Unveiling the Complex Mechanism of Short Video Addiction on English Learning Engagement and Burnout among College EFL Students

Unveiling the Complex Mechanism of Short Video Addiction on English Learning Engagement and Burnout among College EFL Students | 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 Research Article Unveiling the Complex Mechanism of Short Video Addiction on English Learning Engagement and Burnout among College EFL Students Huang Kuilong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5885393/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In the digital era, short-video apps offer students copious learning resources and entertainment. However, short-video addiction has become a notable concern. Based on Media Dependence Theory and Social Cognitive Theory, this research explored how short-video addiction affects English learning engagement and burnout, with English academic self-efficacy as a mediator. Electronic questionnaires were administered to 500 college EFL students in Guangxi, China, and data analysis was conducted with SPSS and SmartPLS. Results showed that : (1) short-video addiction had no direct impact on English learning engagement but significantly and directly contributed to English learning burnout;(2) English academic self-efficacy mediated the relationships between short-video addiction and both learning engagement and burnout. Overall, this study uncovers the intricate connections among short-video addiction, English learning engagement, burnout, and English academic self-efficacy. Theoretically, it enriches the two theories by offering fresh perspectives on how emerging media influence learning behaviors. Practically, to boost college students' English learning engagement and reduce burnout, we should not only address short-video addiction but also focus on cultivating their English academic self-efficacy. short video addiction English academic ability self-efficacy English learning engagement English learning burnout college EFL students Figures Figure 1 Figure 2 1. Introduction In recent years, short video platforms such as TikTok and Bilibili have evolved into crucial channels for entertainment, information dissemination, knowledge acquisition, and social interaction. Short videos are now indispensable in modern society. As reported by Chinese state media [ 1 ], by June 2023, China’s short video user base had grown to 1.026 billion, indicating an immense user scale in the country. Furthermore, a large-scale survey involving 11,267 college students throughout China revealed that over 80% of respondents frequently engaged with short videos and over 70% acknowledging the addictive nature of this media form [ 2 ]. Short videos enrich college students’ lives, but excessive usage becomes worrying. The user-friendly interface and entertainment features of short video applications amplify college students' inclination towards excessive usage, rendering impulse control challenging [ 3 ]. Actually, terms such as “problematic use”, “dependence”, and “addiction” are frequently employed to describe the phenomenon of excessive short video consumption. “Problematic use” often emphasizes the disruptive impacts on daily functioning [ 4 ], while “dependence” highlights a psychological or physical reliance [ 5 ]. However, “addiction” represents a more profound and complex manifestation, taking it a step further than the previous two terms. It not only encompasses the elements of disruption and reliance but also involves a loss of self - control and a high degree of perseverance in the face of negative consequences[ 6 ]. Considering the purpose of this study, which aims to explore the deeper behavioral and psychological mechanisms underlying excessive short video consumption, the term “addiction” was adopted. Short video addiction refers to an individual's excessive and prolonged engagement with short video applications, demonstrating an inability to regulate the frequency and duration of usage, ultimately resulting in detrimental effects on their physical, mental, and behavioral well-being [ 7 ]. Since short video users mainly employ mobile phones and network technology to consume videos, short video addiction shares some characteristics of the Internet and mobile phone addiction, which were concluded as excessive and improper use, withdrawal and tolerance symptoms, compulsive behaviors, and functional impair [ 8 ],[ 9 ],[ 10 ],[ 11 ]. Thus, existing studies on Internet and mobile phone addiction provide valuable insights for the study of short video addiction. According to the Media Dependence Theory [ 12 ], the greater an individual's reliance on media platforms (such as the Internet, mobile phones, and short videos) determines the extent to which their cognitive, emotional, and behavioral patterns are influenced by these media outlets. Existing studies have verified the effects of smartphone addiction on learning engagement and learning burnout. Specifically, it has been shown that smartphone addiction has a direct and significant positive prediction of learning burnout[ 13 ]. However, the impact of smartphone addiction on learning engagement remains a contentious issue. While some studies have found a significant relationship between the two [ 14 ][ 15 ], others argue that a direct association is absent [ 16 ]. Then, what are the impacts of short video addiction on learning engagement and learning burnout? Against this backdrop, the effects of short-video addiction on learning engagement and learning burnout remain under-explored. Short video platforms have unique characteristics, such as highly engaging content personalized by recommendation algorithm and easily consumable short-form videos [ 17 ], which, unlike general smartphone addiction, may have distinct impacts on students’ learning, with both potential distractions and educational value depending on usage. What's more, students' academic ability self-efficacy–a crucial part of academic self-efficacy [ 18 ],[ 19 ],[ 20 ]-- has been empirically confirmed to have significant influence on learners’ engagement and burnout in their learning process [ 21 ],[ 22 ]. However, few studies notice possible association between short video addiction and academic ability self-efficacy and the potential intermediate mechanism of academic ability self-efficacy in the interplay between short video addiction and learning engagement, as well as between short video addiction and learning burnout. To further illuminate the intricacies of short video addiction in the academic context, this study investigated influence of short video addiction on learning engagement and burnout, and the mediating role of academic ability self-efficacy in the two relations with a focus on the Chinese college English majors. It's expected that the findings not only offer theoretical contributions to the field of media dependence theory and educational psychology but also have practical implications for guiding college English major students in achieving a healthy balance between media consumption and academic pursuits, ultimately bolstering engagement and alleviating burnout for more effective and fulfilling learning experiences. 2. Literature Review and Hypothesis 2.1. Short Video Addiction and Learning Engagement Learning engagement refers to the active, fulfilling, stable, and voluntary involvement demonstrated by individuals during learning activities [ 23 ]. According to Fredricks et al. [ 24 ], learning engagement consists of emotional, behavioral, and cognitive dimensions, with emotional engagement involving learners' emotions, behavioral engagement relating to their effort and attention, and cognitive engagement encompassing their usage of strategies and self-regulation during learning. Short video addiction may potentially influence English learning engagement for three main reasons. Firstly, college students' learning engagement is greatly influenced by their academic emotions, with learning engagement and negative academic emotions, such as anxiety, being inversely connected in language learning process [ 25 ]. It has been verified that short video addiction might lead to prolonged feelings of anxiety [ 26 ], and anxiety might reduce the level of engagement in language learning [ 25 ]. Secondly, the time and effort students invest on learning, along with their successful learning experiences and achieved accomplishments, are critical factors influencing their learning engagement[ 27 ]. However, short video addiction can consume a considerable amount of time and energy dedicated to learning, leading to distractions and a decline in attention control abilities [ 28 ]. This decrease in behavioral engagement may result in poor academic performance, which subsequently might reduce learning engagement in a cyclical manner. Thirdly, short video addiction can also affect students’ cognitive engagement by exacerbating their tendency toward inert thinking. When encountering study problems, addicted individuals may habitually opt to directly search for answers on their phones, thus engaging less in personal analysis and critical thinking [ 29 ]. In conclusion, short videos addiction can negatively influence individuals' learning in behavior, emotion, and cognition, subsequently leading to a decrease in their learning engagement levels. Therefore, this study proposed Hypothesis 1: Short video addiction significantly and negatively predicts English learning engagement among college English majors. 2.2. Short Video Addiction and Learning Burnout Learning burnout is a sustained, negative, learning-related state that reflects students' adverse reactions to learning [ 30 ]. As concluded by Xu et al. [ 31 ], in physiology, learning burnout is evidenced by recurrent exhaustion during study sessions; in emotion, it leads to a diminished interest in learning coupled with a reduced sense of accomplishment; and in behavior, this burnout manifests as decreased learning efficiency, a cursory approach to studies, and actions such as absenteeism or inattention in the classroom. Short video addiction may lead to learning burnout based on the following reasons. Firstly, excessive engagement with short videos often disrupts time management, significantly reducing the time allocated for essential activities such as studying and sleeping. This disruption negatively affects sleep quality, learning efficiency, and academic procrastination [ 3 ],[ 4 ], all of which are significant contributors to learning burnout[ 13 ]. Secondly, short video addiction may weaken college students' intrinsic and extrinsic learning motivation [ 32 ], and a decrease in motivation can exacerbate the severity of burnout [ 33 ]. Additionally, addiction behavior may result in adverse emotional reactions such as the sense of study-weariness and low achievement, bad mood, and psychological problems like anxiety and depression [ 34 ],[ 35 ], which are all manifestations of learning burnout. Based on the above, short video addiction might exert detrimental effects on individuals' routine learning behaviors, cognitive functions, emotional states, and psychological well-being, ultimately contributing to the development of learning burnout. Accordingly, this study put forward Hypothesis 2: Short video addiction significantly and positively predicts English learning burnout among college English majors. 2.3. Short Video Addiction and Academic Ability Self-efficacy Academic self-efficacy is grounded in Social Cognitive Theory, which posits that academic self-efficacy refers to learners' subjective assessment and confidence in their own learning abilities [ 36 ]. Pintrich and De Groot [ 18 ] classified academic self-efficacy into two separate dimensions: ability and behavior, and this was accepted in many studies [ 19 ],[ 20 ]. Academic ability self-efficacy pertains to an individual's assessment and confidence in their ability to effectively accomplish academic tasks, attain favourable outcomes, and evade academic setbacks [ 19 ]. According to Bandura [ 36 ], an individual's behavior may affects their self-efficacy through individual, behavioral, and environmental factors. Thus, college students' addictive behaviors towards short videos might affect their self-efficacy in learning ability. Although no direct link between short video addiction and academic self-efficacy or academic ability self-efficacy has been found, the negative connection between academic self-efficacy and other types of media addiction has been verified. For example, Yan et al. [ 20 ] confirmed from their study that smart phone addiction significantly and negatively predicted academic self-efficacy among secondary-vocational-school students. Their study defined academic self-efficacy as encompassing self-efficacy in both learning ability and learning behavior. Given the close relation between academic self-efficacy and academic ability self-efficacy [ 18 ] and the overlapping of smartphone addiction and short video addiction [ 8 ],[ 10 ], it's highly possibly that short video addiction is negatively related to academic ability self-efficacy. Accordingly, this study introduced Hypothesis 3: short video addiction significantly and negatively predicts English academic ability self-efficacy among the college English majors. 2.4. Direct and Mediating Effects of Academic Ability Self-efficacy According to the Self-system Processes Model of Learning Engagement [ 37 ], students need to initially recognize and develop sufficient confidence in their learning abilities as a precursor to attaining comprehensive emotional, behavioral, and cognitive engagement within the learning process. Individuals with high levels of self-efficacy consistently believe in their ability to achieve positive academic outcomes, thereby demonstrating strong motivation and increased dedication [ 38 ]. Research has consistently highlighted a noteworthy correlation between academic self-efficacy and learning engagement, with academic ability self-efficacy having higher explanatory power than academic behavior self-efficacy [ 21 ]. Thus, this study proposed H4: English academic ability self-efficacy significantly positively predicts English learning engagement among the college English majors. High academic self-efficacy enables students to develop strong critical thinking abilities and confidence [ 39 ]. Conversely, lower academic self-efficacy leads to increased academic procrastination among students, consequently resulting in learning burnout [ 40 ]. Li and Chen [ 41 ] confirmed in their study on undergraduate English majors that academic self-efficacy predicts learning burnout, with higher levels of academic self-efficacy predicting lower levels of learning burnout. Moreover, students with lower levels of self-efficacy in learning abilities and learning behaviors tend to experience more severe learning burnout [ 22 ]. Thus, this study proposed Hypothesis 5: English academic ability self-efficacy significantly negatively predicts English learning burnout among college English majors. According to Bandura's Triadic Reciprocal Determinism [ 43 ], there exists a reciprocal and interactive relationship between an individual's behavior, cognition, and the environment. Specifically, an individual's behavioral patterns can significantly shape their cognitive processes, and conversely, their cognitive processes can profoundly influence their behavioral tendencies. Accordingly, students' addictive behaviors may exert influence on their academic ability self-efficacy, and this self-efficacy plays a potential role in shaping their engagement and burnout in learning. Therefore, based on H3 and H4, this study introduced H6: English academic ability self-efficacy acts as a mediator in the relationship between short video addiction and English learning engagement. Likewise, based on H3 and H5, this study posed H7: English academic ability self-efficacy acts as a mediator in the relationship between short video addiction and English learning burnout among the college English majors. In summary, short video addiction may exert direct influence on college English majors' engagement, burnout, and academic ability self-efficacy in their English learning; and English academic ability self-efficacy may mediate the association between short video addiction and English learning engagement, as well as between short video addiction and English learning burnout. Hence, this study presented a hypothesized model (see Fig. 1 ), aiming to offer a structured approach for exploring the intricate dynamics of variables and a guide for designing research instruments. 3. Methodology 3.1. Procedure This study conducted an online survey in December 2023, with 500 electronic questionnaires distributed to students from four colleges in Guangxi, China.The link to the questionnaire was distributed to students via WeChat, along with detailed information about the purpose of the research, the anonymity of responses, confidentiality of data, and the voluntary nature of participation and withdrawal. After reading this information, they could choose to consent and complete the questionnaire or decline by exiting it. The survey required approximately 10–15 minutes for completion, and each student was allowed to submit responses only once to ensure the uniqueness and authenticity of the data. To ensure the quality and validity of the collected data, the online platform was configured to require students to complete all items in the questionnaire before submission, ensuring no unanswered sections or missing critical data. After collecting all responses, the researchers conducted further screening to exclude invalid questionnaires. Specifically, responses with unrealistic completion times, such as those completed in under 3 minutes, were deemed insufficient to reflect thoughtful and accurate answers. Additionally, questionnaires exhibiting uniform or repetitive answer patterns (e.g., selecting the same option throughout) were manually reviewed and excluded if identified as invalid. 3.2. Participants Out of the 500 collected responses, a total of 444 valid questionnaires were obtained, which gives an effective recovery rate of 88.8%. Among the respondents, 104 (23.4%) were male, while the remaining 340 (76.6%) were female. The distribution across academic years included 45 freshmen (10.1%), 289 sophomores (65.1%), 83 juniors (18.7%), and 27 seniors (6.1%). All participants were English majors, each having completed at least one semester of specialized English coursework. 3.3. Measures 3.3.1 Short Video Addiction Scale This study adopted the Short Video Addiction Scale for College Students developed by Qin et al. [ 43 ]. It's a fourteen-item scale with four dimensions: inability to control craving (ICC), anxiety and feeling lost (AFL), withdrawal and escape (WE), and productivity loss (PL). Items such as “You feel restless without watching short videos“, “When you feel lonely, you watch short videos on your phone”, and “It is difficult for you to uninstall the short video app” were included in the scale. The total scale demonstrates an internal consistency reliability coefficient of 0.91, while the individual sub-dimensions range in their reliability from 0.76 to 0.89. The scale adopts a five-point Likert format with 1 for completely disagree, and 5 for completely agree. 3.3.2 English Learning Engagement Scale The English Learning Engagement Scale in this study was adapted from the scale compiled by Fang et al. [ 44 ] to align with the current study context. The scale consists of three dimensions: vigor (V), dedication (D), and absorption (A). It comprises 17 items such as “I find learning English very valuable and meaningful”, “In English learning, I enjoy exploring new problems”, and “I have abundant energy when learning English”. The scale's internal consistency reliability coefficient stands at 0.949, while its Cronbach's alpha coefficient for each dimension falls within a range from 0.736 to 0.845. A 7-point scoring system is employed, with respondents indicating level of agreement on the scale where 1 represents “never” and 7 indicates “always”. 3.3.3 English Learning Burnout Scale The measurement of the English learning burnout of college English majors was conducted using the scale developed by Wu & Dai [ 45 ]. The original scale was adapted to the learning context with regard to burnout among college English majors. The original scale's internal consistency reliability coefficient stands at 0.890, and its Cronbach's alpha coefficient for each dimension ranges from 0.684 to 0.860. The current scale retains the three dimensions of the original: academic alienation (AA), low achievement (LA), and physical and mental exhaustion (PME). It includes 15 items, such as “I perform poorly in English learning and feel like giving up”, “I often feel empty inside and don't know what to do”, and “I can effectively solve problems that arise in my learning”. This scale adopts a Likert 5-point scoring method, with 1 indicating "always" and 5 presenting "never". 3.3.4 English Academic Ability Self-efficacy Scale To assess the English academic ability self-efficacy of Chinese college English majors, the scale of Liang's [ 19 ] was adapted to align with the study context. Liang constructed the scale based on the framework by Pintrich & DeGroot [ 18 ], distinguishing academic self-efficacy into academic ability and academic behavior dimensions. The academic ability self-efficacy scale has 11 items, with a Cronbach's alpha of 0.820. Items, such as “I believe I have the ability to achieve good results in English learning”, “I believe I can grasp the content taught by the teacher in English class in a timely manner”, and “I enjoy choosing challenging English learning tasks”, were included. A Likert 5-point scoring method is adopted, where 1 represents completely disagree, while 5 represents always agree. 3.4 Data Analysis In this study, SPSS 26 and Smart PLS 4.1 were utilized as statistical tools for conducting Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis, which is a comprehensive statistical analysis method. The selection of PLS-SEM for data analysis stems from its lenient requirements regarding sample size and its capability to evaluate relatively complex models, especially when the model encompasses formative constructs (Hair et al., 2019). PLS-SEM has gained widespread acceptance within the social sciences. Data analysis techniques included common method bias (CMB) assessment, reliability and validity testing, structural model explanatory power analysis, path tracing, and mediation effect testing. 4. Results 4.1. Common Method Bias To assess CMB in the statistical analysis, this study applied Kock's [ 46 ] full covariance test to examine the variance inflation factor (VIF) values. Full covariance test results in Table 1 shows that the VIF value for A is 2.460, D 3.984, V 3.976, EAAS 1.548, ICC 2.181, AFL 3.778, WE 2.935, PL 2.527, PME 3.496, LA 2.304, and AA 3.185. According to the criteria established by Hair et al. [ 47 ], a VIF over 5 suggests a significant issue with CMB. All constructs in this study exhibited VIF values under 5, meaning that there exists no CMB affecting this study. Table 1 Structural model collinearity analysis. Construct A D V EAAS ICC AFL VIF 2.460 3.984 3.976 1.548 2.181 3.778 Construct WE PL PME LA AA VIF 2.935 2.527 3.496 2.304 3.185 Note: Absorption (A); Dedication (D); Vigor (V); English Academic Ability Self-efficacy (EAAS); Inability to Control Craving (ICC); Anxiety and Feeling Lost (AFL); Withdrawal and Escape (WE); Productivity Loss (PL); Physical and Mental Exhaustion (PME); Low Achievement (LA); Academic Alienation (AA). 4.2. Measurement Model 4.2.1. Measurement Model The study adopted indicator reliability, internal consistency reliability, convergent validity, and discriminant validity to assess the measurement model. Indicator loadings were used to evaluate indicator reliability. According to Hair et al. [ 47 ], indicator reliability is deemed satisfactory when indicator loading is 0.708 or greater. It can be seen in Table 2 that all the items demonstrate indicator loadings higher than 0.708 (0.735 ~ 0.935). The study assessed internal consistency reliability using Cronbach's alpha (CA) and composite reliability (CR). Hair et al. [ 47 ] recommended a CA and CR threshold of 0.7 or higher for acceptability. It's obvious that all metrics in this study surpasses the threshold of 0.7 (0.822 ~ 0.962), indicating acceptable reliability. The composite reliability (CR) for each construct also meets the required standard 0.7 (0.823 ~ 0.963), further supporting the reliability and validity of the measures. The study evaluated convergent validity using average variance extracted (AVE), with a minimum acceptable value set at 0.5, as recommended by Hair et al. [ 48 ]. In this study, all constructs have AVE values exceeding 0.5 (0.635 ~ 0.859), indicating favorable convergent validity. Short Video Addiction (SVA) is a reflective higher-order structure consisting of four lower order structures: inability to control craving (ICC), anxiety and feeling lost (AFL), withdrawal and escape (WE), productivity loss (PL) [ 43 ]. English Learning Engagement (ELE) is a reflective higher-order structure consisting of three lower order structures: vigor (V), dedication (D), and absorption (A) [ 44 ]. English Learning Burnout (ELB) is a reflective higher-order structure composed of three lower order structures: physical and mental exhaustion (PME), academic alignment (AA), and low achievement (LA) [ 45 ]. From Table 2 , it can be seen that the indicators of the reflective model all meet the ideal indicators suggested by Hair et al. [ 47 ]. Table 2 Convergent validity index of each variable. Variables Items Loading CA CR AVE A 5 0.845–0.922 0.941 0.942 0.809 D 6 0.815–0.926 0.942 0.945 0.777 V 6 0.846–0.894 0.941 0.942 0.771 EAAS 11 0.788-0.900 0.962 0.963 0.725 ICC 4 0.789–0.837 0.822 0.823 0.653 AFL 5 0.735–0.882 0.890 0.892 0.697 WE 3 0.893–0.927 0.894 0.895 0.826 PL 2 0.926–0.927 0.835 0.835 0.859 PME 4 0.862–0.907 0.911 0.912 0.791 AA 5 0.829–0.883 0.907 0.908 0.730 LA 7 0.831–0.901 0.945 0.945 0.752 SVA 2nd 3 0.880–0.919 0.897 0.900 0.765 ELB 2nd 3 0.878–0.923 0.888 0.890 0.818 ELE 2nd 3 0.910–0.935 0.913 0.932 0.851 Note: Absorption (A); Dedication (D); Vigor (V); English Academic Ability Self-efficacy (EAAS); Inability to Control Craving (ICC); Anxiety and Feeling Lost (AFL); Withdrawal and Escape (WE); Productivity Loss (PL)); Physical and Mental Exhaustion (PME); Academic Alienation (AA); Low Achievement (LA); Short Video Addiction (SVA); English Learning Burnout (ELB); English Learning Engagement (ELE). The study evaluate discriminant validity with the Heterotrait-monotrait ratio (HTMT). The HTMT value should be less than 0.90 for the measurement model to establish discriminant validity [ 49 ]. The study findings, as shown in Table 3 , fully meet this criterion, confirming the successful establishment of discriminant validity. Table 3 Discriminant validity assessment (HTMT). A AFL D EAAS ICC PL PME AA LA V A AFL 0.12 D 0.796 0.104 EAAS 0.38 0.475 0.279 ICC 0.187 0.872 0.155 0.424 PL 0.295 0.805 0.234 0.448 0.705 PME 0.102 0.651 0.045 0.447 0.596 0.534 AA 0.242 0.614 0.215 0.396 0.609 0.518 0.866 LA 0.128 0.603 0.057 0.367 0.580 0.496 0.764 0.735 V 0.790 0.094 0.886 0.383 0.172 0.256 0.081 0.269 0.143 WE 0.221 0.849 0.171 0.425 0.681 0.870 0.497 0.519 0.493 0.198 Note: Note: Absorption (A); Anxiety and Feeling Lost (AFL); Dedication (D); English Academic Ability Self-efficacy (EAAS); Inability to Control Craving (ICC); Productivity Loss (PL); Physical and Mental Exhaustion (PME); Academic Alienation (AA); Low Achievement (LA); Vigor (V); Withdrawal and Escape (WE). 4.2.2. Structural Model In structural equation modeling, the primary focus lies in assessing the quality of the structural model, which entails examining its predictive and explanatory abilities. Subsequent evaluation criteria encompass tests for multicollinearity, determination coefficients (R 2 ), effect size (f 2 ), predictive relevance (Q 2 ), as well as the statistical significance and correlation of path coefficients. The multicollinearity test indicated that the VIF values (1.000-1.273) were all below 5, suggesting no significant multicollinearity [ 46 ]. The R 2 value, serving as an indicator of the model's explanatory power, spans from 0 to 1, with higher values indicating stronger explanatory power and lower values weaker explanatory power. As shown in Table 4 , the overall R 2 values for each construct in the model exceeded 0.1 (0.135–0.420), indicating a satisfactory level of explanatory power for the model [ 50 ]. Cohen et al. [ 51 ] posited that effect size is determined by f 2 values, and can be categorized into three distinct levels: small (f 2 = 0.02), medium (f 2 = 0.15), and large (f 2 = 0.35). The results indicated that SVA had a medium effect on EAAS (f² = 0.273), a negligible effect on ELE (f 2 = 0.001), and large effect on ELB (f 2 = 0.417). Additionally, EAAS had a small to medium effect on ELE (f² = 0.112) and ELB (f² = 0.037). Table 4 Model explanatory coefficients. Relationship R² f² VIF SVA→EAAS 0.214 0.273 1.000 SVA→ELE 0.135 0.001 1.273 EAAS→ELE 0.112 1.273 SVA→ELB 0.420 0.417 1.273 EAAS→ELB 0.037 1.273 As recommended by Shmueli & Sarstedt [ 52 ], this study assessed the model's predictivity through the implementation of the PLS prediction process. This involved predicting of potential measurement errors through a comparative analysis of the Root Mean Square Error (RMSE) between Partial Least Squares (PLS) and Linear Modeling (LM). A model is deemed to have strong predictivity when all differences between PLS and LM are less than 0, moderate predictivity when the majority of differences are less than 0, low predictivity when only a few differences are less than 0, and no predictivity when all differences are greater than 0 [ 51 ]. It can be seen in Table 5 that the majority of exogenous variable items exhibit lower values in comparison to those derived from the LM approach, indicating that the model has a moderate level of predictivity. Additionally, as presented in Table 5 , all Q 2 values are above 0, signifying adequate predictive relevance. Table 5 PLS predict. Indicators Q²predict PLS-SEM_RMSE LM_RMSE PLS_LM EAAS1 0.109 0.966 0.981 -0.015 EAAS2 0.156 1.031 1.052 -0.021 EAAS3 0.168 1.035 1.050 -0.015 EAAS4 0.126 1.025 1.052 -0.027 EAAS5 0.128 1.011 1.032 -0.021 EAAS6 0.165 1.026 1.029 -0.003 EAAS7 0.155 1.061 1.076 -0.015 EAAS8 0.160 1.021 1.039 -0.018 EAAS9 0.182 0.976 0.992 -0.016 EAAS10 0.155 1.011 1.028 -0.017 EAAS11 0.155 1.077 1.104 -0.027 PME1 0.313 0.917 0.921 -0.004 PME2 0.244 0.944 0.962 -0.018 PME3 0.270 1.002 1.014 -0.012 PME4 0.254 0.935 0.945 -0.010 AA1 0.266 0.962 0.977 -0.015 AA2 0.287 0.982 1.000 -0.018 AA3 0.232 0.980 0.995 -0.015 AA4 0.200 1.005 1.025 -0.020 AA5 0.203 1.016 1.035 -0.019 LA1 0.248 0.933 0.944 -0.011 LA2 0.243 0.880 0.893 -0.013 LA3 0.237 0.844 0.862 -0.018 LA4 0.206 0.948 0.953 -0.005 LA5 0.248 0.885 0.893 -0.008 LA6 0.254 0.861 0.870 -0.009 LA7 0.242 0.891 0.899 -0.008 A1 0.015 1.493 1.459 0.034 A2 0.026 1.385 1.391 -0.006 A3 0.017 1.384 1.358 0.026 A4 0.028 1.375 1.387 -0.012 A5 0.020 1.329 1.319 0.010 D1 0.000 1.550 1.405 0.145 D2 0.001 1.438 1.388 0.050 D3 0.006 1.382 1.328 0.054 D4 0.005 1.427 1.389 0.038 D5 0.009 1.415 1.441 -0.026 D6 0.006 1.401 1.395 0.006 V1 0.009 1.359 1.337 0.022 V2 0.012 1.349 1.311 0.038 V3 0.031 1.339 1.357 -0.018 V4 0.008 1.331 1.344 -0.013 V5 0.011 1.314 1.320 -0.006 V6 0.005 1.336 1.324 0.012 4.3. Analysis of Path Relationships To further understand the causal relationships between SVA, EAAS, ELE, and ELB in the model, path analysis was conducted (see in Table 6 & Fig. 2 ). This study first verified the five direct hypotheses, H1 - H5. Firstly, the path from SVA to ELE showed a negative coefficient (β=-0.033), yet it lacked statistical significance (p = 0.599 > 0.05). This statistical outcome implies that, while there is a tendency for college English majors addicted to short videos to show reduced levels of learning engagement, this tendency is not strong enough to be considered a significant finding. The non- significant result, thus, fails to support H1. It highlights the complexity of the relationship between short video addiction and English learning engagement, which may be influenced by other factors not fully accounted for in this analysis. Secondly, the path from SVA to ELB exhibited a strong and statistically significant positive correlation (β = 0.555, p < 0.001). This indicates that as the degree of short video addiction among college English majors increases, their level of English learning burnout also rises. This finding not only aligns precisely with the prediction proposed in H2 but is also in perfect harmony with the Media Dependency Theory. Thirdly, SVA exerted a significant negative influence on EAAS (β=-0.463, p < 0.001). This robust statistical finding implies that as the severity of short video addiction intensifies among English major students, their self-perception of academic ability in English learning correspondingly diminishes. This outcome is in line with H3. Fourthly, H4 was supported by the significant positive impact of EAAS on ELE (β = 0.351, p < 0.001). This result indicates that students who hold higher self-efficacy in their English academic abilities are more likely to demonstrate increased levels of engagement in English learning activities. Finally, the analysis revealed that EAAS had a significant negative influence on ELB (β=-0.164, p < 0.001). This means that college English majors with greater self-efficacy in their English academic abilities tend to experience lower levels of burnout in English learning. This finding is consistent with H5. Subsequently, this study carried out the verification of the two mediating effects. For the mediating path "SVA→EAAS→ELE", the path coefficient was significant (β = -0.163, p < 0.001). This indicates that EAAS mediates the relationship between SVA and ELE, thereby supporting H6. In terms of the effect size, the indirect effect along this path is -0.163. Additionally, when considering the direct effect of SVA on ELE from other analyses, the total effect amounts to -0.196. Consequently, the indirect effect accounts for approximately 83.16% of the total effect of short video addiction on students' English learning engagement, highlighting the dominant role of the indirect influence mediated by EAAS in this relationship. Moving on to the second mediating path "SVA→EAAS→ELB", the standardized path coefficient was also significant (β = 0.076, p = 0.001 < 0.005). This indicates that EAAS mediates the relationship between SVA and ELB, thus validating H7. Regarding the effect size, the indirect effect for this path is 0.076. By combining this with the direct effect of SVA on ELB, the total effect is computed as 0.631. Hence, the indirect effect accounts for approximately 12.04% of the total effect of short video addiction on students' English learning burnout, further demonstrating the mediating role of EAAS in this particular relationship. Table 6 Hypothesis Testing. Hypo Relationship Beta T Values p Values Support H1 SVA→ELE -0.033 0.526 0.599 Not supported H2 SVA→ELB 0.555 13.436 0.000 Supported H3 SVA→EAAS -0.463 11.486 0.000 Supported H4 EAAS→ELE 0.351 7.583 0.000 Supported H5 EAAS→ELB -0.164 3.741 0.000 Supported H6 SVA→EAAS→ELE -0.163 6.438 0.000 Supported H7 SVA→EAAS→ELB 0.076 3.415 0.001 Supported 5. Discussion 5.1. The Influence Mechanism of Short Video Addiction on English Learning Engagement The path analysis yielded an unexpected outcome where no significant correlation was found between short video addiction and English learning engagement, leading to the non-verification of Hypothesis 1. This implies that short-video addiction among college English majors does not consistently lead to a substantial decrease in their English learning engagement. When comparing this result with prior research, it was observed that different researchers have presented diverse conclusions regarding the correlation between media addiction, such as smartphone addiction, and learning engagement. For instance, Zhen et al. [ 15 ] identified a strong negative correlation, whereas Yang et al. [ 16 ] reported no significant association. Such differences highlight the variability in findings within the field. The observed non-significant relationship challenges the conventional understanding that media addiction would straightforwardly and significantly reduce learning engagement. A possible explanation for this might be that learning engagement is a multidimensional and complex concept and is influenced by various factors, including individual and environmental factors [ 24 ]. Existing studies have shown that environmental factors such as positive school climate and strong school identification [ 53 ], as well as individual characteristics including grit and academic buoyancy, high academic self-efficacy, and high motivation [ 54 ],[ 55 ],[ 56 ] acted as protective factors of students' learning engagement. Hence, with these buffers, short video addiction exerts limited impact on English learning engagement, and students can sustain high level of learning engagement even in the presence of short video addiction. However, even though the relationship was not statistically significant, the observed trend cannot be completely ignored. This trend still indicates that short video addiction may have some influence on learning engagement, albeit not to a significant extent. Therefore, it is advisable to monitor students' short video usage and take measures to minimize any potential negative impacts it may have on their learning engagement. This study contributes to the existing body of knowledge by emphasizing the importance of considering the intricate interplay of multiple factors when exploring the relationship between media addiction and learning engagement, rather than assuming a simple, direct negative correlation. 5.2. The Influence Mechanism of Short Video Addiction on English Learning Burnout The research findings clearly demonstrate that short-video addiction has a significant and positive impact on students' English learning burnout. This positive relationship is well-aligned with the Media Dependency Theory [ 12 ], which posits that an individual's degree of reliance on media profoundly influences their cognitive, emotional, and behavioral patterns. When individuals become overly dependent on certain media forms, such as short-video platforms, corresponding changes occur in their behavior and mental states within relevant domains, like English learning. This result is also consistent with some previous studies. Similar to research on mobile-phone addiction [ 57 ], where it was found that mobile-phone addiction is a significant positive predictor of learning burnout, short-video addiction, being a form of mobile-media use, also exhibits a positive correlation with English learning burnout. Such similarity further validates the existence of this relationship in the context of media-related addictions and learning burnout. There are several possible explanations for the observed relationship between short-video addiction and English learning burnout. One feasible explanation could be that due to prolonged obsession with mobile phones, students' cognitive faculties become blunt, necessitating a greater allocation of psychological resources to engage in learning activities, and this increase in cognitive load subsequently triggers avoidance of learning in psychology, and then in behavior [ 57 ]. Another plausible explanation could be that long-term addictive behavior can lead to difficulties in concentration, disruptions in time perception, a loss of behavioral control, and abandonment of life goals and future pursuits, all of which potentially harms students' cognition and attitude towards academics, ultimately resulting in learning burnout [ 58 ],[ 59 ]. It can be concluded that although short video addiction does not significantly predict English learning engagement, it significantly predicts English learning burnout in a negative way. This supports the claim that engagement and burnout are not absolutely opposites, as they differ in their respective dimensions[ 23 ]. This study emphasizes the importance of addressing short video addiction to prevent and mitigate learning burnout among students. 5.3. Mediating Effects of English Academic Ability Self-efficacy The results demonstrated that English academic ability self-efficacy plays a significant mediating role in the relationship between short video addiction and English learning engagement, as well as between short video addiction and English learning burnout. This implies that as the intensity of short video addiction increases, individuals experience a notable decline in self-efficacy, which subsequently leads to decreased levels of learning engagement or heightened instances of learning burnout. These findings are consistent with the social cognitive theory [ 42 ], which posits that an individual's behavior can influence their cognitive processes, and vice versa, these cognitive processes can have a reciprocal impact on subsequent behavioral patterns. Specifically, in the context of this study, short video addiction, as a behavioral pattern, may negatively affect students' self-perception of their English academic ability, thereby influencing their cognitive evaluation of their learning abilities. This alteration in self-efficacy then affects their subsequent learning behaviors, either by decreasing engagement or increasing burnout. Furthermore, these findings provide strong support for other related studies. Individuals addicted to short video consumption often find it difficult to allocate sufficient time to their educational pursuits and tend to postpone academic tasks, which may lead to academic failure and ultimately undermine their confidence in learning ability [ 60 ],[ 61 ].Consequently, with impaired academic ability self-efficacy, individuals are less likely to engage in English learning and are more prone to experiencing learning burnout [ 22 ]. On the contrary, individuals with higher self-efficacy in academic abilities are less likely to be addicted to short videos [ 62 ]. High self-efficacy fosters positive learning behaviors, clear goal setting, optimistic outcome expectations, and robust social support networks; these factors collectively enhance students' engagement in English language learning while reducing feelings of burnout [ 38 ]. Therefore, students' English academic ability self-efficacy serve as a protective shield against decrease in engagement and emergence of burnout in English learning due to short video addiction. This study recommend fostering and strengthening students' English academic ability self-efficacy to counteract the potential negative effects of short video addiction, thereby sustaining their learning engagement and preventing burnout. 5.4. Limitations and Future Directions While the hypotheses were validated, this research has several limitations that warrant discussion. First, the study primarily focused on the overall influence of short videos on learning engagement and burnout, without investigating the effects of specific types of short video content. This lack of granularity meant that the study was unable to determine how variations in content type, format, or duration might differentially affect learning outcomes. Future studies could provide deeper insights by conducting more detailed analyses of these factors, potentially revealing distinct pathways through which short video consumption impacts educational performance. Secondly, the study did not take into account key environmental factors such as school climate, teacher-student relationships, and home environment. These contextual variables may significantly shape students' responses to short video content, potentially moderating the relationship between short video addiction and learning engagement or burnout. Including these factors in future studies could enhance the robustness of the findings by highlighting how different groups of students are influenced by short video consumption in varied educational settings. Lastly, the study exclusively examined the negative aspects of short video addiction in learning, overlooking the potential benefits these platforms might offer. Future research should explore how short videos could be strategically used as educational tools to enhance student engagement and enrich learning experiences. By shifting the focus towards potential positive uses, researchers could investigate how these platforms contribute to learning when integrated into teaching strategies. 6. Conclusions This study revealed that short video addiction does not exert a direct influence on English learning engagement. Instead, its impact is mediated through shaping English academic ability self-efficacy. Conversely, short video addiction directly contributes to English learning burnout while also influencing it indirectly via English academic ability self-efficacy. The findings suggest that to improve engagement and reduce burnout in English learning among college students, apart from paying attention to whether they are addicted to media such as short videos, we also need to emphasize and enhance their sense of English academic ability self-efficacy. Students' engagement and burnout in learning during college years not only significantly influence their academic performance [ 63 ],[ 64 ], but also serve as indicative factors for their potential achievement strategies in future careers [ 65 ]. College students are the main force for future social advancement, so their academic endeavors have profound implications for the sustainable development of both individuals and the society. Therefore, this study has some theoretical and practical value. 6.1. Theoretical Contributions This study, based on Social Cognitive Theory and Media Dependency Theory, offers several significant theoretical contributions. Traditional Media Dependence Theory mainly emphasizes the direct impact of media use on individual cognitive, emotional, and behavioral patterns. However, when it comes to short-video addiction, this study reveals that its impact on learning engagement and learning burnout is far from a simple linear relationship. While short-video addiction shows a direct positive prediction of learning burnout, it has no direct predictive power over learning engagement. This calls for further refinement and improvement of the Media Dependence Theory when it comes to explaining the effects of emerging media like short-videos on specific learning behaviors. By taking into account the differential impacts on different learning dimensions, this study provides a new research perspective and empirical basis for the application of this theory in the new media environment. By verifying the mediating role of English academic ability self-efficacy in the relationship between short video addiction and learning engagement and burnout, this research uncovers the complex mechanisms underlying media - learning interactions. By correlating short video addiction, a media - related behavior, with learning engagement and burnout in English learning, this research extends Social Cognitive Theory into the emerging domain of short-video - induced academic impacts. This not only demonstrates how excessive short video consumption (an environmental factor) influences cognitive states (e.g., English academic ability self-efficacy) and subsequent learning behaviors (e.g., engagement and burnout), but also has the potential to prompt the further development of Social Cognitive Theory in better explaining the dynamic interplay between environmental factors and learning behaviors. 6.2 Practical Implications In the digital age, short videos have become a significant influence on college students' English learning. This study underscores the necessity of fostering English academic ability self-efficacy and encouraging responsible media use to enhance learning engagement and mitigate burnout. These findings offer practical guidance for students, teachers, and schools to improve English learning outcomes. Students, as the primary agents of their learning, should strive to balance short video consumption with academic responsibilities. Developing self-discipline and time management skills is vital. Strategies such as setting SMART goals (Specific, Measurable, Achievable, Relevant, and Time-bound), engaging in self-monitoring, and seeking peer support can regulate their media use and enhance their academic focus. To boost English academic ability self-efficacy, students can draw inspiration from successful peers, cultivate a positive mindset, and maintain physical well-being through regular exercise. Furthermore, shifting from passive video consumption to purposeful educational use—such as watching English grammar tutorials or vocabulary lessons—can lead to better learning outcomes. Teachers play a critical role in shaping students’ academic behaviors. By sharing their experiences with online educational videos and integrating such resources into their teaching practices, educators can model effective media use. Creating and disseminating short video content featuring English learning tips can encourage two-way engagement. Additionally, incorporating short videos into classroom activities—like analyzing English speeches or facilitating discussions on video content—can foster enthusiasm and participation. For students exhibiting signs of short video addiction, teachers should intervene early, set personalized learning objectives, and provide constructive feedback to reinforce their confidence in English learning. Educational institutions serve as key platforms for implementing holistic strategies to support students’ academic growth. Schools can introduce media literacy courses to equip students with the skills to critically assess and responsibly use short video content. Hosting English short video creation contests offers opportunities for students to showcase their linguistic abilities while building their self-efficacy. Furthermore, creating dedicated English learning spaces on campus—where English short videos are featured—can foster an immersive and motivational learning environment. Declarations Ethics approval and consent to participate This research was conducted in accordance with the Declaration of Helsinki, and approved by City University Malaysia Research Ethics Committee (CUM-REC), as the author is HPD candidate of this university. Informed consent was obtained from all subjects involved in the study. All participants were over the age of 16, and informed consent was obtained from each of them to participate in this study. Consent for publication Not applicable. Availability of data and material The datasets generated in the study are available from the corresponding author on reasonable request. Competing interests The author declares no conflicts of interest. Funding The study received no external finding. 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J Vocat Behav. 2009;75(2):162–72. https://doi.org/10.1016/j.jvb.2009.03.009 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5885393","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":406591559,"identity":"7c4cd18f-32c4-4c44-8158-b23b185a8975","order_by":0,"name":"Huang Kuilong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYBACCQbGxgMMDAcgvA9AzMZOWEsDXAvjDJAWZoJawOohWph5wCQBLZLthxsO8+64k9jffvbwa5tf2+T5mBkYP3zMwa1FmicRqOXMs8QZZ/LSrHP7bhu2MTMwS87chluLHANIS9vhxIYDOWbGuT23GYFa2Jh58WnhfwjRMv/8GzNjy57b9gS1SEtAbdlwI8f4McOP24kEtUjOeNhwcG7bYeONN96YMfY23E5uY2ZsxusXifPpDx+8bTssO+98jvGHH39u285vbz744SMeLSDAxAOh2SQY20A0YwN+9SAlPyA08weGPwQVj4JRMApGwQgEAEsLXDApmfy0AAAAAElFTkSuQmCC","orcid":"","institution":"Chongzuo College for Preschool Education","correspondingAuthor":true,"prefix":"","firstName":"Huang","middleName":"","lastName":"Kuilong","suffix":""}],"badges":[],"createdAt":"2025-01-23 05:55:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5885393/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5885393/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74925943,"identity":"308b1100-f1f1-41a7-9b4e-f0b6704e4724","added_by":"auto","created_at":"2025-01-28 11:28:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48913,"visible":true,"origin":"","legend":"\u003cp\u003eHypothesized model.\u003c/p\u003e","description":"","filename":"floatimage18.png","url":"https://assets-eu.researchsquare.com/files/rs-5885393/v1/bfd121d9e1d4273575410a1e.png"},{"id":74925950,"identity":"4217f6a5-1964-4577-a0f7-799df4e018e8","added_by":"auto","created_at":"2025-01-28 11:28:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":34933,"visible":true,"origin":"","legend":"\u003cp\u003eHypothesis testing.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5885393/v1/7854a3a0f2a712a3da167101.png"},{"id":84180855,"identity":"c1106049-1c0b-4660-9fe5-abc7c9996990","added_by":"auto","created_at":"2025-06-09 03:46:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1461087,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5885393/v1/35371a45-2ade-477e-90b6-afec4dcdf978.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unveiling the Complex Mechanism of Short Video Addiction on English Learning Engagement and Burnout among College EFL Students","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn recent years, short video platforms such as TikTok and Bilibili have evolved into crucial channels for entertainment, information dissemination, knowledge acquisition, and social interaction. Short videos are now indispensable in modern society. As reported by Chinese state media [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], by June 2023, China\u0026rsquo;s short video user base had grown to 1.026\u0026nbsp;billion, indicating an immense user scale in the country. Furthermore, a large-scale survey involving 11,267 college students throughout China revealed that over 80% of respondents frequently engaged with short videos and over 70% acknowledging the addictive nature of this media form [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Short videos enrich college students\u0026rsquo; lives, but excessive usage becomes worrying.\u003c/p\u003e \u003cp\u003eThe user-friendly interface and entertainment features of short video applications amplify college students' inclination towards excessive usage, rendering impulse control challenging [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Actually, terms such as \u0026ldquo;problematic use\u0026rdquo;, \u0026ldquo;dependence\u0026rdquo;, and \u0026ldquo;addiction\u0026rdquo; are frequently employed to describe the phenomenon of excessive short video consumption. \u0026ldquo;Problematic use\u0026rdquo; often emphasizes the disruptive impacts on daily functioning [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], while \u0026ldquo;dependence\u0026rdquo; highlights a psychological or physical reliance [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, \u0026ldquo;addiction\u0026rdquo; represents a more profound and complex manifestation, taking it a step further than the previous two terms. It not only encompasses the elements of disruption and reliance but also involves a loss of self - control and a high degree of perseverance in the face of negative consequences[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Considering the purpose of this study, which aims to explore the deeper behavioral and psychological mechanisms underlying excessive short video consumption, the term \u0026ldquo;addiction\u0026rdquo; was adopted. Short video addiction refers to an individual's excessive and prolonged engagement with short video applications, demonstrating an inability to regulate the frequency and duration of usage, ultimately resulting in detrimental effects on their physical, mental, and behavioral well-being [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Since short video users mainly employ mobile phones and network technology to consume videos, short video addiction shares some characteristics of the Internet and mobile phone addiction, which were concluded as excessive and improper use, withdrawal and tolerance symptoms, compulsive behaviors, and functional impair [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e],[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e],[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e],[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Thus, existing studies on Internet and mobile phone addiction provide valuable insights for the study of short video addiction.\u003c/p\u003e \u003cp\u003eAccording to the Media Dependence Theory [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], the greater an individual's reliance on media platforms (such as the Internet, mobile phones, and short videos) determines the extent to which their cognitive, emotional, and behavioral patterns are influenced by these media outlets. Existing studies have verified the effects of smartphone addiction on learning engagement and learning burnout. Specifically, it has been shown that smartphone addiction has a direct and significant positive prediction of learning burnout[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, the impact of smartphone addiction on learning engagement remains a contentious issue. While some studies have found a significant relationship between the two [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e][\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], others argue that a direct association is absent [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Then, what are the impacts of short video addiction on learning engagement and learning burnout? Against this backdrop, the effects of short-video addiction on learning engagement and learning burnout remain under-explored. Short video platforms have unique characteristics, such as highly engaging content personalized by recommendation algorithm and easily consumable short-form videos [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], which, unlike general smartphone addiction, may have distinct impacts on students\u0026rsquo; learning, with both potential distractions and educational value depending on usage. What's more, students' academic ability self-efficacy\u0026ndash;a crucial part of academic self-efficacy [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e],[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e],[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]-- has been empirically confirmed to have significant influence on learners\u0026rsquo; engagement and burnout in their learning process [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e],[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, few studies notice possible association between short video addiction and academic ability self-efficacy and the potential intermediate mechanism of academic ability self-efficacy in the interplay between short video addiction and learning engagement, as well as between short video addiction and learning burnout.\u003c/p\u003e \u003cp\u003eTo further illuminate the intricacies of short video addiction in the academic context, this study investigated influence of short video addiction on learning engagement and burnout, and the mediating role of academic ability self-efficacy in the two relations with a focus on the Chinese college English majors. It's expected that the findings not only offer theoretical contributions to the field of media dependence theory and educational psychology but also have practical implications for guiding college English major students in achieving a healthy balance between media consumption and academic pursuits, ultimately bolstering engagement and alleviating burnout for more effective and fulfilling learning experiences.\u003c/p\u003e"},{"header":"2. Literature Review and Hypothesis","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Short Video Addiction and Learning Engagement\u003c/h2\u003e \u003cp\u003eLearning engagement refers to the active, fulfilling, stable, and voluntary involvement demonstrated by individuals during learning activities [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. According to Fredricks et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], learning engagement consists of emotional, behavioral, and cognitive dimensions, with emotional engagement involving learners' emotions, behavioral engagement relating to their effort and attention, and cognitive engagement encompassing their usage of strategies and self-regulation during learning. Short video addiction may potentially influence English learning engagement for three main reasons. Firstly, college students' learning engagement is greatly influenced by their academic emotions, with learning engagement and negative academic emotions, such as anxiety, being inversely connected in language learning process [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. It has been verified that short video addiction might lead to prolonged feelings of anxiety [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and anxiety might reduce the level of engagement in language learning [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Secondly, the time and effort students invest on learning, along with their successful learning experiences and achieved accomplishments, are critical factors influencing their learning engagement[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, short video addiction can consume a considerable amount of time and energy dedicated to learning, leading to distractions and a decline in attention control abilities [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This decrease in behavioral engagement may result in poor academic performance, which subsequently might reduce learning engagement in a cyclical manner. Thirdly, short video addiction can also affect students\u0026rsquo; cognitive engagement by exacerbating their tendency toward inert thinking. When encountering study problems, addicted individuals may habitually opt to directly search for answers on their phones, thus engaging less in personal analysis and critical thinking [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In conclusion, short videos addiction can negatively influence individuals' learning in behavior, emotion, and cognition, subsequently leading to a decrease in their learning engagement levels. Therefore, this study proposed Hypothesis 1: Short video addiction significantly and negatively predicts English learning engagement among college English majors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Short Video Addiction and Learning Burnout\u003c/h2\u003e \u003cp\u003eLearning burnout is a sustained, negative, learning-related state that reflects students' adverse reactions to learning [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. As concluded by Xu et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], in physiology, learning burnout is evidenced by recurrent exhaustion during study sessions; in emotion, it leads to a diminished interest in learning coupled with a reduced sense of accomplishment; and in behavior, this burnout manifests as decreased learning efficiency, a cursory approach to studies, and actions such as absenteeism or inattention in the classroom. Short video addiction may lead to learning burnout based on the following reasons. Firstly, excessive engagement with short videos often disrupts time management, significantly reducing the time allocated for essential activities such as studying and sleeping. This disruption negatively affects sleep quality, learning efficiency, and academic procrastination [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e],[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], all of which are significant contributors to learning burnout[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Secondly, short video addiction may weaken college students' intrinsic and extrinsic learning motivation [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and a decrease in motivation can exacerbate the severity of burnout [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Additionally, addiction behavior may result in adverse emotional reactions such as the sense of study-weariness and low achievement, bad mood, and psychological problems like anxiety and depression [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e],[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], which are all manifestations of learning burnout. Based on the above, short video addiction might exert detrimental effects on individuals' routine learning behaviors, cognitive functions, emotional states, and psychological well-being, ultimately contributing to the development of learning burnout. Accordingly, this study put forward Hypothesis 2: Short video addiction significantly and positively predicts English learning burnout among college English majors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Short Video Addiction and Academic Ability Self-efficacy\u003c/h2\u003e \u003cp\u003eAcademic self-efficacy is grounded in Social Cognitive Theory, which posits that academic self-efficacy refers to learners' subjective assessment and confidence in their own learning abilities [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Pintrich and De Groot [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] classified academic self-efficacy into two separate dimensions: ability and behavior, and this was accepted in many studies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e],[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Academic ability self-efficacy pertains to an individual's assessment and confidence in their ability to effectively accomplish academic tasks, attain favourable outcomes, and evade academic setbacks [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. According to Bandura [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], an individual's behavior may affects their self-efficacy through individual, behavioral, and environmental factors. Thus, college students' addictive behaviors towards short videos might affect their self-efficacy in learning ability. Although no direct link between short video addiction and academic self-efficacy or academic ability self-efficacy has been found, the negative connection between academic self-efficacy and other types of media addiction has been verified. For example, Yan et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] confirmed from their study that smart phone addiction significantly and negatively predicted academic self-efficacy among secondary-vocational-school students. Their study defined academic self-efficacy as encompassing self-efficacy in both learning ability and learning behavior. Given the close relation between academic self-efficacy and academic ability self-efficacy [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and the overlapping of smartphone addiction and short video addiction [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e],[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], it's highly possibly that short video addiction is negatively related to academic ability self-efficacy. Accordingly, this study introduced Hypothesis 3: short video addiction significantly and negatively predicts English academic ability self-efficacy among the college English majors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Direct and Mediating Effects of Academic Ability Self-efficacy\u003c/h2\u003e \u003cp\u003eAccording to the Self-system Processes Model of Learning Engagement [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], students need to initially recognize and develop sufficient confidence in their learning abilities as a precursor to attaining comprehensive emotional, behavioral, and cognitive engagement within the learning process. Individuals with high levels of self-efficacy consistently believe in their ability to achieve positive academic outcomes, thereby demonstrating strong motivation and increased dedication [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Research has consistently highlighted a noteworthy correlation between academic self-efficacy and learning engagement, with academic ability self-efficacy having higher explanatory power than academic behavior self-efficacy [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Thus, this study proposed H4: English academic ability self-efficacy significantly positively predicts English learning engagement among the college English majors.\u003c/p\u003e \u003cp\u003eHigh academic self-efficacy enables students to develop strong critical thinking abilities and confidence [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Conversely, lower academic self-efficacy leads to increased academic procrastination among students, consequently resulting in learning burnout [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Li and Chen [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] confirmed in their study on undergraduate English majors that academic self-efficacy predicts learning burnout, with higher levels of academic self-efficacy predicting lower levels of learning burnout. Moreover, students with lower levels of self-efficacy in learning abilities and learning behaviors tend to experience more severe learning burnout [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Thus, this study proposed Hypothesis 5: English academic ability self-efficacy significantly negatively predicts English learning burnout among college English majors.\u003c/p\u003e \u003cp\u003eAccording to Bandura's Triadic Reciprocal Determinism [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], there exists a reciprocal and interactive relationship between an individual's behavior, cognition, and the environment. Specifically, an individual's behavioral patterns can significantly shape their cognitive processes, and conversely, their cognitive processes can profoundly influence their behavioral tendencies. Accordingly, students' addictive behaviors may exert influence on their academic ability self-efficacy, and this self-efficacy plays a potential role in shaping their engagement and burnout in learning. Therefore, based on H3 and H4, this study introduced H6: English academic ability self-efficacy acts as a mediator in the relationship between short video addiction and English learning engagement. Likewise, based on H3 and H5, this study posed H7: English academic ability self-efficacy acts as a mediator in the relationship between short video addiction and English learning burnout among the college English majors.\u003c/p\u003e \u003cp\u003eIn summary, short video addiction may exert direct influence on college English majors' engagement, burnout, and academic ability self-efficacy in their English learning; and English academic ability self-efficacy may mediate the association between short video addiction and English learning engagement, as well as between short video addiction and English learning burnout. Hence, this study presented a hypothesized model (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), aiming to offer a structured approach for exploring the intricate dynamics of variables and a guide for designing research instruments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Procedure\u003c/h2\u003e \u003cp\u003eThis study conducted an online survey in December 2023, with 500 electronic questionnaires distributed to students from four colleges in Guangxi, China.The link to the questionnaire was distributed to students via WeChat, along with detailed information about the purpose of the research, the anonymity of responses, confidentiality of data, and the voluntary nature of participation and withdrawal. After reading this information, they could choose to consent and complete the questionnaire or decline by exiting it.\u003c/p\u003e \u003cp\u003eThe survey required approximately 10\u0026ndash;15 minutes for completion, and each student was allowed to submit responses only once to ensure the uniqueness and authenticity of the data. To ensure the quality and validity of the collected data, the online platform was configured to require students to complete all items in the questionnaire before submission, ensuring no unanswered sections or missing critical data. After collecting all responses, the researchers conducted further screening to exclude invalid questionnaires. Specifically, responses with unrealistic completion times, such as those completed in under 3 minutes, were deemed insufficient to reflect thoughtful and accurate answers. Additionally, questionnaires exhibiting uniform or repetitive answer patterns (e.g., selecting the same option throughout) were manually reviewed and excluded if identified as invalid.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Participants\u003c/h2\u003e \u003cp\u003eOut of the 500 collected responses, a total of 444 valid questionnaires were obtained, which gives an effective recovery rate of 88.8%. Among the respondents, 104 (23.4%) were male, while the remaining 340 (76.6%) were female. The distribution across academic years included 45 freshmen (10.1%), 289 sophomores (65.1%), 83 juniors (18.7%), and 27 seniors (6.1%). All participants were English majors, each having completed at least one semester of specialized English coursework.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Measures\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Short Video Addiction Scale\u003c/h2\u003e \u003cp\u003eThis study adopted the Short Video Addiction Scale for College Students developed by Qin et al. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. It's a fourteen-item scale with four dimensions: inability to control craving (ICC), anxiety and feeling lost (AFL), withdrawal and escape (WE), and productivity loss (PL). Items such as \u0026ldquo;You feel restless without watching short videos\u0026ldquo;, \u0026ldquo;When you feel lonely, you watch short videos on your phone\u0026rdquo;, and \u0026ldquo;It is difficult for you to uninstall the short video app\u0026rdquo; were included in the scale. The total scale demonstrates an internal consistency reliability coefficient of 0.91, while the individual sub-dimensions range in their reliability from 0.76 to 0.89. The scale adopts a five-point Likert format with 1 for completely disagree, and 5 for completely agree.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 English Learning Engagement Scale\u003c/h2\u003e \u003cp\u003eThe English Learning Engagement Scale in this study was adapted from the scale compiled by Fang et al. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] to align with the current study context. The scale consists of three dimensions: vigor (V), dedication (D), and absorption (A). It comprises 17 items such as \u0026ldquo;I find learning English very valuable and meaningful\u0026rdquo;, \u0026ldquo;In English learning, I enjoy exploring new problems\u0026rdquo;, and \u0026ldquo;I have abundant energy when learning English\u0026rdquo;. The scale's internal consistency reliability coefficient stands at 0.949, while its Cronbach's alpha coefficient for each dimension falls within a range from 0.736 to 0.845. A 7-point scoring system is employed, with respondents indicating level of agreement on the scale where 1 represents \u0026ldquo;never\u0026rdquo; and 7 indicates \u0026ldquo;always\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 English Learning Burnout Scale\u003c/h2\u003e \u003cp\u003eThe measurement of the English learning burnout of college English majors was conducted using the scale developed by Wu \u0026amp; Dai [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The original scale was adapted to the learning context with regard to burnout among college English majors. The original scale's internal consistency reliability coefficient stands at 0.890, and its Cronbach's alpha coefficient for each dimension ranges from 0.684 to 0.860. The current scale retains the three dimensions of the original: academic alienation (AA), low achievement (LA), and physical and mental exhaustion (PME). It includes 15 items, such as \u0026ldquo;I perform poorly in English learning and feel like giving up\u0026rdquo;, \u0026ldquo;I often feel empty inside and don't know what to do\u0026rdquo;, and \u0026ldquo;I can effectively solve problems that arise in my learning\u0026rdquo;. This scale adopts a Likert 5-point scoring method, with 1 indicating \"always\" and 5 presenting \"never\".\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.3.4 English Academic Ability Self-efficacy Scale\u003c/h2\u003e \u003cp\u003eTo assess the English academic ability self-efficacy of Chinese college English majors, the scale of Liang's [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] was adapted to align with the study context. Liang constructed the scale based on the framework by Pintrich \u0026amp; DeGroot [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], distinguishing academic self-efficacy into academic ability and academic behavior dimensions. The academic ability self-efficacy scale has 11 items, with a Cronbach's alpha of 0.820. Items, such as \u0026ldquo;I believe I have the ability to achieve good results in English learning\u0026rdquo;, \u0026ldquo;I believe I can grasp the content taught by the teacher in English class in a timely manner\u0026rdquo;, and \u0026ldquo;I enjoy choosing challenging English learning tasks\u0026rdquo;, were included. A Likert 5-point scoring method is adopted, where 1 represents completely disagree, while 5 represents always agree.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Data Analysis\u003c/h2\u003e \u003cp\u003eIn this study, SPSS 26 and Smart PLS 4.1 were utilized as statistical tools for conducting Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis, which is a comprehensive statistical analysis method. The selection of PLS-SEM for data analysis stems from its lenient requirements regarding sample size and its capability to evaluate relatively complex models, especially when the model encompasses formative constructs (Hair et al., 2019). PLS-SEM has gained widespread acceptance within the social sciences. Data analysis techniques included common method bias (CMB) assessment, reliability and validity testing, structural model explanatory power analysis, path tracing, and mediation effect testing.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Common Method Bias\u003c/h2\u003e \u003cp\u003eTo assess CMB in the statistical analysis, this study applied Kock's [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] full covariance test to examine the variance inflation factor (VIF) values. Full covariance test results in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that the VIF value for A is 2.460, D 3.984, V 3.976, EAAS 1.548, ICC 2.181, AFL 3.778, WE 2.935, PL 2.527, PME 3.496, LA 2.304, and AA 3.185. According to the criteria established by Hair et al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], a VIF over 5 suggests a significant issue with CMB. All constructs in this study exhibited VIF values under 5, meaning that there exists no CMB affecting this study.\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\u003eStructural model collinearity analysis.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEAAS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eICC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAFL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.778\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNote: Absorption (A); Dedication (D); Vigor (V); English Academic Ability Self-efficacy (EAAS); Inability to Control Craving (ICC); Anxiety and Feeling Lost (AFL); Withdrawal and Escape (WE); Productivity Loss (PL); Physical and Mental Exhaustion (PME); Low Achievement (LA); Academic Alienation (AA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Measurement Model\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1. Measurement Model\u003c/h2\u003e \u003cp\u003eThe study adopted indicator reliability, internal consistency reliability, convergent validity, and discriminant validity to assess the measurement model. Indicator loadings were used to evaluate indicator reliability. According to Hair et al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], indicator reliability is deemed satisfactory when indicator loading is 0.708 or greater. It can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e that all the items demonstrate indicator loadings higher than 0.708 (0.735\u0026thinsp;~\u0026thinsp;0.935). The study assessed internal consistency reliability using Cronbach's alpha (CA) and composite reliability (CR). Hair et al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] recommended a CA and CR threshold of 0.7 or higher for acceptability. It's obvious that all metrics in this study surpasses the threshold of 0.7 (0.822\u0026thinsp;~\u0026thinsp;0.962), indicating acceptable reliability. The composite reliability (CR) for each construct also meets the required standard 0.7 (0.823\u0026thinsp;~\u0026thinsp;0.963), further supporting the reliability and validity of the measures. The study evaluated convergent validity using average variance extracted (AVE), with a minimum acceptable value set at 0.5, as recommended by Hair et al. [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In this study, all constructs have AVE values exceeding 0.5 (0.635\u0026thinsp;~\u0026thinsp;0.859), indicating favorable convergent validity.\u003c/p\u003e \u003cp\u003eShort Video Addiction (SVA) is a reflective higher-order structure consisting of four lower order structures: inability to control craving (ICC), anxiety and feeling lost (AFL), withdrawal and escape (WE), productivity loss (PL) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. English Learning Engagement (ELE) is a reflective higher-order structure consisting of three lower order structures: vigor (V), dedication (D), and absorption (A) [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. English Learning Burnout (ELB) is a reflective higher-order structure composed of three lower order structures: physical and mental exhaustion (PME), academic alignment (AA), and low achievement (LA) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. From Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, it can be seen that the indicators of the reflective model all meet the ideal indicators suggested by Hair et al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\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\u003eConvergent validity index of each variable.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLoading\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\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\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.845\u0026ndash;0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.815\u0026ndash;0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.846\u0026ndash;0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEAAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.788-0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.789\u0026ndash;0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.735\u0026ndash;0.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.893\u0026ndash;0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.926\u0026ndash;0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.862\u0026ndash;0.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.829\u0026ndash;0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.831\u0026ndash;0.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVA\u003csup\u003e2nd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.880\u0026ndash;0.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eELB\u003csup\u003e2nd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.878\u0026ndash;0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eELE\u003csup\u003e2nd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.910\u0026ndash;0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.851\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\u003eNote: Absorption (A); Dedication (D); Vigor (V); English Academic Ability Self-efficacy (EAAS); Inability to Control Craving (ICC); Anxiety and Feeling Lost (AFL); Withdrawal and Escape (WE); Productivity Loss (PL)); Physical and Mental Exhaustion (PME); Academic Alienation (AA); Low Achievement (LA); Short Video Addiction (SVA); English Learning Burnout (ELB); English Learning Engagement (ELE).\u003c/p\u003e \u003cp\u003eThe study evaluate discriminant validity with the Heterotrait-monotrait ratio (HTMT). The HTMT value should be less than 0.90 for the measurement model to establish discriminant validity [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The study findings, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, fully meet this criterion, confirming the successful establishment of discriminant validity.\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\u003eDiscriminant validity assessment (HTMT).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\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\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAFL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEAAS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eICC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePME\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12\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\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEAAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.424\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 \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eNote: Note: Absorption (A); Anxiety and Feeling Lost (AFL); Dedication (D); English Academic Ability Self-efficacy (EAAS); Inability to Control Craving (ICC); Productivity Loss (PL); Physical and Mental Exhaustion (PME); Academic Alienation (AA); Low Achievement (LA); Vigor (V); Withdrawal and Escape (WE).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2. Structural Model\u003c/h2\u003e \u003cp\u003eIn structural equation modeling, the primary focus lies in assessing the quality of the structural model, which entails examining its predictive and explanatory abilities. Subsequent evaluation criteria encompass tests for multicollinearity, determination coefficients (R\u003csup\u003e2\u003c/sup\u003e), effect size (f\u003csup\u003e2\u003c/sup\u003e), predictive relevance (Q\u003csup\u003e2\u003c/sup\u003e), as well as the statistical significance and correlation of path coefficients.\u003c/p\u003e \u003cp\u003eThe multicollinearity test indicated that the VIF values (1.000-1.273) were all below 5, suggesting no significant multicollinearity [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The R\u003csup\u003e2\u003c/sup\u003e value, serving as an indicator of the model's explanatory power, spans from 0 to 1, with higher values indicating stronger explanatory power and lower values weaker explanatory power. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the overall R\u003csup\u003e2\u003c/sup\u003e values for each construct in the model exceeded 0.1 (0.135\u0026ndash;0.420), indicating a satisfactory level of explanatory power for the model [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Cohen et al. [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] posited that effect size is determined by f\u003csup\u003e2\u003c/sup\u003e values, and can be categorized into three distinct levels: small (f\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.02), medium (f\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.15), and large (f\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.35). The results indicated that SVA had a medium effect on EAAS (f\u0026sup2; = 0.273), a negligible effect on ELE (f\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.001), and large effect on ELB (f\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.417). Additionally, EAAS had a small to medium effect on ELE (f\u0026sup2; = 0.112) and ELB (f\u0026sup2; = 0.037).\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\u003eModel explanatory coefficients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelationship\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ef\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVA\u0026rarr;EAAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVA\u0026rarr;ELE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEAAS\u0026rarr;ELE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVA\u0026rarr;ELB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEAAS\u0026rarr;ELB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.273\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\u003eAs recommended by Shmueli \u0026amp; Sarstedt [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], this study assessed the model's predictivity through the implementation of the PLS prediction process. This involved predicting of potential measurement errors through a comparative analysis of the Root Mean Square Error (RMSE) between Partial Least Squares (PLS) and Linear Modeling (LM). A model is deemed to have strong predictivity when all differences between PLS and LM are less than 0, moderate predictivity when the majority of differences are less than 0, low predictivity when only a few differences are less than 0, and no predictivity when all differences are greater than 0 [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. It can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e that the majority of exogenous variable items exhibit lower values in comparison to those derived from the LM approach, indicating that the model has a moderate level of predictivity. Additionally, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, all Q\u003csup\u003e2\u003c/sup\u003e values are above 0, signifying adequate predictive relevance.\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\u003ePLS predict.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ\u0026sup2;predict\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePLS-SEM_RMSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLM_RMSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePLS_LM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEAAS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEAAS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEAAS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEAAS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEAAS5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEAAS6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEAAS7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEAAS8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEAAS9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEAAS10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEAAS11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePME1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePME2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePME3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePME4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\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 \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Analysis of Path Relationships\u003c/h2\u003e \u003cp\u003eTo further understand the causal relationships between SVA, EAAS, ELE, and ELB in the model, path analysis was conducted (see in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u0026amp; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This study first verified the five direct hypotheses, H1 - H5. Firstly, the path from SVA to ELE showed a negative coefficient (β=-0.033), yet it lacked statistical significance (p\u0026thinsp;=\u0026thinsp;0.599\u0026thinsp;\u0026gt;\u0026thinsp;0.05). This statistical outcome implies that, while there is a tendency for college English majors addicted to short videos to show reduced levels of learning engagement, this tendency is not strong enough to be considered a significant finding. The non- significant result, thus, fails to support H1. It highlights the complexity of the relationship between short video addiction and English learning engagement, which may be influenced by other factors not fully accounted for in this analysis. Secondly, the path from SVA to ELB exhibited a strong and statistically significant positive correlation (β\u0026thinsp;=\u0026thinsp;0.555, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This indicates that as the degree of short video addiction among college English majors increases, their level of English learning burnout also rises. This finding not only aligns precisely with the prediction proposed in H2 but is also in perfect harmony with the Media Dependency Theory. Thirdly, SVA exerted a significant negative influence on EAAS (β=-0.463, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This robust statistical finding implies that as the severity of short video addiction intensifies among English major students, their self-perception of academic ability in English learning correspondingly diminishes. This outcome is in line with H3. Fourthly, H4 was supported by the significant positive impact of EAAS on ELE (β\u0026thinsp;=\u0026thinsp;0.351, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This result indicates that students who hold higher self-efficacy in their English academic abilities are more likely to demonstrate increased levels of engagement in English learning activities. Finally, the analysis revealed that EAAS had a significant negative influence on ELB (β=-0.164, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This means that college English majors with greater self-efficacy in their English academic abilities tend to experience lower levels of burnout in English learning. This finding is consistent with H5.\u003c/p\u003e \u003cp\u003eSubsequently, this study carried out the verification of the two mediating effects. For the mediating path \"SVA\u0026rarr;EAAS\u0026rarr;ELE\", the path coefficient was significant (β = -0.163, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This indicates that EAAS mediates the relationship between SVA and ELE, thereby supporting H6. In terms of the effect size, the indirect effect along this path is -0.163. Additionally, when considering the direct effect of SVA on ELE from other analyses, the total effect amounts to -0.196. Consequently, the indirect effect accounts for approximately 83.16% of the total effect of short video addiction on students' English learning engagement, highlighting the dominant role of the indirect influence mediated by EAAS in this relationship. Moving on to the second mediating path \"SVA\u0026rarr;EAAS\u0026rarr;ELB\", the standardized path coefficient was also significant (β\u0026thinsp;=\u0026thinsp;0.076, p\u0026thinsp;=\u0026thinsp;0.001\u0026thinsp;\u0026lt;\u0026thinsp;0.005). This indicates that EAAS mediates the relationship between SVA and ELB, thus validating H7. Regarding the effect size, the indirect effect for this path is 0.076. By combining this with the direct effect of SVA on ELB, the total effect is computed as 0.631. Hence, the indirect effect accounts for approximately 12.04% of the total effect of short video addiction on students' English learning burnout, further demonstrating the mediating role of EAAS in this particular relationship.\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\u003eHypothesis Testing.\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=\"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=\"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 \u003cp\u003eHypo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelationship\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT Values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep Values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupport\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\u003eSVA\u0026rarr;ELE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003eSVA\u0026rarr;ELB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\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\u003eSVA\u0026rarr;EAAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003eEAAS\u0026rarr;ELE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003eEAAS\u0026rarr;ELB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003eSVA\u0026rarr;EAAS\u0026rarr;ELE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003eSVA\u0026rarr;EAAS\u0026rarr;ELB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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 \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e5.1. The Influence Mechanism of Short Video Addiction on English Learning Engagement\u003c/h2\u003e \u003cp\u003eThe path analysis yielded an unexpected outcome where no significant correlation was found between short video addiction and English learning engagement, leading to the non-verification of Hypothesis 1. This implies that short-video addiction among college English majors does not consistently lead to a substantial decrease in their English learning engagement. When comparing this result with prior research, it was observed that different researchers have presented diverse conclusions regarding the correlation between media addiction, such as smartphone addiction, and learning engagement. For instance, Zhen et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] identified a strong negative correlation, whereas Yang et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] reported no significant association. Such differences highlight the variability in findings within the field. The observed non-significant relationship challenges the conventional understanding that media addiction would straightforwardly and significantly reduce learning engagement. A possible explanation for this might be that learning engagement is a multidimensional and complex concept and is influenced by various factors, including individual and environmental factors [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Existing studies have shown that environmental factors such as positive school climate and strong school identification [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], as well as individual characteristics including grit and academic buoyancy, high academic self-efficacy, and high motivation [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e],[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e],[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] acted as protective factors of students' learning engagement. Hence, with these buffers, short video addiction exerts limited impact on English learning engagement, and students can sustain high level of learning engagement even in the presence of short video addiction. However, even though the relationship was not statistically significant, the observed trend cannot be completely ignored. This trend still indicates that short video addiction may have some influence on learning engagement, albeit not to a significant extent. Therefore, it is advisable to monitor students' short video usage and take measures to minimize any potential negative impacts it may have on their learning engagement.\u003c/p\u003e \u003cp\u003eThis study contributes to the existing body of knowledge by emphasizing the importance of considering the intricate interplay of multiple factors when exploring the relationship between media addiction and learning engagement, rather than assuming a simple, direct negative correlation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.2. The Influence Mechanism of Short Video Addiction on English Learning Burnout\u003c/h2\u003e \u003cp\u003eThe research findings clearly demonstrate that short-video addiction has a significant and positive impact on students' English learning burnout. This positive relationship is well-aligned with the Media Dependency Theory [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], which posits that an individual's degree of reliance on media profoundly influences their cognitive, emotional, and behavioral patterns. When individuals become overly dependent on certain media forms, such as short-video platforms, corresponding changes occur in their behavior and mental states within relevant domains, like English learning. This result is also consistent with some previous studies. Similar to research on mobile-phone addiction [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], where it was found that mobile-phone addiction is a significant positive predictor of learning burnout, short-video addiction, being a form of mobile-media use, also exhibits a positive correlation with English learning burnout. Such similarity further validates the existence of this relationship in the context of media-related addictions and learning burnout. There are several possible explanations for the observed relationship between short-video addiction and English learning burnout. One feasible explanation could be that due to prolonged obsession with mobile phones, students' cognitive faculties become blunt, necessitating a greater allocation of psychological resources to engage in learning activities, and this increase in cognitive load subsequently triggers avoidance of learning in psychology, and then in behavior [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Another plausible explanation could be that long-term addictive behavior can lead to difficulties in concentration, disruptions in time perception, a loss of behavioral control, and abandonment of life goals and future pursuits, all of which potentially harms students' cognition and attitude towards academics, ultimately resulting in learning burnout [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e],[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. It can be concluded that although short video addiction does not significantly predict English learning engagement, it significantly predicts English learning burnout in a negative way. This supports the claim that engagement and burnout are not absolutely opposites, as they differ in their respective dimensions[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This study emphasizes the importance of addressing short video addiction to prevent and mitigate learning burnout among students.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e5.3. Mediating Effects of English Academic Ability Self-efficacy\u003c/h2\u003e \u003cp\u003eThe results demonstrated that English academic ability self-efficacy plays a significant mediating role in the relationship between short video addiction and English learning engagement, as well as between short video addiction and English learning burnout. This implies that as the intensity of short video addiction increases, individuals experience a notable decline in self-efficacy, which subsequently leads to decreased levels of learning engagement or heightened instances of learning burnout. These findings are consistent with the social cognitive theory [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], which posits that an individual's behavior can influence their cognitive processes, and vice versa, these cognitive processes can have a reciprocal impact on subsequent behavioral patterns. Specifically, in the context of this study, short video addiction, as a behavioral pattern, may negatively affect students' self-perception of their English academic ability, thereby influencing their cognitive evaluation of their learning abilities. This alteration in self-efficacy then affects their subsequent learning behaviors, either by decreasing engagement or increasing burnout. Furthermore, these findings provide strong support for other related studies. Individuals addicted to short video consumption often find it difficult to allocate sufficient time to their educational pursuits and tend to postpone academic tasks, which may lead to academic failure and ultimately undermine their confidence in learning ability [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e],[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].Consequently, with impaired academic ability self-efficacy, individuals are less likely to engage in English learning and are more prone to experiencing learning burnout [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. On the contrary, individuals with higher self-efficacy in academic abilities are less likely to be addicted to short videos [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. High self-efficacy fosters positive learning behaviors, clear goal setting, optimistic outcome expectations, and robust social support networks; these factors collectively enhance students' engagement in English language learning while reducing feelings of burnout [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Therefore, students' English academic ability self-efficacy serve as a protective shield against decrease in engagement and emergence of burnout in English learning due to short video addiction. This study recommend fostering and strengthening students' English academic ability self-efficacy to counteract the potential negative effects of short video addiction, thereby sustaining their learning engagement and preventing burnout.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e5.4. Limitations and Future Directions\u003c/h2\u003e \u003cp\u003eWhile the hypotheses were validated, this research has several limitations that warrant discussion. First, the study primarily focused on the overall influence of short videos on learning engagement and burnout, without investigating the effects of specific types of short video content. This lack of granularity meant that the study was unable to determine how variations in content type, format, or duration might differentially affect learning outcomes. Future studies could provide deeper insights by conducting more detailed analyses of these factors, potentially revealing distinct pathways through which short video consumption impacts educational performance.\u003c/p\u003e \u003cp\u003eSecondly, the study did not take into account key environmental factors such as school climate, teacher-student relationships, and home environment. These contextual variables may significantly shape students' responses to short video content, potentially moderating the relationship between short video addiction and learning engagement or burnout. Including these factors in future studies could enhance the robustness of the findings by highlighting how different groups of students are influenced by short video consumption in varied educational settings.\u003c/p\u003e \u003cp\u003eLastly, the study exclusively examined the negative aspects of short video addiction in learning, overlooking the potential benefits these platforms might offer. Future research should explore how short videos could be strategically used as educational tools to enhance student engagement and enrich learning experiences. By shifting the focus towards potential positive uses, researchers could investigate how these platforms contribute to learning when integrated into teaching strategies.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eThis study revealed that short video addiction does not exert a direct influence on English learning engagement. Instead, its impact is mediated through shaping English academic ability self-efficacy. Conversely, short video addiction directly contributes to English learning burnout while also influencing it indirectly via English academic ability self-efficacy. The findings suggest that to improve engagement and reduce burnout in English learning among college students, apart from paying attention to whether they are addicted to media such as short videos, we also need to emphasize and enhance their sense of English academic ability self-efficacy.\u003c/p\u003e \u003cp\u003eStudents' engagement and burnout in learning during college years not only significantly influence their academic performance [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e],[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], but also serve as indicative factors for their potential achievement strategies in future careers [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. College students are the main force for future social advancement, so their academic endeavors have profound implications for the sustainable development of both individuals and the society. Therefore, this study has some theoretical and practical value.\u003c/p\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e6.1. Theoretical Contributions\u003c/h2\u003e \u003cp\u003eThis study, based on Social Cognitive Theory and Media Dependency Theory, offers several significant theoretical contributions. Traditional Media Dependence Theory mainly emphasizes the direct impact of media use on individual cognitive, emotional, and behavioral patterns. However, when it comes to short-video addiction, this study reveals that its impact on learning engagement and learning burnout is far from a simple linear relationship. While short-video addiction shows a direct positive prediction of learning burnout, it has no direct predictive power over learning engagement. This calls for further refinement and improvement of the Media Dependence Theory when it comes to explaining the effects of emerging media like short-videos on specific learning behaviors. By taking into account the differential impacts on different learning dimensions, this study provides a new research perspective and empirical basis for the application of this theory in the new media environment.\u003c/p\u003e \u003cp\u003eBy verifying the mediating role of English academic ability self-efficacy in the relationship between short video addiction and learning engagement and burnout, this research uncovers the complex mechanisms underlying media - learning interactions. By correlating short video addiction, a media - related behavior, with learning engagement and burnout in English learning, this research extends Social Cognitive Theory into the emerging domain of short-video - induced academic impacts. This not only demonstrates how excessive short video consumption (an environmental factor) influences cognitive states (e.g., English academic ability self-efficacy) and subsequent learning behaviors (e.g., engagement and burnout), but also has the potential to prompt the further development of Social Cognitive Theory in better explaining the dynamic interplay between environmental factors and learning behaviors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Practical Implications\u003c/h2\u003e \u003cp\u003eIn the digital age, short videos have become a significant influence on college students' English learning. This study underscores the necessity of fostering English academic ability self-efficacy and encouraging responsible media use to enhance learning engagement and mitigate burnout. These findings offer practical guidance for students, teachers, and schools to improve English learning outcomes.\u003c/p\u003e \u003cp\u003eStudents, as the primary agents of their learning, should strive to balance short video consumption with academic responsibilities. Developing self-discipline and time management skills is vital. Strategies such as setting SMART goals (Specific, Measurable, Achievable, Relevant, and Time-bound), engaging in self-monitoring, and seeking peer support can regulate their media use and enhance their academic focus. To boost English academic ability self-efficacy, students can draw inspiration from successful peers, cultivate a positive mindset, and maintain physical well-being through regular exercise. Furthermore, shifting from passive video consumption to purposeful educational use\u0026mdash;such as watching English grammar tutorials or vocabulary lessons\u0026mdash;can lead to better learning outcomes.\u003c/p\u003e \u003cp\u003eTeachers play a critical role in shaping students\u0026rsquo; academic behaviors. By sharing their experiences with online educational videos and integrating such resources into their teaching practices, educators can model effective media use. Creating and disseminating short video content featuring English learning tips can encourage two-way engagement. Additionally, incorporating short videos into classroom activities\u0026mdash;like analyzing English speeches or facilitating discussions on video content\u0026mdash;can foster enthusiasm and participation. For students exhibiting signs of short video addiction, teachers should intervene early, set personalized learning objectives, and provide constructive feedback to reinforce their confidence in English learning.\u003c/p\u003e \u003cp\u003eEducational institutions serve as key platforms for implementing holistic strategies to support students\u0026rsquo; academic growth. Schools can introduce media literacy courses to equip students with the skills to critically assess and responsibly use short video content. Hosting English short video creation contests offers opportunities for students to showcase their linguistic abilities while building their self-efficacy. Furthermore, creating dedicated English learning spaces on campus\u0026mdash;where English short videos are featured\u0026mdash;can foster an immersive and motivational learning environment.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was conducted in accordance with the Declaration of Helsinki, and approved by City University Malaysia Research Ethics Committee (CUM-REC), as the author is HPD candidate of this university. Informed consent was obtained from all subjects involved in the study. All participants were over the age of 16, and informed consent was obtained from each of them to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated in the study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares no conflicts of interest. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study received no external finding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026apos;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author, Huang K.L., was solely responsible for all aspects of this study, including the conception, design, data collection, data analysis, and manuscript writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChina Internet Network Information Center. 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J Vocat Behav. 2009;75(2):162\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jvb.2009.03.009\u003c/span\u003e\u003cspan address=\"10.1016/j.jvb.2009.03.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"short video addiction, English academic ability self-efficacy, English learning engagement, English learning burnout, college EFL students","lastPublishedDoi":"10.21203/rs.3.rs-5885393/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5885393/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the digital era, short-video apps offer students copious learning resources and entertainment. However, short-video addiction has become a notable concern. Based on Media Dependence Theory and Social Cognitive Theory, this research explored how short-video addiction affects English learning engagement and burnout, with English academic self-efficacy as a mediator. Electronic questionnaires were administered to 500 college EFL students in Guangxi, China, and data analysis was conducted with SPSS and SmartPLS. Results showed that : (1) short-video addiction had no direct impact on English learning engagement but significantly and directly contributed to English learning burnout;(2) English academic self-efficacy mediated the relationships between short-video addiction and both learning engagement and burnout. Overall, this study uncovers the intricate connections among short-video addiction, English learning engagement, burnout, and English academic self-efficacy. Theoretically, it enriches the two theories by offering fresh perspectives on how emerging media influence learning behaviors. Practically, to boost college students' English learning engagement and reduce burnout, we should not only address short-video addiction but also focus on cultivating their English academic self-efficacy.\u003c/p\u003e","manuscriptTitle":"Unveiling the Complex Mechanism of Short Video Addiction on English Learning Engagement and Burnout among College EFL Students","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-28 11:28:15","doi":"10.21203/rs.3.rs-5885393/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8072b8d4-8634-4263-927f-d601ee1c3c23","owner":[],"postedDate":"January 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-09T03:38:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-28 11:28:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5885393","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5885393","identity":"rs-5885393","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}