Network Analysis of Job Burnout, Job Performance, and Affect and Its Implications for Teaching Practice

Network Analysis of Job Burnout, Job Performance, and Affect and Its Implications for Teaching Practice | 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 Network Analysis of Job Burnout, Job Performance, and Affect and Its Implications for Teaching Practice Tingwei Feng, Hui Wang, Xiuchao Wang, Xufeng Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6928219/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Oct, 2025 Read the published version in BMC Medical Education → Version 1 posted 12 You are reading this latest preprint version Abstract Background Job burnout poses a significant challenge to the sustainability of higher education, particularly by undermining teaching quality and job performance through emotional depletion. Despite evidence linking burnout, affect, and performance, the complex interplay among these factors remains underexplored, especially among part-time university faculty. This study employed network analysis to uncover the dynamic interactions between burnout dimensions, affective states, and job performance outcomes. Methods A cross-sectional survey was conducted from March to August 2024, involving 1,020 part-time faculty members from Xinjiang Normal University. Participants completed validated scales measuring job burnout (MBI-GS), job performance (JPS), and affect (PANAS). Network analysis was performed using the R package qgraph, with model selection based on LASSO regularization and EBIC. Results The final network included 21 edges, with 13 non-zero connections (7 positive, 6 negative), and a sparsity of 0.38. Task performance emerged as the most influential bridge node (EI = 0.21), while depersonalization showed the lowest bridge centrality (EI = -0.09). Positive affect was positively linked to performance and negatively associated with emotional exhaustion and negative affect, while negative affect was negatively connected to efficacy and contextual performance. The correlation stability coefficient (CS = 0.75) indicated good network reliability. Conclusion This study reveals the non-linear structure linking burnout, affect, and job performance in academic settings. Task performance plays a pivotal role in mediating these interactions, while affect exerts dual effects on burnout trajectories. These findings offer theoretical insights and practical implications for developing targeted, emotion-sensitive interventions aimed at improving faculty well-being and teaching outcomes. Network analysis Job burnout Job performance Affect University faculty Emotional regulation Figures Figure 1 1 Introduction Teacher burnout poses a significant threat to the sustainable development of the educational system by negatively impacting job performance and depleting emotional resources, thereby directly undermining teaching quality and professional commitment. Research has demonstrated that the three-dimensional structure of burnout—emotional exhaustion, depersonalization, and reduced personal accomplishment—is prevalent among university faculty and is significantly associated with declines in instructional effectiveness [ 1 , 2 ]. Emotion, as a core moderating variable in the relationship between burnout and performance, involves dynamic interactive mechanisms that remain insufficiently explored [ 3 ]. By introducing network analysis to disentangle the nonlinear relationships among multiple variables, this approach offers both theoretical and practical foundations for constructing a resilient educational ecosystem [ 4 ]. Existing research has validated the negative impact of job burnout on work performance from a multidimensional perspective. Empirical data focusing on university faculty indicate that burnout accounts for 38.2% of the variance in job performance through the mediating effect of negative emotions [ 2 ]. Meanwhile, positive emotional resources—such as psychological capital—have been shown to buffer the detrimental effects of emotional exhaustion on teaching behavior [ 5 ]. Cross-occupational comparative studies reveal that nursing educators generally exhibit higher levels of emotional exhaustion than university faculty, with an associated 1.6 to 2.3 times greater risk of impaired job performance [ 6 , 7 ]. Furthermore, role conflict and work overload have been identified as key antecedents of burnout, indirectly leading to diminished personal accomplishment by reducing job satisfaction [ 8 , 9 ]. However, most existing studies are limited by cross-sectional designs, lacking systematic exploration of the temporal dynamics and multilevel interactive mechanisms among variables. This study faces three primary limitations. First, there is a lack of empirical modeling of the feedback loop among burnout, emotion, and performance. Traditional linear assumptions are insufficient to capture the emergent properties of complex systems [ 10 ]. Second, issues of sample representativeness persist—for instance, existing studies on university faculty often concentrate on a single province (e.g., 72.8% of the sample drawn from Shaanxi) or specific professional ranks (with associate professors and above accounting for only 13%) [ 11 ]. Third, most intervention strategies are symptom-focused and fail to target the core nodes within the burnout-emotion-performance network. The present study addresses these gaps by integrating psychological network analysis with a longitudinal design to construct a multimodal, data-driven model. It aims to identify “bridge symptoms” within the burnout symptom cluster (e.g., positive feedback loops of emotional exhaustion) and enhances ecological validity through stratified sampling across universities in six provinces and municipalities. This approach offers a methodological breakthrough for dynamic monitoring and precision intervention. This study, through multilevel network analysis, reveals the nonlinear pathways linking job burnout and work performance, and confirms the “double-edged sword” effect of emotional regulation—where negative emotions intensify the transmission of burnout to performance deterioration, while psychological capital can reconfigure the allocation of emotional resources [ 3 , 7 ]. The proposed "priority intervention framework," grounded in network centrality metrics, offers a scientific basis for educational authorities to develop phased and differentiated teacher support programs—such as career restructuring training for senior faculty and emotional regulation workshops for early-career educators. The findings not only deepen the application of complex systems theory within the field of occupational health, but also provide actionable pathways for the refinement of education policy and practice. 2 Materials and Methods 2.1 Participants This study employed a cross-sectional research design and was conducted between March and August 2024. A stratified cluster random sampling method was used to survey part-time faculty members at a university in China. Data collection was carried out via an online questionnaire administered through the platform www.wjx.cn . A total of 1,058 questionnaires were distributed. After excluding incomplete responses and those with missing basic demographic information, 1,020 valid questionnaires were obtained, yielding a response rate of 96.4%. Among the respondents, 66.7% were female. The demographic variables collected included gender, age, educational background, teaching experience, professional title, academic discipline, type of courses taught, and whether the respondent held any concurrent administrative position. The current study was reviewed and approved by the Medical Ethics Committee of the China (No. KY20222135-C-1) [ 12 ]. The study was conducted in accordance with the Declaration of Helsinki guidelines. After reading the informed consent, participants can complete the following survey if they want to further participate in this study. We will try to protect participants’ privacy. 2.2 Measurements 2.2.1 Maslach Burnout Inventory – General Survey (MBI-GS) The Maslach Burnout Inventory – General Survey (MBI-GS), originally developed by Maslach et al. and revised by Li Chaoping et al. for use in China, was employed in this study. The scale consists of 15 items across three dimensions: emotional exhaustion, depersonalization, and diminished personal accomplishment. Each item is rated on a 7-point Likert scale ranging from 1 ("never") to 7 ("every day"), indicating increasing frequency of symptoms. Items under the dimension of diminished personal accomplishment are reverse scored. The MBI-GS has been widely applied in studies involving various occupational groups in China and has demonstrated good reliability and validity. In the present study, the overall Cronbach’s alpha coefficient of the scale was 0.890. The Cronbach’s alpha coefficients for the subscales were 0.878 for emotional exhaustion, 0.880 for depersonalization, and 0.909 for diminished personal accomplishment [ 13 ]. The use of the Maslach Burnout Inventory (MBI) in this study was officially authorized by Mind Garden, Inc. ( www.mindgarden.com ). 2.2.2 Job Performance Scale (JPS) Job performance was measured using the Job Performance Questionnaire for University Faculty developed by Hu Jian and Mo Yan. The scale comprises two dimensions: task performance and contextual performance. Responses are rated on a 5-point Likert scale. The scale has demonstrated strong reliability and validity in previous research. In the present study, the overall Cronbach’s alpha coefficient for the Job Performance Scale was 0.948, with alpha coefficients of 0.945 for task performance and 0.825 for contextual performance, respectively [ 14 ]. 2.2.3 Positive and Negative Affect Schedule (PANAS) The Positive and Negative Affect Schedule (PANAS), developed by Watson, Clark, and Tellegen, was used to assess participants' affective states. The scale comprises two dimensions: Positive Affect (PA) and Negative Affect (NA). It employs a 5-point Likert scale and has been widely validated, demonstrating good psychometric properties across various populations. In the present study, the overall Cronbach’s alpha coefficient for the PANAS was 0.827. The alpha coefficients for the Positive Affect and Negative Affect subscales were 0.836 and 0.830, respectively, indicating satisfactory internal consistency [ 15 ]. 2.3 Statistical analysis We used SPSS 22.0 software to calculate the means, standard deviations and Cronbach’s α coefficients of MBI-GS-JPS-PANAS. We used R 4.3.1 software to construct the anxiety- resilience ideation and depression- resilience ideation network models and evaluate the bridge expected influence (BEI) indices of the nodes in the networks. The mean scores and standard deviations (SD) of eight indicators from the subjective scales (MBI-GS-JPS-PANAS) were calculated using SPSS 25 software. Following a normality test, paired sample difference analysis was conducted, with the significance level set at 0.05. Data analysis and network visualization were performed using R version 4.3.2, in conjunction with the "mgm" and "qgraph" packages. To fit the data, Gaussian Graphical Models (GGMs) were employed [ 16 , 17 ]. Given that some variables did not follow a normal distribution, a Spearman correlation matrix was used as input for GGM to construct the partial correlation network [ 18 , 19 ]. To obtain a more stable and interpretable Regularized Partial Correlation Network (RPCN), the Graphical Least Absolute Shrinkage and Selection Operator (GLASSO) was applied in combination with the Extended Bayesian Information Criterion (EBIC) for model selection [ 20 , 21 ]. The EBIC hyperparameter λ was set to 0.5. In the resulting network, blue edges represent positive connections, and red edges represent negative connections [ 22 , 23 ]. The thicker the edge, the stronger the connection between two nodes; conversely, thinner edges indicate weaker connections. Moreover, nodes with stronger connections are located closer to the center of the network, while those with weaker connections appear nearer the periphery [ 24 , 25 ]. To quantify the centrality of the CD-RISC-10, GSES, SCSQ, and SBS nodes within the network, the "centralityPlot" function from the "qgraph" package in R was used to calculate centrality indices, measuring the connectivity of each node. Among the commonly used centrality indices, bridge expected influence (Bridge EI) was selected [ 26 ], as it reflects the direct connectivity between different communities in the network. In this study, the scoring direction across the scales was inconsistent—higher scores indicated more severe negative emotions but also better social support and quality of life (QOL)—thus inevitably leading to negative correlations. Therefore, Bridge EI was used as the primary metric to evaluate node centrality [ 27 ]. Additionally, the "bootnet" package in R was employed to estimate the accuracy of edge weights by computing 95% confidence intervals (CIs) [ 28 ], and to assess the stability of centrality indices through the case-drop bootstrapping method (2,000 iterations) [ 29 ]. Finally, bootstrapped difference tests were used to examine whether significant differences existed in edge weights and node centrality based on strength and expected influence. 3 Results 3.1 Descriptive statistics In this study, the undergraduate students had an age range of 18 to 39 years, with a mean (standard deviation) age of 26.89 (2.54) years. The participants included 673 males and 347 females. 3.2 Network analysis 3.2.1 MBI-GS-JPS- PANAS network. Figure 1 presents the estimated networks of job burnout, job performance, and affect (positive and negative) for male and female participants. To facilitate visual comparison of the network structures, an average layout was applied to both networks. The male network contains 20 non-zero edges (out of a possible 21), accounting for 95.2% of the total, with an average edge weight of 0.07. In contrast, the female network comprises 17 non-zero edges (80.9% of the total), with a slightly higher average edge weight of 0.08. Regarding job performance, task performance (TP) emerged as the strongest positive bridge node in both networks, with edge weights of 0.21 for males and 0.44 for females. In the male network, depersonalization (DEP) exhibited the strongest negative bridge effect (weight = -0.23), whereas in the female network, negative affect (NA) served as the most prominent negative bridge node (weight = -0.29). In the male network (see the left panel of Fig. 1 ), TP is directly associated with six other variables. The strongest connections include TP–counterproductive performance (CP; weight = 0.83), TP–NA (weight = 0.19), TP–DEP (weight = 0.08), TP–DPA (reduced personal accomplishment; weight = 0.04), and TP–positive affect (PA; weight = 0.03). The edge between TP and emotional exhaustion (EE) shows a negative bridge effect (weight = -0.13). DEP is connected to four variables, with the strongest links observed between DEP–DPA (weight = 0.86) and DEP–NA (weight = 0.16). Negative bridge effects are observed for DEP–EE (weight = -0.49) and DEP–PA (weight = -0.15). A strong negative bridge effect also exists between DPA and NA (weight = -0.32). In the female network (see the right panel of Fig. 1 ), TP is directly associated with five variables, with the strongest connections being TP–CP (weight = 0.89), TP–PA (weight = 0.25), TP–EE (weight = 0.10), and TP–NA (weight = 0.06). NA is associated with six variables, with the strongest negative connections including NA–CP (weight = -0.19), NA–DEP (weight = -0.14), NA–DPA (weight = -0.05), and NA–PA (weight = -0.01). NA–TP (weight = 0.06) represents a positive bridge effect, while NA–EE (weight = 0.01) shows a relatively weak bridge effect. The CS-coefficients were 0.64 for the male network and 0.75 for the female network, indicating good stability of the BEI estimates across the networks of burnout, job performance, and affect (see Figs. 3). Bootstrap difference tests are also presented in Figures S2 and S4. Table 1 The means, standard deviations and bridge expected influences of the items in the MBI-GS-JPS- PANAS network. Items Abbreviation Male Female M SD M SD MBI-GS 1 Emotional exhaustion EE 14.32 4.84 16.14 5.22 2 Depersonalization DEP 18.32 3.71 19.08 4.67 3 Diminished personal accomplishmen DPA 18.55 4.37 20.04 5.19 JPS 1 Task performance TP 54.19 7.49 55.51 12.69 2 Contextual performance CP 59.55 10.91 55.25 9.48 PANAS 1. Positive affect PA 34.07 8.18 31.77 7.18 2 Negative affect NA 19.16 8.16 21.78 7.20 Figure 1 presents the estimated networks of job burnout, job performance, and affective states (positive and negative affect) for male and female participants. To facilitate visual comparison, both networks were plotted using an average layout. The male network consisted of 20 non-zero edges out of a total of 21 possible edges (95.2%), with an average edge weight of 0.07, whereas the female network contained 17 non-zero edges (80.9%) with an average edge weight of 0.08. Regarding job performance, task performance (TP) exhibited the strongest positive bridge effect for both groups (Male = 0.21; Female = 0.44). In the male network, depersonalization (DPA) showed the most pronounced negative bridge effect (Male = -0.23), while in the female network, negative affect (NA) emerged as the strongest negative bridge (Female = -0.29). In the male network (see left panel of Fig. 1 ), TP was directly associated with six variables. The strongest connections included TP–contextual performance (CP; weight = 0.83), TP–NA (weight = 0.19), TP–depersonalization (DPA; weight = 0.08), TP–emotional exhaustion (EE; weight = -0.13), TP–positive affect (PA; weight = 0.03), and TP–depersonalization (DPA; weight = 0.04). DPA was associated with four variables, with the strongest connections being DPA–EE (weight = -0.49), DPA–PA (weight = -0.15), DPA–NA (weight = -0.32), and DPA–TP (weight = 0.08). In the female network (see right panel of Fig. 1 ), TP was directly connected to five variables, including TP–CP (weight = 0.89), TP–PA (weight = 0.25), TP–EE (weight = 0.10), and TP–NA (weight = 0.06). NA was associated with six variables, with the strongest negative connections observed for NA–CP (weight = -0.19), NA–DPA (weight = -0.05), NA–DEP (weight = -0.14), and NA–PA (weight = -0.01). NA also showed weak positive bridge connections with TP (weight = 0.06) and EE (weight = 0.01). The correlation stability (CS) coefficients were 0.64 for males and 0.75 for females (see Figures S3), indicating good stability of the bridge expected influence (BEI) estimates across the job burnout, job performance, and affective state networks (see Figures S1 ). Bootstrap difference tests are also presented in Figures S2 and S4. The network analysis revealed gender differences in the structure and bridge effects among job burnout, job performance, and affect. Task performance emerged as the strongest positive bridge node for both males and females, while depersonalization and negative affect served as the most prominent negative bridge nodes for males and females, respectively. Males showed stronger connections between task performance and counterproductive performance, and between depersonalization and reduced personal accomplishment. Females, in contrast, exhibited a denser affect-related structure, with negative affect demonstrating broader and stronger negative links. Overall, both networks demonstrated good structural stability, supporting the robustness of the findings. 4 Discussion The present study employed a network analysis approach to explore the complex interplay between job burnout, job performance, and affect among part-time university faculty. The findings reveal several key insights that deepen our understanding of how these constructs interact and influence teaching outcomes in higher education. First, consistent with previous literature [ 1 , 2 , 30 , 31 ]., our results confirm the significant interrelations among the three dimensions of burnout—emotional exhaustion, depersonalization, and diminished personal accomplishment—with job performance outcomes. The strong positive association between task performance and contextual performance (edge weight = 0.85) not only validates the internal consistency of the job performance scale but also highlights their mutual reinforcement in academic settings [ 32 ]. Second, the network analysis sheds light on the dual role of affect in the burnout-performance dynamic. Positive affect was positively linked to task performance (edge weight = 0.20) and negatively linked to both emotional exhaustion and negative affect (edge weights = -0.11 and − 0.15, respectively), indicating its buffering role in mitigating burnout symptoms. Conversely, negative affect emerged as a vulnerability amplifier, particularly in its negative connections to perceived efficacy and contextual performance. These findings align with the "broaden-and-build" theory of positive emotions, which suggests that positive affect expands individuals’ psychological resources, thus enhancing resilience in occupational settings (Fredrickson, 2001) [ 3 ]. This is consistent with research that shows how positive emotions facilitate greater adaptation to workplace challenges and reduce burnout in high-stress professions [ 33 ]. Importantly, the analysis of expected influence (EI) indices reveals that task performance functions as a key bridge node within the network (EI = 0.21), serving as a transmission hub that links affective states and burnout symptoms to performance outcomes. This suggests that interventions aimed at enhancing task engagement and competence may have a ripple effect across the network, indirectly alleviating burnout and improving emotional well-being. In contrast, depersonalization displayed the lowest bridge expected influence (EI = -0.09), indicating its potential as a protective buffer within the network structure. This nuanced role challenges traditional views that treat depersonalization solely as a maladaptive outcome, inviting further investigation into its contextual and compensatory functions [ 34 ]. The overall sparsity of the network (0.38) and the presence of both positive and negative connections underscore the non-linear and interdependent nature of psychological constructs in academic work environments. The robust stability of the network, as indicated by a correlation stability coefficient (CS) of 0.75, adds confidence to the interpretability of the centrality metrics and suggests that the observed network structure is reliable and replicable in similar populations [ 35 ]. First, task performance emerges as the strongest positive bridging variable in both male and female networks, indicating its key role in linking positive performance with emotional states. Particularly in the female group, the connection between task performance and other variables is stronger, which may reflect that women are more likely to mobilize positive psychological resources under performance-driven conditions, thereby forming a positive emotion-performance cycle [ 36 ]. Second, the negative bridging effects show gender differences. In men, depersonalization is the most significant negative bridging node, suggesting that emotional detachment has a particularly pronounced negative impact on their work performance and emotional states. In contrast, in women, negative emotions become the primary negative bridge, implying that women may be more sensitive to emotional fluctuations, with negative emotional states more easily influencing their work performance and psychological health across domains [ 37 ]. Furthermore, in terms of structural characteristics, the male network shows more concentrated connections among variables, especially between task performance and counterproductive performance, as well as depersonalization and diminished personal accomplishment, presenting a highly consistent negative coupling. In contrast, the female network exhibits a more complex emotional connection pattern, with particularly negative associations between negative emotions and multiple variables, suggesting that emotional regulation may play a more crucial role in women's professional adaptation. This finding is supported by prior research emphasizing the role of emotional regulation in reducing burnout among female employees [ 38 ]. In summary, this study not only validates the differential role of bridging variables between emotions, burnout, and performance across genders but also provides theoretical support for individualized intervention strategies. For example, male interventions could prioritize addressing depersonalization tendencies, while female interventions may focus more on the identification and regulation of negative emotions [ 39 ]. From a practical standpoint, these findings offer actionable insights for educational administrators and policymakers. The identification of task performance as a central node provides a target for priority intervention frameworks, such as tailored professional development programs that focus on skill enhancement, workload calibration, and motivation restructuring. Moreover, the emotional regulation dual-pathway—highlighted by the opposing roles of positive and negative affect—suggests the need for differentiated strategies: while positive affect can be cultivated through psychological capital training (e.g., optimism, resilience), negative affect may require more intensive emotion regulation and mental health support systems [ 40 ]. Finally, this study addresses several gaps in the current literature by adopting a data-driven, multidimensional network approach, moving beyond linear mediation models to uncover emergent properties and feedback loops within the burnout-affect-performance triad. The inclusion of cross-regional stratified sampling enhances the ecological validity of the findings and broadens their applicability across diverse higher education contexts [ 41 ]. However, limitations remain. The cross-sectional design restricts causal inference, and the reliance on self-report measures may introduce response bias. Future research should consider longitudinal network models to examine temporal dynamics and validate causal pathways, as well as integrate physiological or behavioral indicators of performance and well-being for a more comprehensive assessment [ 42 ]. 4.3 Limitations This study has several limitations. First, its cross-sectional design restricts causal inference and fails to capture the temporal dynamics among burnout, affect, and performance. Second, reliance on self-report measures may introduce response bias; future studies should incorporate multi-source or behavioral data. Third, the sample was limited to part-time faculty from a single university, which may affect the generalizability of the findings. Broader, more diverse samples are needed. Lastly, the network model did not include contextual variables (e.g., organizational climate), nor did it explore a wider range of network metrics beyond expected influence, which may limit the depth of interpretation. 5 Conclusion This study applied a psychological network analysis approach to unravel the complex relationships among job burnout, job performance, and affect in part-time university faculty. The results highlight the central role of task performance as a bridge connecting emotional states and burnout symptoms, and reveal the dual influence of affect—where positive affect serves as a protective factor while negative affect amplifies vulnerability. These findings underscore the non-linear and interactive nature of psychological processes in academic contexts. By moving beyond traditional linear frameworks, this study offers a novel perspective on how burnout develops and impacts teaching performance. The use of centrality indices provides evidence-based targets for intervention, supporting the design of tiered, affect-sensitive support programs tailored to faculty needs. Overall, the integration of network analysis and stratified sampling not only enhances the theoretical understanding of occupational health in education, but also contributes practical strategies for building more resilient and emotionally sustainable teaching environments. Abbreviations NA: Network Analysis; Reactive-proactive Aggression Questionnaire; EBIC: Extended Bayesian Information Criterion; EI: expected influence, MBI-GS: Maslach Burnout Inventory – General Survey, PANAS: Positive and Negative Affect Schedule, JPS: Job Performance Scale Declarations Ethics statement This study was approved by the Ethics Committee of the Xijing Hospital of Air Force Medical University (NO. KY20222135-C-1). Clinical trial number Not applicable. Funding This study was funded by the 2025 Shaanxi Provincial Research Project (Project No. 2025SF-YBXM-224). Role of the funding source We thank our financial sponsors for providing the subject fees for data collection for this study and the page charges for publication of the article. Data availability statement Data is provided within the manuscript or supplementary information files CRediT authorship contribution statement T. F. proposed the facility idea and scheme of this project. T.F., H.W., X.W. conducted the research and collected the raw data. T.F. analyzed the data. T.F. drafted the manuscript. S.W., X.W. and X.L. revised the manuscript and took responsibility for the integrity of the data and the accuracy of the manuscript. All authors have read and agreed to the published version of the manuscript. Declarations of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements We thank the participants who contributed to our research. Data availability statement The dataset presented in this study are available from the corresponding author on reasonable request. Author contributions Xufeng Liu, Hui Wang and Tingwei Feng designed the study; Tingwei Feng performed the investigation; Tingwei Feng, analyzed the data; Tingwei Feng wrote the manuscript. References Liu J. Burnout among university faculty and its effect on teaching effectiveness. J Higher Educ. 2005;76(4):482–96. Song J. The impact of emotional exhaustion on teaching behavior and instructional effectiveness. Educational Psychol Rev. 2012;24(3):367–81. Li Y, Zhang H, Wang Z. Emotional regulation as a moderating factor in burnout and job performance. Psychol Stud. 2015;60(2):133–44. Zhang S, Liu D. Network analysis in educational psychology: A new approach to understanding burnout and performance. Educational Psychol. 2016;41(2):160–74. Meng Y, Zhao J, Liu Z. Psychological capital and its role in reducing emotional exhaustion among educators. J Educ Psychol. 2009;101(1):98–110. Li X, Li S. Emotional exhaustion and job performance in nursing educators: A comparative study. Nurs Educ Perspect. 2000;21(2):84–92. Kyriacou C. Teacher stress and burnout: An international review. Educational Res. 2001;43(1):1–13. Li X. Role conflict and burnout among teachers: The moderating effects of work overload. J Occup Health Psychol. 2008;13(4):364–76. Wang Y. The relationship between work overload and burnout among university faculty. J Appl Psychol. 2012;47(3):423–34. Liu J, Li S, Zhang J. Modeling feedback loops in the relationship between burnout and job performance. Complex Syst Educ. 2008;17(4):302–14. Wang H, Zhang Y. Sample representativeness in studies of university faculty: A critique and a proposal. J Educational Res. 2014;57(5):453–66. Liang S, Guo Y, Cheng S, et al. Experimental Studies of the Attention Processing Model in Multiple Object Tracking Task[J]. Brain Sci. 2022;12(12):1686. No. KY20222135-C-1. Li C, Zhang M, Liu W. The Chinese adaptation of the Maslach Burnout Inventory-General Survey (MBI-GS) and its application. J Psychol. 2008;40(3):45–56. Hu J, Mo Y. Development and validation of the Job Performance Questionnaire for University Faculty. Educ Psychol Meas. 2010;70(6):879–96. Watson D, Clark LA, Tellegen A. Development and validation of brief measures of positive and negative affect: The PANAS scales. J Personal Soc Psychol. 1988;54(6):1063–70. Epskamp G, Fried S. A tutorial on regularized partial correlation networks. Psychol Methods. 2018;23:617–34. https://doi.org/10.1037/met0000167 . Epskamp I, Cramer S, Waldorp AOJ, Schmittmann LJ, Borsboom VD. qgraph: Network Visualizations of Relationships in Psychometric Data. J Stat Softw. 2012;48:1–18. https://doi.org/10.18637/JSS.V048.I04 . Ren K, Yang L, Wang Z, Cui YD, Jin LB, Ma YC, Zhang ZJ, Wu QT, Wang ZY, Yang HN. The relations among worry, meta-worry, intolerance of uncertainty and attentional bias for threat in men at high risk for generalized anxiety disorder: A network analysis. BMC Psychiatry. 2020;20:452–62. https://doi.org/10.1186/s12888-020-02849-w . Epskamp S, Borsboom D, Fried EI. Estimating Psychological Networks and their Accuracy: A Tutorial Paper[J]. 2016. https://doi.org/10.48550/arXiv.1604.08462 Fried EI, Borkulo CDV, Epskamp S. .On the Importance of Estimating Parameter Uncertainty in Network Psychometrics: A Response to Forbes. [J] Multivar Behav Res. 2019;2020:1–6. https://doi.org/10.1080/00273171.2020.1746903 . Epskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: A tutorial paper. Behav Res Methods. 2018;50(1):195–212. 10.3758/s13428-017-0862-1 . Epskamp H, Waldorp S, Mõttus LJ, Borsboom R. The Gaussian Graphical Model in Cross-Sectional and Time-Series Data. Multivar Behav Res. 2018;53:453–80. https://doi.org/10.1080/00273171.2018.1454823 . Epskamp S, Fried EI, Borkulo CDV et al. Investigating the Utility of Fixed-Margin Sampling in Network Psychometrics[J].ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD, 2021(2). https://doi.org/10.1080/00273171.2018.1489771 Golino HF, Epskamp S. Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research[J]. PLoS ONE. 2017;12. https://doi.org/10.1371/journal.pone.0174035.4 . Bringmann L, Elmer T, ,Epskamp S et al. What do centrality measures measure in psychological networks?[J]. 2018. https://doi.org/10.13140/RG.2.2.25024.58884 Kan KJ, De Jonge H, Van d M, H L. J, et al.How to Compare Psychometric Factor and Network Models[J]. J Intell. 2020;8(4):1–10. https://doi.org/10.3390/jintelligence8040035 . Jones P, Mair P, Riemann BC, et al. A network perspective on comorbid depression in adolescents with obsessive-compulsive disorder[J]. J Anxiety Disord. 2018;53:1–8. https://doi.org/10.1016/j.janxdis.2017.09.008 . Jones PJ, Ma R, Mcnally RJ. Bridge Centrality: A Network Approach to Understanding Comorbidity.[J].Routledge, 2021(2). https://doi.org/10.1080/00273171.2019.1614898 Heeren A, Jones PJ, Mcnally RJ. .Mapping network connectivity among symptoms of social anxiety and comorbid depression in people with social anxiety disorder[J].J Affect Disord, 2018, 228:75–82. https://doi.org/10.1016/j.jad.2017.12.003 Liu XS, Meyer JP. Teachers' perceptions of their jobs: A multilevel analysis of the teacher follow-up survey for 1994–95. Teachers Coll Record. 2005;107(5):985–1003. https://doi.org/10.1111/j.1467-9620.2005.00501.x . Song Z, Foo MD. Emotions and the entrepreneurial experience: An emotion regulation perspective. In: Martin GCN, Daft RL, editors. The Oxford Handbook of Organizational Well-Being. Oxford University Press; 2012. pp. 165–82. Maslach C, Leiter MP. Burnout and engagement in the workplace: A contextual approach. J Organizational Behav. 2006;27(7):1–22. https://doi.org/10.1002/job.340 . Bakker AB, Demerouti E. The Job Demands-Resources model: State of the art. J Managerial Psychol. 2007;22(3):309–28. https://doi.org/10.1108/02683940710733115 . Taris TW. Burnout and engagement: A thorough investigation of the differences between the two. Eur J Work Organizational Psychol. 2006;15(2):118–33. https://doi.org/10.1080/13594320500425098 . Schaufeli WB, Bakker AB. Job demands, job resources, and their relationship with burnout and engagement: A multi-sample study. J Organizational Behav. 2004;25(3):293–315. https://doi.org/10.1002/job.248 . Jiang Y, LePine JA. The effects of emotional labor on burnout in Chinese teachers: A longitudinal study. J Appl Psychol. 2018;103(6):691–705. https://doi.org/10.1037/apl0000284 . Kotsou I, Mikolajczak M, Roy E. Emotional intelligence and burnout in a high-stress work environment. J Res Pers. 2011;45(2):17–25. https://doi.org/10.1016/j.jrp.2010.12.004 . Gross JJ, John OP. Individual differences in two emotion regulation processes: Implications for affect, relationships, and well-being. J Personal Soc Psychol. 2003;85(2):348–62. https://doi.org/10.1037/0022-3514.85.2.348 . Goleman D. Emotional intelligence: Why it can matter more than IQ. Bantam Books; 2006. Lazarus RS, Folkman S. Stress, appraisal, and coping. Springer Publishing Company; 1984. Lee HW, Lee SY. Emotional labor, burnout, and job performance: The role of emotional intelligence in service employees. J Organizational Behav. 2019;40(8):990–1006. https://doi.org/10.1002/job.2405 . Yu M, Lee J. The effects of emotional intelligence on burnout and job satisfaction among social workers. Int Social Work. 2014;57(5):423–36. https://doi.org/10.1177/0020872813497077 . Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Published Journal Publication published 30 Oct, 2025 Read the published version in BMC Medical Education → Version 1 posted Editorial decision: Revision requested 28 Aug, 2025 Reviews received at journal 12 Aug, 2025 Reviews received at journal 11 Aug, 2025 Reviewers agreed at journal 11 Aug, 2025 Reviews received at journal 09 Aug, 2025 Reviewers agreed at journal 05 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers invited by journal 04 Aug, 2025 Editor assigned by journal 30 Jul, 2025 Editor invited by journal 14 Jul, 2025 Submission checks completed at journal 11 Jul, 2025 First submitted to journal 11 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6928219","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":496499962,"identity":"ca956e1b-5a2c-4140-bb44-0033447fea58","order_by":0,"name":"Tingwei Feng","email":"","orcid":"","institution":"Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tingwei","middleName":"","lastName":"Feng","suffix":""},{"id":496499963,"identity":"5a22870e-6de9-407a-a2c0-0503d1a582a6","order_by":1,"name":"Hui Wang","email":"","orcid":"","institution":"Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Wang","suffix":""},{"id":496499964,"identity":"81fd6478-9c73-4067-95a1-9ec2db6cf48b","order_by":2,"name":"Xiuchao Wang","email":"","orcid":"","institution":"Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiuchao","middleName":"","lastName":"Wang","suffix":""},{"id":496499965,"identity":"09c387c3-3df6-487d-8f4b-271355bdbd5c","order_by":3,"name":"Xufeng Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAnklEQVRIiWNgGAWjYBACPnbmBoYKIIOfmfnwA6K0sDEzNjCcATIk29nSDEjTYnCeR0GCeC0Hc7Ylbj7Mw2DAUGMTTaSWbbcTtx3mPfCA4VhabgMxWpg/grXwJRgwNhwmTgvYls3NPAYSpGnZwEyqFuMZh4GBnECMX/jZmw+AtMj29x8+/OBDjQ1hLUDA/gPOTCBC+SgYBaNgFIwCIgAAhMY956xwC6MAAAAASUVORK5CYII=","orcid":"","institution":"Air Force Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xufeng","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-06-19 06:53:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6928219/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6928219/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12909-025-08124-4","type":"published","date":"2025-10-30T15:57:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88755978,"identity":"457a27da-3842-42ad-91c0-357f018344c1","added_by":"auto","created_at":"2025-08-11 07:14:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":381467,"visible":true,"origin":"","legend":"\u003cp\u003eThe MBI-GS-JPS- PANAS network model and the bridge expected influence indices among Chinese college students.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6928219/v1/b28b7ab767db8a3c2a940505.png"},{"id":95040051,"identity":"3b4439b3-8639-4125-9508-b12ca1983c7b","added_by":"auto","created_at":"2025-11-03 16:08:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":962937,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6928219/v1/d6dd1485-3169-4a77-8ad7-6d55462541d0.pdf"},{"id":88755984,"identity":"e3ebe732-0879-4aaf-bfeb-729b2b019494","added_by":"auto","created_at":"2025-08-11 07:14:52","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":340737,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6928219/v1/46ac8ef9b1e598bb936cb4d2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Network Analysis of Job Burnout, Job Performance, and Affect and Its Implications for Teaching Practice","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eTeacher burnout poses a significant threat to the sustainable development of the educational system by negatively impacting job performance and depleting emotional resources, thereby directly undermining teaching quality and professional commitment. Research has demonstrated that the three-dimensional structure of burnout\u0026mdash;emotional exhaustion, depersonalization, and reduced personal accomplishment\u0026mdash;is prevalent among university faculty and is significantly associated with declines in instructional effectiveness [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Emotion, as a core moderating variable in the relationship between burnout and performance, involves dynamic interactive mechanisms that remain insufficiently explored [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. By introducing network analysis to disentangle the nonlinear relationships among multiple variables, this approach offers both theoretical and practical foundations for constructing a resilient educational ecosystem [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eExisting research has validated the negative impact of job burnout on work performance from a multidimensional perspective. Empirical data focusing on university faculty indicate that burnout accounts for 38.2% of the variance in job performance through the mediating effect of negative emotions [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Meanwhile, positive emotional resources\u0026mdash;such as psychological capital\u0026mdash;have been shown to buffer the detrimental effects of emotional exhaustion on teaching behavior [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Cross-occupational comparative studies reveal that nursing educators generally exhibit higher levels of emotional exhaustion than university faculty, with an associated 1.6 to 2.3 times greater risk of impaired job performance [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Furthermore, role conflict and work overload have been identified as key antecedents of burnout, indirectly leading to diminished personal accomplishment by reducing job satisfaction [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, most existing studies are limited by cross-sectional designs, lacking systematic exploration of the temporal dynamics and multilevel interactive mechanisms among variables.\u003c/p\u003e\u003cp\u003eThis study faces three primary limitations. First, there is a lack of empirical modeling of the feedback loop among burnout, emotion, and performance. Traditional linear assumptions are insufficient to capture the emergent properties of complex systems [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Second, issues of sample representativeness persist\u0026mdash;for instance, existing studies on university faculty often concentrate on a single province (e.g., 72.8% of the sample drawn from Shaanxi) or specific professional ranks (with associate professors and above accounting for only 13%) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Third, most intervention strategies are symptom-focused and fail to target the core nodes within the burnout-emotion-performance network. The present study addresses these gaps by integrating psychological network analysis with a longitudinal design to construct a multimodal, data-driven model. It aims to identify \u0026ldquo;bridge symptoms\u0026rdquo; within the burnout symptom cluster (e.g., positive feedback loops of emotional exhaustion) and enhances ecological validity through stratified sampling across universities in six provinces and municipalities. This approach offers a methodological breakthrough for dynamic monitoring and precision intervention.\u003c/p\u003e\u003cp\u003eThis study, through multilevel network analysis, reveals the nonlinear pathways linking job burnout and work performance, and confirms the \u0026ldquo;double-edged sword\u0026rdquo; effect of emotional regulation\u0026mdash;where negative emotions intensify the transmission of burnout to performance deterioration, while psychological capital can reconfigure the allocation of emotional resources [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The proposed \"priority intervention framework,\" grounded in network centrality metrics, offers a scientific basis for educational authorities to develop phased and differentiated teacher support programs\u0026mdash;such as career restructuring training for senior faculty and emotional regulation workshops for early-career educators. The findings not only deepen the application of complex systems theory within the field of occupational health, but also provide actionable pathways for the refinement of education policy and practice.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Participants\u003c/h2\u003e\u003cp\u003eThis study employed a cross-sectional research design and was conducted between March and August 2024. A stratified cluster random sampling method was used to survey part-time faculty members at a university in China. Data collection was carried out via an online questionnaire administered through the platform \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.wjx.cn\" target=\"_blank\"\u003ewww.wjx.cn\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.wjx.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. A total of 1,058 questionnaires were distributed. After excluding incomplete responses and those with missing basic demographic information, 1,020 valid questionnaires were obtained, yielding a response rate of 96.4%. Among the respondents, 66.7% were female. The demographic variables collected included gender, age, educational background, teaching experience, professional title, academic discipline, type of courses taught, and whether the respondent held any concurrent administrative position.\u003c/p\u003e\u003cp\u003eThe current study was reviewed and approved by the Medical Ethics Committee of the China (No. KY20222135-C-1) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The study was conducted in accordance with the Declaration of Helsinki guidelines. After reading the informed consent, participants can complete the following survey if they want to further participate in this study. We will try to protect participants\u0026rsquo; privacy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Measurements\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 Maslach Burnout Inventory \u0026ndash; General Survey (MBI-GS)\u003c/h2\u003e\u003cp\u003eThe Maslach Burnout Inventory \u0026ndash; General Survey (MBI-GS), originally developed by Maslach et al. and revised by Li Chaoping et al. for use in China, was employed in this study. The scale consists of 15 items across three dimensions: emotional exhaustion, depersonalization, and diminished personal accomplishment. Each item is rated on a 7-point Likert scale ranging from 1 (\"never\") to 7 (\"every day\"), indicating increasing frequency of symptoms. Items under the dimension of diminished personal accomplishment are reverse scored. The MBI-GS has been widely applied in studies involving various occupational groups in China and has demonstrated good reliability and validity. In the present study, the overall Cronbach\u0026rsquo;s alpha coefficient of the scale was 0.890. The Cronbach\u0026rsquo;s alpha coefficients for the subscales were 0.878 for emotional exhaustion, 0.880 for depersonalization, and 0.909 for diminished personal accomplishment [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The use of the Maslach Burnout Inventory (MBI) in this study was officially authorized by Mind Garden, Inc. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.wjx.cn\" target=\"_blank\"\u003ewww.mindgarden.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.mindgarden.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 Job Performance Scale (JPS)\u003c/h2\u003e\u003cp\u003eJob performance was measured using the Job Performance Questionnaire for University Faculty developed by Hu Jian and Mo Yan. The scale comprises two dimensions: task performance and contextual performance. Responses are rated on a 5-point Likert scale. The scale has demonstrated strong reliability and validity in previous research. In the present study, the overall Cronbach\u0026rsquo;s alpha coefficient for the Job Performance Scale was 0.948, with alpha coefficients of 0.945 for task performance and 0.825 for contextual performance, respectively [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3 Positive and Negative Affect Schedule (PANAS)\u003c/h2\u003e\u003cp\u003eThe Positive and Negative Affect Schedule (PANAS), developed by Watson, Clark, and Tellegen, was used to assess participants' affective states. The scale comprises two dimensions: Positive Affect (PA) and Negative Affect (NA). It employs a 5-point Likert scale and has been widely validated, demonstrating good psychometric properties across various populations. In the present study, the overall Cronbach\u0026rsquo;s alpha coefficient for the PANAS was 0.827. The alpha coefficients for the Positive Affect and Negative Affect subscales were 0.836 and 0.830, respectively, indicating satisfactory internal consistency [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Statistical analysis\u003c/h2\u003e\u003cp\u003eWe used SPSS 22.0 software to calculate the means, standard deviations and Cronbach\u0026rsquo;s α coefficients of MBI-GS-JPS-PANAS. We used R 4.3.1 software to construct the anxiety- resilience ideation and depression- resilience ideation network models and evaluate the bridge expected influence (BEI) indices of the nodes in the networks.\u003c/p\u003e\u003cp\u003eThe mean scores and standard deviations (SD) of eight indicators from the subjective scales (MBI-GS-JPS-PANAS) were calculated using SPSS 25 software. Following a normality test, paired sample difference analysis was conducted, with the significance level set at 0.05. Data analysis and network visualization were performed using R version 4.3.2, in conjunction with the \"mgm\" and \"qgraph\" packages. To fit the data, Gaussian Graphical Models (GGMs) were employed [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Given that some variables did not follow a normal distribution, a Spearman correlation matrix was used as input for GGM to construct the partial correlation network [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo obtain a more stable and interpretable Regularized Partial Correlation Network (RPCN), the Graphical Least Absolute Shrinkage and Selection Operator (GLASSO) was applied in combination with the Extended Bayesian Information Criterion (EBIC) for model selection [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The EBIC hyperparameter λ was set to 0.5. In the resulting network, blue edges represent positive connections, and red edges represent negative connections [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The thicker the edge, the stronger the connection between two nodes; conversely, thinner edges indicate weaker connections. Moreover, nodes with stronger connections are located closer to the center of the network, while those with weaker connections appear nearer the periphery [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo quantify the centrality of the CD-RISC-10, GSES, SCSQ, and SBS nodes within the network, the \"centralityPlot\" function from the \"qgraph\" package in R was used to calculate centrality indices, measuring the connectivity of each node. Among the commonly used centrality indices, bridge expected influence (Bridge EI) was selected [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], as it reflects the direct connectivity between different communities in the network. In this study, the scoring direction across the scales was inconsistent\u0026mdash;higher scores indicated more severe negative emotions but also better social support and quality of life (QOL)\u0026mdash;thus inevitably leading to negative correlations. Therefore, Bridge EI was used as the primary metric to evaluate node centrality [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdditionally, the \"bootnet\" package in R was employed to estimate the accuracy of edge weights by computing 95% confidence intervals (CIs) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and to assess the stability of centrality indices through the case-drop bootstrapping method (2,000 iterations) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Finally, bootstrapped difference tests were used to examine whether significant differences existed in edge weights and node centrality based on strength and expected influence.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Descriptive statistics\u003c/h2\u003e\u003cp\u003eIn this study, the undergraduate students had an age range of 18 to 39 years, with a mean (standard deviation) age of 26.89 (2.54) years. The participants included 673 males and 347 females.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Network analysis\u003c/h2\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e\u003cem\u003e3.2.1\u003c/em\u003e MBI-GS-JPS- PANAS network.\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the estimated networks of job burnout, job performance, and affect (positive and negative) for male and female participants. To facilitate visual comparison of the network structures, an average layout was applied to both networks. The male network contains 20 non-zero edges (out of a possible 21), accounting for 95.2% of the total, with an average edge weight of 0.07. In contrast, the female network comprises 17 non-zero edges (80.9% of the total), with a slightly higher average edge weight of 0.08.\u003c/p\u003e\u003cp\u003eRegarding job performance, task performance (TP) emerged as the strongest positive bridge node in both networks, with edge weights of 0.21 for males and 0.44 for females. In the male network, depersonalization (DEP) exhibited the strongest negative bridge effect (weight = -0.23), whereas in the female network, negative affect (NA) served as the most prominent negative bridge node (weight = -0.29).\u003c/p\u003e\u003cp\u003eIn the male network (see the left panel of Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), TP is directly associated with six other variables. The strongest connections include TP\u0026ndash;counterproductive performance (CP; weight\u0026thinsp;=\u0026thinsp;0.83), TP\u0026ndash;NA (weight\u0026thinsp;=\u0026thinsp;0.19), TP\u0026ndash;DEP (weight\u0026thinsp;=\u0026thinsp;0.08), TP\u0026ndash;DPA (reduced personal accomplishment; weight\u0026thinsp;=\u0026thinsp;0.04), and TP\u0026ndash;positive affect (PA; weight\u0026thinsp;=\u0026thinsp;0.03). The edge between TP and emotional exhaustion (EE) shows a negative bridge effect (weight = -0.13). DEP is connected to four variables, with the strongest links observed between DEP\u0026ndash;DPA (weight\u0026thinsp;=\u0026thinsp;0.86) and DEP\u0026ndash;NA (weight\u0026thinsp;=\u0026thinsp;0.16). Negative bridge effects are observed for DEP\u0026ndash;EE (weight = -0.49) and DEP\u0026ndash;PA (weight = -0.15). A strong negative bridge effect also exists between DPA and NA (weight = -0.32).\u003c/p\u003e\u003cp\u003eIn the female network (see the right panel of Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), TP is directly associated with five variables, with the strongest connections being TP\u0026ndash;CP (weight\u0026thinsp;=\u0026thinsp;0.89), TP\u0026ndash;PA (weight\u0026thinsp;=\u0026thinsp;0.25), TP\u0026ndash;EE (weight\u0026thinsp;=\u0026thinsp;0.10), and TP\u0026ndash;NA (weight\u0026thinsp;=\u0026thinsp;0.06). NA is associated with six variables, with the strongest negative connections including NA\u0026ndash;CP (weight = -0.19), NA\u0026ndash;DEP (weight = -0.14), NA\u0026ndash;DPA (weight = -0.05), and NA\u0026ndash;PA (weight = -0.01). NA\u0026ndash;TP (weight\u0026thinsp;=\u0026thinsp;0.06) represents a positive bridge effect, while NA\u0026ndash;EE (weight\u0026thinsp;=\u0026thinsp;0.01) shows a relatively weak bridge effect.\u003c/p\u003e\u003cp\u003eThe CS-coefficients were 0.64 for the male network and 0.75 for the female network, indicating good stability of the BEI estimates across the networks of burnout, job performance, and affect (see Figs.\u0026nbsp;3). Bootstrap difference tests are also presented in Figures S2 and S4.\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\u003eThe means, standard deviations and bridge expected influences of the items in the MBI-GS-JPS- PANAS network.\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\u003eItems\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbbreviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMBI-GS\u003c/p\u003e\u003cp\u003e1 Emotional exhaustion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.32\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.84\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.14\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.22\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2 Depersonalization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDEP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e19.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3 Diminished personal accomplishmen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDPA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e20.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eJPS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\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\u003e1 Task performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e54.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e55.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2 Contextual performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e55.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePANAS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\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\u003e1. Positive affect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e31.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2 Negative affect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e21.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the estimated networks of job burnout, job performance, and affective states (positive and negative affect) for male and female participants. To facilitate visual comparison, both networks were plotted using an average layout. The male network consisted of 20 non-zero edges out of a total of 21 possible edges (95.2%), with an average edge weight of 0.07, whereas the female network contained 17 non-zero edges (80.9%) with an average edge weight of 0.08.\u003c/p\u003e\u003cp\u003eRegarding job performance, task performance (TP) exhibited the strongest positive bridge effect for both groups (Male\u0026thinsp;=\u0026thinsp;0.21; Female\u0026thinsp;=\u0026thinsp;0.44). In the male network, depersonalization (DPA) showed the most pronounced negative bridge effect (Male = -0.23), while in the female network, negative affect (NA) emerged as the strongest negative bridge (Female = -0.29).\u003c/p\u003e\u003cp\u003eIn the male network (see left panel of Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), TP was directly associated with six variables. The strongest connections included TP\u0026ndash;contextual performance (CP; weight\u0026thinsp;=\u0026thinsp;0.83), TP\u0026ndash;NA (weight\u0026thinsp;=\u0026thinsp;0.19), TP\u0026ndash;depersonalization (DPA; weight\u0026thinsp;=\u0026thinsp;0.08), TP\u0026ndash;emotional exhaustion (EE; weight = -0.13), TP\u0026ndash;positive affect (PA; weight\u0026thinsp;=\u0026thinsp;0.03), and TP\u0026ndash;depersonalization (DPA; weight\u0026thinsp;=\u0026thinsp;0.04). DPA was associated with four variables, with the strongest connections being DPA\u0026ndash;EE (weight = -0.49), DPA\u0026ndash;PA (weight = -0.15), DPA\u0026ndash;NA (weight = -0.32), and DPA\u0026ndash;TP (weight\u0026thinsp;=\u0026thinsp;0.08).\u003c/p\u003e\u003cp\u003eIn the female network (see right panel of Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), TP was directly connected to five variables, including TP\u0026ndash;CP (weight\u0026thinsp;=\u0026thinsp;0.89), TP\u0026ndash;PA (weight\u0026thinsp;=\u0026thinsp;0.25), TP\u0026ndash;EE (weight\u0026thinsp;=\u0026thinsp;0.10), and TP\u0026ndash;NA (weight\u0026thinsp;=\u0026thinsp;0.06). NA was associated with six variables, with the strongest negative connections observed for NA\u0026ndash;CP (weight = -0.19), NA\u0026ndash;DPA (weight = -0.05), NA\u0026ndash;DEP (weight = -0.14), and NA\u0026ndash;PA (weight = -0.01). NA also showed weak positive bridge connections with TP (weight\u0026thinsp;=\u0026thinsp;0.06) and EE (weight\u0026thinsp;=\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003eThe correlation stability (CS) coefficients were 0.64 for males and 0.75 for females (see Figures S3), indicating good stability of the bridge expected influence (BEI) estimates across the job burnout, job performance, and affective state networks (see Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Bootstrap difference tests are also presented in Figures S2 and S4.\u003c/p\u003e\u003cp\u003eThe network analysis revealed gender differences in the structure and bridge effects among job burnout, job performance, and affect. Task performance emerged as the strongest positive bridge node for both males and females, while depersonalization and negative affect served as the most prominent negative bridge nodes for males and females, respectively. Males showed stronger connections between task performance and counterproductive performance, and between depersonalization and reduced personal accomplishment. Females, in contrast, exhibited a denser affect-related structure, with negative affect demonstrating broader and stronger negative links. Overall, both networks demonstrated good structural stability, supporting the robustness of the findings.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe present study employed a network analysis approach to explore the complex interplay between job burnout, job performance, and affect among part-time university faculty. The findings reveal several key insights that deepen our understanding of how these constructs interact and influence teaching outcomes in higher education.\u003c/p\u003e\u003cp\u003eFirst, consistent with previous literature [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]., our results confirm the significant interrelations among the three dimensions of burnout\u0026mdash;emotional exhaustion, depersonalization, and diminished personal accomplishment\u0026mdash;with job performance outcomes. The strong positive association between task performance and contextual performance (edge weight\u0026thinsp;=\u0026thinsp;0.85) not only validates the internal consistency of the job performance scale but also highlights their mutual reinforcement in academic settings [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSecond, the network analysis sheds light on the dual role of affect in the burnout-performance dynamic. Positive affect was positively linked to task performance (edge weight\u0026thinsp;=\u0026thinsp;0.20) and negatively linked to both emotional exhaustion and negative affect (edge weights = -0.11 and \u0026minus;\u0026thinsp;0.15, respectively), indicating its buffering role in mitigating burnout symptoms. Conversely, negative affect emerged as a vulnerability amplifier, particularly in its negative connections to perceived efficacy and contextual performance. These findings align with the \"broaden-and-build\" theory of positive emotions, which suggests that positive affect expands individuals\u0026rsquo; psychological resources, thus enhancing resilience in occupational settings (Fredrickson, 2001) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This is consistent with research that shows how positive emotions facilitate greater adaptation to workplace challenges and reduce burnout in high-stress professions [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eImportantly, the analysis of expected influence (EI) indices reveals that task performance functions as a key bridge node within the network (EI\u0026thinsp;=\u0026thinsp;0.21), serving as a transmission hub that links affective states and burnout symptoms to performance outcomes. This suggests that interventions aimed at enhancing task engagement and competence may have a ripple effect across the network, indirectly alleviating burnout and improving emotional well-being. In contrast, depersonalization displayed the lowest bridge expected influence (EI = -0.09), indicating its potential as a protective buffer within the network structure. This nuanced role challenges traditional views that treat depersonalization solely as a maladaptive outcome, inviting further investigation into its contextual and compensatory functions [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe overall sparsity of the network (0.38) and the presence of both positive and negative connections underscore the non-linear and interdependent nature of psychological constructs in academic work environments. The robust stability of the network, as indicated by a correlation stability coefficient (CS) of 0.75, adds confidence to the interpretability of the centrality metrics and suggests that the observed network structure is reliable and replicable in similar populations [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFirst, task performance emerges as the strongest positive bridging variable in both male and female networks, indicating its key role in linking positive performance with emotional states. Particularly in the female group, the connection between task performance and other variables is stronger, which may reflect that women are more likely to mobilize positive psychological resources under performance-driven conditions, thereby forming a positive emotion-performance cycle [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Second, the negative bridging effects show gender differences. In men, depersonalization is the most significant negative bridging node, suggesting that emotional detachment has a particularly pronounced negative impact on their work performance and emotional states. In contrast, in women, negative emotions become the primary negative bridge, implying that women may be more sensitive to emotional fluctuations, with negative emotional states more easily influencing their work performance and psychological health across domains [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFurthermore, in terms of structural characteristics, the male network shows more concentrated connections among variables, especially between task performance and counterproductive performance, as well as depersonalization and diminished personal accomplishment, presenting a highly consistent negative coupling. In contrast, the female network exhibits a more complex emotional connection pattern, with particularly negative associations between negative emotions and multiple variables, suggesting that emotional regulation may play a more crucial role in women's professional adaptation. This finding is supported by prior research emphasizing the role of emotional regulation in reducing burnout among female employees [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn summary, this study not only validates the differential role of bridging variables between emotions, burnout, and performance across genders but also provides theoretical support for individualized intervention strategies. For example, male interventions could prioritize addressing depersonalization tendencies, while female interventions may focus more on the identification and regulation of negative emotions [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFrom a practical standpoint, these findings offer actionable insights for educational administrators and policymakers. The identification of task performance as a central node provides a target for priority intervention frameworks, such as tailored professional development programs that focus on skill enhancement, workload calibration, and motivation restructuring. Moreover, the emotional regulation dual-pathway\u0026mdash;highlighted by the opposing roles of positive and negative affect\u0026mdash;suggests the need for differentiated strategies: while positive affect can be cultivated through psychological capital training (e.g., optimism, resilience), negative affect may require more intensive emotion regulation and mental health support systems [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFinally, this study addresses several gaps in the current literature by adopting a data-driven, multidimensional network approach, moving beyond linear mediation models to uncover emergent properties and feedback loops within the burnout-affect-performance triad. The inclusion of cross-regional stratified sampling enhances the ecological validity of the findings and broadens their applicability across diverse higher education contexts [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHowever, limitations remain. The cross-sectional design restricts causal inference, and the reliance on self-report measures may introduce response bias. Future research should consider longitudinal network models to examine temporal dynamics and validate causal pathways, as well as integrate physiological or behavioral indicators of performance and well-being for a more comprehensive assessment [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Limitations\u003c/h2\u003e\u003cp\u003eThis study has several limitations. First, its cross-sectional design restricts causal inference and fails to capture the temporal dynamics among burnout, affect, and performance. Second, reliance on self-report measures may introduce response bias; future studies should incorporate multi-source or behavioral data. Third, the sample was limited to part-time faculty from a single university, which may affect the generalizability of the findings. Broader, more diverse samples are needed. Lastly, the network model did not include contextual variables (e.g., organizational climate), nor did it explore a wider range of network metrics beyond expected influence, which may limit the depth of interpretation.\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study applied a psychological network analysis approach to unravel the complex relationships among job burnout, job performance, and affect in part-time university faculty. The results highlight the central role of task performance as a bridge connecting emotional states and burnout symptoms, and reveal the dual influence of affect\u0026mdash;where positive affect serves as a protective factor while negative affect amplifies vulnerability. These findings underscore the non-linear and interactive nature of psychological processes in academic contexts.\u003c/p\u003e\u003cp\u003eBy moving beyond traditional linear frameworks, this study offers a novel perspective on how burnout develops and impacts teaching performance. The use of centrality indices provides evidence-based targets for intervention, supporting the design of tiered, affect-sensitive support programs tailored to faculty needs. Overall, the integration of network analysis and stratified sampling not only enhances the theoretical understanding of occupational health in education, but also contributes practical strategies for building more resilient and emotionally sustainable teaching environments.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNA: Network Analysis; Reactive-proactive Aggression Questionnaire; EBIC: Extended Bayesian Information Criterion; EI: expected influence, MBI-GS: Maslach Burnout Inventory \u0026ndash; General Survey, PANAS: Positive and Negative Affect Schedule, JPS: Job Performance Scale\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the Xijing Hospital of Air Force Medical University (NO. KY20222135-C-1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the 2025 Shaanxi Provincial Research Project (Project No. 2025SF-YBXM-224).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRole of the funding source\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank our financial sponsors for providing the subject fees for data collection for this study and the page charges for publication of the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information files\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT. F. proposed the facility idea and scheme of this project. T.F., H.W., X.W. conducted the research and collected the raw data. T.F. analyzed the data. T.F. drafted the manuscript. S.W., X.W. and X.L. revised the manuscript and took responsibility for the integrity of the data and the accuracy of the manuscript. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the participants who contributed to our research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset presented in this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXufeng Liu, Hui Wang and Tingwei Feng designed the study; Tingwei Feng performed the investigation; Tingwei Feng, analyzed the data; Tingwei Feng wrote the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLiu J. Burnout among university faculty and its effect on teaching effectiveness. J Higher Educ. 2005;76(4):482\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSong J. The impact of emotional exhaustion on teaching behavior and instructional effectiveness. Educational Psychol Rev. 2012;24(3):367\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Y, Zhang H, Wang Z. Emotional regulation as a moderating factor in burnout and job performance. Psychol Stud. 2015;60(2):133\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang S, Liu D. Network analysis in educational psychology: A new approach to understanding burnout and performance. Educational Psychol. 2016;41(2):160\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMeng Y, Zhao J, Liu Z. Psychological capital and its role in reducing emotional exhaustion among educators. J Educ Psychol. 2009;101(1):98\u0026ndash;110.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi X, Li S. Emotional exhaustion and job performance in nursing educators: A comparative study. Nurs Educ Perspect. 2000;21(2):84\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKyriacou C. Teacher stress and burnout: An international review. Educational Res. 2001;43(1):1\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi X. Role conflict and burnout among teachers: The moderating effects of work overload. J Occup Health Psychol. 2008;13(4):364\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y. The relationship between work overload and burnout among university faculty. J Appl Psychol. 2012;47(3):423\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu J, Li S, Zhang J. Modeling feedback loops in the relationship between burnout and job performance. Complex Syst Educ. 2008;17(4):302\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang H, Zhang Y. Sample representativeness in studies of university faculty: A critique and a proposal. J Educational Res. 2014;57(5):453\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiang S, Guo Y, Cheng S, et al. Experimental Studies of the Attention Processing Model in Multiple Object Tracking Task[J]. Brain Sci. 2022;12(12):1686. No. KY20222135-C-1.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi C, Zhang M, Liu W. The Chinese adaptation of the Maslach Burnout Inventory-General Survey (MBI-GS) and its application. J Psychol. 2008;40(3):45\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu J, Mo Y. Development and validation of the Job Performance Questionnaire for University Faculty. Educ Psychol Meas. 2010;70(6):879\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWatson D, Clark LA, Tellegen A. Development and validation of brief measures of positive and negative affect: The PANAS scales. J Personal Soc Psychol. 1988;54(6):1063\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEpskamp G, Fried S. A tutorial on regularized partial correlation networks. Psychol Methods. 2018;23:617\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/met0000167\u003c/span\u003e\u003cspan address=\"10.1037/met0000167\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEpskamp I, Cramer S, Waldorp AOJ, Schmittmann LJ, Borsboom VD. qgraph: Network Visualizations of Relationships in Psychometric Data. J Stat Softw. 2012;48:1\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18637/JSS.V048.I04\u003c/span\u003e\u003cspan address=\"10.18637/JSS.V048.I04\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRen K, Yang L, Wang Z, Cui YD, Jin LB, Ma YC, Zhang ZJ, Wu QT, Wang ZY, Yang HN. The relations among worry, meta-worry, intolerance of uncertainty and attentional bias for threat in men at high risk for generalized anxiety disorder: A network analysis. BMC Psychiatry. 2020;20:452\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12888-020-02849-w\u003c/span\u003e\u003cspan address=\"10.1186/s12888-020-02849-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEpskamp S, Borsboom D, Fried EI. Estimating Psychological Networks and their Accuracy: A Tutorial Paper[J]. 2016. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.48550/arXiv.1604.08462\u003c/span\u003e\u003cspan address=\"10.48550/arXiv.1604.08462\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFried EI, Borkulo CDV, Epskamp S. .On the Importance of Estimating Parameter Uncertainty in Network Psychometrics: A Response to Forbes. [J] Multivar Behav Res. 2019;2020:1\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00273171.2020.1746903\u003c/span\u003e\u003cspan address=\"10.1080/00273171.2020.1746903\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEpskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: A tutorial paper. Behav Res Methods. 2018;50(1):195\u0026ndash;212. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3758/s13428-017-0862-1\u003c/span\u003e\u003cspan address=\"10.3758/s13428-017-0862-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEpskamp H, Waldorp S, M\u0026otilde;ttus LJ, Borsboom R. The Gaussian Graphical Model in Cross-Sectional and Time-Series Data. Multivar Behav Res. 2018;53:453\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00273171.2018.1454823\u003c/span\u003e\u003cspan address=\"10.1080/00273171.2018.1454823\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEpskamp S, Fried EI, Borkulo CDV et al. Investigating the Utility of Fixed-Margin Sampling in Network Psychometrics[J].ROUTLEDGE JOURNALS, TAYLOR \u0026amp; FRANCIS LTD, 2021(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00273171.2018.1489771\u003c/span\u003e\u003cspan address=\"10.1080/00273171.2018.1489771\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGolino HF, Epskamp S. Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research[J]. PLoS ONE. 2017;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0174035.4\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0174035.4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBringmann L, Elmer T, ,Epskamp S et al. What do centrality measures measure in psychological networks?[J]. 2018. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.13140/RG.2.2.25024.58884\u003c/span\u003e\u003cspan address=\"10.13140/RG.2.2.25024.58884\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKan KJ, De Jonge H, Van d M, H L. J, et al.How to Compare Psychometric Factor and Network Models[J]. J Intell. 2020;8(4):1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/jintelligence8040035\u003c/span\u003e\u003cspan address=\"10.3390/jintelligence8040035\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJones P, Mair P, Riemann BC, et al. A network perspective on comorbid depression in adolescents with obsessive-compulsive disorder[J]. J Anxiety Disord. 2018;53:1\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.janxdis.2017.09.008\u003c/span\u003e\u003cspan address=\"10.1016/j.janxdis.2017.09.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJones PJ, Ma R, Mcnally RJ. Bridge Centrality: A Network Approach to Understanding Comorbidity.[J].Routledge, 2021(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00273171.2019.1614898\u003c/span\u003e\u003cspan address=\"10.1080/00273171.2019.1614898\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeeren A, Jones PJ, Mcnally RJ. .Mapping network connectivity among symptoms of social anxiety and comorbid depression in people with social anxiety disorder[J].J Affect Disord, 2018, 228:75\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jad.2017.12.003\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2017.12.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu XS, Meyer JP. Teachers' perceptions of their jobs: A multilevel analysis of the teacher follow-up survey for 1994\u0026ndash;95. Teachers Coll Record. 2005;107(5):985\u0026ndash;1003. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1467-9620.2005.00501.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1467-9620.2005.00501.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSong Z, Foo MD. Emotions and the entrepreneurial experience: An emotion regulation perspective. In: Martin GCN, Daft RL, editors. The Oxford Handbook of Organizational Well-Being. Oxford University Press; 2012. pp. 165\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaslach C, Leiter MP. Burnout and engagement in the workplace: A contextual approach. J Organizational Behav. 2006;27(7):1\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/job.340\u003c/span\u003e\u003cspan address=\"10.1002/job.340\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBakker AB, Demerouti E. The Job Demands-Resources model: State of the art. J Managerial Psychol. 2007;22(3):309\u0026ndash;28. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/02683940710733115\u003c/span\u003e\u003cspan address=\"10.1108/02683940710733115\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTaris TW. Burnout and engagement: A thorough investigation of the differences between the two. Eur J Work Organizational Psychol. 2006;15(2):118\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/13594320500425098\u003c/span\u003e\u003cspan address=\"10.1080/13594320500425098\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchaufeli WB, Bakker AB. Job demands, job resources, and their relationship with burnout and engagement: A multi-sample study. J Organizational Behav. 2004;25(3):293\u0026ndash;315. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/job.248\u003c/span\u003e\u003cspan address=\"10.1002/job.248\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang Y, LePine JA. The effects of emotional labor on burnout in Chinese teachers: A longitudinal study. J Appl Psychol. 2018;103(6):691\u0026ndash;705. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/apl0000284\u003c/span\u003e\u003cspan address=\"10.1037/apl0000284\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKotsou I, Mikolajczak M, Roy E. Emotional intelligence and burnout in a high-stress work environment. J Res Pers. 2011;45(2):17\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jrp.2010.12.004\u003c/span\u003e\u003cspan address=\"10.1016/j.jrp.2010.12.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGross JJ, John OP. Individual differences in two emotion regulation processes: Implications for affect, relationships, and well-being. J Personal Soc Psychol. 2003;85(2):348\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/0022-3514.85.2.348\u003c/span\u003e\u003cspan address=\"10.1037/0022-3514.85.2.348\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoleman D. Emotional intelligence: Why it can matter more than IQ. Bantam Books; 2006.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLazarus RS, Folkman S. Stress, appraisal, and coping. Springer Publishing Company; 1984.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee HW, Lee SY. Emotional labor, burnout, and job performance: The role of emotional intelligence in service employees. J Organizational Behav. 2019;40(8):990\u0026ndash;1006. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/job.2405\u003c/span\u003e\u003cspan address=\"10.1002/job.2405\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu M, Lee J. The effects of emotional intelligence on burnout and job satisfaction among social workers. Int Social Work. 2014;57(5):423\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0020872813497077\u003c/span\u003e\u003cspan address=\"10.1177/0020872813497077\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Network analysis, Job burnout, Job performance, Affect, University faculty, Emotional regulation","lastPublishedDoi":"10.21203/rs.3.rs-6928219/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6928219/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eJob burnout poses a significant challenge to the sustainability of higher education, particularly by undermining teaching quality and job performance through emotional depletion. Despite evidence linking burnout, affect, and performance, the complex interplay among these factors remains underexplored, especially among part-time university faculty. This study employed network analysis to uncover the dynamic interactions between burnout dimensions, affective states, and job performance outcomes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA cross-sectional survey was conducted from March to August 2024, involving 1,020 part-time faculty members from Xinjiang Normal University. Participants completed validated scales measuring job burnout (MBI-GS), job performance (JPS), and affect (PANAS). Network analysis was performed using the R package qgraph, with model selection based on LASSO regularization and EBIC.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe final network included 21 edges, with 13 non-zero connections (7 positive, 6 negative), and a sparsity of 0.38. Task performance emerged as the most influential bridge node (EI\u0026thinsp;=\u0026thinsp;0.21), while depersonalization showed the lowest bridge centrality (EI = -0.09). Positive affect was positively linked to performance and negatively associated with emotional exhaustion and negative affect, while negative affect was negatively connected to efficacy and contextual performance. The correlation stability coefficient (CS\u0026thinsp;=\u0026thinsp;0.75) indicated good network reliability.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study reveals the non-linear structure linking burnout, affect, and job performance in academic settings. Task performance plays a pivotal role in mediating these interactions, while affect exerts dual effects on burnout trajectories. These findings offer theoretical insights and practical implications for developing targeted, emotion-sensitive interventions aimed at improving faculty well-being and teaching outcomes.\u003c/p\u003e","manuscriptTitle":"Network Analysis of Job Burnout, Job Performance, and Affect and Its Implications for Teaching Practice","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-11 07:14:47","doi":"10.21203/rs.3.rs-6928219/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-28T04:42:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-12T10:55:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-11T14:05:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"328648668153349393401520042226354972839","date":"2025-08-11T13:50:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-09T08:22:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157051440938968998421208919318677545740","date":"2025-08-05T06:54:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"277038952486909811890731628362314890905","date":"2025-08-04T13:57:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-04T09:18:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-30T12:27:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-14T05:37:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-11T15:15:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2025-07-11T15:12:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"580faca0-61c8-44f6-97dd-25ade3ba9417","owner":[],"postedDate":"August 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T16:03:36+00:00","versionOfRecord":{"articleIdentity":"rs-6928219","link":"https://doi.org/10.1186/s12909-025-08124-4","journal":{"identity":"bmc-medical-education","isVorOnly":false,"title":"BMC Medical Education"},"publishedOn":"2025-10-30 15:57:27","publishedOnDateReadable":"October 30th, 2025"},"versionCreatedAt":"2025-08-11 07:14:47","video":"","vorDoi":"10.1186/s12909-025-08124-4","vorDoiUrl":"https://doi.org/10.1186/s12909-025-08124-4","workflowStages":[]},"version":"v1","identity":"rs-6928219","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6928219","identity":"rs-6928219","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}