTuber in the era of COVID-19? The impact of digital attributes on flow experience, satisfaction, and behavioral intention. \u003cem\u003eJ. Retailing Consumer Serv.\u003c/em\u003e \u003cb\u003e64\u003c/b\u003e, 102778. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jretconser.2021.102778\u003c/span\u003e\u003cspan address=\"10.1016/j.jretconser.2021.102778\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, S., Choi, M. J. \u0026amp; Choi, J. S. Empirical study on the factors affecting individuals\u0026rsquo; switching intention to augmented/virtual reality content services based on push-pull-mooring theory. \u003cem\u003eInformation\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (1), 25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/info11010025\u003c/span\u003e\u003cspan address=\"10.3390/info11010025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlingenberg, S., Bosse, R., Mayer, R. E. \u0026amp; Makransky, G. Does embodiment in virtual reality boost learning transfer? Testing an immersion-interactivity framework. \u003cem\u003eEducational Psychol. Rev.\u003c/em\u003e \u003cb\u003e36\u003c/b\u003e (4), 116. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10648-024-09956-0\u003c/span\u003e\u003cspan address=\"10.1007/s10648-024-09956-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKluger, A. N. \u0026amp; DeNisi, A. The effects of feedback interventions on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. \u003cem\u003ePsychol. Bull.\u003c/em\u003e \u003cb\u003e119\u003c/b\u003e (2), 254\u0026ndash;284. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/0033-2909.119.2.254\u003c/span\u003e\u003cspan address=\"10.1037/0033-2909.119.2.254\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1996).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKowalczuk, P. \u0026amp; Adler, J. Siepmann (N\u0026eacute;e Scheiben), C., Cognitive, affective, and behavioral consumer responses to augmented reality in e-commerce: A comparative study. \u003cem\u003eJournal of Business Research\u003c/em\u003e, \u003cem\u003e124\u003c/em\u003e, 357\u0026ndash;373. (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jbusres.2020.10.050\u003c/span\u003e\u003cspan address=\"10.1016/j.jbusres.2020.10.050\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, M. C. Explaining and predicting users\u0026rsquo; continuance intention toward e-learning: An extension of the expectation\u0026ndash;confirmation model. \u003cem\u003eComput. Educ.\u003c/em\u003e \u003cb\u003e54\u003c/b\u003e (2), 506\u0026ndash;516. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.compedu.2009.09.002\u003c/span\u003e\u003cspan address=\"10.1016/j.compedu.2009.09.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLim, W. M. Generative AI and the future of education: Ragnar\u0026ouml;k or reformation? A paradoxical perspective from management educators. \u003cem\u003eThe Int. J. Manage. Education\u003c/em\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLisana, L. Factors affecting university students switching intention to mobile learning: A push-pull-mooring theory perspective. \u003cem\u003eEduc. Inform. Technol.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e (5), 5341\u0026ndash;5361. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10639-022-11410-z\u003c/span\u003e\u003cspan address=\"10.1007/s10639-022-11410-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, P., Zhang, Y. \u0026amp; Liu, D. Flow experience in foreign language writing: Its effect on students\u0026rsquo; writing process and writing performance. \u003cem\u003eFront. Psychol.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 952044. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpsyg.2022.952044\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2022.952044\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacCallum, R. C. \u0026amp; Hong, S. Power analysis in covariance structure modeling using GFI and AGFI. \u003cem\u003eMultivar. Behav. Res.\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e (2), 193\u0026ndash;210. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1207/s15327906mbr3202_5\u003c/span\u003e\u003cspan address=\"10.1207/s15327906mbr3202_5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1997).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaydeu-Olivares, A. Assessing the size of model misfit in structural equation models. \u003cem\u003ePsychometrika\u003c/em\u003e \u003cb\u003e82\u003c/b\u003e (3), 533\u0026ndash;558. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11336-016-9552-7\u003c/span\u003e\u003cspan address=\"10.1007/s11336-016-9552-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcIntosh, C. N. Rethinking fit assessment in structural equation modelling: A commentary and elaboration on barrett (2007). \u003cem\u003ePersonality and Individual Differences\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e(5), 859\u0026ndash;867. (2007). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.paid.2006.09.020\u003c/span\u003e\u003cspan address=\"10.1016/j.paid.2006.09.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMotaghian, H., Hassanzadeh, A. \u0026amp; Moghadam, D. K. Factors affecting university instructors\u0026rsquo; adoption of web-based learning systems: Case study of Iran. \u003cem\u003eComput. Educ.\u003c/em\u003e \u003cb\u003e61\u003c/b\u003e, 158\u0026ndash;167. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.compedu.2012.09.016\u003c/span\u003e\u003cspan address=\"10.1016/j.compedu.2012.09.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen, T. T. et al. Fintech literacy and digital entrepreneurial intention: Mediator and moderator effect. \u003cem\u003eInt. J. Inform. Manage. Data Insights\u003c/em\u003e. \u003cb\u003e4\u003c/b\u003e (1), 100222. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jjimei.2024.100222\u003c/span\u003e\u003cspan address=\"10.1016/j.jjimei.2024.100222\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOzkara, B. Y., Ozmen, M. \u0026amp; Kim, J. W. Examining the effect of flow experience on online purchase: A novel approach to the flow theory based on hedonic and utilitarian value. \u003cem\u003eJ. Retailing Consumer Serv.\u003c/em\u003e \u003cb\u003e37\u003c/b\u003e, 119\u0026ndash;131. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jretconser.2017.04.001\u003c/span\u003e\u003cspan address=\"10.1016/j.jretconser.2017.04.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePramana, E. Determinants of the adoption of mobile learning systems among university students in Indonesia. \u003cem\u003eJ. Inform. Technol. Education: Res.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 365\u0026ndash;398. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.28945/4119\u003c/span\u003e\u003cspan address=\"10.28945/4119\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQashou, A. Influencing factors in M-learning adoption in higher education. \u003cem\u003eEduc. Inform. Technol.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e (2), 1755\u0026ndash;1785. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10639-020-10323-z\u003c/span\u003e\u003cspan address=\"10.1007/s10639-020-10323-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoy Dholakia, R. \u0026amp; Zhao, M. Retail web site interactivity: How does it influence customer satisfaction and behavioral intentions? \u003cem\u003eInt. J. Retail Distribution Manage.\u003c/em\u003e \u003cb\u003e37\u003c/b\u003e (10), 821\u0026ndash;838. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/09590550910988011\u003c/span\u003e\u003cspan address=\"10.1108/09590550910988011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoy, S. K., Singh, G., Sadeque, S., Harrigan, P. \u0026amp; Coussement, K. Customer engagement with digitalized interactive platforms in retailing. \u003cem\u003eJ. Bus. Res.\u003c/em\u003e \u003cb\u003e164\u003c/b\u003e, 114001. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jbusres.2023.114001\u003c/span\u003e\u003cspan address=\"10.1016/j.jbusres.2023.114001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRyan, R. M. \u003cem\u003eMeta-analytic findings within self-determination theory 2\u003c/em\u003e. (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRyan, R. M. \u0026amp; Deci, E. L. Intrinsic and extrinsic motivations: Classic definitions and new directions. \u003cem\u003eContemp. Educ. Psychol.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e (1), 54\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1006/ceps.1999.1020\u003c/span\u003e\u003cspan address=\"10.1006/ceps.1999.1020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSage, K., Jackson, S., Fox, E. \u0026amp; Mauer, L. The virtual COVID-19 classroom: Surveying outcomes, individual differences, and technology use in college students. \u003cem\u003eSmart Learn. Environ.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e (1), 27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40561-021-00174-7\u003c/span\u003e\u003cspan address=\"10.1186/s40561-021-00174-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaroia, A. I. \u0026amp; Gao, S. Investigating university students\u0026rsquo; intention to use mobile learning management systems in Sweden. \u003cem\u003eInnovations Educ. Teach. Int.\u003c/em\u003e \u003cb\u003e56\u003c/b\u003e (5), 569\u0026ndash;580. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/14703297.2018.1557068\u003c/span\u003e\u003cspan address=\"10.1080/14703297.2018.1557068\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShe, L., Ma, L., Jan, A., Nia, S., Rahmatpour, P. \u0026amp; H., \u0026amp; Online learning satisfaction during COVID-19 pandemic among chinese university students: The serial mediation model. \u003cem\u003eFront. Psychol.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 743936. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpsyg.2021.743936\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2021.743936\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong, J. H. \u0026amp; Zinkhan, G. M. Determinants of perceived web site interactivity. \u003cem\u003eJ. Mark.\u003c/em\u003e \u003cb\u003e72\u003c/b\u003e (2), 99\u0026ndash;113. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1509/jmkg.72.2.99\u003c/span\u003e\u003cspan address=\"10.1509/jmkg.72.2.99\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang, Y. M. et al. Comparative analysis of student\u0026rsquo;s live online learning readiness during the coronavirus (COVID-19) pandemic in the higher education sector. \u003cem\u003eComput. Educ.\u003c/em\u003e \u003cb\u003e168\u003c/b\u003e, 104211. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.compedu.2021.104211\u003c/span\u003e\u003cspan address=\"10.1016/j.compedu.2021.104211\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eT\u0026uuml;rker, C., Altay, B. C. \u0026amp; Okumuş, A. Understanding user acceptance of QR code mobile payment systems in Turkey: An extended TAM. \u003cem\u003eTechnol. Forecast. Soc. Chang.\u003c/em\u003e \u003cb\u003e184\u003c/b\u003e, 121968. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.techfore.2022.121968\u003c/span\u003e\u003cspan address=\"10.1016/j.techfore.2022.121968\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenkatesh, M., Davis \u0026amp; Davis, \u0026amp; User acceptance of information technology: Toward a unified view. \u003cem\u003eMIS Q.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e (3), 425. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/30036540\u003c/span\u003e\u003cspan address=\"10.2307/30036540\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenkatesh, V. Adoption and use of AI tools: A research agenda grounded in UTAUT. \u003cem\u003eAnn. Oper. Res.\u003c/em\u003e \u003cb\u003e308\u003c/b\u003e (1\u0026ndash;2), 641\u0026ndash;652. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10479-020-03918-9\u003c/span\u003e\u003cspan address=\"10.1007/s10479-020-03918-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerplanken, B. \u0026amp; Orbell, S. Attitudes, habits, and behavior change. \u003cem\u003eAnn. Rev. Psychol.\u003c/em\u003e \u003cb\u003e73\u003c/b\u003e (1), 327\u0026ndash;352. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev-psych-020821-011744\u003c/span\u003e\u003cspan address=\"10.1146/annurev-psych-020821-011744\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, H. et al. Framework effect and achievement motivation on college students\u0026rsquo; online learning intention\u0026ndash;based on technology acceptance model (TAM) and theory of planned behaviour (TPB) model. \u003cem\u003eEduc. Inform. Technol.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10639-024-13254-1\u003c/span\u003e\u003cspan address=\"10.1007/s10639-024-13254-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, Y., Dong, C. \u0026amp; Zhang, X. Improving MOOC learning performance in China: An analysis of factors from the TAM and TPB. \u003cem\u003eComput. Appl. Eng. Educ.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e (6), 1421\u0026ndash;1433. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/cae.22310\u003c/span\u003e\u003cspan address=\"10.1002/cae.22310\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWiyono, B. B., Rasyad, A. \u0026amp; Maisyaroh The Effect of Collaborative Supervision Approaches and Collegial Supervision Techniques on Teacher Intensity Using Performance-Based Learning. \u003cem\u003eSage Open.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (2), 21582440211013779. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/21582440211013779\u003c/span\u003e\u003cspan address=\"10.1177/21582440211013779\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu, A. et al. A study of young Chinese intentions to purchase online paid knowledge: An extended technological acceptance model. \u003cem\u003eFront. Psychol.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 695600. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpsyg.2021.695600\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2021.695600\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Q., Ariffin, S. K., Richardson, C. \u0026amp; Wang, Y. Influencing factors of customer loyalty in mobile payment: A consumption value perspective and the role of alternative attractiveness. \u003cem\u003eJ. Retailing Consumer Serv.\u003c/em\u003e \u003cb\u003e73\u003c/b\u003e, 103302. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jretconser.2023.103302\u003c/span\u003e\u003cspan address=\"10.1016/j.jretconser.2023.103302\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Y. Effect of interactive immediacy on online learning satisfaction of international students in chinese universities: The chain mediating role of learning interest and academic engagement. \u003cem\u003eActa Psychologica\u003c/em\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Online Study Room, Technology Acceptance Model (TAM), Flow Theory, Intention to Use, User Behavior","lastPublishedDoi":"10.21203/rs.3.rs-7846720/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7846720/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThough online study rooms have gained popularity among users for their flexibility and interactivity, there remains a lack of in-depth analysis of factors that influence user behaviors in these virtual learning environments. This research delves into the determinants of user behaviors in the context of online study rooms by integrating the Technology Acceptance Model (TAM) and the Flow Theory. Structural Equation Modeling (SEM) was performed to analyze 389 valid responses. The research findings manifest that: (a) external variables (i.e., learning convenience, interactivity, and the incentive and constraint mechanism) significantly influence users\u0026rsquo; perceived ease of use and perceived usefulness of such platforms; (b) perceived ease of use has a direct and positive influence on the intention to use, while perceived usefulness has no direct influence; (c) flow experience is a key determinant of intention to use; (d) gender and habituation significantly moderate the relationships between perceived ease of use, perceived usefulness, flow experience, and intention to use. By integrating the flow theory and extending the application boundary of TAM, this research reveals that flow experience plays a critical role in shaping user behaviors. Our research results offer data support for optimizing the design of online study rooms, theoretical evidence for understanding user behaviors in novel digital learning environments, and practical implications for developers of such platforms and personnel working in this domain.\u003c/p\u003e","manuscriptTitle":"From Physical to Virtual: Understanding User Intention to Use Online Study Rooms Through Technology Acceptance Model (TAM) and Flow Theory","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-23 13:44:03","doi":"10.21203/rs.3.rs-7846720/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-02-19T06:52:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-17T09:18:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-31T08:58:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-23T09:36:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-23T09:31:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"31c0d56f-c23b-457c-93ca-81d84ff59dfe","owner":[],"postedDate":"February 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":63186784,"name":"Business and commerce/Business and management"},{"id":63186785,"name":"Social science/Business and management"},{"id":63186786,"name":"Business and commerce/Information systems and information technology"},{"id":63186787,"name":"Biological sciences/Psychology"},{"id":63186788,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-02-23T13:44:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-23 13:44:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7846720","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7846720","identity":"rs-7846720","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}