Understanding University Students’ Study Engagement through the Lens of Grit: A Serial Mediation Model of Psychological Capital and Life Satisfaction Using a PLS-SEM Approach

Understanding University Students’ Study Engagement through the Lens of Grit: A Serial Mediation Model of Psychological Capital and Life Satisfaction Using a PLS-SEM Approach | 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 Understanding University Students’ Study Engagement through the Lens of Grit: A Serial Mediation Model of Psychological Capital and Life Satisfaction Using a PLS-SEM Approach Xiaomei Zhang, Soon-Yew Ju, Lai-Kuan Kong, Lei Cui, Shan Zhang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6654212/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract The study engagement of university students has gained increasing attention as a crucial determinant of their learning outcomes and overall well-being. Despite the growing body of research examining the predictors of study engagement, there remains a limited understanding of the mechanisms through which non-cognitive traits and psychological resources interact to influence engagement, particularly in non-Western educational contexts. This study investigates the role of grit in predicting university students’ study engagement, while also examining the mediating effects of psychological capital and life satisfaction in the relationship between grit and study engagement. Method : A cross-sectional study was conducted among 484 university students from 17 universities in Ji’nan, Shandong Province, China. The survey questionnaire employed four established scales and data collected through a non-probability purposive sampling method. A partial least squares structural equation model (PLS-SEM) was employed to test the hypothesized relationships and mediation effects. Results : The research findings indicated that grit directly predicted study engagement and psychological capital, but had no significant impact on life satisfaction. Psychological capital directly predicted study engagement and life satisfaction, but life satisfaction did not significantly influence study engagement. The mediation hypothesis asserts that the relationship between grit and study engagement can be enhanced by psychological capital attributes, discounting life satisfaction variables. Finally, psychological capital and life satisfaction did not serve as a chain mediation variable in the link between grit and study engagement. Conclusion : These findings contribute to a deeper understanding of the psychological mechanisms underlying study engagement of the university students, emphasizing the pivotal role of psychological capital in academic persistence. The results have important implications for higher education institutions, suggesting that interventions aimed at fostering psychological capital such as self-efficacy, hope, resilience and optimism may promote university students’ study engagement. study engagement grit psychological capital life satisfaction university students Figures Figure 1 Figure 2 Introduction University students, as a crucial group within higher education, face substantial academic demands [ 1 ] and transitional challenges [ 2 ]. These pressures necessitate a deeper understanding of study engagement, which is increasingly recognized as a critical determinant of educational success and student well-being [ 3 – 7 ]. Study engagement, defined as the degree of students’ cognitive, emotional, and behavioral investment in learning, has emerged as a pivotal research focus in educational psychology due to its strong associations with academic performance, well-being, and career preparedness [ 8 – 9 ]. Despite extensive research on the antecedents of study engagement, the interplay between non-cognitive traits [e.g., grit], psychological resources [e.g., psychological capital], and subjective well-being [e.g., life satisfaction] remains under-explored, particularly in non-Western educational contexts. Existing literature suggests that grit—a sustained passion and perseverance for long-term goals—plays a key role in fostering academic engagement [ 10 – 13 ]. However, the mechanisms underlying this relationship remain unclear, necessitating an examination of potential mediating factors. In particular, psychological capital [PsyCap], a malleable psychological resource encompassing self-efficacy, hope, resilience and optimism [ 14 ], may function as a critical intermediary between grit and study engagement. Additionally, life satisfaction, a central component of subjective well-being [ 15 ], is posited to strengthen students’ motivation and engagement levels [ 16 ]. This study, therefore, explores the chain mediation effect of psychological capital and life satisfaction in the relationship between grit and study engagement, providing a nuanced perspective on student motivation and persistence. Conceptualizing Study Engagement: A Theoretical Perspective Study engagement is conceptualized as a multidimensional construct comprising behavioral, emotional, and cognitive dimensions [ 17 ]. It reflects students’ persistent effort, emotional investment, and intellectual involvement in their academic pursuits [ 18 ]. Given its strong correlation with academic achievement, study engagement has become a central theme in higher education research [ 19 ]. Self-Determination Theory (SDT) [ 20 – 21 ], a widely recognized framework for studying academic engagement, emphasizes that fulfilling individuals’ core psychological needs in autonomy, competence, and relatedness is more likely to foster their deep engagement in relevant activities. In this study, SDT acts as a guiding framework, illuminating how personal characteristics and psychological resources influence student study engagement. Traits such as grit, for instance, strengthen students’ belief in their abilities and their perseverance in pursuing long-term objectives. Similarly, psychological resources like PsyCap—comprising self-efficacy, hope, resilience, and optimism—encourage self-driven motivation, which sustains effort and emotional commitment to academic work. Well-being factors, including life satisfaction, further nurture students’ sense of belonging and emotional stability, enabling them to engage meaningfully with their educational settings. By weaving these elements together, SDT conceptualizes engagement as a motivation-driven process and clarifies how the interplay between intrinsic traits and extrinsic conditions collectively fuels academic participation. This integrative approach underscores the theory’s explanatory power in linking individual and contextual factors to sustained academic involvement. Grit and Study Engagement Grit, defined as passion and perseverance for long-term goals, has gained prominence as a non-cognitive trait critical for academic success [ 10 ]. It consists of two dimensions: consistency of interest and perseverance of effort [ 22 ]. Empirical evidence suggests that grittier students exhibit stronger academic engagement, persisting in their studies despite challenges [ 11 – 13 ]. Cross-cultural studies indicate that the impact of grit on study engagement varies across different educational contexts. For example, research in Western contexts suggests a moderate positive relationship between grit and academic engagement [ 23 ], whereas studies in Asian educational settings indicate a stronger link, potentially due to cultural expectations of academic perseverance [ 24 ]. Besides, developmental factors influence grit’s effectiveness, with findings suggesting that grit’s impact on engagement and literacy achievement is particularly pronounced among younger learners and dual-language students [ 25 – 26 ]. While the direct influence of grit on engagement is well-documented, the mediating mechanisms remain under-explored. Some studies propose that grit enhances engagement through self-regulated learning and resilience [ 27 ], yet the extent to which psychological capital and life satisfaction mediate this effect remains an open question. Psychological Capital as a Mediating Resource PsyCap is a state-like psychological resource encompassing self-efficacy, hope, resilience, and optimism [ 14 ]. Unlike fixed personality traits, PsyCap is malleable and can be facilitated through training, making it a valuable target for interventions aimed at boosting student performance and engagement [ 28 – 30 ]. Research suggests that students with higher PsyCap set more ambitious academic goals, persist in the face of adversity, and experience greater satisfaction in their learning environments [ 31 ]. Notably, PsyCap has been found to mediate the relationship between grit and engagement, amplifying students’ academic resilience and persistence, proving that grit fosters PsyCap, which in turn promotes greater academic studies [ 32 ]. Luthans and Youssef-Morgan further emphasize the malleability of PsyCap, highlighting its potential as an intervention tool for enhancing academic persistence [ 33 ]. Moreover, PsyCap is closely linked to students’ emotional well-being and life satisfaction[ 31 ], reinforcing the argument that it serves as a psychological relief against academic stress [ 34 ]. This raises the possibility that life satisfaction may function as a secondary mediator, reinforcing the positive impact of PsyCap on engagement. Life Satisfaction as a Mediating Resource Life satisfaction, a key indicator of subjective well-being, represents an individual’s overall assessment of their life quality [ 15 ]. Research consistently demonstrates that higher life satisfaction is linked to greater academic engagement, as satisfied students display stronger motivation, persistence, and enthusiasm for learning [ 35 – 36 ]. Recent empirical findings reveal a strong association between life satisfaction and engagement across diverse cultural contexts. For instance, Indonesian university students reporting higher life satisfaction also exhibited greater engagement in academic tasks [ 36 ]. Similarly, Brazilian research identifies grit as a key predictor of life satisfaction, further reinforcing its role in boosting emotional well-being and engagement [ 37 ]. Furthermore, PsyCap has been identified as a significant contributor to life satisfaction, particularly in challenging academic environments [ 38 ]. Empirical studies conducted among multicultural professionals in Poland, hospitality workers in Taiwan, and online learners in UK further suggest that higher psychological capital leads to enhanced life satisfaction[ 39 – 40 ] and greater engagement [ 41 ]. Despite existing literature establishes grit as a predictor of academic engagement[ 12 – 13 ], but the underlying mechanisms remain insufficiently explored. While some studies suggest that grit fosters persistence and academic resilience [ 11 , 32 ], the role of psychological capital and life satisfaction in this process warrants further investigation. This study aims to bridge this gap by investigating the chain mediation effect of psychological capital and life satisfaction on the relationship between grit and study engagement. Research Hypotheses Building on the theoretical and empirical evidence discussed, this study proposes the following hypotheses: H1: Grit has a positive and significant impact on university students’ study engagement. H2: Grit has a positive and significant impact on university students’ psychological capital. H3: Psychological capital has a positive and significant impact on university students’ study engagement. H4: Psychological capital mediates the relationship between grit and university students’ study engagement. H5: Grit has a positive and significant impact on university students’ life satisfaction. H6: Life satisfaction has a positive and significant impact on university students’ study engagement. H7: Life satisfaction mediates the relationship between grit and university students’ study engagement. H8: Psychological capital has a positive and significant impact on university students’ life satisfaction. H9: Psychological capital and life satisfaction jointly mediate the relationship between grit and university students’ study engagement. The proposed serial mediation model is shown in Fig. 1 . Methods Procedure and Measurement The questionnaire was designed by Questionnaire Star and then sent to the respondents via teachers in the public universities in Ji’nan by the WeChat or QQ group. The study employed a non-probability purposive sampling strategy which eventually collected 560 questionnaires. Out of that, 86.43% or 484 data were usable. Prior to the main analysis, the data set was screened for suspicious response patterns. Specifically, cases exhibiting no variance or displaying sequential response patterns were identified and removed. For ethical consideration, the participants were provided with informed consent to assure the confidentiality and anonymity. In relation to that, data were stored securely in encrypted files, with access restricted to the research team. This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Review Board of the School of Foreign Languages, Shandong Jianzhu University (approval date: April 2024). All participants were informed about the purpose and procedures of the study, and written informed consent was obtained prior to participation. Participation was voluntary, and participants had the right to withdraw at any time without penalty. Study Engagement This study utilized the Utrecht Work Engagement Scale (UWES) to assess student study engagement, since it has been effectively used to evaluate students’ academic engagement [ 42 – 43 ]. It has three subscales including 17 items. Six items are used for study engagement absorption (SEA), five items for study engagement dedication (SED) and other six items for Study engagement vigor (SEV), such as “Time flies when I’m studying”, “I find my studies to be full of meaning and purpose”, and “When I’m studying, I feel mentally strong”. The prior study indicated that the reliability coefficients of each dimension was 0.904 for SEA, 0.818 for SED and 0.886 for SEV [ 44 ]. The seven-Likert scale was utilized to rate these items in this study, in which 1 meant strongly disagree and 7 meant strongly agree. In this present study, the Cronbach’s alpha coefficients of the SEA, SED, and SEV were 0.889, 0.861, and 0.907 respectively. Grit The Grit Scale developed by Duckworth and her associateswas adopted in this study[ 10 ]. It contains 12 items attached to two subscales, i.e. consistency of interest (GCI) and perseverance of effort (GPE). Each subscale has six items, such as “I become interested in new pursuits every few months”, and “I have achieved a goal that took years of work”. The Cronbach’s alpha for GCI was 0.840, and GPE was 0.780 [ 45 ]. In this study, the five-Likert scale was employed to evaluate the items (1 = strongly disagree – 5 = strongly agree). The Cronbach’s alpha of the two subscales in this present study was 0.821 for GCI and 0.817 for GPE. Psychological Capital Psychological capital was measured using the Psychological Capital Scale previously developed by Luthans et al. [ 46 ]. As they outlined, this instrument measures four core positive psychological resources—self-efficacy, hope, resilience, and optimism—and its reliability and construct validity have been well‐documented in organizational behavior literature. There are 24 items with 4 subscales, namely psychological capital self-efficacy (PCS), such as “I believe I can analyze the long-term problems and find solutions”, psychological capital hope (PCH) like “If I find myself in a jam, I could think of ways to get out of it”, psychological capital resilience (PCR) like “I can usually take the pressure at school calmly”, and psychological capital optimism (PCO) like “I always see the positive side of my study”. Besides, the psychological capital scale has been successfully employed by the other scholars in their research. Wang et al. displayed the relationship between anxiety, depressive symptoms, and psychological capital among Chinese university students [ 47 ]. And Zhou et al. investigated how psychological capital, organizational commitment, job burnout, and anxiety affected each other among Chinese female nurses [ 48 ]. According to the Peng et al., the Cronbach’s alpha coefficients for the four subscales were 0.710, 0.780, 0.750, and 0.820, respectively [ 49 ]. The seven-Likert scale was adopted to measure these items (i.e. 1 = strongly disagree – 7 = strongly agree). The Cronbach’s alpha of the four subscales in this present study was 0.874 for PCS, 0.845 for PCH, 0.883 for PCR and 0.871 for PCO. Life Satisfaction Satisfaction with Life Scale (SWLS) was widely put into use to measure the life satisfaction [ 15 ]. It consists of five items in all, such as “In most ways my life is close to my ideal”. The previous study showed that Cronbach’s alpha coefficients for this scale was 0.780 [ 33 ]. The seven-Likert scale was employed to score the items with 1 meaning completely disagree and 7 meaning completely agree. The Cronbach’s alpha was 0.888 in the current study. Sampling and data collection This survey involved 484 respondents, including 44.6% male students and 55.4% female students from 17 universities in Ji’nan, Shandong Province, China. Of the respondents, 34.1% were from urban areas, while 65.9% were from rural areas. In terms of academic specialization, 57.9% of the university students majored in science, whereas 42.1% pursued majors in the liberal arts. For the sibling category, 30.6% of the respondents were from single-child families, while 69.4% had siblings. Data Analysis This study utilized SPSS 29 and Smart PLS 4.1.1.2 as tools for statistical data analysis. A total of 484 data sets were analyzed using Smart PLS software via Partial Least Squares Equation Modelling (PLS-SEM). This research utilized PLS-SEM to validate the model, since it adeptly accommodates the interplay among independent, mediator, and dependent variables simultaneously, often rendering it more appropriate for constructing predictive models compared to alternative methodologies [ 50 ]. PLS-SEM is especially suitable when the objective is to optimize explained variance (R²) and forecast relationships among constructs rather than merely validate existing theories. This study analyzed the data sample in two separate phases utilizing Smart PLS software [ 51 ]. Firstly, this study utilizes a validated and mature instrument that has been previously tested for both reliability and validity in earlier research. Accordingly, a confirmatory factor analysis will be performed to assess the measurement model [ 52 ]. Once the measurement model was evaluated, then the structural model was further evaluated using bootstrapping and PLS Predict to assess the significance of the path coefficients and the model's explanatory and predictive power. Results Common Method Bias Common method bias (CMB), usually occurring when research incorporates multiple self-report measures from the same respondent and then look at connections between these self-report measures suggested by Cooper et al. [ 53 ]. There are two methods widely applied to detect the CMV and then protect the data from it. When examining causal relationships between exogenous and endogenous variables, the use of a single respondent to provide data for all constructs (i.e., a single-source design) may introduce common method bias (CMB), which could compromise the validity of the findings [ 54 ]. To address this concern, this study employ both procedural and statistical remedies [ 55 ]. Procedurally, participant anonymity and confidentiality were guaranteed to reduce evaluation fear and social desirability bias, and different Likert scales were utilized to rate exogenous and endogenous variables. For statistical remedy, the Kock’s method was used to measure full collinearity statistically [ 56 ]. As shown in Table 1 , exogenous and endogenous variables variance inflation factor (VIF) values were below 3.3 (ranging from 1.018 to 3.080), showing that CMB was not a significant threat in this study [ 57 ]. Table 1 Full covariance test GCI GPE PCS PCH PCR PCO LS SEA SED SEV 1.372 2.349 1.900 2.622 2.916 2.004 1.018 2.807 3.080 2. 976 Note: GCI = Consistency of interest; GPE = Perseverance of effort; PCS = Psychological capital self-efficacy; PCH = Psychological capital hope; PCR = Psychological capital resilience; PCO = Psychological capital optimism; LS = Life satisfaction; SEA = Study engagement absorption; SED = Study engagement dedication; SEV = Study engagement vigor. Measurement Model It was crucial and necessary to separate the data prior to the data run because the precise measurement model specification was needed to assign meaningful links in the structural model [ 58 ]. The term “reflective measurement” has been utilized to refer to the fact that a latent idea produces variance in a set of observable indicators, which may then be used to get a measurement concept [ 59 ]. Grit, defined as a high-order construct by Duckworth and Quinn [ 21 ], functions as a reflective model akin to psychological capital [ 60 ] and study engagement [ 61 ]. In contrast, life satisfaction, categorized as a first-order construct, is also reflective [ 15 ]. For reflective model, indicator loading, construct reliability as well as convergent and discriminant validity, were broadly adopted to measure it [ 50 ]. Just as Hair et al. suggested [ 50 ], indicator loading larger than 0.708 was recommended, and the indicator loading ranging from 0.500 to 0.708 was acceptable as well. The following Table 2 displayed the indicator loading of all the items was greater than 0.708 except that the indicator loading of GCI06 (0.617), GPE07 (0.683), GPE08 (0.678) and PCH09 (0.682) respectively. Despite the item loadings for GCI06, GPE07, GPE08, and PCH09 being marginally below the suggested threshold of 0.708, Table 2 indicates that the AVE values for the constructs GCI, GPE, and PCH surpass the minimum requirement of 0.500. Therefore, all indicators in this study were retained. Next, construct reliability was assessed by both Cronbach’s alpha (CA) and composite reliability (CR). In line with the suggestion from the researchers [ 62 ], the values in the range from 0.70 to 0.95 were considered to be feasible. For this study, just as Table 2 indicated, all the CA and CR values of each dimension situated in the required scope ranging from 0.817 to 0.933. To assess convergent validity, the average variance extracted (AVE) was utilized, with a threshold value set at 0.50 [ 50 ]. As shown in the Table 2 , all AVE values exceeded the required threshold, ranging from 0.523 to 0.859, thus confirming the convergent validity of the measurement model. For the assessment of higher-order constructs, i.e., grit, psychological capital, and study engagement constructs. Table 2 shows that all indicator loadings range from 0.852 to 0.924. The AVE values range from 0.766 to 0.859, while both Cronbach’s alpha (CA) and composite reliability (CR) values range from 0.837 to 0.933. Therefore, the reliability and validity of the higher-order constructs exceed the required thresholds and are thus confirmed. Table 2 Construct reliability and convergent validity assessment Construct Indicators Loading CA CR AVE GCI GCI01 0.723 0.821 0.870 0.529 GCI02 0.748 GCI03 0.743 GCI04 0.769 GCI05 0.753 GCI06 0.617 GPE GPE07 0.683 0.817 0.868 0.523 GPE08 0.678 GPE09 0.772 GPE10 0.747 GPE11 0.719 GPE12 0.734 PCS PCS01 0.798 0.874 0.905 0.613 PCS02 0.804 PCS03 0.765 PCS04 0.810 PCS05 0.785 PCS06 0.732 PCH PCH07 0.700 0.845 0.885 0.564 PCH08 0.753 PCH09 0.682 PCH10 0.748 PCH11 0.826 PCH12 0.788 PCR PCR13 0.822 0.883 0.911 0.631 PCR14 0.825 PCR15 0.780 PCR16 0.777 PCR17 0.813 PCR18 0.747 PCO PCO19 0.709 0.871 0.903 0.609 PCO20 0.737 PCO21 0.831 PCO22 0.827 PCO23 0.788 PCO24 0.785 LS LS01 0.855 0.888 0.917 0.690 LS02 0.824 LS03 0.864 LS04 0.822 LS05 0.786 SEA SEA01 0.750 0.889 0.916 0.644 SEA02 0.817 SEA03 0.739 SEA04 0.853 SEA05 0.828 SEA06 0.823 SED SED07 0.735 0.861 0.900 0.644 SED08 0.772 SED09 0.865 SED10 0.861 SED11 0.771 SEV SEV12 0.816 0.907 0.928 0.682 SEV13 0.836 SEV14 0.803 SEV15 0.841 SEV16 0.830 SEV17 0.829 GR GCI 0.924 0.837 0.924 0.859 GPE 0.930 PsyCap PCS 0.853 0.898 0.929 0.766 PCH 0.904 PCR 0.889 PCO 0.852 SE SEA 0.920 0.893 0.933 0.823 SED 0.904 SEV 0.897 Note: GCI = Consistency of interest; GPE = Perseverance of effort; PCS = Psychological capital self-efficacy; PCH = Psychological capital hope; PCR = Psychological capital resilience; PCO = Psychological capital optimism; LS = Life satisfaction; SEA = Study engagement absorption; SED = Study engagement dedication; SEV = Study engagement vigor; Note: GR = Grit; PsyCap = Psychological capital; LS = Life satisfaction; SE = Study engagement. The Heterotrait-monotrait ratio (HTMT) advised by Henseler et al. [ 63 ] was adopted to test the discriminant validity. According to Henseler et al. [ 63 ], as long as the HTMT threshold value should not be larger than 0.90. The values in Table 3 indicated that all the value of HTMT completely conformed to the suggested standard, illustrating the establishment of discriminant validity. Table 3 Discriminant validity assessment (HTMT) Contruct GCI GPE PCS PCH PCR PCO LS SEA SED GPE 0.879 PCS 0.552 0.567 PCH 0.648 0.637 0.872 PCR 0.674 0.699 0.731 0.875 PCO 0.643 0.693 0.700 0.774 0.803 LS 0.389 0.349 0.530 0.614 0.483 0.498 SEA 0.664 0.706 0.536 0.617 0.622 0.526 0.387 SED 0.627 0.723 0.571 0.606 0.667 0.561 0.358 0.872 SEV 0.656 0.740 0.536 0.618 0.593 0.555 0.396 0.818 0.768 Note: GCI = Consistency of interest; GPE = Perseverance of effort; PCS = Psychological capital self-efficacy; PCH = Psychological capital hope; PCR = Psychological capital resilience; PCO = Psychological capital optimism; LS = Life satisfaction; SEA = Study engagement absorption; SED = Study engagement dedication; SEV = Study engagement vigor. Structural Model This study used a two-step approach [ 58 ] to verify the measurement model by justifying reliability, convergent validity, and discriminant validity and statistically analyzing full collinearity as suggested by Kock to detect CMB [ 56 ]. After all values were validated against the recommended threshold criteria, the statistical significance and magnitude of the proposed path coefficients were assessed using a bootstrapping approach with 10,000 resamples [ 64 ]. Furthermore, the effect size (f²) was calculated to assess the influence of each exogenous variable on the respective endogenous construct, adhering to Cohen’s criteria for interpreting small (0.02), medium (0.15), and high (0.35) effect sizes [ 65 ]. Finally, the model’s predictive relevance was evaluated by Q² values, and predictive power analysis was carried out using the PLS Predict procedure [ 50 , 66 ]. Collinearity was evaluated using variance inflation factor (VIF) values, which should remain below the threshold of 3.3 [ 57 ]. Table 4 indicated that all VIF values were below 3.3, signifying the absence of problems with collinearity among the predictor variables. Both the direct and indirect effects were illustrated in Table 4 . Five direct hypotheses were tested in this present study. Just as the results of PLS-SEM study showed, grit had a positive and significant effect on study engagement (β = 0.484, t = 10.412, p value < 0.000), grit also had a positive and significant effect on psychological capital (β = 0.671, t = 25.598, p value < 0.000), psychological capital had a positive and significant effect on study engagement (β = 0.300, t = 5.751, p value < 0.000), psychological capital had a positive and significant effect on life satisfaction (β = 0.568, t = 10.847, p value 0.05), and life satisfaction was found to be insignificant to study engagement (β = -0.050, t = 1.309, p value > 0.05), hence H5 and H6 were not supported. For indirect effects, the mediating effect of psychological capital between grit and study engagement was proved to be significant (β = 0.201, t = 5.466, p value 0.05), hence H4 was supported and H7 was not supported. Lastly indirect effect passing from grit to study engagement through psychological capital and life satisfaction serially, with the former impacting the latter, didn’t reach its significance either (β = 0.019, t = 1.291, p value > 0.05), hence, H9 was not supported. The model’s explanatory power was also evaluated in the present study. The results in Table 4 indicated that 55.3% of the variance of study engagement was explained by grit, psychological capital and life satisfaction together, 45.0% of the variance of psychological capital was explained by grit, and 29.2% of the variance of life satisfaction was explained by grit and psychological capital together. Grit was proved to have a large effect on psychological capital (f 2 = 0.818) since the f 2 is larger than 0.35. Grit had a moderate effect on study engagement (f 2 = 0.288), and psychological capital on life satisfaction (f 2 = 0.251) since the f 2 is smaller than 0.35 but larger than 0.15. Psychological capital had a smaller effect on study engagement (f 2 = 0.088) because value ranged from 0.02 to 0.15 [ 65 ]. Figure 2 presents the results of the hypothesis testing. Table 4 Structural model assessment Hypothesis Relationship Beta T Values P Values R² f² VIF H1 GR ->SE 0.484 10.412 0.000 0.553 0.288 1.821 H2 GR ->PsyCap 0.671 25.598 0.000 0.450 0.818 1.000 H3 PsyCap ->SE 0.300 5.751 0.000 0.088 2.274 H4 GR ->PsyCap ->SE 0.201 5.466 0.000 H5 GR ->LS -0.043 0.812 0.208 0.292 1.818 H6 LS ->SE 0.050 1.309 0.095 1.412 H7 GR ->LS ->SE -0.002 0.582 0.560 H8 PayCap ->LS 0.568 10.847 0.000 0.251 1.818 H9 GR ->PsyCap ->LS ->SE 0.019 1.291 0.197 Note: GR = Grit; PsyCap = Psychological capital; LS = Life satisfaction; SE = Study engagement. PLS predict approach was used to predict measurement error by comparing the Root Mean Square Error (RMSE) of PLS and Linear Modeling (LM), as suggested by Shmueli et al. [ 66 ], in order to assess the predictive power of the model. If any difference between PLS and LM is less than 0, the predictive capacity of the model is said to be strong. A modest level of predictive relevance is shown when the bulk of the differences are less than 0. On the other hand, if a small percentage of values goes below 0, it implies a poor degree of predictability. On the other hand, it suggests that the predictive significance has not been verified if every item is greater than 0. Table 5 showed a moderate predictive power in the model, with the majority of items for all exogenous variables in the PLS predict analysis being lower than the values obtained by LM. Additionally, data in Table 5 demonstrated that all of Q²values are greater than 0, indicating that Q² signified adequate predictive relevance. Table 5 PLS predict Indicators Q²predict PLS-SEM_RMSE LM_RMSE PLS-LM LS1 0.079 1.294 1.297 -0.003 LS2 0.085 1.328 1.330 -0.002 LS3 0.062 1.301 1.304 -0.003 LS4 0.075 1.503 1.508 -0.005 LS5 0.072 1.710 1.705 0.005 PCH 0.332 0.819 0.821 -0.002 PCO 0.374 0.793 0.794 -0.001 PCR 0.397 0.778 0.780 -0.002 PCS 0.261 0.862 0.863 -0.001 SEA 0.398 0.778 0.779 -0.001 SED 0.380 0.789 0.785 0.004 SEV 0.428 0.757 0.755 0.002 Note: PCS = Psychological capital self-efficacy; PCH = Psychological capital hope; PCR = Psychological capital resilience; PCO = Psychological capital optimism; LS = Life satisfaction; SEA = Study engagement absorption; SED = Study engagement dedication; SEV = Study engagement vigor. Discussion Student study engagement has emerged as a pivotal focus in educational psychology research due to its substantial correlations with academic success [ 67 ], academic achievement, subjective well-being [ 68 ] and preparedness for work [ 69 ]. Recently, researchers have shown growing interest in the topic of student engagement in education, particularly through the lens of grit [ 13 , 70 , 71 ]. Hence, this study examined the link between grit and university students’ study engagement and the chain mediating role of psychological capital and life satisfaction in the Chinese culture. The research findings demonstrated that grit directly predicted study engagement and psychological capital, but had no significant effect on life satisfaction. Psychological capital directly predicted study engagement and life satisfaction, nevertheless life satisfaction had no significant effect on study engagement. The mediation hypothesis posits that the relationship between grit and study engagement is mediated by psychological capital traits, omitting life satisfaction variables. Lastly, psychological capital and life satisfaction failed to function as a chain mediating variable in the relationship between grit and study engagement. Direct predictive effects This results from the study demonstrate that grit has a positive effect on the study engagement of the university students. This indicates that the university students who possess high degree of grit are more likely to exhibit a strong level of study engagement, which verifies the research hypothesis 1 and is accord with the prior finding [ 12 , 13 ]. Grit defined as consistency of interest and perseverance of effort has gained prominence as a non-cognitive trait that is essential in predicting educational outcomes [ 10 ]. Grit is a crucial factor reinforcing individuals’ confidence in their capabilities while fostering a sustained commitment to achieving future-oriented goals. The more gritty one is, the strongly she or he is engaged in study. Strongly gritty university students usually tend to actively overcome the learning difficulties and adopt deep learning strategies, therefore more inclined to sustain their concentration on academic tasks [ 11 – 13 ]. To sum up, the degree of university students’ grit positively affects their level of study engagement. By improving the level of grit of university students, their basic psychological need like autonomy is more likely to be satisfied [ 72 ], which accordingly leads to their increased study engagement [ 12 , 13 ]. These results reinforce grit as a key determinant of study engagement, particularly in structured educational settings. Mediating effects The findings of the study reveal that grit positively affects psychological capital, which in turn directly influences study engagement. Additionally, psychological capital has a mediating effect on the relationship between grit and study engagement. This indicates that university students’ grit indirectly impacts their study engagement through the mediator of psychological capital. These results in this present study empirically validate the hypotheses 2, 3 and 4, which are consistent with the existing research findings [ 31 , 73 , 74 ]. On the one hand, the individuals with sustained efforts and long-term oriented goals, accumulating successful experiences, are more capable of improving their self-efficacy, hope, resilience and optimism. On the other hand, self-efficacy, hope, resilience, and optimism facilitate students’ ability to cope with academic demands [ 31 ]. In conclusion, psychological capital of the university students serves as a mediator between their grit and study engagement. The study extends prior findings by demonstrating that grit alone is not enough—students require psychological capital resources to translate consistency of interest and perseverance of effort into their study engagement. Contrary to expectations, the results of this study demonstrate that grit had no significant effect on life satisfaction, and life satisfaction also had no significant effect on study engagement. Consequently, life satisfaction did not function as a mediator in the relationship between grit and study engagement, hence the hypotheses 5, 6, and 7 are not supported. These contradict the prior studies proving that grit can positively affect life satisfaction [ 37 , 75 ] and life satisfaction can directly predict study engagement [ 36 ], which were conducted in Brazil, Hongkong and Indonesia respectively. A possible explanation is cultural factor in mainland of China, study engagement is often driven by external pressures (e.g., parental expectations, career competition rather than intrinsic satisfaction [ 76 ]. These findings suggest that life satisfaction may be an outcome rather than a mechanism in study engagement processes. Another possible reason is that respondents in different ages may think and behave differently in academic performance when they are satisfied with their life. Just as the research on Hong Kong adolescents indicates, the grit of adolescents is positively related to their well-being [ 75 ], which underscores that the different age range of the respondents should be taken into consideration. The last cause is that the previous researchers find different factors of grit - consistency of interest and perseverance of effort function differently in the related link [ 12 , 77 ], by which the special attention to the various impact of the separate factors of grit should be paid. These results further extend the current knowledge about the relationship among grit, life satisfaction and study engagement in the educational setting. These findings offer new insights and contribute to the existing body of literature, suggesting that researchers should consider examining the relationship between grit, life satisfaction, and study engagement from a different perspective. Chain mediation effects The other findings in the study indicate that psychological capital positively affect life satisfaction, which is line with the existing research [ 78 ]. Thus, the hypothesis 8 is justified. However, psychological capital and life satisfaction are found not to sequentially mediate grit’s effect on study engagement due to the invalid link between grit and life satisfaction, along with life satisfaction and study engagement, hence the hypothesis 9 is not supported. One explanation is that life satisfaction is more strongly influenced by psychological capital resources like self-efficacy, hope, resilience, and optimism rather than grit - consistency of interest and perseverance of effort. Another explanation is that study engagement tends to be directly impacted by positive psychological traits but not by emotional factors like life satisfaction. Future research should explore possible mediators like intrinsic motivation or external demands, which may provide stronger explanatory power. Implications and limitations of the study University students are the key participants in high education and their study engagement plays a vital role in both their academic achievement and future career development. Consequently, this study has important implications for theory as well as practice. This study has important theoretical implications. First of all, although there have been previous studies on the effect of grit on engagement, the research on the effect of university students’ grit on study engagement is still immature in China. This study explores the influence of psychology mechanism of psychological capital and life satisfaction on the relationship of grit on university students’ study engagement, contributing to a deeper understanding of the relationship between non-cognitive grit and study engagement relationship. Secondly, there are few studies on grit and psychological capital in the prior literature. The study examines the positively predictive effect of grit on psychological capital, which enriches the literature on the relationship between grit and psychological capital. Moreover, it discusses the mediating effect of psychological capital between grit and study engagement and provides a new research perspective for the concept of study engagement. Lastly, the proposed chain mediating model is not justified in this study, suggesting future researchers may consider exploring alternative mediator variables in examining the relationship between grit and study engagement. To the best of the researcher’s knowledge, no prior studies have combined all four of the aforementioned variables within a single serial mediation model. This study has important practical implications. At first, the study found that grit has a positive and significant effect on university students’ study engagement. Therefore, for higher education institutions and educational organizations, it is possible to enhance university students’ study engagement by improving the level of their grit. Moreover, this study found that grit fosters university students’ study engagement through the mediating effect of psychological capital. This exhibits a deep inner enhancement path for university students’ grit toward their study engagement and eventually boost academic performance. Based on this, higher education institutions and educators should pay attention to the formation of psychological capital by cultivating of university students’ grit, guiding them to clarify long-term development goals and visions, developing their excellent qualities that dare to challenge but never give up, and stimulate their enthusiasm and initiative toward academic achievement. It satisfies university students’ intrinsic pursuit of a sense of significance and accomplishments, to ultimately facilitate their study engagement. While this research provides valuable insights, it unavoidably presents some limitations. Firstly, the study employed cross-sectional data and further longitudinal studies or experiments should be adopted in the future. This study only evaluates the relationship among grit, psychological capital, life satisfaction and study engagement from the perspective of university students from Ji’nan, China. Future research could examine the effects of these variables from a more diverse student population from other regions such as Northeast China, North China, Northwest China, Central China, South China, East China, and Southwest China. In addition, future researchers may explore other potential moderators to gain a deeper understanding of why the relationships between grit and life satisfaction, as well as between life satisfaction and study engagement, were not significant in the current model. Conclusion This study verified the direct effect of grit on study engagement of university students in the Chinese context, and deeply analyzed the psychological mechanism of grit on university students’ study engagement, revealing that grit indirectly and positively predicts study engagement through the mediating effects of psychological capital. The findings not only provide a systematic explanation and empirical evidence for the influence mechanism of grit on university students’ study engagement, but also provide references for the realization path of the higher education institutions and educators to enhance university students’ study engagement and improve their comprehensive and competitive ability, particularly in high-pressure educational environments. Abbreviations GR grit GCI consistency of interest GPE perseverance of effort; PsyCap psychological capital PCS psychological capital self-efficacy PCH psychological capital hope PCR psychological capital resilience PCO psychological capital optimism SE study engagement SEA study engagement absorption SED study engagement dedication SEV study engagement vigor LS life satisfaction Declarations Acknowledgments The authors would like to thank all participants and research assistants for their cooperation. We would also like to thank the reviewers for their valuable comments and suggestions. Author contributions Conceptualization: Xiaomei Zhang, Soon-Yew Ju, Lai-Kuan Kong. Data curation:Xiaomei Zhang, Lei Cui, Shan Zhang, Na Liu. Formal analysis: Xiaomei Zhang. Funding acquisition: Xiaomei Zhang. Investigation: Xiaomei Zhang, Xixia Gao, Xiaoning Chu, Jihong Liu Methodology: Xiaomei Zhang, Soon-Yew Ju, Lai-Kuan Kong. Project administration: Xiaomei Zhang Resources: Xiaomei Zhang, Lei Cui, Na Liu. Software: Xiaomei Zhang, Soon-Yew Ju, Lai-Kuan Kong. Supervision: Xiaomei Zhang, Shan Zhang, Lai-Kuan Kong. Validation: Xiaomei Zhang. Visualization: Xiaomei Zhang. Writing – original draft: Xiaomei Zhang. Writing – review & editing: Xiaomei Zhang, Xiaoning Chu, Soon-Yew Ju, Lai-Kuan Kong, Jihong Liu. Funding There is no specific funding to support this research. Data availability The data supporting the findings of this research are obtainable upon request from the first author. Due to privacy or ethical restrictions, the data are not publicly accessible. Ethics approval and Informed Consent This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Review Board of the School of Foreign Languages, Shandong Jianzhu University (approval date: April 2024). And written informed consent from all participants was obtained prior to participation. 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testing.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6654212/v1/c171d571e7dc5fb726959a34.png"},{"id":86931577,"identity":"62e8d319-f4f7-4239-a2ad-f522bd5bc5f6","added_by":"auto","created_at":"2025-07-17 09:52:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1230758,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6654212/v1/86f40afd-77bd-447c-9f97-da9e803047fa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Understanding University Students’ Study Engagement through the Lens of Grit: A Serial Mediation Model of Psychological Capital and Life Satisfaction Using a PLS-SEM Approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUniversity students, as a crucial group within higher education, face substantial academic demands [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and transitional challenges [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These pressures necessitate a deeper understanding of study engagement, which is increasingly recognized as a critical determinant of educational success and student well-being [\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Study engagement, defined as the degree of students\u0026rsquo; cognitive, emotional, and behavioral investment in learning, has emerged as a pivotal research focus in educational psychology due to its strong associations with academic performance, well-being, and career preparedness [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite extensive research on the antecedents of study engagement, the interplay between non-cognitive traits [e.g., grit], psychological resources [e.g., psychological capital], and subjective well-being [e.g., life satisfaction] remains under-explored, particularly in non-Western educational contexts. Existing literature suggests that grit\u0026mdash;a sustained passion and perseverance for long-term goals\u0026mdash;plays a key role in fostering academic engagement [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, the mechanisms underlying this relationship remain unclear, necessitating an examination of potential mediating factors.\u003c/p\u003e\u003cp\u003eIn particular, psychological capital [PsyCap], a malleable psychological resource encompassing self-efficacy, hope, resilience and optimism [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], may function as a critical intermediary between grit and study engagement. Additionally, life satisfaction, a central component of subjective well-being [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], is posited to strengthen students\u0026rsquo; motivation and engagement levels [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This study, therefore, explores the chain mediation effect of psychological capital and life satisfaction in the relationship between grit and study engagement, providing a nuanced perspective on student motivation and persistence.\u003c/p\u003e\n\u003ch3\u003eConceptualizing Study Engagement: A Theoretical Perspective\u003c/h3\u003e\n\u003cp\u003eStudy engagement is conceptualized as a multidimensional construct comprising behavioral, emotional, and cognitive dimensions [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. It reflects students\u0026rsquo; persistent effort, emotional investment, and intellectual involvement in their academic pursuits [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Given its strong correlation with academic achievement, study engagement has become a central theme in higher education research [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSelf-Determination Theory (SDT) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], a widely recognized framework for studying academic engagement, emphasizes that fulfilling individuals\u0026rsquo; core psychological needs in autonomy, competence, and relatedness is more likely to foster their deep engagement in relevant activities. In this study, SDT acts as a guiding framework, illuminating how personal characteristics and psychological resources influence student study engagement. Traits such as grit, for instance, strengthen students\u0026rsquo; belief in their abilities and their perseverance in pursuing long-term objectives. Similarly, psychological resources like PsyCap\u0026mdash;comprising self-efficacy, hope, resilience, and optimism\u0026mdash;encourage self-driven motivation, which sustains effort and emotional commitment to academic work. Well-being factors, including life satisfaction, further nurture students\u0026rsquo; sense of belonging and emotional stability, enabling them to engage meaningfully with their educational settings. By weaving these elements together, SDT conceptualizes engagement as a motivation-driven process and clarifies how the interplay between intrinsic traits and extrinsic conditions collectively fuels academic participation. This integrative approach underscores the theory\u0026rsquo;s explanatory power in linking individual and contextual factors to sustained academic involvement.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eGrit and Study Engagement\u003c/h2\u003e\u003cp\u003eGrit, defined as passion and perseverance for long-term goals, has gained prominence as a non-cognitive trait critical for academic success [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. It consists of two dimensions: consistency of interest and perseverance of effort [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Empirical evidence suggests that grittier students exhibit stronger academic engagement, persisting in their studies despite challenges [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCross-cultural studies indicate that the impact of grit on study engagement varies across different educational contexts. For example, research in Western contexts suggests a moderate positive relationship between grit and academic engagement [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], whereas studies in Asian educational settings indicate a stronger link, potentially due to cultural expectations of academic perseverance [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Besides, developmental factors influence grit\u0026rsquo;s effectiveness, with findings suggesting that grit\u0026rsquo;s impact on engagement and literacy achievement is particularly pronounced among younger learners and dual-language students [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile the direct influence of grit on engagement is well-documented, the mediating mechanisms remain under-explored. Some studies propose that grit enhances engagement through self-regulated learning and resilience [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], yet the extent to which psychological capital and life satisfaction mediate this effect remains an open question.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePsychological Capital as a Mediating Resource\u003c/h3\u003e\n\u003cp\u003ePsyCap is a state-like psychological resource encompassing self-efficacy, hope, resilience, and optimism [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Unlike fixed personality traits, PsyCap is malleable and can be facilitated through training, making it a valuable target for interventions aimed at boosting student performance and engagement [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Research suggests that students with higher PsyCap set more ambitious academic goals, persist in the face of adversity, and experience greater satisfaction in their learning environments [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNotably, PsyCap has been found to mediate the relationship between grit and engagement, amplifying students\u0026rsquo; academic resilience and persistence, proving that grit fosters PsyCap, which in turn promotes greater academic studies [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Luthans and Youssef-Morgan further emphasize the malleability of PsyCap, highlighting its potential as an intervention tool for enhancing academic persistence [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Moreover, PsyCap is closely linked to students\u0026rsquo; emotional well-being and life satisfaction[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], reinforcing the argument that it serves as a psychological relief against academic stress [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This raises the possibility that life satisfaction may function as a secondary mediator, reinforcing the positive impact of PsyCap on engagement.\u003c/p\u003e\n\u003ch3\u003eLife Satisfaction as a Mediating Resource\u003c/h3\u003e\n\u003cp\u003eLife satisfaction, a key indicator of subjective well-being, represents an individual\u0026rsquo;s overall assessment of their life quality [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Research consistently demonstrates that higher life satisfaction is linked to greater academic engagement, as satisfied students display stronger motivation, persistence, and enthusiasm for learning [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecent empirical findings reveal a strong association between life satisfaction and engagement across diverse cultural contexts. For instance, Indonesian university students reporting higher life satisfaction also exhibited greater engagement in academic tasks [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Similarly, Brazilian research identifies grit as a key predictor of life satisfaction, further reinforcing its role in boosting emotional well-being and engagement [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Furthermore, PsyCap has been identified as a significant contributor to life satisfaction, particularly in challenging academic environments [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Empirical studies conducted among multicultural professionals in Poland, hospitality workers in Taiwan, and online learners in UK further suggest that higher psychological capital leads to enhanced life satisfaction[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and greater engagement [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite existing literature establishes grit as a predictor of academic engagement[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], but the underlying mechanisms remain insufficiently explored. While some studies suggest that grit fosters persistence and academic resilience [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], the role of psychological capital and life satisfaction in this process warrants further investigation. This study aims to bridge this gap by investigating the chain mediation effect of psychological capital and life satisfaction on the relationship between grit and study engagement.\u003c/p\u003e\n\u003ch3\u003eResearch Hypotheses\u003c/h3\u003e\n\u003cp\u003eBuilding on the theoretical and empirical evidence discussed, this study proposes the following hypotheses:\u003c/p\u003e\u003cp\u003eH1: Grit has a positive and significant impact on university students\u0026rsquo; study engagement.\u003c/p\u003e\u003cp\u003eH2: Grit has a positive and significant impact on university students\u0026rsquo; psychological capital.\u003c/p\u003e\u003cp\u003eH3: Psychological capital has a positive and significant impact on university students\u0026rsquo; study engagement.\u003c/p\u003e\u003cp\u003eH4: Psychological capital mediates the relationship between grit and university students\u0026rsquo; study engagement.\u003c/p\u003e\u003cp\u003eH5: Grit has a positive and significant impact on university students\u0026rsquo; life satisfaction.\u003c/p\u003e\u003cp\u003eH6: Life satisfaction has a positive and significant impact on university students\u0026rsquo; study engagement.\u003c/p\u003e\u003cp\u003eH7: Life satisfaction mediates the relationship between grit and university students\u0026rsquo; study engagement.\u003c/p\u003e\u003cp\u003eH8: Psychological capital has a positive and significant impact on university students\u0026rsquo; life satisfaction.\u003c/p\u003e\u003cp\u003eH9: Psychological capital and life satisfaction jointly mediate the relationship between grit and university students\u0026rsquo; study engagement.\u003c/p\u003e\u003cp\u003eThe proposed serial mediation model is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eProcedure and Measurement\u003c/p\u003e\u003cp\u003eThe questionnaire was designed by Questionnaire Star and then sent to the respondents via teachers in the public universities in Ji\u0026rsquo;nan by the WeChat or QQ group. The study employed a non-probability purposive sampling strategy which eventually collected 560 questionnaires. Out of that, 86.43% or 484 data were usable. Prior to the main analysis, the data set was screened for suspicious response patterns. Specifically, cases exhibiting no variance or displaying sequential response patterns were identified and removed. For ethical consideration, the participants were provided with informed consent to assure the confidentiality and anonymity. In relation to that, data were stored securely in encrypted files, with access restricted to the research team. This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Review Board of the School of Foreign Languages, Shandong Jianzhu University (approval date: April 2024). All participants were informed about the purpose and procedures of the study, and written informed consent was obtained prior to participation. Participation was voluntary, and participants had the right to withdraw at any time without penalty.\u003c/p\u003e\u003cp\u003eStudy Engagement\u003c/p\u003e\u003cp\u003eThis study utilized the Utrecht Work Engagement Scale (UWES) to assess student study engagement, since it has been effectively used to evaluate students\u0026rsquo; academic engagement [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. It has three subscales including 17 items. Six items are used for study engagement absorption (SEA), five items for study engagement dedication (SED) and other six items for Study engagement vigor (SEV), such as \u0026ldquo;Time flies when I\u0026rsquo;m studying\u0026rdquo;, \u0026ldquo;I find my studies to be full of meaning and purpose\u0026rdquo;, and \u0026ldquo;When I\u0026rsquo;m studying, I feel mentally strong\u0026rdquo;. The prior study indicated that the reliability coefficients of each dimension was 0.904 for SEA, 0.818 for SED and 0.886 for SEV [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The seven-Likert scale was utilized to rate these items in this study, in which 1 meant strongly disagree and 7 meant strongly agree. In this present study, the Cronbach\u0026rsquo;s alpha coefficients of the SEA, SED, and SEV were 0.889, 0.861, and 0.907 respectively.\u003c/p\u003e\u003cp\u003eGrit\u003c/p\u003e\u003cp\u003eThe Grit Scale developed by Duckworth and her associateswas adopted in this study[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. It contains 12 items attached to two subscales, i.e. consistency of interest (GCI) and perseverance of effort (GPE). Each subscale has six items, such as \u0026ldquo;I become interested in new pursuits every few months\u0026rdquo;, and \u0026ldquo;I have achieved a goal that took years of work\u0026rdquo;. The Cronbach\u0026rsquo;s alpha for GCI was 0.840, and GPE was 0.780 [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In this study, the five-Likert scale was employed to evaluate the items (1\u0026thinsp;=\u0026thinsp;strongly disagree \u0026ndash; 5\u0026thinsp;=\u0026thinsp;strongly agree). The Cronbach\u0026rsquo;s alpha of the two subscales in this present study was 0.821 for GCI and 0.817 for GPE.\u003c/p\u003e\u003cp\u003ePsychological Capital\u003c/p\u003e\u003cp\u003ePsychological capital was measured using the Psychological Capital Scale previously developed by Luthans et al. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. As they outlined, this instrument measures four core positive psychological resources\u0026mdash;self-efficacy, hope, resilience, and optimism\u0026mdash;and its reliability and construct validity have been well‐documented in organizational behavior literature. There are 24 items with 4 subscales, namely psychological capital self-efficacy (PCS), such as \u0026ldquo;I believe I can analyze the long-term problems and find solutions\u0026rdquo;, psychological capital hope (PCH) like \u0026ldquo;If I find myself in a jam, I could think of ways to get out of it\u0026rdquo;, psychological capital resilience (PCR) like \u0026ldquo;I can usually take the pressure at school calmly\u0026rdquo;, and psychological capital optimism (PCO) like \u0026ldquo;I always see the positive side of my study\u0026rdquo;. Besides, the psychological capital scale has been successfully employed by the other scholars in their research. Wang et al. displayed the relationship between anxiety, depressive symptoms, and psychological capital among Chinese university students [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. And Zhou et al. investigated how psychological capital, organizational commitment, job burnout, and anxiety affected each other among Chinese female nurses [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAccording to the Peng et al., the Cronbach\u0026rsquo;s alpha coefficients for the four subscales were 0.710, 0.780, 0.750, and 0.820, respectively [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The seven-Likert scale was adopted to measure these items (i.e. 1\u0026thinsp;=\u0026thinsp;strongly disagree \u0026ndash; 7\u0026thinsp;=\u0026thinsp;strongly agree). The Cronbach\u0026rsquo;s alpha of the four subscales in this present study was 0.874 for PCS, 0.845 for PCH, 0.883 for PCR and 0.871 for PCO.\u003c/p\u003e\u003cp\u003eLife Satisfaction\u003c/p\u003e\u003cp\u003eSatisfaction with Life Scale (SWLS) was widely put into use to measure the life satisfaction [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. It consists of five items in all, such as \u0026ldquo;In most ways my life is close to my ideal\u0026rdquo;. The previous study showed that Cronbach\u0026rsquo;s alpha coefficients for this scale was 0.780 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The seven-Likert scale was employed to score the items with 1 meaning completely disagree and 7 meaning completely agree. The Cronbach\u0026rsquo;s alpha was 0.888 in the current study.\u003c/p\u003e\u003cp\u003eSampling and data collection\u003c/p\u003e\u003cp\u003eThis survey involved 484 respondents, including 44.6% male students and 55.4% female students from 17 universities in Ji\u0026rsquo;nan, Shandong Province, China. Of the respondents, 34.1% were from urban areas, while 65.9% were from rural areas. In terms of academic specialization, 57.9% of the university students majored in science, whereas 42.1% pursued majors in the liberal arts. For the sibling category, 30.6% of the respondents were from single-child families, while 69.4% had siblings.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eThis study utilized SPSS 29 and Smart PLS 4.1.1.2 as tools for statistical data analysis. A total of 484 data sets were analyzed using Smart PLS software via Partial Least Squares Equation Modelling (PLS-SEM). This research utilized PLS-SEM to validate the model, since it adeptly accommodates the interplay among independent, mediator, and dependent variables simultaneously, often rendering it more appropriate for constructing predictive models compared to alternative methodologies [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. PLS-SEM is especially suitable when the objective is to optimize explained variance (R\u0026sup2;) and forecast relationships among constructs rather than merely validate existing theories. This study analyzed the data sample in two separate phases utilizing Smart PLS software [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Firstly, this study utilizes a validated and mature instrument that has been previously tested for both reliability and validity in earlier research. Accordingly, a confirmatory factor analysis will be performed to assess the measurement model [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Once the measurement model was evaluated, then the structural model was further evaluated using bootstrapping and PLS Predict to assess the significance of the path coefficients and the model's explanatory and predictive power.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eCommon Method Bias\u003c/p\u003e\u003cp\u003eCommon method bias (CMB), usually occurring when research incorporates multiple self-report measures from the same respondent and then look at connections between these self-report measures suggested by Cooper et al. [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. There are two methods widely applied to detect the CMV and then protect the data from it.\u003c/p\u003e\u003cp\u003eWhen examining causal relationships between exogenous and endogenous variables, the use of a single respondent to provide data for all constructs (i.e., a single-source design) may introduce common method bias (CMB), which could compromise the validity of the findings [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. To address this concern, this study employ both procedural and statistical remedies [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Procedurally, participant anonymity and confidentiality were guaranteed to reduce evaluation fear and social desirability bias, and different Likert scales were utilized to rate exogenous and endogenous variables. For statistical remedy, the Kock\u0026rsquo;s method was used to measure full collinearity statistically [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, exogenous and endogenous variables variance inflation factor (VIF) values were below 3.3 (ranging from 1.018 to 3.080), showing that CMB was not a significant threat in this study [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFull covariance test\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGPE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePCS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePCH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePCR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePCO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSEA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSED\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSEV\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.622\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.916\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2. 976\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote: GCI\u0026thinsp;=\u0026thinsp;Consistency of interest; GPE\u0026thinsp;=\u0026thinsp;Perseverance of effort; PCS\u0026thinsp;=\u0026thinsp;Psychological capital self-efficacy; PCH\u0026thinsp;=\u0026thinsp;Psychological capital hope; PCR\u0026thinsp;=\u0026thinsp;Psychological capital resilience; PCO\u0026thinsp;=\u0026thinsp;Psychological capital optimism; LS\u0026thinsp;=\u0026thinsp;Life satisfaction; SEA\u0026thinsp;=\u0026thinsp;Study engagement absorption; SED\u0026thinsp;=\u0026thinsp;Study engagement dedication; SEV\u0026thinsp;=\u0026thinsp;Study engagement vigor.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMeasurement Model\u003c/p\u003e\u003cp\u003eIt was crucial and necessary to separate the data prior to the data run because the precise measurement model specification was needed to assign meaningful links in the structural model [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The term \u0026ldquo;reflective measurement\u0026rdquo; has been utilized to refer to the fact that a latent idea produces variance in a set of observable indicators, which may then be used to get a measurement concept [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGrit, defined as a high-order construct by Duckworth and Quinn [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], functions as a reflective model akin to psychological capital [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] and study engagement [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. In contrast, life satisfaction, categorized as a first-order construct, is also reflective [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFor reflective model, indicator loading, construct reliability as well as convergent and discriminant validity, were broadly adopted to measure it [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Just as Hair et al. suggested [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], indicator loading larger than 0.708 was recommended, and the indicator loading ranging from 0.500 to 0.708 was acceptable as well. The following Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displayed the indicator loading of all the items was greater than 0.708 except that the indicator loading of GCI06 (0.617), GPE07 (0.683), GPE08 (0.678) and PCH09 (0.682) respectively. Despite the item loadings for GCI06, GPE07, GPE08, and PCH09 being marginally below the suggested threshold of 0.708, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e indicates that the AVE values for the constructs GCI, GPE, and PCH surpass the minimum requirement of 0.500. Therefore, all indicators in this study were retained.\u003c/p\u003e\u003cp\u003eNext, construct reliability was assessed by both Cronbach\u0026rsquo;s alpha (CA) and composite reliability (CR). In line with the suggestion from the researchers [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], the values in the range from 0.70 to 0.95 were considered to be feasible. For this study, just as Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e indicated, all the CA and CR values of each dimension situated in the required scope ranging from 0.817 to 0.933. To assess convergent validity, the average variance extracted (AVE) was utilized, with a threshold value set at 0.50 [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. As shown in the Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, all AVE values exceeded the required threshold, ranging from 0.523 to 0.859, thus confirming the convergent validity of the measurement model.\u003c/p\u003e\u003cp\u003eFor the assessment of higher-order constructs, i.e., grit, psychological capital, and study engagement constructs. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that all indicator loadings range from 0.852 to 0.924. The AVE values range from 0.766 to 0.859, while both Cronbach\u0026rsquo;s alpha (CA) and composite reliability (CR) values range from 0.837 to 0.933. Therefore, the reliability and validity of the higher-order constructs exceed the required thresholds and are thus confirmed.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eConstruct reliability and convergent validity assessment\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstruct\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndicators\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLoading\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAVE\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCI01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.723\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.870\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.529\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCI02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCI03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCI04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCI05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.753\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCI06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGPE07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.683\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.523\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGPE08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGPE09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.772\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGPE10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGPE11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGPE12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.734\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCS01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.798\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.905\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.613\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCS02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCS03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCS04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.810\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCS05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCS06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCH07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.845\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.564\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCH08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.753\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCH09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCH10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCH11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.826\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCH12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.788\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCR13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.883\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.911\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.631\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCR14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCR15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.780\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCR16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCR17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.813\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCR18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCO19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.709\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.871\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.609\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCO20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCO21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.831\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCO22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.827\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCO23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.788\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCO24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLS01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.690\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLS02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.824\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLS03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLS04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLS05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSEA01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.916\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.644\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSEA02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSEA03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.739\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSEA04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSEA05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSEA06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.823\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSED\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSED07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.644\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSED08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.772\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSED09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSED10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSED11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.771\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSEV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSEV12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.907\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.682\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSEV13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSEV14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.803\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSEV15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.841\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSEV16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSEV17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.924\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.837\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.924\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.859\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGPE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePsyCap\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.898\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.766\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.852\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.920\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.823\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSED\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSEV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: GCI\u0026thinsp;=\u0026thinsp;Consistency of interest; GPE\u0026thinsp;=\u0026thinsp;Perseverance of effort; PCS\u0026thinsp;=\u0026thinsp;Psychological capital self-efficacy; PCH\u0026thinsp;=\u0026thinsp;Psychological capital hope; PCR\u0026thinsp;=\u0026thinsp;Psychological capital resilience; PCO\u0026thinsp;=\u0026thinsp;Psychological capital optimism; LS\u0026thinsp;=\u0026thinsp;Life satisfaction; SEA\u0026thinsp;=\u0026thinsp;Study engagement absorption; SED\u0026thinsp;=\u0026thinsp;Study engagement dedication; SEV\u0026thinsp;=\u0026thinsp;Study engagement vigor; Note: GR\u0026thinsp;=\u0026thinsp;Grit; PsyCap\u0026thinsp;=\u0026thinsp;Psychological capital; LS\u0026thinsp;=\u0026thinsp;Life satisfaction; SE\u0026thinsp;=\u0026thinsp;Study engagement.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe Heterotrait-monotrait ratio (HTMT) advised by Henseler et al. [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] was adopted to test the discriminant validity. According to Henseler et al. [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], as long as the HTMT threshold value should not be larger than 0.90. The values in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e indicated that all the value of HTMT completely conformed to the suggested standard, illustrating the establishment of discriminant validity.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDiscriminant validity assessment (HTMT)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eContruct\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGPE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePCS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePCH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePCR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePCO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSEA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSED\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.552\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.648\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.637\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.674\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.731\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.774\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.803\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.530\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.483\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.706\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.622\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.387\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSED\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.627\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.723\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSEV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.656\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.740\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.396\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.768\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote: GCI\u0026thinsp;=\u0026thinsp;Consistency of interest; GPE\u0026thinsp;=\u0026thinsp;Perseverance of effort; PCS\u0026thinsp;=\u0026thinsp;Psychological capital self-efficacy; PCH\u0026thinsp;=\u0026thinsp;Psychological capital hope; PCR\u0026thinsp;=\u0026thinsp;Psychological capital resilience; PCO\u0026thinsp;=\u0026thinsp;Psychological capital optimism; LS\u0026thinsp;=\u0026thinsp;Life satisfaction; SEA\u0026thinsp;=\u0026thinsp;Study engagement absorption; SED\u0026thinsp;=\u0026thinsp;Study engagement dedication; SEV\u0026thinsp;=\u0026thinsp;Study engagement vigor.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eStructural Model\u003c/td\u003e\u003c/tr\u003e\u003cp\u003eThis study used a two-step approach [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] to verify the measurement model by justifying reliability, convergent validity, and discriminant validity and statistically analyzing full collinearity as suggested by Kock to detect CMB [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. After all values were validated against the recommended threshold criteria, the statistical significance and magnitude of the proposed path coefficients were assessed using a bootstrapping approach with 10,000 resamples [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Furthermore, the effect size (f\u0026sup2;) was calculated to assess the influence of each exogenous variable on the respective endogenous construct, adhering to Cohen\u0026rsquo;s criteria for interpreting small (0.02), medium (0.15), and high (0.35) effect sizes [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Finally, the model\u0026rsquo;s predictive relevance was evaluated by Q\u0026sup2; values, and predictive power analysis was carried out using the PLS Predict procedure [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Collinearity was evaluated using variance inflation factor (VIF) values, which should remain below the threshold of 3.3 [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e indicated that all VIF values were below 3.3, signifying the absence of problems with collinearity among the predictor variables.\u003c/p\u003e\u003cp\u003eBoth the direct and indirect effects were illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Five direct hypotheses were tested in this present study. Just as the results of PLS-SEM study showed, grit had a positive and significant effect on study engagement (β\u0026thinsp;=\u0026thinsp;0.484, t\u0026thinsp;=\u0026thinsp;10.412, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.000), grit also had a positive and significant effect on psychological capital (β\u0026thinsp;=\u0026thinsp;0.671, t\u0026thinsp;=\u0026thinsp;25.598, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.000), psychological capital had a positive and significant effect on study engagement (β\u0026thinsp;=\u0026thinsp;0.300, t\u0026thinsp;=\u0026thinsp;5.751, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.000), psychological capital had a positive and significant effect on life satisfaction (β\u0026thinsp;=\u0026thinsp;0.568, t\u0026thinsp;=\u0026thinsp;10.847, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.000), hence H1, H2 H3 and H8 were supported. However, grit was found to be insignificant to life satisfaction (β = -0.043, t\u0026thinsp;=\u0026thinsp;0.812, p value\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and life satisfaction was found to be insignificant to study engagement (β = -0.050, t\u0026thinsp;=\u0026thinsp;1.309, p value\u0026thinsp;\u0026gt;\u0026thinsp;0.05), hence H5 and H6 were not supported. For indirect effects, the mediating effect of psychological capital between grit and study engagement was proved to be significant (β\u0026thinsp;=\u0026thinsp;0.201, t\u0026thinsp;=\u0026thinsp;5.466, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.000), however, life satisfaction failed to mediate the relationship between grit and study engagement (β = -0.002, t\u0026thinsp;=\u0026thinsp;0.582, p value\u0026thinsp;\u0026gt;\u0026thinsp;0.05), hence H4 was supported and H7 was not supported. Lastly indirect effect passing from grit to study engagement through psychological capital and life satisfaction serially, with the former impacting the latter, didn\u0026rsquo;t reach its significance either (β\u0026thinsp;=\u0026thinsp;0.019, t\u0026thinsp;=\u0026thinsp;1.291, p value\u0026thinsp;\u0026gt;\u0026thinsp;0.05), hence, H9 was not supported.\u003c/p\u003e\u003cp\u003eThe model\u0026rsquo;s explanatory power was also evaluated in the present study. The results in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e indicated that 55.3% of the variance of study engagement was explained by grit, psychological capital and life satisfaction together, 45.0% of the variance of psychological capital was explained by grit, and 29.2% of the variance of life satisfaction was explained by grit and psychological capital together. Grit was proved to have a large effect on psychological capital (f\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.818) since the f\u003csup\u003e2\u003c/sup\u003e is larger than 0.35. Grit had a moderate effect on study engagement (f\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.288), and psychological capital on life satisfaction (f\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.251) since the f\u003csup\u003e2\u003c/sup\u003e is smaller than 0.35 but larger than 0.15. Psychological capital had a smaller effect on study engagement (f\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.088) because value ranged from 0.02 to 0.15 [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of the hypothesis testing.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStructural model assessment\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypothesis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRelationship\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBeta\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT Values\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP Values\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eR\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ef\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eVIF\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGR -\u0026gt;SE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.484\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.821\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGR -\u0026gt;PsyCap\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePsyCap -\u0026gt;SE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.751\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.274\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGR -\u0026gt;PsyCap -\u0026gt;SE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.466\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGR -\u0026gt;LS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.812\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.818\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLS -\u0026gt;SE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.412\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGR -\u0026gt;LS -\u0026gt;SE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePayCap -\u0026gt;LS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.568\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.847\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.818\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGR -\u0026gt;PsyCap -\u0026gt;LS -\u0026gt;SE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: GR\u0026thinsp;=\u0026thinsp;Grit; PsyCap\u0026thinsp;=\u0026thinsp;Psychological capital; LS\u0026thinsp;=\u0026thinsp;Life satisfaction; SE\u0026thinsp;=\u0026thinsp;Study engagement.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePLS predict approach was used to predict measurement error by comparing the Root Mean Square Error (RMSE) of PLS and Linear Modeling (LM), as suggested by Shmueli et al. [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], in order to assess the predictive power of the model. If any difference between PLS and LM is less than 0, the predictive capacity of the model is said to be strong. A modest level of predictive relevance is shown when the bulk of the differences are less than 0. On the other hand, if a small percentage of values goes below 0, it implies a poor degree of predictability. On the other hand, it suggests that the predictive significance has not been verified if every item is greater than 0. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e showed a moderate predictive power in the model, with the majority of items for all exogenous variables in the PLS predict analysis being lower than the values obtained by LM. Additionally, data in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e demonstrated that all of Q\u0026sup2;values are greater than 0, indicating that Q\u0026sup2; signified adequate predictive relevance.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePLS predict\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndicators\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ\u0026sup2;predict\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePLS-SEM_RMSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLM_RMSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePLS-LM\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLS1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLS2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLS3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLS4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.503\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLS5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.710\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.705\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.794\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.397\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.780\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.779\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSED\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.789\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSEV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.757\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.755\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: PCS\u0026thinsp;=\u0026thinsp;Psychological capital self-efficacy; PCH\u0026thinsp;=\u0026thinsp;Psychological capital hope; PCR\u0026thinsp;=\u0026thinsp;Psychological capital resilience; PCO\u0026thinsp;=\u0026thinsp;Psychological capital optimism; LS\u0026thinsp;=\u0026thinsp;Life satisfaction; SEA\u0026thinsp;=\u0026thinsp;Study engagement absorption; SED\u0026thinsp;=\u0026thinsp;Study engagement dedication; SEV\u0026thinsp;=\u0026thinsp;Study engagement vigor.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eStudent study engagement has emerged as a pivotal focus in educational psychology research due to its substantial correlations with academic success [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], academic achievement, subjective well-being [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] and preparedness for work [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Recently, researchers have shown growing interest in the topic of student engagement in education, particularly through the lens of grit [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Hence, this study examined the link between grit and university students\u0026rsquo; study engagement and the chain mediating role of psychological capital and life satisfaction in the Chinese culture. The research findings demonstrated that grit directly predicted study engagement and psychological capital, but had no significant effect on life satisfaction. Psychological capital directly predicted study engagement and life satisfaction, nevertheless life satisfaction had no significant effect on study engagement. The mediation hypothesis posits that the relationship between grit and study engagement is mediated by psychological capital traits, omitting life satisfaction variables. Lastly, psychological capital and life satisfaction failed to function as a chain mediating variable in the relationship between grit and study engagement.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eDirect predictive effects\u003c/h2\u003e\u003cp\u003eThis results from the study demonstrate that grit has a positive effect on the study engagement of the university students. This indicates that the university students who possess high degree of grit are more likely to exhibit a strong level of study engagement, which verifies the research hypothesis 1 and is accord with the prior finding [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Grit defined as consistency of interest and perseverance of effort has gained prominence as a non-cognitive trait that is essential in predicting educational outcomes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Grit is a crucial factor reinforcing individuals\u0026rsquo; confidence in their capabilities while fostering a sustained commitment to achieving future-oriented goals. The more gritty one is, the strongly she or he is engaged in study. Strongly gritty university students usually tend to actively overcome the learning difficulties and adopt deep learning strategies, therefore more inclined to sustain their concentration on academic tasks [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. To sum up, the degree of university students\u0026rsquo; grit positively affects their level of study engagement. By improving the level of grit of university students, their basic psychological need like autonomy is more likely to be satisfied [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], which accordingly leads to their increased study engagement [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These results reinforce grit as a key determinant of study engagement, particularly in structured educational settings.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eMediating effects\u003c/h2\u003e\u003cp\u003eThe findings of the study reveal that grit positively affects psychological capital, which in turn directly influences study engagement. Additionally, psychological capital has a mediating effect on the relationship between grit and study engagement. This indicates that university students\u0026rsquo; grit indirectly impacts their study engagement through the mediator of psychological capital. These results in this present study empirically validate the hypotheses 2, 3 and 4, which are consistent with the existing research findings [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. On the one hand, the individuals with sustained efforts and long-term oriented goals, accumulating successful experiences, are more capable of improving their self-efficacy, hope, resilience and optimism. On the other hand, self-efficacy, hope, resilience, and optimism facilitate students\u0026rsquo; ability to cope with academic demands [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In conclusion, psychological capital of the university students serves as a mediator between their grit and study engagement. The study extends prior findings by demonstrating that grit alone is not enough\u0026mdash;students require psychological capital resources to translate consistency of interest and perseverance of effort into their study engagement.\u003c/p\u003e\u003cp\u003eContrary to expectations, the results of this study demonstrate that grit had no significant effect on life satisfaction, and life satisfaction also had no significant effect on study engagement. Consequently, life satisfaction did not function as a mediator in the relationship between grit and study engagement, hence the hypotheses 5, 6, and 7 are not supported. These contradict the prior studies proving that grit can positively affect life satisfaction [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e] and life satisfaction can directly predict study engagement [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], which were conducted in Brazil, Hongkong and Indonesia respectively. A possible explanation is cultural factor in mainland of China, study engagement is often driven by external pressures (e.g., parental expectations, career competition rather than intrinsic satisfaction [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. These findings suggest that life satisfaction may be an outcome rather than a mechanism in study engagement processes. Another possible reason is that respondents in different ages may think and behave differently in academic performance when they are satisfied with their life. Just as the research on Hong Kong adolescents indicates, the grit of adolescents is positively related to their well-being [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e], which underscores that the different age range of the respondents should be taken into consideration. The last cause is that the previous researchers find different factors of grit - consistency of interest and perseverance of effort function differently in the related link [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e], by which the special attention to the various impact of the separate factors of grit should be paid. These results further extend the current knowledge about the relationship among grit, life satisfaction and study engagement in the educational setting. These findings offer new insights and contribute to the existing body of literature, suggesting that researchers should consider examining the relationship between grit, life satisfaction, and study engagement from a different perspective.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eChain mediation effects\u003c/h2\u003e\u003cp\u003eThe other findings in the study indicate that psychological capital positively affect life satisfaction, which is line with the existing research [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Thus, the hypothesis 8 is justified. However, psychological capital and life satisfaction are found not to sequentially mediate grit\u0026rsquo;s effect on study engagement due to the invalid link between grit and life satisfaction, along with life satisfaction and study engagement, hence the hypothesis 9 is not supported. One explanation is that life satisfaction is more strongly influenced by psychological capital resources like self-efficacy, hope, resilience, and optimism rather than grit - consistency of interest and perseverance of effort. Another explanation is that study engagement tends to be directly impacted by positive psychological traits but not by emotional factors like life satisfaction. Future research should explore possible mediators like intrinsic motivation or external demands, which may provide stronger explanatory power.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eImplications and limitations of the study\u003c/h2\u003e\u003cp\u003eUniversity students are the key participants in high education and their study engagement plays a vital role in both their academic achievement and future career development. Consequently, this study has important implications for theory as well as practice.\u003c/p\u003e\u003cp\u003eThis study has important theoretical implications. First of all, although there have been previous studies on the effect of grit on engagement, the research on the effect of university students\u0026rsquo; grit on study engagement is still immature in China. This study explores the influence of psychology mechanism of psychological capital and life satisfaction on the relationship of grit on university students\u0026rsquo; study engagement, contributing to a deeper understanding of the relationship between non-cognitive grit and study engagement relationship. Secondly, there are few studies on grit and psychological capital in the prior literature. The study examines the positively predictive effect of grit on psychological capital, which enriches the literature on the relationship between grit and psychological capital. Moreover, it discusses the mediating effect of psychological capital between grit and study engagement and provides a new research perspective for the concept of study engagement. Lastly, the proposed chain mediating model is not justified in this study, suggesting future researchers may consider exploring alternative mediator variables in examining the relationship between grit and study engagement. To the best of the researcher\u0026rsquo;s knowledge, no prior studies have combined all four of the aforementioned variables within a single serial mediation model.\u003c/p\u003e\u003cp\u003eThis study has important practical implications. At first, the study found that grit has a positive and significant effect on university students\u0026rsquo; study engagement. Therefore, for higher education institutions and educational organizations, it is possible to enhance university students\u0026rsquo; study engagement by improving the level of their grit. Moreover, this study found that grit fosters university students\u0026rsquo; study engagement through the mediating effect of psychological capital. This exhibits a deep inner enhancement path for university students\u0026rsquo; grit toward their study engagement and eventually boost academic performance. Based on this, higher education institutions and educators should pay attention to the formation of psychological capital by cultivating of university students\u0026rsquo; grit, guiding them to clarify long-term development goals and visions, developing their excellent qualities that dare to challenge but never give up, and stimulate their enthusiasm and initiative toward academic achievement. It satisfies university students\u0026rsquo; intrinsic pursuit of a sense of significance and accomplishments, to ultimately facilitate their study engagement.\u003c/p\u003e\u003cp\u003eWhile this research provides valuable insights, it unavoidably presents some limitations. Firstly, the study employed cross-sectional data and further longitudinal studies or experiments should be adopted in the future. This study only evaluates the relationship among grit, psychological capital, life satisfaction and study engagement from the perspective of university students from Ji\u0026rsquo;nan, China. Future research could examine the effects of these variables from a more diverse student population from other regions such as Northeast China, North China, Northwest China, Central China, South China, East China, and Southwest China. In addition, future researchers may explore other potential moderators to gain a deeper understanding of why the relationships between grit and life satisfaction, as well as between life satisfaction and study engagement, were not significant in the current model.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study verified the direct effect of grit on study engagement of university students in the Chinese context, and deeply analyzed the psychological mechanism of grit on university students\u0026rsquo; study engagement, revealing that grit indirectly and positively predicts study engagement through the mediating effects of psychological capital. The findings not only provide a systematic explanation and empirical evidence for the influence mechanism of grit on university students\u0026rsquo; study engagement, but also provide references for the realization path of the higher education institutions and educators to enhance university students\u0026rsquo; study engagement and improve their comprehensive and competitive ability, particularly in high-pressure educational environments.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGR grit\u003c/p\u003e\n\u003cp\u003eGCI consistency of interest\u003c/p\u003e\n\u003cp\u003eGPE perseverance of effort;\u003c/p\u003e\n\u003cp\u003ePsyCap psychological capital\u003c/p\u003e\n\u003cp\u003ePCS psychological capital self-efficacy\u003c/p\u003e\n\u003cp\u003ePCH psychological capital hope\u003c/p\u003e\n\u003cp\u003ePCR psychological capital resilience\u003c/p\u003e\n\u003cp\u003ePCO psychological capital optimism\u003c/p\u003e\n\u003cp\u003eSE study engagement\u003c/p\u003e\n\u003cp\u003eSEA study engagement absorption\u003c/p\u003e\n\u003cp\u003eSED study engagement dedication\u003c/p\u003e\n\u003cp\u003eSEV study engagement vigor\u003c/p\u003e\n\u003cp\u003eLS life satisfaction\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all participants and research assistants for their cooperation. We would also like to thank the reviewers for their valuable comments and suggestions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Xiaomei Zhang, Soon-Yew Ju, Lai-Kuan Kong.\u003c/p\u003e\n\u003cp\u003eData curation:Xiaomei Zhang, Lei Cui, Shan Zhang, Na Liu.\u003c/p\u003e\n\u003cp\u003eFormal analysis: Xiaomei Zhang.\u003c/p\u003e\n\u003cp\u003eFunding acquisition: Xiaomei Zhang.\u003c/p\u003e\n\u003cp\u003eInvestigation: Xiaomei Zhang, Xixia Gao, Xiaoning Chu, Jihong Liu\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethodology: Xiaomei Zhang, Soon-Yew Ju, Lai-Kuan Kong.\u003c/p\u003e\n\u003cp\u003eProject administration: Xiaomei Zhang\u003c/p\u003e\n\u003cp\u003eResources: Xiaomei Zhang, Lei Cui, Na Liu.\u003c/p\u003e\n\u003cp\u003eSoftware: Xiaomei Zhang, Soon-Yew Ju, Lai-Kuan Kong.\u003c/p\u003e\n\u003cp\u003eSupervision: \u0026nbsp; Xiaomei Zhang, Shan Zhang, Lai-Kuan Kong.\u003c/p\u003e\n\u003cp\u003eValidation: Xiaomei Zhang.\u003c/p\u003e\n\u003cp\u003eVisualization: Xiaomei Zhang.\u003c/p\u003e\n\u003cp\u003eWriting\u0026nbsp;–\u0026nbsp;original draft: \u0026nbsp;Xiaomei Zhang.\u003c/p\u003e\n\u003cp\u003eWriting\u0026nbsp;–\u0026nbsp;review \u0026amp; editing: Xiaomei Zhang, Xiaoning Chu, Soon-Yew Ju, Lai-Kuan Kong, Jihong Liu.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no specific funding to support this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this research are obtainable upon request from the first author. Due to privacy or ethical restrictions, the data are not publicly accessible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and Informed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Review Board of the School of Foreign Languages, Shandong Jianzhu University (approval date: April 2024). And written informed consent from all participants was obtained prior to participation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdom D, Chukwuere J, Osei M. 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The role of mindfulness, psychological capital, and social capital in the well-being of refugees in Uganda[J]. Curr Res Ecol Social Psychol. 2023;5:100148.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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},"keywords":"study engagement, grit, psychological capital, life satisfaction, university students","lastPublishedDoi":"10.21203/rs.3.rs-6654212/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6654212/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study engagement of university students has gained increasing attention as a crucial determinant of their learning outcomes and overall well-being. Despite the growing body of research examining the predictors of study engagement, there remains a limited understanding of the mechanisms through which non-cognitive traits and psychological resources interact to influence engagement, particularly in non-Western educational contexts. This study investigates the role of grit in predicting university students\u0026rsquo; study engagement, while also examining the mediating effects of psychological capital and life satisfaction in the relationship between grit and study engagement.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethod\u003c/b\u003e: A cross-sectional study was conducted among 484 university students from 17 universities in Ji\u0026rsquo;nan, Shandong Province, China. The survey questionnaire employed four established scales and data collected through a non-probability purposive sampling method. A partial least squares structural equation model (PLS-SEM) was employed to test the hypothesized relationships and mediation effects.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e: The research findings indicated that grit directly predicted study engagement and psychological capital, but had no significant impact on life satisfaction. Psychological capital directly predicted study engagement and life satisfaction, but life satisfaction did not significantly influence study engagement. The mediation hypothesis asserts that the relationship between grit and study engagement can be enhanced by psychological capital attributes, discounting life satisfaction variables. Finally, psychological capital and life satisfaction did not serve as a chain mediation variable in the link between grit and study engagement.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e: These findings contribute to a deeper understanding of the psychological mechanisms underlying study engagement of the university students, emphasizing the pivotal role of psychological capital in academic persistence. The results have important implications for higher education institutions, suggesting that interventions aimed at fostering psychological capital such as self-efficacy, hope, resilience and optimism may promote university students\u0026rsquo; study engagement.\u003c/p\u003e","manuscriptTitle":"Understanding University Students’ Study Engagement through the Lens of Grit: A Serial Mediation Model of Psychological Capital and Life Satisfaction Using a PLS-SEM Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-17 09:28:52","doi":"10.21203/rs.3.rs-6654212/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-07-23T01:40:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"255033062092973083859945495146025942494","date":"2025-07-19T06:25:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-14T12:19:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-09T11:50:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-20T14:40:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-19T11:02:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2025-06-19T10:58:11+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":"86b8afe8-19f4-42f6-a6cd-34c91b79c197","owner":[],"postedDate":"July 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-17T09:28:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-17 09:28:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6654212","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6654212","identity":"rs-6654212","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}