Digital Burnout and Academic Procrastination among University Psychology Students: A Correlational Study

Introduction

Academic procrastination, defined as the habitual and intentional postponement of academic tasks despite full awareness of their potential negative consequences, remains one of the most widespread forms of self-regulatory failure identified in higher education contexts across the world. This phenomenon extends far beyond a simple issue of ineffective time management, as it is associated with substantial downstream consequences such as lower academic achievement, increased levels of psychological distress, delayed completion of study programs, and diminished subjective well-being (1, 2). Epidemiological findings further highlight the magnitude of this concern, with numerous studies indicating that more than half of university students experience procrastination at a clinically meaningful level (3), while prevalence rates among Indonesian university students have likewise been reported to exceed 50%. Importantly, the rapid digitalization of higher education following the pandemic has added a new layer of complexity to this already significant issue. As universities worldwide, including institutions in Indonesia where more than 98% of students use digital media daily for approximately three to five hours, have shifted toward increasingly technology-dependent learning systems, students are now confronted with unprecedented demands on their attention and self-regulatory resources. These structural transformations in the delivery of education have also intensified students’ exposure to persistent digital stressors, thereby raising important questions regarding whether technology-related psychological exhaustion may represent an underrecognized factor contributing to academic procrastination.

Existing scholarship has identified a range of psychological antecedents of academic procrastination, including perfectionism, fear of failure, low self-efficacy, and deficits in self-regulation and time management (4, 5). Current intervention frameworks predominantly rooted in cognitive-behavioral therapy, motivational interviewing, and time-management training have demonstrated moderate effectiveness in reducing procrastinatory behavior, yet their long-term efficacy remains inconsistent and their scope inherently limited (4)Notably, these approaches have largely overlooked the role of digital exhaustion as a proximal contributor to task avoidance, despite growing evidence that sustained engagement with digital technologies is associated with impaired concentration, emotional depletion, and reduced motivational resources among university students (6, 7). This is further supported by findings indicating that digital burnout is significantly associated with higher levels of perceived stress among university students (8). Digital burnout broadly conceptualized as a state of physical, cognitive, and emotional exhaustion resulting from prolonged and excessive immersion in digital environments (5, 9) has emerged as a construct of particular relevance in this regard. Drawing on the Conservation of Resources theory (Hobfoll, 1989), which posits that resource depletion drives individuals toward protective withdrawal from demanding tasks, and self-regulation theory (Baumeister & Vohs, 2007), which frames procrastination as a failure of finite executive resources under conditions of chronic cognitive overload, there exists a theoretically coherent basis for expecting digital burnout to be meaningfully associated with academic procrastination (10, 11). This suggests that digital burnout may reduce cognitive resources necessary for task initiation and persistence, thereby increasing the likelihood of procrastination. However, direct empirical examination of this association within higher education contexts remains relatively limited, particularly in student-specific and non-Western contexts (6, 12).

The present study addresses this gap by examining the association between digital burnout and academic procrastination among undergraduate psychology students at Maulana Malik Ibrahim State Islamic University Malang, Indonesia a context in which both phenomena are prevalent yet underexamined in conjunction. While a nascent body of literature has begun to explore related pathways, including the mediating roles of fatigue and life satisfaction in this relationship (9), evidence from non-Western, developing-country educational contexts remains particularly scarce. This study contributes to the field by providing empirical data from an Indonesian university sample (N = 304), employing validated psychometric instruments and simple linear regression analysis to quantify the magnitude and direction of the association between digital burnout and procrastination. It is important to note that, consistent with its cross-sectional correlational design, this study does not purport to establish causal directionality; rather, it aims to determine whether a statistically significant associative relationship exists between these constructs. The findings are intended to offer preliminary evidence that may inform the development of more ecologically comprehensive intervention frameworks ones that extend beyond conventional psychological approaches to incorporate digital well-being as a modifiable and clinically relevant target in academic support systems.

Methodology

Study Design and Rationale

This study employed a quantitative, cross-sectional correlational design to examine the association between digital burnout and academic procrastination among university students. A correlational framework was selected as it allows for the quantification of the magnitude and direction of association between variables without experimental manipulation an approach appropriate for investigating psychological and behavioral constructs in naturalistic academic settings. It is important to acknowledge, however, that the cross-sectional nature of this design precludes any inference of causal directionality; findings should therefore be interpreted as reflecting statistical associations rather than cause-and-effect relationships.

Participants, Population, and Sampling

The target population comprised undergraduate psychology students enrolled at Maulana Malik Ibrahim State Islamic University Malang during the 2024/2025 academic year, totaling 1, 269 registered students. Psychology students were selected as the target population given their intensive academic workload and sustained daily exposure to digital technologies for learning, assessment, and communication activities.

Sample size was determined using the Slovin Eq. 1 with a margin of error of 5%.

This yielded a required sample of 304 participants, which was retained as the final analytic sample following data screening. Participants were recruited through simple random sampling, implemented by obtaining a complete list of eligible students from the faculty academic registry, assigning each student a unique numerical identifier, and selecting participants using a computer-generated random number sequence. This procedure ensured that every eligible student had an equal and independent probability of inclusion. No additional inclusion or exclusion criteria were applied beyond active enrollment status, as all registered psychology students were considered eligible to participate.

Variables and Operational Definitions

This study examined the association between one independent variable and one dependent variable, both treated as continuous constructs for parametric analysis. The independent variable, digital burnout (X), was operationalized as a state of physical and emotional exhaustion arising from prolonged and excessive engagement with digital technologies, assessed across three dimensions: digital aging (cognitive and emotional fatigue from sustained digital exposure), digital deprivation (discomfort and anxiety from digital dependency), and emotional exhaustion (depletion of emotional resources from continuous digital demands).

The dependent variable, academic procrastination (Y), was defined as the habitual tendency to delay or avoid academic tasks despite awareness of negative consequences. It was operationalized across six dimensions: psychological beliefs about abilities, distraction of attention, social factors of procrastination, time management skills, personal initiative, and laziness. Higher scores on both scales indicate greater severity of the respective construct.

Instruments and Measurement

Academic procrastination was assessed using the Academic Procrastination Scale (APS; McCloskey, 2011), a 25-item instrument capturing procrastination tendencies across six theoretically grounded dimensions. Items are rated on a Likert-type scale, with higher composite scores indicating greater procrastination. Digital burnout was measured using the Digital Burnout Scale (13), a 24-item instrument operationalizing burnout across the dimensions of digital aging, digital deprivation, and emotional exhaustion.

Both instruments underwent validity and reliability testing within the present study sample prior to hypothesis testing. Content validity was established through expert judgment by two academic psychologists. Construct validity was assessed using corrected item-total correlation analysis. For the APS, 19 of the original 25 items met the minimum validity threshold, yielding item-total correlation indices ranging from 0.493 to 0.737. For the DBS, all 24 items were retained as valid, with item-total correlation indices ranging from 0.379 to 0.675. Internal consistency reliability was evaluated using Cronbach's alpha; the APS yielded α = 0.935 and the DBS yielded α = 0.918, both substantially exceeding the minimum acceptable threshold of α ≥ 0.70 and indicating excellent internal consistency. These results confirm the psychometric adequacy of both instruments for use in the present study.

Data Collection Procedure

Data were collected during the 2024/2025 academic semester via an online survey platform to maximize accessibility and minimize disruption to participants' academic activities. Prior to completing the survey, all participants received standardized written instructions detailing the study purpose, voluntary nature of participation, and confidentiality safeguards. Estimated completion time ranged from 15 to 20 minutes. All responses were automatically recorded upon submission and subsequently screened for completeness prior to inclusion in the final dataset.

Data Analysis Strategy

All statistical analyses were conducted using IBM SPSS Statistics version 25 for Windows, supplemented by Microsoft Excel for descriptive computations. Analyses proceeded in two sequential stages.

First, descriptive statistical analysis was performed to characterize the distribution of both study variables. This included computation of hypothetical and empirical means, standard deviations, and frequency-based categorization. Categorization of variable levels followed a norm-referenced framework: scores above (M + 1SD) were classified as high, scores within ±1SD of the mean as moderate, and scores below (M − 1SD) as low.

Second, simple linear regression analysis was employed to test the primary research hypothesis. This technique was selected given that the study involved a single continuous predictor and a single continuous outcome variable. Prior to regression, classical assumption testing was conducted: data normality was assessed using the Kolmogorov–Smirnov test (α = 0.05), and linearity was evaluated using ANOVA-based linearity testing. Both assumptions were satisfied, confirming the suitability of the data for regression analysis. The regression model is expressed in Eq. 2.

where Y = academic procrastination score, X = digital burnout score, a = intercept, b = regression coefficient, and e = error term. The coefficient of determination (R²) was reported to quantify the proportion of variance in academic procrastination statistically attributable to digital burnout, and statistical significance was evaluated at α = 0.05.

Ethical Considerations

This study was conducted in full accordance with ethical standards governing psychological research involving human participants. Ethical approval was secured at the faculty level prior to data collection. All participants provided informed consent and were assured of the anonymity of their responses, the confidentiality of all data, and their unconditional right to withdraw at any point without penalty. All data were used exclusively for academic research purposes and stored securely in accordance with institutional data governance protocols.

Results

Preliminary Analyses and Assumption Testing

Prior to hypothesis testing, a series of preliminary analyses were conducted to verify that the data satisfied the assumptions required for parametric statistical procedures. As presented in Table 1, the Kolmogorov–Smirnov normality test indicated that the academic procrastination variable showed a statistically significant deviation from normality (Sig. = 0.034), while the digital burnout variable met the normality assumption (Sig. = 0.200). Although one variable did not strictly conform to a normal distribution, this deviation was considered acceptable in the context of the present sample size (N = 304). Parametric procedures such as linear regression are well-documented to be robust against minor violations of normality in large samples, as the Central Limit Theorem supports approximate normality of sampling distributions at this sample size (14, 15).

Furthermore, linearity testing was subsequently conducted to examine whether the relationship between digital burnout and academic procrastination conformed to a linear pattern, as required for simple linear regression. As shown in Table 2, the deviation from linearity yielded a non-significant result (Sig. = 0.202), confirming that the assumption of linearity was tenable and that regression analysis was an appropriate analytical approach for the present data.

Descriptive Statistics of Study Variables

Descriptive analysis was performed to compare hypothetical and empirical mean scores of the study variables. As shown in Table 3, the empirical mean score of academic procrastination (M = 49.85) exceeded its hypothetical mean (M = 47.5), suggesting a tendency toward moderate levels of procrastination within the sample. A comparable pattern was observed for digital burnout, where the empirical mean (M = 62.26) similarly exceeded the hypothetical mean (M = 60), suggesting that participants experienced a degree of digital fatigue that was slightly above the midpoint of the scale.

To further characterize the distribution of scores, categorization analyses were conducted based on norm-referenced criteria. As presented in Table 4, the majority of students (53%) fell within the moderate category of academic procrastination, followed by 30% in the high category and 17% in the low category. These findings suggest that more than half of the participants exhibited procrastination tendencies that may be associated with disruptions to academic functioning, though the cross-sectional nature of the data precludes conclusions about actual academic impact.

A broadly similar distributional pattern was observed for digital burnout. As reported in Table 5, the majority of students (60%) fell within the moderate digital burnout category, while 25% were classified as high and 15% as low. This distribution suggests that digital exhaustion is a relatively common experience among psychology students in this sample, a pattern broadly consistent with recent findings indicating moderate-to-elevated levels of digital strain in post-pandemic higher education contexts (6, 16).

Dimensional Analysis of Academic Procrastination and Digital Burnout

An analysis of the contributing dimensions of academic procrastination revealed differential weight across aspects. As shown in Table 6, the dimension distraction of attention contributed the highest proportion (22.1%) to overall academic procrastination, followed by psychological beliefs about abilities (20.9%) and laziness (16.4%). In contrast, personal initiative contributed the smallest proportion (9.4%). These findings suggest that attentional difficulties may play a more prominent role in procrastination behavior than motivational deficits alone, a pattern broadly consistent with recent empirical evidence identifying attentional control of self-regulation as the strongest predictor of academic procrastination among university students (17, 18).

Regarding digital burnout, Table 7 indicates that digital aging emerged as the dominant contributing dimension, accounting for 55.8% of the total digital burnout score, followed by digital deprivation (25.9%) and emotional exhaustion (18.3%). The predominance of digital aging suggests that students' experience of digital exhaustion may be primarily characterized by a cumulative sense of cognitive weariness from sustained digital engagement — a pattern consistent with recent evidence demonstrating that prolonged digital exposure disrupts concentration, decision-making, and students' capacity to retain information, thereby undermining academic functioning (6).

Hypothesis Testing: The Association Between Digital Burnout and Procrastination

The primary research hypothesis was tested using simple linear regression analysis. As reported in Table 8, the correlation coefficient between digital burnout and academic procrastination was R = 0.615, suggesting a moderately strong positive association between the two variables. The coefficient of determination indicated that digital burnout was statistically associated with 37.8% of the variance in academic procrastination scores (R² = 0.378, p < 0.001), supporting the hypothesis that higher levels of digital burnout are associated with greater tendencies toward academic task delay. It is important to note, however, that these findings reflect a statistical association within a cross-sectional design and should not be interpreted as evidence of a causal relationship.

The remaining 62.2% of unexplained variance in academic procrastination suggests that additional psychological and contextual factors beyond digital burnout are likely to contribute to procrastination behavior among university students. These may include, but are not limited to, academic self-efficacy, learning motivation, emotional regulation difficulties, personality traits, and environmental demands all of which have been identified as relevant antecedents of academic procrastination in recent empirical literature.

Discussion

The present study examined the association between digital burnout and academic procrastination among undergraduate psychology students at Universitas Islam Negeri Maulana Malik Ibrahim Malang. The descriptive findings indicate that the majority of students experienced moderate levels of both digital burnout (60%) and academic procrastination (53%), suggesting that these phenomena are prevalent yet not uniformly severe within this population. The moderate level of digital burnout observed in the present sample appears comparatively lower than levels reported during the COVID-19 pandemic, when enforced remote learning substantially intensified compulsory digital engagement (16). This discrepancy is theoretically plausible, as the present study was conducted in a post-pandemic context in which face-to-face learning had largely resumed, affording students greater opportunities for offline recovery. Dimensional analysis further revealed that digital aging was the dominant contributor to digital burnout (55.8%), suggesting that students' exhaustion may be primarily characterized by cumulative cognitive weariness from sustained platform use, notification management, and continuous digital adaptation — rather than acute emotional breakdown. This pattern is consistent with recent evidence linking prolonged digital exposure to impaired concentration and diminished academic engagement among university students (6).

Regarding academic procrastination, the dimensional analysis indicated that distraction of attention was the strongest contributing dimension (22.1%), followed by psychological beliefs about abilities (20.9%). These findings are broadly consistent with recent empirical evidence identifying attentional control as a key self-regulatory mechanism underlying procrastination behavior (17–19). The prominence of attentional distraction as the leading dimension rather than motivational deficits such as personal initiative (9.4%) suggests that procrastination among these students may be more strongly driven by cognitive overload and competing digital stimuli than by a simple lack of willingness to engage. This pattern appears to support contemporary conceptualizations of academic procrastination as a failure of self-regulation rather than a moral or motivational deficit, particularly within digitally saturated academic environments (2, 4).

The central finding of this study is that digital burnout was significantly and positively associated with academic procrastination (R = 0.615, R² = 0.378, p < 0.001). This finding is broadly consistent with prior research suggesting that burnout whether academic, occupational, or digital may be associated with impaired motivation, reduced attentional capacity, and increased task avoidance tendencies (9, 20). One plausible explanation, grounded in Conservation of Resources theory (Hobfoll, 1989), is that sustained digital engagement may be associated with the gradual depletion of students' cognitive and emotional resources, which could make them more susceptible to avoidant coping strategies such as procrastination (11). Similarly, from a self-regulation perspective (10) chronic digital overload may impair executive functioning, reducing students' capacity to initiate and sustain goal-directed academic behavior. It is important to note, however, that these mechanistic explanations remain theoretical inferences; the present correlational data do not permit conclusions about directionality or underlying pathways. However, it is also possible that academic procrastination contributes to increased digital fatigue, suggesting a potentially reciprocal relationship between the two variables. The remaining 62.2% of unexplained variance further underscores that academic procrastination is a multifactorial phenomenon (2, 17). From a practical standpoint, these findings tentatively suggest that academic support strategies targeting procrastination may benefit from incorporating digital well-being components alongside conventional time-management and motivational approaches, though experimental evidence is needed before firm intervention recommendations can be made. This study has several limitations. The cross-sectional design precludes causal inference, self-report measures may introduce response bias, and the single-institution sample limits generalizability. Future research employing longitudinal or experimental designs, broader sampling strategies, and additional mediating variables would substantially advance understanding of the digital burnout–procrastination relationship.

Conclusion

This study found a significant positive association between digital burnout and academic procrastination among undergraduate students. The findings suggest that higher levels of digital burnout are linked to a greater tendency toward procrastination, although no causal relationship can be established. Overall, digital burnout may represent one of several contributing factors to academic procrastination, highlighting the need for further research using more robust designs.