Skip to main content
Download PDF

The relationship between Internet addiction and academic burnout in undergraduates: a chain mediation model

BMC Public Health volume 25, Article number: 1523 (2025) Cite this article

Abstract

Background

In the hybrid teaching context, the impact of Internet addiction on academic burnout and its underlying mechanism is still unclear. This study aims to investigate the effect of Internet addiction on academic burnout, and the chain mediating effect of academic engagement and academic self-efficacy in the relationship between Internet addiction and academic burnout.

Methods

This study adopted a quantitative and correlational research design. A stratified random sampling method was used and a sample of 534 undergraduates (148 male and 386 female) from three normal universities participated in this study. Instruments used were the Chinese version of the Internet Addiction Test, the Chinese version of the Utrecht Work Engagement Scale-Student, the Chinese version of the Academic Self-efficacy Scale, and the Learning Burnout Scale of Undergraduates. Data were collected through self-report questionnaires and structural equation modeling was adopted to test the hypotheses using AMOS 24.0 software.

Results

The findings revealed that Internet addiction demonstrated a statistically significant positive association with academic burnout among undergraduate students. Analysis of mediating effects indicated that academic engagement served as a significant mediator in the relationship between Internet addiction and academic burnout. However, academic self-efficacy failed to exhibit a significant mediating effect between these variables. Further examination revealed a significant chain mediating effect of academic engagement and academic self-efficacy in the relationship between Internet addiction and academic burnout.

Conclusion

Internet addiction influences academic burnout of undergraduate students both directly and indirectly through academic engagement and academic self-efficacy. Effective interventions could be implemented in universities to mitigate the negative effects of Internet addiction by enhancing academic engagement and academic self-efficacy, which may reduce students’ vulnerability to academic burnout and its associated educational consequences such as course withdrawal, decreased academic performance, or dropping out.

Peer Review reports

Introduction

Academic burnout among undergraduate students has emerged as a critical concern in higher education, with prevalence rates demonstrating a consistent and alarming upward trajectory [1], from 7.4% in 2017 [2], 33.4% in 2019 [3], 39.29% in 2021 [4], 44.26% in 2022 [5], to 59.9% in 2023 [6], which is associated with significant educational consequences including course withdrawal, decreased academic performance, and increased dropout rates [7]. Moreover, this phenomenon is particularly concerning in normal universities, institutions pivotal for cultivating future educators [8]. The academic well-being of normal students, who bear the responsibility of shaping the next generation’s educational landscape, directly impacts educational quality.

Simultaneously, Internet addiction has emerged as a parallel worldwide concern [9], the Internet addiction proportion of undergraduates in China is 13.8%, which is the highest compared with Japan, Singapore, and the United States [10]. The COVID-19 pandemic and the subsequent shift towards online and hybrid learning modalities have further exacerbated this issue. Research suggests that online learning environments may increase susceptibility to Internet addiction and academic burnout [11, 12], characterized by decreased academic engagement and diminished self-efficacy [13, 14].

Recent longitudinal studies reveal the dynamic and heterogeneous nature of these relationships among Chinese university students [15, 16, 17], yet few have explored this in the context of normal universities. Furthermore, while previous research has examined the relationships between these variables individually [7, 18, 19, 20, 21], comprehensive examinations integrating these constructs within a unified framework remain scarce. This study addresses that gap by proposing a chain mediation model linking Internet addiction to academic burnout through academic engagement and self-efficacy based on a theoretical framework integrating Conservation of Resources (COR) theory [22] and self-efficacy theory [23].

According to this theoretical framework, Internet addiction depletes academic resources through multiple mechanisms: reducing academic engagement via time displacement and attention division [24], while undermining academic self-efficacy through decreased academic engagement and repeated academic underperformance [25]. Revealing the chain mediation effect is more meaningful than examining each mediator independently, as it demonstrates how the loss of one academic resource (engagement) triggers the decline of another (self-efficacy), creating a loss spiral toward academic burnout.

This synergy and sequential depletion process of academic resources is largely overlooked in the existing literature and it extends COR theory’s application beyond workplace contexts by demonstrating how the resource caravan principle and resource loss spiral corollary operate in educational settings [22, 26]. Additionally, normal university students face unique dual academic pressures: mastering both subject knowledge and pedagogical skills while meeting strict professional standards. This makes them particularly susceptible to academic resource loss, positioning them as an ideal group for testing and extending COR theory’s applicability within educational contexts.

Therefore, by focusing on undergraduates from normal universities in H province of China, this study primarily aimed to investigate the effect of Internet addiction on academic burnout and to test the mediating role of academic engagement and academic self-efficacy based on COR theory and self-efficacy theory, which may help researchers find the predictive factors for academic burnout and tailor intervention programs to prevent the negative effect of Internet addiction on academic burnout.

Internet addiction and academic burnout

The COR theory was originally developed in the 1980s and has been extensively applied to stress and burnout research [22, 27, 28]. Its basic tenet is individuals actively seek to acquire and protect their valued resources including personal and energy resources, which typically exist in caravans [22]. The resource loss spiral corollary explicates that initial resource depletion can precipitate a cascade of subsequent losses [22].

Internet addiction represents a significant resource-depleting behavior that can systematically erode students’ personal and energy resources [29]. As stress occurs during resource loss, each iteration of the stress spiral leaves individuals with diminishing resources to offset further losses, resulting in accelerating loss momentum and magnitude [22]. When students’ academic resource depletion reaches the edge of their resource reserves, they will feel exhausted and have a diminished sense of accomplishment and academic underachievement [30]. In academic settings, this manifests as academic burnout caused by gradually escalating stress from sequential chronic and minor losses [31].

Studies have demonstrated that the loss or depletion of resources, such as time, energy, and social support, can contribute to burnout symptoms [31, 32]. Cross-sectional research has consistently demonstrated a significant positive correlation between Internet addiction and academic burnout among Chinese students [18, 33, 34]. Longitudinal studies across diverse adolescent populations have corroborated this relationship [35, 36]. Building upon the existing body of research, the first research hypothesis is proposed:

H1

Internet addiction has a significant positive effect on academic burnout.

The mediating effect of academic engagement

Academic engagement is defined as the positive learning attitude, abundant learning energy, and full immersion shown by individuals in the learning process and it is marked by vigor, dedication and absorption [19, 37]. Consistent with COR theory, Internet addiction initiates a resource loss spiral by depleting valuable resources including time, energy, and attention invested in academic activities manifests as reduced academic engagement [20, 22]. The state of low engagement accelerates the resource loss spiral and makes additional academic resource investment more difficult, resulting in fewer resources to cope with academic stress, ultimately leading to academic burnout [20, 32].

Furthermore, Internet addiction is widely recognized as a risk factor for reduced academic engagement [38, 39], potentially exacerbated by increased Internet availability and persistent use [19]. Students often use the Internet to chat with friends and play games, even during their class time, while completing homework, which leads to low academic engagement [40]. Drawing from the theoretical and empirical evidence, the second hypothesis is proposed:

H2

There is a significant mediating effect of academic engagement in the relationship between Internet addiction and academic burnout.

The mediating effect of academic self-efficacy

Self-efficacy theory suggests that high academic self-efficacy enhances students’ motivation in the face of challenges and stress [23]. According to the resources gain spiral principle of COR theory, academic self-efficacy represents a personal resource as it can foster students’ competency to cope with academic challenges, facilitate adaptive stress management strategies, and create a resource gain spiral [22, 41]. Internet addiction depletes students’ personal resources including academic self-efficacy, leading to poor academic performance, which further undermines students’ academic self-efficacy [32, 42].

Following COR theory’s loss spiral principle, diminished self-efficacy accelerates the resource loss spiral making students less capable of effectively managing academic stress, leading to increased susceptibility to burnout [22, 42]. In fact, academic self-efficacy has been consistently identified as a protective factor against academic burnout, with a negative correlation between them [7, 21]. Thus, Internet addiction may lead to academic burnout through the attenuation of academic self-efficacy and the third hypothesis has been drawn:

H3

There is a significant mediating effect of academic self-efficacy in the relationship between Internet addiction and academic burnout.

The chain mediating effect of academic engagement and academic self-efficacy

Internet addiction triggers the initial resource loss through reduced academic engagement, this engagement deficit then undermines academic self-efficacy since resources typically travel and deteriorate together based on COR theory’s resource caravan principle, leading students to fall into a progressive resource loss spiral where each step intensifies vulnerability to further resource depletion, ultimately leads to academic burnout due to insufficient resources for managing academic stress [22, 32]. According to self-efficacy theory, continuous engagement in learning strengthens belief, and improves knowledge reserves and judgment abilities, thereby reinforcing academic self-efficacy, lower engagement leads to reduced academic self-efficacy, aligning with the resource caravan principle of COR theory [22, 23, 32]. Studies have also consistently demonstrated a significant positive correlation between academic engagement and academic self-efficacy [39, 43].

While the resource loss spiral corollary and resource caravan principle of COR theory has been extensively applied to occupational contexts [22, 26], its application to academic settings remains underexplored. This study addresses this theoretical gap by examining how Internet addiction triggers a sequential depletion of academic resources (academic engagement and academic self-efficacy), ultimately leading to academic burnout, thereby extending these core principles of COR theory to educational contexts (see Fig. 1 for the hypothesized chain mediation model). Based on the evidence presented, the fourth hypothesis is proposed:

H4

There is a significant chain mediating effect of academic engagement and academic self-efficacy in the relationship between Internet addiction and academic burnout.

Fig. 1
figure 1

Hypothesized chain mediation model

Full size image

Methods

Participants

The research design of this study is quantitative and correlational research design. The participants in this study are from three normal universities, which are located in the H province of China. Considering that the data analysis method used in this study includes structural equation modeling (SEM), the current study referred to the A-priori Sample Size Calculator for Structural Equation Models [44]. Using an anticipated effect size of 0.2, the desired statistical power level of 0.8, a total of 4 latent variables with 79 observed variables, and a probability level of 0.05, a sample size of 342 is considered appropriate for this study, which is the minimum sample size to detect an effect. As SEM is a technique that requires a large sample, the researchers collected an extra sample by 50% to account for the loss of samples and non-responders [45], which means an additional sample of 171 (50% of 342) was needed. Consequently, a total sample size of more than 513 participants was necessary. With this sample size, most of the rules of thumb listed in the related literature of SEM are satisfied.

This study employed stratified random sampling from a population of 55,250 undergraduate students across three normal universities in H province, China. The participating institutions had student populations of 25,000 (45.3%), 17,684 (32.0%), and 12,566 (22.8%), respectively. Following the stratified sampling formula (\(Sample = \frac{{size\:of\:entire\:sample}}{{population\:size}} \times layer\,size\)) [46], the minimum required sample sizes were calculated as 232, 164, and 117 students from each respective institution. Participants were subsequently selected using SPSS 26.0’s random sample function applied to the student ID number, with inclusion criteria specifically targeting undergraduate students enrolled in these three normal universities.

Procedure

After obtaining approval from the Ethics Committee for Research involving Human Subjects of University Putra Malaysia Research (JKEUPM-2023-137), the study proceeded to the data collection phase. Participants received comprehensive instructions detailing the study’s purpose, requirements, and procedures. They were assured that participation was voluntary, their responses would remain confidential and anonymized, and they could withdraw at any time without consequences. All participants reviewed these instructions and gave their informed consent before participating in the research.

The expert panel’s evaluation regarding the instrument’s content and face validity yielded satisfactory results. Specifically, the panel found no significant issues with item relevance, content representation, linguistic clarity, or overall alignment with the study’s objectives. After experts reviewed the questionnaires, the electronic questionnaires were distributed in classroom settings through the Questionnaire Star tool. A pilot study was conducted among 236 undergraduate students who were subsequently excluded from the actual study to assess the validity and reliability of the instruments. Finally, the actual research involved 534 participants from the target population.

Measurement instruments

Internet addiction scale

Internet addiction was measured by the Chinese version of the Internet Addiction Test (IAT), which was developed by Young [47] and validated in 2008 based on Hong Kong undergraduates [48]. It contains 20 items and adopts a 5-point Likert scoring method ranging from 1 (rarely) to 5 (always) with an average score of 3. More serious Internet addiction is indicated by a higher score. An example item is: “Do you find that you stay online longer than you intended?” Previous psychometric validation reports suggested that some items of this scale are outdated [49, 50]. To make sure the items are more suitable to the population of this study, items 2 [51], 3 [50], 7 [49], and 8 [52] were rephrased. The revised items and the scale are provided in the Supplementary file (see Supplementary file).

Exploratory factor analysis was conducted based on the pilot study with a sample (n = 208) similar to the actual study and a three-factor solution was supported. The three factors are emotional and cognitive preoccupation (ECP), withdrawal and social problems (WSP), and loss of control and interference with daily life (LCI). The Cronbach’s alpha coefficient for each sub-construct was 0.88, 0.83, and 0.77, respectively. The confirmatory factor analysis (CFA) results indicated that the three-factor model fits the data of the actual study well: χ2(113) = 235.52, p < 0.001; χ2/df = 2.084, RMSEA = 0.047, CFI = 0.948, and SRMR = 0.040. The results showed that the reliability and validity of IAT were good in this study.

Academic engagement scale

Academic engagement was evaluated by the Chinese version of UWES-Student (UWES-S), which was compiled by Schaufeli et al. [53] and the Chinese version of it was validated based on a sample of college students [37]. There are three dimensions including vigor, dedication, and absorption, with a total of 17 items. It is a 7-point Likert scale with 1 representing “never” and 7 representing “always”. A sample item is: “I experience happiness when I concentrate on learning.” The Cronbach’s alpha coefficient for each sub-construct was 0.83, 0.83, and 0.79, respectively. The CFA results suggested that the three-factor model fits the data well: χ2(61) = 175.98, p < 0.001; χ2/df = 2.885, RMSEA = 0.062, CFI = 0.955, and SRMR = 0.036. Hence, the reliability and validity of the scale were good in this study.

Academic self-efficacy scale

The Academic Self-Efficacy Scale (ASS) was used to test academic self-efficacy. The ASS was developed by Pintrich and DeGroot [54] and was revised by Liang based on a sample of Chinese undergraduate students [55]. Learning ability self-efficacy (LAS) and learning behavior self-efficacy (LBS) are two distinct dimensions of ASS, which have 22 items, and each dimension has 11 items. The scale was scored at 5 points on a Likert scale with 1–5 points from “completely inconsistent” to “completely consistent”. Higher scores correspond to higher levels of academic self-efficacy. A representative item is: “I believe in my ability to do well in my studies.” Cronbach’s alpha coefficients for the two sub-constructs were 0.88 and 0.83. The CFA results showed that the two-factor model fits the data well: χ2(74) = 175.09, p < 0.001; χ2/df = 2.366, RMSEA = 0.053, CFI = 0.962, and SRMR = 0.038. To conclude, the scale has good reliability and validity in this study.

Academic burnout scale

The current study adopted the Learning Burnout Scale of Undergraduates (LBSU) compiled by Lian et al. [56] to assess academic burnout, which has 20 items and covers three dimensions, namely, low mood (LM), misbehavior, and low sense of accomplishment (LSA). Each item was rated from 1 (not at all like me) to 5 (very much like me) with an average score of 3. Higher scores indicate greater academic burnout. An example item is: “I felt exhausted after learning for a whole day”. The Cronbach’s alpha coefficient for each sub-construct was 0.81, 0.71, and 0.71, respectively. The CFA results indicated that the three-factor model fits the data well: χ2(74) = 165.47, p < 0.001; χ2/df = 2.236, RMSEA = 0.050, CFI = 0.961, and SRMR = 0.042. Therefore, the reliability and validity of LBSU were good in this study.

Statistical analysis

Preliminary to hypothesis testing, data were subjected to examination, reliability analysis, and descriptive statistical analysis using SPSS version 26.0. The measurement model was evaluated through CFA using AMOS version 24.0. Model fit was appraised using multiple indices: Chi-Square/Degrees of Freedom ratio (χ2/df), Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR). Acceptable model fit was determined based on the following criteria: χ2/df ≤ 5, CFI ≥ 0.90, RMSEA ≤ 0.08, and SRMR ≤ 0.08 [57]. A non-significant χ2 generally indicates good model fit; however, in large samples and complex models, the test becomes overly sensitive, making significance less informative for evaluating fit [58]. Therefore, the chi-square results are reported in line with standard practice for completeness. The hypothesized chain mediation model was examined using path analysis within the SEM framework. To assess the stability of indirect effects, bootstrap resampling (5000 samples) was employed to compute bias-corrected 95% confidence intervals. Significant mediation was inferred when confidence intervals excluded zero [59].

Results

Data examination

A total of 534 respondents (148 male and 386 female) participated in the actual study. It took about 10 min to complete the entire questionnaire and the questionnaires answered in less than 3 min were excluded from further analysis. As a result, the sample size decreased to 525.

Initial data analysis commenced with an examination of missing values through frequency distribution analysis [60], which revealed no missing data. Subsequently, unengaged responses were identified by calculating the standard deviation (SD) of individual response patterns. Following Lanning’s recommendations [61], three cases exhibiting SD values below 0.50 were eliminated, reducing the sample size to 522. The next phase involved outlier detection. Univariate outliers were identified using standardized scores (z-scores), with cases falling outside the range of ± 3.29 considered outliers [62]. This process resulted in the removal of 8 cases, further reducing the sample to 514. Multivariate outliers were assessed using the squared Mahalanobis distance (Mahalanobis d2). In accordance with Collier’s criterion [63], cases of Mahalanobis d2 with both p1 and p2 values below 0.001 were deemed potential outliers and subsequently removed, leading to the elimination of an additional 22 cases. Following this comprehensive data screening and cleaning process, the final sample size was 492, with an effective rate of 92.13%.

Normality examination of the data (n = 492) showed that the values of the skewness and kurtosis of items ranged from − 0.49 to 1.32 and − 1.06 to 1.63, respectively, which means the data were distributed normally [62].

Common method bias test

Since this study adopts a questionnaire survey method and the data come from participants’ self-reports, there may be common method bias. Harman’s One-Factor Test was performed to detect whether common method bias exists or not [64]. Exploratory factor analysis was conducted based on all the items of this study and principal components un-rotated factor analysis was used. The results showed that the first component explained 22.20% of the total variance, which was less than 50% of the variance, indicating the common method bias problem was not serious in this study [65].

Testing for the measurement model

Results revealed that all items from the sub-constructs have a factor loading of more than 0.50, ranging from 0.73 to 0.98. The measurement model demonstrated an adequate fit to the data (χ2(1569) = 2661.62, p < 0.001; χ2/df = 1.696; RMSEA = 0.038; CFI = 0.903; SRMR = 0.051), supporting the model’s validity for further structural analyses. The Average Variance Extracted (0.67–0.74) and Composite Reliability (0.86–0.95) values surpassed their respective thresholds of 0.50 and 0.60 [60, 66], establishing satisfactory convergent validity and composite reliability for all constructs.

Descriptive analysis

The demographic profiles of participants

The study included 492 valid responses from three normal universities. Participants’ mean age was 19.13 ± 1.00 years. The sample was predominantly female (75.4%) and evenly distributed across academic years. Most students majored in humanities (53.7%), came from rural areas (67.5%) and middle-income families (82.1%). This distribution, particularly the gender and major imbalances, reflects the typical demographic composition of normal universities in the region. Detailed demographic breakdowns are provided in Table 1.

Table 1 Distribution of participants by demographic information (n = 492)
Full size table

Testing for the structural model

The structural equation model demonstrated satisfactory fit indices across multiple criteria (χ2(1569) = 2661.62, p < 0.001; χ2/df = 1.696; RMSEA = 0.038; CFI = 0.903; SRMR = 0.051), warranting subsequent hypothesis testing. The structural relationships among the studied variables are depicted in Fig. 2. Examination of the direct effect demonstrated that Internet addiction had a significant positive effect on academic burnout (β = 0.22, 95% CI [0.13, 0.32]), accounting for 45.83% of the total effect and confirming the first hypothesis (Table 2).

Fig. 2
figure 2

Summarize of structural model. Note: The values shown are the standardized coefficients. ***p < 0.001

Full size image
Table 2 Summary of mediating effect
Full size table

The summary of mediating effect is presented in Table 2. Utilizing bias-corrected bootstrap resampling procedures (5000 samples), it was identified a significant mediating effect of academic engagement in the relationship between Internet addiction and academic burnout (β = 0.16, 95% CI [0.08, 0.25]). This mediating pathway explained 33.33% of the total effect, supporting the second hypothesis regarding the indirect influence of Internet addiction on academic burnout through academic engagement. Notably, the mediating effect of academic self-efficacy in the relationship between Internet addiction and academic burnout was non-significant (β = 0.00, 95% CI [-0.04, 0.04]), leading to the rejection of the third hypothesis. Further analysis revealed a significant chain mediating effect through academic engagement and academic self-efficacy (β = 0.10, 95% CI [0.05, 0.17]), supporting the fourth hypothesis. This chain mediation accounted for 20.83% of the total effect. Collectively, all mediating pathways explained 54.17% of the total effect between Internet addiction and academic burnout.

Discussion

The effect of Internet addiction on academic burnout

The present study aimed to investigate the effect of Internet addiction on academic burnout among undergraduate students from normal universities. Results indicated that Internet addiction was a significant positive predictor for academic burnout, suggesting that higher levels of Internet addiction were associated with increased levels of academic burnout. In the context of previous related research, this finding aligns with and extends upon existing literature highlighting the detrimental effects of Internet addiction on academic outcomes and mental health [10].

Prior studies have consistently demonstrated a link between excessive Internet use and negative academic outcomes, including decreased academic performance and increased psychological distress [9]. Furthermore, research has indicated that Internet addiction is associated with heightened levels of stress, anxiety, and depression, all of which are key components of academic burnout [10, 18, 33]. The result is also in line with past studies conducted on adolescents [34, 67] and undergraduate students from other types of universities [18, 33]. Moreover, this finding is consistent with the results that mobile phone addiction predicts academic burnout [5, 67]. The above evidence collectively supports the notion that Internet addiction can serve as a significant risk factor for the emergence of academic burnout among undergraduate students from normal universities.

The mediating effect of academic engagement

There is a significant mediating effect of academic engagement in the relationship between Internet addiction and academic burnout, which indicates that Internet addiction influences academic burnout through academic engagement. To be more specific, the higher the level of Internet addiction the lower the level of academic engagement, which results in a higher level of academic burnout.

The finding demonstrated that the positive effect of Internet addiction among undergraduate students from normal universities on increasing academic burnout may have worked through weakening their academic engagement. Previous investigations have also established a strong correlation between problematic Internet use and reduced engagement in academic activities, such as decreased motivation, lower levels of concentration, and diminished self-regulation, all of which contribute to the development of academic burnout [7, 14, 38]. Moreover, previous studies have also highlighted the importance of addressing problematic Internet use in promoting academic engagement and preventing burnout [34].

Building upon these existing findings, the current study highlights the importance of academic engagement in understanding the internal mechanism between Internet addiction and academic burnout. According to COR theory, when undergraduate students put many of their resources into Internet entertainment activities, their resources including their time, energy, and cognitive resources would be depleted, which would result in very few resources being left for academic tasks [22]. By this, their engagement in academics would be reduced, so when facing academic stress, they are prone to fall into the lose spiral and experience academic burnout [32].

Moreover, research has indicated that Internet addiction can act as a barrier to active learning behaviors, such as participation in class discussions, completion of assignments on time, and seeking out additional educational resources [32, 42], aligning with the resource investment principle of COR theory. This lack of engagement can further exacerbate feelings of isolation, stress, and disconnection from academic pursuits, ultimately impacting the academic experience of students, which may lead to academic burnout.

The mediating effect of academic self-efficacy

The study found that academic self-efficacy can independently mediate the relationship between Internet addiction and academic burnout, suggesting that students’ beliefs in their academic abilities play a crucial role in determining their experience of academic burnout. However, in the chain mediation model including academic engagement, its mediating effect becomes non-significant. It is suggested that in the chain mediation model, the mediating effect of academic self-efficacy is overshadowed by the mediating effect of academic engagement, which indicates that academic engagement plays a more dominant role in mediating the relationship between Internet addiction and academic burnout compared to academic self-efficacy.

This can be explained using COR theory [22] and self-efficacy theory [23]. According to COR theory [22], Internet addiction depletes critical resources like time and energy, directly affecting academic engagement, which involves active, behavioral involvement (vigor, dedication, absorption). In contrast, academic self-efficacy, a more stable belief shaped by accumulated experiences, changes more gradually based on self-efficacy theory [23], making academic engagement more sensitive to immediate resource loss.

Internet addiction has been linked to various negative outcomes, including decreased academic self-efficacy, which has been shown to be a protective factor against academic burnout [7, 21, 39]. Students with higher levels of academic self-efficacy tend to be more resilient and motivated in the face of academic challenges [15], which will make them less susceptible to academic burnout. This may be the reason that academic self-efficacy independently mediates the relationship between Internet addiction and academic burnout. This also confirms the speculation on this mediating mechanism based on COR theory and self-efficacy theory.

The chain mediating effect of academic engagement and academic self-efficacy

There is a chain mediating effect of academic engagement and academic self-efficacy in the link between Internet addiction and academic burnout, suggesting that Internet addiction indirectly affects academic burnout through academic engagement and academic self-efficacy. Specifically, students addicted to the Internet may be less engaged in academic pursuits, leading to lower levels of academic self-efficacy and increased academic burnout symptoms. The higher the level of Internet addiction, the lower the level of academic engagement, which leads to the decline of academic self-efficacy level and, ultimately, increases the risk of academic burnout.

The COR theory posits that individuals strive to acquire, protect, and maintain their resources, which typically exist in caravans [22]. The resources lost through addictive behaviors (such as Internet addiction) would leave fewer resources to use for academic tasks (academic engagement), contributing to lower academic self-efficacy based on the resource loss spiral corollary and resource caravan principle of COR theory, and resulting in the experience of academic burnout. Studies have shown that Internet addiction has negative effects on academic performance and mental health including increased levels of stress, anxiety, and depression [10]. It highlights the importance of both academic engagement and academic self-efficacy in influencing the academic outcomes of students [7, 15, 39]. Academic engagement and academic self-efficacy have been consistently linked to resilience, well-being, higher levels of confidence, adaptive coping strategies, and academic achievement [15, 38, 43].

Combined with the findings of previous studies, the current study has proved the mediating mechanisms underlying the relationship between Internet addiction and academic burnout by focusing on academic engagement and academic self-efficacy and further revealing the complex relationship between these variables. This study also further confirms that the previous explanation of the mediating mechanism of Internet addiction affecting academic burnout based on COR theory and self-efficacy theory is in line with the actual situation.

Implications

The findings of this research showed that Internet addiction influences academic burnout of undergraduate students both directly and indirectly through academic engagement and academic self-efficacy, which contribute to a deep understanding of the internal mediating mechanisms underlying the impact of Internet addiction on academic burnout among undergraduate students, particularly in the online and offline hybrid teaching context, and provide an empirical basis for targeted interventions.

For practical implications, reducing the level of academic burnout among undergraduate students from normal universities is of significant importance for promoting their academic performance, which can help cultivate a high-quality teacher workforce and enhance education quality because they are future educators. Correspondingly, reducing students’ addiction to the Internet may be the key to preventing or mitigating academic burnout. To address this, higher education institutions should implement comprehensive interventions that foster healthier digital behaviors. These interventions may include structured educational programs and campus-wide digital detox initiatives designed to recalibrate students’ online habits [68]. Additionally, universities could implement network management systems that regulate non-academic Internet access during instructional periods, particularly in online learning contexts. Dormitory administrators can establish technology-free zones or designated offline hours within residence halls, promoting a more academically supportive residential environment [69]. Moreover, counseling centers could provide specialized counseling services including targeted group therapy sessions and workshops focused on technology-related maladaptive behaviors.

Furthermore, advancements in technology further enable universities to adopt AI-driven interventions such as early detection mechanisms, chatbot-based behavioral support systems and virtual coaches, AI-enhanced time management tools and wearable technology that tracks online behavior, offering more personalized and proactive support systems [70, 71]. Beyond reducing Internet addiction, fostering academic engagement and self-efficacy is crucial. Universities can design programs to boost these factors, including gamified learning platforms to enhance engagement, virtual reality simulations to build self-efficacy, and structured academic coaching to support students’ goal-setting and progress monitoring, creating a more immersive, rewarding academic experience that competes with the appeal of non-academic online content [72].

Theoretically, the study contributes to the literature by validating the COR theory in explaining academic burnout. The structural model demonstrates that academic resource loss is crucial in the mechanism linking Internet addiction to academic burnout. While previous research has predominantly focused on direct resource depletion processes, this study illuminates how Internet addiction leads to academic burnout by triggering a complex resource loss spiral through multiple mediating pathways. In addition, it advances COR theory by extending its resource caravan principle to academic settings, a domain where this principle has been underutilized compared to occupational contexts. Specifically, it highlights how these academic resources travel together in a sequential pattern of depletion when exposed to stressors. They do not exist in isolation but instead form an interconnected system that, once depleted, collectively contributes to academic burnout. The chain mediation model validates both the resource loss spiral corollary and the resource caravan principle in an educational context beyond the well-established domain of work-related burnout [22, 26]. This extension marks a contribution to COR theory, demonstrating its relevance and explanatory power in understanding academic burnout among undergraduate students.

Limitations and recommendations

As the data was collected by questionnaire survey, it relies heavily on the self-report of the participants. Their self-report may not be as accurate as the actual situation because of the social approval effect and subjective bias, and their answers may tend to be less serious than the real situation. Although filling out the questionnaires of this study is anonymous and this study screened the data using multiple methods, this may still not completely eliminate the negative impact of the social approval effect and subjective bias.

Another research limitation lies in the generalizability of the research findings. The participants of this study were undergraduate students from three normal universities and the survey was conducted in one province in China due to the limitation of research resources, so it needs to be treated with caution when generalizing the research findings to other groups. Subsequent investigations should aim to broaden the scope and diversity of the sample, which would not only enhance the generalizability of findings but also facilitate more nuanced analyses of underrepresented demographic subgroups, leading to the possibility of making a more comprehensive examination of whether individual characteristics may moderate the relationships. Furthermore, future studies should consider incorporating additional psychosocial and environmental variables that may exert moderating or mediating effects on the established relationships, such as achievement motivation orientations, social support, coping strategies, and learning environment (fully online, hybrid, and traditional in-person).

This study adopts a correlational research design, which provides a snapshot of the relationship between Internet addiction and academic burnout. However, to gain a deeper understanding of the causal relationship between these two variables, it is recommended that future research employs a longitudinal research design to investigate this relationship and to explore the underlying mechanisms and the potential moderating factors that may impact this relationship, by which researchers can track changes in Internet addiction and academic burnout over time, allowing for the examination of causality and the identification of potential risk factors and protective factors. This is particularly necessary given that previous longitudinal studies have illuminated the dynamic nature of the interrelationships among several key variables pertinent to the current study [14, 15, 16].

Conclusions

The findings reveal that Internet addiction has a significant positive effect on academic burnout, indicating that elevated levels of Internet addiction correspond to increased manifestations of academic burnout symptomatology. Furthermore, this relationship is mediated by academic engagement, suggesting that Internet addiction’s impact on academic burnout operates through the mechanism of diminished academic engagement. More notably, there is a chain mediating effect of academic engagement and academic self-efficacy in the relationship between Internet addiction and academic burnout, which illustrates that Internet addiction exerts its influence on academic burnout through a progressive deterioration of academic engagement and academic self-efficacy. Specifically, heightened levels of Internet addiction are associated with decreased academic engagement, which subsequently leads to reduced academic self-efficacy, ultimately culminating in elevated levels of academic burnout. These findings provide crucial insights into how Internet addiction affects academic burnout through engagement and self-efficacy, offering both theoretical validation of COR theory in educational contexts and practical intervention strategies to protect student well-being and enhance educational outcomes.

Data availability

The datasets used in the current study are available from the corresponding author on reasonable request.

References

  1. Yu X, Yin M, Zhao Y, Xin S. A cross-temporal meta-analysis of changes in Chinese college students’ learning burnout. Psychology: Techniques Appl. 2020;8(2):74–83. https://doiorg.publicaciones.saludcastillayleon.es/10.16842/j.cnki.issn2095-5588.2020.02.002

    Article  Google Scholar 

  2. Al-Alawi M, Al-Sinawi H, Al-Qubtan A, Al-Lawati J, Al-Habsi A, Al-Shuraiqi M, et al. Prevalence and determinants of burnout syndrome and depression among medical students at Sultan Qaboos University: A cross-sectional analytical study from Oman. Arch Environ Occup Health. 2017;74(3):130–9. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/19338244.2017.1400941

    Article  PubMed  Google Scholar 

  3. Frajerman A, Morvan Y, Krebs MO, Gorwood P, Chaumette B. Burnout in medical students before residency: A systematic review and meta-analysis. Eur Psychiatry. 2019;55:36–42. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.eurpsy.2018.08.006

    Article  PubMed  Google Scholar 

  4. Wang J, Bu L, Li Y, Song J, Li N. The mediating effect of academic engagement between psychological capital and academic burnout among nursing students during the COVID-19 pandemic: A cross-sectional study. Nurse Educ Today. 2021;102:104938. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.nedt.2021.104938

    Article  PubMed  PubMed Central  Google Scholar 

  5. Zhou Z, Liu H, Zhang D, Wei H, Zhang M, Huang A. Mediating effects of academic self-efficacy and smartphone addiction on the relationship between professional attitude and academic burnout in nursing students: A cross-sectional study. Nurse Educ Today. 2022;116:105471. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.nedt.2022.105471

    Article  PubMed  Google Scholar 

  6. Liu Z, Xie Y, Sun Z, Liu D, Yin H, Shi L. Factors associated with academic burnout and its prevalence among university students: A cross-sectional study. BMC Med Educ. 2023;23(1):317. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12909-023-04316-y

    Article  PubMed  PubMed Central  Google Scholar 

  7. Ma Y. The impact of academic self-efficacy and academic motivation on Chinese EFL students’ academic burnout. Learn Motiv. 2024;85:101959. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.lmot.2024.101959

    Article  Google Scholar 

  8. Dong J, Hassan NC, Hassan A, Bin, Chen D, Guo W. Effect of achievement motivation and Self-Efficacy on general Well-Being among students at normal universities in Ningxia: the mediating role of time management. Behav Sci. 2024;14(1):15. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/bs14010015

    Article  Google Scholar 

  9. Meng SQ, Cheng JL, Li YY, Yang XQ, Zheng JW, Chang XW, et al. Global prevalence of digital addiction in general population: A systematic review and meta-analysis. Clin Psychol Rev. 2022;92:102128. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.cpr.2022.102128

    Article  PubMed  Google Scholar 

  10. Duc TQ, Chi VTQ, Huyen NTH, Quang PN, Thuy BT, Nguyen Di K. Growing propensity of internet addiction among Asian college students: Meta-analysis of pooled prevalence from 39 studies with over 50,000 participants. BMC Public Health. 2024;227:250–8. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.puhe.2023.11.040

    Article  Google Scholar 

  11. Niu H, Wang S, Tao Y, Tang Q, Zhang L, Liu X. The association between online learning, parents’ marital status, and internet addiction among adolescents during the COVID-19 pandemic period: A cross-lagged panel network approach. J Affect Disord. 2023;333:553–61. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jad.2023.04.096

    Article  PubMed  Google Scholar 

  12. Huang C, Tu Y, Han Z, Jiang F, Wu F, Jiang Y. Examining the relationship between peer feedback classified by deep learning and online learning burnout. Comput Educ. 2023;207:104910. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.compedu.2023.104910

    Article  Google Scholar 

  13. Baskici C, Aytar A, Ersoy H, Wiktsröm-Grotell C, Arell-Sundberg M, Neves H, et al. Being in the digital box. Academic staff experiences in online practical teaching: A qualitative study from six universities and countries. Heliyon. 2024;10(2):e24275. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.heliyon.2024.e24275

    Article  PubMed  PubMed Central  Google Scholar 

  14. Huang C, Tu Y, He T, Han Z, Wu X. Longitudinal exploration of online learning burnout: the role of social support and cognitive engagement. Eur J Psychol Educ. 2024;39(1):361–88. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s10212-023-00693-6

    Article  Google Scholar 

  15. Cao X, Liu X. Self-esteem as a predictor of anxiety and academic self-efficacy among Chinese university students: A cross-lagged analysis. Curr Psychol. 2024;43:19628–38. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s12144-024-05781-4

    Article  Google Scholar 

  16. Liu X, Zhang Y, Cao X. Achievement goal orientations in college students: longitudinal trajectories, related factors, and effects on academic performance. Eur J Psychol Educ. 2024;39:2033–55. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s10212-023-00764-8

    Article  Google Scholar 

  17. Gao W, Ji J, Zhang W, Liu X. Depression and approach-avoidance achievement goals of Chinese undergraduate students: A four‐wave longitudinal study. Br J Educ Psychol. 2024;94(1):151–64. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/bjep.12638

    Article  PubMed  Google Scholar 

  18. Qin Y, Liu S, Xu X. The causalities between learning burnout and internet addiction risk: A moderated-mediation model. Soc Psychol Educ. 2023;26(5):1455–77. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s11218-023-09799-7

    Article  Google Scholar 

  19. Liu S, Zou S, Zhang D, Wang X, Wu X. Problematic internet use and academic engagement during the COVID-19 lockdown: the indirect effects of depression, anxiety, and insomnia in early, middle, and late adolescence. J Affect Disord. 2022;309:9–18. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jad.2022.04.043

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Salmela-Aro K, Tang X, Upadyaya K. Study demands-resources model of student engagement and burnout. Handbook of research on student engagement. Cham: Springer International Publishing; 2022. pp. 77–93. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/978-3-031-07853-8_4

    Chapter  Google Scholar 

  21. Huang S, Li X, Chen SH, Fang Z, Lee CY, Chiang YC. Enhancing academic self-efficacy on decreasing adolescents’ unmonitored internet usage and depressive mood. Heliyon. 2024;10(1):e23286. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.heliyon.2023.e23286

    Article  PubMed  Google Scholar 

  22. Hobfoll SE, Halbesleben J, Neveu JP, Westman M. Conservation of resources in the organizational context: the reality of resources and their consequences. Annual Rev Organizational Psychol Organizational Behav. 2018;5:103–28. https://doiorg.publicaciones.saludcastillayleon.es/10.1146/annurev-orgpsych-032117-104640

    Article  Google Scholar 

  23. Bandura A. Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev. 1977;84(2):191–215. https://doiorg.publicaciones.saludcastillayleon.es/10.1037/0033-295x.84.2.191

    Article  CAS  PubMed  Google Scholar 

  24. Marin MG, Nuñez X, de Almeida RMM. Internet addiction and attention in adolescents: A systematic review. Cyberpsychol Behav Soc Netw. 2021;24(4):237–49. https://doiorg.publicaciones.saludcastillayleon.es/10.1089/cyber.2019.0698

    Article  PubMed  Google Scholar 

  25. Kleppang AL, Steigen AM, Finbråten HS. Explaining variance in self-efficacy among adolescents: the association between mastery experiences, social support, and self-efficacy. BMC Public Health. 2023;23(1):1665. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12889-023-16603-w

    Article  PubMed  PubMed Central  Google Scholar 

  26. McLinton SS, Jamieson SD, Tuckey MR, Dollard MF, Owen MS. Evidence for a negative loss spiral between co-worker social support and burnout: can psychosocial safety climate break the cycle? Healthcare. 2023;11(24):3168. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/healthcare11243168

    Article  PubMed  PubMed Central  Google Scholar 

  27. Fu L. Social support in class and learning burnout among Chinese EFL learners in higher education: are academic buoyancy and class level important? Curr Psychol. 2024;43(7):5789–803. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s12144-023-04778-9

    Article  Google Scholar 

  28. Gonçalves T, Muñoz-Pascual L, Galende J, Oliveira M, Curado C. Techno-social systems and conservation of resources theory for workplace happiness: evidence of linear and non-linear influences in healthcare. Technol Forecast Soc Change. 2025;212:123910. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.techfore.2024.123910

    Article  Google Scholar 

  29. Fu S, Cai Z, Lim E, Liu Y, Tan CW, Lin Y, et al. Unraveling the effects of mobile application usage on users’ health status: insights from conservation of resources theory. J Assoc Inf Syst. 2023;24(2):452–89. https://doiorg.publicaciones.saludcastillayleon.es/10.17705/1jais.00808

    Article  Google Scholar 

  30. Chen G, Wang J, Huang Q, Sang L, Yan J, Chen R, et al. Social support, psychological capital, multidimensional job burnout, and turnover intention of primary medical staff: a path analysis drawing on conservation of resources theory. Hum Resour Health. 2024;22(1):42. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12960-024-00915-y

    Article  PubMed  PubMed Central  Google Scholar 

  31. Khan AN. Students are at risk? Elucidating the impact of health risks of COVID-19 on emotional exhaustion and academic performance: role of mindfulness and online interaction quality. Curr Psychol. 2024;43(13):12285–98. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s12144-023-04355-0

    Article  Google Scholar 

  32. Vîrgă D, Pattusamy M, Kumar DP. How psychological capital is related to academic performance, burnout, and boredom? The mediating role of study engagement. Curr Psychol. 2022;41(10):6731–43. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s12144-020-01162-9

    Article  Google Scholar 

  33. Wang K, Li Y, Yang Y, Zhang T, Luo J. The role of loneliness and learning burnout in the regulation of physical exercise on internet addiction in Chinese college students. BMC Public Health. 2023;23(1):1994. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12889-023-16783-5

    Article  PubMed  PubMed Central  Google Scholar 

  34. Wu H, Meng G, Wang L, Xiao J, Hu K, Li Q. Understanding the relationships among adolescents’ internet dependence, reward, cognitive control processing, and learning burnout: A network perspective in China. BMC Psychiatry. 2024;24:599. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12888-024-06025-2

    Article  PubMed  PubMed Central  Google Scholar 

  35. Salmela-Aro K, Upadyaya K, Hakkarainen K, Lonka K, Alho K. The dark side of internet use: two longitudinal studies of excessive internet use, depressive symptoms, school burnout and engagement among Finnish early and late adolescents. J Youth Adolesc. 2017;46(2):343–57. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s10964-016-0494-2

    Article  PubMed  Google Scholar 

  36. Liou PY, Huang SC, Chen S. Longitudinal relationships between school burnout, compulsive internet use, and academic decrement: A three-wave cross-lagged study. Comput Hum Behav. 2022;135:107363. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.chb.2022.107363

    Article  Google Scholar 

  37. Li X, Huang R. A revise of the UWES-S of Chinese college samples. Psychol Res. 2010;3(1):84–8.

    Google Scholar 

  38. Liu H, Zhong Y, Chen H, Wang Y. The mediating roles of resilience and motivation in the relationship between students’ english learning burnout and engagement: A conservation-of-resources perspective. Int Rev Appl Linguist Lang Teach. 2023;13:915456. https://doiorg.publicaciones.saludcastillayleon.es/10.1515/iral-2023-0089

    Article  Google Scholar 

  39. Cong Y, Yang L, Ergün ALP. Exploring the relationship between burnout, learning engagement and academic self-efficacy among EFL learners: A structural equation modeling analysis. Acta Psychol (Amst). 2024;248:104394. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.actpsy.2024.104394

    Article  PubMed  Google Scholar 

  40. Anthony WL, Zhu Y, Nower L. The relationship of interactive technology use for entertainment and school performance and engagement: evidence from a longitudinal study in a nationally representative sample of middle school students in China. Comput Hum Behav. 2021;122:106846. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.chb.2021.106846

    Article  Google Scholar 

  41. Teuber Z, Nussbeck FW, Wild E. School burnout among Chinese high school students: the role of teacher-student relationships and personal resources. Educ Psychol (Lond). 2021;41(8):985–1002. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/01443410.2021.1917521

    Article  Google Scholar 

  42. Paloș R. Students’ core self-evaluations and academic burnout: the mediating role of personal resources. J Appl Res High Educ. 2024;16(3):904–18. https://doiorg.publicaciones.saludcastillayleon.es/10.1108/JARHE-04-2023-0152

    Article  Google Scholar 

  43. Bai Y, Wang J, Huo Y, Huo J. The desire for self-control and academic achievement: The mediating roles of self-efficacy and learning engagement of sixth-grade Chinese students. Current Psychology. 2023;42(25):21945–53. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s12144-022-03275-9

  44. Soper DS. A-priori sample size calculator for structural equation models. 2024 [cited 2024 May 28]. Available from: http://www.danielsoper.com/statcalc/caculator.aspx?id=89

  45. Kline P. A handbook of test construction (psychology revivals): introduction to psychometric design. Routledge; 2015. https://doiorg.publicaciones.saludcastillayleon.es/10.4324/9781315695990

  46. Hayes N. Doing psychological research. Second. McGraw-Hill Education; 2021.

  47. Young KS. Caught in the net: how to recognize the signs of internet addiction and a winning strategy for recovery. New York: Wiley; 1998.

    Google Scholar 

  48. Chang MK, Man Law SP. Factor structure for Young’s internet addiction test: A confirmatory study. Comput Hum Behav. 2008;24(6):2597–619. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.chb.2008.03.001

    Article  Google Scholar 

  49. Lu X, Yeo KJ, Guo F, Zhao Z, Wu O. Psychometric property and measurement invariance of internet addiction test: the effect of socio-demographic and internet use variables. BMC Public Health. 2022;22(1):1548. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12889-022-13915-1

    Article  PubMed  PubMed Central  Google Scholar 

  50. Feleke N, Mihretu A, Habtamu K, Amare B, Teferra S. Validation of the amharic version of internet addiction Test-20: A cross-sectional study. Front Psychiatry. 2024;14:1243035. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fpsyt.2023.1243035

    Article  PubMed  PubMed Central  Google Scholar 

  51. Ngai SSY. Exploring the validity of the internet addiction test for students in grades 5–9 in Hong Kong. Int J Adolesc Youth. 2007;13(3):221–37. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/02673843.2007.9747976

    Article  Google Scholar 

  52. Hawi NS. Arabic validation of the internet addiction test. Cyberpsychol Behav Soc Netw. 2013;16(3):200–4. https://doiorg.publicaciones.saludcastillayleon.es/10.1089/cyber.2012.0426

    Article  PubMed  Google Scholar 

  53. Schaufeli WB, Salanova M, González-Romá V, Bakker AB. The measurement of engagement and burnout: A two sample confirmatory factor analytic approach. J Happiness Stud. 2002;3:71–92. https://doiorg.publicaciones.saludcastillayleon.es/10.1023/A:1015630930326

    Article  Google Scholar 

  54. Pintrich PR, DE Groot EV. Motivational and Self-Regulated learning components of classroom academic performance. J Educ Psychol. 1990;82(1):33–40. https://doiorg.publicaciones.saludcastillayleon.es/10.13140/RG.2.1.1714.1201

    Article  Google Scholar 

  55. Liang Y. Correlation between self-efficacy to school work and mental health of university students. Chin J Clin Rehabilitation. 2004;8(24):4962–3.

    Google Scholar 

  56. Lian R, Yang L, Wu L. Relationship between professional commitment and learning burnout of undergraduates and scales developing. Acta Physiol Sinica. 2005;37(5):632–6.

    Google Scholar 

  57. Hu LT, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Model. 1999;6(1):1–55. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/10705519909540118

    Article  Google Scholar 

  58. Kline RB. Principles and practice of structural equation modeling. 5th ed. New York: Guilford; 2023.

    Google Scholar 

  59. Preacher KJ, Hayes AF. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res Methods. 2008;40(3):879–91. https://doiorg.publicaciones.saludcastillayleon.es/10.3758/BRM.40.3.879

    Article  PubMed  Google Scholar 

  60. Hair JFJ, Black WC, Babin BJ, Anderson RE. Multivariate data analysis. 7th ed. London: Prentice Hall; 2010.

    Google Scholar 

  61. Lanning K. Detection of invalid response patterns on the California psychological inventory. Appl Psychol Meas. 1989;13(1):45–56. https://doiorg.publicaciones.saludcastillayleon.es/10.1177/014662168901300105

    Article  Google Scholar 

  62. Tabachnick BG, Fidell LS, Ullman JB. Using multivariate statistics. 6th ed. Boston, MA: Pearson; 2013.

    Google Scholar 

  63. Collier JE. Applied structural equation modeling using AMOS: basic to advanced techniques. 1st ed. New York: Routledge; 2020. https://doiorg.publicaciones.saludcastillayleon.es/10.4324/9781003018414

    Book  Google Scholar 

  64. Lindell MK, Whitney DJ. Accounting for common method variance in cross-sectional research designs. J Appl Psychol. 2001;86(1):114–21. https://doiorg.publicaciones.saludcastillayleon.es/10.1037/0021-9010.86.1.114

    Article  CAS  PubMed  Google Scholar 

  65. Podsakoff PM, MacKenzie SB, Podsakoff NP. Sources of method bias in social science research and recommendations on how to control it. Annu Rev Psychol. 2012;63:539–69. https://doiorg.publicaciones.saludcastillayleon.es/10.1146/annurev-psych-120710-100452

    Article  PubMed  Google Scholar 

  66. Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res. 1981;18(1):39–50. https://doiorg.publicaciones.saludcastillayleon.es/10.2307/3151312

    Article  Google Scholar 

  67. Bai C, Chen X, Han K. Mobile phone addiction and school performance among Chinese adolescents from low-income families: A moderated mediation model. Child Youth Serv Rev. 2020;118:105406. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.childyouth.2020.105406

    Article  Google Scholar 

  68. Schmuck D. Does digital detox work? Exploring the role of digital detox applications for problematic smartphone use and well-being of young adults using multigroup analysis. Cyberpsychol Behav Soc Netw. 2020;23(8):526–32. https://doiorg.publicaciones.saludcastillayleon.es/10.1089/cyber.2019.0578

    Article  PubMed  Google Scholar 

  69. Kivrak Y, Kivrak I. Internet addiction in adolescents and staying at a dormitory: A controlled study. Eur Psychiatry. 2017;41(S1):S690. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.eurpsy.2017.01.1208

    Article  Google Scholar 

  70. Li Y, Liang S, Zhu B, Liu X, Li J, Chen D, et al. Feasibility and effectiveness of artificial intelligence-driven conversational agents in healthcare interventions: A systematic review of randomized controlled trials. Int J Nurs Stud. 2023;143:104494. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.ijnurstu.2023.104494

    Article  PubMed  Google Scholar 

  71. Lin H, Chen Q. Artificial intelligence (AI) -integrated educational applications and college students’ creativity and academic emotions: students and teachers’ perceptions and attitudes. BMC Psychol. 2024;12:487. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40359-024-01979-0

    Article  PubMed  PubMed Central  Google Scholar 

  72. Kuznetcova I, Glassman M, Tilak S, Wen Z, Evans M, Pelfrey L, et al. Using a mobile virtual reality and computer game to improve visuospatial self-efficacy in middle school students. Comput Educ. 2023;192:104660. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.compedu.2022.104660

    Article  Google Scholar 

Download references

Acknowledgements

The authors express their gratitude to everyone who dedicated their valuable time to participate in this research.

Funding

This study was funded by Science Research Project of Hebei Education Department (Grant ID: QN2025751). The funding body had no role in the whole process of the research.

Author information

Authors and Affiliations

  1. Hebei Key Laboratory of Children’s Cognition and Digital Education, School of Educational Studies, Langfang Normal University, Langfang, 065000, Hebei Province, China

    Zhixia Wei

  2. Faculty of Educational Studies, Universiti Putra Malaysia, Serdang, 43400, Selangor, Malaysia

    Zhixia Wei, Norlizah Che Hassan, Siti Aishah Hassan, Normala Ismail, Xiaoxia Gu & Jingyi Dong

Authors
  1. Zhixia Wei
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Norlizah Che Hassan
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Siti Aishah Hassan
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Normala Ismail
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Xiaoxia Gu
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Jingyi Dong
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

All authors contributed to the research design. Zhixia Wei collected and analyzed the data, and wrote the first draft. Norlizah Che Hassan, Siti Aishah Hassan and Normala Ismail revised the draft. Xiaoxia Gu checked the data and reviewed the content and format of the manuscript. Jingyi Dong checked the literature and reviewed the manuscript. All authors contributed to and approved the final manuscript.

Corresponding authors

Correspondence to Zhixia Wei or Norlizah Che Hassan.

Ethics declarations

Ethics approval and consent to participate

The study was conducted in compliance with the Declaration of Helsinki and was approved by the Ethics Committee for Research involving Human Subjects of University Putra Malaysia Research (JKEUPM-2023-137). Informed consent was obtained from the participants prior to the survey.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wei, Z., Hassan, N.C., Hassan, S.A. et al. The relationship between Internet addiction and academic burnout in undergraduates: a chain mediation model. BMC Public Health 25, 1523 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12889-025-22719-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12889-025-22719-y

Keywords

BMC Public Health

ISSN: 1471-2458

Contact us