The Impact of Technostress on Work Meaningfulness Among University Teachers in the Context of Digital Transformation of Education: The Chain Mediating Role of Digital Resilience and Job Burnout

The Impact of Technostress on Work Meaningfulness Among University Teachers in the Context of Digital Transformation of Education: The Chain Mediating Role of Digital Resilience and Job Burnout | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Impact of Technostress on Work Meaningfulness Among University Teachers in the Context of Digital Transformation of Education: The Chain Mediating Role of Digital Resilience and Job Burnout Yu-Qiao Luo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7493995/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract With the accelerating digital transformation of education, university teachers face unprecedented challenges in instructional design, technology application, and professional role identification, leading to a significant increase in technostress, which may undermine their perception of work meaningfulness. Based on the Conservation of Resources Theory, this study constructed a chain mediating model involving technostress, digital resilience, job burnout, and work meaningfulness to explore the mechanism of psychological resources among university teachers in the context of educational digital transformation. A questionnaire survey was conducted to collect data from 1029 teachers at multiple universities in China, and structural equation modeling was used to test the research hypotheses. The results showed that: (1) Technostress significantly and negatively predicted work meaningfulness; (2) Digital resilience and job burnout played a significant chain mediating role between technostress and work meaningfulness; (3) The improvement of digital resilience could alleviate job burnout, thereby enhancing teachers' perception of work meaningfulness. This study not only enriches the theoretical exploration of the relationship between technostress and positive psychological outcomes in the context of digital education but also provides empirical evidence for universities to implement teacher support interventions and construct resilience training mechanisms during digital transformation. Digital transformation of education Technostress Digital resilience Job burnout Work meaningfulness Chain mediation Figures Figure 1 Figure 2 1. Introduction Against the backdrop of the deepening global digital transformation of education, the widespread application of digital technologies in teaching, research, and educational management is reshaping the professional ecology of university teachers. On the one hand, digitalization endows teaching with greater flexibility and interactivity, providing technical support for educational innovation; on the other hand, rapidly evolving technological tools, blended online-offline teaching models, and data-driven performance evaluation systems have exposed teachers to unprecedented challenges in knowledge updating, instructional design, and role positioning [1]. In this process, teachers are required to invest substantial cognitive and emotional resources to adapt to the demands of digital teaching. However, if such resource investment lacks corresponding compensation, it is likely to trigger psychological stress—particularly stress related to technology application, namely "technostress". Technostress manifests not only as anxiety about the difficulty of mastering new technologies but also involves information overload, time pressure, and persistent anxiety about skill adaptation. Long-term accumulation of technostress may erode teachers' mental health and professional identity [2]. Therefore, exploring the impact of technostress on teachers' work experience in the context of educational digital transformation holds important theoretical and practical significance. Work meaningfulness refers to the sense of value and purpose that individuals perceive in their professional activities, and it serves as a crucial psychological foundation for teachers' professional well-being and teaching engagement [3]. In the field of education, teachers typically view their profession as a key means to fulfill educational missions and social responsibilities. However, with the continuous increase in technostress, teachers may feel that their original teaching philosophies and practical models have been disrupted, and their role identity has been challenged—ultimately undermining their work meaningfulness. This change not only affects teachers' personal professional satisfaction and well-being but may also impact teaching quality and students' learning experience. Therefore, revealing the mechanism through which technostress influences teachers' work meaningfulness is essential for safeguarding teachers' professional health and ensuring the sustainability of educational digital reform. From a theoretical perspective, the Conservation of Resources (COR) Theory provides a robust framework for understanding this issue. The theory posits that individuals tend to acquire, maintain, and protect their own resources; when resources are threatened or lost, stress responses are triggered [4]. During the digital transformation of education, teachers must continuously learn and master new technologies to meet teaching needs, which not only consumes cognitive resources but also may occupy emotional and time resources, leading to an imbalance in overall resource allocation. In this context, technostress— as a typical source of resource threat—may trigger a series of negative psychological consequences. However, individuals are not entirely passive; adaptive and coping abilities play a key role in mitigating the impact of stress. In recent years, the academic community has begun to focus on the emerging concept of "digital resilience", which refers to an individual's ability to maintain psychological stability and actively adjust behaviors in the face of digital challenges [5]. As a type of psychological resource, digital resilience can not only buffer the negative impact of technostress on individuals' mental health but also reduce job burnout by promoting positive coping strategies. Job burnout is a comprehensive syndrome characterized by emotional exhaustion, depersonalization, and reduced personal accomplishment under long-term work stress [6]. Studies have shown that job burnout not only impairs teaching quality but also significantly reduces work meaningfulness [7]. In the context of digital education, if technostress leads to a decline in teachers' digital resilience, it may further trigger job burnout, thereby eroding their perception of work value. This chain mechanism aligns with the "resource loss spiral" effect emphasized by the COR Theory—i.e., initial resource loss triggers further resource depletion, forming a vicious cycle. Therefore, exploring the mechanism between technostress, digital resilience, job burnout, and work meaningfulness can reveal the psychological adaptation path of teachers during the digital transformation of education. Although existing studies have paid some attention to the relationship between technostress and teachers' psychological states, there are still several gaps. First, most existing studies have focused on the impact of technostress on job satisfaction or mental health [2, 8, 9], while few have systematically explored this issue from the perspective of work meaningfulness. As a key dimension of teachers' professional well-being, the dynamic changes of work meaningfulness in digital contexts deserve in-depth investigation [10, 11]. Second, as an emerging psychological resource, the buffering role of digital resilience has not been fully empirically tested in the field of education [12, 13]. Third, most existing studies have adopted single mediation or moderation models, lacking a holistic examination of the resource loss chain and failing to reveal how technostress affects work meaningfulness through resource loss and emotional exhaustion [14, 15]. Based on this, this study introduces digital resilience and job burnout to construct a chain mediation model, aiming to explain how teachers' psychological resources influence their professional experience in the context of educational digitalization. The innovations of this study are reflected in three aspects: (1) Theoretically, it introduces the COR Theory into the context of educational digitalization, reveals the mechanism through which technostress affects work meaningfulness, and expands the applicability of this theory in higher education; (2) Conceptually, it integrates digital resilience and job burnout into the same model, verifies their mediating roles in the resource loss chain, and enriches the research on the psychological mechanism of teachers' adaptation to digital transformation; (3) Practically, it provides empirical evidence for universities to formulate teacher support policies, develop digital resilience training, and prevent job burnout. In summary, this study aims to address the following research questions: (1) Does technostress significantly affect the work meaningfulness of university teachers? (2) Does digital resilience play a mediating role between technostress and work meaningfulness? (3) Does job burnout play a mediating role between technostress and work meaningfulness? (4) Do digital resilience and job burnout play a chain mediating role between technostress and work meaningfulness? To this end, this study constructs a chain mediation model of "technostress → digital resilience → job burnout → work meaningfulness" and tests it based on empirical data from university teachers in China. 2. Theoretical Foundation and Research Hypotheses 2.1 Conservation of Resources Theory (COR Theory) Proposed by [4] , the COR Theory is an important theoretical framework for explaining stress and individual coping mechanisms. The theory argues that individuals possess a certain amount of resources (e.g., time, energy, skills, social support); when resources are threatened, actually lost, or fail to yield expected gains, individuals experience stress and adopt corresponding resource protection or compensation behaviors [16]. In the context of educational digital transformation, technostress is regarded as a major threat to teachers' resources, as they need to invest additional time in learning new platforms, adjusting instructional design, and bearing the risk of technical failures [17]. If teachers cannot supplement resources in a timely manner (e.g., improving digital resilience), a resource loss spiral will occur, leading to emotional exhaustion and a decline in work meaningfulness [18]. This study regards technostress as a source of resource threat, digital resilience as an emerging psychological resource, job burnout as an outcome of resource loss, and work meaningfulness as the final outcome of resource recovery or value experience—thus constructing a chain psychological process framework. Therefore, the COR Theory provides a solid theoretical foundation for this study's chain mediation model of "technostress—digital resilience—job burnout—work meaningfulness". 2.2 Technostress and Work Meaningfulness Technostress, proposed by Brod [19], refers to the negative psychological experience caused by individuals' use or adaptation to information technology. Tarafdar et al. [17] further divided technostress into dimensions such as technology overload, technology complexity, and technology insecurity. With the advancement of educational digitalization, university teachers are required to master online teaching platforms, data-driven teaching tools, and multimedia resources. Frequent technology updates and platform iterations increase teachers' learning burden and even threaten the effectiveness of their existing teaching methods. According to the COR Theory, technostress consumes teachers' time, energy, and emotional resources, leading to a sense of resource scarcity and further triggering negative emotional and cognitive responses [4]. Work meaningfulness refers to the sense of purpose and value that individuals experience in their work [3]. Studies have shown that work meaningfulness not only affects individuals' work engagement and well-being but also relates to professional persistence and organizational commitment [10]. In educational contexts, teachers typically view their profession as a key means to impart knowledge and fulfill educational missions. However, technostress may make teachers feel that their educational value has been weakened and their traditional teaching achievements have been diluted by digital tools—ultimately reducing work meaningfulness. Empirical studies have shown that high levels of technostress are negatively correlated with job satisfaction and work engagement [20], indirectly indicating that technostress may impair work meaningfulness. Based on the above analysis, this study proposes: H1: Technostress has a significant negative predictive effect on the work meaningfulness of university teachers. 2.3 Technostress and Digital Resilience Digital resilience is an emerging concept developed in recent years with the advancement of digital education and work scenarios. It refers to an individual's ability to maintain psychological balance and actively cope with technological changes and digital challenges [5]. Digital resilience is reflected not only in the mastery of technical skills but also in cognitive flexibility, emotional regulation, and positive adaptive beliefs. The COR Theory points out that when facing threats of resource loss, individuals will use existing resources or develop new resources to cope with stress. However, when technostress persists and exceeds individuals' coping capacity, their psychological resources will be severely consumed, leading to a decline in digital resilience. Existing studies have found that high levels of work stress weaken individuals' psychological resilience [21], and technostress may also hinder teachers from maintaining or improving digital resilience by increasing anxiety and a sense of powerlessness. Therefore, this study proposes: H2: Technostress has a significant negative predictive effect on the digital resilience of university teachers. 2.4 Digital Resilience and Job Burnout Job burnout refers to the occurrence of emotional exhaustion, depersonalization, and reduced personal accomplishment in individuals under long-term work stress [6]. Teachers' job burnout not only affects their physical and mental health but also reduces teaching quality and students' learning experience. According to the COR Theory, resilience is an important psychological resource that helps individuals recover and maintain balance in stressful situations [18]. When teachers possess high digital resilience, they are more likely to face digital challenges with a positive attitude, use strategies to reduce anxiety, and reduce resource loss—thus alleviating job burnout. Empirical studies have shown that psychological resilience is significantly negatively correlated with burnout [22]; in digital education scenarios, digital resilience, as a specialized form of psychological resilience, plays a similar role. Based on this, this study proposes: H3: Digital resilience has a significant negative predictive effect on the job burnout of university teachers. 2.5 Job Burnout and Work Meaningfulness There is a close relationship between work meaningfulness and job burnout. Maslach et al. [23] pointed out that burnout not only reflects energy exhaustion and cognitive alienation but also is accompanied by a decline in personal accomplishment and sense of meaning. In the context of digital education, if teachers experience burnout due to technostress and adaptation difficulties, they are likely to feel that their work value has been weakened and their professional goals have become vague—ultimately reducing work meaningfulness. A large number of studies have confirmed that burnout is an important negative predictor of work meaningfulness, and this effect is more pronounced in high-load work environments [24]. Therefore, this study proposes: H4: Job burnout has a significant negative predictive effect on the work meaningfulness of university teachers. 2.6 The Mediating Role of Digital Resilience Between Technostress and Work Meaningfulness In recent years, digital resilience has gradually been recognized as an important psychological resource for alleviating teachers' technostress and enhancing positive professional experiences. Studies have shown that when facing technostress, teachers with high digital resilience can offset the negative effects of technology by improving their digital capabilities and reflective practice [12]. Smith et al. [13] found that digital resilience not only helps teachers cope with unexpected challenges in online teaching but also promotes them to gain deeper value and meaning from their work. A study by Zhao et al. [9] in the Chinese context further pointed out that digital resilience can buffer the adverse impact of technostress on teachers' well-being. That is, by enhancing teachers' adaptability and psychological resources, technostress can be transformed into an opportunity for constructing positive professional meaning. Therefore, this study proposes: H5: Digital resilience plays a mediating role between technostress and work meaningfulness among university teachers. 2.7 The Mediating Role of Job Burnout Between Technostress and Work Meaningfulness Job burnout is considered an important transmission mechanism through which technostress affects teachers' work attitudes and sense of meaning. Existing studies have shown that long-term technology overload and information pressure can lead to teachers' emotional exhaustion and depersonalization, thereby weakening their positive identification with work [8]. Hakanen et al. [25] found in a sample of Finnish teachers that job burnout was not only negatively correlated with well-being but also significantly undermined teachers' sense of meaning derived from work. Similarly, a study by Zhang et al. [26] pointed out that job burnout played a mediating role between work stress and work meaningfulness—i.e., stress reduced teachers' experience of professional value by increasing burnout. In summary, job burnout may be a key psychological path explaining how technostress undermines teachers' work meaningfulness. Therefore, this study proposes: H6: Job burnout plays a mediating role between technostress and work meaningfulness among university teachers. 2.8 The Chain Mediating Role of Digital Resilience and Job Burnout In summary, technostress may not only directly undermine work meaningfulness but also indirectly affect this outcome through digital resilience and job burnout. Specifically, increased technostress reduces digital resilience, which in turn increases job burnout and ultimately weakens teachers' perception of work meaningfulness. This path is consistent with the "resource loss spiral" model emphasized by the COR Theory—i.e., initial resource loss leads to further resource scarcity, ultimately triggering negative psychological outcomes. Technostress reduces work meaningfulness by lowering digital resilience, which in turn increases job burnout. Therefore, this study proposes: H7: Digital resilience and job burnout play a chain mediating role between technostress and work meaningfulness among university teachers. 2.9 Differences in Work Meaningfulness by Gender, Professional Title, and Teaching Experience 2.9.1 Gender and Work Meaningfulness Studies in the past five years have generally shown that gender has no significant difference in teachers' work meaningfulness and professional satisfaction. Hatlevik [27] found in a study of Norwegian teachers that gender was not a key determinant of teachers' professional meaning or satisfaction; instead, self-efficacy and school environmental support played a more important role. In addition, a study by Zhang et al. [1] on Chinese samples also found that gender had no significant impact on teachers' professional identity. In contrast, Latif [28] found in a survey of Pakistani teachers that female teachers had higher job satisfaction than male teachers, suggesting that gender differences may be influenced by cultural contexts. In educational systems with a higher degree of gender equality, the impact of gender on sense of meaning tends to weaken. 2.9.2 Professional Title (Professional Identity) and Work Meaningfulness Teachers' professional titles represent their level of professional development and identity, and they are important factors affecting work meaningfulness. A study by Zhang et al. [1] showed that teachers with more than 10 years of teaching experience were more likely to identify with the teaching profession and exhibit a higher sense of work value. Similarly, Hatlevik [27] also found that professional promotion was closely related to teachers' professional identity, thereby enhancing work meaningfulness. These results indicate that during the process of professional title promotion, teachers not only gain external recognition but also strengthen their internal sense of meaning through professional identity. 2.9.3 Teaching Experience (Seniority) and Work Meaningfulness Recent studies have consistently pointed out that teachers with longer teaching experience have a higher level of work meaningfulness. Smith [29] found in a study of American teachers that teachers' age and experience were significantly correlated with professional satisfaction and sense of meaning. Experienced teachers are usually more likely to construct positive professional identities in educational activities and gain in-depth professional meaning through long-term practice. Similarly, the results of Zhang et al. [1] also showed that senior teachers had a significantly stronger sense of professional identity than novice teachers, further confirming the positive relationship between teaching experience and sense of meaning. In summary, this study proposes H8: There are significant differences in the work meaningfulness of university teachers across different background variables (gender, professional title, and teaching experience). Based on the above theoretical analysis and hypotheses, this study constructs a research model (see Figure 1) and proposes the following hypotheses: H1: Technostress has a significant negative predictive effect on the work meaningfulness of university teachers. H2: Technostress has a significant negative predictive effect on the digital resilience of university teachers. H3: Digital resilience has a significant negative predictive effect on the job burnout of university teachers. H4: Job burnout has a significant negative predictive effect on the work meaningfulness of university teachers. H5: Digital resilience plays a mediating role between technostress and work meaningfulness among university teachers. H6: Job burnout plays a mediating role between technostress and work meaningfulness among university teachers. H7: Digital resilience and job burnout play a chain mediating role between technostress and work meaningfulness among university teachers. H8: There are significant differences in the work meaningfulness of university teachers across different background variables (gender, professional title, and teaching experience). 3. Methodology 3.1 Sample and Data Collection This study adopted a cross-sectional questionnaire survey method to examine the mechanism through which technostress affects the work meaningfulness of university teachers in the context of educational digital transformation, and to explore the chain mediating role of digital resilience and job burnout. Based on the COR Theory, a structural equation model was constructed to quantitatively test the theoretical hypotheses. The survey targeted teachers at private universities in western China. Convenience sampling was used to select multiple universities to ensure sample representativeness. Questionnaires were distributed through online platforms (e.g., Wenjuanxing, WJX) and promoted with the assistance of the academic affairs departments of participating universities. Participation was entirely voluntary, and respondents were required to read and sign an electronic informed consent form before completing the questionnaire. Data collection was conducted in July 2025. A total of 1136 questionnaires were distributed, 1110 were returned, and 1029 valid questionnaires were obtained after excluding invalid ones, resulting in a valid response rate of 92.7%. Among the valid samples: 514 were male teachers (49.95%) and 515 were female teachers (50.05%); 313 were teaching assistants (30.42%), 412 were lecturers (40.04%), 218 were associate professors (21.19%), and 86 were professors (8.36%); in terms of teaching experience: 206 had 0–5 years (20.02%), 255 had 6–10 years (24.78%), 314 had 11–20 years (30.52%), 187 had 21–30 years (18.17%), and 67 had more than 30 years (6.51%). 3.2 Measures 3.2.1 Technostress Scale The Teacher Technostress Scale developed by Çoklar et al. [ 30 ] was used. This scale consists of 28 items and 5 dimensions: learning-teaching process orientation, professional orientation, technology problem orientation, personal orientation, and social orientation. A 5-point Likert scale was adopted (1 = strongly disagree; 5 = strongly agree). In this study, reliability analysis showed that the Cronbach's α coefficient of the scale was 0.960 (> 0.700), indicating good reliability [ 31 ]. Confirmatory factor analysis (CFA) results showed that RMSEA = 0.062 (< 0.080), reflecting good consistency between the model and the observed data [ 32 ]; RMR = 0.048 and SRMR = 0.041 (both 0.800), indicating good construct validity [ 34 ]; PNFI = 0.703, PCFI = 0.715, and PGFI = 0.652 (all > 0.500), indicating good model fit [ 35 ]. 3.2.2 Digital Resilience Scale This study used the Digital Resilience Scale developed by Makrakis [ 36 ], which consists of 10 items covering two dimensions: digital competence and reflective practice. A 5-point Likert scale was adopted (1 = strongly disagree; 5 = strongly agree), and the scale has good reliability and validity. To adapt to the research context (university teachers in the digital transformation of education), the scale was linguistically adjusted following Brislin's [ 37 ] back-translation method: two bilingual experts with educational technology backgrounds independently translated the original English items into Chinese, and another group of experts back-translated them to ensure semantic consistency and contextual adaptability to Chinese. Meanwhile, considering that the research subjects were university teachers, the term "learners" in the original scale was adjusted to "teachers" while maintaining the structure and core meaning of the items to ensure content validity. In this study, reliability analysis showed that the Cronbach's α coefficient of the scale was 0.909 (> 0.700), indicating good reliability [ 31 ]. CFA results showed that RMSEA = 0.058 (< 0.100), reflecting good consistency between the model and the observed data [ 32 ]; RMR = 0.045 and SRMR = 0.038 (both 0.800), indicating good construct validity [ 34 ]; PNFI = 0.710, PCFI = 0.722, and PGFI = 0.660 (all > 0.500), indicating good model fit [ 35 ]. 3.2.3 Job Burnout Scale The university teacher burnout scale developed by Wang and Li [ 38 ] was used. This scale consists of 19 items and 3 dimensions: emotional exhaustion, depersonalization, and reduced personal accomplishment. A 5-point Likert scale was adopted (1 = strongly disagree; 5 = strongly agree). In this study, reliability analysis showed that the Cronbach's α coefficient of the scale was 0.923 (> 0.700), indicating good reliability [ 31 ]. CFA results showed that RMSEA = 0.065 (< 0.100), reflecting good consistency between the model and the observed data [ 32 ]; RMR = 0.050 and SRMR = 0.045 (both 0.800), indicating good construct validity [ 34 ]; PNFI = 0.698, PCFI = 0.709, and PGFI = 0.645 (all > 0.500), indicating good model fit [ 35 ]. 3.2.4 Work Meaningfulness Scale The Work and Meaning Inventory (WAMI) developed by Steger et al. [ 3 ] was used. This scale consists of 10 items and 3 dimensions: positive meaning, meaning-making through work, and motivation for greater meaning. A 5-point Likert scale was adopted (1 = strongly disagree; 5 = strongly agree). In this study, reliability analysis showed that the Cronbach's α coefficient of the scale was 0.911 (> 0.700), indicating good reliability [ 31 ]. CFA results showed that RMSEA = 0.060 (< 0.100), reflecting good consistency between the model and the observed data [ 32 ]; RMR = 0.047 and SRMR = 0.040 (both 0.800), indicating good construct validity [ 34 ]; PNFI = 0.705, PCFI = 0.718, and PGFI = 0.655 (all > 0.500), indicating good model fit [ 35 ]. 3.3 Statistical Analysis Data analysis was conducted using SPSS 22.0 and AMOS 21.0. First, reliability analysis and confirmatory factor analysis were used to test the reliability and validity of the measurement tools in this study; second, Harman's One-Factor Test was used to test for common method bias; third, descriptive analysis, difference analysis, and Pearson correlation analysis were conducted to examine the overall performance of participants on each variable and the correlation between variables; fourth, AMOS 21.0 was used to test the structural model. 4. Results 4.1 Common Method Bias Test Harman's one-factor test was used to assess common method bias. An unrotated principal component factor analysis was conducted on all items of the variables, and 24 factors with eigenvalues greater than 1 were obtained. The variance explained by the first factor was 28.27%, which was lower than the critical standard of 50%, indicating that common method bias was not a serious issue in this study [ 39 ]. 4.2 Correlation Analysis Descriptive statistics and correlation analysis were conducted on the four variables (technostress, digital resilience, job burnout, and work meaningfulness). As shown in Table 1 , technostress was significantly negatively correlated with digital resilience (r = -0.315, p < 0.001); technostress was significantly positively correlated with job burnout (r = 0.380, p < 0.001); technostress was significantly negatively correlated with work meaningfulness (r = -0.207, p < 0.001); digital resilience was significantly negatively correlated with job burnout (r = -0.376, p < 0.001); digital resilience was significantly positively correlated with work meaningfulness (r = 0.418, p < 0.001); job burnout was significantly negatively correlated with work meaningfulness (r = -0.408, p < 0.001). The correlation coefficients between the four variables ranged from 0.207 to 0.418, indicating moderate to low correlations between variables and no serious multicollinearity issues [ 40 ]. Table 1 Correlation Analysis Results Variable M SD technostress Digital Resilience job burnout work meaningfulness technostress 2.992 0.911 1 Digital Resilience 2.998 0.984 -0.315*** 1 job burnout 2.996 0.962 0.380*** -0.376*** 1 work meaningfulness 3.092 1.023 -0.207*** 0.418*** -0.408*** 1 Note: ***p < 0.001 4.3 Difference Test of Work Meaningfulness by Background Variables 4.3.1 Gender Difference Independent samples t-test results showed that there was no significant difference in work meaningfulness between male (n = 514) and female (n = 515) teachers (t = -1.835, p = 0.067 > 0.050). This indicates that gender has no significant impact on work meaningfulness and is not a key determinant of it. 4.3.2 Professional Title Difference One-way ANOVA results showed that there were significant differences in work meaningfulness among teachers with different professional titles (F = 9.497, p < 0.001). Further post-hoc tests (Tukey HSD) revealed that associate professors and professors scored significantly higher than teaching assistants and lecturers. This indicates that teachers with higher professional titles have a stronger sense of work meaningfulness. 4.3.3 Teaching Experience Difference One-way ANOVA results showed that there were significant differences in work meaningfulness among teachers with different teaching experience (F = 8.084, p < 0.001). Further post-hoc tests showed that teachers with 11–20 years and 21–30 years of teaching experience had significantly higher work meaningfulness than those with 0–5 years and 6–10 years; teachers with more than 30 years of teaching experience also had higher scores, but the difference was not significant compared with the 21–30 years group. This indicates that teachers with longer teaching experience have a stronger sense of work meaningfulness. 4.4 Structural Model Test As shown in Fig. 2 , a structural equation model was constructed to examine the relationships between technostress, digital resilience, job burnout, and work meaningfulness among university teachers. The results showed that χ²/df = 0.980 (< 5), and other fit indices were as follows: SRMR = 0.0071, RMSEA = 0.045, GFI = 0.985, AGFI = 0.978, PGFI = 0.666, PNFI = 0.777, PCFI = 0.780, NFI = 0.996, IFI = 0.957, TLI = 0.932, RFI = 0.984, and CFI = 0.998. All fit indices met acceptable standards [ 34 ]. To further verify the chain mediation effect, a bias-corrected nonparametric percentile Bootstrap method (5000 resamples) was used to test the mediating paths, See Table 2 for details. The results showed that technostress had a significant negative direct effect on work meaningfulness (β = − 0.257, p < 0.001, 95% CI [–0.366, − 0.218]), verifying Hypothesis H1. Meanwhile, digital resilience and job burnout respectively played significant mediating roles: (1) Technostress had an indirect effect on work meaningfulness through digital resilience (β = − 0.529, 95% CI [–0.404, − 0.308]); (2) Technostress had an indirect effect on work meaningfulness through job burnout (β = − 0.560, 95% CI [–0.306, − 0.207]); (3) Technostress had a chain mediating effect on work meaningfulness sequentially through digital resilience and job burnout (β = − 0.275, 95% CI [–0.545, − 0.395]). The confidence intervals of the above results did not include zero, indicating that all mediating path effects were significant, verifying Hypotheses H5, H6, and H7. Table 2 Summary of Indirect Path Effects Parameter Effect(β) 95% CI (BC) direct –0.257*** [–0.366, − 0.218] Indirect effect1 –0.529*** [–0.404, − 0.308] Indirect effect2 –0.560*** [–0.306, − 0.207] Indirect effect3 –0.275*** [–0.545, − 0.395] Total effect -0.207*** [–0.266, − 0.147] Note: ***p < 0.001; BC = Bias-Corrected 5. Discussion 5.1 The Relationship Between Technostress and Digital Resilience This study found that teachers' technostress had a significant negative effect on digital resilience. This result is consistent with the study by Çoklar et al. [ 41 ], who pointed out that high levels of technostress weaken teachers' confidence and initiative in using information technology in teaching. Similarly, Tarafdar et al. [ 42 ] also found that technology overload and complexity erode individuals' coping abilities. However, some studies have suggested that in certain contexts, technostress may stimulate individuals' learning motivation, thereby improving digital skills—this contradicts the results of this study. The possible reason for this difference lies in sample characteristics: compared with corporate employees, the technostress of university teachers is more derived from the additional burden of teaching tasks, and they lack sufficient external support, thus being more likely to exhibit negative effects. 5.2 The Relationship Between Technostress and Job Burnout The results showed that technostress positively predicted teachers' job burnout. This is consistent with Maslach and Leiter's [ 43 ] burnout theory and Salanova et al.'s [ 44 ] empirical results, which indicated that information technology requirements increase teachers' emotional exhaustion and professional alienation. Some literature, however, found that the relationship between technology integration and job burnout is not directly significant and is mediated by teaching self-efficacy [ 45 ]. This difference may be related to the professional context of the sample in this study: teachers at private universities generally bear high teaching workloads and performance evaluation pressure, making it easier for technostress to be directly transformed into burnout experiences. 5.3 The Relationship Between Digital Resilience and Work Meaningfulness Digital resilience had a significant positive effect on work meaningfulness, which is consistent with Masten's [ 46 ] theory that resilience promotes the construction of positive meaning. Educational studies have also shown that teachers with higher digital competence are more likely to gain value experiences in teaching activities [ 47 ]. However, some studies have not found a significant relationship in other contexts, possibly because the role of resilience often depends on the external support environment. The significant effect in this study indicates that digital resilience is an important psychological resource for teachers to achieve positive meaning in contexts where technostress is prevalent. 5.4 The Relationship Between Job Burnout and Work Meaningfulness This study found that job burnout was significantly negatively correlated with work meaningfulness, which is consistent with the conclusions of Maslach & Leiter [ 43 ] and Hakanen et al. [ 48 ], who found that burnout significantly undermines teachers' identification with work value. However, some studies have pointed out that social support may moderate the relationship between burnout and sense of meaning: when peer support is strong, the negative effect of burnout is weakened. This suggests that future studies can further examine the moderating effect of social support. 5.5 The Mediating Role of Digital Resilience This study verified the mediating role of digital resilience between technostress and work meaningfulness. Specifically, when facing high levels of technostress, teachers who can maintain high levels of digital competence and reflective practice ability can buffer the adverse effects of external stress, thereby maintaining or even enhancing their work meaningfulness. This result is consistent with Howard et al.'s [ 12 ] view that resilience in education is not only a coping resource but also an important psychological capital for meaning construction. It also echoes Smith et al.'s [ 13 ] finding that digital resilience helps teachers adapt to online teaching challenges and enhance professional value experiences. This study further shows that digital resilience not only plays a protective role in general well-being [ 9 ] but also has a key mediating function in teachers' work meaningfulness. This finding expands the applicability of resilience in the context of educational digital transformation and suggests that universities should strengthen digital resilience training in teacher professional development to help teachers transform technostress into an opportunity for constructing positive meaning. 5.6 The Mediating Role of Job Burnout This study also found that job burnout played a significant mediating role between technostress and work meaningfulness. Teachers under high technostress are more likely to experience emotional exhaustion, depersonalization, and reduced personal accomplishment, thereby weakening their identification with work value and sense of meaning. This is consistent with Salanova et al.'s [ 8 ] conclusions on technostress and burnout and Hakanen et al.'s [ 25 ] finding that burnout levels significantly undermine teachers' work meaningfulness. Furthermore, this study echoes Zhang et al.'s [ 26 ] results, who also found that burnout had a significant mediating effect between work stress and professional meaning. The contribution of this study lies in verifying job burnout as a key link in the "resource consumption chain" and revealing how technostress erodes teachers' sense of meaning by weakening their psychological resources. This implies that while promoting digital teaching, educational managers should focus on reducing teachers' burnout levels—for example, by reducing technical burdens, optimizing performance evaluation mechanisms, and strengthening mental health support—to promote teachers' positive experiences of professional meaning. 5.7 Verification of the Chain Mediation Effect The chain mediation path was verified: technostress significantly negatively affected digital resilience, which further negatively predicted job burnout, and job burnout ultimately negatively predicted work meaningfulness. This finding indicates that in high technostress contexts, teachers who lack digital resilience are more likely to experience job burnout, thereby weakening their sense of work meaningfulness. In other words, digital resilience and job burnout play a continuous transmission role. This result not only reveals the in-depth mechanism through which technostress affects teachers' well-being but also emphasizes the central role of resilience in buffering stress and promoting sense of meaning. 5.8 Differences in Background Variables This study further examined the mechanism through which gender, professional title, and teaching experience influence teachers' work meaningfulness. Regarding gender, no significant differences were found in this study, which is consistent with Hatlevik's [ 27 ] finding that teacher gender is not a key determinant of sense of meaning. Although Latif [ 28 ] found that female teachers had higher job satisfaction than male teachers, such differences often occur in specific cultural backgrounds or societies where gender roles are prominent. The non-significant gender difference in work meaningfulness among Chinese university teachers in digital environments may reflect relatively equal career opportunities and gender role socialization. Regarding professional title, significant differences were found: teachers with higher professional titles had stronger work meaningfulness. This is consistent with Zhang et al.'s [ 49 ] finding that teachers with rich teaching experience and strong professional identity have higher professional identity and sense of meaning. As a symbol of professional identity and authority, professional titles help teachers gain in-depth meaning experiences in educational activities. Regarding teaching experience, this study found that teachers with more experience had a higher sense of meaning. This is consistent with Smith's [ 29 ] study, which pointed out that teachers' age and experience are significantly correlated with professional satisfaction. The accumulation of experience not only improves skills but also deepens individuals' sense of value and mission toward educational work. Overall, these results indicate that in the Chinese university teacher group, career development stages (professional title and teaching experience) play an important role in shaping sense of meaning, while the impact of gender is relatively weak. This provides a basis for the formulation of subsequent teacher professional development policies, suggesting that attention should be paid to psychological support and growth opportunities for young teachers and teachers with low professional titles to promote the improvement of their professional sense of meaning. The findings of this study not only enrich the applicability of the COR Theory theoretically but also respond to the practical needs of China's educational digitalization. Currently, the Ministry of Education of China is implementing the "Education Digitalization Strategy Action", emphasizing the improvement of teacher professional development and educational quality through digital means [ 50 ]. Against this policy background, teachers' digital resilience has become a key psychological resource for ensuring the implementation of educational digitalization, while job burnout is a risk factor that needs to be focused on in digital reform. Therefore, the results of this study reveal that teachers' psychological adaptability and resource management are particularly important in the Chinese context, which not only provides evidence from emerging economies for the international academic community but also offers empirical support for local educational reform. 6. Research Contributions 6.1Theoretical Contributions This study has the following innovations at the theoretical level: (1) Expanding the application of the COR Theory in the context of educational digitalization. Previous studies have mostly focused on verifying the COR Theory in traditional professional environments, while this study introduces the theory into the context of university teachers' response to digital transformation, revealing how technostress— as a resource threat—affects teachers' sense of meaning through a chain psychological mechanism (reducing digital resilience and increasing job burnout). This not only enriches the explanatory power of the COR Theory in digital education but also deepens its theoretical extension in mental health research in higher education. (2) Constructing the path of action between digital resilience and sense of meaning, and expanding research on positive psychology and professional adaptability. Previous studies have mainly focused on the negative effects of job burnout, while paying less attention to the buffering role of resilience under digital teaching stress. This study verifies the mediating role of digital resilience between technostress and sense of meaning and further reveals its chain effect through job burnout, providing a new explanatory framework for understanding university teachers' psychological adaptation and expanding the application of career construction theory in digital education contexts. (3) Proposing sense of meaning as an important indicator of professional well-being and verifying its impact path. Existing studies have mostly used job satisfaction or well-being as outcome variables, while this study incorporates teacher sense of meaning into the context of digital education, emphasizing its core role in the realization of teaching value and professional identity, and enriching the research dimensions of "sense of meaning" in work motivation theory and educational psychology. 6.2 Practical Contributions This study not only has innovative value in theory but also provides important implications for educational management and policy formulation: (1) Providing intervention ideas for alleviating teachers' technostress. Technostress has been proven to be an important risk factor affecting teachers' professional health. Universities should reduce teachers' technical burden and anxiety by optimizing digital teaching platforms, providing timely technical support, and offering flexible training. (2) Providing strategic basis for improving teachers' digital resilience. The results show that digital resilience is a key resource for alleviating technostress, reducing job burnout, and enhancing sense of meaning. Universities can enhance teachers' digital skills and psychological coping abilities through systematic training, resilience workshops, and peer support groups. (3) Providing management implications for preventing job burnout and enhancing sense of meaning. Managers should focus on teaching innovation and teachers' value contributions in performance evaluation, avoid psychological burden caused by single-index assessment, and help teachers reconnect with educational missions through organizational support and incentive mechanisms—thus enhancing professional sense of meaning and improving work engagement and educational quality. 7. Conclusion Against the backdrop of the accelerating digital transformation of education, technostress faced by university teachers has become a key factor affecting their professional health and teaching effectiveness. Based on the COR Theory, this study constructed and verified a chain mediation model of "technostress—digital resilience—job burnout—teacher sense of meaning", revealing the psychological adaptation mechanism of teachers in digital contexts. The research results show that: (1) Technostress significantly negatively predicts teachers' sense of meaning; (2) Digital resilience plays a mediating role between technostress and sense of meaning, indicating that resilience is an important psychological resource for alleviating the impact of digital transformation; (3) Job burnout plays a negative mediating role between technostress and sense of meaning, and together with digital resilience, forms a chain mediation path. This finding not only enriches psychological theories in the context of digital education but also provides a new perspective for understanding how university teachers maintain their educational missions amid technological changes. Based on the above findings, this study puts forward the following practical implications: Universities should optimize the digital teaching environment to reduce teachers' technostress; enhance teachers' digital resilience through training, psychological support, and organizational resources; and help teachers maintain professional sense of meaning and prevent burnout through reasonable performance evaluation and value guidance—thus promoting the sustainable development of educational digital reform. 8. Limitations and Suggestions for Future Research Although this study has achieved certain results in theory and practice, it still has the following limitations, which need to be improved in future research: 8.1 Limitations in Research Design This study adopts a cross-sectional design and collects data through one-time questionnaires, making it difficult to make strict inferences about the causal relationships between variables. Future studies can adopt longitudinal designs or follow-up studies to form mixed-methods research, so as to verify the dynamic evolution process between technostress, digital resilience, job burnout, and sense of meaning. At the same time, moderating variables such as organizational support and self-efficacy can be introduced to expand the research on the mechanism of technostress and further examine the long-term psychological impact of educational digitalization. 8.2 Limitations in Data Collection and Sample Representativeness The sample of this study mainly comes from some universities in China, which may have limitations in cultural and institutional backgrounds, affecting the external validity of the conclusions. Future studies can adopt cross-regional and cross-cultural comparative studies to explore the differences in technostress coping strategies, digital resilience levels, and sense of meaning perception among teachers in different educational systems—thus enhancing the generalizability of the research conclusions. 8.3 Limitations in Measurement Tools Although this study uses mature scales, the Digital Resilience Scale is still based on Western educational backgrounds, and its adaptability in Chinese university scenarios needs further verification. Future studies can combine localized interviews and scale revisions to develop a more culturally adaptable measurement tool for teachers' digital resilience and test its reliability and validity through confirmatory factor analysis. 8.4 Limitations in Model Variable Selection This study mainly focuses on technostress, digital resilience, job burnout, and sense of meaning, and does not include moderating variables that may play a role, such as organizational support, digital teaching self-efficacy, and emotional intelligence. Future studies can introduce moderated mediation models based on the existing model to explore how individual and contextual factors interact to influence teachers' psychological adaptation. 8.5 Methodological Limitations This study adopts a single quantitative method, which may have the risk of common method bias. Future studies can adopt mixed-methods research, combining questionnaire surveys with in-depth interviews, to verify the chain mediation model from multiple dimensions and explore teachers' subjective experiences of digital teaching challenges and professional meaning—thus enhancing the explanatory power and theoretical depth of the research results. Declarations Ethics approval and consent to participate This study adhered to the provisions of the Helsinki Declaration. The participation was voluntary and anonymous. All participants had signed an informed consent form before completing the questionnaire. The study was approved by the Academic Ethics Committee of Hengshui University. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The author declares that there are no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors' contributions The author solely conceived the study, collected and analyzed the data, and wrote the manuscript. Acknowledgements The author would like to thank all university teachers who participated in the survey. Authors’ information Dr.Yuqiao Luo is a researcher in educational management and psychology, specializing in digital transformation in higher education, and well-being. Data availability The data is confidential. Ifnecessary, please contact the corresponding author to request it. (Email: [email protected] ) References Zhang Y, Zhang M, Wu L, Li J. Digital transition framework for higher education in AI-assisted engineering teaching: Challenge, strategy, and initiatives in China. Sci Educ. 2025;34(2):933–54. https://doi.org/10.1007/s11191-024-00575-3 . Tarafdar M, Maier C, Laumer S, Weitzel T. 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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-7493995","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":532309379,"identity":"839e3ef3-7837-4977-ab5c-5976b448a371","order_by":0,"name":"Yu-Qiao 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1","display":"","copyAsset":false,"role":"figure","size":28374,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eResearch Model\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7493995/v1/65718f92cf53e751f0ae9445.png"},{"id":94036953,"identity":"eef6a545-b15e-4ed4-9b2f-dbc8cb2b7cae","added_by":"auto","created_at":"2025-10-21 16:46:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52854,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eStructural Equation Model\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e***p \u0026lt; 0.001), JY = Technostress, SR = Digital Resilience, ZJ = Job Burnout, GY = Work Meaningfulness\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7493995/v1/83138d9da99b1fcc888054e3.png"},{"id":94489438,"identity":"9756b8a1-7c1d-4060-8b0f-52aff807f54e","added_by":"auto","created_at":"2025-10-27 17:04:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1109241,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7493995/v1/5fc00ec6-850a-4355-b548-abbbe3bbc303.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Impact of Technostress on Work Meaningfulness Among University Teachers in the Context of Digital Transformation of Education: The Chain Mediating Role of Digital Resilience and Job Burnout","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAgainst the backdrop of the deepening global digital transformation of education, the widespread application of digital technologies in teaching, research, and educational management is reshaping the professional ecology of university teachers. On the one hand, digitalization endows teaching with greater flexibility and interactivity, providing technical support for educational innovation; on the other hand, rapidly evolving technological tools, blended online-offline teaching models, and data-driven performance evaluation systems have exposed teachers to unprecedented challenges in knowledge updating, instructional design, and role positioning [1]. In this process, teachers are required to invest substantial cognitive and emotional resources to adapt to the demands of digital teaching. However, if such resource investment lacks corresponding compensation, it is likely to trigger psychological stress—particularly stress related to technology application, namely \"technostress\". Technostress manifests not only as anxiety about the difficulty of mastering new technologies but also involves information overload, time pressure, and persistent anxiety about skill adaptation. Long-term accumulation of technostress may erode teachers' mental health and professional identity [2]. Therefore, exploring the impact of technostress on teachers' work experience in the context of educational digital transformation holds important theoretical and practical significance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWork meaningfulness refers to the sense of value and purpose that individuals perceive in their professional activities, and it serves as a crucial psychological foundation for teachers' professional well-being and teaching engagement [3]. In the field of education, teachers typically view their profession as a key means to fulfill educational missions and social responsibilities. However, with the continuous increase in technostress, teachers may feel that their original teaching philosophies and practical models have been disrupted, and their role identity has been challenged—ultimately undermining their work meaningfulness. This change not only affects teachers' personal professional satisfaction and well-being but may also impact teaching quality and students' learning experience. Therefore, revealing the mechanism through which technostress influences teachers' work meaningfulness is essential for safeguarding teachers' professional health and ensuring the sustainability of educational digital reform.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFrom a theoretical perspective, the Conservation of Resources (COR) Theory provides a robust framework for understanding this issue. The theory posits that individuals tend to acquire, maintain, and protect their own resources; when resources are threatened or lost, stress responses are triggered [4]. During the digital transformation of education, teachers must continuously learn and master new technologies to meet teaching needs, which not only consumes cognitive resources but also may occupy emotional and time resources, leading to an imbalance in overall resource allocation. In this context, technostress— as a typical source of resource threat—may trigger a series of negative psychological consequences. However, individuals are not entirely passive; adaptive and coping abilities play a key role in mitigating the impact of stress. In recent years, the academic community has begun to focus on the emerging concept of \"digital resilience\", which refers to an individual's ability to maintain psychological stability and actively adjust behaviors in the face of digital challenges [5]. As a type of psychological resource, digital resilience can not only buffer the negative impact of technostress on individuals' mental health but also reduce job burnout by promoting positive coping strategies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJob burnout is a comprehensive syndrome characterized by emotional exhaustion, depersonalization, and reduced personal accomplishment under long-term work stress [6]. Studies have shown that job burnout not only impairs teaching quality but also significantly reduces work meaningfulness [7]. In the context of digital education, if technostress leads to a decline in teachers' digital resilience, it may further trigger job burnout, thereby eroding their perception of work value. This chain mechanism aligns with the \"resource loss spiral\" effect emphasized by the COR Theory—i.e., initial resource loss triggers further resource depletion, forming a vicious cycle. Therefore, exploring the mechanism between technostress, digital resilience, job burnout, and work meaningfulness can reveal the psychological adaptation path of teachers during the digital transformation of education.\u003c/p\u003e\n\u003cp\u003eAlthough existing studies have paid some attention to the relationship between technostress and teachers' psychological states, there are still several gaps. First, most existing studies have focused on the impact of technostress on job satisfaction or mental health [2, 8, 9], while few have systematically explored this issue from the perspective of work meaningfulness. As a key dimension of teachers' professional well-being, the dynamic changes of work meaningfulness in digital contexts deserve in-depth investigation [10, 11]. Second, as an emerging psychological resource, the buffering role of digital resilience has not been fully empirically tested in the field of education [12, 13]. Third, most existing studies have adopted single mediation or moderation models, lacking a holistic examination of the resource loss chain and failing to reveal how technostress affects work meaningfulness through resource loss and emotional exhaustion [14, 15]. Based on this, this study introduces digital resilience and job burnout to construct a chain mediation model, aiming to explain how teachers' psychological resources influence their professional experience in the context of educational digitalization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe innovations of this study are reflected in three aspects: (1) Theoretically, it introduces the COR Theory into the context of educational digitalization, reveals the mechanism through which technostress affects work meaningfulness, and expands the applicability of this theory in higher education; (2) Conceptually, it integrates digital resilience and job burnout into the same model, verifies their mediating roles in the resource loss chain, and enriches the research on the psychological mechanism of teachers' adaptation to digital transformation; (3) Practically, it provides empirical evidence for universities to formulate teacher support policies, develop digital resilience training, and prevent job burnout.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn summary, this study aims to address the following research questions: (1) Does technostress significantly affect the work meaningfulness of university teachers? (2) Does digital resilience play a mediating role between technostress and work meaningfulness? (3) Does job burnout play a mediating role between technostress and work meaningfulness? (4) Do digital resilience and job burnout play a chain mediating role between technostress and work meaningfulness? To this end, this study constructs a chain mediation model of \"technostress → digital resilience → job burnout → work meaningfulness\" and tests it based on empirical data from university teachers in China.\u003c/p\u003e"},{"header":"2. Theoretical Foundation and Research Hypotheses","content":"\u003cp\u003e2.1 Conservation of Resources Theory (COR Theory)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eProposed by [4] , the COR Theory is an important theoretical framework for explaining stress and individual coping mechanisms. The theory argues that individuals possess a certain amount of resources (e.g., time, energy, skills, social support); when resources are threatened, actually lost, or fail to yield expected gains, individuals experience stress and adopt corresponding resource protection or compensation behaviors [16]. In the context of educational digital transformation, technostress is regarded as a major threat to teachers' resources, as they need to invest additional time in learning new platforms, adjusting instructional design, and bearing the risk of technical failures [17]. If teachers cannot supplement resources in a timely manner (e.g., improving digital resilience), a resource loss spiral will occur, leading to emotional exhaustion and a decline in work meaningfulness [18]. This study regards technostress as a source of resource threat, digital resilience as an emerging psychological resource, job burnout as an outcome of resource loss, and work meaningfulness as the final outcome of resource recovery or value experience—thus constructing a chain psychological process framework. Therefore, the COR Theory provides a solid theoretical foundation for this study's chain mediation model of \"technostress—digital resilience—job burnout—work meaningfulness\".\u003c/p\u003e\n\u003cp\u003e2.2 Technostress and Work Meaningfulness\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTechnostress, proposed by Brod [19], refers to the negative psychological experience caused by individuals' use or adaptation to information technology. Tarafdar et al. [17] further divided technostress into dimensions such as technology overload, technology complexity, and technology insecurity. With the advancement of educational digitalization, university teachers are required to master online teaching platforms, data-driven teaching tools, and multimedia resources. Frequent technology updates and platform iterations increase teachers' learning burden and even threaten the effectiveness of their existing teaching methods. According to the COR Theory, technostress consumes teachers' time, energy, and emotional resources, leading to a sense of resource scarcity and further triggering negative emotional and cognitive responses [4].\u003c/p\u003e\n\u003cp\u003eWork meaningfulness refers to the sense of purpose and value that individuals experience in their work [3]. Studies have shown that work meaningfulness not only affects individuals' work engagement and well-being but also relates to professional persistence and organizational commitment [10]. In educational contexts, teachers typically view their profession as a key means to impart knowledge and fulfill educational missions. However, technostress may make teachers feel that their educational value has been weakened and their traditional teaching achievements have been diluted by digital tools—ultimately reducing work meaningfulness. Empirical studies have shown that high levels of technostress are negatively correlated with job satisfaction and work engagement [20], indirectly indicating that technostress may impair work meaningfulness. Based on the above analysis, this study proposes:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eH1: Technostress has a significant negative predictive effect on the work meaningfulness of university teachers.\u003c/p\u003e\n\u003cp\u003e2.3 Technostress and Digital Resilience\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDigital resilience is an emerging concept developed in recent years with the advancement of digital education and work scenarios. It refers to an individual's ability to maintain psychological balance and actively cope with technological changes and digital challenges [5]. Digital resilience is reflected not only in the mastery of technical skills but also in cognitive flexibility, emotional regulation, and positive adaptive beliefs. The COR Theory points out that when facing threats of resource loss, individuals will use existing resources or develop new resources to cope with stress. However, when technostress persists and exceeds individuals' coping capacity, their psychological resources will be severely consumed, leading to a decline in digital resilience. Existing studies have found that high levels of work stress weaken individuals' psychological resilience [21], and technostress may also hinder teachers from maintaining or improving digital resilience by increasing anxiety and a sense of powerlessness. Therefore, this study proposes:\u003c/p\u003e\n\u003cp\u003eH2: Technostress has a significant negative predictive effect on the digital resilience of university teachers.\u003c/p\u003e\n\u003cp\u003e2.4\u0026nbsp;Digital Resilience and Job Burnout\u003c/p\u003e\n\u003cp\u003eJob burnout refers to the occurrence of emotional exhaustion, depersonalization, and reduced personal accomplishment in individuals under long-term work stress [6]. Teachers' job burnout not only affects their physical and mental health but also reduces teaching quality and students' learning experience. According to the COR Theory, resilience is an important psychological resource that helps individuals recover and maintain balance in stressful situations [18]. When teachers possess high digital resilience, they are more likely to face digital challenges with a positive attitude, use strategies to reduce anxiety, and reduce resource loss—thus alleviating job burnout. Empirical studies have shown that psychological resilience is significantly negatively correlated with burnout [22]; in digital education scenarios, digital resilience, as a specialized form of psychological resilience, plays a similar role. Based on this, this study proposes:\u003c/p\u003e\n\u003cp\u003eH3: Digital resilience has a significant negative predictive effect on the job burnout of university teachers.\u003c/p\u003e\n\u003cp\u003e2.5 Job Burnout and Work Meaningfulness\u003c/p\u003e\n\u003cp\u003eThere is a close relationship between work meaningfulness and job burnout. Maslach et al. [23] pointed out that burnout not only reflects energy exhaustion and cognitive alienation but also is accompanied by a decline in personal accomplishment and sense of meaning. In the context of digital education, if teachers experience burnout due to technostress and adaptation difficulties, they are likely to feel that their work value has been weakened and their professional goals have become vague—ultimately reducing work meaningfulness. A large number of studies have confirmed that burnout is an important negative predictor of work meaningfulness, and this effect is more pronounced in high-load work environments [24]. Therefore, this study proposes:\u003c/p\u003e\n\u003cp\u003eH4: Job burnout has a significant negative predictive effect on the work meaningfulness of university teachers.\u003c/p\u003e\n\u003cp\u003e2.6 The Mediating Role of Digital Resilience Between Technostress and Work Meaningfulness\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn recent years, digital resilience has gradually been recognized as an important psychological resource for alleviating teachers' technostress and enhancing positive professional experiences. Studies have shown that when facing technostress, teachers with high digital resilience can offset the negative effects of technology by improving their digital capabilities and reflective practice [12]. Smith et al. [13] found that digital resilience not only helps teachers cope with unexpected challenges in online teaching but also promotes them to gain deeper value and meaning from their work. A study by Zhao et al. [9] in the Chinese context further pointed out that digital resilience can buffer the adverse impact of technostress on teachers' well-being. That is, by enhancing teachers' adaptability and psychological resources, technostress can be transformed into an opportunity for constructing positive professional meaning. Therefore, this study proposes:\u003c/p\u003e\n\u003cp\u003eH5: Digital resilience plays a mediating role between technostress and work meaningfulness among university teachers.\u003c/p\u003e\n\u003cp\u003e2.7 The Mediating Role of Job Burnout Between Technostress and Work Meaningfulness\u003c/p\u003e\n\u003cp\u003eJob burnout is considered an important transmission mechanism through which technostress affects teachers' work attitudes and sense of meaning. Existing studies have shown that long-term technology overload and information pressure can lead to teachers' emotional exhaustion and depersonalization, thereby weakening their positive identification with work [8]. Hakanen et al. [25] found in a sample of Finnish teachers that job burnout was not only negatively correlated with well-being but also significantly undermined teachers' sense of meaning derived from work. Similarly, a study by Zhang et al. [26] pointed out that job burnout played a mediating role between work stress and work meaningfulness—i.e., stress reduced teachers' experience of professional value by increasing burnout. In summary, job burnout may be a key psychological path explaining how technostress undermines teachers' work meaningfulness. Therefore, this study proposes:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eH6: Job burnout plays a mediating role between technostress and work meaningfulness among university teachers.\u003c/p\u003e\n\u003cp\u003e2.8 The Chain Mediating Role of Digital Resilience and Job Burnout\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn summary, technostress may not only directly undermine work meaningfulness but also indirectly affect this outcome through digital resilience and job burnout. Specifically, increased technostress reduces digital resilience, which in turn increases job burnout and ultimately weakens teachers' perception of work meaningfulness. This path is consistent with the \"resource loss spiral\" model emphasized by the COR Theory—i.e., initial resource loss leads to further resource scarcity, ultimately triggering negative psychological outcomes. Technostress reduces work meaningfulness by lowering digital resilience, which in turn increases job burnout. Therefore, this study proposes:\u003c/p\u003e\n\u003cp\u003eH7: Digital resilience and job burnout play a chain mediating role between technostress and work meaningfulness among university teachers.\u003c/p\u003e\n\u003cp\u003e2.9 Differences in Work Meaningfulness by Gender, Professional Title, and Teaching Experience\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.9.1 Gender and Work Meaningfulness\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStudies in the past five years have generally shown that gender has no significant difference in teachers' work meaningfulness and professional satisfaction. Hatlevik [27] found in a study of Norwegian teachers that gender was not a key determinant of teachers' professional meaning or satisfaction; instead, self-efficacy and school environmental support played a more important role. In addition, a study by Zhang et al. [1] on Chinese samples also found that gender had no significant impact on teachers' professional identity. In contrast, Latif [28] found in a survey of Pakistani teachers that female teachers had higher job satisfaction than male teachers, suggesting that gender differences may be influenced by cultural contexts. In educational systems with a higher degree of gender equality, the impact of gender on sense of meaning tends to weaken.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.9.2 Professional Title (Professional Identity) and Work Meaningfulness\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTeachers' professional titles represent their level of professional development and identity, and they are important factors affecting work meaningfulness. A study by Zhang et al. [1] showed that teachers with more than 10 years of teaching experience were more likely to identify with the teaching profession and exhibit a higher sense of work value. Similarly, Hatlevik [27] also found that professional promotion was closely related to teachers' professional identity, thereby enhancing work meaningfulness. These results indicate that during the process of professional title promotion, teachers not only gain external recognition but also strengthen their internal sense of meaning through professional identity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.9.3 Teaching Experience (Seniority) and Work Meaningfulness\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRecent studies have consistently pointed out that teachers with longer teaching experience have a higher level of work meaningfulness. Smith [29] found in a study of American teachers that teachers' age and experience were significantly correlated with professional satisfaction and sense of meaning. Experienced teachers are usually more likely to construct positive professional identities in educational activities and gain in-depth professional meaning through long-term practice. Similarly, the results of Zhang et al. [1] also showed that senior teachers had a significantly stronger sense of professional identity than novice teachers, further confirming the positive relationship between teaching experience and sense of meaning.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn summary, this study proposes H8: There are significant differences in the work meaningfulness of university teachers across different background variables (gender, professional title, and teaching experience).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on the above theoretical analysis and hypotheses, this study constructs a research model (see Figure 1) and proposes the following hypotheses:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eH1: Technostress has a significant negative predictive effect on the work meaningfulness of university teachers.\u003c/p\u003e\n\u003cp\u003eH2: Technostress has a significant negative predictive effect on the digital resilience of university teachers.\u003c/p\u003e\n\u003cp\u003eH3: Digital resilience has a significant negative predictive effect on the job burnout of university teachers.\u003c/p\u003e\n\u003cp\u003eH4: Job burnout has a significant negative predictive effect on the work meaningfulness of university teachers.\u003c/p\u003e\n\u003cp\u003eH5: Digital resilience plays a mediating role between technostress and work meaningfulness among university teachers.\u003c/p\u003e\n\u003cp\u003eH6: Job burnout plays a mediating role between technostress and work meaningfulness among university teachers.\u003c/p\u003e\n\u003cp\u003eH7: Digital resilience and job burnout play a chain mediating role between technostress and work meaningfulness among university teachers.\u003c/p\u003e\n\u003cp\u003eH8: There are significant differences in the work meaningfulness of university teachers across different background variables (gender, professional title, and teaching experience).\u0026nbsp;\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Sample and Data Collection\u003c/h2\u003e\u003cp\u003eThis study adopted a cross-sectional questionnaire survey method to examine the mechanism through which technostress affects the work meaningfulness of university teachers in the context of educational digital transformation, and to explore the chain mediating role of digital resilience and job burnout. Based on the COR Theory, a structural equation model was constructed to quantitatively test the theoretical hypotheses.\u003c/p\u003e\u003cp\u003eThe survey targeted teachers at private universities in western China. Convenience sampling was used to select multiple universities to ensure sample representativeness. Questionnaires were distributed through online platforms (e.g., Wenjuanxing, WJX) and promoted with the assistance of the academic affairs departments of participating universities. Participation was entirely voluntary, and respondents were required to read and sign an electronic informed consent form before completing the questionnaire.\u003c/p\u003e\u003cp\u003eData collection was conducted in July 2025. A total of 1136 questionnaires were distributed, 1110 were returned, and 1029 valid questionnaires were obtained after excluding invalid ones, resulting in a valid response rate of 92.7%. Among the valid samples: 514 were male teachers (49.95%) and 515 were female teachers (50.05%); 313 were teaching assistants (30.42%), 412 were lecturers (40.04%), 218 were associate professors (21.19%), and 86 were professors (8.36%); in terms of teaching experience: 206 had 0\u0026ndash;5 years (20.02%), 255 had 6\u0026ndash;10 years (24.78%), 314 had 11\u0026ndash;20 years (30.52%), 187 had 21\u0026ndash;30 years (18.17%), and 67 had more than 30 years (6.51%).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Measures\u003c/h2\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 Technostress Scale\u003c/h2\u003e\u003cp\u003eThe Teacher Technostress Scale developed by \u0026Ccedil;oklar et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] was used. This scale consists of 28 items and 5 dimensions: learning-teaching process orientation, professional orientation, technology problem orientation, personal orientation, and social orientation. A 5-point Likert scale was adopted (1\u0026thinsp;=\u0026thinsp;strongly disagree; 5\u0026thinsp;=\u0026thinsp;strongly agree). In this study, reliability analysis showed that the Cronbach's α coefficient of the scale was 0.960 (\u0026gt;\u0026thinsp;0.700), indicating good reliability [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Confirmatory factor analysis (CFA) results showed that RMSEA\u0026thinsp;=\u0026thinsp;0.062 (\u0026lt;\u0026thinsp;0.080), reflecting good consistency between the model and the observed data [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]; RMR\u0026thinsp;=\u0026thinsp;0.048 and SRMR\u0026thinsp;=\u0026thinsp;0.041 (both \u0026lt;\u0026thinsp;0.080) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]; GFI\u0026thinsp;=\u0026thinsp;0.912, AGFI\u0026thinsp;=\u0026thinsp;0.884, NFI\u0026thinsp;=\u0026thinsp;0.913, RFI\u0026thinsp;=\u0026thinsp;0.902, IFI\u0026thinsp;=\u0026thinsp;0.932, TLI\u0026thinsp;=\u0026thinsp;0.921, and CFI\u0026thinsp;=\u0026thinsp;0.933 (all \u0026gt;\u0026thinsp;0.800), indicating good construct validity [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]; PNFI\u0026thinsp;=\u0026thinsp;0.703, PCFI\u0026thinsp;=\u0026thinsp;0.715, and PGFI\u0026thinsp;=\u0026thinsp;0.652 (all \u0026gt;\u0026thinsp;0.500), indicating good model fit [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 Digital Resilience Scale\u003c/h2\u003e\u003cp\u003eThis study used the Digital Resilience Scale developed by Makrakis [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], which consists of 10 items covering two dimensions: digital competence and reflective practice. A 5-point Likert scale was adopted (1\u0026thinsp;=\u0026thinsp;strongly disagree; 5\u0026thinsp;=\u0026thinsp;strongly agree), and the scale has good reliability and validity. To adapt to the research context (university teachers in the digital transformation of education), the scale was linguistically adjusted following Brislin's [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] back-translation method: two bilingual experts with educational technology backgrounds independently translated the original English items into Chinese, and another group of experts back-translated them to ensure semantic consistency and contextual adaptability to Chinese. Meanwhile, considering that the research subjects were university teachers, the term \"learners\" in the original scale was adjusted to \"teachers\" while maintaining the structure and core meaning of the items to ensure content validity.\u003c/p\u003e\u003cp\u003eIn this study, reliability analysis showed that the Cronbach's α coefficient of the scale was 0.909 (\u0026gt;\u0026thinsp;0.700), indicating good reliability [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. CFA results showed that RMSEA\u0026thinsp;=\u0026thinsp;0.058 (\u0026lt;\u0026thinsp;0.100), reflecting good consistency between the model and the observed data [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]; RMR\u0026thinsp;=\u0026thinsp;0.045 and SRMR\u0026thinsp;=\u0026thinsp;0.038 (both \u0026lt;\u0026thinsp;0.080) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]; GFI\u0026thinsp;=\u0026thinsp;0.918, AGFI\u0026thinsp;=\u0026thinsp;0.891, NFI\u0026thinsp;=\u0026thinsp;0.920, RFI\u0026thinsp;=\u0026thinsp;0.909, IFI\u0026thinsp;=\u0026thinsp;0.937, TLI\u0026thinsp;=\u0026thinsp;0.925, and CFI\u0026thinsp;=\u0026thinsp;0.939 (all \u0026gt;\u0026thinsp;0.800), indicating good construct validity [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]; PNFI\u0026thinsp;=\u0026thinsp;0.710, PCFI\u0026thinsp;=\u0026thinsp;0.722, and PGFI\u0026thinsp;=\u0026thinsp;0.660 (all \u0026gt;\u0026thinsp;0.500), indicating good model fit [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3 Job Burnout Scale\u003c/h2\u003e\u003cp\u003eThe university teacher burnout scale developed by Wang and Li [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] was used. This scale consists of 19 items and 3 dimensions: emotional exhaustion, depersonalization, and reduced personal accomplishment. A 5-point Likert scale was adopted (1\u0026thinsp;=\u0026thinsp;strongly disagree; 5\u0026thinsp;=\u0026thinsp;strongly agree). In this study, reliability analysis showed that the Cronbach's α coefficient of the scale was 0.923 (\u0026gt;\u0026thinsp;0.700), indicating good reliability [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. CFA results showed that RMSEA\u0026thinsp;=\u0026thinsp;0.065 (\u0026lt;\u0026thinsp;0.100), reflecting good consistency between the model and the observed data [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]; RMR\u0026thinsp;=\u0026thinsp;0.050 and SRMR\u0026thinsp;=\u0026thinsp;0.045 (both \u0026lt;\u0026thinsp;0.080) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]; GFI\u0026thinsp;=\u0026thinsp;0.905, AGFI\u0026thinsp;=\u0026thinsp;0.877, NFI\u0026thinsp;=\u0026thinsp;0.908, RFI\u0026thinsp;=\u0026thinsp;0.896, IFI\u0026thinsp;=\u0026thinsp;0.929, TLI\u0026thinsp;=\u0026thinsp;0.918, and CFI\u0026thinsp;=\u0026thinsp;0.931 (all \u0026gt;\u0026thinsp;0.800), indicating good construct validity [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]; PNFI\u0026thinsp;=\u0026thinsp;0.698, PCFI\u0026thinsp;=\u0026thinsp;0.709, and PGFI\u0026thinsp;=\u0026thinsp;0.645 (all \u0026gt;\u0026thinsp;0.500), indicating good model fit [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003e3.2.4 Work Meaningfulness Scale\u003c/h2\u003e\u003cp\u003eThe Work and Meaning Inventory (WAMI) developed by Steger et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] was used. This scale consists of 10 items and 3 dimensions: positive meaning, meaning-making through work, and motivation for greater meaning. A 5-point Likert scale was adopted (1\u0026thinsp;=\u0026thinsp;strongly disagree; 5\u0026thinsp;=\u0026thinsp;strongly agree). In this study, reliability analysis showed that the Cronbach's α coefficient of the scale was 0.911 (\u0026gt;\u0026thinsp;0.700), indicating good reliability [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. CFA results showed that RMSEA\u0026thinsp;=\u0026thinsp;0.060 (\u0026lt;\u0026thinsp;0.100), reflecting good consistency between the model and the observed data [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]; RMR\u0026thinsp;=\u0026thinsp;0.047 and SRMR\u0026thinsp;=\u0026thinsp;0.040 (both \u0026lt;\u0026thinsp;0.080) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]; GFI\u0026thinsp;=\u0026thinsp;0.910, AGFI\u0026thinsp;=\u0026thinsp;0.882, NFI\u0026thinsp;=\u0026thinsp;0.915, RFI\u0026thinsp;=\u0026thinsp;0.903, IFI\u0026thinsp;=\u0026thinsp;0.934, TLI\u0026thinsp;=\u0026thinsp;0.922, and CFI\u0026thinsp;=\u0026thinsp;0.935 (all \u0026gt;\u0026thinsp;0.800), indicating good construct validity [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]; PNFI\u0026thinsp;=\u0026thinsp;0.705, PCFI\u0026thinsp;=\u0026thinsp;0.718, and PGFI\u0026thinsp;=\u0026thinsp;0.655 (all \u0026gt;\u0026thinsp;0.500), indicating good model fit [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Statistical Analysis\u003c/h2\u003e\u003cp\u003eData analysis was conducted using SPSS 22.0 and AMOS 21.0. First, reliability analysis and confirmatory factor analysis were used to test the reliability and validity of the measurement tools in this study; second, Harman's One-Factor Test was used to test for common method bias; third, descriptive analysis, difference analysis, and Pearson correlation analysis were conducted to examine the overall performance of participants on each variable and the correlation between variables; fourth, AMOS 21.0 was used to test the structural model.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Common Method Bias Test\u003c/h2\u003e\n \u003cp\u003eHarman\u0026apos;s one-factor test was used to assess common method bias. An unrotated principal component factor analysis was conducted on all items of the variables, and 24 factors with eigenvalues greater than 1 were obtained. The variance explained by the first factor was 28.27%, which was lower than the critical standard of 50%, indicating that common method bias was not a serious issue in this study [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Correlation Analysis\u003c/h2\u003e\n \u003cp\u003eDescriptive statistics and correlation analysis were conducted on the four variables (technostress, digital resilience, job burnout, and work meaningfulness). As shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, technostress was significantly negatively correlated with digital resilience (r = -0.315, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); technostress was significantly positively correlated with job burnout (r\u0026thinsp;=\u0026thinsp;0.380, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); technostress was significantly negatively correlated with work meaningfulness (r = -0.207, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); digital resilience was significantly negatively correlated with job burnout (r = -0.376, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); digital resilience was significantly positively correlated with work meaningfulness (r\u0026thinsp;=\u0026thinsp;0.418, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); job burnout was significantly negatively correlated with work meaningfulness (r = -0.408, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The correlation coefficients between the four variables ranged from 0.207 to 0.418, indicating moderate to low correlations between variables and no serious multicollinearity issues [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eCorrelation Analysis Results\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003etechnostress\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDigital Resilience\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ejob burnout\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ework meaningfulness\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etechnostress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDigital Resilience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.315***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ejob burnout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.380***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.376***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ework meaningfulness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.207***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.418***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.408***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eNote: ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Difference Test of Work Meaningfulness by Background Variables\u003c/h2\u003e\n \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\n \u003ch2\u003e4.3.1 Gender Difference\u003c/h2\u003e\n \u003cp\u003eIndependent samples t-test results showed that there was no significant difference in work meaningfulness between male (n\u0026thinsp;=\u0026thinsp;514) and female (n\u0026thinsp;=\u0026thinsp;515) teachers (t = -1.835, p\u0026thinsp;=\u0026thinsp;0.067\u0026thinsp;\u0026gt;\u0026thinsp;0.050). This indicates that gender has no significant impact on work meaningfulness and is not a key determinant of it.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e\n \u003ch2\u003e4.3.2 Professional Title Difference\u003c/h2\u003e\n \u003cp\u003eOne-way ANOVA results showed that there were significant differences in work meaningfulness among teachers with different professional titles (F\u0026thinsp;=\u0026thinsp;9.497, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Further post-hoc tests (Tukey HSD) revealed that associate professors and professors scored significantly higher than teaching assistants and lecturers. This indicates that teachers with higher professional titles have a stronger sense of work meaningfulness.\u003c/p\u003e\n \u003cp\u003e4.3.3 Teaching Experience Difference One-way ANOVA results showed that there were significant differences in work meaningfulness among teachers with different teaching experience (F\u0026thinsp;=\u0026thinsp;8.084, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Further post-hoc tests showed that teachers with 11\u0026ndash;20 years and 21\u0026ndash;30 years of teaching experience had significantly higher work meaningfulness than those with 0\u0026ndash;5 years and 6\u0026ndash;10 years; teachers with more than 30 years of teaching experience also had higher scores, but the difference was not significant compared with the 21\u0026ndash;30 years group. This indicates that teachers with longer teaching experience have a stronger sense of work meaningfulness.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 Structural Model\u003c/h2\u003e\n \u003cp\u003eTest As shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, a structural equation model was constructed to examine the relationships between technostress, digital resilience, job burnout, and work meaningfulness among university teachers. The results showed that \u0026chi;\u0026sup2;/df\u0026thinsp;=\u0026thinsp;0.980 (\u0026lt;\u0026thinsp;5), and other fit indices were as follows: SRMR\u0026thinsp;=\u0026thinsp;0.0071, RMSEA\u0026thinsp;=\u0026thinsp;0.045, GFI\u0026thinsp;=\u0026thinsp;0.985, AGFI\u0026thinsp;=\u0026thinsp;0.978, PGFI\u0026thinsp;=\u0026thinsp;0.666, PNFI\u0026thinsp;=\u0026thinsp;0.777, PCFI\u0026thinsp;=\u0026thinsp;0.780, NFI\u0026thinsp;=\u0026thinsp;0.996, IFI\u0026thinsp;=\u0026thinsp;0.957, TLI\u0026thinsp;=\u0026thinsp;0.932, RFI\u0026thinsp;=\u0026thinsp;0.984, and CFI\u0026thinsp;=\u0026thinsp;0.998. All fit indices met acceptable standards [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eTo further verify the chain mediation effect, a bias-corrected nonparametric percentile Bootstrap method (5000 resamples) was used to test the mediating paths, See Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e for details. The results showed that technostress had a significant negative direct effect on work meaningfulness (\u0026beta; = \u0026minus;\u0026thinsp;0.257, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 95% CI [\u0026ndash;0.366, \u0026minus;\u0026thinsp;0.218]), verifying Hypothesis H1. Meanwhile, digital resilience and job burnout respectively played significant mediating roles: (1) Technostress had an indirect effect on work meaningfulness through digital resilience (\u0026beta; = \u0026minus;\u0026thinsp;0.529, 95% CI [\u0026ndash;0.404, \u0026minus;\u0026thinsp;0.308]); (2) Technostress had an indirect effect on work meaningfulness through job burnout (\u0026beta; = \u0026minus;\u0026thinsp;0.560, 95% CI [\u0026ndash;0.306, \u0026minus;\u0026thinsp;0.207]); (3) Technostress had a chain mediating effect on work meaningfulness sequentially through digital resilience and job burnout (\u0026beta; = \u0026minus;\u0026thinsp;0.275, 95% CI [\u0026ndash;0.545, \u0026minus;\u0026thinsp;0.395]). The confidence intervals of the above results did not include zero, indicating that all mediating path effects were significant, verifying Hypotheses H5, H6, and H7.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of Indirect Path Effects\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEffect(\u0026beta;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI (BC)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edirect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.257***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u0026ndash;0.366, \u0026minus;\u0026thinsp;0.218]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndirect effect1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.529***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u0026ndash;0.404, \u0026minus;\u0026thinsp;0.308]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndirect effect2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.560***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u0026ndash;0.306, \u0026minus;\u0026thinsp;0.207]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndirect effect3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.275***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u0026ndash;0.545, \u0026minus;\u0026thinsp;0.395]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.207***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u0026ndash;0.266, \u0026minus;\u0026thinsp;0.147]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003eNote: ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; BC\u0026thinsp;=\u0026thinsp;Bias-Corrected\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003e5.1 The Relationship Between Technostress and Digital Resilience\u003c/h2\u003e\u003cp\u003eThis study found that teachers' technostress had a significant negative effect on digital resilience. This result is consistent with the study by \u0026Ccedil;oklar et al. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], who pointed out that high levels of technostress weaken teachers' confidence and initiative in using information technology in teaching. Similarly, Tarafdar et al. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] also found that technology overload and complexity erode individuals' coping abilities. However, some studies have suggested that in certain contexts, technostress may stimulate individuals' learning motivation, thereby improving digital skills\u0026mdash;this contradicts the results of this study. The possible reason for this difference lies in sample characteristics: compared with corporate employees, the technostress of university teachers is more derived from the additional burden of teaching tasks, and they lack sufficient external support, thus being more likely to exhibit negative effects.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e\u003ch2\u003e5.2 The Relationship Between Technostress and Job Burnout\u003c/h2\u003e\u003cp\u003eThe results showed that technostress positively predicted teachers' job burnout. This is consistent with Maslach and Leiter's [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] burnout theory and Salanova et al.'s [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] empirical results, which indicated that information technology requirements increase teachers' emotional exhaustion and professional alienation. Some literature, however, found that the relationship between technology integration and job burnout is not directly significant and is mediated by teaching self-efficacy [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This difference may be related to the professional context of the sample in this study: teachers at private universities generally bear high teaching workloads and performance evaluation pressure, making it easier for technostress to be directly transformed into burnout experiences.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec33\" class=\"Section2\"\u003e\u003ch2\u003e5.3 The Relationship Between Digital Resilience and Work Meaningfulness\u003c/h2\u003e\u003cp\u003eDigital resilience had a significant positive effect on work meaningfulness, which is consistent with Masten's [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] theory that resilience promotes the construction of positive meaning. Educational studies have also shown that teachers with higher digital competence are more likely to gain value experiences in teaching activities [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. However, some studies have not found a significant relationship in other contexts, possibly because the role of resilience often depends on the external support environment. The significant effect in this study indicates that digital resilience is an important psychological resource for teachers to achieve positive meaning in contexts where technostress is prevalent.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec34\" class=\"Section2\"\u003e\u003ch2\u003e5.4 The Relationship Between Job Burnout and Work Meaningfulness\u003c/h2\u003e\u003cp\u003eThis study found that job burnout was significantly negatively correlated with work meaningfulness, which is consistent with the conclusions of Maslach \u0026amp; Leiter [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] and Hakanen et al. [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], who found that burnout significantly undermines teachers' identification with work value. However, some studies have pointed out that social support may moderate the relationship between burnout and sense of meaning: when peer support is strong, the negative effect of burnout is weakened. This suggests that future studies can further examine the moderating effect of social support.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec35\" class=\"Section2\"\u003e\u003ch2\u003e5.5 The Mediating Role of Digital Resilience\u003c/h2\u003e\u003cp\u003eThis study verified the mediating role of digital resilience between technostress and work meaningfulness. Specifically, when facing high levels of technostress, teachers who can maintain high levels of digital competence and reflective practice ability can buffer the adverse effects of external stress, thereby maintaining or even enhancing their work meaningfulness. This result is consistent with Howard et al.'s [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] view that resilience in education is not only a coping resource but also an important psychological capital for meaning construction. It also echoes Smith et al.'s [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] finding that digital resilience helps teachers adapt to online teaching challenges and enhance professional value experiences. This study further shows that digital resilience not only plays a protective role in general well-being [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] but also has a key mediating function in teachers' work meaningfulness. This finding expands the applicability of resilience in the context of educational digital transformation and suggests that universities should strengthen digital resilience training in teacher professional development to help teachers transform technostress into an opportunity for constructing positive meaning.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec36\" class=\"Section2\"\u003e\u003ch2\u003e5.6 The Mediating Role of Job Burnout\u003c/h2\u003e\u003cp\u003eThis study also found that job burnout played a significant mediating role between technostress and work meaningfulness. Teachers under high technostress are more likely to experience emotional exhaustion, depersonalization, and reduced personal accomplishment, thereby weakening their identification with work value and sense of meaning. This is consistent with Salanova et al.'s [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] conclusions on technostress and burnout and Hakanen et al.'s [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] finding that burnout levels significantly undermine teachers' work meaningfulness. Furthermore, this study echoes Zhang et al.'s [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] results, who also found that burnout had a significant mediating effect between work stress and professional meaning. The contribution of this study lies in verifying job burnout as a key link in the \"resource consumption chain\" and revealing how technostress erodes teachers' sense of meaning by weakening their psychological resources. This implies that while promoting digital teaching, educational managers should focus on reducing teachers' burnout levels\u0026mdash;for example, by reducing technical burdens, optimizing performance evaluation mechanisms, and strengthening mental health support\u0026mdash;to promote teachers' positive experiences of professional meaning.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec37\" class=\"Section2\"\u003e\u003ch2\u003e5.7 Verification of the Chain Mediation Effect\u003c/h2\u003e\u003cp\u003eThe chain mediation path was verified: technostress significantly negatively affected digital resilience, which further negatively predicted job burnout, and job burnout ultimately negatively predicted work meaningfulness. This finding indicates that in high technostress contexts, teachers who lack digital resilience are more likely to experience job burnout, thereby weakening their sense of work meaningfulness. In other words, digital resilience and job burnout play a continuous transmission role. This result not only reveals the in-depth mechanism through which technostress affects teachers' well-being but also emphasizes the central role of resilience in buffering stress and promoting sense of meaning.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec38\" class=\"Section2\"\u003e\u003ch2\u003e5.8 Differences in Background Variables\u003c/h2\u003e\u003cp\u003eThis study further examined the mechanism through which gender, professional title, and teaching experience influence teachers' work meaningfulness.\u003c/p\u003e\u003cp\u003eRegarding gender, no significant differences were found in this study, which is consistent with Hatlevik's [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] finding that teacher gender is not a key determinant of sense of meaning. Although Latif [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] found that female teachers had higher job satisfaction than male teachers, such differences often occur in specific cultural backgrounds or societies where gender roles are prominent. The non-significant gender difference in work meaningfulness among Chinese university teachers in digital environments may reflect relatively equal career opportunities and gender role socialization.\u003c/p\u003e\u003cp\u003eRegarding professional title, significant differences were found: teachers with higher professional titles had stronger work meaningfulness. This is consistent with Zhang et al.'s [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] finding that teachers with rich teaching experience and strong professional identity have higher professional identity and sense of meaning. As a symbol of professional identity and authority, professional titles help teachers gain in-depth meaning experiences in educational activities.\u003c/p\u003e\u003cp\u003eRegarding teaching experience, this study found that teachers with more experience had a higher sense of meaning. This is consistent with Smith's [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] study, which pointed out that teachers' age and experience are significantly correlated with professional satisfaction. The accumulation of experience not only improves skills but also deepens individuals' sense of value and mission toward educational work.\u003c/p\u003e\u003cp\u003eOverall, these results indicate that in the Chinese university teacher group, career development stages (professional title and teaching experience) play an important role in shaping sense of meaning, while the impact of gender is relatively weak. This provides a basis for the formulation of subsequent teacher professional development policies, suggesting that attention should be paid to psychological support and growth opportunities for young teachers and teachers with low professional titles to promote the improvement of their professional sense of meaning.\u003c/p\u003e\u003cp\u003eThe findings of this study not only enrich the applicability of the COR Theory theoretically but also respond to the practical needs of China's educational digitalization. Currently, the Ministry of Education of China is implementing the \"Education Digitalization Strategy Action\", emphasizing the improvement of teacher professional development and educational quality through digital means [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Against this policy background, teachers' digital resilience has become a key psychological resource for ensuring the implementation of educational digitalization, while job burnout is a risk factor that needs to be focused on in digital reform. Therefore, the results of this study reveal that teachers' psychological adaptability and resource management are particularly important in the Chinese context, which not only provides evidence from emerging economies for the international academic community but also offers empirical support for local educational reform.\u003c/p\u003e\u003c/div\u003e"},{"header":"6. Research Contributions","content":"\u003cdiv id=\"Sec40\" class=\"Section2\"\u003e\u003ch2\u003e6.1Theoretical Contributions This study has the following innovations at the theoretical level:\u003c/h2\u003e\u003cp\u003e(1) Expanding the application of the COR Theory in the context of educational digitalization. Previous studies have mostly focused on verifying the COR Theory in traditional professional environments, while this study introduces the theory into the context of university teachers' response to digital transformation, revealing how technostress\u0026mdash; as a resource threat\u0026mdash;affects teachers' sense of meaning through a chain psychological mechanism (reducing digital resilience and increasing job burnout). This not only enriches the explanatory power of the COR Theory in digital education but also deepens its theoretical extension in mental health research in higher education.\u003c/p\u003e\u003cp\u003e(2) Constructing the path of action between digital resilience and sense of meaning, and expanding research on positive psychology and professional adaptability. Previous studies have mainly focused on the negative effects of job burnout, while paying less attention to the buffering role of resilience under digital teaching stress. This study verifies the mediating role of digital resilience between technostress and sense of meaning and further reveals its chain effect through job burnout, providing a new explanatory framework for understanding university teachers' psychological adaptation and expanding the application of career construction theory in digital education contexts.\u003c/p\u003e\u003cp\u003e(3) Proposing sense of meaning as an important indicator of professional well-being and verifying its impact path. Existing studies have mostly used job satisfaction or well-being as outcome variables, while this study incorporates teacher sense of meaning into the context of digital education, emphasizing its core role in the realization of teaching value and professional identity, and enriching the research dimensions of \"sense of meaning\" in work motivation theory and educational psychology.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec41\" class=\"Section2\"\u003e\u003ch2\u003e6.2 Practical Contributions\u003c/h2\u003e\u003cp\u003eThis study not only has innovative value in theory but also provides important implications for educational management and policy formulation:\u003c/p\u003e\u003cp\u003e(1) Providing intervention ideas for alleviating teachers' technostress. Technostress has been proven to be an important risk factor affecting teachers' professional health. Universities should reduce teachers' technical burden and anxiety by optimizing digital teaching platforms, providing timely technical support, and offering flexible training.\u003c/p\u003e\u003cp\u003e(2) Providing strategic basis for improving teachers' digital resilience. The results show that digital resilience is a key resource for alleviating technostress, reducing job burnout, and enhancing sense of meaning. Universities can enhance teachers' digital skills and psychological coping abilities through systematic training, resilience workshops, and peer support groups.\u003c/p\u003e\u003cp\u003e(3) Providing management implications for preventing job burnout and enhancing sense of meaning. Managers should focus on teaching innovation and teachers' value contributions in performance evaluation, avoid psychological burden caused by single-index assessment, and help teachers reconnect with educational missions through organizational support and incentive mechanisms\u0026mdash;thus enhancing professional sense of meaning and improving work engagement and educational quality.\u003c/p\u003e\u003c/div\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eAgainst the backdrop of the accelerating digital transformation of education, technostress faced by university teachers has become a key factor affecting their professional health and teaching effectiveness. Based on the COR Theory, this study constructed and verified a chain mediation model of \"technostress\u0026mdash;digital resilience\u0026mdash;job burnout\u0026mdash;teacher sense of meaning\", revealing the psychological adaptation mechanism of teachers in digital contexts.\u003c/p\u003e\u003cp\u003eThe research results show that: (1) Technostress significantly negatively predicts teachers' sense of meaning; (2) Digital resilience plays a mediating role between technostress and sense of meaning, indicating that resilience is an important psychological resource for alleviating the impact of digital transformation; (3) Job burnout plays a negative mediating role between technostress and sense of meaning, and together with digital resilience, forms a chain mediation path. This finding not only enriches psychological theories in the context of digital education but also provides a new perspective for understanding how university teachers maintain their educational missions amid technological changes.\u003c/p\u003e\u003cp\u003eBased on the above findings, this study puts forward the following practical implications: Universities should optimize the digital teaching environment to reduce teachers' technostress; enhance teachers' digital resilience through training, psychological support, and organizational resources; and help teachers maintain professional sense of meaning and prevent burnout through reasonable performance evaluation and value guidance\u0026mdash;thus promoting the sustainable development of educational digital reform.\u003c/p\u003e"},{"header":"8. Limitations and Suggestions for Future Research","content":"\u003cp\u003eAlthough this study has achieved certain results in theory and practice, it still has the following limitations, which need to be improved in future research:\u003c/p\u003e\u003cdiv id=\"Sec44\" class=\"Section2\"\u003e\u003ch2\u003e8.1 Limitations in Research Design\u003c/h2\u003e\u003cp\u003eThis study adopts a cross-sectional design and collects data through one-time questionnaires, making it difficult to make strict inferences about the causal relationships between variables. Future studies can adopt longitudinal designs or follow-up studies to form mixed-methods research, so as to verify the dynamic evolution process between technostress, digital resilience, job burnout, and sense of meaning. At the same time, moderating variables such as organizational support and self-efficacy can be introduced to expand the research on the mechanism of technostress and further examine the long-term psychological impact of educational digitalization.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec45\" class=\"Section2\"\u003e\u003ch2\u003e8.2 Limitations in Data Collection and Sample Representativeness\u003c/h2\u003e\u003cp\u003eThe sample of this study mainly comes from some universities in China, which may have limitations in cultural and institutional backgrounds, affecting the external validity of the conclusions. Future studies can adopt cross-regional and cross-cultural comparative studies to explore the differences in technostress coping strategies, digital resilience levels, and sense of meaning perception among teachers in different educational systems\u0026mdash;thus enhancing the generalizability of the research conclusions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec46\" class=\"Section2\"\u003e\u003ch2\u003e8.3 Limitations in Measurement Tools\u003c/h2\u003e\u003cp\u003eAlthough this study uses mature scales, the Digital Resilience Scale is still based on Western educational backgrounds, and its adaptability in Chinese university scenarios needs further verification. Future studies can combine localized interviews and scale revisions to develop a more culturally adaptable measurement tool for teachers' digital resilience and test its reliability and validity through confirmatory factor analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec47\" class=\"Section2\"\u003e\u003ch2\u003e8.4 Limitations in Model Variable Selection\u003c/h2\u003e\u003cp\u003eThis study mainly focuses on technostress, digital resilience, job burnout, and sense of meaning, and does not include moderating variables that may play a role, such as organizational support, digital teaching self-efficacy, and emotional intelligence. Future studies can introduce moderated mediation models based on the existing model to explore how individual and contextual factors interact to influence teachers' psychological adaptation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec48\" class=\"Section2\"\u003e\u003ch2\u003e8.5 Methodological Limitations\u003c/h2\u003e\u003cp\u003eThis study adopts a single quantitative method, which may have the risk of common method bias. Future studies can adopt mixed-methods research, combining questionnaire surveys with in-depth interviews, to verify the chain mediation model from multiple dimensions and explore teachers' subjective experiences of digital teaching challenges and professional meaning\u0026mdash;thus enhancing the explanatory power and theoretical depth of the research results.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study adhered to the provisions of the Helsinki Declaration. The participation was voluntary and anonymous. All participants had signed an informed consent form before completing the questionnaire. The study was approved by the Academic Ethics Committee of Hengshui University.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe author declares that there are no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\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\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eThe author solely conceived the study, collected and analyzed the data, and wrote the manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe author would like to thank all university teachers who participated in the survey.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; information\u003c/p\u003e\n\u003cp\u003eDr.Yuqiao Luo is a researcher in educational management and psychology, specializing in digital transformation in higher education, and well-being.\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe data is confidential. 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High Educ. 2022;83(3):695\u0026ndash;710. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10734-021-00695-7\u003c/span\u003e\u003cspan address=\"10.1007/s10734-021-00695-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":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":"Digital transformation of education, Technostress, Digital resilience, Job burnout, Work meaningfulness, Chain mediation","lastPublishedDoi":"10.21203/rs.3.rs-7493995/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7493995/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the accelerating digital transformation of education, university teachers face unprecedented challenges in instructional design, technology application, and professional role identification, leading to a significant increase in technostress, which may undermine their perception of work meaningfulness. Based on the Conservation of Resources Theory, this study constructed a chain mediating model involving technostress, digital resilience, job burnout, and work meaningfulness to explore the mechanism of psychological resources among university teachers in the context of educational digital transformation. A questionnaire survey was conducted to collect data from 1029 teachers at multiple universities in China, and structural equation modeling was used to test the research hypotheses. The results showed that: (1) Technostress significantly and negatively predicted work meaningfulness; (2) Digital resilience and job burnout played a significant chain mediating role between technostress and work meaningfulness; (3) The improvement of digital resilience could alleviate job burnout, thereby enhancing teachers' perception of work meaningfulness. This study not only enriches the theoretical exploration of the relationship between technostress and positive psychological outcomes in the context of digital education but also provides empirical evidence for universities to implement teacher support interventions and construct resilience training mechanisms during digital transformation.\u003c/p\u003e","manuscriptTitle":"The Impact of Technostress on Work Meaningfulness Among University Teachers in the Context of Digital Transformation of Education: The Chain Mediating Role of Digital Resilience and Job Burnout","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-21 16:46:52","doi":"10.21203/rs.3.rs-7493995/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-11-13T08:45:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"297273826661754806912148197402303509344","date":"2025-10-11T00:58:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"63699316512884522180377873620676028626","date":"2025-10-09T02:55:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-08T18:41:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-10T13:54:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-09T12:33:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-05T08:42:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2025-09-05T08:37:05+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":"7f61abfc-08d2-4e14-9574-664b455afa47","owner":[],"postedDate":"October 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-21T16:46:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-21 16:46:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7493995","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7493995","identity":"rs-7493995","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}