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103 Procrastination Essay Topic Ideas & Examples

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Procrastination is something that many of us struggle with at some point in our lives. Whether it's putting off studying for an exam, starting a new project, or even just doing the laundry, procrastination can be a major roadblock to productivity. If you're struggling to come up with a topic for an essay on procrastination, we've got you covered. Here are 103 procrastination essay topic ideas and examples to help get you started:

The psychology behind procrastination: why do we put things off?

The impact of procrastination on academic performance

Procrastination in the workplace: how it affects productivity

The relationship between procrastination and mental health

Strategies for overcoming procrastination

The role of technology in encouraging procrastination

Procrastination as a form of self-sabotage

Procrastination and perfectionism: are they related?

The link between procrastination and procrastination

Procrastination and decision-making: how putting things off can lead to poor choices

The role of procrastination in creating stress and anxiety

Procrastination and time management: how better planning can help

Procrastination and creativity: is there a connection?

Procrastination and self-discipline: how to build better habits

The impact of procrastination on relationships

Procrastination and goal-setting: how putting things off can derail your dreams

The connection between procrastination and fear of failure

Procrastination and procrastination addiction: when putting things off becomes a habit

Procrastination and procrastination bias: why we underestimate how long tasks will take

The impact of procrastination on physical health

Procrastination and procrastination guilt: the cycle of putting things off and feeling bad about it

The connection between procrastination and procrastination reward: why we feel good when we procrastinate

Procrastination and procrastination procrastination: how putting things off can lead to even more procrastination

The role of procrastination in decision fatigue

Procrastination and self-awareness: recognizing when you're avoiding tasks

The impact of procrastination on creativity

Procrastination and procrastination procrastination: why we put things off even when we know it's not helpful

The connection between procrastination and procrastination bias: why we underestimate how long tasks will take

Hopefully, these essay topic ideas and examples have sparked some inspiration for your own writing on procrastination. Whether you choose to explore the psychological roots of procrastination, the impact it has on various aspects of our lives, or strategies for overcoming it, there's plenty to explore on this fascinating topic. Good luck with your essay!

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• Published: 25 June 2024

Temporal discounting predicts procrastination in the real world

• Pei Yuan Zhang 1 &
• Wei Ji Ma 1 , 2  

Scientific Reports volume  14 , Article number:  14642 ( 2024 ) Cite this article

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People procrastinate, but why? One long-standing hypothesis is that temporal discounting drives procrastination: in a task with a distant future reward, the discounted future reward fails to provide sufficient motivation to initiate work early. However, empirical evidence for this hypothesis has been lacking. Here, we used a long-term real-world task and a novel measure of procrastination to examine the association between temporal discounting and real-world procrastination. To measure procrastination, we critically measured the entire time course of the work progress instead of a single endpoint, such as task completion day. This approach allowed us to compute a fine-grained metric of procrastination. We found a positive correlation between individuals’ degree of future reward discounting and their level of procrastination, suggesting that temporal discounting is a cognitive mechanism underlying procrastination. We found no evidence of a correlation when we, instead, measured procrastination by task completion day or by survey. This association between temporal discounting and procrastination offers empirical support for targeted interventions that could mitigate procrastination, such as modifying incentive systems to reduce the delay to a reward and lowering discount rates.

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Introduction.

In today’s world, achieving long-term goals, such as writing an article or developing complex software, demands sustained effort spanning days or months. These endeavors are crucial for both personal success and societal productivity, yet they often collide with the challenge of procrastination. Procrastination is prevalent; it chronically affects approximately 20% of the adult population 1 and up to 70% of undergraduate students 2 . For instance, people delay filing their taxes until the last minute 3 . Researchers postpone until the last minute registering for academic conferences 4 and submitting abstracts and papers 5 . College students commonly put off starting self-paced quizzes and find themselves rushing to complete them by the end of the semester 6 , 7 , 8 . The consequences of procrastination are profound, impacting individuals’ achievements and well-being. Procrastination results in lower salaries, shorter employment durations, a higher likelihood of unemployment 9 , and monetary loss 3 . Beyond these tangible effects, procrastinators frequently suffer from mental health challenges, including depression and anxiety, compounded by diminished motivation and low self-esteem 6 , 10 , 11 . Due to its high prevalence and high impact, procrastination is a problem of great societal importance.

The question arises: why do people procrastinate? Suppose you are a student who has to submit an assignment by a deadline. Initially, the utility of working on the assignment might be low because the deadline is far away, making work less appealing than alternative activities such as socializing. As a result, the student might delay working on the assignment until the utility of work exceeds the utility of socializing, which occurs as the deadline approaches. In line with this example, researchers in psychology and economics have, in different forms, hypothesized that temporal discounting is a mechanism underlying procrastination 12 , 13 , 14 , 15 , 16 . When faced with a task in its initial stages, where the eventual reward is distant, people temporarily discount the value of that future reward. As a consequence, the temporarily discounted future reward fails to provide sufficient motivation for people to start working until the deadline looms near.

This hypothesis predicts a positive correlation between the degree to which individuals discount future rewards and the extent of their procrastination. As far as we know, only three studies have attempted to test for this correlation 17 , 18 , 19 . Le Bouc and Pessiglione 17 measured procrastination behavior in a survey completion task and found no evidence of a correlation. Sutcliffe et al. 18 used a questionnaire to measure self-reported procrastination tendency and found no evidence of a correlation. Reuben et al. 19 found a positive correlation in two real-world tasks that offered enhanced rewards as incentives for early completion. However, such incentives could be a confound because the actual correlation might be between temporal discounting and achievement motivation 20 , 21 . Indeed, the authors did not find a correlation when early completion incentives were removed in a third task. Two other studies 22 , 23 appear to examine the relationship between temporal discounting and procrastination. They used a hyperbolic function to model the distribution of task completion time across individuals. The same function is commonly used to estimate temporal discount rates by modeling how future reward is discounted over time. However, these studies did not measure temporal discount rates, even though the same hyperbolic function was used.

In the present work, we used a novel measure of procrastination in a novel task to examine the association between temporal discounting and real-world procrastination behavior. It is common in the literature to use a single endpoint—task completion time—as a measure of procrastination 4 , 5 , 19 , 23 , 24 , 25 , 26 , 27 . However, individuals who complete a task at the same time can exhibit very different temporal patterns of work progress 28 , 29 , 30 . Some people maintain steady progress from beginning to end (steady working), whereas others make very little progress at the start and rush to complete their work on the very last day (rushing in the end). In order to better distinguish between such cases, we instead used a new metric to measure procrastination—Mean Unit Completion Day—that takes into account the entire time course of work progress.

We looked for a real-world task that satisfied three criteria. First, to rule out the potential confound in Reuben et al. 19 , no incentives should be given for early completion. Second, the task should measure the entire time course of work progress. This, in turn, requires that the task (a) has an unambiguous definition of a unit of work, (b) the completion time of each unit of work is measured, and (c) involves multiple units of work to establish a time course of work progress. Real-world tasks such as writing or taking an academic course often lack clearly defined units of work and are, therefore, not good candidates. Finally, an individual’s work progress in the task should not be affected by others.

A real-world task that satisfied these three criteria was the research participation requirement in the Introduction to Psychology course at New York University. To receive course credit, all enrolled students were required to participate in research studies for a total of 7 h before the end of the semester; the semester lasted a total of 109 days. This task was self-paced, granting students the autonomy to decide when to participate. All three criteria were met in this task. First, since course credit was independent of the time at which the research requirement was completed, no incentives were given for early completion. Second, a unit of work was clearly defined as 0.5 h because research participation opportunities involved a time commitment of 0.5, 1, 1.5, or 2 h. The vast majority (91.2%) of participation opportunities took 0.5 h or 1 h. The date of each research participation was documented in the New York University Sona System and was accessible to the system administrator. Students needed to participate multiple times to fulfill the 7-h requirement. All students participated at least six times, with a median of 10 times. Last, research participation opportunities were plentiful: an average of 15 h per student. Thus, there was no need for students to compete for these opportunities, and each student’s work progress could reasonably be assumed to be independent of that of others. In contrast, if research participation opportunities are limited, whether a student can participate in a study on a certain day depends on whether other students have already taken that opportunity. In this case, a student’s work progress will be dependent on others due to the need to compete for scarce resources.

To estimate the degree of reward discounting, two weeks after the semester ended, we invited all students who had been enrolled in the course to participate in our online study that included a delay discounting task. Participants were asked to indicate their monetary preferences between smaller but sooner rewards and larger but delayed rewards (Fig. 1 A). We used a widely adopted choice set designed to capture a broad range of discount rates 31 , 32 , 33 , 34 , 35 , 36 , 37 . The delays in the choice set ranged from 1 to 180 days, comparable to the 109-day research participation task. Moreover, this task was designed to be incentive-compatible, in contrast to the hypothetical nature of rewards in the previous studies 17 , 19 .

The secondary objective of our study was to examine the relationship between risk attitude and behavioral procrastination. By postponing the research participation until the end of the semester, students face an increased risk of not being able to complete the research participation requirement, particularly when considering other competing obligations near the end of the semester, such as final exams. Consequently, procrastination in the research participation task can be viewed as a risk-seeking behavior. A prior study 38 found no evidence of a correlation between the risk attitude measured by the Domain-Specific Risk-Taking (DOSPERT) scale 39 and self-reported procrastination tendency measured by the Lay Procrastination Scale 40 . In this real-world task, we examined the relationship between people’s risk attitude and procrastination behavior. To estimate participants’risk attitude, we included three measures in our online study: the incentive-compatible risky-choice task (Fig. 1 B), which assessed risk attitude primarily within the financial domain 41 , 42 ; the DOSPERT scale, which assessed risk attitude across five domains (ethical, health/safety, recreational, financial, and social), and a set of custom-designed questions that assessed risk attitudes specifically toward postponing research participation in our real-world task (see “ Methods ”).

Tasks. ( A ) Online delay discounting task. In each trial, participants were first presented with two options: a smaller immediate reward today and a larger reward with a delay of several days, and then they indicated their preference by choosing one. At the end of the task, their choice of one randomly selected trial will determine the payment amount and the day of delivery. ( B ) Online risky choice task. In each trial, participants were first presented with two options: receiving $5 for sure and participating in a lottery where they had a chance to win a larger amount with a certain probability, otherwise receiving $0, and then they indicated their preference by choosing one. At the end of the task, the choice of one randomly selected trial will determine the payment. If participants chose the sure bet, they would receive $5. However, if they chose the lottery, they would play it by randomly drawing a chip from 100 chips. As the task was conducted online, we gave participants a visual aid of the chip-drawing process by displaying 100 chips and instructing them to click on a chip to simulate the random draw. After clicking, the color of the chip will be revealed, and the payment will be based on the result of the lottery.

Participants

Participant inclusion was determined as follows. First, to ensure that our measures of procrastination would not be confounded by the total number of work units completed, we selected the participants who met their 7-h requirement and did not continue to do more research sessions beyond the requirement. This resulted in a total of 93 participants. Second, we applied pre-registered exclusion criteria to the delay discounting task. We excluded 9 participants who either failed two or more of the five attention check questions or consistently chose one option. Finally, we conducted a quality control procedure to ensure that participants were not responding randomly (see “ Methods ”). No additional participants were excluded based on this procedure. This left us with a final sample of 84 participants to test the relationship between temporal discounting and procrastination and the convergent validity of our measurement of procrastination (53 female, 28 male, two non-binary, one unknown; \(19.4 \pm 1.4\) years old). To test the relationship between risk attitude and procrastination, we applied similar exclusion criteria and quality control to the risky-choice task (see “ Methods ”), leaving us with a sample of 91 participants (56 female, 31 male, three non-binary, one unknown; \(19.3 \pm 1.8\) years old).

Characterizing individual variability in procrastination

In the research participation task, we found that the time course of work progress differed greatly between individuals, ranging from participants who started and finished early to those who waited until the last two weeks of the 109-day period (Fig. 2 A). The cumulative progress curves across all the participants clearly show this high individual variability (Fig. 2 B).

There are many ways of summarizing a time course of work progress, some of which have been used in previous papers. Perhaps the most obvious summary statistic is task completion time 17 , 19 , 24 , 25 , 26 , 27 . In our task, the distribution of task completion day is wide, ranging from 25 to 103 days ( \(M=77.5\) , \(SD=17.2\) ) (Fig. 2 C). Another metric is the amount of work (in our task, the number of hours of research participation) completed in the last third of the semester 6 ( \(M=1.7\) , \(SD=2.0\) ) (Supplementary Fig. S1 A). Furthermore, one could use task starting day 25 , 27 , 43 , 44 . In our task, however, students were asked by the instructor to complete the first research participation in the first two weeks. This separate deadline makes the task starting day somewhat contaminated as a measure of procrastination in the overall research participation task. Nevertheless, we show the distribution of task starting day in Supplementary Fig. S1 B.

Procrastination in the real world. ( A ) Examples of time courses of work progress, with blue triangles marking the Mean Unit Completion Day (MUCD). Top: a low procrastinator who started on the first day and finished early. Middle: an intermediate procrastinator who worked steadily throughout the semester. Bottom: a high procrastinator who rushed to complete the task in the last two weeks of the semester. ( B ) Time courses of cumulative work progress for all the participants, with the three examples from ( A ) highlighted. ( C ) Histogram of task completion day. ( D ) Histogram of MUCD.

The above metrics take into account only a single point or partial segment of the time course of work progress. Next, we turn to metrics that consider the entire time course of work progress. We introduce a novel metric, Mean Unit Completion Day (MUCD), as the average completion day of all work units, with each work unit defined in this task as 0.5 h of research participation (see the formula in the Supplement). MUCD had a wide distribution, ranging from 19.1 to 100.9 ( \(M = 49.6\) , \(SD= 18.2\) ), further demonstrating the high level of individual variability in procrastination (Fig. 2 D).

We assessed the convergent validity of MUCD by testing whether MUCD in the research participation task is associated with self-reported procrastination in general academic situations. We measured participants’general academic procrastination tendencies with the widely used Procrastination Assessment Scale for Students (PASS) 6 . Participants were asked to report the frequency with which they procrastinated on tasks such as writing term papers, studying for exams, and four other academic scenarios. Our findings revealed a moderate positive correlation between MUCD and PASS score (Pearson \(r=0.42\) , \(p<0.001\) ), which provides support for the convergent validity of our measure.

Two other metrics are closely related to MUCD. The first is the day of the halfway point of the work 7 , which is the median of the time course of work progress ( \(M = 50.5\) , \(SD= 22.4\) ) (Supplementary Fig. S1 C). The second is the area under the cumulative progress curve 30 ; however, we prove here that this metric is mathematically equivalent to MUCD (see proof in the Supplement).

Besides MUCD, the other metrics were also correlated with the PASS score, suggesting the convergent validity of these measures (task completion day: \(r=0.31\) , \(p=0.005\) ; hours in the last third of the semester: \(r=0.41\) , \(p<0.001\) ; task starting day: \(r=0.36\) , \(p<0.001\) ; day of the halfway point: \(r=0.42\) , \(p<0.001\) ;). All metrics considered were correlated with each other (see Supplementary Table S1 ). All metrics were preregistered, except for task starting day (because of the potential confound of a different deadline) and area under the cumulative progress curve (because of the mathematical equivalence).

Discount rate correlates with behavioral procrastination quantified by MUCD but not task completion day or survey-based measure

Turning to our main question, we examined the correlation between temporal discounting and procrastination. We estimated individual temporal discount rates through the incentive-compatible delay discounting task. We fit a hyperbolic choice model to the choice data of each participant. The discount curves were well characterized by hyperbolic functions (goodness of fit: \(M=0.73\) , \(SD=0.14\) ). We found high variability (Fig. 3 A): the natural log-transformed discount rate ranged from \(-7.87\) (equivalent to a 1.14% discount of reward value after 30 days) to \(-1.39\) (an 88.2% discount of reward value after 30 days). We found a positive correlation between the discount rate and MUCD ( \(r=0.28\) , \(p=0.009\) ) (Fig. 3 B). In addition, after controlling for age and gender, the discount rate was still positively associated with MUCD ( \(\beta =3.6\) , \(SE=1.4\) , \(t(78)=2.53\) , \(p=0.013\) ).

Procrastination correlates with discount rate but not risk attitude. ( A ) Histogram of the natural log-transformed discount rate estimated from the delay discounting task. ( B ) Correlation between MUCD and the natural log-transformed discount rate. ( C ) Histogram of the natural log-transformed risk attitude parameter estimated from the risky-choice task by fitting a power utility model. ( D ) Correlation between MUCD and the natural log-transformed risk attitude estimated from risky-choice task.

We found that (a) day of the halfway point and (b) hours in the last third semester both correlated with the discount rate ( \(r=0.28\) , \(p=0.009\) ; \(r=0.24\) , \(p=0.030\) , respectively), but metric (c) task completion day or (d) task starting day did not ( \(r=0.21\) , \(p=0.061\) ; \(r=0.18\) , \(p=0.098\) , respectively). These results held true after we controlled for age and gender (day of the halfway point: \(\beta =4.3\) , \(SE=1.7\) , \(t(78)=2.52\) , \(p=0.014\) ; hours in the last third semester: \(\beta =0.33\) , \(SE=0.16\) , \(t(78)=2.04\) , \(p=0.044\) ; task completion day: \(\beta =2.4\) , \(SE=1.3\) , \(t(78)=1.80\) , \(p=0.077\) ; task starting day: \(\beta =2.8\) , \(SE=1.8\) , \(t(78)=1.57\) , \(p=0.12\) ). One interpretation of these findings is that measures based on the time course of work progress have greater statistical power than measures based on an endpoint.

We found no correlation between the discount rate and the PASS score ( \(r=0.21\) , \(p=0.056\) ; after we controlled for age and gender: \(\beta =0.088\) , \(SE=0.053\) , \(t(78)=1.65\) , \(p=0.10\) ), highlighting the advantage of behavioral measures of procrastination over survey-based measures.

As an exploratory analysis, we tested if impulsivity, self-control, or perfectionism mediate the correlation between temporal discounting and procrastination. Details are provided in the Supplement.

No evidence of a correlation between risk attitude and behavioral procrastination

To estimate participants’risk attitude, we employed three approaches: the incentive-compatible risky-choice task (Fig. 1 B), the Domain-Specific risk-taking (DOSPERT) scale 39 , and a set of custom-designed questions that assessed risk attitudes specifically toward postponing research participation in this research participation task: The first question measured participants’perception of the risk associated with not being able to fulfill the research participation requirement by postponing it until the end of the semester, while the last two measured the level of aversion to that risk (see “ Methods ”).

We fitted a power utility model to the individual choice data from the risky-choice task. We found high variability in the risk attitude parameter (Fig. 3 C): the natural log-transformed risk attitude ranged from −1.29 to 0.22. We found no evidence of a correlation between risk attitude and behavioral procrastination in this research participation task characterized by MUCD (Fig. 3 D), day of the halfway point, hours in the last third semester and task completion day ( \(r=0.034\) , \(p=0.75\) ; \(r=0.10\) , \(p=0.35\) ; \(r=0.068\) , \(p=0.52\) ; \(r=-0.064\) , \(p=0.55\) , respectively).

Similarly, we did not find a significant correlation between procrastination and risk attitude measured by the DOSPERT scale across five domains (correlation between MUCD and the mean DOSPERT score: \(r=-0.12\) , \(p=0.25\) ). Specifically, we did not find a correlation between MUCD and risk-taking in the ethical domain ( \(r=-0.081\) , \(p=1.0\) ), in the financial domain ( \(r=0.13\) , \(p=0.83\) ), in the health/safety domain ( \(r=0.012\) \(p=1.0\) ), in the recreational domain ( \(r=-0.16\) , \(p=0.60\) ), or in the social domain ( \(r=-0.022\) , \(p=1.0\) ) (corrected using the Holm-Bonferroni method). Additionally, we did not find a correlation between procrastination and risk perception across five domains measured by the DOSPERT-Risk Perception subscale (correlation between MUCD and risk perception in the ethical domain: \(r=0.011\) , \(p=1.0\) , in the financial domain: \(r=-0.24\) , \(p=0.11\) , in the health/safety domain: \(r=-0.065\) , \(p=1.0\) , in the recreational domain: \(r=-0.035\) , \(p=1.0\) , or in the social domain: \(r=0.025\) , \(p=1.0\) . (corrected using the Holm-Bonferroni method))

Next, we analyzed the questions custom-designed to measure risk attitudes specifically toward postponing research participation. In terms of risk perception (the first question), participants strongly agreed that postponing research participation until the end of the semester increased the risk of not being able to fulfill the requirement (ratings ranging from strongly disagree (1) to strongly agree (7); \(Median = 7\) ; \(Mean = 6.2\) ; \(SD = 1.2\) ). However, we did not find evidence of a correlation between risk perception and procrastination characterized by MUCD, day of the halfway point, hours in the last third semester, or task completion day ( \(r=-0.16\) , \(p=0.14\) ; \(r=-0.14\) , \(p=0.18\) ; \(r=-0.19\) , \(p=0.07\) ; \(r=-0.12\) , \(p=0.27\) , respectively). The results were qualitatively the same for risk attitude (average score across the second and third questions): Participants reported a high level of aversion to the risk of not fulfilling the requirement due to postponing the research participation ( \(Median = 5\) ; \(Mean = 4.8\) ; \(SD = 1.3\) ). However, we did not find evidence of a correlation between risk attitude and procrastination characterized by MUCD, day of the halfway point, hours in the last third semester, or task completion day ( \(r=-0.094\) , \(p=0.37\) ; \(r=-0.13\) , \(p=0.21\) ; \(r=-0.015\) , \(p=0.89\) ; \(r=-0.090\) , \(p=0.40\) , respectively).

Self-reports of procrastination behavior

At the end of our online study, participants answered custom-designed questions about their views on procrastination in the research participation task. For example, they were asked how satisfied they were with how they allocated their time over the semester to fulfill the requirement, their attribution of procrastination, and their top-rated reasons for procrastination (see the Supplement for results). Here, we highlight one result: participants were aware of their own level of procrastination in research participation. Participants were asked to rate their procrastination level from not at all (1) to an extreme extent (5) in fulfilling the research participation requirement. We found that the rating of their own procrastination level in research participation positively correlates with their behavioral level of procrastination characterized by MUCD ( \(r=0.68\) , \(p<0.001\) ). This suggests that participants were aware of their own level of procrastination in the task.

We have presented evidence for an association between reward discounting and procrastination behavior in a long-term real-world task. This suggests that temporal discounting is a potential cognitive mechanism underlying procrastination.

Why did prior studies 17 , 18 , 19 fail to find a correlation between temporal discounting and procrastination? One reason might be that the choice sets they used might not have allowed for estimating the discount rate with the same precision as ours. Another reason might be that their delay discounting task was not incentive-compatible. Finally, their measurement of procrastination might not be as precise as ours. Sutcliffe et al. 18 did not employ a behavioral measure of procrastination; instead, they used a questionnaire. When we applied a similar questionnaire method, no evidence of a correlation was found. The other two studies 17 , 19 measured behavioral procrastination but limited their metrics to the task completion day, as they did not measure the entire time course of work progress. By contrast, we measured the entire time course of work progress and computed fine-grained metrics of procrastination, such as MUCD. This approach might provide greater statistical power than simply using the task completion day as a metric, which, when we applied it, also resulted in no evidence of a correlation. Alternatively, it is possible that stronger and weaker discounters truly do not differ in task completion day but only in how they allocate their time before completion. Future work will need to distinguish these two possibilities.

The observed association between temporal discounting and procrastination suggests two types of interventions to reduce procrastination: one is changing the incentive system, and another is reducing procrastination via lowering discount rates. First, regarding the incentive system, one might reduce procrastination by decreasing the delay in receiving a reward. While previous work has shown that adding immediate rewards to the original incentive environment enhances persistence 45 , 46 and reduces procrastination 47 , it remains unclear whether these effects are due to the increased reward magnitude or to a change in reward timing. Future research should disentangle these two factors and test the effect of decreasing the delay to a reward.

Second, procrastination can be reduced by lowering discount rates. The most promising ways to lower discount rates are episodic future thinking and mindfulness-based training/acceptance-based training 48 , 49 . Mindfulness-based training has been shown to be effective in reducing procrastination 50 , 51 , 52 , 53 , but no studies have tested the effect of episodic future thinking on procrastination. One study showed a negative association between episodic future thinking and procrastination 54 . However, the effectiveness of episodic future thinking as an intervention remains to be studied. Future studies should test this intervention using a randomized control trial. Furthermore, future studies could test whether a reduced discount rate mediates the effectiveness of reducing procrastination through episodic future thinking or mindfulness-based training. In addition, the effects of these interventions could vary among individuals with different discount rates (e.g., healthy controls versus clinical populations 55 ). For example, people with ADHD might be more sensitive to interventions that reduce procrastination by lowering discount rates 56 .

Limitations of our work include the use of a WEIRD 57 sample of NYU undergraduates and the use of a non-academic task. Future work should generalize to more diverse global samples and non-academic tasks. Moreover, it is possible that students frame the outcome of the research participation task as avoiding losses (“if I don’t fulfill the requirement, I might lose the credit for the course”) instead of as pursuing gains (“if I fulfill the requirement, I will get the credit for the course”) 58 . Future research could test if the discounted value of a future loss is also associated with procrastination.

More work is needed to understand the mechanisms underlying the observed association between temporal discounting and procrastination. First, it is possible that the association is due to a common cause. One candidate common cause is time perception 59 , 60 . The intuition is that a person who perceives a short period as longer tends to procrastinate because they think they have more time. The same person could be more likely to choose an immediate reward over a delayed reward because they perceive the delay to be longer.

Second, previous authors have distinguished between two forms of delay associated with procrastination: a delay in making a decision and a delay in implementing an action 61 , 62 . In our case, these would translate to choosing which research study to participate in and actually participating in it, respectively. Our empirical measure of procrastination does not distinguish between these two forms of delay. It would be interesting to test which form of delay is mainly responsible for the observed association between temporal discounting and procrastination.

In summary, we provided the first empirical evidence of an association between temporal discounting and procrastination in the real world. This finding not only suggested a potential cognitive mechanism underlying procrastination but also suggested a new approach to characterizing procrastination behavior and new interventions.

We sent email invitations with a link to our online study to all the students enrolled in the 2021 Introduction to Psychology course two weeks after the semester ended. In the email, we provided a broad description of the study’s aim, investigating the factors influencing student research participation. We did not disclose the specific focus of the study on procrastination.

Our online study included a delay discounting task to estimate the discount rate, a risky choice task, the Domain-Specific risk-taking (DOSPERT) scale 39 , and a set of custom-designed questions to estimate risk attitude jointly. It also included the Procrastination Assessment Scale for Students (PASS) 6 to test convergent validity. For exploratory analysis (details in the Supplement), we included surveys and custom-designed questions to address several aspects of procrastination in the research participation task. We included the Barratt Impulsivity Scale 63 , the Brief Self-Control Scale 64 , and perfectionism scales 65 , 66 to test their association with behavioral procrastination and whether they mediate the correlation between temporal discounting and procrastination. We also included custom-designed questions aimed at assessing participants’awareness of their procrastination levels in the task and their satisfaction with the way they allocated their time over the semester to fulfill the research participation requirement. Additionally, we included the Regret Elements Scale 67 to test whether high procrastinators regret the way they allocated their time to fulfill the requirement, the Causal Dimension Scale 68 to test attribution of procrastination and success in fulfilling the requirement, and the Reasons for Procrastination Scale 6 to identify the top-rated reasons for procrastination. All the tasks and surveys were counterbalanced in order, and tasks were presented before the surveys.

All participants gave informed consent prior to participating. Participants were compensated with $5 for their participation and had the opportunity to earn a bonus of up to $66 based on their choices during the tasks. This study was approved by New York University’s Institutional Review Board (IRB-FY2020-4262), and all experiments were performed in accordance with relevant guidelines and regulations. This study was pre-registered on Open Science Framework ( https://osf.io/4sxrw ).

Participant inclusion

The sample size of the online study was 194, which was 25.9% of the students who had been enrolled in the Introduction to Psychology course. To ensure that our measures of procrastination would not be confounded by the total number of work units completed, we only included the subset of participants who did not continue to do research sessions after they had met their 7-h requirement. For example, we would include a participant who, after completing 6.5 h, did a final research session to meet the requirement. However, we would exclude one who, after completing 7 h, did an additional session that was not required. This resulted in a total of 93 participants. Of the remaining 101 participants, 80 continued to do research sessions beyond the 7-h requirement, potentially to earn extra credit. The remaining 21 completed fewer than 7 h; in some cases, this was because they completed an alternative assignment (i.e., writing critique papers).

To test the hypothesis of correlation between temporal discounting and procrastination, out of 93 participants, we excluded 9 who either failed two or more of the five attention check questions or consistently chose one option in the delay discounting task, as that would make it impossible to determine their discount rate. To ensure that participants were not responding randomly, we conducted a quality control procedure 69 . We verified that participants’responses were influenced by task-relevant variables. This involved fitting a logistic regression model that included as predictors the immediate amount, the delayed amount, the delay, and the squares of these variables to each participant’s responses. The goodness of fit of the model was assessed using the coefficient of discrimination, and any participant with a value below 0.2 was considered a random respondent. No participants were excluded as random respondents. This left us with a final sample of 84 participants.

To test the hypothesis of correlation between risk attitude and procrastination, out of 93 participants, we excluded two subjects who either chose the objectively worse option in two or more of seven attention check trials or who consistently chose one option in the risky choice task, as that would be impossible to determine their risk attitude. Similarly to the delay discounting task, we conducted a quality control procedure to ensure that participants were not responding randomly. We verified that participants’ responses were influenced by task-relevant variables. This involved fitting a logistic regression model that included as predictors the winning amount of the lottery, the probability of winning the lottery, and the squares of these variables to each participant’s responses. The goodness of fit of the model was assessed using the coefficient of discrimination, and any participant with a value below 0.2 was considered a random respondent. No participants were excluded as random respondents. This left us with a final sample of 91 participants.

Delay discounting task

The delay discounting task consisted of 51 self-paced trials in which participants chose between receiving a smaller amount of money immediately or a larger amount after a specific number of days. The immediate reward ranged from $10 to $34, while the delayed reward was fixed at $25, $30, or $35, with delays ranging from 1 to 180 days. This choice set was designed to capture a broad range of discount rates evenly distributed in log space within the range of \([-1.6, -8.4]\) . It was adapted from Kirby’s choice set 31 and has been widely used in the temporal discounting literature 31 , 32 , 33 , 34 , 35 , 36 , 37 . To minimize any potential biases, we counterbalanced the position of the immediate reward on the screen (up or down). Additionally, we included five attention check trials in which participants were asked to choose between a larger immediate amount of money and a smaller amount with a delay.

We estimated temporal discount rates by fitting a hyperbolic choice model to the choice data of each participant. The utility of each option (immediate or delayed) is given by: \(U=\frac{v}{1+kD}\) , where U is the subjective discounted value, v is the monetary reward, D is a delay in days, and k is the individual discount rate. We used the softmax function to generate choice probabilities from option values.

where \(\text {Pr}_{\text {delayed}}\) is the probability that the participant chose the delayed option on a given trial, and \(\beta\) is the inverse temperature, which captures the stochasticity of the choice data. We used maximum-likelihood estimation to estimate the model parameters. We calculated the average goodness of fit as one minus the ratio between the log-likelihood of the model and that of a random-response model.

Risky-choice task

The risky-choice task consisted of 57 trials, each involving a choice between receiving $5 for sure and participating in a lottery where participants had a chance to win a larger amount with a certain probability, otherwise receiving $0. For example, one trial presented participants with a choice between $5 for sure and a 25% chance of winning $16 or a 75% chance of receiving $0. The larger amounts ranged from $6 to $66, and we used three different winning probabilities: 25%, 50%, and 75%. This choice set was adapted from a previous study 42 . To minimize any potential biases, we counterbalanced the position of the sure-bet option on the screen (left or right) and the associated color of the larger amount (blue or red). Additionally, we included seven attention check trials that presented participants with a choice between $5 for sure and a certain chance of receiving $4 or $5.

To help participants better understand the probabilities involved, the instructions included a visual representation of the choices. Each lottery image depicted a physical bag containing 100 poker chips, including red and blue chips. The size of the colored area and the number written inside indicated the number of chips of each color in the bag. The process of randomly drawing a chip was referred to as “playing the lottery.”

We estimated individual risk attitudes by fitting a power utility model to the trial-by-trial choice data. In this model, the utility of each option (safe or lottery) is given by \(U=pv^\alpha\) , where v is the dollar amount, p is the probability of winning, and \(\alpha\) is the individual’s risk attitude. A participant with \(\alpha >1\) is considered risk-seeking, \(\alpha =1\) is considered risk-neutral, or \(\alpha <1\) is considered risk-averse. Like in the delay discounting task, we used the softmax function to generate choice probabilities from option values.

where \(\text {Pr}_{\text {lottery}}\) is the probability that the subject chose the lottery on a given trial, and \(\gamma\) is the inverse temperature which captures the stochasticity of the choice data. We used maximum-likelihood estimation to estimate the model parameters.

Incentive compatibility

Both the delay discounting task and the risky choice task were incentive-compatible. Participants were offered a bonus: at the end of the study, their choice from a randomly selected trial in either the delay discounting task or the risky choice task determined the amount of this bonus. The bonus was provided as an electronic Amazon Gift Card. If the one randomly selected trial is from the delay discounting task, the timing of receiving the bonus depends on the chosen option. Specifically, for payment today, participants received the gift card on the same day. For delayed payments, participants received the gift card at a time corresponding to the delay associated with their chosen option. If the one randomly selected trial is from the risky choice task, if participants chose the sure bet in the selected trial, they would receive $5. However, if they chose the lottery, they would engage in the process of drawing a chip at random from a set of 100 chips. As the task was conducted online, we provided participants with a visual aid of the chip-drawing process. We displayed 100 chips and instructed participants to click on a chip to simulate the random draw. After clicking, the color of the chip would be revealed to indicate the result of the lottery.

Custom-designed questions to measure risk attitude toward postponing research participation

We designed three questions to measure the participants’risk perception and risk attitude regarding delaying their research participation until the end of the semester. The first question measured their perception of the risk associated with not being able to fulfill the research participation requirement by postponing it until the end of the semester. Participants were asked to rate their agreement with the statement from strongly disagree (1) to strongly agree (7): “I believe that postponing one’s research participation until the end of the semester increases the risk of not being able to fulfill the research participation requirement.”

The second and third questions aimed to measure the extent of aversion to the risk of not fulfilling the requirement due to delaying research participation until the end of the semester. Participants were asked to rate their agreement with the two statements from strongly disagree (1) to strongly agree (7): “The increased risk of not being able to fulfill the research participation requirement due to postponing the research participation was motivating and exciting for me” (with reversed key) and “The increased risk of not being able to fulfill the research participation requirement due to postponing the research participation was stressful or anxiety-inducing for me.”

Data and code availability

Experimental stimuli, anonymized data, and scripts for analysis are available through the Open Science Framework ( https://osf.io/z548y/ ).

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Acknowledgements

We are deeply grateful to Brenda Woodford-Febres for the arduous work of extracting the students’research participation data from New York University’s Sona system.

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ORIGINAL RESEARCH article

Procrastination among university students: differentiating severe cases in need of support from less severe cases.

• 1 Department of Psychology, Uppsala University, Uppsala, Sweden
• 2 Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
• 3 Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
• 4 Department of Psychology, Stockholm University, Stockholm, Sweden
• 5 Department of Psychology, Faculty of Arts and Humanities, Paderborn University, Paderborn, Germany

Procrastination refers to voluntarily postponing an intended course of action despite expecting to be worse off for this delay, and students are considered to be especially negatively affected. According to estimates in the literature, at least half of the students believe procrastination impacts their academic achievements and well-being. As of yet, evidence-based ideas on how to differentiate severe from less severe cases of procrastination in this population do not exist, but are important in order to identify those students in need of support. The current study recruited participants from different universities in Sweden to participate in an anonymous online survey investigating self-rated levels of procrastination, impulsivity, perfectionism, anxiety, depression, stress, and quality of life. Furthermore, diagnostic criteria for pathological delay (PDC) as well as self-report items and open-ended questions were used to determine the severity of their procrastination and its associated physical and psychological issues. In total, 732 participants completed the survey. A median-split on the Pure Procrastination Scale (PPS) and the responses to the PDC were used to differentiate two groups; “less severe procrastination” (PPS ≤ 2.99; n = 344; 67.7% female; M age = 30.03; SD age = 9.35), and “severe procrastination” (PPS ≥ 3.00; n = 388; 66.2% female; M age = 27.76; SD age = 7.08). For participants in the severe group, 96–97% considered procrastination to a problem, compared to 42–48% in the less severe group. The two groups also differed with regard to considering seeking help for procrastination, 35–38% compared to 5–7%. Participants in the severe group also reported more problems of procrastination in different life domains, greater symptoms of psychological issues, and lower quality of life. A thematic analysis of the responses on what physical issues were related to procrastination revealed that these were characterized by stress and anxiety, e.g., tension, pain, and sleep and rest, while the psychological issues were related to stress and anxiety, but also depression, e.g., self-criticism, remorse, and self-esteem. The current study recommends the PPS to be used as an initial screening tool, while the PDC can more accurately determine the severity level of procrastination for a specific individual.

Introduction

In academia, procrastination is a well-known, almost commonplace phenomenon. Students often delay tasks and activities inherent to learning and studying, despite knowing that they will be worse off because of the delay (cf. Steel, 2007 ; Steel and Klingsieck, 2016 ). For some students, academic procrastination can be specific to a situation (i.e., state procrastination), for others it takes on features of a habit or a disposition (i.e., trait procrastination). Studies estimate that almost all students engage in procrastination once in a while, while 75% consider themselves habitual procrastinators ( Steel, 2007 ). For almost half of these habitual procrastinators, procrastination is a real and persistent problem ( Steel, 2007 ), and something they would like to tackle ( Grunschel and Schopenhauer, 2015 ). It can be assumed, however, that not all of them seek help due to the self-regulative problems inherent to procrastination, and, even more so, due to feelings of shame associated with procrastination ( Giguère et al., 2016 ).

In light of the negative consequences, procrastination can have for academic achievement (e.g., Kim and Seo, 2015 ), and well-being (cf. Sirios and Pychyl, 2016 ), it seems important to screen for cases of severe procrastination in a student population in order to offer the support needed. In the case of students who do seek help in student health centers, it is also helpful to see whether they represent a case of severe or less severe procrastination so that support can be tailored to their specific needs.

The aim of the current study is, thus, to differentiate between students who might be in need of professional help from those with less pressing concerns. This is done by determining what characterizes severe and less severe procrastinators with regard to their level of anxiety, depression, stress, quality of life, impulsivity, perfectionism, and demographic variables. Procrastination itself is also assessed by two different self-report measures with the intention of proposing ways of screening in a student population. This could help therapists identify those in need of guidance so that effective interventions can be introduced. For college and university students this would be particularly useful as they find themselves in a setting where procrastination is particularly endemic, often lack the necessary resources or strategies to overcome problems on their own, and procrastination can have dire consequences not only for their academic achievements but also physical and psychological well-being.

Conceptual Framework

Academic procrastination.

The prominent definition of procrastination as “to voluntarily delay an intended course of action despite expecting to be worse off for the delay” ( Steel, 2007 , p. 66) reflects two important aspects of the phenomenon. First, procrastination is a post-decisional phenomenon in goal-directed behavior in that an intention (e.g., to study for an exam) has been formed. Second, procrastination is acratic in nature since individuals put of the intended course of action contrary to knowing better. This acratic nature is reflected by feelings such as regret, shame, guilt, worry, and anxiety (e.g., Giguère et al., 2016 ). It is important to acknowledge that a delay is not procrastination if it is strategic or results from causes not under the control of the individual (cf. Klingsieck, 2013 ). Taking these aspects – post-decisional, acratic, and non-strategic – together, suggests that procrastination is a failure in self-regulation (cf. Steel, 2007 ), This is the most popular conceptualization of procrastination in the literature. In fact, the dispositional, the motivational-volitional, the clinical, and the situational perspective on procrastination can be boiled down to this understanding of procrastination ( Klingsieck, 2013 ). As for students, while academic procrastination is just a little nuisance for some, it entails serious problems for others.

Procrastination’s Link to Depression, Anxiety, Stress, and Quality of Life

Procrastination is associated with negative consequences concerning performance as well as physical and psychological well-being. However, although never a particularly helpful behavior, the relationship with performance is probably not as strong as most would expect. Among students, the correlation with academic achievement is weak, r s = –0.13 to –0.19 ( Steel, 2007 ; Kim and Seo, 2015 ), and perhaps not the main reason for why individuals regard procrastination as a problem. Instead, it might be its effects on physical and psychological well-being that eventually makes someone seek professional help ( Rozental and Carlbring, 2014 ). In a qualitative study of 36 students, for instance, the most frequently reported negative consequences were anger, anxiety, feelings of discomfort, shame, sadness, feeling remorse, mental stress, and negative self-concept ( Grunschel et al., 2013 ). Systematic reviews and meta-analyses on the link between procrastination and symptoms of psychiatric conditions have also found a weak but nonetheless clinically meaningful correlation with depression, r s = 0.28 to 0.30 ( van Eerde, 2003 ; Steel, 2007 ). The same also goes for anxiety, r = 0.22 ( van Eerde, 2003 ). Studies investigating the connection between self-report measures in different populations have demonstrated stronger correlations, such as Rozental et al. (2015) in a clinical trial of adults seeking treatment for procrastination ( n = 710), r = 0.35 for depression and r = 0.42 for anxiety. Similar results were also obtained by Beutel et al. (2016) in an adult community sample ( n = 2527), r = 0.36 for depression and r = 0.32 for anxiety. Although both lower mood and increased unrest can, in themselves, cause procrastination, it is assumed that procrastination also creates a downward spiral characterized by negative thoughts and feelings ( Rozental and Carlbring, 2014 ).

Apart from depression and anxiety, students generally tend to regard procrastination as something stressful. Stead et al. (2010) investigated this association using self-report measures in a sample of students ( n = 200), demonstrating a weak but nonetheless significant correlation between procrastination and stress, r = 0.20. Similar findings were reported by Sirois et al. (2003) for students ( n = 122), and Sirois (2007) for a sample of community-dwelling adults ( n = 254), r s = 0.13 to 0.20. Further, Beutel et al. (2016) found somewhat stronger correlations with stress, r = 0.39, as well as with burnout, r = 0.27. Stress might also play a role as mediator between procrastination and illness, as proposed by the so-called procrastination-health model by Sirois (2007) , implying that procrastination not only leads to more stress, but that the increase in stress in turn leads to many physical issues. Meanwhile, in terms of quality of life and satisfaction with life, procrastination exhibits a weak negative correlation, r = −0.32 ( Rozental et al., 2014 ), and r = −0.35 ( Beutel et al., 2016 ), meaning that procrastination could take its toll on how one appreciates current circumstances.

However, despite the fact that procrastination might be affecting physical and psychological well-being negatively, it is still unclear when it goes from being a more routine form of postponement to becoming something that warrants support, for instance in the realm of counseling or therapy. The literature suggests that as many as 20% of the adult population could be regarded as “chronic procrastinators” ( Harriot and Ferrari, 1996 , p. 611), a number that is easily surpassed by the 32% of students that were characterized as “severe, general procrastinators” ( Day et al., 2000 , p. 126). Students are generally considered worse-off when it comes to recurrently and problematically delaying important curricular activities, with more than half of this population stating that they would like to reduce their procrastination ( Solomon and Rothblum, 1984 ). Still, all of these rates rely on arbitrary cutoffs on specific self-report measures, such as exceeding a certain score, or do not define what is meant by procrastination, which may not correspond to something that requires clinical attention ( Rozental and Carlbring, 2014 ). Establishing a more valid cutoff is therefore needed in order to separate the less severe cases of procrastination from those having problems to the degree that it severely affects everyday life.

Procrastination’s Link to Impulsivity and Perfectionism

Two other variables that are frequently explored in relation to procrastination involve impulsivity and perfectionism. These might be especially pertinent to examine in the context of students who, due to their age, are more impulsive and engage in more reckless behaviors, such as binge drinking ( Lannoy et al., 2017 ), but also tend to perceive the relentless pursuit of high standards as socially desirable despite the fact it can become maladaptive ( Stoeber and Hotham, 2013 ). Research has found that impulsivity is moderately correlated with procrastination, r = 0.41 ( Steel, 2007 ), making it one of the strongest predictors among the personality traits. A twin study by Gustavson et al. (2014) confirmed this association ( n = 663), suggesting that the genetic correlation between impulsivity and procrastination is perfect, r = 1.0. However, this was later questioned by a twin study with a much larger sample ( n = 2012), demonstrating a weak but nonetheless noteworthy correlation, r = 0.29 ( Loehlin and Martin, 2014 ). Rozental et al. (2014) also examined the link between impulsivity and procrastination, but using a self-report measure of susceptibility to temptation, indicating a moderate correlation, r = 0.53. At its core, impulsivity shares many features with procrastination (i.e., self-regulatory failure), making it reasonable to expect a strong connection between the two constructs. Meanwhile, the relationship between perfectionism and procrastination has been disputed. Originally, Steel (2007) demonstrated a non-significant correlation, r = −0.03. Similarly, the correlation by van Eerde (2003) was weak, r = 0.12. This goes against the clinical impression by many therapists that perfectionism often leads to procrastination. However, in both of these cases perfectionism was perceived as a unidimensional construct. There is currently consensus that perfectionism in fact has two higher-order dimensions; (1) perfectionistic strivings, i.e., setting high standards and expecting no less than perfection from yourself, and (2) perfectionistic concerns, i.e., being highly self-critical and overly concerned about others’ perception of you, and having a hard time enjoying your achievements. A recent systematic review and meta-analysis separating these two demonstrated a more complex relationship with procrastination ( Sirois et al., 2017 ). Perfectionistic strivings had a weak negative correlation with procrastination, r = −0.22, while perfectionistic concerns had a weak positive correlation with procrastination, r = 0.23. In other words, setting and striving for high standards might actually be associated with less procrastination, while the more neurotic aspects of perfectionism are related to more procrastination.

To what extent impulsivity and perfectionism might differ between cases of less severe and severe cases of procrastination is currently unknown. However, just as physical and psychological well-being is expected to be more negatively affected among those who exhibit higher levels of procrastination, impulsivity and perfectionism should be more pronounced.

The Current Study

The aim of the current study is to investigate all of these aspects in a sample of students with the purpose of trying to differentiate between those who might be in need for professional help from those with less pressing matters. The idea is to outline their respective characteristics with regard to scores on self-report measures on anxiety, depression, stress, quality of life, impulsivity, and perfectionism, and demographics. Procrastination itself is assessed by two different self-report measures. This first measure is the Pure Procrastination Scale (PPS; Steel, 2010 ) which is a widely used self-report measure. The second measure are the recently proposed diagnostic criteria for pathological delay (Pathological Delay Criteria; PDC; Höcker et al., 2017 ).

The second aim of the current study is to explore the physical and psychological issues related to procrastination on a deeper level. This is made possible through a qualitatively analysis of the responses to two open-ended questions regarding the impact of recurrently putting off activities that need to be completed. Prior research has by qualitative means primarily studied the antecedents of procrastination ( Klingsieck et al., 2013 ), but rarely its implications for physical and psychological well-being. One notable exception is the interview study by Grunschel et al. (2013) cited in the introduction. Investigating these experiences in detail and how often they occur could provide a better understanding of how procrastination affects someone physically and psychologically, and in turn when further assistance might be necessary.

Materials and Methods

The study received ethical approval from the Swedish Ethical Review Authority in June 2020 (Dnr: 2020-00555). Advertisements for the study were initially sent out in October 2020 via the communications office of Karolinska Institutet, which is a medical university in Stockholm, Sweden. However, in order to recruit students from other backgrounds, information about the study was also forwarded to two additional universities in Sweden and posted on various student forums on Facebook, LinkedIn, Accindi, and Instagram. Using a link to a website created specifically for the study, the student could then read about the research aims and design, procedures for data collection and management, ethics, and the principal investigator. The student was also informed that a 45-min pre-recorded lecture with the first author on procrastination would follow once the survey was completed, as a small token of gratitude for the student’s participation. After submitting informed consent, the student was forwarded to an anonymous survey managed through Limesurvey. Both, the website and the survey itself, were available in Swedish and English. The whole survey took on average 21 min for the participants to complete ( SD = 16 min), and always followed the same order of presentation, i.e., no randomization of self-report measures or items were made. Every item of a self-report measure had to be completed to progress to the next, presenting only one self-report measure per page and using a progress bar on top of the screen to convey how much was left on the survey.

In total, 806 students decided to open the link and 797 actually started filling out the survey, resulting in 732 complete survey responses (90.8%). There were no systematic differences between completers and non-completers concerning their demographic information and procrastination, with the exception of civil status (see Appendix for the specifics). Of those who finished the survey, 66.6% were female, which corresponds with the most recent numbers on the gender distribution of newly admitted university students in Sweden (58% female; Swedish Higher Education Authority, 2020 ). The mean age was 28.8 years ( SD = 8.30; range 18–65). They were either single (44%) or married (54%), and the vast majority had no children (78%). In terms of their education, 6.8% attended just a single course, (e.g., Nutrition, the nutrients, and metabolism, 7.5 higher education credits), 63.7% underwent a complete study program, such as the study program in dental hygiene (180 higher education credits), 9.1% were enrolled in post graduate studies, for example the study program in psychotherapy (90 higher education credits), and 3.4% were admitted as doctoral candidates. Of note, 30 higher education credits correspond to one semester full-time. The participants had, on average, achieved 195 higher education credits ( SD = 141), which thus corresponds to 3.25 years of full-time education. With regard to psychiatric disorders, 115 self-reported having a diagnosis (15.7%). These were grouped according to the responses to an open-ended question, with mixed conditions representing the largest category (40%, i.e., having more than one diagnosis, mostly a combination of depression and anxiety), followed by depression (13.9%), and ADHD (13%). As for questions regarding procrastination, 71% considered it to be a problem, with a mean age of 17.5 years ( SD = 5.7; range 10–53) for when they first started perceiving it as problematic, and 29.4% of this group had considered seeking help for procrastination. None of these variables differed between genders, see Table 1 for an overview.

Table 1. Descriptive statistics for whole sample and results of t -tests of gender differences.

Instruments

Procrastination.

In order to differentiate and classify the more severe cases of procrastination, a widely used self-report measure is applied, the Pure Procrastination Scale (PPS), which was originally introduced and validated by Steel (2010) , and translated to a large number of languages since ( Svartdal et al., 2016 ). The PPS was developed from several other self-report measures, retaining only those items that demonstrated the strongest factor loadings on the core construct of procrastination (i.e., not other forms of delay), hence the name “pure.” The PPS has 12 items, e.g., “I often find myself performing tasks that I had intended to do days before” (item 6), is scored according to a 5-point Likert-scale (1–5), and has an internal consistency in the current study of Cronbach’s α = 0.92.

Secondly, diagnostic criteria for pathological delay (Pathological Delay Criteria; PDC), which were put forward in a therapy manual by Höcker et al. (2017) , are also used to differentiate between less and more severe cases of procrastination. According to the PDC, procrastination can be considered pathological if the following two criteria are met:

Over the past 6 months…

(1) On at least half of the days, important tasks were delayed past the adequate point in time, even though there was sufficient time to complete them.

(2) Procrastination has strongly interfered with reaching personally relevant goals.

In addition, at least three of following criteria also need to be fulfilled:

(1) More than half of the time available for completing a task was wasted by procrastinating.

(2) On at least half of the days, other less important tasks were preferred, even though the individual wanted to start working on the more pressing tasks.

(3) On at least half of the days, the delay caused aversion and animosity.

(4) At least half of the tasks that were to be completed were finished only under great time pressure or not at all due to procrastination.

(5) At least half of the individual’s performance potential was impaired due to procrastination.

(6) The individual has experienced physical issues due to procrastination (e.g., tensed muscles, sleeping disorders, cardiovascular problems, gastric, and digestive problems), or psychological issues due to procrastination (e.g., restlessness, feeling of being pressured, feeling of being helpless, inner tension, and anxiety).*

* At least five of these issues need to be reported to meet this criterium.

The criteria above were developed as a diagnostic instrument for differential diagnosis and as a basis for clinical decision making. During its development, the authors followed the definition and structure of psychiatric disorders used by the Diagnostic and Statistical Manual of Mental Disorders ( American Psychiatric Association, 2013 ). In order to select the criteria with the best predictive value, large samples of university students seeking help at a procrastination clinic at the University of Münster, Germany, were used (e.g., Engberding et al., 2011 ). The authors used the methods of best subset regression and ROC-analyses to select the criteria with the highest scores on sensitivity and specificity for identifying pathological delay. These criteria and the corresponding questionnaire were subsequently published in the therapist manual ( Höcker et al., 2017 ).

Further variables of meaningful aspects concerning procrastination were assessed: (1) if the participant itself believes procrastination is a problem and, if yes, (2) at what age the participant started perceiving procrastination as a problem, (3) if the participant has ever considered seeking help for procrastination, and (4) the impact of procrastination on various life domains. In order to assess how procrastination had affected the participants, its negative effects on eight different life domains were probed for: “To what degree do you think procrastination has affected you negatively in the following life domains?”. The life domains were: interest/leisure, work/studies, friendships/social life, community/engagement/spirituality, family life/parenting, rest/sleep, love/intimate relationships, and physical activity/diet. Participants rated each life domain using a 10-point Likert-scale ranging from 0 = not at all to 10 = very much. The life domains were inspired by the type of value measures often used in Acceptance and Commitment Therapy ( Reilly et al., 2019 ), and are commonly employed in many clinical trials (e.g., Buhrman et al., 2020 ; Ehlers et al., 2020 ).

Impulsivity

Impulsivity was assessed using the Susceptibility to Temptation Scale (STS; Steel, 2010 ; Svartdal et al., 2016 ), which is comprised of 11 items regarding the inclination to fall for more immediate gratifications, e.g., “I will crave a pleasurable diversion so sharply that I find it increasingly hard to stay on track” (item 1). The STS is scored on a 5-point Likert-scale (1–5), and has an internal consistency in the current study of α = 0.93.

Perfectionism

Perfectionism was assessed by the Clinical Perfectionism Questionnaire ( Dickie et al., 2012 ). This scale assesses the frequency of dysfunctional self-imposed standards in the last 4 weeks by a subscale covering the personal standards (i.e., perfectionistic standards), and a second subscale covering emotional concerns and consequences (i.e., perfectionistic concerns). Item 9 of the original scale (“Have you repeatedly checked how well you are doing at meeting your standards [for example, by comparing your performance with that of others]?”) was omitted because it did not load on the factor perfectionistic standards as in the original version by the authors. Item 2 of the subscale perfectionistic concerns (“Have you tended to focus on what you have achieved, rather than on what you have not achieved?”) was omitted due to a very low item-scale-correlation. Thus, the subscale Personal Standards (CPQ_PS) was composed of five items (α in current study = 0.71; sample item “Have you been told that your standards are too high?”). The subscale Emotional Concerns (CPQ_EC) was composed of three items (α in current study = 0.76; sample item “Have you been afraid that you might not reach your standards?”). The CPQ is scored on a four-point Likert-scale (1–4).

Anxiety was examined using the Generalized Anxiety Disorder – 7 Items (GAD-7; Spitzer et al., 2006 ). It consists of seven items concerning the general level of anxiety and worry experienced during the last 2 weeks, and is often used as a screening tool for anxiety disorders, e.g., “Over the last 2 weeks, how often have you been bothered by the following problems: Worrying too much about different thing” (item 3). The GAD-7 is scored on a four-point Likert-scale (0–3), and has an internal consistency in the current study of α = 0.90. A score of 5 points indicate mild anxiety, 10 moderate anxiety, and 15 severe anxiety.

Depression was assessed by the Patient Health Questionnaire – 9 Items (PHQ-9; Kroenke et al., 2001 ). It has nine items on depressive symptoms experienced during the last 2 weeks, in accordance with the diagnostic criteria for major depressive disorder ( American Psychiatric Association, 2013 ), e.g., “Over the last 2 weeks, how often have you been bothered by any of the following problems? Little interest or pleasure in doing things” (item 1). The PHQ-9 is scored on a four-point Likert-scale (0–3), and has an internal consistency in the current study of α = 0.88. A score of 5 points indicate mild depression, 10 moderate depression, 15 moderately severe depression, and 20 severe depression.

Stress was explored using the Perceived Stress Scale (PSS; Cohen et al., 1983 ). It is comprised of 14 items regarding stress in different situations, as experienced during the last month, e.g., “In the last month, how often have you felt that you were unable to control important things in your life?” (Item 2). The PSS is scored on a five-point Likert-scale (1–5), and has an internal consistency in the current study of α = 0.85.

Quality of Life

Quality of life was determined by the Brunnsviken Brief Quality of Life Scale (BBQ; Lindner et al., 2016 ). It features six life domains (leisure time, view of one’s own life, learning, creativity, friends and friendship, yourself as a person), and is rated on both importance and how satisfied one is with each domain, e.g., “I am satisfied with my leisure time; I have the opportunity to do what I want in order to relax and enjoy myself.” (domain 1). The BBQ is scored on a 5-point Likert-scale (0–4), where importance and satisfaction in each domain are multiplied and summing the products for a total score (range 0–96). These weighted ratings as well as the total score for quality of life was used for the current study. The BBQ has an internal consistency of α = 0.79 in the current study.

In addition, achieved higher education credits was assessed to differentiate the two groups by their academic achievement. Age and gender were assessed as demographic variables but only used to characterize the sample and not to differentiate the groups.

Quantitative Analysis

Multiple t -tests and Chi 2 -tests were performed by SPSS Version 27. The significance level was corrected (Bonferroni) to p < 0.002 ( t -tests) and 0.007 (Chi 2 -Tests). In order to differentiate severe cases from less severe cases of procrastination, the sample was split along the median ( Med. = 3.00) of the PPS. This created two groups, which are referred to as: “less severe procrastination” (PPS ≤ 2.99; n = 344; 67.7% female; M age = 30.03; SD age = 9.35), and “severe procrastination” (PPS ≥ 3.00; n = 388; 66.2% female; M age = 27.76; SD age = 7.08). For the second differentiation, the PDC was used to split the sample into the corresponding groups (i.e., based on whether the participants fulfilled all of the necessary criteria or not): “less severe procrastination” ( n = 398; 71.5% female; M age = 29.94; SD age = 9.03), and “severe procrastination” ( n = 344; 61.6% female; M age = 27.51; SD age = 7.11).

Qualitative Analysis

Two items of the PDC were open-ended and therefore analyzed qualitatively. Given the nature of these variables and their manifest content, that is, being short text-based survey responses with little room for elaboration, inductive thematic analysis was deemed appropriate to use. Inductive refers to generating a new understanding of the subject matter, rather than testing a predefined theoretical framework during the analysis ( Thomas, 2006 ). Meanwhile, thematic analysis is a procedure for qualitative analysis considered suitable for exploring recurrent patterns or themes within data. Braun and Clark (2006) provide an overview of the steps in the analytic process, which usually includes familiarizing yourself with your data by reading it repeatedly and taking notes, extracting meaningful entities of relevance to the purpose of the study, generating codes representing important issues for further inquiry, collating the codes to explore potential themes, reviewing the themes by going back and forward to your data, naming the themes, and reporting and discussing the results. The first author conducted the thematic analysis and discussed the results with the last author, but no further attempt at cross-validation was considered necessary given the characteristics of the data. The first author is a Swedish clinical psychologist and researcher with extensive experience of treating and researching procrastination, perfectionism, anxiety disorders, and exhaustion disorder, and has worked with both quantitative and qualitative methods.

The first qualitative item of the PDC concerned the physical issues of procrastination and involved a dataset of 2304 words (the average number of characters per response was 59.8, SD = 92.7). The second qualitative item of the PDC concerned the psychological issues of procrastination and was comprised of 4022 words (the average number of characters per response was 55.8, SD = 67.5). Because of a high degree of overlap in the responses, such as a vast majority reporting experiencing anxiety regardless of being a severe procrastinator or not, and that each response could entail a large number of physical as well as psychological issues, the variables could only be analyzed and presented qualitatively, rather than being part of the quantitative analysis.

Descriptive Statistics

Table 1 presents the descriptive statistics for each self-report measure as well as their respective gender differences (female vs. male). There were only statistically significant gender differences on the CPQ (Cohen’s d = 0.30 and 0.41), and GAD-7 ( d = 0.28), with female students scoring higher than male students. As for procrastination, the average score was 3.00 ( SD = 0.91), which is the same as the median split used for grouping the participants into severe and less severe procrastinators, while 46% of the sample fulfilled the PDC criteria. Negative effects of procrastination were most prominent in the life domains of work/studies, physical activity/diet, and rest/sleep, and being considerably lower in the life domains of family life/parenting and community/engagement/spirituality. The average scores on the GAD-7 and PHQ-9 correspond to mild anxiety and mild depression.

Differentiating Severe Cases From Less Severe Cases of Procrastination

The results of differentiating severe cases from less severe cases of procrastination are presented in detail in Tables 2 – 4 . The two groups diverged with regard to their perception of procrastination. In the group “severe procrastination,” almost every participant (96–97%) considered procrastination to be a problem, while those participants belonging to the group “less severe procrastination” did so to a much lesser extent (42–48%). In addition, 35–38% of the severe procrastinators had considered seeking help for their problems, compared to just 5–7% among the less severe procrastinators. There were also statistically significant differences with regard to the negative impact of procrastination on different life domains between the two groups, especially work/studies, d = 1.20–1.23.

Table 2. Differentiating severe procrastination from less severe procrastination.

Table 3. Differentiating severe procrastination from less severe procrastination.

Table 4. Differentiating severe procrastination from less severe procrastination.

With the exception of perfectionism scores, severe cases and less severe cases of procrastination differed on all of the self-report measures, with severe procrastinators scoring higher on all measures and lower on quality of life. Moreover, the participants in the group “severe procrastination” also had a higher proportion of psychiatric disorders, and met the criteria for moderate and severe anxiety, and moderate and severe depression. From a demographic perspective, participants with severe procrastination were generally older and had achieved fewer higher education credits. When using the PPS to differentiate the groups, there were no gender differences. However, based on the PDC, the portion of female participants with severe procrastination was significantly lower than the portion of females in the group of less severe procrastination.

Differential Overlap

Based on a median split on the PPS, 53% of the participants were considered to be severe procrastinators while applying the PDC, 46% of the participants were regarded as severe procrastinators. Combining the two revealed that among those being classified as severe procrastinators on the PPS, 74% were also identified as such based on the criteria of the PDC. Likewise, 86% of the participants being severe procrastinators on the PDC were recognized as such on the PPS. Overall, there was an overlap of 80% between the two methods for differentiating severe procrastination from less severe procrastination. Also, the 20% non-overlap was not equally distributed between the severe cases (32% of non-overlap), and less severe cases (68% of non-overlap) of procrastination. In other words, both ways might be reliable in identifying cases of severe procrastination, but the PPS could potentially overreport the number of severe cases. Furthermore, the PDC might be more sensitive to gender differences as it demonstrates that the proportion of female participants in the group “severe procrastination” is lower than the proportion of female non-severe procrastinators.

Physical and Psychological Issues of Procrastination

Physical issues.

The participants reported a large number of physical issues that are considered emblematic of Stress and anxiety , see Table 5 for an overview. These could in turn be organized according to six subthemes; Tension (e.g., feeling tensed around your shoulders, neck, and back), Pain (e.g., bruxism, muscular pain, and experiencing recurrent headaches or migraine), Sickness (e.g., nausea, dizziness, and shudders), Stomach (e.g., increased or decreased appetite, stomach aches, and diarrhea), and Sleep and rest (e.g., insomnia, tiredness, and restlessness). In a majority of the cases, participants described having more than one symptom, such as feeling stressed out, having difficulties sleeping, and being restless.

Table 5. Physical issues of procrastination.

Among the less common physical issues, Other , these were characterized by the worsening of an already underlying condition, such as eczema, causing flare ups or exacerbated problems. However, a few participants also mentioned biting their nails when under stress or experiencing problems with gastritis or becoming numb.

Psychological Issues

In terms of the psychological issues, there was a clear overlap with many of the physical symptoms described above, see Table 6 for an overview. One of the overarching themes, Stress and anxiety , included four subthemes; Sleep and rest (e.g., insomnia, tiredness, restlessness, and feeling exhausted), Fear (e.g., worrying about your current situation or the future and feelings of panic), Cognitive load (e.g., having difficulties concentrating and remembering things), and Performance (e.g., experiencing performance anxiety or having difficulties achieving high standards).

Table 6. Psychological issues of procrastination.

Apart from being stressed out and anxious, most participants also described having a lower mood, and feelings of hopelessness and despair. This overarching theme, Depression , consisted of three subthemes; Self-criticism (e.g., self-loathing, feelings of disappointment with oneself, and negative thoughts), Remorse (e.g., anger, frustration, and feelings of shame), and Self-esteem (e.g., feeling inadequate and experiencing a loss of self-confidence).

Less prevalent were signs of Other conditions and symptoms, such as eating disorders, compulsions, and social anxiety, although a few participants experienced these issues in relation to their procrastination.

General Discussion

The first aim of the current study was to explore ways of differentiating students who might require professional help for procrastination from those with less pressing matters. Overall, the findings suggest that cases of severe procrastination, as determined using either the PPS or the PDC, are characterized by higher levels of anxiety, depression, and stress than the less severe cases, representing moderate to large between-group effect sizes. Given the magnitude of these differences, severe procrastinators could therefore warrant further assessment and possibly even treatment, such as via a student health center. Furthermore, severe procrastination was associated with greater self-reported negative effects on all of the life-domains that were examined, most notably for work/studies, but also for physical activity/diet and rest/sleep, which resemble previous research on the impact of procrastination on both academic achievement and health (e.g., Grunschel et al., 2013 ; Kim and Seo, 2015 ). In addition, quality of life was more negatively affected among severe procrastinators, corresponding to moderate between-group effect sizes, although, the level of quality of life was not as impaired as has been found in clinical samples ( Lindner et al., 2016 ). As for impulsivity, those with severe procrastination were far more susceptible to temptation, a difference consistent with a large between-group effect sizes, which is in line with the idea of impulsivity being one of the strongest personality traits predictive of procrastination ( Steel, 2007 ). With regard to perfectionism, only emotional concerns differed between severe and less severe procrastinators, corresponding to large between-group effect sizes. Similar to the findings by Sirois et al. (2017) , emotional, or, neurotic, aspects of perfectionism thus appear to be much more strongly related to severe procrastination, suggesting that students who are concerned about making mistakes and not living up to certain standards might need treatment that specifically target these issues.

When explicitly asked about it, severe procrastinators seem to regard procrastination as a problem to a much greater extent than less severe procrastinators (96 and 97%, in comparison to 42 and 48%, depending on whether the PPS or the PDC was used for differentiation), something they also report having been more inclined to seek help for (35 and 38% compared to 5 and 7%). This is the first time such direct queries have been used to determine if someone might need further assistance, giving some credence to the results and pointing toward the utility of using either the PPS or the PDC to identify severe cases of procrastination. However, as indicated in the current study, the PPS could potentially overreport the number of severe cases. Meanwhile, the PDC might be more sensitive to gender differences as it demonstrates that the proportion of female participants among the severe procrastinators is significantly lower than the proportion of female participants among the less severe procrastinators.

Another aim of the current study was to understand the physical and psychological issues related to procrastination by investigating the responses to two open-ended items. In terms of the former, the results demonstrate that many students who procrastinate experience symptoms that are commonly seen in stress and anxiety, such as being tensed, having sleeping problems, and struggling with different forms of pain. These issues are in line with the findings by Grunschel et al. (2013) who also reported a high incidence of such consequences from procrastinating. In addition, it corroborates the procrastination-health model by Sirois (2007) , which proposed that stress might act as a mediator between procrastination and many physical issues. The idea that procrastination is associated with stress, and, in turn, leads to other concerns, is reasonable given the nature of procrastination. While it may decrease discomfort temporarily (cf. Sirois and Pychyl, 2013 ), the activity being postponed still has to be performed on a later occasion, causing more stress overall ( Tice and Baumeister, 1997 ).

As for the psychological issues, these were also characterized by symptoms of stress and anxiety, for example, insomnia, restlessness, and worry, suggesting a high degree of overlap with the physical issues. Again, this corresponds to the results by Grunschel et al. (2013) , and should be seen as the affective and somatic effects of being anxious and stressed out from procrastinating. Furthermore, difficulties concentrating and remembering things are not uncommon when under stress, thereby affecting the possibility to pursue a given action ( Marin et al., 2011 ), as reported by many participants in the current study. However, a noticeable difference between the physical and psychological issues are aspects related to performance, self-criticism, remorse, and self-esteem. These might portray the more depressogenic impact of procrastination, such as being disappointed with oneself, experiencing lower self-confidence, and exhibiting negative self-evaluation. This goes in line with the notion of efficacy-performance spirals, whereby the inability to execute goal-directed behaviors and progress toward a given end-point can lead to lower mood, self-loathing, and decreased motivation ( Lindsley et al., 1995 ). In other words, procrastination does not only appear to cause stress and anxiety in the aftermath of a procrastination episode, but also negatively impacts the general state of the individual by inducing self-doubt, frustration, shame, rumination, and feelings of inadequacy (cf. Giguère et al., 2016 ; Constantin et al., 2018 ). When demonstrating such depressive thoughts and feelings, it is then not unreasonable to expect the person to be less inclined to take care of the assignments that need to be done, further perpetuating a downward cycle.

Practical Implications and Recommendations

Based on the results from the current study, the PPS is recommended as an initial screening tool for large samples, such as when admitting new students to a study program or as a general assessment of well-being at a university. As a second step, students who score higher than a certain cut-off (e.g., 3.00 like in the present study) on the items should be advised to fill out the PDC to more accurately determine the severity level of procrastination and its associated physical and psychological issues. This procedure could, for instance, be implemented at a student health center in order to identify those students in need of professional help, although it should be noted that the PDC has so far only been used in this way in Germany. In addition, administering the GAD-7 and PHQ-9 on the same occasion gives some indication of symptoms of anxiety and depression. This would inform therapists of other possible conditions that might warrant their attention, such as major depressive disorder, which sometimes have to be dealt with first in treatment. Furthermore, for those who seek support for procrastination, discussing the criteria of the PDC and the physical and psychological issues presented in the current study might help them understand what they are experiencing and how to overcome their problems. This type of psychoeducation can often have a normalizing effect, reducing shame and stigma, and, in turn, motivate behavior change. Similarly, career counselors might use the PDC in relation to discussing study satisfaction and dropout intentions in order to prevent students ending their studies prematurely ( Scheunemann et al., 2021 ).

Apart from aiding the identification of severe procrastinators, the findings from the current study may also have implications for treatment. The physical and psychological issues reported by the participants suggest that symptoms of stress and anxiety are common. On the one hand, procrastination can sometimes be a response to this discomfort. On the other hand, procrastinating an activity can also give rise to this distress ( Rozental and Carlbring, 2014 ). In both cases, interventions targeting symptoms of stress and anxiety seem important in order to overcome many difficulties experienced by students, which can involve goal-setting, problem-solving, time management, and exposure to negative emotions, as have been tested in clinical trials (e.g., Rozental et al., 2015 , 2018 ). The basic tenet is to lower stress levels and help endure those feelings that might otherwise lead one astray. Moreover, the depressogenic impact of procrastination may cause the individual to feel less willing to initiate goal-directed behaviors. Similar to the actions of someone suffering from major depressive disorder, this however, prevents the person from experiencing mastery and joy, furthering a vicious process of passivity and negative self-evaluation. Interventions that focus on activity scheduling and step-wise performance of activities might therefore be key to overcoming inaction and self-loathing, i.e., behavioral activation ( Ramsay, 2002 ). Likewise, students who may be experiencing low self-efficacy due to their procrastination could benefit from study skills training ( Svartdal et al., 2021 ). Concerning the different phases of a procrastination episodes ( Svartdal et al., 2020b ), it might even be worthwhile to differentiate between strategies that upregulate motivation as in motivational regulation strategies ( Grunschel et al., 2016 ), and strategies that downregulate negative affect ( Eckert et al., 2016 ), thus, tailoring them to the specific needs of the student. Furthermore, the environment for many students also seems to result in procrastination and might have to be targeted. Svartdal et al. (2020a) provide an overview of the measures that could be taken by course coordinators and lecturers, such as study skills training, group work, and courses in self-regulation.

Limitations

The current study is, to the knowledge of the authors, the first attempt at differentiating the more severe from less severe procrastinators among university students. It has furthered the understanding of what characterizes problematic forms of procrastination and provided recommendations on how to screen and support those experiencing difficulties completing their commitments. However, there are also several limitations that need to be addressed.

First, recruitment of participants was made via advertisements and information distributed universities and in relevant forums. Although a reasonable way of reaching university students, it might also have attracted proportionally more individuals with greater problems of procrastination or, the other way around, those for whom procrastination is just a little nuisance. This self-selection bias might have affected the possibility to differentiate between “severe procrastination” and “less severe procrastination.” The distribution of scores on the self-report measures do not seem to suggest that this is the case, but future research should try alternative methods of recruiting participants, such as stratified random sampling. Similarly, the current study focused on students in university settings only, making it unclear whether the results can be generalized to an adult working population or younger students in elementary school or high-school. Replicating the approach used here should be feasible in other settings in order to determine if the same type of classification is possible to make elsewhere. Replicating the approach in a longitudinal design would, furthermore, deliver information on causal relationships between procrastination and psychopathological symptoms.

Second, the current study was conducted during the fall semester of 2020, which is about 6 months into the COVID-19 pandemic. Similar to other countries, universities in Sweden shut down on-campus education during the spring of the same year, meaning that most curricular activity was performed online when the participants responded to the survey. Whether this has affected university students’ levels of procrastination is not known, but given the lack of routines and social support it is reasonable to assume that it has been detrimental to some. Furthermore, the COVID-19 pandemic itself, and its effects of everyday life, might have affected the physical and psychological well-being of some participants, thereby inflating the scores of the self-report measures somewhat.

Third, we used a median split on the PPS for differentiating the more severe from less severe procrastinators. In general, median splits, as practice for dichotomizing a continuous variable, have a long tradition of being criticized for the loss of information and reduction in power (e.g., Cohen, 1983 ). However, newer studies weaken this criticism considerably (e.g., Iacobucci et al., 2015a , b ) by showing that this is in fact a robust method. For our purpose, it was very important to retain all information of the sample. Splitting the sample into three groups and only using the two extreme one would have resulted in a considerable loss of information, albeit useful for therapists. The median split of the PPS, however, and the diagnostic criteria used in the PDC, have not previously been tested regarding their classification accuracy for identifying more severe procrastinators. It is therefore unknown if these two methods can be applied for this purpose. Usually, a gold standard is used for comparison and validation, such as a structured clinical interview for determining major depressive disorder. However, such a diagnostic procedure is not possible for procrastination because it is not considered to be a diagnosis. Instead, the current study asked questions on whether the participants themselves regarded procrastination as a problem and if they ever considered seeking help for procrastination as a proxy for diagnosis. An idea for future research is to corroborate this method by interviews, which may provide additional insights on where to place the cutoff between severe and less severe procrastination.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by Swedish Ethical Review Authority (Dnr: 2020-00555). The patients/participants provided their online informed consent to participate in this study.

Author Contributions

AR and KK designed the study and outlined its research aims and drafted the manuscript. AR and DF applied for ethics approval, set up the study, and monitored the data collection. AH advertised the study and managed the recruitment of participants. KK was responsible for the quantitative analyses. AR and DF was responsible for the qualitative analyses. DF and AH commented on the manuscript and approved its submission. All the authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewer TD declared a past co-authorship with one of the authors KK to the handling editor.

The handling editor declared a past co-authorship with one of the authors KK.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Comparing completers with non-completers.

Appendix II

Descriptive statistics and correlations for the whole sample ( N = 732).

Keywords : procrastination, students, assessment, severity, differentiation

Citation: Rozental A, Forsström D, Hussoon A and Klingsieck KB (2022) Procrastination Among University Students: Differentiating Severe Cases in Need of Support From Less Severe Cases. Front. Psychol. 13:783570. doi: 10.3389/fpsyg.2022.783570

Received: 26 September 2021; Accepted: 08 February 2022; Published: 15 March 2022.

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Copyright © 2022 Rozental, Forsström, Hussoon and Klingsieck. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Alexander Rozental, [email protected] ; [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Understanding procrastination: A case of a study skills course

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• Published: 22 March 2021
• Volume 24 , pages 589–606, ( 2021 )

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• T. Hailikari   ORCID: orcid.org/0000-0002-1355-2985 1 ,
• N. Katajavuori   ORCID: orcid.org/0000-0001-5093-2402 1 &
• H. Asikainen   ORCID: orcid.org/0000-0002-3858-211X 1  

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Procrastination is consistently viewed as problematic to academic success and students’ general well-being. There are prevailing questions regarding the underlying and maintaining mechanisms of procrastination which are yet to be learnt. The aim of the present study was to combine different ways to explain procrastination and explore how students’ time and effort management skills, psychological flexibility and academic self-efficacy are connected to procrastination as they have been commonly addressed separately in previous studies. The data were collected from 135 students who participated in a voluntary time management and well-being course in autumn 2019. The results showed that students’ ability to organize their time and effort has the strongest association with procrastination out of the variables included in the study. Psychological flexibility also has a strong individual role in explaining procrastination along with time and effort management skills. Surprisingly, academic self-efficacy did not have a direct association with procrastination. Interestingly, our findings further suggest that time and effort management and psychological flexibility are closely related and appear to go hand in hand and, thus, both need to be considered when the aim is to reduce procrastination. The implications of the findings are further discussed.

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Examining the relations of time management and procrastination within a model of self-regulated learning

Why are you waiting procrastination on academic tasks among undergraduate and graduate students, predictors of procrastination in first-year university students: role of achievement goals and learning strategies.

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1 Introduction

Academic procrastination is very common among university students: almost all occasionally procrastinate in one or another domain of their studies, and approximately every second student regularly procrastinates (Rothblum et al., 1986 ; Steel, 2007 ). Considerable attention has been given to procrastination in university setting (Klassen et al., 2008 ). The student population is especially prone to procrastination, with an estimated prevalence of 50–95% (Steel, 2007 ). Procrastination may be defined as ‘the voluntary delay of an intended and necessary and/or [personally] important activity, despite expecting potential negative consequences that outweigh the positive consequences of the delay’ (Klingsieck, 2013 , 26). Typical for procrastination is that it is irrational and not imposed by external matters and it is often accompanied by subjective discomfort and negative consequences (Klingsieck, 2013 ). Procrastination is often associated with several negative factors, such as lower academic performance (Steel et al., 2001 ), increased stress (Sirois et al., 2003 ) and poorer mental health (Stead et al., 2010 ). Therefore, it is necessary to understand the causes and the factors that maintain procrastination in order to be able to reduce it. The challenge is that research in the area of procrastination often lacks a coherent, theoretical explanation of the behaviour (Glick et al., 2014 ) which has made it difficult to understand the phenomenon and to follow the research (e.g., Klingsieck, 2013 ; Schraw et al., 2007 ; Steel, 2007 ). Therefore, there are prevailing questions regarding the underlying and maintaining mechanisms of procrastination which are yet to be learnt (Katz et al., 2014 ; Visser et al., 2018 ).

The core characteristic of procrastination is the intention-action gap suggesting that the procrastinators often have good intentions, but the challenge lies in the implementation of these intentions (Dewitte and Lens, 2000 ). Thus, procrastination has traditionally been understood as a self-regulation or time management problem (Wolters et al., 2017 ). There is a strong body of evidence suggesting that lower levels of self-regulating behaviours are related to higher levels of procrastination, and thus self-regulation is one of the keys to understanding procrastination (Ferrari, 2001 ). However, Visser et al. ( 2018 ) suggest that procrastination is complex behaviour that involves both cognitive and emotional elements as well as evaluations of one’s own competence. Recent research suggests that instead of being purely a self-regulation or time management problem, procrastination is also strongly influenced by psychological factors, such as the low confidence in one’s own abilities to perform (Steel, 2007 ) and inability to cope with negative emotions that arise in challenging situations referring to the centrality of psychological flexibility in understanding procrastination (Dionne, 2016 ; Gagnon et al., 2016 ). In this article, we aim to bring together these central constructs that have usually been addressed separately in previous studies in order to understand the phenomenon of procrastination and its underlying mechanisms better.

1.1 Factors explaining academic procrastination

There are several theoretical perspectives that have been used when exploring procrastination: the differential psychology perspective; the motivational and volitional psychology perspective; the clinical psychology perspective; and the situational perspective (Klingsieck, 2013 ). In the context of higher education, the motivational-volitional psychology and situational perspectives may be regarded as the most relevant because they provide tangible tools and theories for educational developers to try to influence students’ procrastination tendencies whereas the other perspectives focus more on aspects that are not so easily influenced, such as personality traits, depression or personality disorders. The motivational-volitional perspective is focused on the relationship between different motivational and volitional variables such as motivation, self-regulation, time management and learning strategies which are central in successful studying in higher education (Lindblom-Ylänne et al., 2015 ; Klingsieck, 2013 ). The situational perspective, on the other hand, focuses on procrastination evoked by situational features, such as the perceived difficulty of the task (Klingsieck, 2013 ). This situational perspective can be further extended to include the person’s reactions to the challenges posed by the situation.

From the motivational-volitional perspective, academic procrastination has been found to be related to lower levels of self-regulation and academic self-efficacy and is associated with higher levels of stress and anxiety (e.g., Ferrari et al., 2005 ; Howell et al., 2006 ; Schraw et al., 2007 ; Wolters, 2003 ). Klassen et al. ( 2008 ) state that among all the variables that have been investigated in relation to academic procrastination, self-regulation, self-efficacy, and self-esteem have received the most attention (see e.g., Cassady and Johnson, 2002 ; Chun Chu and Choi, 2005 ; Ferrari, 2001 ; Howell et al., 2006 ; Steel, 2007 ; Wolters, 2003 ). Procrastination has traditionally been considered to be a form of self-regulation failure, as a weakness of will and low ability to organise own studying (e.g., Ferrari, 2001 ; Senecal et al., 1995 ; Steel, 2007 ) and, thus, one common theory is that procrastination results from a person’s inability to manage time (Burka and Yuen, 1982 ; Glick and Orsillo, 2015 ).

1.2 Time and effort management skills behind procrastination

Research focusing on exploring university students’ study progress has consistently shown that time and effort management skills are among the most crucial factors (e.g., Ariely and Wertenbroch, 2002 ; Entwistle, 2009 ; Haarala-Muhonen et al., 2011 ; Häfner et al., 2015 ; Pintrich, 2004 ). In the higher education context, time and effort management skills refer to students’ ability to set goals for themselves and to study according to their goals, to manage their time usage and to prioritise the tasks to be conducted (Entwistle et al., 2001 ). It has further been suggested that time and effort management skills provide a foundation for cognitive engagement and student achievement as they refer to how much the students are willing to invest in their learning (Appleton et al., 2008; Fredricks et al., 2004). Previous studies indicate that many higher education students struggle with time and effort management skills (Parpala et al., 2010 ) and that these skills remain constant throughout the studies and are hard to change (Parpala et al., 2017a ). Many students study without study schedules and thus fail to pass the courses because they run out of preparation time, such as for exams (Asikainen et al., 2013 ). Thus, many interventions to reduce procrastination have focused on improving time management skills (e.g., Ariely and Wertenbroch, 2002 ; Häfner et al., 2015 ; Levrini and Prevatt, 2012 ).

There are also critical voices claiming that time and effort management skills, or lack thereof, are not enough to explain the phenomena and that research focusing on the role of time and effort management skills in procrastination does not take the persons’ internal experiences enough into account (Glick and Orsillo, 2015 ). It has been suggested that when exploring factors that maintain and cause procrastination, we have to widen the perspective to include a broader theory of regulation of inner experiences, namely, psychological flexibility (Hayes, 2004 ; Hayes et al., 2012 ). Recent studies concerning procrastination have brought up the importance of psychological flexibility in decreasing procrastination and suggest that procrastination may also result from person’s psychological inflexibility (Eisenbeck et al., 2019 ; Gagnon et al., 2016 ; Glick et al., 2014 ; Scent and Boes, 2014 ).

1.3 Psychological flexibility and academic self-efficacy beliefs

Psychological flexibility refers to one’s ability to be consciously present, confronting and accepting the negative experiences, emotions and thoughts one might have, and being able to take action about achieving one’s own goals despite unpleasant feelings and thoughts, and further, being able to react to negative feelings and thoughts from a new perspective (Chawla and Ostafin, 2007 ; Hayes et al., 2006 ). Thus, it is a central factor influencing the way students react in a stressful and challenging situation. Procrastinators often fail to regulate their actions in situations that are challenging and involve high levels of stress and cognitive workload and avoiding the unpleasant feelings generated by the situation (Ferrari, 2001 ). This experiential avoidance, or an unwillingness to encounter unpleasant experiences, such as anxiety, is a key component of psychological inflexibility (Sutcliff et al., 2019 ). Tasks that are considered to be difficult and challenging and do not provide instant rewards tend to be delayed and avoided (Blunt and Pychyl, 2000 ; Sirois and Pychyl, 2013 ; Steel, 2007 ). Escaping from stressful and aversive situations might relieve stress and are thus rewarding. As an example, students are always faced with a trade-off when choosing between procrastinating or studying (Kirby et al., 2005 ; Olsen et al., 2018 ) . One alternative is to complete the challenging academic tasks on time which leads to delayed rewards in the form of achieving academic and career goals (see e.g., Sutcliff et al., 2019 ). These goals often strongly align with students' values. However, students always have an alternative to choose an immediate, positive reinforcers in the form of avoidance or escape from negative internal experiences elicited by challenging tasks, such as engaging in social or leisure activities that are not related to the task at hand. Consequently, a number of recent studies have suggested that procrastination is strongly characterised by avoidant tendencies and aversive experiences and is thus mainly involved with the person’s ability to deal with negative emotions, in addition to their time and effort management skills (Sirois, 2014 ; Ticeand Bratslavsky, 2000 ; Hailikari et al., submitted).

Psychological flexibility is thought to be constructed of six core psychological processes, which are cognitive defusion, self-as-context, being present, acceptance, values and committed actions (Hayes et al., 2012 ). These processes include the ability to observe and recognise ones’ own thoughts and seeing them just as thoughts rather than truths; keeping a flexible perspective-taking attitude on one’s thinking and feeling; the ability to remain in the present moment and be mindful of thoughts, feelings, and sensations without judging them; confronting negative thoughts and emotions without attempting to change them; clarifying one’s hopes, values and goals in life and finally, doing and taking actions which are consistent with one’s hopes, values and goals (Flaxman et al., 2013 ; Hayes et al., 2012 ). Each of these processes is a psychological skill that can be enhanced in different life domains.

Previous research has clearly shown a link between high levels of procrastination and psychological inflexibility. Eisenbeck et al. ( 2019 ) found that procrastination and psychological distress were associated with psychological inflexibility and further, psychological inflexibility mediated the relationship between general psychological distress and procrastination. The role of psychological flexibility’s sub-processes in procrastination among university students has also been studied, and it was found that committed actions were moderately negatively correlated with procrastination suggesting that committed action could be a promising variable in the study of procrastination (Gagnon et al. 2016 ). Another study showed that procrastination was negatively and moderately related to lower levels of acceptance, adding support to the negative link between psychological flexibility and procrastination (Glick et al., 2014 ). The significance of psychological flexibility in the university context has been studied less, but recent research in this context showed that psychological flexibility has a strong relationship with student engagement and study progression (Asikainen, 2018 ; Asikainen et al., 2018 ).

A recent study by Jeffords et al. ( 2018 ), showed that psychological flexibility is closely related to self-efficacy. Self-efficacy has often been studied previously, focusing on procrastination with results showing an inverse relationship with procrastination (Howell and Watson, 2007; Steel, 2007 ; Wolters, 2003 ). Academic Self-efficacy beliefs describe students’ beliefs in their own capabilities to learn new things and to complete given tasks successfully (Bandura, 1997 ). According to the study by Jeffords et al. ( 2018 ) students who reported greater psychological flexibility felt more efficacious in their ability to complete their studies, whereas students who reported greater inflexibility also reported feeling less efficacious. Similar findings have been reported in relation to students’ time and effort management skills. Bembenutty ( 2009 ) showed that college students who have greater academic self-efficacy also tend to show increased management of their time and study environment (see also Burlison et al., 2009 ; Park and Sperling, 2012 ). Academic Self-efficacy beliefs have been proposed as a possible explanation for procrastination in the academic context, indicating that low academic self-efficacy beliefs are associated with an increased tendency to procrastinate (Judge and Bono, 2001 ). If one’s academic self-efficacy beliefs are low, the motivation to initiate work or to commit to required action should also be low, resulting in avoidance behaviour and consequently procrastination (Grunschel et al. 2013 ). On the other hand, students who believe that they can and will do well are more likely to be motivated to self-regulate, persist and engage in studying (Pintrich and Schunk, 2002 ; Zimmerman, 2000 ). Academic Self-efficacy beliefs have been found to be among the strongest predictive factors of performance in various domains (e.g., Lane and Lane, 2001 ; Pajares, 1996 ). Thus, when exploring the maintaining factors of procrastination, it is important to include academic self-efficacy.

1.4 Aim of the study

Taken together, previous research suggests that time and effort management skills, psychological flexibility and self-efficacy are all closely related to procrastination. Although the studies in this area support a tentative connection between these factors, it is far from conclusive. To our knowledge, no previous study has brought together these central constructs in explaining procrastination. They have been explored separately as they represent different research traditions. The aim of the present study is to include all these variables and explore their interrelations and how they together predict procrastination among students that experiences challenges with their study skills. There is a need to understand the underlying mechanisms of procrastination and which constructs are especially important if the aim is to reduce procrastination among higher education students. This research focuses on answering the following research question: How are university students’ time and effort management skills, psychological flexibility and self-efficacy associated with (a) each other and (b) to their reported level of procrastination.

2 Methodology

2.1 participants.

The data were collected from students studying arts and humanities at a Finnish university. Prolonged study times are a great challenge at the Faculty of Arts and Humanities (Kurri, 2006 ). Recent research also suggests that students procrastinate more in the field of arts and humanities compared to other academic fields (Nordby et al., 2017 ). The data came from the students who participated in a voluntary time management and well-being course, and who were willing/eager to improve their study skills. This course was advertised for students who have challenges with their time-management and well-being. A total of 149 students voluntarily participated in the study and answered the questionnaire in autumn 2019. Students responded to the questionnaires at the beginning of the course as a part of their pre-assignment. Of these students, 14 were excluded because their answers had many missing values concerning the measured dimensions (> 50%). Thus, a total of 135 students provided the data. In the questionnaire, the students were asked to evaluate their own time and effort management skills, academic self-efficacy, tendency to procrastinate and psychological flexibility. Of these students, 22 were male students and 110 female students. Two students identified as ‘other gender’, and one did not answer this question. Approximately a quarter of the students in the Faculty of Arts are male and, thus, the sample distribution is similar to the population. The average age of the participants was 28.1 years (SD = 7.62).

2.2 Instruments

We used two scales, focusing on time and effort management skills and academic self-efficacy, from the HowULearn questionnaire (Parpala and Lindblom-Ylänen, 2012 ). HowULearn -questionnaire and its scales are widely used and validated in Finnish and international contexts (e.g., Cheung et al., 2020; Parpala et al., 2010 ; Postareff et al., 2018; Ruohoniemi et al., 2017 ; Rytkönen et al., 2012). The HowULearn questionnaire has also been translated in the context of Danish higher education (Herrmann et al., 2017 ). Time and effort management skills are measured with four items on a Likert-scale from 1 to 5 (e.g. 'I am generally systematic and organised in my studies’). Concerning students’ academic self-efficacy, we used a scale from HowULearn questionnaire which has been constructed based on (Pintrich and Garcia ( 1991 ) Motivated Strategies for Learning Questionnaire (MSLQ). Five items, using a Likert scale from 1 to 5, were modified to suit the academic self-efficacy. As it is applied here, academic self-efficacy refers to students’ appraisal of their ability to master academic tasks including their judgements about their ability to accomplish a task as well as their confidence in their skill to perform that task. Based on these items, an academic self-efficacy scale for constructed (5 items, e.g., ‘I believe I will do well in my studies as long as I make an effort’). Psychological flexibility was measured according to the work-related acceptance and action questionnaire (WAAQ) (Bond et al., 2013) which was recently developed to fit the higher education context in Finland (7 items, e.g., ‘My worries do not prevent me from succeeding in my studies’ (Asikainen, 2018 ). The items used a 7-point Likert scale (1 = totally disagree, 7 = totally agree). Procrastination was measured with a short version of the Pure procrastination scale (PPS) (Svartdahl and Steel, 2017 ) using a 5-point Likert scale (5 items, e.g.,’ In preparation for some deadlines, I often waste time by doing other things’). This short version of the original pure procrastination scale has been proven to be a robust instrument to measure academic procrastination (Svartdahl et al., 2017; see also Klein et al., 2019 ).

2.3 Statistical analysis

Missing value analysis was conducted on the items measuring the scales. There were only four separate missing values concerning different items and, thus, these were replaced with means. The relationships between the scales were analysed with Pearson’s correlation analysis. In addition, linear regression analysis was conducted on the scales measuring academic self-efficacy, time and effort management (= organised studying) and psychological flexibility explaining procrastination. In addition, the students were then divided into three score groups (low/medium/high) based on their scores measuring time and effort management and psychological flexibility where the middle group was formed using the mean + − a half standard deviation. The groups were combined and thus, six score groups were conducted. The differences in these groups in procrastination was analysed with One-way ANOVA and Tukey’s test.

According to the Cronbach alpha analysis, the scales measuring psychological flexibility, procrastination and academic self-efficacy had very good reliability (α = 0.83–0.90). The reliability for the scale measuring time and effort management can be regarded as acceptable (see Table 1 ). Adding more items to measure the same dimension, would most probably have increased the alpha on Organised studying (Taber 2018). However, as the scale has been used in many previous studies with good reliability (Herrmann et al., 2017 ; Parpala et al., 2010 ; Ruohoniemi et al., 2017 ) its use can be considered to be acceptable.

The correlational analysis showed that there was a clear relationship between procrastination, psychological flexibility, academic self-efficacy and time and effort management skills. Procrastination was statistically significantly and negatively correlated with time and effort management skills (r =  − 0.584, p  < 0.001), academic self-efficacy ( p  =  − 0.358, p  < 0.001) and psychological flexibility (r =  − 0.461, p  < 0.001). In addition, academic self-efficacy was positively related to psychological flexibility ( p  = 0.322, p  < 0,001) and time and effort management skills ( p  = 0.357, p  < 0.001). In addition, time and effort management skills and psychological flexibility correlated positively with each other (r = 0.332, p  < 0.001). The correlations can be seen in Table 2 .

3.1 Regression analysis

A linear regression model was conducted with psychological flexibility, time and effort management and academic self-efficacy as predictors of procrastination. As presented in Table 3 , time and effort management skills, psychological flexibility and academic self-efficacy explained a significant level of variance in procrastination (Adjusted R Square = 0.382). Both time and effort management (t =  − 5.63, p  < 0.001) and psychological flexibility (t =  − 3.06, p  = 0.003) explained the variance in procrastination statistically significantly meaning that students who reported greater use of time and effort management strategies and higher psychological flexibility reported less tendency to procrastinate. Academic self-efficacy failed to emerge as an individual predictor of procrastination t =  − 1.04, p  = 0.301). The results of the regression analysis can be seen in Table 3 .

3.2 Differences in score groups

The One-way ANOVA of the score groups showed that there were differences in experiences of procrastination according to the score groups. According to the Tukey’s test, the group with a high score on time and effort management as well as psychological flexibility scored statistically significantly lower on procrastination than the other score groups (see Table 4 ). In addition, the group with a low score in time and effort management as well as on psychological flexibility scored higher in procrastination than the group scoring average on time and effort management and high on psychological flexibility as well as the group scoring high on time and effort management and average on psychological flexibility. The group scoring average on time and effort management and low on psychological flexibility also scored statistically significantly higher on procrastination than the group scoring high on time and effort management and average on psychological flexibility.

4 Discussion

Procrastination is consistently viewed as problematic to academic success and students’ general well-being (Steel, 2007 ). Students’ time management skills as well as ability to manage their own actions despite the negative feelings have been identified as central factors associated with procrastination along with students’ academic self-efficacy beliefs. To this point, however, only a few studies have included all these measures and compared their impact on procrastination. Thus, an aim with the present study was to explore how students’ time and effort management skills, psychological flexibility and academic self-efficacy are interrelated and associated with procrastination as they have been commonly addressed separately in previous studies.

Designed to address this limitation, our findings support three noteworthy findings regarding academic procrastination among students who experience problems in their time management skills. Firstly, our findings show that students’ ability to organise their time and effort had the strongest association with procrastination out of the variables included in the study. Secondly, our findings indicate that psychological flexibility has a strong individual role in explaining procrastination along with time and effort management skills, although to a slightly smaller degree. And thirdly, our findings suggest that these two constructs appear to be closely related and clearly go hand in hand and, thus, both need to be considered. In the remainder of this section, we review the findings that support these points, identify implications for research and practice, and discuss some limitations to these conclusions.

Time management has been repeatedly identified in previous studies as a major factor contributing to procrastination (Ferrari, 2001 ; Senécal et al., 1995 ; Steel, 2007 ; Wolters, 2003 ). Our findings add to this work by showing that in our study time and effort management skills were strongly related to self-reported level of procrastination and explained the largest variance of procrastination in the regression. This finding implies that students’ time and effort management skills can be used to understand their self-reported levels of academic procrastination. However, it appears that time and effort management skills alone are not enough to explain the phenomenon of procrastination as we assumed. In our study, psychological flexibility also had a strong individual role in explaining large variation of procrastination. This is in line with the recent research suggesting that psychological flexibility is also a central construct explaining procrastination (Dionne, 2016 ; Gagnon et al. 2016 ). These two factors were also strongly correlated with each other as well as with academic self-efficacy beliefs which suggests that they share common variance. Their central role was further explained by regression analysis which showed that together they explained almost 40% of the variance in procrastination. Interestingly, in the present study academic self-efficacy beliefs did not have a direct association with procrastination. This finding is in contrast with previous studies showing that lower self-efficacy beliefs are associated with an increased tendency to procrastinate (Judge and Bono, 2001 ; Wolters, 2003 ). However, some studies have similarly reported a non-significant association between self-efficacy and procrastination. For example, Klassen et al. ( 2010 ) showed with Canadian and Singaporean students that although procrastination negatively and significantly correlated with academic self-efficacy, in the regression model there was no association between academic self-efficacy and procrastination. Only self-efficacy for self-regulation and self-esteem had a significant relationship with procrastination (Klassen et al., 2010 ). This finding is very similar to our result. The most likely explanation for the result is that time and effort management skills and psychological flexibility have a more direct and stronger relationship with procrastination than academic self-efficacy even though it is closely related to all these constructs. It might be that if one is committed to value-based actions which are at the core of psychological flexibility, the negative thoughts one might have about oneself may not be hindering one’s goal-based actions (Hayes et al., 2006 ). This is an interesting finding, and it would be useful to study it in more detail in subsequent studies.

As a third noteworthy finding, our findings provide insight into the relations between time and effort management and psychological flexibility as factors contributing to procrastination. Psychological flexibility and time and effort management skills appear to go hand in hand. When the students were divided to three groups based on their scores on psychological flexibility and time and effort management, the largest groups were the ones in which both time and effort managements skills and psychological flexibility were either low or high. The groups where one of these measures would be high and the other would be low were the smallest in implicating their close relationship. Therefore, it seems that if a person rates his/her time and effort management skills highly, he/she rates his/her psychological flexibility high as well. Also, significant correlations between these measures support this notion. A significant positive correlation between time and effort management and psychological flexibility has also been found in previous study (Asikainen et al., 2019 ). Interestingly, the group that rated both time and effort management and psychological flexibility highly rated their tendency to procrastinate as markedly low compared to other groups. The opposite phenomenon was true for the group that rated their time and effort management skills and psychological flexibility low. This group rated their tendency to procrastinate very highly. Interestingly, if the rating on one of these measures, especially on time and effort management studying was lower, the tendency to procrastinate increased drastically. Although this conclusion fits with common-sense expectations regarding these constructs and their relationship, our findings are the first to establish this relationship empirically.

One implication of this finding is that future efforts to remediate students’ procrastination should account for both these factors. Only when accounting for both time and effort management and psychological flexibility can students’ procrastination be understood. Instead of taking procrastination merely as a self-regulation problem, it is also strongly influenced by a person’s inability to cope with negative emotions that arise in challenging situations (Eisenbeck et al., 2019 ; Gagnon et al., 2016 ; Glick et al., 2014 ). It may be suggested that time and effort management support psychological flexibility. Some studies on time allocation suggest that psychological flexibility process includes allocating one’s time to important and value-based actions in everyday life (Kashdan and Rottenberg, 2010 ). Thus, when time is allocated to support value-based action well-being also increases (Sheldon et al., 2010 ). Thinking about your own values and setting goals can also be considered to be a central part of both time and effort management (Entwistle and McCune, 2004 ) and psychological flexibility (Hayes et al., 2006 ). Thus, we could suggest that when practising psychological flexibility, time management is a part of the process in which one needs to plan how to allocate time to support one’s own personal values. Fostering students’ psychological flexibility as well as time and effort managements skills, could be a promising tool to decrease procrastination. As procrastinators often fail to regulate their actions in challenging or stressful situations (Ferrari, 2001 ), it might be that psychological flexibility could be a central construct. More attention should be paid to encouraging students to pursue value-based committed actions, despite the negative thoughts and feelings one might have. Thus, students’ capacity to cope with their negative thoughts and emotions should be enhanced during their studying (Asikainen, 2018 ).

4.1 Limitations

There are also some limitations that should be addressed. The participants consisted of a selected sample of students which most probably influenced the results. The students took part in a time management and well-being course which was directed especially at those students who had experienced problems with their studies. Thus, the sample of the students in this study was selected and most probably consisted mostly of students who were eager and motivated to improve their time management skills and studying. That might also explain why the time and effort management skills were the strongest explanatory variable of procrastination in the present study. Thus, these results of the study are not generalisable to general student population and the selected sample most probably influenced the results. More research is still needed with a bigger and more representative population. Studies should also explore the role of time and effort management skills in procrastination with a more representative student population. The number of participants was rather low which gave limited opportunities for analysis. For example, the number of students in different score groups was rather low, and in some cases too low for the analysis. Therefore, the results should be interpreted with care. Still, we wanted to include the One Way Anova analysis in our study as it clearly showed that psychological flexibility and time and effort management skills are aligned with each other and students with high scores in both of these dimensions report much less procrastination than other students. Furthermore, one major limitation of the study is that the data are based solely on self-reports. This means that we have measured students’ experiences of these variables. However, we used validated questionnaires which have been shown to be reliable in measuring these constructs and thus, we argue that these results also bring valuable insights to research in procrastination which should be further explored. Future research should also include other measures such as accumulation of credits to see how these measures relate to students’ study progression. In addition, our data are also cross-sectional in nature and thus represents only one particular timeframe. Thus, it is not possible to draw any conclusions regarding the predictive value of the variables. In future research we should also include longitudinal data to explore more closely the relationship between these measures. Despite of the numerous limitations in our study, we argue that this paper provides a novel exploration of these predictors of procrastination together which has not been provided in previous studies.

4.2 Practical implications and conclusions

One promising way to support students’ psychological flexibility and learning processes could be to combine study skills courses, such as time and effort management intervention courses with acceptance and commitment therapy (ACT)-based intervention courses, in which students could practise tolerating stress and negative thoughts as well as developing their time and effort management. Recent studies (Asikainen et al., 2019 ) have shown that this kind of ACT-interventions including reflection of one’s own study processes and practising new ways to study, in this way practising new ways to study, can enhance students’ psychological flexibility and time and effort management and in this way, foster students’ well-being and study skills. ACT-based intervention has shown to have multiple positive effects on students’ well-being and studying (Asikainen et al., 2019 ; Levin et al. 2017 ; Räsänen et al. 2016 ). In addition, ACT-based training can help students to manage psychological inflexibility and encourage persistence behaviour, which in turn is likely to have a positive impact on students’ self-efficacy and further, to their academic performance (Jeffords et al. 2018 ). Earlier studies have found that ACT-based interventions targeted at students who suffer from procrastination can decrease experiences of procrastination (Scent and Boes, 2014 ; Wang et al., 2015 ). One study has suggested that different core processes of psychological flexibility have different effects on procrastination. That is, although all the components correlate with procrastination, acceptance and committed actions significantly predict experiences of procrastination (Gagnon et al., 2016 ). Thus, it seems that being more open and accepting of one’s emotional experiences or thoughts and being willing to engage in difficult activities to persist in the direction of important values is important in reducing procrastination.

As time and effort management in our study was the predominant factor associated with procrastination, we suggest that time management should be promoted for higher education students. It has been shown that many students have trouble with time management (Parpala et al., 2010 ). Many studies have shown that different time management strategies are beneficial for different students. These include things like setting goals and planning how to achieve these (Häfner et al., 2015 ), setting deadlines (Ariely and Wertenbroch, 2002 ) and monitoring time use (Asikainen et al., 2019 ). These skills should be enhanced during university study because it has been shown that time and effort management skills remain rather constant without a conscious effort to influence them (Lindblom-Ylänne et al., 2017 ).

To conclude, our study brings novel insights into the underlying mechanisms of procrastination. Our study showed that both psychological flexibility and time management are important factors influencing procrastination, and furthermore, they appear to be closely related factors and together influence procrastination behavior. Thus, both these factors should be considered when the focus is on reducing procrastination. Students who tend to procrastinate might benefit from trainings that focus on training both time management skills and psychological flexibility and not focusing on only either one. This might produce the best results.

Data availability

The data is available on demand.

Ariely, D., & Wertenbroch, K. (2002). Procrastination, deadlines, and performance: Self-control by precommitment. Psychological Science, 13 (3), 219–224.

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Hailikari, T., Katajavuori, N. & Asikainen, H. Understanding procrastination: A case of a study skills course. Soc Psychol Educ 24 , 589–606 (2021). https://doi.org/10.1007/s11218-021-09621-2

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Home › Brain News

Scientists discover what really causes us to procrastinate

By Jocelyn Solis-Moreira

Reviewed by Chris Melore

Research led by Sahiti Chebolu, Max Planck Institute for Biological Cybernetics

Jun 28, 2024

(Credit: ntkris/Shutterstock)

TÜBINGEN, Germany — Chronic procrastinators are often seen as lazy, but a new study suggests that it’s more than just a lack of motivation. A new study published in the Proceedings of the Annual Meeting of the Cognitive Science Society examined the cost-benefit risks the brain goes through when deciding to put off tasks, especially in the face of serious consequences or failure. According to researchers in Germany, understanding why people wait until the last minute to finish important tasks would help create more effective strategies when it comes to productivity.

Procrastination is a complex issue, especially when you consider that most people have been guilty of doing this at least once. Whether it’s filing taxes, meeting a project deadline for work, or simply cleaning out the garage, procrastination causes people to delay tasks despite having the time to do them right away. Given the stress, anxiety, and guilt that can come with procrastination, it’s surprising the human brain continues to support this bad habit.

One issue with procrastination is more than waiting until the last minute to complete a task. While they might look alike, there are different forms of procrastination.

“Procrastination is an umbrella term for different behaviors,” explains Sahiti Chebolu, a computational neuroscientist from the Max Planck Institute for Biological Cybernetics, in a media release. “If we want to understand it, we need to differentiate between its various types.”

A common pattern of procrastination, for example, is not following through on a decision. You might have set aside time to do laundry in the evening, but when the time comes, you decide to watch a movie instead. Usually, something is stopping a person from committing to the original task and waiting for the right conditions or motivation to start the work.

In the current study, Chebolu categorized each type of procrastination and narrowed it down to two explanations: misjudging the time needed to complete the task and protecting the ego from prospective failure.

The Theory Behind a Distracted Brain

The theory of procrastination is that it is a series of temporal decisions or making a choice now that would have consequences later. For example, deciding to file taxes on Friday but then choosing to watch a new show on TV when the time comes. Obviously, missing the Tax Day deadline results in penalties and other financial consequences — yet people do it anyway.

According to the authors, the brain weighs all the rewards and penalties of choosing an alternative behavior. However, the brain is biased and prefers immediate gratification over delayed pleasure. The joy of watching television right now is a more appealing option to the brain than the relief of filing taxes three weeks later. It’s too long of a wait for the reward, so the brain prefers the quicker option.

Now, if this were the case all the time, no one would get anything done. That’s why the brain also considers the penalties for making a different decision. However, the study finds the negative outcomes have less weight than the option that gives immediate pleasure. The brain will always try to find the easiest and most immediately pleasurable option.

Evolutionarily, this makes sense. The distant future is always full of uncertainties, so the emphasis should be on helping yourself in the present moment. Procrastination comes when this mental process becomes maladaptive. Chebolu says people’s decision-making skills become flawed as they put too much emphasis on experiences in the present and not enough on the future.

Methodology

The research team looked at large datasets to study real-life procrastination. The data included a log of students who needed to complete a certain number of hours taking part in experiments over the course of a school semester.

Some students completed the task right away. Other students spaced out the requirement by enrolling in experiments over several weeks. Others waited until the very last minute to complete all their experiment requirements. Chebolu then ran simulations to reproduce the students’ behavior and understand why people procrastinated on a task that would affect their course grades.

Key Results

The theory behind procrastination is that the brain always chooses the option that will give the most immediate pleasure. However, the current study showed this is only one factor in the decision to procrastinate. Another issue is uncertainty .

This could look like failing to predict how much time is needed to complete a task, such as the time needed to dig up receipts and W-2s to file taxes and calculate deductions. Uncertainty also looks like a lack of confidence in a person’s ability or doubting whether a task will help them achieve their goals.

Discussion and Takeaways

According to Chebolu, understanding how procrastination works and why someone would want to delay a task can help tailor strategies to prevent it. 

If procrastination is caused by a bias towards instant gratification, the best solution is to set up short-term rewards. The Pomodoro method, for example, gives people breaks for working a set period of time. People who underestimate the time needed to complete a task could try time-bound goals, such as setting a personal deadline.

If you abandon the task once you start, the authors recommend avoiding distracting environments. The most important thing, however, is not to beat yourself up if you fall into the trap of procrastination. Everyone does it. Forgiving yourself is also one of the most important steps towards better productivity.

About Jocelyn Solis-Moreira

Jocelyn is a New York-based science journalist whose work has appeared in Discover Magazine, Health, and Live Science, among other publications. She holds a Master's of Science in Psychology with a concentration in behavioral neuroscience and a Bachelor's of Science in integrative neuroscience from Binghamton University. Jocelyn has reported on several medical and science topics ranging from coronavirus news to the latest findings in women's health.

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• Published: 28 June 2024

Unraveling symptom interplay: a network analysis of procrastination in gifted students

• Sajjad Bagheri 1 ,
• Hojjatollah Farahani 2 ,
• Peter Watson 3 ,
• Timea Bezdan 4 &
• Kosar Rezaiean 5  

BMC Psychology volume  12 , Article number:  370 ( 2024 ) Cite this article

Metrics details

This study explores the intricate web of symptoms experienced by academically gifted high school students, focusing on procrastination, rumination, perfectionism, and cognitive flexibility. The well-being of these gifted adolescents remains a pivotal concern, and understanding the dynamics of these symptoms is vital.

A diverse sample of 207 academically gifted high school students from Mashhad, Iran, participated in this study. Using convenience sampling, participants from grades 10, 11, and 12 were included, with detailed assessments conducted through questionnaires measuring the mentioned symptoms.

Our network analysis uncovers compelling insights into the interplay of these symptoms: Procrastination, though moderately central, exerts significant influence within the network, underscoring its relevance. Cognitive flexibility, while centrally positioned, curiously exhibits a negative influence, potentially serving as a protective factor. Negative perfectionism emerges as the keystone symptom, with both high centrality and a positive influence. Rumination displays substantial centrality and a positive influence, indicating its role in symptom exacerbation. Positive perfectionism, moderately central, lacks direct influence on other symptoms.

This network analysis provides a nuanced understanding of the relationships among procrastination, rumination, perfectionism, and cognitive flexibility in academically gifted adolescents. Negative perfectionism and cognitive flexibility emerge as critical factors deserving attention in interventions aimed at enhancing the well-being of this unique group. Further research should explore causal relationships to refine targeted interventions.

Peer Review reports

Introduction

The identification of gifted students has long perplexed educators, giving rise to the Elitism movement in many developed countries [ 1 ]. In the modern world, formal education is designed to be inclusive, offering opportunities from kindergarten through higher education to individuals of all genders, ethnic backgrounds, and social statuses [ 2 ]. Within this educational framework, some students exhibit typical academic performance, with their efforts aligning with a median level of achievement. Conversely, there exists a distinct group of students whose exceptional abilities set them apart, consistently outshining their peers of the same age [ 3 ]. These academically gifted students often receive early recognition as high achievers, a distinction that holds promise for their future success [ 4 ]. However, the concept of giftedness remains complex and can be subject to misconceptions within school systems [ 5 ]. Gifted students display superior performance across various domains of development compared to their peers [ 6 ]. Yet, their heightened sensitivity and perfectionist tendencies, spanning mental, psychomotor, emotional, and sensory dimensions, can sometimes hinder their personal communication and social interactions [ 7 , 8 ]. These gifted individuals, often characterized by unique thinking patterns, a penchant for questioning, and the ability to provide innovative solutions, hold great importance for the advancement of societies [ 9 ]. Notably, talent, manifesting in mental, musical, artistic, physical, and social realms, often accompanies high intelligence levels [ 10 ].

In their pursuit of excellence, gifted individuals may exhibit behaviors such as withdrawal, giving up, or disengaging from their environment if they believe they cannot attain the desired results [ 11 ]. Loneliness can also be a consequence of their unique experiences, as evidenced by Ünal and Sak’s (2020) study titled " The extraordinary ones: Lonely adolescents with giftedness” which highlighted the reactions of gifted students in educational settings and the potential loneliness resulting from their distinctiveness [ 12 ]. The research demonstrated that gifted students frequently face reactions such as jealousy and exclusion from their peers due to their distinctive thinking, advanced skills, and higher levels of success and talent. In the pursuit of competence in achievement-oriented environments, some gifted students implicitly internalize self-worth and strive to be the best [ 13 ]. This pursuit of excellence is often intertwined with perfectionism, a personality trait that has been associated with various adaptive and maladaptive outcomes in academically gifted youth [ 13 , 14 ]. Perfectionism, recognized as a multidimensional construct, is categorized into normal perfectionism, characterized by the pursuit of high standards and excellence, and neurotic perfectionism, which results in anxiety and dissatisfaction [ 15 , 16 ]. Procrastination, defined as the nonadaptive behavior of involuntarily postponing planned tasks without a clear reason [ 17 ], has been linked to adverse academic performance and negative emotional outcomes, including depression, anxiety, and shame, among college students [ 18 , 19 , 20 , 21 , 22 ]. The relationship between perfectionism and procrastination has been the subject of investigation, with specific facets of perfectionism predicting procrastination tendencies [ 23 , 24 ].

Rumination, a stable personality trait involving repetitive and passive contemplation of the causes and potential consequences of negative life events, has also been associated with perfectionism and depressive symptoms [ 25 , 26 ]. Rumination is a process wherein individuals with high levels of this trait tend to dwell on negative experiences rather than engage in constructive coping strategies [ 27 ]. Cognitive flexibility, on the other hand, is the ability to adapt one’s thinking and behavior to changing environmental demands [ 28 ]. High levels of cognitive flexibility enable individuals to navigate challenging situations, generate alternative ideas, and employ problem-solving skills effectively [ 29 ]. The interplay among perfectionism, procrastination, rumination, and cognitive flexibility in gifted adolescents represents a complex psychological landscape. To explore these intricate relationships, network analysis offers a novel approach. Network analysis allows for the examination of the interactions and central symptoms within this unique population, shedding light on the structure of psychological disturbances and potential intervention targets [ 30 , 31 , 32 , 33 , 34 ]. In this study, we aim to contribute to a deeper understanding of these psychological dynamics and provide insights for interventions that promote the well-being and academic success of gifted adolescents.

Network analysis has emerged as a novel approach to conceptualizing psychological phenomena in a manner that addresses the limitations of the traditional approach. In network theory, central symptoms are more likely to activate other symptoms and play a major role in causing the onset and/or maintenance of a syndrome/disorder. Network analysis has the potential to map specific relationships among individual symptoms of a disorder and identify targets for treatment [ 35 ]. Furthermore, network analysis can be used to extract the structure of psychiatric disturbances from clinical data [ 31 ] and highlight meaningful associations between individual symptoms within and/or between disorders [ 36 ]. Additionally, a network model is useful in understanding the mechanism of comorbidities and provides suggested strategies for clinicians to prevent and treat comorbidities [ 34 ]. Researchers have used network analysis to assess these symptom-symptom interactions. Network analysis, a set of procedures based on the modeling of dynamical systems [ 37 ], provides a visual depiction of the complex associations among symptoms. A tightly connected network with many strong connections among symptoms is considered a ‘riskier’ network because activation of one symptom can quickly spread to other symptoms, leading to more chronic symptoms over time [ 38 ]. Network analysis also allows the identification of highly ‘central’ or influential symptoms, defined by having, on average, strong connections with other symptoms. When a highly central symptom is activated (i.e. a person reports the presence of the symptom), it will influence other symptoms to become activated as well, maintaining the symptom network. To date, most network studies have examined symptom relationships and centrality within a single disorder. However, network analysis may be particularly useful for understanding co-morbidity because it permits the identification of potential pathways from one disorder to another [ 39 ].

In this section, we present a detailed account of the methodology employed in our study. The research design was carefully crafted to address key research questions and objectives, employing a combination of quantitative measures and advanced analytical techniques. The following subsections outline the participant selection process, measurement tools utilized, and the analytical framework applied for data interpretation.

Participants and study design

In this comprehensive study, the target population comprised high school students with outstanding talents at the secondary level in the city of Mashhad. The participants were exclusively enrolled in Hashemi Nejad High School, an institution under the auspices of the National Organization for the Development of Exceptional Talents of Iran. Admission to this school is contingent upon successfully passing specialized entrance tests assessing both intelligence and academic aptitude. To determine the sample size, the Morgan table was employed, considering a total population of 450 gifted male students in Mashhad. The target population was calculated to encompass 207 students from the second-grade secondary school at Hashemi Nejad High School during the academic year 2022–2023. Convenience sampling was chosen as the method due to the accessibility of the sample. The process of filling out the questionnaire and the required time commitment were clearly explained to all participants to ensure uniformity in data collection. It is noteworthy that the participants in this study represent a diverse group of academically gifted high school boys, encompassing various ethnic backgrounds and socio-economic statuses. This diversity enhances the generalizability of the findings to a broader population of academically talented high school students.

To gather data and information for analysis and hypothesis testing in the research, the following questionnaires will be employed:

Positive and negative perfectionism scale (PANPS)

This scale was developed by Terry-Short et al. (1995) and consists of two sub-scales, each comprising 20 questions [ 40 ]. These sub-scales assess positive and negative perfectionism, with each sub-scale containing 20 questions. Responses to these questions are based on a 5-point Likert scale ranging from 1) strongly disagree to 5) strongly agree, resulting in scores ranging from 20 to 100. Scoring for this questionnaire is structured in such a way that if an individual scores high on questions related to positive perfectionism, they are categorized as having a positive perfectionism orientation, while a high score on negative perfectionism indicates an orientation towards negative perfectionism. The cutoff score for individuals displaying negative perfectionism tendencies is 69 or higher.

Cognitive flexibility inventory (CFI)

Developed by Dennis and Vander Wal in 2010, this questionnaire comprises 20 seven-point Likert scale questions, with response options ranging from 1 (strongly disagree) to 7 (strongly agree) [ 28 ]. It measures cognitive flexibility, with higher scores indicating greater cognitive flexibility. The questionnaire demonstrates good validity, with a correlation of 0.75 with the Beck Depression Inventory [ 41 ]. In Iran, Shahre et al., (2014) reported a Cronbach’s alpha of 0.71 for the entire scale [ 41 ].

Rumination response scale (RRS)

This 22-item scale, developed by Nolen-Hoeksema, is rated on a 4-point scale from 1 to 4 and assesses cognitive distortions. The Cronbach’s alpha of this scale has been reported to range from 0.88 to 0.92, indicating high internal consistency [ 42 ]. Intra-class correlation for the 5 retest measurements was 0.75, and the test-retest reliability over more than 12 months was 0.67. In Iran, Bagherinejad et al. (2010) reported correlations of 0.79 with depression and 0.56 with anxiety [ 43 ].

Tuckman procrastination scale

For assessing procrastination tendencies, the standard Tuckman Procrastination Scale (1991) is employed [ 44 ]. This self-report scale consists of 16 items, rated on a Likert scale. Higher scores on this scale indicate higher levels of procrastination. Tuckman (1991) reported a reliability coefficient of 0.86 for this scale. In Kazemi et al.‘s (2010) research, Cronbach’s alpha for the entire scale was 0.71, indicating good reliability [ 45 ].

Statistical analysis

The data utilized in this study underwent a comprehensive multi-step analytical approach, integrating symptom network analysis and correlation stability analysis. The primary objectives of the research were as follows:

Aim 1: Characterization of the symptom network at admission

Edges calculation.

The initial phase involved computing polychoric correlations [ 46 ] between all items in the dataset. Polychoric correlations, selected due to the ordinal nature of the variables, were employed to estimate associations assumed to be continuous and normally distributed. The resulting correlation matrix served as input for constructing the symptom network. The network, modeled using a Graphical Gaussian Model (GGM), represented conditional independence relationships between nodes. To ensure a concise network, the graphical lasso (glasso) algorithm was applied, effectively shrinking smaller edges to zero [ 47 , 48 , 49 ].

Network visualization

The resultant symptom network underwent visualization using the qgraph R package. The edge thickness in the visualization indicated the strength of associations, while the Fruchterman-Reingold algorithm determined the spatial arrangement of nodes. Nodes with stronger average associations were strategically positioned closer to the center of the network [ 50 , 51 ].

Centrality measures

Node centrality was assessed through three key indices: strength (sum of absolute edge weights connected to a node), closeness (average distance to all other nodes), and betweenness (number of times a node lies on the shortest path between two other nodes). These centrality measures provided valuable insights into the relative importance of individual symptoms within the network [ 36 , 52 ].

Aim 2: Stability assessment of the symptom network

Network stability.

Network stability was rigorously evaluated using a permutation-based method. The dataset underwent random division into multiple sub-samples, and independent networks were estimated from each subsample. Edge and centrality values were then correlated across these independent networks. This process was iterated 10,000 times to comprehensively assess the stability of the symptom network [ 53 ].

Confidence intervals

Confidence intervals (CIs) for edge values were calculated using a bootstrap approach [ 38 ]. Additionally, the stability of centrality values was examined by repeatedly correlating values derived from the complete dataset with those obtained from subsamples with varying percentages of nodes or participants missing [ 54 ].

Aim 3: Comparison between admission and discharge networks

Global network strength.

Changes in the global network strength between admission and discharge were systematically assessed using the Network Comparison Test (NCT). A null distribution was created through random swapping of participants’ admission and discharge data, constructing networks, and computing NCT scores over 10,000 iterations [ 55 ].

Network structure

Alterations in network structure were evaluated by correlating edge values and centrality indices between admission and discharge networks. The magnitude of these correlations provided valuable insights into the stability of the network structure over time.

All data analyses were executed using the R statistical programming language, with relevant packages and functions employed for network estimation, inference, stability assessment, and regularization.

Demographic findings

The distribution of ages in our sample is presented in Table  1 . The majority (88.5%) of the high school students are aged 16 or 17.

Network analysis and correlation stability

In this section, we present an analysis of the network structure and centrality measures based on the provided data. The analysis encompasses various aspects of the network, including node centrality, edge betweenness, shortest path lengths, and correlation stability.

Figure  1 estimated network model for procrastination, perfectionism, rumination and cognitive flexibility in gifted students. Notably, the depiction includes visual elements to enhance understanding. Nodes with stronger connections are depicted closer to each other in the diagram, reflecting the intensity of their relationships. Red lines indicate negative correlations, while blue lines represent positive correlations. The thickness of the edges corresponds to the strength of the association between symptom nodes. This visual representation aids in comprehending the intricate relationships and dynamics within the network of procrastination, perfectionism, rumination, and cognitive flexibility among gifted students.

Network structure of procrastination, perfectionism, rumination and cognitive flexibility in gifted students

Node centrality measures

The network consists of several nodes representing different variables. Node centrality measures, such as betweenness (looking at how many short paths between nodes feature the node of interest), closeness (how influential a node is in indirect connections to all other nodes), and strength (how influential a node is in direct connections to all other nodes), provide insights into the importance of each node within the network. These measures are crucial for understanding the flow of information or influence within the network and are further described below.

Here are some key findings regarding node centrality:

Procrastinations has a closeness centrality of 0.0376 and a strength centrality of 0.6773.

Cognitive Flexibility exhibits a closeness centrality of 0.0398 and a strength centrality of 0.7380.

Negative Perfectionism shows a closeness centrality of 0.0510 and a strength centrality of 0.9234.

Rumination has a closeness centrality of 0.0495 and a strength centrality of 0.9144.

Positive Perfectionism has a closeness centrality of 0.0244 and a strength centrality of 0.2228.

These centrality measures highlight the nodes’ relative importance within the network and suggest all the above variables except Positive Perfectionism have direct connections between one another (Fig. 2 ).

Edge betweenness

Edge betweenness measures the number of pairs of nodes whose shortest path includes the edge which runs between a specified pair of nodes. For example:

There are four pairs of nodes whose shortest path includes the edge between Negative Perfectionism and Rumination, indicating their relationship represents a relatively strong central connection in the network acting as a bridge between other nodes.

Procrastinations and Cognitive Flexibility have a direct relationship since only one shortest path includes the edge between them.

These edge betweenness values provide insights into the flow of influence or information in the network that runs between specific pairs of nodes.

Please refer to Fig.  3 for the bootstrap results, which determine the 95% confidence intervals for the edge weight test.

Centrality indices of network nodes based on z scores

Bootstrap results to determine 95% confidence intervals for the edge weight test

Stability of centrality indices by case dropping subset bootstrap. The x-axis represents the percentage of cases of the original sample used at each step. The y-axis represents the average of correlations between the centrality indices in the original network and the centrality indices from the re-estimated networks after excluding increasing percentages of cases. The line indicates the correlations between bridge strengths in the reduced and original samples.

In Fig.  3 , we present the bootstrap results to determine the 95% confidence intervals for the edge weight test. These confidence intervals provide an indication of the precision of our estimates, representing a range of values within which we are reasonably confident that the true population parameter lies. The edge weights in our analysis signify the strength of associations between different symptom nodes in the network. Through bootstrap resampling, we generate multiple samples from our original data to estimate the variability in the edge weights, allowing us to quantify the uncertainty surrounding our estimates. A narrower confidence interval suggests a more precise estimate of the edge weight, indicating less variability across different samples. Conversely, a wider confidence interval suggests greater uncertainty and less precision in our estimates. Therefore, by presenting the 95% confidence intervals for the edge weights in Fig.  3 , we provide readers with a measure of the precision of our estimates and the degree of certainty surrounding the strength of associations between symptom nodes in the network.

Shortest path lengths

The shortest path lengths represent the minimum number of edges that must be traversed to move from one node to another. Closeness and betweenness both depend on the concept of shortest path lengths. The shortest path length between two given nodes refers to the shortest distance between these two nodes based on the edges that directly or indirectly connect these two nodes. Dijkstra’s algorithm is used to find shortest path lengths in weighted networks [ 55 , 56 , 57 ]. Based on this algorithm, shortest path lengths represent the inverse of edge weights that have to be “travelled” on the shortest path. Closeness sums the shortest path lengths between a given node and all other nodes in the network and takes the inverse of the resulting value. Therefore, closeness represents how likely it is that information from a given node “travels” through the whole network either directly or indirectly. Betweenness represents how strongly a given node can disrupt information flow in the network, as betweenness calculates the number of shortest paths a given node lies on. For instance:

The shortest path length between Procrastinations and Cognitive Flexibility is approximately 4.8163. This value is obtained by determining the shortest path between these two nodes, accounting for the weights of the edges traversed along the path.

Between Cognitive Flexibility and Negative Perfectionism, the shortest path length is around 4.1795. Similarly, this value is calculated based on the shortest path between these nodes within the network structure.

Negative Perfectionism and Procrastinations have a shortest path length of approximately 5.7302. Again, this value is computed by finding the shortest path between these nodes while considering the network topology and edge weights.

These path lengths provide valuable insights into the proximity and accessibility between nodes in the network, offering a quantitative measure of the distance between symptom nodes. By employing established algorithms for calculating shortest paths, we ensure robustness and accuracy in our analyses, allowing for a comprehensive understanding of the network structure.

Correlation stability analysis

In network analysis, ensuring the stability of the results is paramount to establish the reliability of the findings. To address this, we employed the Correlation Stability (CS) coefficient as a pivotal measure. The CS coefficient serves to quantify the stability of the network by evaluating the consistency of correlations across various subsets of the data, indicating the extent to which the network structure remains intact when parts of the sample are excluded. CS-C values represented the maximum proportion of samples that could be removed. Generally, a CS-C above 0.50 is preferred, ensuring a robust network structure [ 56 ]. Key findings include:

The strength of the network exhibits excellent stability, with a CS coefficient of 0.517. This value indicates that approximately 51% of the sample could be dropped without substantially affecting the network’s overall structure.

Moreover, it’s important to note that this CS coefficient is associated with a 95% confidence interval, providing a measure of certainty around the reported stability metric.

Additionally, bootstrap analyses were conducted to further confirm the reliability and stability of the estimated edge weights. These analyses involved resampling the data to assess the variability in the edge weights and ensure robustness in our findings.

The significance of stability analysis lies in its ability to reassure readers about the robustness of the network findings, demonstrating that they are not unduly influenced by specific data points (Fig 4 ). By reporting the CS coefficient alongside its associated confidence interval, we offer a comprehensive assessment of the network’s stability, instilling greater confidence in the reliability of our results.

Network comparison tests

Comparative analyses were conducted to evaluate potential differences in network models based on demographic factors. These tests aimed to identify variations in network strength and edge weights between different groups, such as gender, school grade, and residence [ 57 ].

Key findings include

No significant differences in network global strength were observed between male and female adolescents.

Some specific edge weights showed differences between genders, suggesting variations in symptom associations.

Subdividing the sample by school grade or residence did not reveal significant differences in network global strength or edge weight distribution.

These network comparison tests provide insights into how demographic factors may influence the network structure and symptom associations. However, it is important to note that due to the relatively small sample size, the network comparison tests might not fully capture the demographic influences. The limited sample size restricts the statistical power necessary for a robust comparison, potentially resulting in non-positive definite correlation matrices. This limitation should be considered when interpreting the results, and future research with larger samples is recommended to more definitively explore these demographic influences.

In summary, network analysis and correlation stability assessment offer valuable insights into the relationships between variables, the importance of specific nodes, and the stability of the network findings. Additionally, while network comparison tests suggest potential demographic influences on symptom associations, the current sample size limits the ability to draw definitive conclusions. Future studies with larger samples are needed to further explore these demographic influences and validate the preliminary findings of this study.

The network analysis conducted in this study unveiled intriguing insights into the symptom interplay among academically gifted adolescents.

Procrastination, a pervasive issue among high-achieving students, emerged as a symptom of moderate centrality within the network. This finding aligns with previous research highlighting the prevalence of procrastination among academically gifted individuals [ 47 , 48 , 49 , 50 ]. However, our study extends this understanding by demonstrating its central role in shaping the symptom network, emphasizing the need for targeted interventions tailored to this population.

Similarly, cognitive flexibility, although centrally positioned, exhibited an unexpected negative influence on the network. This contrasts with conventional views of cognitive flexibility as a positive trait and suggests its potential role as a protective factor among academically gifted students [ 50 , 51 , 52 ]. This nuanced understanding challenges traditional assumptions and underscores the importance of considering context-specific factors in intervention development.

The standout revelation of our analysis was the dominance of negative perfectionism, characterized by both high centrality and a positive influence. While previous research has acknowledged the prevalence of perfectionism among gifted students, our study elucidates its pivotal role in shaping symptom dynamics [ 54 , 55 ,– 57 ]. This underscores the urgency of addressing negative perfectionism in interventions aimed at promoting the well-being of academically gifted adolescents.

Furthermore, rumination, another prevalent symptom in this group, displayed substantial centrality and positive influence, highlighting its contribution to symptom development [ 58 , 59 , 60 , 61 , 62 ]. Our findings complement existing literature on the detrimental effects of rumination and emphasize the need for targeted interventions addressing this symptom among academically gifted individuals.

To translate these findings into practical applications, educators and mental health professionals working with academically gifted students should consider interventions that specifically address procrastination, negative perfectionism, and rumination. Strategies focusing on enhancing cognitive flexibility may serve as a protective factor against the co-occurrence of these symptoms. For instance, incorporating mindfulness practices or cognitive-behavioral interventions tailored to the unique characteristics of gifted students could be explored. Additionally, educators could implement time management and goal-setting techniques to mitigate procrastination tendencies. These practical implications emphasize the importance of tailored interventions to support the well-being of academically gifted adolescents.

The analysis of shortest path lengths provided insights into how quickly information or influence can spread within the network. Notably, procrastination, cognitive flexibility, and negative perfectionism had relatively long shortest path lengths to other symptoms, indicating their centrality. This suggests that these symptoms may act as central nodes in the network, influencing the overall dynamics [ 63 , 64 , 65 , 66 , 67 , 68 ].

In contrast, rumination had shorter shortest path lengths, suggesting that it may play a key role in the rapid dissemination of influence or information within the symptom network. This underscores the urgency of addressing rumination in interventions designed for academically gifted adolescents [ 69 , 70 ].

The correlation stability analysis revealed the robustness of the network’s correlations across various sampling levels. Even with substantial drop percentages, the network’s correlations remained relatively stable, supporting the validity of the network structure and relationships. This finding enhances our confidence in the identified symptom network and its implications for intervention development.

Examining edge betweenness and strength values provided further insights into the network’s dynamics. The high edge value between Negative Perfectionism and Rumination suggests that these symptoms act as bridges, connecting other symptoms in the network. This highlights the potential importance of these two symptoms in mediating the interactions among other symptoms and suggests that interventions targeting them may have broader effects on the symptom network [ 71 , 72 , 73 , 74 , 75 ].

In light of these findings, interventions aimed at enhancing the psychological well-being of academically gifted adolescents should prioritize addressing negative perfectionism, procrastination, and rumination.

Additionally, interventions should consider leveraging the potential protective role of cognitive flexibility [ 76 , 77 , 78 , 79 ]. Future research should delve deeper into the causal relationships between these symptoms and explore the development of targeted interventions that take into account the complex network dynamics uncovered in this study.

First and foremost, the centrality measures of the symptoms in the network reveal important patterns. Procrastination, while not having the highest centrality, emerges as a significant factor, underlining the need for targeted interventions to address this behavior among gifted students. On the contrary, cognitive flexibility, with its notable centrality and negative influence, acts as a potential protective factor, deterring the co-occurrence of other symptoms. Understanding the role of cognitive flexibility in this context is crucial for developing interventions that harness its positive impact. Negative perfectionism, identified as a keystone symptom, exhibits the highest centrality and a positive influence on other symptoms. This highlights the critical role of negative perfectionism in shaping the symptom landscape among academically talented students, suggesting that interventions targeting negative perfectionism could significantly contribute to the well-being of this population. Rumination, with its substantial centrality and positive influence, is closely tied to other symptoms, emphasizing its potential to exacerbate or contribute to the development of additional negative symptoms. Recognizing the role of rumination is essential for designing interventions that effectively address this specific aspect. In contrast, positive perfectionism, while moderately central, lacks direct influence on other symptoms. This finding suggests that interventions may need to prioritize mitigating the negative aspects of perfectionism rather than promoting its positive aspects.

The practical implications of addressing procrastination, negative perfectionism, and rumination, along with the recognition of the protective role of cognitive flexibility, highlight the importance of tailored interventions for this unique population. This study contributes significantly to the field by offering insights that can guide educators, mental health professionals, and researchers in supporting the psychological well-being and academic success of academically gifted students. As we continue to explore the dynamics and causal relationships among these symptoms, the findings pave the way for more targeted interventions that address the specific needs of this talented student population, ultimately enriching their academic journey and overall well-being.

In conclusion, this network analysis has shed light on the symptom interplay among academically gifted adolescents, emphasizing the significance of addressing procrastination, negative perfectionism, and rumination while considering the potential role of cognitive flexibility. Building on these insights, future research could explore the causal relationships between these symptoms to better understand the mechanisms driving their interconnected dynamics. For instance, investigating whether high levels of negative perfectionism contribute to increased procrastination tendencies or exploring how cognitive flexibility influences the development of other symptoms could provide valuable insights. By delving into these potential causal links, researchers can refine targeted interventions and further contribute to our understanding of the psychological well-being of academically gifted students.

Limitations

Sample size.

One significant limitation of this study is the sample size, which may not be sufficient for extensive network comparison tests across multiple demographic variables. The small sample size could lead to non-positive definite correlation matrices, limiting the reliability of the network comparison findings. Future research should aim to include larger and more diverse samples to enhance the robustness of network comparison tests and better understand the potential demographic influences on symptom associations.

Sample characteristics

The study primarily focused on academically gifted high school students from a single school in Mashhad, Iran. The limited geographical and institutional scope may restrict the generalizability of findings to a broader population of academically gifted adolescents. Future research should consider diverse samples from multiple schools and regions to enhance the external validity of the results.

Cross-sectional design

The study utilized a cross-sectional design, capturing a snapshot of the symptoms and their relationships at a specific point in time. Longitudinal studies could provide a more dynamic understanding of how these symptoms evolve over time and allow for the exploration of causal relationships between them.

Self-report measures

The data relied on self-report measures, which may be subject to response biases, including social desirability or recall bias. Future research could incorporate multi-method assessments, including observational or interview-based measures, to provide a more comprehensive understanding of the studied constructs.

Cultural specificity

The study focused on academically gifted students in a specific cultural context (Iran). Cultural factors may influence the expression and perception of symptoms. Future research should consider cultural variations to determine the generalizability of the findings across diverse cultural settings.

Intervention implications

While the study provides insights into potential intervention targets, the effectiveness of specific interventions was not directly assessed. Future research should incorporate intervention studies to evaluate the impact of targeted strategies on reducing symptoms and enhancing the well-being of academically gifted adolescents.

Suggestions for future research

Longitudinal investigations.

Conduct longitudinal studies to track the development and interaction of symptoms over time. This approach would allow for a more nuanced understanding of causality and changes in symptom dynamics, providing valuable insights for targeted interventions.

Diverse cultural samples

Extend the research to include academically gifted students from diverse cultural backgrounds. Examining how cultural factors influence symptom patterns and relationships can contribute to a more comprehensive understanding of the experiences of gifted adolescents.

Comparative studies

Undertake comparative studies to explore potential variations in symptom networks among academically gifted students and their non-gifted peers. Understanding the unique challenges faced by gifted students in comparison to their peers can inform tailored interventions.

Intervention studies

Implement and evaluate targeted interventions based on the identified symptom network. Assess the effectiveness of interventions designed to address procrastination, negative perfectionism, and rumination, while promoting cognitive flexibility, in improving the well-being of academically gifted adolescents.

Qualitative approaches

Complement quantitative findings with qualitative approaches to gain a deeper understanding of the subjective experiences and contextual factors influencing the symptoms. Qualitative data can provide rich insights into the lived experiences of academically gifted students.

Incorporate multimodal assessments

Expand assessment methods to include multimodal approaches, such as neurobiological measures or behavioral observations, to triangulate findings and enhance the robustness of symptom characterization.

Parental and teacher perspectives

Investigate the perspectives of parents and teachers regarding the observed symptoms in academically gifted students. Understanding how external stakeholders perceive and interact with these symptoms can provide a more holistic view of the challenges faced by gifted adolescents.

Data availability

Data is available from the corresponding authors upon request.

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Bagheri, S., Farahani, H., Watson, P. et al. Unraveling symptom interplay: a network analysis of procrastination in gifted students. BMC Psychol 12 , 370 (2024). https://doi.org/10.1186/s40359-024-01868-6

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5 Research-Based Strategies for Overcoming Procrastination

• Chris Bailey

Understanding why you put off certain tasks.

Why do we procrastinate, even though we know it’s against our best interests? And how can we overcome it? A careful look at the science behind procrastination reveals five tips. First, figure out which of seven triggers are set off by the task you want to avoid. Is it boring, frustrating, or difficult? Or perhaps it’s not personally meaningful to you? Then, try to reverse those triggers. If it’s boring, find a way to make getting it done fun. If it’s unstructured, create a detailed plan for completing it. Then, only spend as much time working on the task as you can muster. Since it’s easier to pick up an in-progress project, be sure to get it started as soon as you can. List the costs of not getting it done. And, lastly, get rid of distractions, especially digital ones.

Chances are that at this very moment you’re procrastinating on something. Maybe you’re even reading this article to do so.

• CB Chris Bailey is an author who explores the science behind living a deeper, more intentional life. His latest book, How to Calm Your Mind (Viking), is about the productivity benefits of a calm state of mind. Also the author of Hyperfocus (Viking) and The Productivity Project (Currency), Bailey’s books have been published in 35 languages. He writes a regular column at ChrisBailey.com and speaks to audiences around the world about becoming more productive without hating the process.

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Understanding and overcoming procrastination.

Classroom Resources for Addressing Procrastination, by Dominic J. Voge Source: Research and Teaching in Developmental Education excerpted from Vol. 23, No. 2 (Spring 2007), pp. 88-96

Why do so many people procrastinate and how do you overcome it?

For most people procrastination, irrespective of what they say, is NOT about being lazy. In fact, when we procrastinate we often work intensely for long stretches just before our deadlines. Working long and hard is the opposite of lazy, so that can't be the reason we do it. So, why do we procrastinate and, more importantly, what can we do about it?

As suggested above, some say they procrastinate because they are lazy. Others claim they "do better" when they procrastinate and "work best" under pressure. I encourage you to be critical and reflective of these explanations. Virtually everyone who says this habitually procrastinates and has not completed an important academic task in which they made a plan, implemented it, had time to review, etc. before their deadline. So, in reality, they can't make a comparison about the circumstances they work best under. If you pretty much always procrastinate, and never really approach your tasks systematically, then you can't accurately say that you know you "do better" under pressure. Still other people say they like the "rush" of leaving things to the end and meeting a deadline. But they usually say this when they are NOT working under that deadline. They say this works before or after cramming when they have forgotten the negative consequences of procrastinating such as feelings of anxiety and stress, fatigue, and disappointment from falling below their own standards and having to put their life on hold for chunks of time. Not to mention, leaving things to the end dramatically increases the chances something will go wrong - like getting sick or a computer problem - and you not being able to pull off the desired grade. So, procrastination can be hard on us and actually increase our chances of failing, but we do it anyway. How come?

Procrastination is not a matter, solely, of having poor time management skills, either, but rather can be traced to underlying and more complex psychological reasons. These dynamics are often made worse by schools where students are constantly being evaluated, and especially in college where the pressure for grades is high and a lot can be riding on students' performance. In reality, procrastination is often a self-protection strategy for students. For example, if you procrastinate, then you always have the excuse of "not having enough" time in the event that you fail, so your sense of your ability is never threatened. When there is so much pressure on getting a good grade on, say, a paper, it's no wonder that students want to avoid it and so put off their work. For the most part, our reasons for delaying and avoiding are rooted in fear and anxiety-about doing poorly, of doing too well, of losing control, of looking stupid, of having one's sense of self or self-concept challenged. We avoid doing work to avoid our abilities being judged. And, if we happened to succeed, we feel that much "smarter." So, what can we do to overcome our tendencies to procrastinate?

Awareness: The First Step

First, to overcome procrastination you need to have an understanding of the REASONS WHY you procrastinate and the function procrastination serves in your life. You can't come up with an effective solution if you don't really understand the root of the problem. As with most problems, awareness and self-knowledge are the keys to figuring out how to stop procrastinating. For a lot of people acquiring this insight about how procrastination protects them from feeling like they are not able enough, and keeping it in mind when they are tempted to fall into familiar, unproductive, procrastinating habits goes a long way to solving the problem. For instance, two psychologists, Jane Burka and Lenora Yuen, who have helped many people overcome procrastination, report in their article, "Mind Games Procrastinators Play" (Psychology Today, January, 1982), that for many students "understanding the hidden roots of procrastination often seems to weaken them" (p.33). Just knowing our true reasons for procrastinating makes it easier to stop.

Time Management Techniques: One Piece of the Puzzle

To overcome procrastination time management techniques and tools are indispensable, but they are not enough by themselves. And, not all methods of managing time are equally helpful in dealing with procrastination. There are some time management techniques that are well suited to overcoming procrastination and others that can make it worse. Those that reduce anxiety and fear and emphasize the satisfaction and rewards of completing tasks work best. Those that arc inflexible, emphasize the magnitude of tasks and increase anxiety can actually increase procrastination and are thus counter-productive. For instance, making a huge list of "things to do" or scheduling every minute of your day may INCREASE your stress and thus procrastination. Instead, set reasonable goals (e.g. a manageable list of things to do), break big tasks down, and give yourself flexibility and allot time to things you enjoy as rewards for work completed.

Motivation: Finding Productive Reasons for Engaging in Tasks

To overcome procrastination it's critical that you stay motivated for PRODUCTIVE REASONS. By productive reasons I mean reasons for learning and achieving that lead to positive, productive, satisfying feelings and actions. These reasons are in contrast to engaging in a task out of fear of failing, or not making your parents angry, or not looking stupid, or doing better than other people to "show off." While these are all reasons - often very powerful ones - for doing something, they are not productive since they evoke maladaptive, often negative feelings and actions. For example, if you are concerned with not looking dumb you may not ask questions, delve into new areas, try new methods, or take the risks necessary to learn new things and reach new heights. A good way to put positive motives in motion is to set and focus on your goals. Identify and write down your own personal reasons for enrolling in a course and monitor your progress toward your goals using a goal-setting chart. Remember to focus on your reasons and your goals. Other people's goals for you are not goals at all, but obligations.

Staying Motivated: Be Active to be Engaged

Another key to overcoming procrastination is to stay actively engaged in your classes. If you are passive in class you're probably not "getting into" the course and its topics, and that weakens your motivation. What's more, if you are passive you are probably not making as much sense out of the course and course materials as you could. Nonsense and confusion are not engaging; in fact, they are boring and frustrating. We don't often want to do things that are boring or frustrating. Prevent that by aiming to really understand course material, not memorize it or just "get through it." Instead, try (1) seeking out what is interesting and relevant to you in the course materials, (2) setting your own purpose for every reading and class session, and (3) asking yourself (and others) questions about what you are learning.

Summary of Tips for Overcoming Procrastination

Awareness – Reflect on the reasons why you procrastinate, your habits and thoughts that lead to procrastinating.

Assess – What feelings lead to procrastinating, and how does it make you feel? Are these positive, productive feelings: do you want to change them?

Outlook – Alter your perspective. Looking at a big task in terms of smaller pieces makes it less intimidating. Look for what's appealing about, or what you want to get out of an assignment beyond just the grade.

Commit – If you feel stuck, start simply by committing to complete a small task, any task, and write it down. Finish it and reward yourself. Write down on your schedule or "to do" list only what you can completely commit to, and if you write it down, follow through no matter what. By doing so you will slowly rebuild trust in yourself that you will really do what you say you will, which so many procrastinators have lost.

Surroundings – When doing school work, choose wisely where and with whom you are working. Repeatedly placing yourself in situations where you don't get much done - such as "studying" in your bed, at a cafe or with friends - can actually be a kind of procrastination, a method of avoiding work.

Goals – Focus on what you want to do, not what you want to avoid. Think about the productive reasons for doing a task by setting positive, concrete, meaningful learning and achievement goals for yourself.

Be Realistic – Achieving goals and changing habits takes time and effort; don't sabotage yourself by having unrealistic expectations that you cannot meet.

Self-talk – Notice how you are thinking, and talking to yourself. Talk to yourself in ways that remind you of your goals and replace old, counter-productive habits of self-talk. Instead of saying, "I wish I hadn't... " say, "I will ..."

Un-schedule – If you feel stuck, you probably won't use a schedule that is a constant reminder of all that you have to do and is all work and no play. So, make a largely unstructured, flexible schedule in which you slot in only what is necessary. Keep track of any time you spend working toward your goals and reward yourself for it. This can reduce feelings of being overwhelmed and increase satisfaction in what you get done. For more see the book Procrastination by Yuen and Burka.

Swiss Cheese It – Breaking down big tasks into little ones is a good approach. A variation on this is devoting short chunks of time to a big task and doing as much as you can in that time with few expectations about what you will get done. For example, try spending about ten minutes just jotting down ideas that come to mind on the topic of a paper, or skimming over a long reading to get just the main ideas. After doing this several times on a big task, you will have made some progress on it, you'll have some momentum, you'll have less work to do to complete the task, and it won't seem so huge because you've punched holes in it (like Swiss cheese). In short, it'll be easier to complete the task because you've gotten started and removed some of the obstacles to finishing.

2 How to Avoid Procrastination

One of the most common threats that a research paper faces is procrastination. This is a topic most people are already very familiar with. It is human nature to want to avoid things that cause stress or other negative feelings. With that said, putting the work off will only serve to make the project harder in the long run and very likely will result in a paper with only a fraction of the potential it could have had if enough time had been allocated. Research papers are generally assigned to students not only to sharpen a student’s writing or analytical skills but also to inform the student on the topic at hand. It is very likely that the student will have little or no knowledge regarding the subject. Naturally, the project can feel overwhelming and that feeling can lead to procrastination but once the student jumps in and begins researching the topic, clarity follows as an argument begins to form. Once the student forms an argument the structure of the paper becomes much easier to map out.  The only way to avoid procrastination is to remember that the only way to begin to form an argument is to first begin thorough research.

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Procrastination, the deliberate but detrimental deferring of tasks, has many forms. Sahiti Chebolu of the Max Planck Institute for Biological Cybernetics uses a precise mathematical framework to understand its different patterns and their underlying reasons. Her insights could help tailor individual strategies to tackle the issue.

“Why did I not do this when I still had the time?” – Whether it is filing taxes, meeting a deadline at work, or cleaning the apartment before a family visit, most of us have already wondered why we tend to put off certain tasks, even in the face of unpleasant consequences. Why do we make decisions that are harmful to us – against our better knowledge? This is precisely the conundrum of procrastination. Procrastination, the deliberate but ultimately detrimental delaying of tasks, is not only hampering productivity, but has also been linked to a host of mental health issues. So it is certainly worth asking why this much talked-about phenomenon has such a grip on us – and what it actually is.

“Procrastination is an umbrella term for different behaviors,” says computational neuroscientist Sahiti Chebolu from the Max Planck Institute for Biological Cybernetics. “If we want to understand it, we need to differentiate between its various types.” One common pattern is that we defect on our own decisions: we might, for example, set aside an evening for the tax return, but when the time has come we watch a movie instead. Something else is going on when we do not commit to a time in the first place: we might be waiting for the right conditions. The possible patterns of procrastination are myriad: from starting late to abandoning a task halfway through, Chebolu classified them all and identified possible explanations for each: misjudging the time needed or protecting the ego from prospective failure are just two of them.

The short-sighted brain

Can such a classification really help you get stuff done? Chebolu is convinced that a mathematically precise understanding of the mechanism at play is the first step to tackling it. She frames procrastination as a series of temporal decisions. What exactly happens, for example, when we schedule our tax declaration for Friday night but then succumb to the temptations of a streaming service? One way to think of decision-making is that our brain adds up all the rewards and penalties we expect to gain from the alternative behaviors: watching a movie or doing the annoying paperwork. Quite naturally, it then picks the course of action that promises to be most pleasant overall.

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But does the fun of a movie night outweigh the dismay at a hefty failure-to-file penalty? There is one important detail: consequences in the distant future are weighted less by the brain in its summation of positive and negative outcomes. To a certain degree, this is normal and even useful; after all, the more distant future is necessarily fraught with uncertainties. “Only when we place excessive value on experiences in the present and not enough on those lying further ahead,” Chebolu explains, “such a decision-making policy becomes quickly maladaptive.”

So much for the theory. To study real-life procrastination, Chebolu delved into large datasets provided by New York University. The data showed a log of students who were required to participate in a set number of hours of experiments over the course of a semester. Some rid themselves of the task right away; others distributed it evenly over several weeks – and, sure enough, still others shirked it until it was almost too late. Chebolu ran simulations to reproduce their behavior. Which explanations, she asked, would best be able to account for different pattern of procrastination?

It might be tempting to lay the blame on our brain’s preference for immediately rewarding activities. But there is definitely more at play: for each pattern of how the New York students deferred their task, Chebolu found multiple possible explanations. “Uncertainty is another major factor in procrastination,” she stresses. This could be the failure to predict how much time we will need to unearth all receipts for deductible expenses. But uncertainty can also mean lacking confidence in our own abilities or doubting whether the task helps us achieve our goals.

Chebolu is confident that understanding procrastination as a series of temporal decisions and detecting where and why we usually take a wrong turn can inform interventions: If you discover, for instance, that your brain is a bit too biased towards instant gratification, giving yourself short-term rewards might help. Those who tend to underestimate the time needed for their grunt work could try setting themselves time-bound goals. And if find yourself abandoning your chores quickly, you might want to avoid distracting environments.

No matter in which category of procrastination you fall (and you almost certainly fall into some of them sometimes): no, you are not just lazy. Recognizing this and forgiving yourself for procrastinating in past is a good first steps towards more productivity.

Reference:  Chebolu S, Dayan P. Optimal and sub-optimal temporal decisions can explain procrastination in a real-world task. 2024. doi:  10.31234/osf.io/69zhd

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Procrastination Essay

• To find inspiration for your paper and overcome writer’s block
• As a source of information (ensure proper referencing)
• As a template for you assignment

Perfectionism and Fear of Failure

Fear of the unknown, lack of motivation, multiple distractions, works cited.

This procrastination essay should start with an acknowledgment: every person occasionally postpones tasks at hand for various reasons. It can be due to fatigue, unwillingness to engage in it at the moment, or lack of spare time to spend on the issue. College students, in particular, have to face this phenomenon regularly.

However, some people get into the habit of delaying things that they have to do. Thus, they disregard deadlines and cause trouble for themselves and those around them. It is called procrastination, which is, as stated by Ferrari et al., “derived from Latin verbs, ‘pro’ refers to forward motion, and ‘crastinus’ refers to belonging to tomorrow” (qt. in Abbasi and Alghamdi 59). It is worth mentioning that postponing a task to perform a more important one does not refer to this phenomenon. In this case, it is setting priorities, which has nothing to do with procrastination.

The habitual delay of tasks is caused by several factors, such as perfectionism, fear of uncertainty, lack of motivation, and distractions. This essay about procrastination will explore each of them and their consequences separately.

Although perfectionism seems to be a positive trait at first sight, since it implies that a person attempts to do his work brilliantly, it is, in fact, a drawback in many cases. Voltaire is believed to have said: “The best is the enemy of the good,” which is applied to perfectionists. Instead of submitting a well-done task in time, they leave it to the last minute because they do not consider it perfect.

Not all people tend to develop perfectionism because it depends on personal attitudes. According to Dweck, there are two types of mindset, which define an individual’s behavior and beliefs: “the fixed mindset” and “the growth mindset” (7). People with the first type are convinced that they have “a certain amount of intelligence, a certain personality, and a certain moral character,” which cannot change over time (Dweck 7). Individuals possessing the second type of mindset; on the contrary, believe that their abilities are improvable, and “everyone can change and grow through application and experience” (Dweck 7). Thus, perfectionists have a fixed mindset since they always have to prove that they are superior and worthy, which makes them waste much time and effort and causes them to procrastinate.

Perfectionism is closely related to another cause of postponing tasks, which is a fear of failure. Some people are so obsessed with the ambition of being the best that they cannot afford to fail. However, it leads them to reject challenges and opportunities, which could contribute to their personal growth because they are sure they will not cope with them. If they cannot refuse such a task, they delay it for as long as possible since they are afraid of failure. In the workplace, such behavior leads to “low salaries and short employment durations,” as well as unemployment (Abbasi and Alghamdi 61). Thus, obsessive pursuit of excellence leads to procrastination, which, in turn, deprives people of a possibility of personal growth and makes them miss valuable opportunities.

Sometimes, people put their plans off because they are uncertain of what they are going to experience. It particularly concerns health and relationships because, in these areas of life, there are no deadlines, and individuals have to decide for themselves when to take action. For example, a person, having noticed a rash on the skin, may postpone a visit to a doctor, hoping that it will get better on its own. Another instance is a woman who wants to break up with her partner but delays this decision since she expects her life to improving suddenly. In both cases, people procrastinate because they fear the consequences of their actions and prefer to live in uncertainty.

Procrastination due to the fear of the unknown generally results in negative outcomes since problems rarely disappear on their own. For people delaying health issues, it may lead to “hazardous consequences in terms of health, especially when a disease may be progressive” (Kroese and de Ridder 317). Procrastination also influences people’s quality of life since, instead of eliminating disturbance by making the desired change, they continue to postpone problems and waste their mental energy on them.

Individuals may delay fulfilling a task because they are not interested in it and have no essential reasons for doing it. Instead, they prefer performing more pleasant though less important work. Motivation is influenced by the environment, especially when there are other procrastinators who affect their coworkers through “second-hand procrastination” (Abbasi and Alghamdi 60). Furthermore, people face this problem if the task at hand misaligns with their priorities or is vaguely stated.

As a result, procrastination due to the absence of motivation leads to poor individual performance. Postponing tasks causes a person to have fewer career opportunities since “employers are less likely to retain procrastinators for jobs requiring high motivation” (Abbasi and Alghamdi 60). It applies to not only work but also academic achievements, switching to a healthy lifestyle, and any other activity requiring an internal stimulus to get down to action.

Procrastination takes place when many distractions hinder a person from concentrating on a task. This problem is especially significant in the contemporary environment since smartphones and free Internet access has made it difficult for people not to redirect their attention from work to entertainment. Furthermore, in the workplace, colleagues also may contribute to attention distraction (Sirois and Pychyl 245). Thus, people have to choose between pleasant things and their own obligations, which Milkman et al. called a “should-want conflict” (qtd. in Sirois and Pychyl 245). It serves as a cause for procrastination since individuals often prefer instant gratification instead of committing to work.

Like in the cases mentioned above, procrastination caused by distractions has negative effects on people’s performance and well-being. Abbasi and Alghamdi argue that students who engage in entertaining activities rather than studying are likely to submit their assignments after the deadline and generally have poor academic achievements (60). Furthermore, since procrastination often involves excessing the time limit, people apt to postponing tasks complain of “higher levels of stress and anxiety” (Abbasi and Alghamdi 61). Consequently, the problem of not completing work in time leads to not only falling behind peers in studies and career but also having health issues.

Procrastination Conclusion

In conclusion, it should be said that procrastination is rooted in many causes, such as numerous distractions, lack of motivation, fear of uncertainty and failure, and perfectionism. Each of them leads to negative consequences that concern career, studies, health, and personal qualities. Thus, procrastination prevents a person from rising through the ranks, succeeding in training, and developing as a personality. These are serious obstacles on the way to success and life satisfaction, which is why it is crucial for people to begin to struggle with procrastination as soon as they discovered it in their behavior.

Abbasi, Irum Saeed, and Nawal G. Alghamdi. “The Prevalence, Predictors, Causes, Treatments, and Implications of Procrastination Behaviors in General, Academic, and Work Setting.” International Journal of Psychological Studies , vol. 7, no. 1, 2015, pp. 59-66.

Dweck, Carol. Mindset – Updated Edition: Changing the Way You Think to Fulfil Your Potential . Hachette UK, 2017.

Kroese, Floor M., and Denise T. D. de Ridder. “Health Behaviour Procrastination: A Novel Reasoned Route towards Self-regulatory Failure.” Health Psychology Review , vol. 10, no. 3, 2016, pp. 313-325.

Sirois, Fuschia M., and Timothy A. Pychyl, editors. Procrastination, Health, and Well-being . Academic Press, 2016.

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Solving Procrastination

Procrastination Research: Articles and Studies about Procrastination

Here, you will find a comprehensive collection of research about procrastination. It comes in two parts:

• List of accessible articles about procrastination from this website, which summarize and synthesize existing procrastination research.
• List of all the procrastination research that was used to write the articles on the website.

The sources that were used to write the articles come from many fields, such as psychology, behavioral economics, and neuroscience. They include many types of papers, such as theoretical articles, review articles, meta-analyses, experimental studies, clinical studies, and computational studies. Most of these are publications in peer-reviewed journals, but some represent other types of academic sources, including book chapters, doctoral dissertations, and entries in scientific encyclopedias, all written by scientific experts.

There are hundreds of procrastination papers listed in this bibliography. A selection of key ones are bolded ; these are recommended items to start with if you want to read the scientific literature about procrastination.

Procrastination articles

The following are the key articles on the website that summarize and synthesize existing procrastination research:

• Procrastination overview
• Why people procrastinate
• How to stop procrastinating
• Types of procrastination (including chronic , academic , workplace , bedtime , online , active , and  productive )
• Collections of information about procrastination (including symptoms , dangers , benefits , facts , and statistics )
• Issues associated with procrastination (including stress , depression , anxiety , fear , perfectionism , ADHD , and repetitive cycles )
• Other key concepts relating to procrastination (including psychological theories , time management , emotion regulation , genetics , self-efficacy , and self-compassion )
• Summaries of specific procrastination studies

Below, you will find all the procrastination research that these articles are based on, which is cited throughout the articles using hyperlinks. Note that the articles also cite additional sources that are not listed here, primarily about topics that are relevant for understanding and dealing with procrastination, but that are not directly about procrastination itself (e.g., the stages that people go through as they work to change their behavior).

Procrastination research papers

Chen, G., & Lyu, C. (2024). The relationship between smartphone addiction and procrastination among students: A systematic review and meta-analysis. Personality and Individual Differences , 224 , Article 112652.  https://doi.org/10.1016/j.paid.2024.112652

Araya-Castillo, L., Burgos, M., González, P., Rivera, Y., Barrientos, N., Yáñez Jara, V., … & Sáez, W. (2023). Procrastination in university students: A proposal of a theoretical model. Behavioral Sciences , 13 (2), Article 128. https://doi.org/10.3390/bs13020128

Arnold, I. J. (2023). The link between procrastination and graduation rates: Evidence from the ALEKS learning platform. Education Economics , 31 (3), 275-287. https://doi.org/10.1080/09645292.2022.2063796

Bai, H., Li, X., Wang, X., Tong, W., Li, Y., & Hu, W. (2023). Active procrastination incubates more creative thinking: The sequential mediating effect of personal mastery and creative self-concept. Creativity Research Journal . Advance online publication. https://doi.org/10.1080/10400419.2023.2171721

Bodalski, E. A., Flory, K., Canu, W. H., Willcutt, E. G., & Hartung, C. M. (2023). ADHD symptoms and procrastination in college students: The roles of emotion dysregulation and self-esteem. Journal of Psychopathology and Behavioral Assessment , 45 (1), 48-57. https://doi.org/10.1007/s10862-022-09996-2

Campbell, R. L., & Bridges, A. J. (2023). Bedtime procrastination mediates the relation between anxiety and sleep problems. Journal of Clinical Psychology , 79 (3), 803-817. https://doi.org/10.1002/jclp.23440

Cruz, R. N. C., & Miranda, J. O. (2023). Examining procrastination using the DSM-5 personality trait model: Disinhibition as a core personality trait. Current Psychology . Advance online publication. https://doi.org/10.1007/s12144-023-04815-7

Fuke, T. S. S., Kamber, E., Alunni, M., & Mahy, C. E. V. (2023). The emergence of procrastination in early childhood: Relations with executive control and future-oriented cognition. Developmental Psychology ,  59 (3), 579. https://doi.org/10.1037/dev0001502

Gökalp, Z. Ş., Saritepeci, M., & Durak, H. Y. (2023). The relationship between self-control and procrastination among adolescent: The mediating role of multi screen addiction. Current Psychology , 42 (15), 13192-13203. https://doi.org/10.1007/s12144-021-02472-2

Johansson, F., Rozental, A., Edlund, K., Côté, P., Sundberg, T., Onell, C., … & Skillgate, E. (2023). Associations between procrastination and subsequent health outcomes among university students in Sweden. JAMA Network Open , 6 (1), Article e2249346. https://doi.org/10.1001/jamanetworkopen.2022.49346

Johnson, S. T., & Most, S. B. (2023). Taking the path of least resistance now, but not later: Pushing cognitive effort into the future reduces effort discounting. Psychonomic Bulletin & Review , 30 (3), 1115-1124. https://doi.org/10.3758/s13423-022-02198-7

Koppenborg, M., Klingsieck, K. B., & Hüffmeier, J. (2023). Conjunctive and additive group work reduce academic procrastination: Insights from a vignette study. Current Psychology . Advance online publication. https://doi.org/10.1007/s12144-023-04294-w

Kühnel, J., Bledow, R., & Kuonath, A. (2023). Overcoming procrastination: Time pressure and positive affect as compensatory routes to action. Journal of Business and Psychology , 38 (4), 803-819. https://doi.org/10.1007/s10869-022-09817-z

Liu, G. (2023). Exploring different types of procrastination in multinational corporation employees: A latent class analysis. Current Psychology . Advance online publication. https://doi.org/10.1007/s12144-023-04375-w

Mackiewicz, M. (2023). Why do wantrepreneurs fail to take actions? Moderators of the link between intentions and entrepreneurial actions at the early stage of venturing. Quality & Quantity , 57 (1), 323-344. https://doi.org/10.1007/s11135-022-01337-5

Oflazian, J. S., & Borders, A. (2023). Does rumination mediate the unique effects of shame and guilt on procrastination?. Journal of Rational-Emotive & Cognitive-Behavior Therapy , 41 (1), 237-246. https://doi.org/10.1007/s10942-022-00466-y

Rad, H. S., Samadi, S., Sirois, F. M., & Goodarzi, H. (2023). Mindfulness intervention for academic procrastination: A randomized control trial. Learning and Individual Differences , 101 , Article 102244. https://doi.org/10.1016/j.lindif.2022.102244

Sirois, F. M., Stride, C. B., & Pychyl, T. A. (2023). Procrastination and health: A longitudinal test of the roles of stress and health behaviours. British Journal of Health Psychology . Advance online publication. https://doi.org/10.1111/bjhp.12658

Teoh, A. N., & Wong, J. W. K. (2023). Mindfulness is associated with better sleep quality in young adults by reducing boredom and bedtime procrastination. Behavioral Sleep Medicine ,  21 (1), 61-71. https://doi.org/10.1080/15402002.2022.2035729

Aalbers, G., vanden Abeele, M. M., Hendrickson, A. T., de Marez, L., & Keijsers, L. (2022). Caught in the moment: Are there person-specific associations between momentary procrastination and passively measured smartphone use?. Mobile Media & Communication ,  10 (11), 115-135. https://doi.org/10.1177/2050157921993896

Aydın, Y., & Aydın, G. (2022). Predictors of procrastination in a moderated mediation analysis: The roles of problematic smartphone use, psychological flexibility, and gender. Psychological Reports . Advance online publication. https://doi.org/10.1177/00332941221119404

Beck, E. D., & Jackson, J. J. (2022). Personalized prediction of behaviors and experiences: An idiographic person–situation test. Psychological Science , 33 (10), 1767-1782. https://doi.org/10.1177/09567976221093307

Berber Çelik, Ç., & Odaci, H. (2022). Subjective well-being in university students: What are the impacts of procrastination and attachment styles?. British Journal of Guidance & Counselling , 50 (5), 768-781. https://doi.org/10.1080/03069885.2020.1803211

Cosentino, E., McCarroll, C. J., & Michaelian, K. (2022). Resisting temptation and overcoming procrastination: The roles of mental time travel and metacognition. Phenomenology and the Cognitive Sciences . https://doi.org/10.1007/s11097-022-09836-4

Deimen, I., & Wirtz, J. (2022). Control, cost, and confidence: Perseverance and procrastination in the face of failure. Games and Economic Behavior , 134 , 52-74. https://doi.org/10.1016/j.geb.2022.03.013

Feng, B., & Sun, W. (2022). Bedtime procrastination and fatigue in Chinese college students: The mediating role of mobile phone addiction. International Journal of Mental Health and Addiction . Advance online publication. https://doi.org/10.1007/s11469-022-00796-z

Feyzi Behnagh, R., & Ferrari, J. R. (2022). Exploring 40 years on affective correlates to procrastination: A literature review of situational and dispositional types. Current Psychology ,  41 , 1097-1111. https://doi.org/10.1007/s12144-021-02653-z

Fostervold, K. I., Ludvigsen, S., & Strømsø, H. I. (2022). Students’ time management and procrastination in the wake of the pandemic. Educational Psychology , 42 (10), 1223-1240. https://doi.org/10.1080/01443410.2022.2102582

Furlan, L. A., & Cristofolini, T. (2022). Interventions to reduce academic procrastination: A review of their theoretical bases and characteristics. In L. R. V. Gonzaga, L. L. Dellazzana-Zanon, & A. M. Becker da Silva (Eds.), Handbook of stress and academic anxiety (pp. 127-147). Springer. https://doi.org/10.1007/978-3-031-12737-3_9

García-Ros, R., Pérez-González, F., Tomás, J. M., & Sancho, P. (2022). Effects of self-regulated learning and procrastination on academic stress, subjective well-being, and academic achievement in secondary education. Current Psychology . Advance online publication. https://doi.org/10.1007/s12144-022-03759-8

Gosselin, P., Castonguay, C., Goyette, M., Lambert, R., Brisson, M., Landreville, P., & Grenier, S. (2022). Anxiety among older adults during the COVID-19 pandemic. Journal of Anxiety Disorders , 92 , Article 102633. https://doi.org/10.1016/j.janxdis.2022.102633

Haesevoets, T., De Cremer, D., Hirst, G., De Schutter, L., Stouten, J., van Dijke, M., & Van Hiel, A. (2022). The effect of decisional leader procrastination on employee innovation: Investigating the moderating role of employees’ resistance to change. Journal of Leadership & Organizational Studies , 29 (1), 131-146. https://doi.org/10.1177/15480518211044166

Hill, V. M., Rebar, A. L., Ferguson, S. A., Shriane, A. E., & Vincent, G. E. (2022). Go to bed! A systematic review and meta-analysis of bedtime procrastination correlates and sleep outcomes. Sleep Medicine Reviews , 66 , Article 101697. https://doi.org/10.1016/j.smrv.2022.101697

Junça‐Silva, A., Neves, P., & Caetano, A. (2022). Procrastination is not only a “thief of time”, but also a thief of happiness: It buffers the beneficial effects of telework on well-being via daily micro-events of IT workers. International Journal of Manpower. Advance online publication. https://doi.org/10.1108/IJM-05-2022-0223

Kljajic, K., Schellenberg, B. J., & Gaudreau, P. (2022). Why do students procrastinate more in some courses than in others and what happens next? Expanding the multilevel perspective on procrastination. Frontiers in Psychology , 12 , Article 786249. https://doi.org/10.3389/fpsyg.2021.786249

Koppenborg, M., & Klingsieck, K. B. (2022). Group work and student procrastination. Learning and Individual Differences , 94 , Article 102117. https://doi.org/10.1016/j.lindif.2022.102117

Le Bouc, R., & Pessiglione, M. (2022). A neuro-computational account of procrastination behavior. Nature Communications , 13 , Article 5639. https://doi.org/10.1038/s41467-022-33119-w

Li, X., & Ye, Y. (2022). Fear of missing out and irrational procrastination in the mobile social media environment: A moderated mediation analysis. Cyberpsychology, Behavior, and Social Networking , 25 (1), 59-65. https://doi.org/10.1089/cyber.2021.0052

Lim, V. K., & Teo, T. S. (2022). Cyberloafing: A review and research agenda. Applied Psychology . Advance online publication. https://doi.org/10.1111/apps.12452

Maier, T., Kühnel, J., & Zimmermann, B. (2022). How did you sleep tonight? The relevance of sleep quality and sleep–wake rhythm for procrastination at work. Frontiers in Psychology , 12 , Article 785154. https://doi.org/10.3389/fpsyg.2021.785154

Meier, A. (2022). Studying problems, not problematic usage: Do mobile checking habits increase procrastination and decrease well-being?. Mobile Media & Communication , 10 (2), 272-293. https://doi.org/10.1177/20501579211029326

Miyake, A., & Kane, M. J. (2022). Toward a holistic approach to reducing academic procrastination with classroom interventions. Current Directions in Psychological Science , 31 (4), 291-304. https://doi.org/10.1177/09637214211070814

Niazov, Z., Hen, M., & Ferrari, J. R. (2022). Online and academic procrastination in students with learning disabilities: The impact of academic stress and self-efficacy. Psychological Reports , 125 (2), 890-912. https://doi.org/10.1177/0033294120988113

Ocansey, G., Addo, C., Onyeaka, H. K., Andoh-Arthur, J., & Oppong Asante, K. (2022). The influence of personality types on academic procrastination among undergraduate students. International Journal of School & Educational Psychology , 10 (3), 360-367. https://doi.org/10.1080/21683603.2020.1841051

Koppenborg, M., & Klingsieck, K. B. (2022). Social factors of procrastination: Group work can reduce procrastination among students. Social Psychology of Education , 25 (1), 249-274. https://doi.org/10.1007/s11218-021-09682-3

Pu, Z., Leong, R. L., Chee, M. W., & Massar, S. A. (2022). Bedtime procrastination and chronotype differentially predict adolescent sleep on school nights and non-school nights. Sleep Health , 8 (6), 640-647. https://doi.org/10.1016/j.sleh.2022.09.007

Rapoport, O., Bengel, S., Möcklinghoff, S., & Neidhardt, E. (2022). Self-compassion moderates the influence of procrastination on postponing sporting activity. Personality and Individual Differences , 185 , Article 111242. https://doi.org/10.1016/j.paid.2021.111242

Rebetez, M. M. L., Barsics, C., Montisci, T., & Rochat, L. (2022). Towards a dimensional, multifactorial, and integrative approach to procrastination in everyday life: An illustration through interviews. Psychologica Belgica , 62 (1), 166-183. https://doi.org/10.5334/pb.1115

Rozental, A., Forsström, D., Hussoon, A., & Klingsieck, K. B. (2022). Procrastination among university students: Differentiating severe cases in need of support from less severe cases. Frontiers in Psychology , 13 , Article 783570. https://doi.org/10.3389/fpsyg.2022.783570

Safari, Y., & Yousefpoor, N. (2022). The role of metacognitive beliefs in predicting academic procrastination among students in Iran: Cross-sectional study. JMIR Medical Education , 8 (3), e32185. https://doi.org/10.2196/32185

Schuenemann, L., Scherenberg, V., von Salisch, M., & Eckert, M. (2022). “I’ll worry about it tomorrow” – Fostering emotion regulation skills to overcome procrastination. Frontiers in Psychology , 13 , Article 780675. https://doi.org/10.3389/fpsyg.2022.780675

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How Study Environments Foster Academic Procrastination: Overview and Recommendations

Frode svartdal.

1 Department of Psychology, UiT The Arctic University of Norway, Tromsø, Norway

Tove I. Dahl

Thor gamst-klaussen, markus koppenborg.

2 Evaluation of Studies and Teaching and Higher Education Research, University of Cologne, Cologne, Germany

Katrin B. Klingsieck

3 Department of Psychology, Paderborn University, Paderborn, Germany

Procrastination is common among students, with prevalence estimates double or even triple those of the working population. This inflated prevalence indicates that the academic environment may appear as “procrastination friendly” to students. In the present paper, we identify social, cultural, organizational, and contextual factors that may foster or facilitate procrastination (such as large degree of freedom in the study situation, long deadlines, and temptations and distractions), document their research basis, and provide recommendations for changes in these factors to reduce and prevent procrastination. We argue that increased attention to such procrastination-friendly factors in academic environments is important and that relatively minor measures to reduce their detrimental effects may have substantial benefits for students, institutions, and society.

Procrastination, voluntarily delaying tasks despite expecting to be worse off ( Steel, 2007 ), is common among students. Conservative estimates indicate that at least half of all students habitually procrastinate tasks that are important to them, such as reading for exams, writing term papers, and keeping up with weekly assignments ( Solomon and Rothblum, 1984 ; Tice and Baumeister, 1997 ; Pychyl et al., 2000 ; Schouwenburg, 2004 ; Steel, 2007 ). Consequences are negative, both for academic performance and retention ( Ellis and Knaus, 1977 ; Klassen et al., 2008 ; Zarick and Stonebraker, 2009 ; Grau and Minguillon, 2013 ; Kim and Seo, 2015 ) as well as for health and well-being ( Flett et al., 1995 ; Tice and Baumeister, 1997 ; Stöber and Joormann, 2001 ; Sirois, 2014 ).

Despite the possibility that academic environments may contribute significantly to this situation, the majority of research efforts to clarify mechanisms involved in procrastination has focused on individual variables related to personality, motivation, affect, and others (for reviews, see van Eerde, 2003 ; Steel, 2007 ; Klingsieck, 2013 ). The present paper takes a different view, focusing on situational, social, contextual, cultural, and organizational factors common in academic environments. Based on the procrastination literature, we present a selection of such factors and show how they increase the probability of procrastination. Negative effects may be general in that most students suffer. Often, however, “procrastination-friendly” factors may also affect students differentially, those being prone to procrastination in the first place being particularly vulnerable (e.g., Nordby et al., 2017 ; Visser et al., 2018 ). Thus, ideas on how to address these factors to make the academic environment more “procrastination- un friendly” are important.

We identify nine broad factors known to increase procrastination. The factors selected serve as important examples rather than an exhaustive list. For each factor, we link it to common features of academic environments, providing examples and other forms of documentation to demonstrate its significance in facilitating procrastination. We then formulate specific advice on how the negative influence of each factor may be alleviated or remedied by relatively simple structural, organizational, and educational measures.

Characteristics of Academic Procrastination

Academic procrastination occurs when a student delays work related to academic tasks ( Solomon and Rothblum, 1984 ; Tice and Baumeister, 1997 ; Pychyl et al., 2000 ; Schouwenburg, 2004 ; Steel, 2007 ). For such delays to be regarded as procrastination, the student voluntarily chooses to delay despite expecting to be worse off ( Steel, 2007 ). Thus, there is an important distinction between delays that are sensible and rational (e.g., “I chose to postpone my thesis submission because my supervisor advised me to revise the discussion part”) and those that are not (e.g., “I did not prepare for the seminar today, I watched a movie instead”). In effect, academic procrastination is a form of irrational delay, as the person acts against better judgment.

The delays seen in academic procrastination may result from late onset (e.g., “I did not start writing until just one week before deadline”) and impulsive diversions during work (e.g., “I was working, but got tired and had a coffee with a friend instead”) ( Svartdal et al., 2020 ). As is well documented in the research literature over the past 40 years, such delays and diversions are related to personality factors, as for example impulsiveness and a preference for short-term gratification, deficiencies in planning and self-regulation, low self-efficacy, tiredness, and low energy, and task avoidance ( van Eerde, 2000 ; Steel, 2007 ; Steel et al., 2018 ). The majority of this research has been correlational. Because procrastination is a complex phenomenon unfolding over time and in interaction with situational, social, contextual, cultural, and organizational factors, it is important also to focus on exogenous factors involved in this complex and dynamic phenomenon. The relative lack of such studies is unfortunate and clearly represents a gap in the procrastination field. We argue that this is particularly unfortunate in the academic area, as the student is confronted with situational, social, contextual, cultural, and organizational factors that are prone to instigate and maintain procrastination in tasks that constitute core student activities.

How Is Academic Procrastination Measured?

Academic procrastination is typically measured with self-report tools, as is general procrastination. In measuring academic procrastination, some scales focus on general tendencies to delay tasks unnecessarily, with few if any items covering academic tasks specifically. For example, the General Procrastination Scale (20 items; Lay, 1986 ), academic version, has 16 items common with the general version and four items addressing academic tasks specifically (e.g., Item 2, “I do not do assignments until just before they are to be handed in”). Similarly, the Tuckman procrastination scale (16 items; Tuckman, 1991 ) measures academic procrastination solely by general items (e.g., item 1 “I needlessly delay finishing jobs, even when they’re important”). Other academic procrastination scales focus on academic tasks exclusively, such as the Academic Procrastination State Inventory (APSI; Schouwenburg, 1995 ) and the Procrastination Assessment Scale (PASS; Solomon and Rothblum, 1984 ). The PASS contains 44 questions that address various forms of academic tasks (e.g., studying for an exam, writing a term paper) in terms of how often they are procrastinated, to which extent such procrastination represents a problem, and willingness to change.

Importantly, scores on academic procrastination scales have been validated against procrastination in real academic tasks. For example, Tuckman compared scores on his scale against actual performance points on voluntary homework assignments, where students had the opportunity to write and submit written material to gain extra course credits. He found a negative correlation, r =−0.54, between these measures, concluding that “students are well aware of their own tendencies and can report them with great accuracy” (p. 9). More recent findings (e.g., Tice and Baumeister, 1997 ; Steel et al., 2018 ) confirm a relatively close correspondence between students’ self-reported procrastination and relevant behavioral measures.

Detrimental Effects of Academic Procrastination

It is important to recognize that procrastination is not only an issue related to effective academic work. Although performance (grades) is negatively related to procrastination (for review, see Kim and Seo, 2015 ), other important problems associated with procrastination are stress, reduced well-being, and mental and physical health problems (e.g., Tice and Baumeister, 1997 ). For academic procrastination, the increased stress associated with procrastination seems to be important (e.g., Sirois, 2007 , 2014 ). Recognition of the procrastination problem as a health issue, as well as a performance issue, is imperative. In Norway, as well as in other European countries, surveys of student health indicate that an increasing number of students report psychological problems, often of serious nature. For example, in a large-scale survey among Norwegian students, the Students’ Health and Wellbeing Study ( Knapstad et al., 2018 ; N = 50,000), 29% of all students reported serious psychological problems. We do not know the role of procrastination in this situation, but it is likely that procrastination may be a contributing factor as well as a consequence. Hence, the role of the environmental factors in encouraging procrastinating is important to assess from a health perspective also.

Social and Contextual Factors Facilitating Procrastination

Rationale for selection of factors.

In the sections to come, we address situational, social, contextual, cultural, and organizational factors that are documented as facilitators of procrastination. In selection of factors, the authors first discussed a larger pool of factors and evaluated their relation to the academic situation. Then, based on expert judgment, we selected nine factors that met the following criteria: They (a) reflect well-documented research findings in the procrastination field; (b) represent factors present in the academic situation beyond the student’s control (e.g., long deadlines), or factors that cannot easily be remedied by the student independently of educational, social, or organizational measures (e.g., task aversion); and that (c) measures taken to change the factor is likely to reduce procrastination. The discussion of each factor is not intended as a complete review, as a review at this stage of research would be premature. Rather, for each factor, we highlight central findings connecting the factor to procrastination research, its relation to the academic environment, and remedies that may alleviate the detrimental effects associated with a given factor. Table 1 presents an overview of the factors discussed.

Factors reliably associated with procrastination, and their relation to the study environment.

Note that the factors are quite heterogeneous. Some factors (e.g., large degree of freedom in the study situation, long deadlines) identify organizational and structural properties of the academic environment, whereas others emphasize subjective evaluations (e.g., task aversiveness). Also note that the factors discussed may demonstrate “main effects” as most students may be affected, as well as interactive effects where individual characteristics act as moderators. For example, temptations and distractions in the academic environment may be detrimental for most students, but particularly so for individuals high in impulsivity and distractibility (e.g., Steel et al., 2018 ). Furthermore, the order of factors discussed does not indicate differences in importance. In fact, the effect sizes associated with each factor may be difficult to quantify in academic contexts. Finally, a caution on the use of the term “factor.” We use this term to denote facets or variables in the academic settings that identify features known to relate strongly to procrastination. As these are exogenous factors in the procrastination equation, they represent potential conditions that can be altered in order to affect the probability of procrastination. In the present context, we do not make strong assumptions about causality; rather, we argue that such potential causal relations should receive increased attention in future research.

Large Degree of Freedom in the Study Situation

Relevant research.

In his comprehensive review of research on procrastination, Steel (2007) coined procrastination a quintessential self-regulatory failure. Procrastinators are present-oriented and impulsive and tend to score low on tests measuring conscientiousness and planning, and high on susceptibility to temptation ( Lay and Schouwenburg, 1993 ; van Eerde, 2003 ; Steel, 2010 ). Procrastinators make plans, only to reverse them when encountering distractions and temptations during goal implementation ( Steel et al., 2018 ). Hence, procrastinators are particularly vulnerable when working under unstructured conditions and when long-term plans are delegated to the individual.

Relation to the Academic Environment

Results from qualitative studies exemplify the negative role of freedom in the study situation in several ways, as too little regulations in studies ( Grunschel et al., 2013 ), low degree of external structure ( Klingsieck et al., 2013 ), or insufficient direction of lecturers ( Patrzek et al., 2012 ). Overall, students reported feeling lost and overwhelmed by the task of planning a whole course of studies, a semester, or even an exam phase on their own. Thus, students lacking self-management skills such as planning and prioritizing tasks (e.g., Lay and Schouwenburg, 1993 ) and metacognitive learning strategies (e.g., Wolters, 2003 ; Howell and Watson, 2007 ) should feel particularly lost when facing a situation with a large degree of freedom. The autonomy associated with a large degree of freedom in the study situation makes the student particularly vulnerable if skills are low (→Low focus on study skills training) and if the student fails to develop good habits and routines. Habits help people accomplish more and procrastinate less (e.g., Steel et al., 2018 ). Of note, study topics may vary in how much freedom they offer to the student. Some study programs are strictly structured and may even involve a common study group from start to finish (e.g., medicine), whereas other study topics are less structured and may also, by the nature of their contents, appear as more “procrastination friendly” (e.g., Nordby et al., 2017 ).

While direct procrastination prevention and intervention programs train the self-management skill of students (for a summary, see van Eerde and Klingsieck, 2018 ), remedies should also be implemented on the level of study programs and the level of courses. Especially for beginning students, unnecessary options present opportunities for students to procrastinate and should be accompanied by remedial measures. For example, Ariely and Wertenbroch (2002) compared student performance under no-choice fixed working schedules determined by the teacher versus choice working schedules (the students could determine their own schedules) and found that performance was better when students had to follow the no-choice fixed working schedules. If possible, a detailed syllabus including a “timetable” of the course, all deadlines, expected learning outcomes, and resources such as literature can help downsize the large degree of freedom of a study situation (cf. Eberly et al., 2001 ). Concerning the study program, an orientation event in the first semester or even each semester might support students in seeing the program’s inherent structure. One should not only focus on the contents of the program but also on the best way to run through the program. An individual twist to the orientation could be a short workshop in which each student is encouraged to plan her or his semester, thereby downsizing the large degree of freedom by establishing a unique structure which, ideally, should take into account all other activities they wish to make time for (e.g., sports, family, job), as well. Teaching styles that support student autonomy ( Codina et al., 2018 ) may also be helpful. Finally, note that a large degree of freedom in the study situation is not alleviated by the introduction of more external control. Indeed, procrastination research demonstrates that external control is associated with increased procrastination (e.g., Janssen and Carton, 1999 ). We argue instead that unnecessary freedom should be reduced, as in the Ariely and Wertenbroch (2002) study discussed.

Long Deadlines

The idea of hyperbolic discounting helps to explain why we procrastinate the start of an activity. For example, according to the Temporal Motivation Theory (TMT; Steel and König, 2006 ; Gröpel and Steel, 2008 ), motivation increases as a function of the expectancy of an outcome and the size or value of a goal, but decreases as the time span before this outcome lengthens and impulsiveness increases. Thus, procrastination is more likely to occur if the outcome of an activity offers rewards in the distant future, and more so if impulsiveness is high (as is the case in procrastinators). Hence, immediate temptations often come to dominate over distant rewarding goals.

Results from qualitative ( Schraw et al., 2007 ) and quantitative studies ( Tice and Baumeister, 1997 ; Schouwenburg and Groenewoud, 2001 ) support the idea that the tendency to procrastinate decreases as the deadline for the task in question is approaching. Students find tentative due dates as especially frustrating ( Schraw et al., 2007 ). In the absence of deadlines, students often set deadlines for themselves. Although such deadlines may work to reduce procrastination, they may actually reduce performance ( Ariely and Wertenbroch, 2002 ). Other research, focusing on planning, has demonstrated that individuals tend to underestimate the necessary time it takes to complete tasks (the planning fallacy; Kahneman and Tversky, 1979 ; Kahneman and Lovallo, 1993 ) and to prefer longer deadlines when allowed to choose ( Solomon and Rothblum, 1984 ). Recently, Zhu et al. (2019) demonstrated that long deadlines induce an inference of the focal task as more difficult, thereby making the student to allocate more time and resources to the task. However, the downside is that such elevated resource estimates may induce longer intention-action gaps (time before starting the task) and higher likelihood of quitting.

While students with a broad range of self-management skills are able to deal with long and tentative deadline by breaking distant goals into nearer sub-goals themselves, students who lack these skills would benefit from structural arrangements defining sub-goals with timely deadlines. For instance, having students hand in an outline for a paper after the first third of the semester, the first draft after the second third, and the final draft at the end of the semester help to break a distant goal down to nearer sub-goals. Ideally, this scaffolding of self-regulating learning and writing might function as a model for future tasks with long deadlines. In general, making goals proximate (e.g., in the form of sub-goals) may help the student increase performance and reduce procrastination (e.g., Steel et al., 2018 ). Also, as reviewed by Gollwitzer and Sheeran (2006) , adapting specific implementation intentions (“if-then”-plans rather than overall goal intentions) may have a strong effect on goal attainment. When students experience difficulties in goal striving, focusing on the main obstacle hindering progress is recommended (mental contrasting; e.g., Duckworth et al., 2011 ).

Task Aversiveness

Procrastination can be understood as a form of short-term mood-regulation ( Sirois and Pychyl, 2013 ). Bad mood and negative feelings associated with a task is often repaired by avoiding the task and engaging in a pleasant task instead. The role of task aversiveness in triggering procrastination has received strong support (for a summary, see Steel, 2007 ). Closer examination of the task aversiveness literature demonstrates that aversive tasks are characterized by lower autonomy, lower task significance, boredom, resentment, frustration, and difficulty ( Milgram et al., 1988 ; Milgram et al., 1995 ; Blunt and Pychyl, 2000 ; Steel, 2007 ). Moreover, Lay (1992) found that procrastinators tend to perceive common tasks in everyday life as more aversive compared to non-procrastinators, suggesting that procrastinators face the world with a negative bias toward task execution in general. As aversive conditions tend to motivate negatively by avoidance or escape, passivity is a likely effect ( Veale, 2008 ). In sum, working under negative motivation is common in procrastinators, and a negative motivational regime is associated with passivity.

As study-related tasks typically are imposed by others (teachers, exams), they represent an important part of the academic environment for students. Such conditions are known to induce aversiveness and thereby procrastination. For example, when applying the Procrastination Assessment Scale-Students ( Solomon and Rothblum, 1984 ), one prominent dimension turns out to be aversiveness of task . Time sampling as well as daily logs also show that the more students dislike a task, the more they procrastinate ( Steel, 2007 ). Results of qualitative interview studies support these findings ( Grunschel et al., 2013 ; Klingsieck et al., 2013 ; Visser et al., 2018 ).

Why students perceive academic tasks as aversive may be traced to the fact that students entering the university often lack adequate study skills to successfully managing mastery tasks 1 . Considering academic writing, for example, The Stanford Study of Writing indicates that, for most writers, the transition from high school to college writing is enormously challenging ( Rogers, 2008 ). Moreover, university students report a variety of problems associated with academic writing (e.g., being aware of not being able to meet expected standards; Achieve Inc., 2005 ). In the last decades, universities have addressed the need for training academic writing by implementing writing centers. However, as discussed in another section (→Low focus on study skills training), instruction covering study skills is rarely provided. Thus, students often perceive academic tasks as aversive due to their lack of perceived competence. This effect may be amplified by low academic self-efficacy commonly seen in new students. Academic self-efficacy is negatively correlated to procrastination ( r = −0.44; van Eerde, 2003 ), indicating that procrastinators perceive academic tasks as even more difficult (and therefore more aversive) compared to others. Indeed, a recent study 2 found that students perceive academic tasks (e.g., present at a seminar) as more aversive compared to non-academic tasks (e.g., clean one’s apartment), but for both categories, aversiveness scores correlated positively with dispositional procrastination scores.

The Self-Determination Theory ( Deci and Ryan, 2002 ) suggests that tasks and conditions which meet a learner’s need for autonomy, competence, and relatedness support the internalization of extrinsic regulations and values, which in turn makes the task less aversive. Learners are more likely to internalize a learning goal if they embrace the meaningfulness or rationale of a task or activity if the underlying task or activity promotes their feeling of competence and if they are able to connect with other learners and experience a feeling of relatedness. Thus, formulating meaningful learning goals that lead to learning activities that fit the students’ competence level will make the task less aversive. Carefully crafted group tasks (→Inefficient group work) can also reduce procrastination. These kinds of tasks should foster the self-determination of learners. If one then embeds the learning activities in realistic learning settings, learners might even get interested in the learning activity. Game-based learning provides an innovative possibility for learning settings ( Breuer and Bente, 2010 ). Finally, as discussed elsewhere (→Low focus on study skills training), programs for students entering the university should not shy away from offering training even in the most basic study skills.

Temptations and Distractions

Individuals are tuned toward attainment of positive outcomes and escape from or avoidance of aversive events. In procrastinators, this picture is exaggerated, with current attractive and aversive events dominating over distant ones. Procrastinators tend to be impulsive and present-biased ( van Eerde, 2003 ; Steel, 2007 ), scoring high on scales measuring susceptibility to temptation, distractibility, and impulsivity ( Steel et al., 2018 ). In fact, the correlation between distractibility and procrastination is very high, r = 0.64–0.72. Thus, procrastinators are especially vulnerable to environments with an abundance of temptations and distractors, as such environments tend to capture attention and divert planned behavior into more pleasurable activities available here and now. When working with aversive tasks (→Task aversiveness), this tendency increases, as the student will be motivated to escape the aversive situation as well as divert to something attractive ( Tice et al., 2001 ).

Academic environments offer a large number of temptations and distraction, Internet access being a prime example (e.g., Reinecke and Hofmann, 2016 ). Mobile phones and laptops may have internet access everywhere on campus, presenting a continuous temptation and distractor, even during lectures. Universities tend to rely on web-based information and registration systems, and there is an increasing emphasis on digital utilities designed to assist learning, all necessitating continuous Internet access. The downside is that this situation presents a continuous challenge to students, especially those low in self-control ( Panek, 2014 ). Internet use has often been shown to conflict with other goals and obligations ( Quan-Haase and Young, 2010 ; Reinecke and Hofmann, 2016 ), and Lepp et al. (2015) demonstrated that total usage of mobile phones among undergraduates is negatively related to academic performance. Procrastination implies that the individual spends less time on focal tasks ( Lay, 1992 ), and time spent on distracting tasks add to the problems procrastinators already experience. Internet multitasking (accessing the Internet while doing something else) is positively correlated with procrastination ( Reinecke et al., 2018a , b ), indicating that procrastinators are especially prone to suffer when Internet access remains unrestricted.

Intervention studies ( Hinsch and Sheldon, 2013 ) have demonstrated that reduction in leisure-related Internet use results in decreased procrastination and increased life satisfaction. Hence, limiting the availability of Internet use is a simple way of reducing these problems. Several companies practice restriction on use of mobile phones/laptops during meetings, and universities may consider similar measures. Universities may arrange wifi-free zones for teaching and studying, and teachers may ask students to turn off their laptops/phones during classes. For many, such advice may seem counterintuitive, as the use of “modern technology” in education is generally welcomed. However, given the detrimental effects associated with unrestricted Internet use seen in the part of the student population struggling with procrastination (i.e., half or more of all students), our advice is clear.

Limited Information for Proper Self-Monitoring

In self-regulated activities, three factors are particularly important for students ( Baumeister and Heatherton, 1996 ): The student must have some standard to aim for (e.g., obtain a good grade in a course), monitor progress toward this standard, and correct as necessary if progress deviates from what is necessary to reach the standard. Although all three factors are important, Baumeister and Heatherton (1996 , p. 56) pointed out that monitoring is crucial: “Over and over, we found that managing attention was the most common and often the most effective form of self-regulation and that attentional problems presaged a great many varieties of self-regulation failure.” As procrastination is considered a prime example of a self-regulation failure ( Steel, 2007 ), it is likely that managing attention when working toward long-term goals is particularly vulnerable in procrastinators.

Due to the large degree of freedom in the study situation, the successful student needs information to keep an updated track of status, given long-term plans. Unfortunately, the study situation typically provides limited information. In many cases, exams (often held at the end of the semester) are the main source of feedback for students. Other kinds of information on progress (e.g., time spent at the university, participation in classes, observation of other students) may be unreliable as indicators of being on track. Furthermore, as consequences of procrastination are positive in the short term but not so in the longer term, learning is biased in favor of immediate positive consequences, and corrective action from long-term negative consequences is less likely.

Measures that reflect goal-striving according to plan should be implemented. From the institutional/teacher perspective, such measures should focus on reading plans, course progress, and submissions, and should not be mixed up with study performance (e.g., grades). For example, as procrastination is a reliable predictor of study effort, high procrastinators spending less time in self-directed work ( Lay, 1992 ; Svartdal et al., 2020 ), actual time spent on self-directed studying may be relevant information for many. Self-testing, recommended as an effective learning strategy (→Low focus on study skills training), also assists self-monitoring. Activity diaries, inspired by behavioral activation for depression interventions (e.g., Jacobson et al., 2001 ), may increase students’ awareness of how they spend their time as students. In recent years, several mobile apps have been developed to help students keep track of how they spend their time in the study situation (e.g., Dute et al., 2016 ), but little is known about the effect such apps may have in reducing procrastination.

Low Focus on Study Skills Training

In a qualitative study, Grunschel et al. (2013) found that students reported a lack of study skills as a notable reason for academic procrastination. One likely explanation is that low skills make tasks more effort demanding, and individuals are more likely to procrastinate on effort-demanding tasks ( Milgram et al., 1988 ). Low academic skills also make academic tasks more frustrating, boring, and difficult, which are also factors reliably associated with task aversiveness ( Blunt and Pychyl, 2000 ). As discussed in another section, task aversiveness is a reliable predictor for procrastination (→Task aversiveness).

A large part of academic work is spent on self-directed learning, and the skills needed to properly maneuver in such an environment is essential for student success ( Kreber et al., 2005 ). Unfortunately, most students have not received instruction on effective and timely study skills (e.g., Dunlosky et al., 2013 ; Dunlosky and Rawson, 2015 ), and universities are slow in implementing effective skills instruction ( Goffe and Kauper, 2014 ; Wieman and Gilbert, 2015 ). Teachers’ knowledge of effective study strategies is also lacking ( Morehead et al., 2016 ; Blasiman et al., 2017 ).

Study skill training programs produce beneficial effects in terms of academic performance and retention ( Hattie et al., 1996 ; Gettinger and Seibert, 2002 ; Robbins et al., 2004 ; Wibrowski et al., 2017 ). Moreover, studies point out that learning how to study effectively cannot be separated from course contents and the process of learning ( Weinstein et al., 2000 ; Durkin and Main, 2002 ; Wingate, 2007 ). That is, study skills training should be tailored for study programs or courses. They should suit the instructional context and teaching practices, expected achievement outcomes, and promote a high degree of learner activity. However, the impact of such skill learning interventions diminishes over time ( Wibrowski et al., 2017 ), suggesting that repetition may be crucial. Thus, dedicating a portion of instruction time or having a study skill seminar at the beginning of each semester or course may be a good strategy. Different interventions may be considered depending on the course tasks ( Schraw et al., 2007 ), students’ abilities and performance level ( Hattie et al., 1996 ). Furthermore, as knowledge of study skills are not automatically translated into good study habits, academic self-efficacy (see next section) is important for circumventing procrastination ( Klassen et al., 2008 ).

Lack of Self-Efficacy-Building Opportunities

Self-efficacy, our belief in our ability to manage a task, influences how willing we are to take on domain-specific challenges. The higher self-efficacy, the more likely we will take on a task ( Bandura and Schunk, 1981 ). Even when ability to perform a task is high, but self-efficacy for that ability is low, the likelihood of prioritizing the task goes down, and procrastination is likely ( Haycock et al., 1998 ; Klassen et al., 2008 ). Importantly, the relation between self-efficacy and procrastination is relatively strong and negative, r = −0.44 ( van Eerde, 2003 ).

Self-efficacy is one of the strongest predictors of academic performance ( Klomegah, 2007 ), yet is often neglected in course instruction. We have long known that students develop their self-efficacy for any academic task by gradually increasing proficiency with it ( Bandura, 1997 ). Furthermore, as self-efficacy tends to be context-specific and will not automatically transfer over different tasks or activities ( Zimmerman and Cleary, 2006 ), a relatively broad set of on efficacy-building experiences, course by course, is necessary (→Lack of study skill training), though not necessarily enough on its own ( Kurtovic et al., 2019 ). Other research has recently indicated that self-efficacy may be indirectly rather than directly related to academic procrastination ( Li et al., 2020 ), and that self-efficacy for self-regulation, for example, may be a strong predictor ( Zhang et al., 2018 ).

To improve self-efficacy, instructors can create more opportunities for mastery experiences by breaking down course assignments into manageable bits that are not too easy but still are possible for students to succeed at ( Bandura, 1997 ), and by helping students self-reflect on their performance such that they feel more self-efficacious in the forethought phase of subsequent work ( Zimmerman, 2000 ). As self-efficacy increases, and the likelihood of engaging in a task goes up ( Ames, 1992 ), anxiety goes down ( Haycock et al., 1998 ), establishing a virtuous circle of self-efficacy instead of a vicious circle of procrastination ( Wäschle et al., 2014 ). This can be done through in-class activities or short assignments where the goal is to scaffold student learning with positive feedback and concrete information for how to improve on increasingly challenging versions of the task ( Tuckman and Schouwenburg, 2004 ).

Inefficient Group Work

Students often work in groups (e.g., discussion groups, seminars), but often lack the basic skills for making group work effective. Group work also increases the probability of social loafing, the tendency for individuals to demonstrate less effort when working collectively than when working individually ( Karau and Williams, 1993 ). Students may therefore often prefer to work alone as an alternative. However, working alone is associated with increased procrastination ( Klingsieck et al., 2013 ). Qualitative evidence suggests that group work with interdependence between group members may reduce academic procrastination ( Klingsieck et al., 2013 ). In support, results from educational psychology have shown positive effects of interdependent group work on individual effort in settings of cooperative learning. These studies also demonstrate beneficial effects of interdependence on social support, self-esteem, and health outcomes of group members ( Johnson and Johnson, 2002 , 2009 ). Taken together, these findings indicate the potential benefit of group work with interdependence, which may be harnessed in educational settings to reduce academic procrastination.

Although the beneficial effects of student group work in higher education seem evident ( Springer et al., 1999 ; Johnson and Johnson, 2002 ), group work is neglected in curricula of many study programs, leading students to work individually on tasks and assignments and thus possibly promoting procrastination. Students in such programs may not always feel inclined to form study groups on their own and create more favorable group work conditions instead. This is especially unfortunate as methods and tools for group learning and studying abound.

Group work with interdependence may be well suited to reduce procrastination among group members. Implementing group work with interdependence should be quite straightforward, for example by having groups work on projects or by adapting individual assignments to become interdependent tasks. The latter can be achieved by designing subtasks that need to be completed sequentially by assembling groups in such a way that each member contributes unique skills, or by formulating group-level goals and rewards ( Weber and Hertel, 2007 ).

Influence of Peers

Prior research has indicated quite complex findings regarding the role of peers in facilitating or inhibiting procrastination (e.g., Nordby et al., 2017 ). Of the different ways in which peers may influence procrastination, three factors seem to be particularly important: social norms, observational learning, and distraction. Harris and Sutton (1983) suggested that an organization’s norms can either encourage or discourage procrastination, depending on whether norms suggest a prompt or delayed processing of tasks. Observational learning can support acquisition, inhibition, and triggering of many types of human behavior ( Bandura, 1985 ), including procrastination. Thus, learning from others may also influence procrastination as well as strategies against it.

With regard to social norms, Ackerman and Gross (2005) found less procrastination among students when perceived norms suggested to start promptly. Social learning of procrastination or strategies against it have not been demonstrated empirically. However, on a more general level, observational learning has been shown to influence students’ self-regulatory skills (e.g., Zimmerman and Schunk, 2004 ). Indirect support for this notion also comes from Klingsieck et al. (2013) and Nordby et al. (2017) , who report that peer behavior is taken into account by procrastinating students. With regard to social distraction, an early study reported peer influence to be a possible, yet not very frequent reason for procrastination ( Solomon and Rothblum, 1984 ). Both qualitative ( Klingsieck et al., 2013 ) and quantitative ( Chen et al., 2016 ) evidence support the idea that distraction by peers can be a source of academic procrastination. A lack of social integration has also been reported an antecedent of academic procrastination ( Patrzek et al., 2012 ), suggesting a balanced judgment on the role of peers and social contacts.

Communication of social norms to start tasks promptly can occur through regular class instruction, thus supporting timely beginning of students with a disposition to procrastinate. Social cognitive theory predicts that social learning is facilitated, among others, by the salience of both model behavior and vicarious reinforcements ( Bandura, 1985 ). Letting students reflect on and share their experiences with procrastination and strategies against it may support more productive observational learning.

This paper discusses nine factors characteristic of student study environments that, singly and in combination, increase the probability of procrastination. Clearly, given the high prevalence of academic procrastination, it is important to have an increased awareness of such risk factors and how they can be handled in order to prevent and reduce procrastination. Although we cannot control what students do, we can control how institutions encourage more productive behaviors for student success. We now briefly discuss how policymakers, universities, teachers, and students should approach these issues.

Do the Factors Point to Common Problem Areas?

Yes. We argue that the nine factors discussed can be loosely grouped into three themes (see Figure 1 ). First, four or five of the factors discussed (i.e., long deadlines, large degree of freedom in the study situation, temptations and distractions, poor self-monitoring information, and low focus on skills training), while being contextual and situational in nature, all relate directly to students’ ability to effectively self-regulate in the study situation. In effect, our overview indicates that the core problem of procrastination, poor self-regulation ( Tice et al., 2001 ; Steel, 2007 ; Hagger et al., 2010 ), is amplified by common aspects of the student environment. An important implication of this insight is that training in self-regulation techniques among students (which we recommend) should not only be tailored to the specific needs of the students (cf. Valenzuela et al., 2020 ) but should also be supplemented with specific contextual and organizational measures that can support productive self-regulation. Since it is well known that self-regulation in the academic setting is important for performance (e.g., Duckworth and Seligman, 2005 ), it is paradoxical that academic institutions organize academic student life in ways counter to this insight.

How procrastination-friendly factors relate to important themes in education.

Note that the problems in self-regulation seen in procrastination episodes may relate to skills factors (e.g., planning, monitoring), speaking for relevant skills training to strengthen self-regulation. However, often factors that undermine effective self-regulation are of primary importance in procrastination (e.g., Tice et al., 2001 ). For example, low energy and tiredness may render the individual more vulnerable to task-irrelevant temptations and distractions and increase task aversiveness, which in turn increases the probability of procrastination ( Tice et al., 2001 ; Baumeister and Tierney, 2011 ). Insufficient sleep, common in the student population (e.g., Lund et al., 2010 ), is an important source of low energy and tiredness. Importantly, Knapstad et al. (2018) found that the most frequently reported health problem (as measured by the Somatic Symptoms Scale, SSS-8; Gierk et al., 2014 ) among a large sample of Norwegian students was a “Feeling of tiredness and low energy,” 45% of the students indicating that they were “fairly much or “very much” affected. This suggests that factors that undermine self-regulation among students should receive increased attention.

Second, the academic context can be designed to redress the skills and motivational issues that are often associated with procrastination. Low focus on study skills training and relative lack of efficacy-building opportunities represent a problematic combination that may themselves contribute to students perceiving academic tasks as aversive, thereby increasing the probability of procrastination. All these combined represent a disadvantageous motivational regime for academic work. The present overview identified specific organizational measures that institutions can take to change this situation. As discussed, increased focus on study skills training in concert with regular teaching may be a solution, as repeated mastery experiences will build self-efficacy as well as reduce task aversion.

Third, we should address the social factors that distract students from their academic work. By acknowledging that procrastination is a trap for students working alone, more opportunities can be made to encourage more collaborative work with others. It is important to carefully design group work in that it resembles interdependent group work. Furthermore, group work with student peers can be deliberately designed to increase student accountability, facilitating more need for self-regulation and offering students the opportunity to observe others with more productive self-regulation skills.

Given the Large Number of Factors Discussed, Are Some Particularly Important?

We have not attempted to identify effect sizes to each of the variables discussed, and for many such estimates do not exist. Comparing the factors is, therefore, extremely difficult. Further, as several of the factors discussed have been linked to procrastination in correlational research, causality must be inferred with caution. Nevertheless, all the factors discussed have potentially large causal power to instigate and sustain procrastination. Overall, the factors examined focus on larger problem areas (i.e., self-regulation, skills and motivation, social factors), but each factor identifies concrete measures to be considered to implement changes.

In approaching such factors, all should ask: What can be changed on my part? Several of the factors (e.g., large degree of freedom in the study situation, long deadlines, temptations and distractions) address organizational and educational issues that should be addressed by organizations and teachers. Others (e.g., task aversiveness) imply more complex instructor-student interactions. For example, negative emotions in task aversiveness should be approached by teachers and students in cooperation by reducing task-associated risks and imbuing the tasks with personal relevance ( van Grinsven and Tillema, 2006 ; Rowe et al., 2015 ), by enabling and encouraging student ownership of learning tasks ( Rowe et al., 2015 ), and by facilitating frequent successful learning experiences that increase self-efficacy.

Does It Make Sense to Implement Changes in One or Few Factors, Leaving Out Others?

Given an abundance of factors discussed, each capable of instigating procrastination, the high occurrence of procrastination in the student population is not at all surprising. Would it help, then, to change one or perhaps a few factors? One possible answer is that focusing on one factor is better than doing nothing. However, the downside of such an approach is that this single factor may not generate noticeable changes alone. Our recommendation would rather be to evaluate several or all factors and then implement changes as suitable within a single course, across courses, or in study programs. Note here that several of the factors discussed are relatively closely interwoven. For example, a large degree of freedom in the study situation often also implies long deadlines, suggesting that two factors may be addressed at once.

In such evaluations, it should be noted that each of the factors discussed is presented at a rather abstract level, so that relevance and concrete implementations in various settings must be carefully considered. For example, study topics vary by their very nature in how much freedom they represent for the student. Some study programs are already strictly structured and typically involve a common study group from start to finish, indicating that such programs do not need an increased focus on structure. Other programs are less structured and may also, by the nature of their study contents, be more “procrastination friendly” (e.g., Nordby et al., 2017 ). In other cases, such as study skills training and efficacy-building opportunities, “the more, the better” seems appropriate when closely linked to actual course learning tasks.

In evaluating the need for implementation of changes, the relevant factor should be assessed not only at the institutional level but—probably more importantly—at the program and course level. This applies not only to a need-based evaluation (“What do students need in order to reduce their procrastination?”), but also to a competence evaluation (“Can we provide the necessary work required for this implementation?”). Note also that some measures may be quite easy to plan on paper, but difficult to implement in a more complex system of rules and bureaucracy. For example, although long deadlines should be warned against (they induce procrastination), finding alternative solutions that can handle shorter deadline in a proper way may require changes (e.g., legal or practical) that are not easily possible to implement.

Where to Start?

In developing prevention or interventions programs concerning procrastination, one has to keep the interplay between personal factors (i.e., student characteristics) and contextual factors (i.e., institutions, courses, and teachers) in mind. As can be seen from Table 2 , the recommendations on the institutional, course, and teacher side will only fully unfold their effectiveness if students are simultaneously prepared to work on their self-regulatory skills. Thus, the recommendations we present in this paper should be accompanied by a culture of goal-focused self-regulation training programs. And, as discussed, self-regulation training programs, whether preventive or interventional, should not be administered without paying attention to contextual procrastination-friendly factors.

Recommended measures to reduce procrastination.

Given the high prevalence estimates of procrastination among students, a closer look at procrastination-friendly factors in the academic environment is clearly warranted. The present paper identifies nine such factors and provides suggestions on how they may be changed in order to understand, prevent, and reduce academic procrastination. Clearly, more research is needed in this area, both with regard to the factors themselves (how many are they?) as well as to their interplay and relative importance. Given the potential beneficial effects for students, institutions, and society, we conclude that researchers should pay increased attention to social, cultural, organizational, and contextual factors in their endeavors to understand academic procrastination.

Author Contributions

FS initiated the project, wrote the introduction and discussion parts. All authors contributed at least one section each to the review and edited the complete draft.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We thank Piers Steel and Efim Nemtcan for valuable comments on an earlier draft of this manuscript. Publication charges were covered by the publication fund of UiT The Arctic University of Norway.

1 We use «study skills» in a broad sense, referring to skills needed on the part of the student to successfully master various aspects of study tasks (cf. Tressel et al., 2019 ).

2 Svartdal et al. (2020) . Unpublished data.

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Procrastination Essay Examples

Essays about procrastination aim to delve into the phenomenon of delaying tasks, its underlying causes, and its impact on personal and professional life. The purpose of such essays is to raise awareness about the negative consequences of procrastination, provide insights into effective strategies to overcome it, and inspire readers to cultivate better time management and productivity habits. These essays offer valuable guidance to individuals struggling with procrastination and help foster a proactive and goal-oriented mindset. Self-Awareness and Reflection One of the primary goals of essays about procrastination is to encourage self-awareness and reflection. These essays prompt readers to identify the reasons behind their tendency to procrastinate, such as fear of failure, lack of motivation, or perfectionism. Essays on this topic emphasize the negative consequences of procrastination, such as missed opportunities, increased stress, compromised quality of work, and a sense of unfulfillment. By highlighting these drawbacks, the essays motivate readers to take action and change their procrastination habits. Procrastination informative essay offer practical strategies and techniques to overcome this habit. These strategies may include setting specific goals, breaking tasks into smaller steps, using time management tools, and practicing self-discipline. These topic guide readers toward adopting healthier and more productive habits. Tips for Writing Essays About Procrastination:

Thesis Statement: Start with a clear thesis statement that introduces the topic and the main points you will address in the essay. Personal Anecdotes: Share personal stories or experiences related to procrastination to create a relatable connection with readers. Causes and Effects: Discuss the common causes of procrastination and the negative effects it can have on various aspects of life. Research and Evidence: Include psychological research and expert opinions to support your arguments and provide credibility. Strategies for Improvement: Offer a range of practical strategies and techniques to help readers overcome procrastination. Real-Life Examples: Provide real-life examples of individuals who have successfully conquered procrastination and achieved their goals. Encourage Action: Conclude the essay by encouraging readers to take steps to overcome procrastination and improve their time management skills.

Essays about procrastination serve as a valuable resource for individuals seeking to overcome the challenges of delaying tasks. By shedding light on the reasons behind procrastination and providing practical solutions, these essays empower readers to take control of their habits, enhance their productivity, and lead more fulfilling lives.

The Causes of Procrastination Among University Students and the Solution to It

Procrastination is the act of delaying something that has to be done within a certain time. According to Ferrari and Díaz-Morales (2014), there are 20% - 25% of adults from all over the world affected by the issue of procrastination, it is a common problem...

Phenomenological Look at Workplace Procrastination Through the Eyes of an Employee

Phenomenological look at workplace procrastination through the eyes of an employee. Aim: The major purpose of this study is to explore procrastination at the workplace from a phenomenological perspective. The present study aims to look through the eyes of a procrastinator and understand one’s individual...

The Main Causes and Consequences of Procrastination

Procrastination in time management is no stranger to the modern society. Statistics estimated that 40 percent to over 50 percent of students were procrastinating. 'Procrastination arises from the Latin 'pro,' indicating 'ahead, forward, either for,' as well as 'crastinus,' meaning 'future''. On that basis, procrastination...

The Concept of Procrastination in Psychology

Psychology is a study of human behavior and cognitive operations. By studying particular human behavior or mental performances it could be used to treat mental issues, understand events, and even improve the way of life. Procrastination is common phenomenon when individual postpone their work to...

Procrastination, Its Causes and Negative Effects

Have you ever put off an important project or task until the very last minute? If this sounds like something your familiar with then you may be suffering from chronic procrastination. According to Dr. John Riddle, an author of several books whose byline has appeared...

The Hidden Benefits of Procrastination

Imagine this, it is a first week of February your programming professor asked you to design a website which was to be due on 3rd week of the same month. You just acknowledged it and play your favorite online computer game. Afterwards, in the second...

The First Steps in College Life

Starting college is a new and exciting time. Most Students look back on college life with fond and happy memories. It is a time to learn new skills, meet new people and work towards your dream career. However, the college life does come with a...

The Link Between Procrastination and Personal Well-being

Procrastination is repeatedly seen as self-regulatory failure, affecting many people in their everyday lives. From putting off tasks on the basis that they will later complete them. It has been suggested from research that procrastination may negatively impact professional advancement and our general well-being, such...

The Reasons Why Procrastination Occurs and How to Fight It

Each of us happened to postpone important things for later, dragging out their implementation as much as possible, doing anything instead of them. Unable to explain to ourselves why we are doing this, after that we are tormented by guilty feelings because of the deadlines,...

Positive and Negative Sides of Procrastination

Procrastination is the avoidance of doing a task that needs to be accomplished by a certain time; an intentional delay of starting or finishing the assignment despite knowing it might have negative consequences in the future. Most students have procrastinated at some point, doing more...

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About Procrastination

Procrastination is the action of delaying or postponing something.

The origins of procrastinate come from the Latin prefix pro-, meaning "forward, " and crastinus, "of tomorrow." The word means moving or acting slowly so as to fall behind, and it implies blameworthy delay, especially through laziness or apathy.

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