Unlocking Academic Success: The Impact of Time Management on College Students’ Study Engagement

Unlocking Academic Success: The Impact of Time Management on College Students’ Study Engagement | 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 Unlocking Academic Success: The Impact of Time Management on College Students’ Study Engagement Yangyang Fu, Qiuju Wang, Xiaofeng Wang, Haoxuan Zhong, Junqi Chen, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4271082/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Apr, 2025 Read the published version in BMC Psychology → Version 1 posted 16 You are reading this latest preprint version Abstract Background: In this study, the purpose was to examine the impact of time management on college students’ study engagement and to determine the mechanisms involved. Consequently, we examined the relationship between time management and engagement in study, as well as self-control and mobile phone dependence. Methods: The Adolescence Time Management Disposition Scale (ATMD), College Student Mobile Phone Dependence Questionnaire (CSMPDQ), Utrecht Work Engagement Scale-student (UWES-S), and Self-Control Scale (SCS) were administered to 1016 college students. A Pearson’s correlation analysis and a mediation analysis using bootstrapping were performed in order to test for standard method bias using SPSS 22.0. Results: ①Time management was positively associated with self-control and study engagement, and negatively associated with mobile phone dependence ( p < 0.001). self-control was positively associated with study engagement，and negatively associated with mobile phone dependence ( p < 0.001). Mobile phone dependence was negatively associated with study engagement ( p < 0.01). ②Time management can not only directly predict study engagement (95%CI, 0.102-0.208) but also affects study engagement through three indirect paths: self-control was a mediator (95%CI, 0.066-0.158), mobile phone dependence was a mediator (95%CI, 0.043-0.109), and self-control and mobile phone dependence were a chain mediator (95%CI, 0.012-0.032). Conclusion: Time management not only influences study engagement directly, but also through the mediating effect of self-control and mobile phone dependenceindirectly. Chinese college students time management self-control mobile phone dependence study engagement 1 Introduction The quality of education serves as a crucial metric for evaluating the effectiveness of educational efforts, serving as the foundation for higher education and essential for the sustainability and advancement of academic institutions. Enhancing educational quality is a central objective in the advancement of higher education. With the continual growth of China’s higher education sector, there is a growing emphasis on the importance of maintaining high standards in educational quality across various sectors of society. Critics have pointed out that the conventional assessment framework for evaluating the quality of education places excessive emphasis on external factors, such as physical infrastructure and research output. In recent years, scholars have shifted towards a student-centric approach to evaluating education quality, prioritizing the learning and development of students. Learning engagement has gained increasing attention from researchers as a determinant of students' experiential growth and of higher education quality [1,2]. Estell and Perdue (2013) defined learning engagement as the perceptions and attitudes of students towards school, which impact their participation in learning activities and sense of belonging to the school community [3]. Study engagement represents a novel approach to assessing the quality of undergraduate education in China by prioritizing students' subjective perspectives. This paradigm shift holds significant implications for enhancing the quality of undergraduate education in China, advancing theoretical research on higher education assessment, and fostering deeper exploration of quality assessment in higher education. It can enhance the theoretical and methodological framework for assessing the quality of undergraduate education, advance the field of higher education quality assessment, and contribute to the enhancement of higher education quality, particularly in the realm of fostering talent. 1.1 Time management and Study engagement Brition and Glynn (1989) assert a connection between time and psychology, positing time management as a form of self-psychological management [4]. Macan (1990) contends that time management involves allocating time based on the immediacy of tasks, prioritizing urgent and important tasks to facilitate rational planning of time [5]. Schaufeli (2002) emphasizes that study engagement encompasses vitality, dedication, and concentration, indicating a positive state characterized by initiative in thought and behavior, enthusiastic participation, and focused attention on learning [6]. Research has shown that time management tendencies play a crucial role in influencing levels of learning engagement. The relationship between individual time management and learning engagement has been explored in several studies. Pan et al. (2011) found that higher levels of time management were associated with increased learning engagement [7]. Similarly, Zhao et al. (2012) demonstrated that a tendency towards effective time management positively predicted levels of learning engagement [8]. Additionally, Huang et al. (2017) showed that improving students' time management skills led to greater dedication to important learning tasks, ultimately enhancing learning engagement [9]. Based on these findings, it is hypothesized that time management significantly predicts learning engagement (Hypothesis 1). 1.2 The mediating role of self- control As previously stated, our hypothesis posits that time management significantly impact learning engagement. However, mere confirmation of a positive correlation between time management tendencies and learning engagement is insufficient; it is imperative to elucidate the specific processes or mechanisms by which time management tendencies operate, including identifying potential mediating variables. Muraven and Baumeister (2000) proposed that self-control encompasses the capacity of an individual to consciously restrain impulses, desires, and manage their own conduct in order to enhance the attainment of enduring objectives [10]. Diamond (2013) posited that self-control is demonstrated through the capability to maintain concentration amidst external diversions, restrain impulsive actions, and consistently fulfill assigned duties [11]. According to the power model of self-control, self-control ability is primarily shaped by personality traits, emotions, and the tendency for effective time management. Specifically, the trait of time management plays a significant role in enhancing self-control, as evidenced by its positive correlation with the regulation of emotions, behaviors, and cognitive processes in individuals [12]. Individuals who exhibit higher levels of time management tendencies are also likely to demonstrate greater self-control [13]. Additionally, Mercer et al. (2011) found a positive correlation between self-control and academic engagement [14]. As a result of our findings, we propose Hypothesis 2: Self-control mediates academic engagement and time management. 1.3 The mediating role of mobile phone dependence The emerging field of mobile phone dependence exhibits addiction withdrawal symptoms akin to those of cell phone addiction. Time management propensity, as a facet of personality traits related to the temporal dimension, reflects an individual’s capacity for self-regulation over time, a factor closely associated with addictive behaviors. Tao and Li (2009) demonstrated the impact of parenting styles on mobile phone addiction through their influence on self-control [15]. He et al. (2012) research findings revealed that self-control mechanisms serve as a complete mediator between individual self-esteem levels and mobile phone addiction [16]. Peng and Jiang’s (2011) study revealed a negative correlation between college students’ time management tendencies and mobile phone dependence rates [17]. In their study, Liu and Yang (2012) highlighted the significant negative correlation between college students’ ability to manage time and their dependence on mobile phones [18]. Additionally, Li et al. (2019) discovered that mobile phone dependence can predict study engagement levels, with the development of mobile phone dependence directly impacting the amount of study engagement [19]. Huang et al. (2019) discovered a negative correlation between study engagement and mobile phone dependence [20], while Gao et al. (2021) found that core self-evaluation moderates the predictive effect of mobile phone dependence on study engagement [21]. Hypothesis 3 suggests that mobile phone dependence mediates the relationship between time management and study engagement. 1.4 The chain intermediary role of self-control and mobile phone dependence In their research on the association between mobile phone dependence and self-control among college students, Li et al. (2017) [22] found a significant negative correlation between mobile phone dependence and self-control. Similarly, Zhang et al. (2017) [23] reported that mobile phone dependence was significantly negatively correlated with self-control and was associated with lower levels of self-control in individuals. Zhang et al. (2019) [24] research revealed that mobile phone dependence is a substantial predictor of self-control, leading to a decrease in students’ ability to regulate their behavior. Zhao (2021) [25] study demonstrated that time management tendencies can indirectly influence mobile phone dependence through self-control. Additionally, Wang and Jia (2020) [26] findings indicated that individuals with higher levels of time management tendencies exhibit greater self-control, which in turn can mitigate the likelihood of developing mobile phone dependence. Consequently, Hypothesis 4 posits that self-control and mobile phone dependence serve as mediators in the relationship between time management and study engagement. The tertiary education phase is a critical period for academic growth, where the degree of students’ engagement in learning serves as a pivotal indicator of their academic success. Therefore, this study focuses on college students as participants to delve deeper into the factors that impact study engagement. This study examines the characteristics and interrelationships of time management, self-control, mobile phone dependence, and study engagement. It explores the impact of time management on study engagement, investigating the mediating roles of self-control and mobile phone dependence. Additionally, it uncovers the connections among these four variables. This research contributes to the empirical literature on study engagement and offers theoretical insights for mental health education in higher education settings. 2. Materials and Methods 2.1 Participants This research utilized a randomized questionnaire survey to gather data from undergraduate college students in Shandong Province, utilizing the Questionnaire Star platform. The research protocol received approval from the Ethics Committee of Jining Medical University. Participation in the study required completion of an informed consent form, with additional parental or guardian consent obtained for participants under the age of 18. Upon obtaining subjects’ consent, online surveys were administered adhering to protocols for voluntary participation, confidentiality, and anonymity. The surveys were completed within a time frame of 10 to 20 minutes, and all data collected were kept confidential. Monetary incentives were not provided to volunteers during the trial. The survey was successfully completed by 1,100 participants, representing 92.36% of the selected sample, during the period between October and December 2023. The sample comprised 487 male students (47.93%) and 529 female students (52.07%). 2.2. Measurements 2.2.1 Adolescence Time Management Disposition Scale (ATMD) Chinese scholars Huang and Zhang (2001) [27] compiled the Adolescence Time Management Disposition Scale (ATMD) according to the domestic situation in China based on foreign research literature. A sense of time value, a view of time monitoring, and a sense of time effectiveness make up the three dimensions of the scale. It consists of a total of 44 questions, such as “I think the phrase “an ounce of time is worth an ounce of gold” is true.” “I usually organize my daily activities into a schedule.” and “The phrase “time is money” is true.” among others. The scale is assessed using a five-point scale ranging from 1 (hardly at all) to 5 (always), with higher scores indicating a better time management skills. The scale exhibited a commendable overall consistency coefficient of 0.962. These test findings offer substantiation for the reliability and validity of the scale. 2.2.2 College Student Mobile Phone Dependence Questionnaire (CSMPDQ) The study employed the Mobile Phone Dependence Scale for College Students, which was developed by Wang (2013) [28]. This scale includes five dimensions: conflict, salience, withdrawal, persistence, and technology. It consists of a total of 20 questions, such as “Mobile phones are more important than clothes and food” “I feel uneasy without my cell phone” and “I'd rather lose my wallet than my cell phone” among others. The scale is assessed using a five-point scale ranging from 1 (hardly at all) to 5 (always), with higher scores indicating a stronger inclination towards mobile phone dependence. The criteria for mobile phone dependence tendency were established as a total score of ≥ 70, while a total score of ≥ 80 was used to define mobile phone dependence syndrome. The questionnaire exhibited a commendable overall consistency coefficient of 0.936, indicating satisfactory construct validity and acceptable internal consistency. 2.2.3 Utrecht Work Engagement Scale-student (UWES-S) In this study, the utilization of the Utrecht Work Engagement Scale-student (UWES-S) developed by Liao (2011) was implemented [29]. This scale comprises three distinct dimensions: behavioral input, cognitive input, and emotional input. It comprises a total of 20 inquiries, including statements such as “The usual holiday will not relax study” “Spare time will not relax study” and “After class will be self-review” among others. The assessment instrument was evaluated using a five-point Likert scale, ranging from “not at all” to “completely”. Higher scores on this scale indicate higher levels of learning engagement. The scale demonstrated a high internal consistency, with an alpha coefficient of 0.916. The findings of the test indicated favorable structural validity for the scale. 2.2.4 Self- Control Scale （ SCS ） Tan and Guo (2008)[30] revised Tangney's (2004)[31] Self-Control Scale based on the reality of Chinese college students. In addition to impulse control, healthy habits, resisting temptation, and focusing on work, the scale also considers entertainment moderation. There are 19 questions, including “I can resist temptation well” “It is difficult for me to break bad habits” and “I am lazy”. With higher scores, greater self-control was indicated, as measured by a five-point Likert scale. The internal consistency reliability of the SCS was 0.941. 2.3 Statistical analysis SPSS 22.0 was used to study descriptive statistics, independent samples t-tests, one-way analysis of variance, repeated measures analysis of variance, and correlation analysis of product-differences. A Mediating Effects Test and a Moderated Mediating Effects Test were performed with Hayes’ PROCESS macro program, Models 4 and 6. 3 Results 3.1 Common method bias test Harman’s single-factor test was used to determine whether the dataset under examination had a common method bias in order to validate the precision of the statistical analysis. A total of 18 common factors exhibiting eigenvalues exceeding 1 were identified, with the unrotated first factor explaining 25.32% of the variance, falling short of the recommended threshold of 40%. Consequently, it can be deduced that the outcomes derived from the survey instrument are not substantially influenced by common method bias. 3.2 Descriptive statistics and correlation analysis of the research variables The mean scores on time management, self-control, mobile phone dependence, and study engagement were 3.760 ± 0.697, 3.698 ± 0.796, 3.000 ± 0.939, and 3.508 ± 0.763, respectively. Table 1 displays the relationships between each variable. Pearson correlation analysis showed that time management was positively correlated with self-control and study engagement and negatively correlated with mobile phone dependence. A negative correlation was found between study engagement and dependence on mobile phones and a positive correlation was found between study engagement and self-control. Table 1 The main variables and their correlation analysis M SD Time management Self-control Mobile phone dependence Study engagement Time management 3.760 0.697 1 Self-control 3.698 0.796 0.481** 1 Mobile phone dependence 3.000 0.939 -0.462** -0.385** 1 Study engagement 3.508 0.763 0.365** 0.367** -0.350** 1 N=1016; M, mean; SD standard deviation. ** p < 0.01. 3.3 Analysis of the mediating effect The mediation effects were tested using the process v4.1 macro program model 6 developed by Hayes et al.(2013) [32]. Self-control and mobile phone dependence were used as mediating variables, time management as the independent variable, and study engagement as the dependent variable. The mediating effects of self-control and mobile phone dependence between time management and study engagement were explored. The analysis results are shown in Table 2. In Model 1, the independent variable time management has a significant positive effect on the dependent variable study engagement (β = 0.365, t = 12.474, p < 0.001), indicating that the total effect of time management on the impact of study engagement is significant. Model 2 independent variable time management (β = 0.481, t = 17.453, p < 0.001) has a significant positive effect on the mediating variable self-control. Model 3: Time management (β = -0.360, t = -11.602, p < 0.001), self-control (β = -0.212 , t = -6.835, p < 0.001) has a significant negative effect on the mediator variable mobile phone dependence, indicating that the first half of the two mediating paths are significant. Model 4: Time management (β = 0.177, t = 5.222, p < 0.001), self-control (β = 0.209 , t = 6.407, p < 0.001) have a significant positive effect on the dependent variable study engagement, and mobile phone dependence (β = -0.188, t = -5.815, p < 0.001) have a significant negative effect on the dependent variable study engagement, indicating that the mediator's direct effect was significant and the two second half paths were significant. The mediating effect exists, and self-control and mobile phone dependence partially mediate the relationship between time management and study engagement. Table 2 Tests of the mediation model for each variable Model 1 Model 2 Model 3 Model 4 Study engagement Self-control Mobile phone dependence Study engagement β t β t β t β t Time management 0.365 12.474*** 0.481 17.453*** -0.360 -11.602*** 0.177 5.222*** Self-control -0.212 -6.835*** 0.209 6.407*** Mobile phone dependence -0.188 -5.815*** R 0.365 0.481 0.499 0.455 R 2 0.133 0.231 0.249 0.207 F 155.594*** 304.594*** 167.457*** 88.169*** ***P ＜ 0.001 The mediating roles of self-control and mobile phone dependence between time management and study engagement were tested by bootstrap method, and the results are shown in Table 3 and Figure 1 below. The trust interval of bias correction of bootstap for time management → self-control → study engagement is [0.066, 0.158], and the trust interval of bias correction of bootstap for time management → mobile phone dependence → study engagement is [0.043, 0.109], and these two mediating effects hold. The trust interval for the bias correction of bootstap for time management → self-control → mobile phone dependence → study engagement is [0.012, 0.032], and the chained mediation effect holds for this article. The trust intervals for the direct effect deviation correction were [0.102, 0.280], and the trust intervals for the total effect deviation correction were [0.314, 0.473], indicating that self-control and mobile phone dependence played a partially mediating role between time management and study engagement. Table 3 Tests of the mediation model for each variable Benefit type Effect BootSE BootLLCI BootULCI Proportion of relative effect Total effect 0.399 0.040 0.314 0.473 / Direct effect 0.194 0.045 0.102 0.280 48.63% Indirect effect TOTAL 0.205 0.027 0.153 0.260 51.37% Ind1 0.110 0.024 0.066 0.158 27.56% Ind2 0.074 0.017 0.043 0.109 18.54% Ind3 0.021 0.005 0.012 0.032 5.26% (C1) 0.036 0.032 -0.027 0.099 / (C2) 0.089 0.024 0.045 0.139 / (C3) 0.053 0.015 0.028 0.084 / Ind1: Time management→Self-control→Study engagement Ind2: Time management→Mobile phone dependence→Study engagement Ind3: Time management→Self-control→Mobile phone dependence→Study engagement (C1): Ind1-Ind2 (C2): Ind1-Ind3 (C3): Ind2-Ind3 The results of this study show that time management predicts study engagement indirectly through self-efficacy and mobile phone dependence, as well as a chain mediation pathway. 4 Discussion In this study, time management and study engagement among college students were examined, along with possible mediating factors. The results indicate that time management may influence study engagement by way of self-control and mobile phone dependence, offering theoretical backing for enhancing study engagement. 4.1 The relationship between time management and study engagement This study examined 1,016 Chinese college students using a survey to determine the relationship between time management and study engagement. The results indicated that individuals who excel in time management also exhibit higher levels of study engagement, supporting the validity of Hypothesis 1. This finding is consistent with previous research conducted by various scholars [7-9]. Time management tendency, considered a multidimensional personality trait, comprises cognitive, emotional, and behavioral sub-dimensions. These dimensions not only reflect an individual's attitude towards time but also indicate how effectively they control and utilize time. Students who exhibit a high propensity for time management are able to effectively prioritize tasks, allocate time efficiently, experience a sense of accomplishment, enhance learning efficacy, and proactively address challenges. Conversely, students with a low inclination towards time management struggle to appreciate the importance of time, lack effective planning skills, exhibit weak control over their learning attitudes, and fail to fully engage in their academic pursuits, resulting in subpar academic performance. The research conducted by Zhao et al. (2012) [33] demonstrated that students who possess proficient time management skills are able to appreciate the importance of time, effectively assess and organize their time, and allocate the majority of their time to essential learning activities [8]. This results in enhanced personal investment of time and energy in learning and practice. Enhancing students' time management and planning capabilities facilitates their accurate and complete allocation of time to significant learning tasks, thereby progressively enhancing their study engagement [9]. 4.2 The mediating effect of self-control The findings of this research indicate that time management has a significant impact on study engagement, mediated by self-control. Improved self-control can enhance both time management skills and study engagement. Individuals with higher levels of time management tendencies demonstrate a belief in their ability to effectively manage their time, allocate tasks appropriately, and exhibit greater self-control [13]. Self-control has been found to be a significant predictor of study engagement, as evidenced by the positive correlation between levels of self-control and study engagement [21]. This phenomenon can be elucidated through the lenses of volitional control theory and self-regulated learning theory. According to the volitional control theory, successful learning requires not only internal motivation to drive individuals towards their goals, but also the presence of strong willpower to sustain their efforts until the desired outcome is achieved [34]. According to Simons et al. (2004) [35], setting valuable goals can enhance individuals’ sense of control and discipline, leading to improved self-control behaviors towards achieving their ultimate objectives as suggested by Miller and Brickman (2004) [36]. Additionally, the self-regulation learning theory underscores the proactive nature of individuals in regulating their behaviors and perceptions to effectively attain their learning objectives. Self-control, a key component of self-regulation, necessitates students to utilize their willpower to manage their actions, sustain focus during learning tasks, and enhance their engagement amidst learning challenges. Consequently, individuals with robust self-control tend to exhibit high levels of mental toughness, enabling them to mitigate the influence of adverse factors on goal attainment and enhance their engagement in learning activities [37]. 4.3 The mediating effect of mobile phone dependence This study demonstrates that mobile phone dependence serves as an indirect mediator in the relationship between time management and study engagement among college students, providing support for Hypothesis 3. Existing research on Internet addiction indicates that effective time management strategies are crucial for addressing problematic Internet usage. Furthermore, time management tendencies, considered as a dimension of personality traits, are significantly associated with addictive behaviors. Effective time management involves reducing reliance on mobile phones by enhancing self-control, minimizing impulsive phone use, and bolstering self-efficacy. Time management plays a crucial role in enabling individuals to regulate their behavior and decision-making processes, thereby diminishing their reliance on mobile phones. Additionally, effective time management aids individuals in managing their attention and curbing impulsive mobile phones usage. Moreover, the practice of time management empowers individuals to take charge of their personal and professional responsibilities, fostering a heightened sense of self-efficacy. By successfully managing their time and accomplishing tasks, individuals may experience increased confidence and self-esteem, ultimately reducing their dependence on mobile phones. The theory of media dependence posits that increased reliance on a medium, such as a mobile phone, leads to a greater influence of the medium on the individual [38]. Higher levels of mobile phone dependence are associated with more pronounced negative effects on the individual, particularly in the context of college students' study engagement. Research has demonstrated that mobile phone dependence is a significant predictor of decreased study time and effort, aligning with the findings of this study [39]. Excessive reliance on mobile phones among college students can impede study time, disrupt normal work and rest routines, diminish sleep quality [40], deplete energy needed for study engagement, and ultimately decrease overall study engagement. Additionally, mobile phone dependence is associated with heightened risk of negative emotions like depression and anxiety [41], which can further contribute to decreased attention and reduced learning efficacy [42]. Based on the above, mobile phone dependence has a negative impact on individuals' cognition, emotions, and daily learning behaviors, which in turn leads to a decrease in the level of individuals’ engagement in learning. 4.4 The chain mediating effects of self-control and mobile phone dependence Self-control and mobile phone dependence mediated the chain between time management and study engagement in college students, which tested Hypothesis 4. Both time management and self-control play crucial roles in influencing the academic performance of adolescents. Within the framework of the three-dimensional structure of time management tendency, the dimension of time monitoring, which encompasses activities such as scheduling, goal setting, and time allocation [27], serves as a tangible representation of an individual’s self-control capacity in managing time effectively. Numerous studies have confirmed a significant positive relationship between self-control and time management, with findings suggesting that individuals with low self-control tend to exhibit poor time management tendencies as a result of challenges in regulating and restraining their own psychological and behavioral impulses, ultimately leading to decreased investment in learning. This relationship has been supported by previous research [42]. Self-control, as posited by Billieux et al (2007), is a crucial individual factor impacting mobile phone dependence [43]. This phenomenon can be elucidated through the dual-systems theoretical model and the use-satisfaction theory. The dual-systems theoretical model posits that individuals with higher levels of self-control possess a reflexive system that is sufficiently robust to regulate impulsive behaviors, thereby enabling them to manage their urges to use mobile phones and mitigate problematic usage patterns [44]. Parker and Plank’s (2000) use-satisfaction theory suggests that the interactive and convenient nature of mobile phones fulfills an individual’s social needs, with lower levels of self-control correlating with increased difficulty in suppressing the impulse to use mobile phones and a heightened likelihood of developing dependence on them [45]. Empirical research has further indicated that an individual’s self-control capacity, defined as the ability to resist immediate temptations, suppress inappropriate impulses and behaviors through logical reasoning, and attain objectives in the absence of external limitations, serves as a detrimental predictor of mobile phone dependence [46]. Research has established a correlation between mobile phone dependency and study engagement, particularly among college students. Studies have indicated that the extent of mobile phone dependency among college students is inversely related to their level of study engagement [47]. The abundance of content available on mobile phones serves as an external source of distraction for college students, potentially undermining their academic focus. Failure to effectively manage the balance between mobile phone usage and academic responsibilities may predispose individuals to diminished study engagement. 5 Limitations While the study successfully validated its hypotheses, it is important to acknowledge the limitations inherent in its research design. Specifically, the study’s reliance on a cross-sectional approach, which gathered all data at a single point in time, precluded the ability to conduct a longitudinal follow-up study. Furthermore, due to the constraints of the cross-sectional design, the study is unable to definitively establish a causal relationship between the variables under investigation. To address these limitations and enhance the understanding of the mediating roles of self-control and mobile phone dependence in the relationship between time management and study engagement, future research should incorporate follow-up and experimental studies. Furthermore, the potential for participants to provide inaccurate information or rely on incomplete recollection of data may compromise the validity of the results when utilizing self-report measures. 6 Conclusion The findings of the study suggest that time management plays a crucial role in predicting college students’ level of study engagement. Additionally, the results indicate that self-control and mobile phone dependence act as significant mediators in the relationship between time management and study engagement. This study provides further evidence supporting the importance of time management in improving self-control and study engagement, while also decreasing reliance on mobile phones. The findings of this research have the potential to enhance college students’ comprehension of the significance of time management, foster awareness of the importance of bolstering self-discipline and diminishing reliance on mobile phones, and ultimately facilitate heightened engagement in study engagement. Consequently, institutions of higher education should implement strategies aimed at enhancing college students’ time management skills and self-regulation, reducing their reliance on mobile devices, and thereby fostering increased study engagement and enhancing learning outcomes. Declarations Acknowledgements We would like to thank the participants of this study for their time completing the survey. Authors’ contributions YF: Data analysis and manuscript revision. QW: Data acquisition, drafting and manuscript revision. XW: Drafted the manuscript. HZ: Drafted the manuscript. JC: Drafted the manuscript. HF: Data acquisition. YY: Data acquisition. YX: Data acquisition. WL: Design and manuscript revision. NL: Study conception, design and manuscript revision. The authors read and approved the final manuscript. Data Availability Statement Data is available on reasonable request from the corresponding author. Funding The writer(s) affirm that they have received financial assistance for the investigation, writing, and/or publication of this article.The study received financial support from the initial funding for doctoral research provided by Jining Medical University (No.6001/600949001), Ministry of Education Industry-University Cooperation Collaborative Education Program (No.220904727062522), Innovative Training Program for University Students (No.cx2023098z; cx2023266). The sponsors did not participate in designing the study, collecting and analyzing data, deciding to publish, or preparing the manuscript. 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China Journal of Health Psychology . 2017;01:145-148. doi:10.13342/j.cnki.cjhp.2017.01.034 Zhang B, Cheng SZ, Zhang YJ, Xiao W. Mobile phone addiction and learning burnout: The mediating effect of self-control. China Journal of Health Psychology . 2019;27(03):121-124. doi:CNKI:SUN:JKXL.0.2019-03-030 Zhao J. The relationship between time management tendencies and cell phone addiction in middle school students: the mediating role of self-control. Master's thesis. West China Normal University. 2021. Wang Y, Jia L. High school students' time management tendencies and handball addiction: a moderated mediation model. Chinese Journal of Ergonomics . 2020;26(5):68-73. doi:10.13837/j.issn.1006-8309.2020.05.0013 Huang XT, Zhang ZJ. The compiling of adolescence time management disposition inventory. Acta Psychologica Sinica . 2001;33(4):338-343. Wang ZX. The association between mobile phone dependence and impulsivity among college students. Master's thesis. Soochow university. 2013. Liao YG. Developing questionnaire of learning engagement for college students and surveying the current situation. Journal of Jimei University:Education Science Edition. 2011;2:39-44. doi:10.3969/j.issn.1671-6493.2011.02.010 Tan SH, Guo YY. Revision of self-control scale for Chinese college students. Chinese Journal of Clinical Psychology . 2008;16(5):468‐470. doi:CNKI:SUN:ZLCY.0.2008-05-010 Tangney JP, Baumeister RF, Boone AL. Highself‐control predicts good adjustment, less pathology, better grades, and interpersonal success. Journal of Personality . 2004;72:271‐322. doi:DOI:10.1111/j.0022-3506.2004.00263.x Hayes A. Introduction to mediation, moderation, and conditional process analysis. J. Educ. Meas . 2013;51:335-337. doi: 10.1111/jedm.12050 Li HX, Lin X, Lin J, Si JW. The relationship between time management disposition and academic achievement in boarding primary school students: The mediating role of self-regulate learning. Psychological Research . 2015;8(6):90-96. Corno L, Mandinach EB. The role of cognitive engagement in classroom learning and notivation. Educational Psychologist . 1983;18(2):88-108. doi:10.1080/00461528309529266 Simons J, Dewitte S, Lens W. The role of different types of instrumentality in motivation, study strategies, and performance: Know why you learn, so you'll know what you learn! British Journal of Educational psychology .2004;74:343-360. doi:10.1348/0007099041552314. Miller RB, Brickman SJ. A Model of future-oriented motivation and self-regulation. Educational Psychology Review . 2004;16:9-33. doi:10.1023/B:EDPR.0000012343.96370.39 Zhou GY. Research on the relationship among self-control, study adaptation and life satisfaction of college students. China Journal of Health Psychology . 2011;19(11):1394-1396. doi:CNKI:SUN:JKXL.0.2011-11-050 Ball-Rokeach SJ, DeFleur ML. A dependency model of mass-media effects. Communication Research . 1976;3(1):3-21. doi:10.1177/009365027600300101 Hong W, Liu RD, Zhen R, Jiang SY, Jin FK. Relations between achievement goal orientations and mathematics engagement among pupils: The mediating roles of academic procrastination and mathematics anxiety. Psychological Development and Education . 2018;34(2):191-199. doi:10.16187/j.cnki.issn1001-4918.2018.02.08 Boumosleh J, Jaalouk D． Depression，anxiety，and smartphone addiction in university students: A cross sectional study． PLoS ONE . 2017;12(8):e0182239. doi:org /10. 1371 /journal． pone． 0182239. https://doi.org/10.1371/journal.pone.0182239 Elhai JD, Dvorak RD, Levine JC, Hall BJ．Problematic smartphone use: A conceptual overview and systematic review of relations with anxiety and depression psychopathology. Journal of Affective Disorders . 2017;207(1):251-259. doi:10.1016/j.jad.2016.08.030 Li YM, Liu RD, Hong W, Gu D, Jin FK. The Impact of conscientiousness on problematic mobile phone use: Time management and self-control as chain mediator. Journal of Psychological Science . 2020;43(3):666-672. doi: 10.16719/j.cnki.1671-6981.20200322 Billieux J, Linden MVD, D'Acremont M, Ceschi G, Zermatten A. Does impulsivity relate to perceived dependence on and actual use of the mobile phone? Applied Cognitive Psychology . 2007;21(4):527-537. doi:10.1002/ACP.1289 Soror AA, Hammer BI, Steelman ZR, Davis FD, Limayem MM. Good habits gone bad: Explaining negative consequences associated with the use of mobile phones from a dual-systems perspective. Information Systems Journal . 2015;25(4):403-427. doi:10.1111/isj.12065 Parker BJ，Plank RE. A uses and gratifications perspective on the Internet as a new information source. Latin American Business Review . 2000;18(2):43-49. doi:10.15655/mw/2017/v8i3/49148 Vellekoop RB, Worell JP. Development of self-control in delay of gratification: The effects of goal contingency and response feedback on delay time and active work accomplished. Atlanta, GA: Southeastern Psychological Association Convention. 1981. Ma XY, Zhang HZ, Yu SQ, Jin TL, Zhang YL. Boredom and Procrastination among Undergraduates:The Mediating Role of Problematic Mobile Phone Use. Chinese Journal of Clinical Psychology . 2020;28(6):1250-1253. doi:10.16128/j.cnki.1005-3611.2020.06.035 Additional Declarations No competing interests reported. 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Enhancing educational quality is a central objective in the advancement of higher education. With the continual growth of China\u0026rsquo;s higher education sector, there is a growing emphasis on the importance of maintaining high standards in educational quality across various sectors of society. Critics have pointed out that the conventional assessment framework for evaluating the quality of education places excessive emphasis on external factors, such as physical infrastructure and research output. In recent years, scholars have shifted towards a student-centric approach to evaluating education quality, prioritizing the learning and development of students. Learning engagement has gained increasing attention from researchers as a determinant of students' experiential growth and of higher education quality [1,2].\u003c/p\u003e\n\u003cp\u003eEstell and Perdue (2013) defined learning engagement as the perceptions and attitudes of students towards school, which impact their participation in learning activities and sense of belonging to the school community [3]. Study engagement represents a novel approach to assessing the quality of undergraduate education in China by prioritizing students' subjective perspectives. This paradigm shift holds significant implications for enhancing the quality of undergraduate education in China, advancing theoretical research on higher education assessment, and fostering deeper exploration of quality assessment in higher education. It can enhance the theoretical and methodological framework for assessing the quality of undergraduate education, advance the field of higher education quality assessment, and contribute to the enhancement of higher education quality, particularly in the realm of fostering talent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.1 Time management and Study engagement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBrition and Glynn (1989) assert a connection between time and psychology, positing time management as a form of self-psychological management [4]. Macan (1990) contends that time management involves allocating time based on the immediacy of tasks, prioritizing urgent and important tasks to facilitate rational planning of time [5]. Schaufeli (2002) emphasizes that study engagement encompasses vitality, dedication, and concentration, indicating a positive state characterized by initiative in thought and behavior, enthusiastic participation, and focused attention on learning [6]. Research has shown that time management tendencies play a crucial role in influencing levels of learning engagement. The relationship between individual time management and learning engagement has been explored in several studies. Pan et al. (2011) found that higher levels of time management were associated with increased learning engagement [7]. Similarly, Zhao et al. (2012) demonstrated that a tendency towards effective time management positively predicted levels of learning engagement [8]. Additionally, Huang et al. (2017) showed that improving students' time management skills led to greater dedication to important learning tasks, ultimately enhancing learning engagement [9]. Based on these findings, it is hypothesized that time management significantly predicts learning engagement (Hypothesis 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 The mediating role of self- control\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs previously stated, our hypothesis posits that time management significantly impact learning engagement. However, mere confirmation of a positive correlation between time management tendencies and learning engagement is insufficient; it is imperative to elucidate the specific processes or mechanisms by which time management tendencies operate, including identifying potential mediating variables. Muraven and Baumeister (2000) proposed that self-control encompasses the capacity of an individual to consciously restrain impulses, desires, and manage their own conduct in order to enhance the attainment of enduring objectives [10]. Diamond (2013) posited that self-control is demonstrated through the capability to maintain concentration amidst external diversions, restrain impulsive actions, and consistently fulfill assigned duties [11]. According to the power model of self-control, self-control ability is primarily shaped by personality traits, emotions, and the tendency for effective time management. Specifically, the trait of time management plays a significant role in enhancing self-control, as evidenced by its positive correlation with the regulation of emotions, behaviors, and cognitive processes in individuals [12]. Individuals who exhibit higher levels of time management tendencies are also likely to demonstrate greater self-control [13]. Additionally, Mercer et al. (2011) found a positive correlation between self-control and academic engagement [14]. As a result of our findings, we propose Hypothesis 2: Self-control mediates academic engagement and time management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 The mediating role of mobile phone dependence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe emerging field of mobile phone dependence exhibits addiction withdrawal symptoms akin to those of cell phone addiction. Time management propensity, as a facet of personality traits related to the temporal dimension, reflects an individual\u0026rsquo;s capacity for self-regulation over time, a factor closely associated with addictive behaviors. Tao and Li (2009) demonstrated the impact of parenting styles on mobile phone addiction through their influence on self-control [15]. He et al. (2012) research findings revealed that self-control mechanisms serve as a complete mediator between individual self-esteem levels and mobile phone addiction [16]. Peng and Jiang\u0026rsquo;s (2011) study revealed a negative correlation between college students\u0026rsquo; time management tendencies and mobile phone dependence rates [17]. In their study, Liu and Yang (2012) highlighted the significant negative correlation between college students\u0026rsquo; ability to manage time and their dependence on mobile phones [18]. Additionally, Li et al. (2019) discovered that mobile phone dependence can predict study engagement levels, with the development of mobile phone dependence directly impacting the amount of study engagement [19]. Huang et al. (2019) discovered a negative correlation between study engagement and mobile phone dependence [20], while Gao et al. (2021) found that core self-evaluation moderates the predictive effect of mobile phone dependence on study engagement [21]. Hypothesis 3 suggests that mobile phone dependence mediates the relationship between time management and study engagement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4 The chain intermediary role of self-control and mobile phone dependence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn their research on the association between mobile phone dependence and self-control among college students, Li et al. (2017) [22] found a significant negative correlation between mobile phone dependence and self-control. Similarly, Zhang et al. (2017) [23] reported that mobile phone dependence was significantly negatively correlated with self-control and was associated with lower levels of self-control in individuals. Zhang et al. (2019) [24] research revealed that mobile phone dependence is a substantial predictor of self-control, leading to a decrease in students\u0026rsquo; ability to regulate their behavior. Zhao (2021) [25] study demonstrated that time management tendencies can indirectly influence mobile phone dependence through self-control. Additionally, Wang and Jia (2020) [26] findings indicated that individuals with higher levels of time management tendencies exhibit greater self-control, which in turn can mitigate the likelihood of developing mobile phone dependence. Consequently, Hypothesis 4 posits that self-control and mobile phone dependence serve as mediators in the relationship between time management and study engagement.\u003c/p\u003e\n\u003cp\u003eThe tertiary education phase is a critical period for academic growth, where the degree of students\u0026rsquo; engagement in learning serves as a pivotal indicator of their academic success. Therefore, this study focuses on college students as participants to delve deeper into the factors that impact study engagement. This study examines the characteristics and interrelationships of time management, self-control, mobile phone dependence, and study engagement. It explores the impact of time management on study engagement, investigating the mediating roles of self-control and mobile phone dependence. Additionally, it uncovers the connections among these four variables. This research contributes to the empirical literature on study engagement and offers theoretical insights for mental health education in higher education settings.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research utilized a randomized questionnaire survey to gather data from undergraduate college students in Shandong Province, utilizing the Questionnaire Star platform. The research protocol received approval from the Ethics Committee of Jining Medical University. Participation in the study required completion of an informed consent form, with additional parental or guardian consent obtained for participants under the age of 18. Upon obtaining subjects\u0026rsquo; consent, online surveys were administered adhering to protocols for voluntary participation, confidentiality, and anonymity. The surveys were completed within a time frame of 10 to 20 minutes, and all data collected were kept confidential. Monetary incentives were not provided to volunteers during the trial. The survey was successfully completed by 1,100 participants, representing 92.36% of the selected sample, during the period between October and December 2023. The sample comprised 487 male students (47.93%) and 529 female students (52.07%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. Measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.1 Adolescence Time Management Disposition Scale (ATMD)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChinese scholars Huang and Zhang (2001) [27] compiled the Adolescence Time Management Disposition Scale (ATMD) according to the domestic situation in China based on foreign research literature. A sense of time value, a view of time monitoring, and a sense of time effectiveness make up the three dimensions of the scale. It consists of a total of 44 questions, such as \u0026ldquo;I think the phrase \u0026ldquo;an ounce of time is worth an ounce of gold\u0026rdquo; is true.\u0026rdquo; \u0026ldquo;I usually organize my daily activities into a schedule.\u0026rdquo; and \u0026ldquo;The phrase \u0026ldquo;time is money\u0026rdquo; is true.\u0026rdquo; among others. The scale is assessed using a five-point scale ranging from 1 (hardly at all) to 5 (always), with higher scores indicating a better time management skills. The scale exhibited a commendable overall consistency coefficient of 0.962. These test findings offer substantiation for the reliability and validity of the scale.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.2\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCollege Student Mobile Phone Dependence Questionnaire (CSMPDQ)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study employed the Mobile Phone Dependence Scale for College Students, which was developed by Wang (2013) [28]. This scale includes five dimensions: conflict, salience, withdrawal, persistence, and technology. It consists of a total of 20 questions, such as \u0026ldquo;Mobile phones are more important than clothes and food\u0026rdquo; \u0026ldquo;I feel uneasy without my cell phone\u0026rdquo; and \u0026ldquo;I\u0026apos;d rather lose my wallet than my cell phone\u0026rdquo; among others.\u0026nbsp;The scale is assessed using a five-point scale ranging from 1 (hardly at all) to 5 (always), with higher scores indicating a stronger inclination towards mobile phone dependence. The criteria for mobile phone dependence tendency were established as a total score of \u0026ge; 70, while a total score of \u0026ge; 80 was used to define mobile phone dependence syndrome. The questionnaire exhibited a commendable overall consistency coefficient of 0.936, indicating satisfactory construct validity and acceptable internal consistency.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.3 Utrecht Work Engagement Scale-student (UWES-S)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, the utilization of the Utrecht Work Engagement Scale-student (UWES-S) developed by Liao (2011) was implemented [29]. This scale comprises three distinct dimensions: behavioral input, cognitive input, and emotional input. It comprises a total of 20 inquiries, including statements such as \u0026ldquo;The usual holiday will not relax study\u0026rdquo; \u0026ldquo;Spare time will not relax study\u0026rdquo; and \u0026ldquo;After class will be self-review\u0026rdquo; among others. The assessment instrument was evaluated using a five-point Likert scale, ranging from \u0026ldquo;not at all\u0026rdquo; to \u0026ldquo;completely\u0026rdquo;. Higher scores on this scale indicate higher levels of learning engagement. The scale demonstrated a high internal consistency, with an alpha coefficient of 0.916. The findings of the test indicated favorable structural validity for the scale.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.4 \u003ca href=\"https://www.xinlixue.cn/wb/archives/SCS.html\" title=\"自我控制量表(Self- Control Scale, SCS)\"\u003eSelf- Control\u0026nbsp;Scale\u003c/a\u003e\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003eSCS\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTan and Guo (2008)[30] revised Tangney\u0026apos;s (2004)[31] Self-Control Scale based on the reality of Chinese college students. In addition to impulse control, healthy habits, resisting temptation, and focusing on work, the scale also considers entertainment moderation. There are 19 questions, including \u0026ldquo;I can resist temptation well\u0026rdquo; \u0026ldquo;It is difficult for me to break bad habits\u0026rdquo; and \u0026ldquo;I am lazy\u0026rdquo;. With higher scores, greater self-control was indicated, as measured by a five-point Likert scale. The internal consistency reliability of the SCS was 0.941.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSPSS 22.0 was used to study descriptive statistics, independent samples t-tests, one-way analysis of variance, repeated measures analysis of variance, and correlation analysis of product-differences. A Mediating Effects Test and a Moderated Mediating Effects Test were performed with Hayes\u0026rsquo; PROCESS macro program, Models 4 and 6.\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003e\u003cstrong\u003e3.1\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCommon method bias test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHarman\u0026rsquo;s single-factor test was used to determine whether the dataset under examination had a common method bias in order to validate the precision of the statistical analysis. A total of 18 common factors exhibiting eigenvalues exceeding 1 were identified, with the unrotated first factor explaining 25.32% of the variance, falling short of the recommended threshold of 40%. Consequently, it can be deduced that the outcomes derived from the survey instrument are not substantially influenced by common method bias.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Descriptive statistics and correlation analysis of the research variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean scores on time management, self-control, mobile phone dependence, and study engagement were 3.760 \u0026plusmn; 0.697, 3.698 \u0026plusmn; 0.796, 3.000 \u0026plusmn; 0.939, and 3.508 \u0026plusmn; 0.763, respectively. Table 1 displays the relationships between each variable. Pearson correlation analysis showed that\u0026nbsp;time management\u0026nbsp;was positively correlated with\u0026nbsp;self-control\u0026nbsp;and\u0026nbsp;study engagement\u0026nbsp;and negatively correlated with\u0026nbsp;mobile phone dependence. A negative correlation was found between study engagement and dependence on mobile phones and a positive correlation was found between study engagement and self-control.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e The main variables and their correlation analysis\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime management\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-control\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMobile phone dependence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy engagement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003eTime management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e3.760\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.697\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003eSelf-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e3.698\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.796\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.481**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003eMobile phone dependence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e3.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.939\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-0.462**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e-0.385**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003eStudy engagement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e3.508\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.763\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.365**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e0.367**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-0.350**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eN=1016; M, mean; SD standard deviation.\u003c/p\u003e\n\u003cp\u003e**\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Analysis of the mediating effect\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mediation effects were tested using the process v4.1 macro program model 6 developed by Hayes et al.(2013) [32]. Self-control and mobile phone dependence were used as mediating variables, time management as the independent variable, and study engagement as the dependent variable. The mediating effects of self-control and mobile phone dependence between time management and study engagement were explored. The analysis results are shown in Table 2. In Model 1, the independent variable time management has a significant positive effect on the dependent variable study engagement (\u0026beta; = 0.365, t = 12.474, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), indicating that the total effect of time management on the impact of study engagement is significant. Model 2 independent variable time management (\u0026beta; = 0.481, t = 17.453, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) has a significant positive effect on the mediating variable self-control. Model 3: Time management (\u0026beta; = -0.360, t = -11.602, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), self-control (\u0026beta; = -0.212 , t = -6.835, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) has a significant negative effect on the mediator variable mobile phone dependence, indicating that the first half of the two mediating paths are significant. Model 4: Time management (\u0026beta; = 0.177, t = 5.222, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), self-control (\u0026beta; = 0.209 , t = 6.407, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) have a significant positive effect on the dependent variable study engagement, and mobile phone dependence (\u0026beta; = -0.188, t = -5.815, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) have a significant negative effect on the dependent variable study engagement, indicating that the mediator\u0026apos;s direct effect was significant and the two second half paths were significant. The mediating effect exists, and self-control and mobile phone dependence partially mediate the relationship between time management and study engagement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Tests of the mediation model for each variable\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.367346938775512%\" rowspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.25%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy engagement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.75%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-control\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMobile phone dependence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.75%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy engagement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.688311688311689%\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.38961038961039%\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.584415584415584%\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.38961038961039%\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.94736842105263%\"\u003e\n \u003cp\u003eTime management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.365\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e12.474***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\"\u003e\n \u003cp\u003e0.481\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e17.453***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e-0.360\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e-11.602***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e0.177\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e5.222***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.94736842105263%\"\u003e\n \u003cp\u003eSelf-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e-0.212\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e-6.835***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e0.209\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e6.407***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.94736842105263%\"\u003e\n \u003cp\u003eMobile phone dependence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e-0.188\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e-5.815***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"bottom\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" colspan=\"2\"\u003e\n \u003cp\u003e0.365\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" colspan=\"2\"\u003e\n \u003cp\u003e0.481\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" colspan=\"2\"\u003e\n \u003cp\u003e0.499\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" colspan=\"2\"\u003e\n \u003cp\u003e0.455\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"bottom\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" colspan=\"2\"\u003e\n \u003cp\u003e0.133\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" colspan=\"2\"\u003e\n \u003cp\u003e0.231\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" colspan=\"2\"\u003e\n \u003cp\u003e0.249\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" colspan=\"2\"\u003e\n \u003cp\u003e0.207\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"bottom\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" colspan=\"2\"\u003e\n \u003cp\u003e155.594***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" colspan=\"2\"\u003e\n \u003cp\u003e304.594***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" colspan=\"2\"\u003e\n \u003cp\u003e167.457***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" colspan=\"2\"\u003e\n \u003cp\u003e88.169***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e***P\u003c/em\u003e\u003cem\u003e＜\u003c/em\u003e\u003cem\u003e0.001\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe mediating roles of self-control and mobile phone dependence between time management and study engagement were tested by bootstrap method, and the results are shown in Table 3 and Figure 1 below. The trust interval of bias correction of bootstap for time management \u0026rarr; self-control \u0026rarr; study engagement is [0.066, 0.158], and the trust interval of bias correction of bootstap for time management \u0026rarr; mobile phone dependence \u0026rarr; study engagement is [0.043, 0.109], and these two mediating effects hold. The trust interval for the bias correction of bootstap for time management \u0026rarr; self-control \u0026rarr; mobile phone dependence \u0026rarr; study engagement is [0.012, 0.032], and the chained mediation effect holds for this article. The trust intervals for the direct effect deviation correction were [0.102, 0.280], and the trust intervals for the total effect deviation correction were [0.314, 0.473], indicating that self-control and mobile phone dependence played a partially mediating role between time management and study engagement.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Tests of the mediation model for each variable\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.804123711340207%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBenefit type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBootSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBootLLCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBootULCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProportion of relative effect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.804123711340207%\" colspan=\"2\"\u003e\n \u003cp\u003eTotal effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.399\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e0.040\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e0.314\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e0.473\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.804123711340207%\" colspan=\"2\"\u003e\n \u003cp\u003eDirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.194\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e0.045\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e0.102\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e0.280\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e48.63%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.402061855670103%\" rowspan=\"7\"\u003e\n \u003cp\u003eIndirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003eTOTAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.205\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e0.027\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e0.153\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e0.260\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e51.37%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\"\u003e\n \u003cp\u003eInd1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.110\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.095238095238095%\"\u003e\n \u003cp\u003e0.024\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.857142857142858%\"\u003e\n \u003cp\u003e0.066\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.857142857142858%\"\u003e\n \u003cp\u003e0.158\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e27.56%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\"\u003e\n \u003cp\u003eInd2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.074\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.095238095238095%\"\u003e\n \u003cp\u003e0.017\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.857142857142858%\"\u003e\n \u003cp\u003e0.043\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.857142857142858%\"\u003e\n \u003cp\u003e0.109\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e18.54%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\"\u003e\n \u003cp\u003eInd3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.021\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.095238095238095%\"\u003e\n \u003cp\u003e0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.857142857142858%\"\u003e\n \u003cp\u003e0.012\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.857142857142858%\"\u003e\n \u003cp\u003e0.032\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e5.26%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\"\u003e\n \u003cp\u003e(C1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.036\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.095238095238095%\"\u003e\n \u003cp\u003e0.032\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.857142857142858%\"\u003e\n \u003cp\u003e-0.027\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.857142857142858%\"\u003e\n \u003cp\u003e0.099\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\"\u003e\n \u003cp\u003e(C2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.089\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.095238095238095%\"\u003e\n \u003cp\u003e0.024\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.857142857142858%\"\u003e\n \u003cp\u003e0.045\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.857142857142858%\"\u003e\n \u003cp\u003e0.139\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\"\u003e\n \u003cp\u003e(C3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.053\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.095238095238095%\"\u003e\n \u003cp\u003e0.015\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.857142857142858%\"\u003e\n \u003cp\u003e0.028\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.857142857142858%\"\u003e\n \u003cp\u003e0.084\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eInd1:\u0026nbsp;Time management\u0026rarr;Self-control\u0026rarr;Study engagement\u003c/p\u003e\n\u003cp\u003eInd2:\u0026nbsp;Time management\u0026rarr;Mobile phone dependence\u0026rarr;Study engagement\u003c/p\u003e\n\u003cp\u003eInd3:\u0026nbsp;Time management\u0026rarr;Self-control\u0026rarr;Mobile phone dependence\u0026rarr;Study engagement\u003c/p\u003e\n\u003cp\u003e(C1): Ind1-Ind2\u003c/p\u003e\n\u003cp\u003e(C2): Ind1-Ind3\u003c/p\u003e\n\u003cp\u003e(C3): Ind2-Ind3\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe results of this study show that time management predicts study engagement indirectly through self-efficacy and mobile phone dependence, as well as a chain mediation pathway.\u0026nbsp;\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIn this study, time management and study engagement among college students were examined, along with possible mediating factors. The results indicate that time management may influence study engagement by way of self-control and mobile phone dependence, offering theoretical backing for enhancing study engagement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1 The relationship between time management and study engagement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study examined 1,016 Chinese college students using a survey to determine the relationship between time management and study engagement. The results indicated that individuals who excel in time management also exhibit higher levels of study engagement, supporting the validity of Hypothesis 1. This finding is consistent with previous research conducted by various scholars [7-9]. Time management tendency, considered a multidimensional personality trait, comprises cognitive, emotional, and behavioral sub-dimensions. These dimensions not only reflect an individual's attitude towards time but also indicate how effectively they control and utilize time. Students who exhibit a high propensity for time management are able to effectively prioritize tasks, allocate time efficiently, experience a sense of accomplishment, enhance learning efficacy, and proactively address challenges. Conversely, students with a low inclination towards time management struggle to appreciate the importance of time, lack effective planning skills, exhibit weak control over their learning attitudes, and fail to fully engage in their academic pursuits, resulting in subpar academic performance. The research conducted by Zhao et al. (2012) [33] demonstrated that students who possess proficient time management skills are able to appreciate the importance of time, effectively assess and organize their time, and allocate the majority of their time to essential learning activities [8]. This results in enhanced personal investment of time and energy in learning and practice. Enhancing students' time management and planning capabilities facilitates their accurate and complete allocation of time to significant learning tasks, thereby progressively enhancing their study engagement [9].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 The mediating effect of self-control\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings of this research indicate that time management has a significant impact on study engagement, mediated by self-control. Improved self-control can enhance both time management skills and study engagement. Individuals with higher levels of time management tendencies demonstrate a belief in their ability to effectively manage their time, allocate tasks appropriately, and exhibit greater self-control [13].\u003c/p\u003e\n\u003cp\u003eSelf-control has been found to be a significant predictor of study engagement, as evidenced by the positive correlation between levels of self-control and study engagement [21]. This phenomenon can be elucidated through the lenses of volitional control theory and self-regulated learning theory. According to the volitional control theory, successful learning requires not only internal motivation to drive individuals towards their goals, but also the presence of strong willpower to sustain their efforts until the desired outcome is achieved [34]. According to Simons et al. (2004) [35], setting valuable goals can enhance individuals\u0026rsquo; sense of control and discipline, leading to improved self-control behaviors towards achieving their ultimate objectives as suggested by Miller and Brickman (2004) [36]. Additionally, the self-regulation learning theory underscores the proactive nature of individuals in regulating their behaviors and perceptions to effectively attain their learning objectives. Self-control, a key component of self-regulation, necessitates students to utilize their willpower to manage their actions, sustain focus during learning tasks, and enhance their engagement amidst learning challenges. Consequently, individuals with robust self-control tend to exhibit high levels of mental toughness, enabling them to mitigate the influence of adverse factors on goal attainment and enhance their engagement in learning activities [37].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 The mediating effect of mobile phone dependence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study demonstrates that mobile phone dependence serves as an indirect mediator in the relationship between time management and study engagement among college students, providing support for Hypothesis 3.\u003c/p\u003e\n\u003cp\u003eExisting research on Internet addiction indicates that effective time management strategies are crucial for addressing problematic Internet usage. Furthermore, time management tendencies, considered as a dimension of personality traits, are significantly associated with addictive behaviors. Effective time management involves reducing reliance on mobile phones by enhancing self-control, minimizing impulsive phone use, and bolstering self-efficacy. Time management plays a crucial role in enabling individuals to regulate their behavior and decision-making processes, thereby diminishing their reliance on mobile phones. Additionally, effective time management aids individuals in managing their attention and curbing impulsive mobile phones usage. Moreover, the practice of time management empowers individuals to take charge of their personal and professional responsibilities, fostering a heightened sense of self-efficacy. By successfully managing their time and accomplishing tasks, individuals may experience increased confidence and self-esteem, ultimately reducing their dependence on mobile phones.\u003c/p\u003e\n\u003cp\u003eThe theory of media dependence posits that increased reliance on a medium, such as a mobile phone, leads to a greater influence of the medium on the individual [38]. Higher levels of mobile phone dependence are associated with more pronounced negative effects on the individual, particularly in the context of college students' study engagement. Research has demonstrated that mobile phone dependence is a significant predictor of decreased study time and effort, aligning with the findings of this study [39]. Excessive reliance on mobile phones among college students can impede study time, disrupt normal work and rest routines, diminish sleep quality [40], deplete energy needed for study engagement, and ultimately decrease overall study engagement. Additionally, mobile phone dependence is associated with heightened risk of negative emotions like depression and anxiety [41], which can further contribute to decreased attention and reduced learning efficacy [42]. Based on the above, mobile phone dependence has a negative impact on individuals' cognition, emotions, and daily learning behaviors, which in turn leads to a decrease in the level of individuals\u0026rsquo; engagement in learning.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 The chain mediating effects of self-control and mobile phone dependence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSelf-control and mobile phone dependence mediated the chain between time management and study engagement in college students, which tested Hypothesis 4.\u003c/p\u003e\n\u003cp\u003eBoth time management and self-control play crucial roles in influencing the academic performance of adolescents. Within the framework of the three-dimensional structure of time management tendency, the dimension of time monitoring, which encompasses activities such as scheduling, goal setting, and time allocation [27], serves as a tangible representation of an individual\u0026rsquo;s self-control capacity in managing time effectively. Numerous studies have confirmed a significant positive relationship between self-control and time management, with findings suggesting that individuals with low self-control tend to exhibit poor time management tendencies as a result of challenges in regulating and restraining their own psychological and behavioral impulses, ultimately leading to decreased investment in learning. This relationship has been supported by previous research [42].\u003c/p\u003e\n\u003cp\u003eSelf-control, as posited by Billieux et al (2007), is a crucial individual factor impacting mobile phone dependence [43]. This phenomenon can be elucidated through the dual-systems theoretical model and the use-satisfaction theory. The dual-systems theoretical model posits that individuals with higher levels of self-control possess a reflexive system that is sufficiently robust to regulate impulsive behaviors, thereby enabling them to manage their urges to use mobile phones and mitigate problematic usage patterns [44]. Parker and Plank\u0026rsquo;s (2000) use-satisfaction theory suggests that the interactive and convenient nature of mobile phones fulfills an individual\u0026rsquo;s social needs, with lower levels of self-control correlating with increased difficulty in suppressing the impulse to use mobile phones and a heightened likelihood of developing dependence on them [45]. Empirical research has further indicated that an individual\u0026rsquo;s self-control capacity, defined as the ability to resist immediate temptations, suppress inappropriate impulses and behaviors through logical reasoning, and attain objectives in the absence of external limitations, serves as a detrimental predictor of mobile phone dependence [46]. Research has established a correlation between mobile phone dependency and study engagement, particularly among college students. Studies have indicated that the extent of mobile phone dependency among college students is inversely related to their level of study engagement [47]. The abundance of content available on mobile phones serves as an external source of distraction for college students, potentially undermining their academic focus. Failure to effectively manage the balance between mobile phone usage and academic responsibilities may predispose individuals to diminished study engagement.\u003c/p\u003e"},{"header":"5 Limitations ","content":"\u003cp\u003eWhile the study successfully validated its hypotheses, it is important to acknowledge the limitations inherent in its research design. Specifically, the study\u0026rsquo;s reliance on a cross-sectional approach, which gathered all data at a single point in time, precluded the ability to conduct a longitudinal follow-up study. Furthermore, due to the constraints of the cross-sectional design, the study is unable to definitively establish a causal relationship between the variables under investigation. To address these limitations and enhance the understanding of the mediating roles of self-control and mobile phone dependence in the relationship between time management and study engagement, future research should incorporate follow-up and experimental studies. Furthermore, the potential for participants to provide inaccurate information or rely on incomplete recollection of data may compromise the validity of the results when utilizing self-report measures.\u003c/p\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eThe findings of the study suggest that time management plays a crucial role in predicting college students\u0026rsquo; level of study engagement. Additionally, the results indicate that self-control and mobile phone dependence act as significant mediators in the relationship between time management and study engagement. This study provides further evidence supporting the importance of time management in improving self-control and study engagement, while also decreasing reliance on mobile phones. The findings of this research have the potential to enhance college students\u0026rsquo; comprehension of the significance of time management, foster awareness of the importance of bolstering self-discipline and diminishing reliance on mobile phones, and ultimately facilitate heightened engagement in study engagement. Consequently, institutions of higher education should implement strategies aimed at enhancing college students\u0026rsquo; time management skills and self-regulation, reducing their reliance on mobile devices, and thereby fostering increased study engagement and enhancing learning outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the participants of this study for their time completing the survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYF: Data analysis and manuscript revision. QW: Data acquisition, drafting and manuscript revision. XW: Drafted the manuscript. HZ: Drafted the manuscript. JC: Drafted the manuscript. HF: Data acquisition. YY: Data acquisition. YX: Data acquisition. WL: Design and manuscript revision. NL: Study conception, design and manuscript revision. The authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is available on reasonable request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe writer(s) affirm that they have received financial assistance for the investigation, writing, and/or publication of this article.The study received financial support from the initial funding for doctoral research provided by Jining Medical University (No.6001/600949001), Ministry of Education Industry-University Cooperation Collaborative Education Program (No.220904727062522), Innovative Training Program for University Students (No.cx2023098z; cx2023266). The sponsors did not participate in designing the study, collecting and analyzing data, deciding to publish, or preparing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe researchers affirm that the study was carried out without any business or monetary affiliations that could be interpreted as a possible clash of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants were provided full information on the study, and provided their informed consent to participate. The research protocol (Code: JNMC-YX-2024-057) obtained approval from the Ethics Committee of Jining Medical University. The study was performed following the standards for medical research involving human subjects recommended by the Declaration of Helsinki for human research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest in relation to the subject of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKuh GD. Assessing what really matters to student learning inside the national survey of student engagement. \u003cem\u003eChange:The Magazine of Higher Learning\u003c/em\u003e. 2001;33(3):10-17.\u003c/li\u003e\n\u003cli\u003eKuh, George D. 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Consequently, we examined the relationship between time management and engagement in study, as well as self-control and mobile phone dependence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The Adolescence Time Management Disposition Scale (ATMD), College Student Mobile Phone Dependence Questionnaire (CSMPDQ), Utrecht Work Engagement Scale-student (UWES-S), and \u003ca href=\"https://www.xinlixue.cn/wb/archives/SCS.html\" title=\"自我控制量表(Self- Control Scale, SCS)\"\u003eSelf-Control Scale\u003c/a\u003e (SCS) were administered to 1016 college students. A Pearson’s correlation analysis and a mediation analysis using bootstrapping were performed in order to test for standard method bias using SPSS 22.0.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e ①Time management was positively associated with self-control and study engagement, and negatively associated with mobile phone dependence (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). self-control was positively associated with study engagement，and negatively associated with mobile phone dependence (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Mobile phone dependence was negatively associated with study engagement (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01). ②Time management can not only directly predict study engagement (95%CI, 0.102-0.208) but also affects study engagement through three indirect paths: self-control was a mediator (95%CI, 0.066-0.158), mobile phone dependence was a mediator (95%CI, 0.043-0.109), and self-control and mobile phone dependence were a chain mediator (95%CI, 0.012-0.032).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Time management not only influences study engagement directly, but also through the mediating effect of self-control and mobile phone dependenceindirectly.\u003c/p\u003e","manuscriptTitle":"Unlocking Academic Success: The Impact of Time Management on College Students’ Study Engagement","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-29 05:57:56","doi":"10.21203/rs.3.rs-4271082/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-15T17:15:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-15T12:24:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-13T21:52:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-08T05:34:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"227600377572279154298231491624365149668","date":"2024-11-08T04:46:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-05T15:24:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"324242224793603582372751697300828026224","date":"2024-11-05T03:09:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"171669402595475414936572213559758555539","date":"2024-11-04T16:50:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17790626479586795832764483453850345363","date":"2024-11-04T07:14:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"85731298258764110998135976614937204940","date":"2024-11-02T18:46:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103084126480565443615023124003568810061","date":"2024-11-02T15:07:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-28T16:22:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-25T12:12:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-24T07:09:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-24T07:09:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2024-04-15T17:02:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"96f24029-3fa0-4245-a21f-e067cea49ead","owner":[],"postedDate":"April 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-07T16:03:25+00:00","versionOfRecord":{"articleIdentity":"rs-4271082","link":"https://doi.org/10.1186/s40359-025-02619-x","journal":{"identity":"bmc-psychology","isVorOnly":false,"title":"BMC Psychology"},"publishedOn":"2025-04-02 15:57:16","publishedOnDateReadable":"April 2nd, 2025"},"versionCreatedAt":"2024-04-29 05:57:56","video":"","vorDoi":"10.1186/s40359-025-02619-x","vorDoiUrl":"https://doi.org/10.1186/s40359-025-02619-x","workflowStages":[]},"version":"v1","identity":"rs-4271082","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4271082","identity":"rs-4271082","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}