Examining the Mediating Role of Zoom Fatigue in Personality Traits and Professional Burnout

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Understanding how individual differences in personality traits influence susceptibility to these technological stressors and subsequent burnout outcomes remains underexplored. Methods This cross-sectional study employed structural equation modeling to examine the mediating role of Zoom fatigue in the relationship between neurotic personality traits and professional burnout among 884 teachers across Turkey. The participants completed validated measures, including the Zoom Fatigue Inventory, the Big Five Personality Inventory (short form), and the Maslach Burnout Inventory-Educator Form. Data were collected between January and March 2022 through stratified random sampling across seven geographical regions. Results Structural equation modeling revealed significant positive relationships between neurotic personality traits and Zoom fatigue (β = 0.40, p < 0.01) and between Zoom fatigue and teacher burnout (β = 0.46, p < 0.01). Crucially, Zoom fatigue significantly mediated the relationship between neurotic personality traits and occupational burnout (indirect effect β = 0.18). The model demonstrated excellent fit indices (χ²/df = 2.89, RMSEA = 0.07, CFI = 0.89) and substantial effect sizes for both personality traits (f² = 0.38) and Zoom fatigue (f² = 0.27) on burnout outcomes. Conclusions Teachers with greater neuroticism experience greater professional burnout, partially through their increased susceptibility to video-conferencing fatigue. These findings suggest that technological demands in online education environments may disproportionately affect individuals with specific personality vulnerabilities. Targeted interventions addressing video conferencing fatigue could mitigate burnout pathways for at-risk educators. Zoom fatigue Online education Teacher burnout Personality traits Neuroticism COVID-19 pandemic Structural equation modeling Digital wellness Figures Figure 1 Background The COVID-19 pandemic precipitated an unprecedented global transformation, compelling an abrupt shift from conventional face-to-face instruction to emergency remote teaching and fundamentally redefining educational delivery worldwide [1,2]. This sudden transition, which impacted approximately 1.6 billion students across the globe [3,4], necessitated rapid adaptation to video conferencing technologies and digital pedagogical methodologies—domains for which many educators possessed insufficient training and preparation [5]. While online education presents distinct advantages, such as increased flexibility and accessibility [6], it has simultaneously engendered novel occupational stressors that pose significant threats to educators’ well-being and the sustainability of their professional engagement [7,8]. Among these emergent challenges is the phenomenon commonly referred to as “zoom fatigue,” characterized by feelings of exhaustion, heightened stress, and cognitive depletion arising from prolonged participation in virtual interactions [9,10]. This multifaceted phenomenon encompasses several dimensions, including general fatigue, visual strain, social exhaustion, motivational depletion, and emotional drain [11]. Empirical evidence suggests that Zoom fatigue adversely affects not only professional functioning but also individuals’ personal lives, manifesting in difficulties with concentration, negative affective states, and anticipatory anxiety regarding future virtual engagements [12]. The theoretical foundation underpinning this investigation synthesizes perspectives from personality psychology, occupational stress theory, and research on technology-mediated communication. The five-factor model of personality serves as a robust framework for elucidating individual differences in stress reactivity and coping processes [13]. Within this model, individuals scoring higher on neuroticism tend to exhibit greater negative emotionality and heightened stress sensitivity [14], potentially rendering them more vulnerable to the adverse effects of online teaching environments. Complementing this perspective, the job demands-resources (JD-R) model provides a valuable lens through which to conceptualize the interaction between technological demands and personal resources in shaping occupational well-being [15]. In this context, zoom fatigue can be conceptualized as a specific response to the psychological demands inherent in video-mediated communication—demands that include cognitive overload, increased self-monitoring, constrained physical mobility, and limitations in nonverbal communicative cues [16]. Such demands may be particularly burdensome for individuals with specific personality vulnerabilities. Drawing on these theoretical perspectives, the present study proposes a mediation model positing that personality traits—especially neuroticism—influence individuals’ susceptibility to Zoom fatigue, which, in turn, contributes to the development of professional burnout. This conceptualization is consistent with transactional stress theory [17], which emphasizes that individual differences in cognitive appraisal processes mediate the relationship between environmental stressors and resultant stress outcomes. Literature Review Personality and Technology Adaptation Cross-cultural research has yielded important insights into the influence of personality on technology adaptation. Wang et al. [18] demonstrated that openness to experience and extraversion positively predict technology adoption among Chinese teachers, whereas neuroticism negatively predicts acceptance. Similarly, Savić and Čorkalo Biruški [19] reported that neuroticism was associated with greater perceived stress in online teaching among Croatian educators, although this relationship was moderated by digital competence. Research consistently demonstrates that neurotic individuals experience greater difficulty adapting to technological changes and exhibit heightened stress responses to novel work environments [20,21]. These findings suggest that the relationship between personality and technology-related stress may be culturally influenced and potentially mediated by skill development and institutional support. Zoom Fatigue: Mechanisms and Consequences Recent empirical investigations have elucidated the multidimensional nature of video conferencing fatigue. Fauville et al. [11] developed the Zoom Exhaustion & Fatigue Scale, which identifies five distinct but related dimensions: general, visual, social, motivational, and emotional fatigue. Their research revealed demographic differences in susceptibility, with women and younger individuals reporting higher fatigue levels. Bailenson [16] proposed four primary mechanisms underlying Zoom fatigue: excessive close-up eye contact, cognitive overload from monitoring nonverbal behavior, increased self-evaluation through self-view, and reduced physical mobility. Peper et al. [22] further explored physiological mechanisms, noting that disrupted attention patterns and reduced mobility contribute significantly to this phenomenon. Organizational factors also influence Zoom fatigue beyond individual differences. Shockley et al. [23] demonstrated that meeting frequency and duration significantly predict fatigue levels, suggesting that institutional policies regarding virtual meetings could mitigate adverse effects. Teacher Burnout in Online Environments The pandemic context has intensified research focused on educator burnout. Herman et al. [24] reported that teacher burnout during the COVID-19 pandemic was significantly associated with both individual factors (coping strategies and perceived stress) and organizational factors (administrative support and workload). Sokal et al. [25] reported varying burnout trajectories among teachers throughout the pandemic, with some experiencing increasing burnout and others showing recovery patterns. These findings suggest that burnout represents a complex phenomenon shaped by interactions between personal resources and environmental demands. Understanding the specific pathways through which technological stressors contribute to burnout could inform targeted intervention strategies. Study Rationale and Hypotheses This investigation addresses a critical gap in the understanding of how individual personality differences influence adaptation to online teaching environments and subsequent burnout outcomes. The identification of Zoom fatigue as a potential mediating mechanism offers novel insights for developing targeted support strategies for at-risk educators. On the basis of the theoretical framework and empirical literature, we formulate four specific hypotheses: H1: Neurotic personality traits are positively related to Zoom fatigue among teachers. H2: Neurotic personality traits are positively related to teacher burnout. H3: Zoom fatigue is positively related to teacher burnout. H4: Zoom fatigue mediates the relationship between neurotic personality traits and teacher burnout. Methods Study Design This cross-sectional investigation employed a relational survey design to examine naturally occurring covariation among variables without experimental manipulation. The relational survey approach is appropriate for exploring associations among personality traits (independent variable), zoom fatigue (mediator variable), and teacher burnout (dependent variable) while acknowledging that causal inferences cannot be definitively established [26]. Participants and Sampling The study utilized stratified random sampling to ensure representation across educational levels and geographical regions throughout Turkey. Participants were recruited through official communication channels within the Istanbul University Cerrahpaşa Publication Ethics Committee for Social Sciences and Humanities following institutional ethical approval (ETH-2021--043). All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. All participants provided informed consent prior to participation. The final sample comprised 884 teachers representing urban and rural areas across Turkey's seven geographical regions. The patients’ demographic characteristics are presented in Table 1. The sample consisted of 762 female (86.2%) and 122 male (13.8%) participants. The age distributions included 23–29 years (n = 128, 14.5%), 30–34 years (n = 158, 17.9%), 35–39 years (n = 216, 24.4%), 40–44 years (n = 217, 24.5%), and 45+ years (n = 165, 18.7%). Teaching experience ranged from 1--5 years (n = 132, 14.9%) to over 25 years (n = 122, 13.8%). The educational levels represented included preschool (n = 50, 5.7%), primary school (n = 343, 38.8%), middle school (n = 251, 28.4%), and high school (n = 240, 27.1%). Table 1. Demographic characteristics of the study participants (n = 884) Variable Category n % Gender Female 762 86.2 Male 122 13.8 Age Group 23-29 years 128 14.5 30-34 years 158 17.9 35-39 years 216 24.4 40-44 years 217 24.5 45+ years 165 18.7 Teaching Experience 1-5 years 132 14.9 6-10 years 190 21.5 11-15 years 183 20.7 16-20 years 257 29.1 25+ years 122 13.8 Educational Level Preschool 50 5.7 Primary School 343 38.8 Middle School 251 28.4 High School 240 27.1 Procedures Data collection occurred between January and March 2022, approximately two years after the initial emergency remote teaching transition. This timing allowed participants to accumulate substantial experience with online instruction and video conferencing platforms. The participants completed an online survey package containing demographic questions and three validated measurement instruments through a secure platform ensuring data privacy and response anonymity. Survey completion required 15–20 minutes. All participants provided informed consent and were informed of their right to withdraw from the study at any time. Measures Zoom Fatigue Inventory The Zoom Fatigue Scale, developed by Deniz et al. [27], employs a 5-point Likert response format (1 = strongly disagree, 5 = strongly agree) across five dimensions: general, visual, social, motivational, and emotional fatigue. The general fatigue dimension assesses overall exhaustion related to video conferencing. Visual fatigue focuses on eye strain and visual discomfort. Social fatigue measures exhaustion related to virtual social interactions. Motivational fatigue assesses decreased engagement and participation motivation. Emotional fatigue measures emotional exhaustion specifically related to video conferencing. Higher scores indicate greater fatigue across dimensions. The Cronbach's alpha coefficients for the subscales ranged from 0.89 to 0.94, indicating excellent internal consistency. Big Five Personality Inventory (Short Form) The Big Five Personality Inventory, developed by Benet-Martinez and John [28] and adapted for Turkish populations by Sümer et al. [29], comprises 44 items assessing five personality dimensions via a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). The inventory measures openness to experience, agreeableness, conscientiousness, neuroticism, and extraversion. Sample items include "I see myself as someone who is talkative" (extraversion) and "I see myself as someone who worries a lot" (neuroticism). Higher scores indicate greater expression of respective personality traits. The Cronbach's alpha coefficients ranged from 0.94 to 0.97, indicating excellent reliability. Maslach Burnout Inventory-Educator Form (MBI-EF) The MBI-EF, originally developed by Maslach et al. [30] and adapted for Turkish educators by İnce and Şahin [31], contains 22 items across three dimensions: emotional exhaustion, depersonalization, and personal accomplishment. The instrument employs a 7-point Likert scale (0 = never, 6 = every day). Sample items include "I feel emotionally drained from my work" (emotional exhaustion) and "I have become more callous toward people since I took this job" (depersonalization). The Cronbach's alpha coefficients ranged from 0.91 to 0.94, indicating excellent internal consistency. Sample size calculation The sample size for this study was determined on the basis of multiple considerations appropriate for structural equation modeling (SEM) analyses. Following established guidelines for SEM, we employed several approaches to ensure adequate statistical power and model stability. Primary approach - effect size-based calculation: On the basis of previous research examining personality burnout relationships [20,21], we anticipated medium effect sizes (Cohen's f² ≥ 0.15) for the structural pathways in our model. Using G*Power 3.1.9.7 software [42], power analysis for multiple regression (as a conservative estimate for SEM) indicated that a minimum sample size of 550 participants would be required to detect medium effect sizes (f² = 0.15) with 80% power, α = 0.05, and three predictors (neuroticism direct effect, zoom fatigue mediator effect, and their interaction). Secondary Approach - SEM-Specific Guidelines: Following the recommendations of Hair et al. [43] and Kline [44] for SEM analyses, we applied the "10:1 rule", which requires a minimum of 10 participants per estimated parameter. Our proposed model included 23 estimated parameters (18 factor loadings, 3 structural paths, and 2 error covariances), necessitating a minimum sample of 230 participants. However, given the complexity of mediation testing and the need for stable parameter estimates, we aimed for a more conservative 20:1 ratio, targeting approximately 460 participants. Tertiary Approach - Statistical Power for Mediation: For mediation analysis using bootstrap procedures, Fritz and MacKinnon [45] recommend larger sample sizes to achieve adequate power for detecting indirect effects. On the basis of their simulation studies for medium effect sizes in both the a-path (predictor to mediator) and b-path (mediator to outcome), a minimum of 558 participants would be needed to achieve 80% power for detecting significant mediation effects. Final Sample Size Determination: Considering all approaches and anticipating potential data loss due to incomplete responses, missing data, and outliers, we targeted a minimum sample size of 800 participants. This target would provide: Adequate power (>95%) for detecting medium to large effect sizes in structural paths Stable parameter estimates with a participant-to-parameter ratio exceeding 30:1 Sufficient power (>85%) for mediation testing via bootstrap procedures Appropriate representation across demographic subgroups for generalizability Sample and post hoc power analysis : The final sample of 884 participants exceeded our target, providing robust statistical power for all planned analyses. Post hoc power analysis using the observed effect sizes confirmed that our sample provided >99% power for detecting the structural relationships identified in the study, with the mediation effect (β = 0.18) having >95% power for detection via bootstrap confidence intervals. Missing Data Considerations: Of the initial 934 responses collected, 50 (5.4%) were excluded because of incomplete data (>20% missing responses on any single instrument), resulting in the final analytic sample of 884 participants. The low missing data rate and substantial sample size ensured robust statistical conclusions without the need for complex missing data imputation procedures. Statistical analysis Data analysis was performed via structural equation modeling (SEM) via AMOS version 26.0 [32]. Prior to SEM analyses, descriptive statistics and correlation coefficients were calculated via SPSS version 27.0 [33]. The analysis proceeded through measurement model evaluation followed by structural model assessment. Mediation testing employs Baron and Kenny's [34] four criteria: (1) significant relationship between the independent and dependent variables; (2) significant relationship between the independent and mediator variables; (3) significant relationship between the mediator and dependent variables; and (4) reduced relationship between the independent and dependent variables when the mediator is included. Bootstrap resampling (5000 samples) was used to test the significance of the indirect effects [35]. Model fit was evaluated via multiple indices: the chi-square to degrees of freedom ratio (χ²/df), the comparative fit index (CFI), the incremental fit index (IFI), the goodness-of-fit index (GFI), the adjusted goodness-of-fit index (AGFI), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR). Acceptable fit criteria included χ²/df 0.90, and RMSEA/SRMR < 0.08 [36,37]. Results Descriptive Statistics and Correlations Table 2 presents the means, standard deviations, and intercorrelations among the study variables. Neuroticism demonstrated significant positive correlations with all the Zoom fatigue dimensions: general (r = 0.43, p < 0.01), visual (r = 0.20, p < 0.01), social (r = 0.20, p < 0.01), motivational (r = 0.21, p < 0.01), and emotional (r = 0.30, p < 0.01). Other personality traits were negatively correlated with the Zoom fatigue dimension. Agreeableness and conscientiousness demonstrated moderate negative correlations (ranging from r = -0.23 to r = -0.40, p < 0.01), suggesting protective effects against video conferencing fatigue. Extraversion and openness also showed negative correlations with zoom fatigue (ranging from r = -0.10 to r = -0.51, p < 0.01). Zoom fatigue dimensions showed substantial intercorrelations, with the strongest relationships observed between general fatigue and motivational fatigue (r = 0.78, p < 0.01), social fatigue (r = 0.72, p < 0.01), and visual fatigue (r = 0.68, p < 0.01). The burnout dimension was significantly correlated with zoom fatigue. Personal accomplishment was positively correlated with all the Zoom fatigue dimensions (r = 0.10 to 0.12, p < 0.01). Depersonalization demonstrated positive correlations ranging from r = 0.21 to r = 0.54, p < 0.01. Emotional exhaustion showed the strongest correlations with the Zoom fatigue dimensions (r = 0.31 to 0.41, p < 0.01). Table 2. Means, Standard Deviations, and Intercorrelations Among Study Variables Variable M SD α 1 2 3 4 5 6 7 8 9 10 11 12 13 1. Openness 3.10 0.42 0.95 - 2. Agreeableness 3.20 0.41 0.97 0.64** - 3. Conscientiousness 2.97 0.51 0.97 0.67** 0.76** - 4. Neuroticism 3.31 0.54 0.94 0.67** 0.64** 0.61** - 5. Extraversion 3.01 0.46 0.95 0.59** 0.75** 0.67** 0.65** - 6. General Fatigue 3.07 0.71 0.91 -0.10** -0.36** -0.34** 0.43** -0.17** - 7. Visual Fatigue 3.00 0.82 0.94 -0.17** -0.38** -0.36** 0.20** -0.43** 0.68** - 8. Social Fatigue 3.00 0.74 0.92 -0.13** -0.36** -0.37** 0.20** -0.46** 0.72** 0.66** - 9. Motivational Fatigue 3.08 0.62 0.91 -0.12** -0.40** -0.39** 0.21** -0.51** 0.78** 0.64** 0.80** - 10. Emotional Fatigue 3.05 0.58 0.89 -0.14** -0.23** -0.35** 0.30** -0.41** 0.55** 0.32** 0.41** 0.38** - 11. Personal Accomplishment 3.75 0.62 0.93 -0.38** -0.34** -0.29** 0.27** 0.24** 0.12** 0.11** 0.10** 0.10** 0.37** - 12. Emotional Exhaustion 4.13 0.71 0.91 -0.12** -0.25** -0.19** 0.22** 0.37** 0.43** 0.31** 0.34** 0.43** 0.41** 0.43** - 13. Depersonalization 4.21 0.67 0.94 -0.10** -0.20** -0.16** 0.11** 0.18** 0.21** 0.25** 0.26** 0.54** 0.25** 0.31** 0.29** - Note: **p < 0.01; α = Cronbach's alpha coefficient Structural equation modeling results The measurement model demonstrated good fit to the data: χ²/df = 2.89, RMSEA = 0.07, AGFI = 0.90, GFI = 0.90, CFI = 0.89. These indices meet the established criteria for acceptable model fit [38]. The structural model (Figure 1) revealed significant relationships supporting all the hypotheses. The standardized direct, indirect, and total effects are presented in Table 3. Table 3. Standardized Direct, Indirect, and Total Effects on Teacher Burnout Pathway β SE Z p Direct Effects Neuroticism → Burnout 0.13 0.10 8.71 < 0.001 Neuroticism → Zoom Fatigue 0.40 0.11 4.69 < 0.001 Zoom Fatigue → Burnout 0.46 0.03 9.06 < 0.001 Indirect Effect Neuroticism → Zoom Fatigue → Burnout 0.18 - - < 0.001 Total Effect Neuroticism → Burnout 0.31 - - < 0.001 Hypothesis Testing Results H1: Neurotic personality traits are positively related to Zoom fatigue among teachers. The analysis confirmed a significant positive relationship (β = 0.40, p < 0.001), supporting H1. H2: Neurotic personality traits are positively related to teacher burnout. The direct effect was significant (β = 0.13, p < 0.001), supporting H2. H3: Zoom fatigue is positively related to teacher burnout. A significant positive relationship was found (β = 0.46, p < 0.001), supporting H3. H4: Zoom fatigue mediates the relationship between neurotic personality traits and teacher burnout. The indirect effect was significant (β = 0.18), supporting the mediation hypothesis. Effect size analysis via Cohen's f² criteria (small ≥ 0.02, medium ≥ 0.15, large ≥ 0.35) [39] revealed large effect sizes for neuroticism on teacher burnout (f² = 0.38) and Zoom fatigue (f² = 0.27), indicating substantial explanatory power. Discussion The present study is noteworthy in that it represents one of the first empirical investigations to elucidate the mediating role of video-conferencing fatigue, commonly referred to as Zoom fatigue, in the relationship between neurotic personality traits and professional burnout among educators during the COVID-19 pandemic. Furthermore, the study provides important evidence regarding how individual differences in personality may exacerbate susceptibility to technology-induced stressors and, consequently, occupational exhaustion. In this context, while efforts to mitigate the technological demands associated with prolonged online teaching are essential, interventions aimed at supporting educators with higher levels of neuroticism may also serve to reduce the adverse effects of video-conferencing fatigue and contribute to sustaining professional well-being in digitally mediated educational environments. This investigation provides novel empirical evidence for the relationships among personality traits, zoom fatigue, and professional burnout in online educational contexts. The findings support our hypothesized mediation model, in which neurotic personality traits influence teacher burnout both directly and indirectly through increased susceptibility to video-conferencing fatigue. This study investigated how neurotic personality traits are associated with professional burnout among teachers, and whether this relationship is mediated by Zoom fatigue. The results indicated significant positive relationships between neuroticism and Zoom fatigue, and between Zoom fatigue and burnout, highlighting the crucial role of video-conferencing fatigue as a technological stressor during the COVID-19 pandemic. The significant positive relationship between neuroticism and zoom fatigue aligns with established research on personality and technological stress. Individuals with greater neuroticism typically experience greater anxiety, emotional instability, and stress reactivity [ 13 ], potentially predisposing them to more severe video-conferencing fatigue symptoms. This finding extends previous work connecting neuroticism to burnout in various professional contexts [ 40 , 41 ] by identifying Zoom fatigue as a specific technological stressor in online teaching environments. The multidimensional nature of Zoom fatigue is particularly relevant for neurotic individuals, who may be especially susceptible to the general, visual, social, motivational, and emotional demands of video conferencing [ 11 , 22 ]. This susceptibility may be explained by heightened attention to social cues and the increased self-awareness characteristic of neurotic individuals [ 14 ], making video conferencing cognitive demands particularly taxing. Conversely, other personality traits demonstrated protective effects against zoom fatigue. The negative correlations observed for openness, agreeableness, conscientiousness, and extraversion suggest that these characteristics may facilitate adaptation to video conferencing demands. This aligns with research showing that openness and extraversion positively predict technology adoption [ 18 ] and that digital competence can moderate relationships between neuroticism and online teaching stress [ 19 ]. The identification of Zoom fatigue as a mediating mechanism offers important theoretical insights. Rather than neurotic individuals inherently experiencing greater burnout, our findings suggest that their predisposition to technological stress increases their susceptibility to video-conferencing fatigue, which subsequently contributes to professional burnout. This mediation pathway provides a more nuanced understanding of how individual differences interact with technological environments to affect occupational well-being. These findings support the job demands-resources model [ 15 ] by demonstrating how technological demands interact with personal resources to influence professional outcomes. The results also align with transactional stress theory [ 17 ], emphasizing how individual appraisal processes mediate relationships between environmental stressors and stress outcomes. The mediation pathway identified in this study suggests several concrete intervention targets that educational institutions, administrators, and policymakers can implement to support teacher well-being in online environments. Rather than focusing solely on personality traits (which are relatively stable) or advanced burnout symptoms (which represent costly late-stage interventions), our findings suggest that interventions targeting the intermediary factor of zoom fatigue may offer the most effective and practical approach. Limitations Several important limitations warrant careful consideration when these findings are interpreted. First, the cross-sectional design fundamentally constrains our ability to establish causal relationships among variables. While our theoretical model suggests that neuroticism influences Zoom fatigue, which subsequently affects burnout, the temporal sequence of these relationships cannot be definitively established without longitudinal data. Future research employing repeated measurements over time would provide stronger evidence for the proposed causal pathways and help determine whether these relationships are stable or change as educators gain experience with online teaching platforms. Second, the exclusive reliance on self-report measures introduces potential methodological concerns. Social desirability bias may have influenced responses, particularly for personality assessments and burnout measures, as participants might have been reluctant to report high levels of neuroticism or professional difficulties. Additionally, common method variance could have inflated the observed correlations among the variables. Future investigations would benefit from incorporating multiple assessment methods, including peer evaluations, supervisor ratings, physiological stress indicators (such as cortisol levels or heart rate variability), and objective measures of technology usage patterns, to provide convergent validity for self-reported experiences. Third, while our sample was substantial (N = 884) and geographically diverse within Turkey, it may not adequately represent the global diversity of educational contexts. Cultural factors significantly influence both personality expression and technology adoption patterns, potentially limiting the generalizability of our findings to other cultural contexts. For instance, collectivistic versus individualistic cultural orientations might moderate the relationships between personality traits and stress responses to technology. Furthermore, differences in technological infrastructure, institutional support systems, and educational policies across countries could substantially alter the observed relationships. Fourth, the sample exhibited notable demographic limitations. The overwhelming representation of female teachers (86.2%) reflects the gender distribution in Turkish education but limits generalizability to more gender-balanced teaching populations. Additionally, the study did not account for potentially relevant individual differences such as prior technology experience, digital literacy levels, teaching subject areas, or access to technical support, all of which might moderate the relationships under investigation. Fifth, pandemic timing represents both a unique strength and a significant limitation. While our data capture responses during an extraordinary historical moment in education, the extreme circumstances of the COVID-19 pandemic may have intensified stress responses and technology-related difficulties beyond what would be observed under normal conditions. The emergent nature of the transition to online teaching meant that many participants lacked adequate preparation, training, or institutional support, potentially exaggerating the relationships between personality traits and negative outcomes. Whether these mediation pathways persist as online education becomes more normalized and educators receive proper training remains unclear. Sixth, our focus primarily on neuroticism, while theoretically justified, represents a limitation in understanding the full personality picture. We did not extensively examine potential interactions among personality traits or investigate how other traits might serve as protective factors. Additionally, we did not explore three-way interactions between personality, technological competence, and institutional support, which might reveal more nuanced intervention targets. Seventh, the study did not distinguish between different types of online teaching activities or platforms. Zoom fatigue might vary considerably depending on whether teachers are conducting live lectures, facilitating small group discussions, providing individual student support, or engaging in administrative meetings. Similarly, different video conferencing platforms may have varying effects on user fatigue due to differences in interface design. Finally, our measure of burnout, while well validated, captured only teacher burnout and did not assess broader indicators of psychological well-being or job satisfaction. The relationships observed might differ when other important outcomes, such as teaching efficacy, job engagement, or intentions to leave the profession, are examined. Additionally, we did not examine potential recovery processes or protective factors that might buffer against the negative effects of Zoom fatigue. Future Directions Several research directions emerge from these findings. Longitudinal studies could examine how these relationships evolve as educators gain experience with online teaching. An investigation of three-way interactions among personality traits, technological competence, and zoom fatigue could inform more targeted interventions. Research examining how different Zoom fatigue dimensions are related to specific burnout aspects could suggest more nuanced intervention strategies. Additionally, exploring protective factors that buffer against video-conferencing fatigue would provide practical guidance for supporting educator well-being. The investigation of different pedagogical approaches within virtual environments and their differential effects on fatigue could inform evidence-based teaching practices. Finally, research examining the organizational and cultural factors that moderate these relationships would enhance the understanding of contextual influences. Conclusions This study provides empirical evidence that Zoom fatigue mediates the relationship between neurotic personality traits and teacher burnout in online educational contexts. Teachers with greater neuroticism appear particularly vulnerable to video conferencing demands, which subsequently contributes to professional burnout. These findings highlight the importance of considering individual differences when designing support systems for online education. The identification of zoom fatigue as a mediating mechanism offers promising intervention targets. By addressing video conferencing fatigue through modified protocols, digital wellness training, and personalized support approaches, educational institutions may be able to mitigate burnout pathways for vulnerable educators. As educational institutions continue incorporating online components beyond the pandemic, understanding and addressing the factors contributing to technological stress will be essential for supporting educator well-being and, by extension, educational quality and sustainability. The present findings contribute to a growing body of evidence informing evidence-based approaches to supporting teachers in increasingly digital educational environments. Declarations Ethics Approval and Consent to Participate This study was approved by the institutional ethics review board (approval number: ETH-2021-043). All participants provided informed consent prior to participation. Consent for Publication Not applicable. Availability of Data and Materials The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request. Competing Interests The authors declare that they have no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Acknowledgments The authors contributed equally to the preparation of the article. The authors received no financial support for the research, authorship, or publication of this article. References Chen H, Liu F, Pang L, Liu F, Fang T, Wen Y, Chen S, Xie Z, Zhang X, Zhao Y, Gu X. Are you tired of working amid the pandemic? the role of professional identity and job satisfaction against job burnout. Int J Environ Res Public Health. 2020;17(24):9188. El Maarouf MD, Belghazi T, El Maarouf F. COVID-19: A critical ontology of the present. Educ Philos Theory. 2020:1-19. UNESCO. School closures caused by Coronavirus (COVID-19). 2020. Available from: https://en.unesco.org/covid19/educationresponse UNICEF. UNICEF and Microsoft launch global learning platform to help address COVID-19 education crisis. 2020. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7003801","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498234119,"identity":"d8b877e4-d84d-4047-be76-b5ab02b4a9f3","order_by":0,"name":"Serkan Demir¹","email":"","orcid":"","institution":"Istanbul University Cerrahpasa","correspondingAuthor":false,"prefix":"","firstName":"Serkan","middleName":"","lastName":"Demir¹","suffix":""},{"id":498234120,"identity":"c87fdbff-7098-4613-96e5-dd25390a1f90","order_by":1,"name":"Emre Er²","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIie3RsQrCMBCA4SsHdRG6SUvpO0SyiuKbWApxdXSsCLqIs+LgKyiuDlcKOqkP0EURnHUrKGhL0TFmFMw/ZbgPLgmATve71cAsAaPsZISKRICJH4JKJIZskIESsSq70/G2PvhjxGWUQs1bkLW5yogza/Pq5JL4AzQ7ZIPgC0KcyAhLhOmWKSdlRgxiPyfSxRqJKN0ftC9IC57fCXNF9lZEBSGg78ROBDojCnh+lyhkAZ/GyKXEmgnjmlLdmw/7q1varXvjbe8sJe+aYbEngOJPZs+gOKfT6XT/2Au9Tkh6XxUWIgAAAABJRU5ErkJggg==","orcid":"","institution":"Yildiz Technical University","correspondingAuthor":true,"prefix":"","firstName":"Emre","middleName":"","lastName":"Er²","suffix":""},{"id":498234121,"identity":"60093f90-8b07-499d-8ad5-c2e2b9ef65df","order_by":2,"name":"Ahmet ÖZBAY","email":"","orcid":"","institution":"Ministry of National Education","correspondingAuthor":false,"prefix":"","firstName":"Ahmet","middleName":"","lastName":"ÖZBAY","suffix":""}],"badges":[],"createdAt":"2025-06-29 16:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7003801/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7003801/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89066061,"identity":"f6973b15-b297-4b02-9183-2a7b42116488","added_by":"auto","created_at":"2025-08-14 10:42:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":84439,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7003801/v1/29ad00be58589bb62fe28902.png"},{"id":89069987,"identity":"71ec2ab7-6a27-4108-a387-9449c5acf2af","added_by":"auto","created_at":"2025-08-14 10:54:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1207548,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7003801/v1/781e5f2b-601e-43ab-84bc-e4fbe84d594e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Examining the Mediating Role of Zoom Fatigue in Personality Traits and Professional Burnout","fulltext":[{"header":"Background","content":"\u003cp\u003eThe COVID-19 pandemic precipitated an unprecedented global transformation, compelling an abrupt shift from conventional face-to-face instruction to emergency remote teaching and fundamentally redefining educational delivery worldwide [1,2]. This sudden transition, which impacted approximately 1.6 billion students across the globe [3,4], necessitated rapid adaptation to video conferencing technologies and digital pedagogical methodologies\u0026mdash;domains for which many educators possessed insufficient training and preparation [5].\u003c/p\u003e\n\u003cp\u003eWhile online education presents distinct advantages, such as increased flexibility and accessibility [6], it has simultaneously engendered novel occupational stressors that pose significant threats to educators\u0026rsquo; well-being and the sustainability of their professional engagement [7,8]. Among these emergent challenges is the phenomenon commonly referred to as \u0026ldquo;zoom fatigue,\u0026rdquo; characterized by feelings of exhaustion, heightened stress, and cognitive depletion arising from prolonged participation in virtual interactions [9,10]. This multifaceted phenomenon encompasses several dimensions, including general fatigue, visual strain, social exhaustion, motivational depletion, and emotional drain [11]. Empirical evidence suggests that Zoom fatigue adversely affects not only professional functioning but also individuals\u0026rsquo; personal lives, manifesting in difficulties with concentration, negative affective states, and anticipatory anxiety regarding future virtual engagements [12].\u003c/p\u003e\n\u003cp\u003eThe theoretical foundation underpinning this investigation synthesizes perspectives from personality psychology, occupational stress theory, and research on technology-mediated communication. The five-factor model of personality serves as a robust framework for elucidating individual differences in stress reactivity and coping processes [13]. Within this model, individuals scoring higher on neuroticism tend to exhibit greater negative emotionality and heightened stress sensitivity [14], potentially rendering them more vulnerable to the adverse effects of online teaching environments.\u003c/p\u003e\n\u003cp\u003eComplementing this perspective, the job demands-resources (JD-R) model provides a valuable lens through which to conceptualize the interaction between technological demands and personal resources in shaping occupational well-being [15]. In this context, zoom fatigue can be conceptualized as a specific response to the psychological demands inherent in video-mediated communication\u0026mdash;demands that include cognitive overload, increased self-monitoring, constrained physical mobility, and limitations in nonverbal communicative cues [16]. Such demands may be particularly burdensome for individuals with specific personality vulnerabilities.\u003c/p\u003e\n\u003cp\u003eDrawing on these theoretical perspectives, the present study proposes a mediation model positing that personality traits\u0026mdash;especially neuroticism\u0026mdash;influence individuals\u0026rsquo; susceptibility to Zoom fatigue, which, in turn, contributes to the development of professional burnout. This conceptualization is consistent with transactional stress theory [17], which emphasizes that individual differences in cognitive appraisal processes mediate the relationship between environmental stressors and resultant stress outcomes.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003e\u003cstrong\u003ePersonality and Technology Adaptation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCross-cultural research has yielded important insights into the influence of personality on technology adaptation. Wang et al. [18] demonstrated that openness to experience and extraversion positively predict technology adoption among Chinese teachers, whereas neuroticism negatively predicts acceptance. Similarly, Savić and Čorkalo Biru\u0026scaron;ki [19] reported that neuroticism was associated with greater perceived stress in online teaching among Croatian educators, although this relationship was moderated by digital competence.\u003c/p\u003e\n\u003cp\u003eResearch consistently demonstrates that neurotic individuals experience greater difficulty adapting to technological changes and exhibit heightened stress responses to novel work environments [20,21]. These findings suggest that the relationship between personality and technology-related stress may be culturally influenced and potentially mediated by skill development and institutional support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZoom Fatigue: Mechanisms and Consequences\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRecent empirical investigations have elucidated the multidimensional nature of video conferencing fatigue. Fauville et al. [11] developed the Zoom Exhaustion \u0026amp; Fatigue Scale, which identifies five distinct but related dimensions: general, visual, social, motivational, and emotional fatigue. Their research revealed demographic differences in susceptibility, with women and younger individuals reporting higher fatigue levels.\u003c/p\u003e\n\u003cp\u003eBailenson [16] proposed four primary mechanisms underlying Zoom fatigue: excessive close-up eye contact, cognitive overload from monitoring nonverbal behavior, increased self-evaluation through self-view, and reduced physical mobility. Peper et al. [22] further explored physiological mechanisms, noting that disrupted attention patterns and reduced mobility contribute significantly to this phenomenon.\u003c/p\u003e\n\u003cp\u003eOrganizational factors also influence Zoom fatigue beyond individual differences. Shockley et al. [23] demonstrated that meeting frequency and duration significantly predict fatigue levels, suggesting that institutional policies regarding virtual meetings could mitigate adverse effects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTeacher Burnout in Online Environments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pandemic context has intensified research focused on educator burnout. Herman et al. [24] reported that teacher burnout during the COVID-19 pandemic was significantly associated with both individual factors (coping strategies and perceived stress) and organizational factors (administrative support and workload). Sokal et al. [25] reported varying burnout trajectories among teachers throughout the pandemic, with some experiencing increasing burnout and others showing recovery patterns.\u003c/p\u003e\n\u003cp\u003eThese findings suggest that burnout represents a complex phenomenon shaped by interactions between personal resources and environmental demands. Understanding the specific pathways through which technological stressors contribute to burnout could inform targeted intervention strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Rationale and Hypotheses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis investigation addresses a critical gap in the understanding of how individual personality differences influence adaptation to online teaching environments and subsequent burnout outcomes. The identification of Zoom fatigue as a potential mediating mechanism offers novel insights for developing targeted support strategies for at-risk educators.\u003c/p\u003e\n\u003cp\u003eOn the basis of the theoretical framework and empirical literature, we formulate four specific hypotheses:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH1:\u003c/strong\u003e Neurotic personality traits are positively related to Zoom fatigue among teachers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH2:\u003c/strong\u003e Neurotic personality traits are positively related to teacher burnout.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH3:\u003c/strong\u003e Zoom fatigue is positively related to teacher burnout.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH4:\u003c/strong\u003e Zoom fatigue mediates the relationship between neurotic personality traits and teacher burnout.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cross-sectional investigation employed a relational survey design to examine naturally occurring covariation among variables without experimental manipulation. The relational survey approach is appropriate for exploring associations among personality traits (independent variable), zoom fatigue (mediator variable), and teacher burnout (dependent variable) while acknowledging that causal inferences cannot be definitively established [26].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants and Sampling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study utilized stratified random sampling to ensure representation across educational levels and geographical regions throughout Turkey. Participants were recruited through official communication channels within the Istanbul University Cerrahpaşa Publication Ethics Committee for Social Sciences and Humanities following institutional ethical approval (ETH-2021--043).\u0026nbsp;All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. All participants provided informed consent prior to participation.\u003c/p\u003e\n\u003cp\u003eThe final sample comprised 884 teachers representing urban and rural areas across Turkey\u0026apos;s seven geographical regions. The patients\u0026rsquo; demographic characteristics are presented in Table 1. The sample consisted of 762 female (86.2%) and 122 male (13.8%) participants. The age distributions included 23\u0026ndash;29 years (n = 128, 14.5%), 30\u0026ndash;34 years (n = 158, 17.9%), 35\u0026ndash;39 years (n = 216, 24.4%), 40\u0026ndash;44 years (n = 217, 24.5%), and 45+ years (n = 165, 18.7%). Teaching experience ranged from 1--5 years (n = 132, 14.9%) to over 25 years (n = 122, 13.8%). The educational levels represented included preschool (n = 50, 5.7%), primary school (n = 343, 38.8%), middle school (n = 251, 28.4%), and high school (n = 240, 27.1%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Demographic\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003echaracteristics of the study participants (n\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;= 884)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e86.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23-29 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30-34 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35-39 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40-44 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45+ years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTeaching Experience\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1-5 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6-10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11-15 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16-20 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25+ years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEducational Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePreschool\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePrimary School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMiddle School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eProcedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData collection occurred between January and March 2022, approximately two years after the initial emergency remote teaching transition. This timing allowed participants to accumulate substantial experience with online instruction and video conferencing platforms. The participants completed an online survey package containing demographic questions and three validated measurement instruments through a secure platform ensuring data privacy and response anonymity. Survey completion required 15\u0026ndash;20 minutes. All participants provided informed consent and were informed of their right to withdraw from the study at any time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZoom Fatigue Inventory\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Zoom Fatigue Scale, developed by Deniz et al. [27], employs a 5-point Likert response format (1 = strongly disagree, 5 = strongly agree) across five dimensions: general, visual, social, motivational, and emotional fatigue. The general fatigue dimension assesses overall exhaustion related to video conferencing. Visual fatigue focuses on eye strain and visual discomfort. Social fatigue measures exhaustion related to virtual social interactions. Motivational fatigue assesses decreased engagement and participation motivation. Emotional fatigue measures emotional exhaustion specifically related to video conferencing. Higher scores indicate greater fatigue across dimensions. The Cronbach\u0026apos;s alpha coefficients for the subscales ranged from 0.89 to 0.94, indicating excellent internal consistency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBig Five Personality Inventory (Short Form)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Big Five Personality Inventory, developed by Benet-Martinez and John [28] and adapted for Turkish populations by S\u0026uuml;mer et al. [29], comprises 44 items assessing five personality dimensions via a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). The inventory measures openness to experience, agreeableness, conscientiousness, neuroticism, and extraversion. Sample items include \u0026quot;I see myself as someone who is talkative\u0026quot; (extraversion) and \u0026quot;I see myself as someone who worries a lot\u0026quot; (neuroticism). Higher scores indicate greater expression of respective personality traits. The Cronbach\u0026apos;s alpha coefficients ranged from 0.94 to 0.97, indicating excellent reliability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaslach Burnout Inventory-Educator Form (MBI-EF)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MBI-EF, originally developed by Maslach et al. [30] and adapted for Turkish educators by İnce and Şahin [31], contains 22 items across three dimensions: emotional exhaustion, depersonalization, and personal accomplishment. The instrument employs a 7-point Likert scale (0 = never, 6 = every day). Sample items include \u0026quot;I feel emotionally drained from my work\u0026quot; (emotional exhaustion) and \u0026quot;I have become more callous toward people since I took this job\u0026quot; (depersonalization). The Cronbach\u0026apos;s alpha coefficients ranged from 0.91 to 0.94, indicating excellent internal consistency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003esize calculation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sample size for this study was determined on the basis of multiple considerations appropriate for structural equation modeling (SEM) analyses. Following established guidelines for SEM, we employed several approaches to ensure adequate statistical power and model stability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrimary\u0026nbsp;\u003c/strong\u003eapproach - effect size-based calculation: On the basis of previous research examining personality burnout relationships [20,21], we anticipated medium effect sizes (Cohen\u0026apos;s f\u0026sup2; \u0026ge; 0.15) for the structural pathways in our model. Using G*Power 3.1.9.7 software [42], power analysis for multiple regression (as a conservative estimate for SEM) indicated that a minimum sample size of 550 participants would be required to detect medium effect sizes (f\u0026sup2; = 0.15) with 80% power, \u0026alpha; = 0.05, and three predictors (neuroticism direct effect, zoom fatigue mediator effect, and their interaction).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSecondary Approach - SEM-Specific Guidelines:\u003c/strong\u003e Following the recommendations of Hair et al. [43] and Kline [44] for SEM analyses, we applied the \u0026quot;10:1 rule\u0026quot;, which requires a minimum of 10 participants per estimated parameter. Our proposed model included 23 estimated parameters (18 factor loadings, 3 structural paths, and 2 error covariances), necessitating a minimum sample of 230 participants. However, given the complexity of mediation testing and the need for stable parameter estimates, we aimed for a more conservative 20:1 ratio, targeting approximately 460 participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTertiary Approach - Statistical Power for Mediation:\u003c/strong\u003e For mediation analysis using bootstrap procedures, Fritz and MacKinnon [45] recommend larger sample sizes to achieve adequate power for detecting indirect effects. On the basis of their simulation studies for medium effect sizes in both the a-path (predictor to mediator) and b-path (mediator to outcome), a minimum of 558 participants would be needed to achieve 80% power for detecting significant mediation effects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinal Sample Size Determination:\u003c/strong\u003e Considering all approaches and anticipating potential data loss due to incomplete responses, missing data, and outliers, we targeted a minimum sample size of 800 participants. This target would provide:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eAdequate power (\u0026gt;95%) for detecting medium to large effect sizes in structural paths\u003c/li\u003e\n \u003cli\u003eStable parameter estimates with a participant-to-parameter ratio exceeding 30:1\u003c/li\u003e\n \u003cli\u003eSufficient power (\u0026gt;85%) for mediation testing via bootstrap procedures\u003c/li\u003e\n \u003cli\u003eAppropriate representation across demographic subgroups for generalizability\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eSample and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003epost hoc power analysis\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e The final sample of 884 participants exceeded our target, providing robust statistical power for all planned analyses. Post hoc power analysis using the observed effect sizes confirmed that our sample provided \u0026gt;99% power for detecting the structural relationships identified in the study, with the mediation effect (\u0026beta; = 0.18) having \u0026gt;95% power for detection via bootstrap confidence intervals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMissing Data Considerations:\u003c/strong\u003e Of the initial 934 responses collected, 50 (5.4%) were excluded because of incomplete data (\u0026gt;20% missing responses on any single instrument), resulting in the final analytic sample of 884 participants. The low missing data rate and substantial sample size ensured robust statistical conclusions without the need for complex missing data imputation procedures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eanalysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData analysis was performed via structural equation modeling (SEM) via AMOS version 26.0 [32]. Prior to SEM analyses, descriptive statistics and correlation coefficients were calculated via SPSS version 27.0 [33]. The analysis proceeded through measurement model evaluation followed by structural model assessment.\u003c/p\u003e\n\u003cp\u003eMediation testing employs Baron and Kenny\u0026apos;s [34] four criteria: (1) significant relationship between the independent and dependent variables; (2) significant relationship between the independent and mediator variables; (3) significant relationship between the mediator and dependent variables; and (4) reduced relationship between the independent and dependent variables when the mediator is included. Bootstrap resampling (5000 samples) was used to test the significance of the indirect effects [35].\u003c/p\u003e\n\u003cp\u003eModel fit was evaluated via multiple indices: the chi-square to degrees of freedom ratio (\u0026chi;\u0026sup2;/df), the comparative fit index (CFI), the incremental fit index (IFI), the goodness-of-fit index (GFI), the adjusted goodness-of-fit index (AGFI), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR). Acceptable fit criteria included \u0026chi;\u0026sup2;/df \u0026lt; 5, CFI/IFI/GFI/AGFI \u0026gt; 0.90, and RMSEA/SRMR \u0026lt; 0.08 [36,37].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDescriptive Statistics and Correlations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 presents the means, standard deviations, and intercorrelations among the study variables. Neuroticism demonstrated significant positive correlations with all the Zoom fatigue dimensions: general (r = 0.43, p \u0026lt; 0.01), visual (r = 0.20, p \u0026lt; 0.01), social (r = 0.20, p \u0026lt; 0.01), motivational (r = 0.21, p \u0026lt; 0.01), and emotional (r = 0.30, p \u0026lt; 0.01).\u003c/p\u003e\n\u003cp\u003eOther personality traits were negatively correlated with the Zoom fatigue dimension. Agreeableness and conscientiousness demonstrated moderate negative correlations (ranging from r = -0.23 to r = -0.40, p \u0026lt; 0.01), suggesting protective effects against video conferencing fatigue. Extraversion and openness also showed negative correlations with zoom fatigue (ranging from r = -0.10 to r = -0.51, p \u0026lt; 0.01).\u003c/p\u003e\n\u003cp\u003eZoom fatigue dimensions showed substantial intercorrelations, with the strongest relationships observed between general fatigue and motivational fatigue (r = 0.78, p \u0026lt; 0.01), social fatigue (r = 0.72, p \u0026lt; 0.01), and visual fatigue (r = 0.68, p \u0026lt; 0.01).\u003c/p\u003e\n\u003cp\u003eThe burnout dimension was significantly correlated with zoom fatigue. Personal accomplishment was positively correlated with all the Zoom fatigue dimensions (r = 0.10 to 0.12, p \u0026lt; 0.01). Depersonalization demonstrated positive correlations ranging from r = 0.21 to r = 0.54, p \u0026lt; 0.01. Emotional exhaustion showed the strongest correlations with the Zoom fatigue dimensions (r = 0.31 to 0.41, p \u0026lt; 0.01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Means, Standard Deviations, and Intercorrelations Among Study Variables\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026alpha;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e1. Openness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e2. Agreeableness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.64**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e3. Conscientiousness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.67**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.76**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e4. Neuroticism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.67**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.64**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.61**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e5. Extraversion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.59**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.75**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.67**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.65**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e6. General Fatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.10**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.36**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.34**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.43**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.17**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e7. Visual Fatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.17**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.38**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.36**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.20**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.43**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.68**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e8. Social Fatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.13**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.36**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.37**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.20**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.46**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.72**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.66**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e9. Motivational Fatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.12**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.40**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.39**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.21**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.51**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.78**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.64**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.80**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e10. Emotional Fatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.14**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.23**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.35**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.30**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.41**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.55**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.32**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.41**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.38**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e11. Personal Accomplishment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.38**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.34**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.29**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.27**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.24**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.11**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.10**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.10**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.37**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e12. Emotional Exhaustion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.12**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.25**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.19**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.22**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.37**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.43**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.31**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.34**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.43**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.41**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.43**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e13. Depersonalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.10**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.20**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.16**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.11**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.18**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.21**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.25**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.26**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.54**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.25**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.31**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.29**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e **p \u0026lt; 0.01; \u0026alpha; = Cronbach\u0026apos;s alpha coefficient\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructural\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eequation modeling results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe measurement model demonstrated good fit to the data: \u0026chi;\u0026sup2;/df = 2.89, RMSEA = 0.07, AGFI = 0.90, GFI = 0.90, CFI = 0.89. These indices meet the established criteria for acceptable model fit [38].\u003c/p\u003e\n\u003cp\u003eThe structural model (Figure 1) revealed significant relationships supporting all the hypotheses. The standardized direct, indirect, and total effects are presented in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Standardized Direct, Indirect, and Total Effects on Teacher Burnout\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePathway\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eZ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDirect Effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNeuroticism \u0026rarr; Burnout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNeuroticism \u0026rarr; Zoom Fatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eZoom Fatigue \u0026rarr; Burnout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eIndirect Effect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNeuroticism \u0026rarr; Zoom Fatigue \u0026rarr; Burnout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Effect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNeuroticism \u0026rarr; Burnout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis Testing Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH1: Neurotic personality traits are positively related to Zoom fatigue among teachers.\u003c/strong\u003e The analysis confirmed a significant positive relationship (\u0026beta; = 0.40, p \u0026lt; 0.001), supporting H1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH2: Neurotic personality traits are positively related to teacher burnout.\u003c/strong\u003e The direct effect was significant (\u0026beta; = 0.13, p \u0026lt; 0.001), supporting H2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH3: Zoom fatigue is positively related to teacher burnout.\u003c/strong\u003e A significant positive relationship was found (\u0026beta; = 0.46, p \u0026lt; 0.001), supporting H3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH4: Zoom fatigue mediates the relationship between neurotic personality traits and teacher burnout.\u003c/strong\u003e The indirect effect was significant (\u0026beta; = 0.18), supporting the mediation hypothesis.\u003c/p\u003e\n\u003cp\u003eEffect size analysis via Cohen\u0026apos;s f\u0026sup2; criteria (small \u0026ge; 0.02, medium \u0026ge; 0.15, large \u0026ge; 0.35) [39] revealed large effect sizes for neuroticism on teacher burnout (f\u0026sup2; = 0.38) and Zoom fatigue (f\u0026sup2; = 0.27), indicating substantial explanatory power.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study is noteworthy in that it represents one of the first empirical investigations to elucidate the mediating role of video-conferencing fatigue, commonly referred to as Zoom fatigue, in the relationship between neurotic personality traits and professional burnout among educators during the COVID-19 pandemic. Furthermore, the study provides important evidence regarding how individual differences in personality may exacerbate susceptibility to technology-induced stressors and, consequently, occupational exhaustion. In this context, while efforts to mitigate the technological demands associated with prolonged online teaching are essential, interventions aimed at supporting educators with higher levels of neuroticism may also serve to reduce the adverse effects of video-conferencing fatigue and contribute to sustaining professional well-being in digitally mediated educational environments.\u003c/p\u003e\u003cp\u003eThis investigation provides novel empirical evidence for the relationships among personality traits, zoom fatigue, and professional burnout in online educational contexts. The findings support our hypothesized mediation model, in which neurotic personality traits influence teacher burnout both directly and indirectly through increased susceptibility to video-conferencing fatigue.\u003c/p\u003e\u003cp\u003eThis study investigated how neurotic personality traits are associated with professional burnout among teachers, and whether this relationship is mediated by Zoom fatigue. The results indicated significant positive relationships between neuroticism and Zoom fatigue, and between Zoom fatigue and burnout, highlighting the crucial role of video-conferencing fatigue as a technological stressor during the COVID-19 pandemic.\u003c/p\u003e\u003cp\u003eThe significant positive relationship between neuroticism and zoom fatigue aligns with established research on personality and technological stress. Individuals with greater neuroticism typically experience greater anxiety, emotional instability, and stress reactivity [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], potentially predisposing them to more severe video-conferencing fatigue symptoms. This finding extends previous work connecting neuroticism to burnout in various professional contexts [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] by identifying Zoom fatigue as a specific technological stressor in online teaching environments.\u003c/p\u003e\u003cp\u003eThe multidimensional nature of Zoom fatigue is particularly relevant for neurotic individuals, who may be especially susceptible to the general, visual, social, motivational, and emotional demands of video conferencing [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This susceptibility may be explained by heightened attention to social cues and the increased self-awareness characteristic of neurotic individuals [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], making video conferencing cognitive demands particularly taxing.\u003c/p\u003e\u003cp\u003eConversely, other personality traits demonstrated protective effects against zoom fatigue. The negative correlations observed for openness, agreeableness, conscientiousness, and extraversion suggest that these characteristics may facilitate adaptation to video conferencing demands. This aligns with research showing that openness and extraversion positively predict technology adoption [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and that digital competence can moderate relationships between neuroticism and online teaching stress [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe identification of Zoom fatigue as a mediating mechanism offers important theoretical insights. Rather than neurotic individuals inherently experiencing greater burnout, our findings suggest that their predisposition to technological stress increases their susceptibility to video-conferencing fatigue, which subsequently contributes to professional burnout. This mediation pathway provides a more nuanced understanding of how individual differences interact with technological environments to affect occupational well-being.\u003c/p\u003e\u003cp\u003eThese findings support the job demands-resources model [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] by demonstrating how technological demands interact with personal resources to influence professional outcomes. The results also align with transactional stress theory [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], emphasizing how individual appraisal processes mediate relationships between environmental stressors and stress outcomes.\u003c/p\u003e\u003cp\u003eThe mediation pathway identified in this study suggests several concrete intervention targets that educational institutions, administrators, and policymakers can implement to support teacher well-being in online environments. Rather than focusing solely on personality traits (which are relatively stable) or advanced burnout symptoms (which represent costly late-stage interventions), our findings suggest that interventions targeting the intermediary factor of zoom fatigue may offer the most effective and practical approach.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSeveral important limitations warrant careful consideration when these findings are interpreted. First, the cross-sectional design fundamentally constrains our ability to establish causal relationships among variables. While our theoretical model suggests that neuroticism influences Zoom fatigue, which subsequently affects burnout, the temporal sequence of these relationships cannot be definitively established without longitudinal data. Future research employing repeated measurements over time would provide stronger evidence for the proposed causal pathways and help determine whether these relationships are stable or change as educators gain experience with online teaching platforms.\u003c/p\u003e\u003cp\u003eSecond, the exclusive reliance on self-report measures introduces potential methodological concerns. Social desirability bias may have influenced responses, particularly for personality assessments and burnout measures, as participants might have been reluctant to report high levels of neuroticism or professional difficulties. Additionally, common method variance could have inflated the observed correlations among the variables. Future investigations would benefit from incorporating multiple assessment methods, including peer evaluations, supervisor ratings, physiological stress indicators (such as cortisol levels or heart rate variability), and objective measures of technology usage patterns, to provide convergent validity for self-reported experiences.\u003c/p\u003e\u003cp\u003eThird, while our sample was substantial (N\u0026thinsp;=\u0026thinsp;884) and geographically diverse within Turkey, it may not adequately represent the global diversity of educational contexts. Cultural factors significantly influence both personality expression and technology adoption patterns, potentially limiting the generalizability of our findings to other cultural contexts. For instance, collectivistic versus individualistic cultural orientations might moderate the relationships between personality traits and stress responses to technology. Furthermore, differences in technological infrastructure, institutional support systems, and educational policies across countries could substantially alter the observed relationships.\u003c/p\u003e\u003cp\u003eFourth, the sample exhibited notable demographic limitations. The overwhelming representation of female teachers (86.2%) reflects the gender distribution in Turkish education but limits generalizability to more gender-balanced teaching populations. Additionally, the study did not account for potentially relevant individual differences such as prior technology experience, digital literacy levels, teaching subject areas, or access to technical support, all of which might moderate the relationships under investigation.\u003c/p\u003e\u003cp\u003eFifth, pandemic timing represents both a unique strength and a significant limitation. While our data capture responses during an extraordinary historical moment in education, the extreme circumstances of the COVID-19 pandemic may have intensified stress responses and technology-related difficulties beyond what would be observed under normal conditions. The emergent nature of the transition to online teaching meant that many participants lacked adequate preparation, training, or institutional support, potentially exaggerating the relationships between personality traits and negative outcomes. Whether these mediation pathways persist as online education becomes more normalized and educators receive proper training remains unclear.\u003c/p\u003e\u003cp\u003eSixth, our focus primarily on neuroticism, while theoretically justified, represents a limitation in understanding the full personality picture. We did not extensively examine potential interactions among personality traits or investigate how other traits might serve as protective factors. Additionally, we did not explore three-way interactions between personality, technological competence, and institutional support, which might reveal more nuanced intervention targets.\u003c/p\u003e\u003cp\u003eSeventh, the study did not distinguish between different types of online teaching activities or platforms. Zoom fatigue might vary considerably depending on whether teachers are conducting live lectures, facilitating small group discussions, providing individual student support, or engaging in administrative meetings. Similarly, different video conferencing platforms may have varying effects on user fatigue due to differences in interface design.\u003c/p\u003e\u003cp\u003eFinally, our measure of burnout, while well validated, captured only teacher burnout and did not assess broader indicators of psychological well-being or job satisfaction. The relationships observed might differ when other important outcomes, such as teaching efficacy, job engagement, or intentions to leave the profession, are examined. Additionally, we did not examine potential recovery processes or protective factors that might buffer against the negative effects of Zoom fatigue.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFuture Directions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSeveral research directions emerge from these findings. Longitudinal studies could examine how these relationships evolve as educators gain experience with online teaching. An investigation of three-way interactions among personality traits, technological competence, and zoom fatigue could inform more targeted interventions.\u003c/p\u003e\u003cp\u003eResearch examining how different Zoom fatigue dimensions are related to specific burnout aspects could suggest more nuanced intervention strategies. Additionally, exploring protective factors that buffer against video-conferencing fatigue would provide practical guidance for supporting educator well-being.\u003c/p\u003e\u003cp\u003eThe investigation of different pedagogical approaches within virtual environments and their differential effects on fatigue could inform evidence-based teaching practices. Finally, research examining the organizational and cultural factors that moderate these relationships would enhance the understanding of contextual influences.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study provides empirical evidence that Zoom fatigue mediates the relationship between neurotic personality traits and teacher burnout in online educational contexts. Teachers with greater neuroticism appear particularly vulnerable to video conferencing demands, which subsequently contributes to professional burnout. These findings highlight the importance of considering individual differences when designing support systems for online education.\u003c/p\u003e\u003cp\u003eThe identification of zoom fatigue as a mediating mechanism offers promising intervention targets. By addressing video conferencing fatigue through modified protocols, digital wellness training, and personalized support approaches, educational institutions may be able to mitigate burnout pathways for vulnerable educators.\u003c/p\u003e\u003cp\u003eAs educational institutions continue incorporating online components beyond the pandemic, understanding and addressing the factors contributing to technological stress will be essential for supporting educator well-being and, by extension, educational quality and sustainability. The present findings contribute to a growing body of evidence informing evidence-based approaches to supporting teachers in increasingly digital educational environments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the institutional ethics review board (approval number: ETH-2021-043). All participants provided informed consent prior to participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors contributed equally to the preparation of the article. The authors received no financial support for the research, authorship, or publication of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChen H, Liu F, Pang L, Liu F, Fang T, Wen Y, Chen S, Xie Z, Zhang X, Zhao Y, Gu X. Are you tired of working amid the pandemic? the role of professional identity and job satisfaction against job burnout. Int J Environ Res Public Health. 2020;17(24):9188.\u003c/li\u003e\n\u003cli\u003eEl Maarouf MD, Belghazi T, El Maarouf F. COVID-19: A critical ontology of the present. Educ Philos Theory. 2020:1-19.\u003c/li\u003e\n\u003cli\u003eUNESCO. School closures caused by Coronavirus (COVID-19). 2020. Available from: https://en.unesco.org/covid19/educationresponse\u003c/li\u003e\n\u003cli\u003eUNICEF. UNICEF and Microsoft launch global learning platform to help address COVID-19 education crisis. 2020. 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G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. 2007;39(2):175-91.\u003c/li\u003e\n\u003cli\u003eHair JF, Black WC, Babin BJ, Anderson RE. Multivariate data analysis. 8th ed. Boston: Cengage Learning; 2019.\u003c/li\u003e\n\u003cli\u003eKline RB. Principles and practice of structural equation modeling. 4th ed. New York: Guilford Press; 2016.\u003c/li\u003e\n\u003cli\u003eFritz MS, MacKinnon DP. Required sample size to detect the mediated effect. Psychol Sci. 2007;18(3):233-9.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Zoom fatigue, Online education, Teacher burnout, Personality traits, Neuroticism, COVID-19 pandemic, Structural equation modeling, Digital wellness","lastPublishedDoi":"10.21203/rs.3.rs-7003801/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7003801/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe rapid transition to online education during the coronavirus disease 2019 (COVID-19) pandemic has introduced novel stressors for educators, including video-conferencing fatigue. Understanding how individual differences in personality traits influence susceptibility to these technological stressors and subsequent burnout outcomes remains underexplored.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis cross-sectional study employed structural equation modeling to examine the mediating role of Zoom fatigue in the relationship between neurotic personality traits and professional burnout among 884 teachers across Turkey. The participants completed validated measures, including the Zoom Fatigue Inventory, the Big Five Personality Inventory (short form), and the Maslach Burnout Inventory-Educator Form. Data were collected between January and March 2022 through stratified random sampling across seven geographical regions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eStructural equation modeling revealed significant positive relationships between neurotic personality traits and Zoom fatigue (β\u0026thinsp;=\u0026thinsp;0.40, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and between Zoom fatigue and teacher burnout (β\u0026thinsp;=\u0026thinsp;0.46, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Crucially, Zoom fatigue significantly mediated the relationship between neurotic personality traits and occupational burnout (indirect effect β\u0026thinsp;=\u0026thinsp;0.18). The model demonstrated excellent fit indices (χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;2.89, RMSEA\u0026thinsp;=\u0026thinsp;0.07, CFI\u0026thinsp;=\u0026thinsp;0.89) and substantial effect sizes for both personality traits (f\u0026sup2; = 0.38) and Zoom fatigue (f\u0026sup2; = 0.27) on burnout outcomes.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eTeachers with greater neuroticism experience greater professional burnout, partially through their increased susceptibility to video-conferencing fatigue. These findings suggest that technological demands in online education environments may disproportionately affect individuals with specific personality vulnerabilities. Targeted interventions addressing video conferencing fatigue could mitigate burnout pathways for at-risk educators.\u003c/p\u003e","manuscriptTitle":"Examining the Mediating Role of Zoom Fatigue in Personality Traits and Professional Burnout","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-14 09:59:52","doi":"10.21203/rs.3.rs-7003801/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-08-13T08:24:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"54795490269368417017262111358967693598","date":"2025-08-10T05:54:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10168764013344985463609845980254573284","date":"2025-08-08T06:27:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-08T05:48:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-06T18:12:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-15T10:08:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-14T10:16:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2025-07-14T08:51:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6d6fbec6-2381-4169-acfd-0bfadd93ab71","owner":[],"postedDate":"August 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-14T09:59:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-14 09:59:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7003801","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7003801","identity":"rs-7003801","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}