procrastination research paper

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What Research Has Been Conducted on Procrastination? Evidence From a Systematical Bibliometric Analysis

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The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding author/s.

Procrastination is generally perceived as a common behavioral tendency, and there are a growing number of literatures to discuss this complex phenomenon. To elucidate the overall perspective and keep abreast of emerging trends in procrastination research, this article presents a bibliometric analysis that investigates the panorama of overviews and intellectual structures of related research on procrastination. Using the Web of Science Database, we collected 1,635 articles published between 1990 and 2020 with a topic search on “procrastination” and created diverse research maps using CiteSpace and VOS viewer. Bibliometric analysis in our research consists of category distribution, keyword co-occurrence networks, main cluster analysis, betweenness centrality analysis, burst detection analysis, and structure variation analysis. We find that most research has focused on students' samples and has discussed the definition, classification, antecedents, consequences and interventions to procrastination, whereas procrastination in diverse contexts and groups remains to be investigated. Regarding the antecedents and consequences, research has mainly been about the relationship between procrastination and personality differences, such as the five-factor model, temperament, character, emotional intelligence, and impulsivity, but functions of external factors such as task characteristics and environmental conditions to procrastination have drawn scant attention. To identify the nature and characteristics of this behavior, randomized controlled trials are usually adopted in designing empirical research. However, the predominant use of self-reported data collection and for a certain point in time rather than longitudinal designs has limited the validation of some conclusions. Notably, there have been novel findings through burst detection analysis and structure variation analysis. Certain research themes have gained extraordinary attention in a short time period, have evolved progressively during the time span from 1990 to 2020, and involve the antecedents of procrastination in a temporal context, theoretical perspectives, research methods, and typical images of procrastinators. And emerging research themes that have been investigated include bedtime procrastination, failure of social media self-control, and clinical interventions. To our knowledge, this is almost the first time to conduct systematically bibliometric analysis on the topic of procrastination and findings can provide an in-depth view of the patterns and trends in procrastination research.

Introduction

Procrastination is commonly conceptualized as an irrational tendency to delay required tasks or assignments despite the negative effects of this postponement on the individuals and organizations (Lay, 1986 ; Steel, 2007 ; Klingsieck, 2013 ). Poets have even written figuratively about procrastination, with such phrases as “ Procrastination is the Thief of Time ,” and “ Procrastination is the Art of Keeping Up with Yesterday ” (Ferrari et al., 1995 ). Literal meanings are retained today in terms of time management. The conceptualizations of procrastination imply inaction, or postponing, delaying, or putting off a decision, in keeping with the Latin origins of the term “pro-,” meaning “forward, forth, or in favor of,” and “-crastinus,” meaning “tomorrow” (Klein, 1971 ). Time delay is just the behavioral reflection, while personality traits, cognitive and motivational process, as well as contextual conditions are in-depth inducements to procrastination. Procrastination can be viewed as purposive and irrational delay so as to miss the deadlines (Akerlof, 1991 ; Schraw et al., 2007 ).

Procrastination is believed to be a self-regulation failure that is associated with a variety of personal and situational determinants (Hen and Goroshit, 2018 ). Specifically, research suggests that task characteristics (e.g., unclear instructions, the timing of rewards and punishment, as well as task aversiveness), personality facets (e.g., the five-factor model, motivation, and cognition), and environmental factors (e.g., temptation, incentives, and accountability) are the main determinants of procrastination (Harris and Sutton, 1983 ; Johnson and Bloom, 1995 ; Green et al., 2000 ; Wypych et al., 2018 ). Procrastination can be an impediment to success, and may influence the individual's mood, and increase the person's anxiety, depression, and low self-esteem (Ferrari, 1991 ; Duru and Balkis, 2017 ). Furthermore, a person with procrastination is prone to poor performance, with lower exam scores, slower job promotions, and poorer health (Sirois, 2004 ; Legood et al., 2018 ; Bolden and Fillauer, 2020 ). Importantly, if policymakers postpone conducting their decision-making until after the proper timing, that procrastination can cause a significant and negative impact on the whole society, such as the cases with the COVID-19 pandemic management in some countries (Miraj, 2020 ).

In practice, procrastination is stable and complex across situations, ranging from students' academic procrastination, to staffs' work procrastination, to individuals' bedtime procrastination, to administrative behavior procrastination when government organizations face multiple tasks in national governance, and even to delayed leadership decision-making in crisis situations in global governance (Nevill, 2009 ; Hubner, 2012 ; Broadbent and Poon, 2015 ; Legood et al., 2018 ). As for science research, procrastination has attracted more and more attention and been studied extensively. Personally, possible explanations for emerging research focuses mainly consist of two aspects. On one hand, procrastination with high prevalence and obvious consequences highlights the importance to explore the complex phenomenon deeply, especially the meteoric rise in availability of information and communications technologies (ICTs) amplifies chronic procrastination, such as problematic social media use, smartphone addictions as well as mobile checking habit intrusion (Ferrari et al., 2007 ; Przepiorka et al., 2021 ; Aalbers et al., 2022 ). On the other hand, more and more basic and milestone research emerges in large numbers, which set the foundation for latecomer' further exploration toward procrastination. In particular, it can't be ignored the efforts of those productive authors in different periods to drive the knowledge development of procrastination.

Procrastination research has experienced tremendous expansion and diversification, but systematic and overview discussion is lacking. Several meta-analyses about procrastination have emerged, but they emphasize more on specific topics (Steel, 2007 ; Sirois et al., 2017 ; Malouff and Schutte, 2019 ). Furthermore, the number of newly published articles is increasing, so it becomes difficult to fully track the relevant domain literature. In order to grasp knowledge development about the fast-moving and complex research field, bibliometric analysis is necessary to construct diagram-based science mapping, so as to provide a comprehensive and intuitive reference for subsequent researchers. Thus, this article emphasizes on the following major research question: what is the intellectual base and structure of procrastination research? How does the emerging direction of procrastination develop? In our research, bibliometric analysis included the annual distribution of literature, distribution of categories, keyword co-occurrence networks, main research clusters, high citation betweenness centrality, and the strongest citation bursts, as well as the recent publications with transformative potential, in order to look back on the early development of procrastination research and look forward to the future transformation of that research. For both scholars and members of the public, this study can comprehensively enhance their understanding of procrastination and can provide overall perspectives for future research.

Data and Methodology

Bibliometric analysis is a quantitative method to investigate intellectual structures of topical field. On the basis of co-citation assumption that if two articles are usually cited together, then there are high associations between those articles, bibliometric analysis can reflect the scientific communicational structures holistically (Garfield, 1979 ; Chen et al., 2012 ). Bibliometric techniques, such as CiteSpace, VOSviewer, HistCite, can generate the science maps based on plenty of literature concerning certain domain. Through the process of charting, mining, analyzing, sorting, and displaying knowledge, science mapping can extract pivotal information from huge complex literature, present knowledge base and intellectual structure of a given field visually, then researchers even general individual can quickly grasp one subject's core structure, development process, frontier field and the whole knowledge framework (Chen, 2017 ; Widziewicz-Rzonca and Tytla, 2020 ). Bibliometric analysis is commonly regarded as a complementary method to traditional structured literature reviews such as narrative analysis and meta-analysis (Fang et al., 2018 ; Jiang et al., 2019 ). Traditional literature analysis tends to labor intensive with subjective preferences, and faces difficulties in analyzing larger body of literature, whereas bibliometric analysis provides a more objective approach for investigating considerable literature's intellectual structure through statistical analysis and interactive visual exploration.

In order to master the characteristics of procrastination research, the study adopted the bibliometric software of CiteSpace and VOSviewer to analyze the literature on procrastination during the time period 1990–2020. The software tool VOSviewer is designed for creating maps of authors, journals, and keyword co-occurrences based on network data (van Eck and Waltman, 2010 ), whereas CiteSpace is applied to conduct co-citation analysis, including centrality betweenness analysis, burst detection, and the emerging trends of research (Chen, 2006 , 2017 ). In our study, we adopted the CiteSpace (5.7.R1) and VOSviewer (1.6.15) software together. Specifically, co-citation analysis mainly depends on CiteSpace software, and co-occurrence analysis is conducted through VOS viewer (Markscheffel and Schroeter, 2021 ).

Though there is one similar bibliometrics analysis toward this topic (Tao et al., 2021 ), related research just focuses on academic procrastination, and mainly conducts co-occurrence analysis using VOSviewer, so as to there is a lack of analysis to core co-citation structures including high betweenness centrality articles, citation burst research and structure variation analysis. To offer insight into the intellectual structure of procrastination research, we further employ CiteSpace — a java application including bibliometric analysis, data mining algorithms and visualization methods developed by Chen — to visualize and elucidate vital trends and pivotal points about knowledge development.

To conduct our bibliometric analysis of procrastination research, we collected bibliographic records from the Web of Science Core Collection as of December 31, 2020. Web of Science is currently the most relevant scientific platform regarding systematic review needs, allowing for a “Topic” query, including searching a topic in the documents' “title”, “abstract”, “author keywords” and “keywords plus” of the documents being reviewed (Yi et al., 2020 ). A topic search strategy is broad enough to be used in science mapping (Olmeda-Gomez et al., 2019 ). Given the aim of the study, records were downloaded if they had the term “procrastination” in the “Topic” field. After restricting the type of publication to “Article” for the years 1900–2020, we had searched 2105 papers about procrastination research.

Figure 1 shows the yearly distribution of 2105 literature during 1900–2020, and it can be classified into three phases. In phase I (1900–1989), the annual number of publications never exceeded 10. In phase II (1990–2010), the annual quantity gradually increased from 11 papers in 1991 to 48 in 2010. The annual number of publications had begun to grow in this period, but remained below 50 papers yearly. In phase III (2011–2020), however, the procrastination research experienced a dramatic growth, with 255 literature in the year 2020. Although procrastination research appeared as early as 1900s, it had a stable total volume until the 1990s, when it developed sustained growth, and that growth became extraordinary during the 2010s. Therefore, this research emphasized centered on 1,635 literature that were published during the time span 1990–2020.

Distribution of publications on the topic of procrastination, 1900-2020.

Panoramic Overview of Procrastination Research

Category distribution.

Procrastination research has been attracting increasing attention from scholars, and it has been successfully integrated into various scientific fields. With the help of CiteSpace software, we present in Figure 2 the timelines of the various disciplines that are involved in procrastination research, and the cumulative numbers of literature that have been published.

Distribution of categories involved in procrastination research.

As Figure 2 shows, the size of node on the horizontal lines represents the quantity of literature published. Node colors denote the range of years of occurrence, and purple outlining is an indication of those articles with prominent betweenness centrality, and red nodes present references with high citation burst (Chen, 2017 ). Besides, the uppermost line shows the timeline of different disciplines, and the numbers on the longitudinal lines describe the distinct categories of procrastination research, of which are arranged vertically in the descending order of cluster's size. Clusters are numbered from 0, i.e Cluster #0 is the largest cluster and Cluster #1 is the second largest one. Specifically, the earlier research about procrastination occurs in the Psychology and Social Science disciplines. Subsequently, research has expanded into Computer Science and Information Systems, Economics, the Neurosciences, the Environmental Sciences, Ethics, Surgery, and general Medicine. As the connections arc in the Figure 2 presents, those categories #0 Psychology and Social Sciences, #1 Computer Science, and #2 Economics interact actively, but the interdisciplinary research about the remaining categories, such as #9 Medicine, #5 Ethics, and #4 Environmental Science, is not active.

Our analysis of the category distribution reveals two aspects of the characteristics about procrastination research. One, related research mostly has its roots in the Psychology and Social Science disciplines, and interdisciplinary research needs to be improved. And Two, the foundational literature dates back to the 1990s, and transformational exploration is currently needed in order to further develop the research on procrastination.

Keyword Co-occurrence Network: Core Contents

Analysis of co-occurring keywords is often used to obtain the content of research fields. Using the VOS viewer, we obtained a total of 5,203 keywords and created a co-occurrence network. As mentioned above, the size of a node represents the number of times that a specific keyword occurs. Several keywords turn up frequently, such as Procrastination, Performance, Academic Procrastination, Motivation, Personality, Self-regulation, Self-control, and Behavior. To create a readable map, the “minimum number of occurrences” is set to 20, and the final network includes 90 high-frequency keywords and five clusters with 2,650 links, as is shown in Figure 3 .

Keywords co-occurrence network for procrastination research.

Among the five clusters depicted in Figure 3 , the blue cluster is mainly related to the definition of procrastination, with keywords such as Procrastination, Delay, Deadlines, Choice, Self-Control, and Implementation Intentions. Procrastination is a complex phenomenon, and previous research has elaborated on the core traits about procrastination from various dimensions. Mainstream views hold that procrastination can be defined as the intentional delay of work because of a self-regulation failure, time-management inefficiency, short-term benefits, a gap between intention and action (Tice and Baumeister, 1997 ; Steel, 2007 ; Pychyl and Flett, 2012 ; Klingsieck, 2013 ), or missing a deadline and causing negative outcomes (Johnson and Bloom, 1995 ; Howell and Watson, 2007 ; Sirois, 2021 ).

The cluster in red in Figure 3 involves procrastination performance in relation to different life-domains, including Academic Achievement, Life Satisfaction, Online Learning, and Technology Uses. Previous research has elaborated on procrastination as being negatively correlated with performance. However, intrinsic motivation, self-regulated learning, and time-management have been shown to relieve the procrastination behavior (Wolters, 2003 ; Howell and Watson, 2007 ; Baker et al., 2019 ).

The green cluster highlights traits associated with procrastination. Related research in that cluster mostly discusses the correlation between the five-factor model (neuroticism, extraversion, openness to experience, agreeableness, conscientiousness) and procrastination (Schouwenburg and Lay, 1995 ). In addition, personality traits including indecisiveness, indecision, and perfectionism have been elaborated upon (Klingsieck, 2013 ; Tibbett and Ferrari, 2019 ). Furthermore, to measure the trait of procrastination itself, various scales have been developed, such as the General Procrastination Scale, Decisional Procrastination Questionnaire, Procrastination at Work Scale, Irrational Procrastination Scale, Adult Inventory of Procrastination Scale and so on (Lay, 1986 ; Ferrari et al., 1995 ; Steel, 2010 ; Metin et al., 2016 ). The validity and reliability of those scales have also been investigated fully.

The cluster presented in yellow depicts studies that focuses on academic procrastination, and especially those that discuss the antecedents of the prevalent behavior, such as Anxiety, Perfectionism, Self-efficacy, Depression, and Stress (Schraw et al., 2007 ; Goroshit, 2018 ). Owing to their accessibility for use as a research sample, a large body of procrastination research has chosen students in an academic setting as the research objects. Researchers have found that academic procrastination is an impediment to academic performance, especially for very young students. Notably, too, female students may perform lower levels of academic procrastination than males do.

The last cluster, presented in purple, relates to chronic procrastination's involvement in health and addiction, for either adults or adolescents. Discussion about chronic procrastination is growing, and interventions can be effective in relieving this behavior.

From the analysis of co-occurrence keywords, we can infer that procrastination research has been developing steadily. The fundamental discussion has become more adequate and persuasive in regard to the definition, the individual differences, and the antecedents of procrastination, and a discussion of how to relieve the behavior has begun.

Main Research Cluster: Core Theme and Hot Topics

Comparing to keyword co-occurrence network analyses, cluster analysis can help us grasp the primary themes in procrastination research. Clusters are based on the assumption that if two references are often cited together, they may be associated in some way (Chen et al., 2012 ; Pan et al., 2019 ). Eventually, related references shape diverse co-citation networks. Clustering is a procedure to classify co-cited references into groups, with references in the same clusters being tightly connected with each other but loosely associated with other clusters (Chen et al., 2010 ).

Based on the references of the top 50 articles with the most citations every year (if the number was less than 50 in a certain year, then all of the articles were combined), the final network contained 982 references and we were able to develop the final cluster landscape. Two procedures are used to label each cluster: (1) retrieval of keywords from the citing articles using the log likelihood ratio, and (2) retrieval of terms contained in the cited articles with latent semantic indexing (Olmeda-Gomez et al., 2019 ). In our research, we adopted the log-likelihood ratio (LLR) method to label the clusters automatically. Given the related structural and time-based values, articles in the co-citation network are assigned to each cluster. Eventually, the network was divided into 23 co-citation clusters.

In addition, two critical parameters, silhouette and modularity, are used to measure whether clusters are available and whether they are well-constructed. Silhouette indicates the homogeneity of clusters, whereas modularity measures whether the network is reasonably divided into independent clusters. The silhouette value ranges from −1 to 1, and the modularity score ranges from 0 to 1. When values of the two metrics are high, the co-citation network is well-constructed (Chen et al., 2010 ; Widziewicz-Rzonca and Tytla, 2020 ). As is shown in Figure 4 , the mean silhouette score of 0.9223 suggested that the homogeneity of these clusters was acceptable, and the modularity score of 0.7822 indicated that the network was reasonably divided.

Landscape view of co-citation network of procrastination research.

In our research, we summed the largest nine clusters. As is shown in Table 1 , the silhouette value for all clusters was higher than 0.8, suggesting the references in each cluster were highly homogeneous. The labels of these clusters were controlled trial, avoidant procrastination, conscientiousness procrastination, smoking cessation, explaining lack, academic achievement, procrastinatory media use, career indecision, and goal orientation.

Summary of the nine largest clusters in procrastination research.

In Table 1 , the year in the far-right column indicated the average year when the reference was cited. Ranking the clusters by the mean cited year, we can follow the development of research themes. During the 1990s, research themes focused on discussions about the antecedents of procrastination. For example, Lay ( 1988 ) discussed that the self-regulation model cannot explain procrastination fully, and errors in estimations of the time taken to complete a task may be attributed to procrastination. Procrastinators were thought to tend to lack conscientiousness and goal orientation as well as to be motivated by neurotic avoidance (Ferrari et al., 1995 ; Elliot and Harackiewicz, 1996 ). Besides, procrastination was prevalent throughout our lifespan, and empirical research on procrastination conducted through controlled trials had considered various settings or scenarios, such as academic procrastination, smoking cessation, career indecision, and in the most recent years, media use (Klassen et al., 2008 ; Germeijs and Verschueren, 2011 ; Du et al., 2019 ). Because procrastination was negatively associated with performance, life satisfaction, health and well-being, research on procrastination avoidance and intervention, including strengths-based training and cognitive behavioral therapy had attracted the most attention from scholars (van Eerde, 2003 ; Balkis and Duru, 2016 ; Visser et al., 2017 ).

Intellectual Structure of Procrastination Research

Co-citation analysis and clustering analysis form the cornerstone for bibliometric investigation (Olmeda-Gomez et al., 2019 ), especially for the microscopic intellectual structures of the science, such as betweenness centrality, burst detection, and structural variation analysis (Pan et al., 2019 ). Based on the cited references network during the period of 1990–2020, we generated a landscape visualization of intellectual structures about procrastination research. The section consists of three parts: (1) Betweenness Centrality Analysis captures the bridge nodes, which represents the landmark and pivotal literature of a scientific field (Freeman, 1978 ). (2) Burst Detection Analysis is used to detect the emergent and sharp increases of interest in a research field (Kleinberg, 2003 ), which is a useful method for easily tracing the development of research focus and research fronts. (3) Structural Variation Analysis (SVA) is an optional measurement to identify whether newly published articles have the potential to transform the citation network in the latest years. Newly published articles initially have fewer citations and may be overlooked. To overcome the limitation, structural variation analysis often employs zero-inflated negative binomial (ZINB) and negative binomial (NB) models to detect these transformative and potential literature (Chen, 2013 ).

Betweenness Centrality Analysis

Literature with high betweenness centrality tends to represent groundbreaking and landmark research. On the basis of our co-citation network on procrastination research for the period 1990–2020, we chose the top 10 articles to explore (see Supplementary Material for details). Related research mainly focuses on three areas.

Definition and Classification of Procrastination

Procrastination is described as the postponement of completion of a task or the failure to meet deadlines, even though the individual would meet adverse outcomes and feel uncomfortable as a result (Johnson and Bloom, 1995 ). Extracting from authoritative procrastination scales, Diaz-Morales et al. ( 2006 ) proposed a four-factor model of procrastination: dilatory behaviors, indecision, lack of punctuality, and lack of planning. Procrastination is commonly considered to be a pattern of self-regulation failure or self-defeating behavior (Tice and Baumeister, 1997 ; Sirois and Pychyl, 2013 ).

The most popular classification is the trinity of procrastination: decisional, arousal, and avoidant procrastination (Ferrari, 1992 ). Using the General Behavioral Procrastination Scale and Adult Inventory of Procrastination Scale, Ferrari et al. ( 2007 ) measured the difference between arousal and avoidant procrastination, and they elaborated that those two patterns of procrastination showed similarity and commonality across cultural values and norms. However, by conducting a meta-analytic review and factor analyses, Steel ( 2010 ) found that evidence for supporting the tripartite model of procrastination may not be sufficient. Research has reached a consensus about the basic definition of procrastination, but how to classify procrastination needs further discussion.

Procrastination Behavior in a Temporal Context

Procrastination is related to time management in its influence on one's behavior. Non-procrastinators or active procrastinators have better time control and purposive use of time (Corkin et al., 2011 ). However, time management is an obstacle to procrastinators. From the temporal disjunction between present and future selves, Sirois and Pychyl ( 2013 ) pointed out that procrastinators tended to give priority to short-term mood repair in the present, even though their future self would pay for the inaction. Similarly, in a longitudinal study Tice and Baumeister ( 1997 ) pointed out that maladjustment about benefits-costs in participants' timeframe shaped their procrastination. When a deadline is far off, procrastination can bring short-term benefits, such as less stress suffering and better health, whereas early benefits are often outweighed by possible long-term costs, including poor performance, low self-esteem, and anxiety. These viewpoints confirm that procrastination is a form of self-regulation failure, and that it involves the regulation of mood and emotion, as well as benefit-cost tradeoffs.

Causes of and Interventions for Procrastination

Procrastination shows significant stability among persons across time and situations. Predictors of procrastination include personality traits, task characteristics, external environments, and demographics (Steel, 2007 ). However, typically, empirical research has mostly focused on the relationship between the five-factor model and procrastination behavior. Johnson and Bloom ( 1995 ) systematically discussed five factors of personality to variance in academic procrastination. Research also had found that facets of conscientiousness and neuroticism were factors that explained most procrastination. In alignment with these findings above, Schouwenburg and Lay ( 1995 ) elaborated that procrastination was largely related to a lack of conscientiousness, which was associated with six facets: competence, order, dutifulness, achievement-striving, self-discipline, and deliberation. Meanwhile, impulsiveness (a facet of neuroticism) has some association with procrastination, owing to genetic influences (Gustavson et al., 2014 ). These discussions have established a basis for research about personality traits and procrastination (Flett et al., 2012 ; Kim et al., 2017 ).

To relieve procrastination, time management (TM) strategies and clinical methods are applied in practice. Glick and Orsillo ( 2015 ) compared the effectiveness of those interventions and found that acceptance-based behavior therapies (ABBTs) were more effective for chronic procrastinators. Regarding academic procrastination, Balkis ( 2013 ) discussed the role of rational beliefs in mediating procrastination, life satisfaction, and performance. However, there is no “Gold Standard” intervention for procrastination. How to manage this complex behavior needs further investigation.

Burst Detection Analysis

A citation burst indicates that one reference has gained extraordinary attention from the scientific community in a short period of time, and thus it can help us to detect and identify emergent research in a specialty (Kleinberg, 2003 ). A citation burst contains two dimensions: the burst strength and the burst status duration. Articles with high strength values can be considered to be especially relevant to the research theme (Widziewicz-Rzonca and Tytla, 2020 ). Burst status duration is labeled by the red segment lines in Figure 5 , which presents active citations' beginning year and ending year during the period 1990-2020. As can be seen in Figure 5 , we ranked the top 20 references (see Supplementary Material for details) with the strongest citation bursts, from the oldest to the most recent.

Top 20 references with the strongest citation bursts.

To systematically investigate the active areas of procrastination research in different time periods, we divided the study's overall timespan into three time periods. During the period 1990 through 1999, there were six references with high citation bursts, with two of them by Ferrari and a third by Ferrari, Johnson, and McCown. Subsequently, in 2000 through 2009, there were eight reference bursts, and the meta-analysis and theoretical review by Steel ( 2007 ) had the highest citation burst among those 20 references. From the period 2010 through 2020, six references showed high citation bursts.

Period I (1990–1999): Preliminary Understanding of Procrastination's Antecedents

How one defines procrastination is important to interventions. During the early period of procrastination research, scholars paid significant attention to define procrastination and discuss its antecedents. Time delay in completing tasks constitutes the vital dimension that distinguishes procrastination behavior, and that distinction has set the foundation for future exploration of the behavior. Lay ( 1988 ) found that errors in estimations of time led to procrastination, then identified two types of procrastinators: pessimistic procrastinators and optimistic ones, according to whether one is optimistic or pessimistic about judgments of time. In addition, the timeframe or constraint scenario influences one's behavioral choices. Procrastinators tend to weigh short-term benefits over long-term costs (Tice and Baumeister, 1997 ).

However, time delay is just a behavioral representation, and personality traits may be in-depth inducements to procrastination behavior (Ferrari, 1991 ; Ferrari et al., 1995 ). Schouwenburg and Lay ( 1995 ) empirically studied and elaborated upon the relationship between the five-factor model and procrastination facing a sample of students, and their findings showed consistency with research by Ferrari ( 1991 ) which demonstrated that the trait facets of lacking conscientiousness and of neurotic avoidance were associated with procrastination. In addition, Ferrari ( 1992 ) evaluated two popular scales to measure procrastination: the General Procrastination (GP) scale and the Adult Inventory for Procrastination (AIP) scale. Regarding the measurement of procrastination, a variety of scales have been constructed to further enhance the development of procrastination research.

Period II (2000–2009): Investigation of Cognitive and Motivational Facets and Emergence of Various Research Methods

During period II, procrastination research with high citation bursts focused largely on two dimensions: behavioral antecedences and empirical methods. On one hand, discussions about cognitive and motivational antecedents spring up. A series of studies find that cognitive and motivational beliefs, including goal orientation, perceived self-efficacy, self-handicapping, and self-regulated learning strategies, are strongly related to procrastination (Wolters, 2003 ; Howell and Watson, 2007 ; Klassen et al., 2008 ). Specifically, Howell and Watson ( 2007 ) examined the achievement goal framework with two variables, achievement goal orientation and learning strategies usage, in which four types of goal orientation can be derived by the performance vs. mastery dimension and the approach vs. avoidance dimension. Their research found that procrastination was attributed to a mastery-avoidance orientation, whereas it was adversely related to a mastery-approach orientation. Moreover, Chu and Choi ( 2005 ) identified two types of procrastinators, active procrastinators versus passive procrastinators, in terms of the individual's time usage and perception, self-efficacy beliefs, motivational orientation, stress-coping strategies, and final outcomes. This classification of procrastinators has aroused a hot discussion about procrastination research (Zohar et al., 2019 ; Perdomo and Feliciano-Garcia, 2020 ). Cognitive and motivational antecedents are complementary to personality traits, and the antecedents and traits together reveal the complex phenomenon.

In addition, there are various research methods being applied in the research, such as meta-analyses and grounded theory. Having the strongest citation burst in period II, research that was based on a meta-analysis of procrastination by Steel ( 2007 ) elaborated on temporal motivation theory (TMT). Temporal motivational theory provides an innovative foothold for understanding self-regulation failure, using four critical indicators: expectancy, value, sensitivity to delay, and delay itself. Similarly, van Eerde ( 2003 ) conducted a meta-analysis to examine the relationship between procrastination and personality traits, and proposed that procrastination was negatively related to conscientiousness and self-efficacy, but was also actively associated with self-handicapping. Procrastinators commonly set deadlines, but research has found that external deadlines may be more effective than self-imposed ones (Ariely and Wertenbroch, 2002 ). Furthermore, Schraw et al. ( 2007 ) constructed a paradigm model through grounded theory to analyze the phenomenon of academic procrastination, looking at context and situational conditions, antecedents, phenomena, coping strategies, and consequences. These diverse research methods are enhancing our comprehensive and systematical understanding of procrastination.

Period III (2010–2020): Diverse Focuses on Procrastination Research

After nearly two decades of progressive developments, procrastination research has entered a steady track with diverse current bursts, on topics such as type distinction, theoretical perspective, temporal context, and the typical image of procrastinators. Steel ( 2010 ) revisited the trinity of procrastination — arousal procrastinators, avoidant procrastinators, and decisional procrastinators — and using the Pure Procrastination Scale (PPS) and the Irrational Procrastination Scale (IPS), he found that there was no distinct difference among the three types. Regarding research settings, a body of literature has focused on academic procrastination in-depth, and that literature has experienced a significant citation burst (Kim and Seo, 2015 ; Steel and Klingsieck, 2016 ). For example, academic procrastination is associated more highly with performance for secondary school students than for other age groups.

Notably, theoretical discussions and empirical research have been advancing synchronously. Klingsieck ( 2013 ) investigated systematic characteristics of procrastination research and concluded that theoretical perspectives to explain the phenomenon, whereas Steel and Ferrari ( 2013 ) portrayed the “typical procrastinator” using the variables of sex, age, marital status, education, community location, and nationality. Looking beyond the use of time control or time perception to define procrastination, Sirois and Pychyl ( 2013 ) compared the current self and the future self, then proposed that procrastination results from short-term mood repair and emotion regulation with the consequences being borne by the future self. In line with the part of introduction, in the last 10 years, research on procrastination has flourished and knowledge about this complex phenomenon has been emerging and expanding.

Structure Variation Analysis

Structure variation analysis (SVA) can predict the literature that will have potential transformative power in the future. Proposed by Chen ( 2012 ), structure variation analysis includes three primary metrics — the modularity change rate, cluster linkage, and centrality divergence — to monitor and discern the potential of newly published articles in specific domains. The modularity change rate measures the changes in and interconnectivity of the overall structure when newly published articles are introduced into the intellectual network. Cluster linkage focuses on these differences in linkages before and after a new between-cluster link is added by an article, whereas centrality divergence measures the structural variations in the divergence of betweenness centrality that a newly published article causes (Chen, 2012 ; Hou et al., 2020 ). The values of these metrics are higher, and the newly published articles are expected to have more potential to transform the intellectual base (Hou et al., 2020 ). Specifically, cluster linkage is a direct measure of intellectual potential and structural change (Chen, 2012 ). Therefore, we adopted cluster linkage as an indicator by which to recognize and predict the valuable ideas in newly published procrastination research. These top 20 articles with high transformative potential that were published during the period 2016-2020 were listed (see Supplementary Material for details). Research contents primarily consist of four dimensions.

Further Investigations Into Academic Procrastination

Although procrastination research has drawn mostly on samples of students, innovative research contents and methods have been emerging that enhance our understanding of academic procrastination. In the past five years, different language versions of scales have been measured and validated (Garzon Umerenkova and Gil-Flores, 2017a , b ; Svartdal, 2017 ; Guilera et al., 2018 ), and novel research areas and contents have arisen, such as how gender difference influences academic procrastination, what are the effective means of intervention, and what are the associations among academic procrastination, person-environment fit, and academic achievement (Balkis and Duru, 2016 ; Garzon Umerenkova and Gil-Flores, 2017a , b ; Goroshit, 2018 ). Interestingly, research has found that females perform academic procrastination less often and gain better academic achievements than males do (Balkis and Duru, 2017 ; Perdomo and Feliciano-Garcia, 2020 ).

In addition, academic procrastination is viewed as a fluid process. Considering the behavior holistically, three different aspects of task engagement have been discussed: initiation, completion, and pursuit. Vangsness and Young ( 2020 ) proposed the metaphors of “turtles” (steady workers), “task ninjas” (precrastinators), and “time wasters” (procrastinators) to elaborate vividly on task completion strategies when working toward deadlines. Individual differences and task characteristics can influence one's choices of a task-completion strategy. To understand the fluid and multifaceted phenomenon of procrastination, longitudinal research has been appearing. Wessel et al. ( 2019 ) observed behavioral delay longitudinally through tracking an undergraduate assignment over two weeks to reveal how passive and active procrastination each affected assignment completion.

Relationships Between Procrastination and Diverse Personality Traits

In addition to the relationship between procrastination and the five-factor model, other personality traits, such as temperament, character, emotional intelligence, impulsivity, and motivation, have been investigated in connection with procrastination. Because the five-factor model is not effective for distinguishing the earlier developing temperamental tendencies and the later developing character traits, Zohar et al. ( 2019 ) discussed how temperament and character influence procrastination in terms of active and passive procrastinators, and revealed that a dependable temperament profile and well-developed character predicted active procrastination.

Procrastination is commonly defined as a self-regulation failure that includes emotion and behavior. Emotional intelligence (EI) is an indicator with which to monitor one's feelings, thinking, and actions, and hot discussions about its relationship with procrastination have sprung up recently. Sheybani et al. ( 2017 ) elaborated on how the relationship between emotional intelligence and the five-factor model influence decisional procrastination on the basis of a students' sample. As a complement to the research above, Wypych et al. ( 2018 ) explored the roles of impulsivity, motivation, and emotion regulation in procrastination through path analysis. Motivation and impulsivity reflecting a lack of value, along with delay discounting and lack of perseverance, are predicators of procrastination, whereas emotion regulation, especially for suppression of procrastination, has only appeared to be significant in student and other low-age groups. How personality traits influence procrastination remains controversial, and further research is expected.

Procrastination in Different Life-Domains and Settings

Newly published research is paying more attention to procrastination in different sample groups across the entire life span. Not being limited to student samples, discussions about procrastination in groups such as teachers, educated adults, and workers have been emerging. With regard to different life domains, the self-oriented domains including health and leisure time, tend to procrastinate, whereas parenting is low in procrastination among highly educated adults. Although the achievement-oriented life domains of career, education, and finances are found with moderate frequency in conjunction with procrastination, these three domains together with health affect life the most (Hen and Goroshit, 2018 ). Similarly, Tibbett and Ferrari ( 2019 ) investigated the main regret domains facing cross-cultural samples, so as to determine which factors increased the likelihood of identifying oneself as a procrastinator. Their research found that forms of earning potential, such as education, finances, and career, led participants to more easily label themselves as procrastinators. Procrastination can lead to regret, and this research adopted reverse thinking to discuss the antecedents of procrastination.

In addition to academic procrastination, research about the behavior in diverse-context settings has begun to draw scholars' attention. Nauts et al. ( 2019 ) used a qualitative study to investigate why people delay their bedtime, and the study identified three forms of bedtime procrastination: deliberate procrastination, mindless procrastination, and strategic delay. Then, those researchers proposed coached interventions involving time management, priority-setting skills, and reminders according to the characteristics of the bedtime procrastination. Interestingly, novel forms of procrastination have been arising in the attention-shortage situations of the age of the internet, such as social media self-control failure (SMSCF). Du et al. ( 2019 ) found that habitual checking, ubiquity, and notifications were determinants for self-control failures due to social media use, and that finding provided insight into how to better use ICTs in a media-pervasive environment. Moreover, even beyond those life-related-context settings, procrastination in the workplace has been further explored. Hen ( 2018 ) emphasized the factor of professional role ambiguity underlying procrastination. Classification of procrastination context is important for the effectiveness of intervention and provides us with a better understanding of this multifaceted behavior.

Interventions to Procrastination

Overcoming procrastination is a necessary topic for discussion. Procrastination is prevalent and stable across situations, and it is commonly averse to one's performance and general well-being. Various types of interventions are used, such as time management, self-management, and cognitive behavioral therapy. To examine the effectiveness of those interventions, scholars have used longitudinal studies or field experimental designs to investigate these methods of intervention for procrastination. Rozental et al. ( 2017 ) examined the efficacy of internet-based cognitive behavior therapy (ICBT) to relieve procrastination, from the perspective of clinical trials. Through a one-year follow-up in a randomized controlled trial, researchers found that ICBT could be beneficial to relieve severe, chronic procrastination. Taking the temporal context into consideration, Visser et al. ( 2017 ) discussed a strengths-based approach — one element of the cognitive behavioral approach — that showed greater usefulness for students at an early stage of their studies than it did at later ages. Overall, research on the effectiveness of intervention for procrastination is relatively scarce.

Discussion and Conclusion

Discussion on procrastination research.

This article provides a systematic bibliometric analysis of procrastination research over the past 30 years. The study identifies the category distribution, co-occurrence keywords, main research clusters, and intellectual structures, with the help of CiteSpace and VOS viewer. As is shown in Figure 6 , the primary focuses for research themes have been on the definition and classification of procrastination, the relationships between procrastination and personality traits, the influences brought by procrastination, and how to better intervene in this complex phenomenon.

Bibliometric analysis and science map of the literature on procrastination.

Those contents have built the bases for procrastination research, but determining how those bases are constructed is important to the development of future research. Therefore, this article primarily discusses three aspects of intellectual structure of procrastination research: betweenness centrality, burst detection, and structural variation analysis. From the betweenness centrality analysis, three research themes are identifiable and can be generally summarized as: definition and classification of procrastination, procrastination behavior in a temporal context, and causes and interventions for procrastination.

However, procrastination research themes have evolved significantly across the time period from 1990–2020. Through burst detection analysis, we are able to infer that research has paid extraordinary attention to diverse themes at different times. In the initial stage, research is mainly about the antecedents of procrastination from the perspectives of time-management, self-regulation failure, and the five-factor model, which pays more attention to the behavior itself, such as delays in time. Subsequently, further discussions have focused on how cognitive and motivational facets such as goal orientation, perceived self-efficacy, self-handicapping, as well as self-regulated learning strategies influence procrastination. In the most recent 10 years, research has paid significant attention to expanding diverse themes, such as theoretical perspectives, typical images of procrastinators, and procrastination behavior in diverse temporal contexts. Research about procrastination has been gaining more and more attention from scholars and practitioners.

To explore newly published articles and their transformative potential, we conduct structural variation analysis. Beyond traditional research involving academic procrastination, emerging research themes consist of diverse research settings across life-domains, such as bedtime procrastination, social media self-control failure, procrastination in the workplace, and procrastination comparisons between self-oriented and achievement-oriented domains. Furthermore, novel interventions from the perspective of clinical and cognitive orientations to procrastination have been emerging in response to further investigation of procrastination's antecedents, such as internet-based cognitive behavior therapy (ICBT) and the strengths-based approach.

Conclusions and Limitations

In summary, research on procrastination has gained increasing attention during 1990 to 2020. Specifically in Figure 7 , research themes have involved in the definition, classification, antecedents, consequences, interventions, and diverse forms of procrastination across different life-domains and contexts. Furthermore, empirical research has been conducted to understand this complex and multifaceted behavior, including how best to design controlled trial experiments, how to collect and analyze the data, and so on.

Brief conclusions on procrastination research.

From the perspective of knowledge development, related research about procrastination has experienced tremendous expansion in the last 30 years. There are three notable features to describe the evolutionary process.

First, research focuses are moving from broader topics to more specific issues. Prior research mostly explored the definition and antecedents of procrastination, as well as the relationship between personality traits and procrastination. Besides, earlier procrastination research almost drew on students' setting. Based on previous research above, innovative research starts to shed light on procrastination in situation-specific domains, such as work procrastination, bedtime procrastination, as well as the interaction between problematic new media use and procrastination (Hen, 2018 ; Nauts et al., 2019 ; Przepiorka et al., 2021 ). With the evolvement of research aimed at distinct contexts, more details and core contents about procrastination have been elaborated. For example, procrastination in workplace may have association with professional role ambiguity, abusive supervision, workplace ostracism and task characteristics (Hen, 2018 ; He et al., 2021 ; Levin and Lipshits-Braziler, 2021 ). In particular, owing to the use of information and communication technology (ICTs), there currently are ample temptations to distract our attention, and those distractions can exacerbate the severity of procrastination (Du et al., 2019 ; Hong et al., 2021 ). Therefore, how to identify those different forms of procrastination, and then to reduce their adverse outcomes, will be important to discuss.

Second, antecedents and consequences of procrastination are further explored over time. On one hand, how procrastination occurs arises hot discussions from diverse dimensions including time management, personality traits, contextual characteristics, motivational and cognitive factors successively. Interestingly, investigations about neural evidences under procrastination have been emerging, such as the underlying mechanism of hippocampal-striatal and amygdala-insula to procrastination (Zhang et al., 2021 ). Those antecedents can be divided into internal factors and external factors. Internal factors including character traits and cognitive maladjustments have been elucidated fully, but scant discussion has occurred about how external factors, such as task characteristics, peers' situations, and environmental conditions, influence procrastination (Harris and Sutton, 1983 ; He et al., 2021 ). On the other hand, high prevalence of procrastination necessitates the importance to identify the negative consequences including direct and indirect. Prior research paid more attention to direct consequences, such as low performance, poor productivity, stress and illness, but the indirect consequences that can be brought about by procrastination remain to be unclear. For example, “second-hand” procrastination vividly describes the “spillover effect” of procrastination, which is exemplified by another employee often working harder in order to compensate for the lost productivity of a procrastinating coworker (Pychyl and Flett, 2012 ). Although such phenomena are common, adverse outcomes are less well investigated. Combining the contexts and groups involved, targeted discussions about the external antecedents and indirect consequences of procrastination are expected.

Third, empirical research toward procrastination emphasizes more on validity. When it comes to previous research, longitudinal studies are often of small numbers. However, procrastination is dynamic, so when most studies focus on procrastination of students' sample during just one semester or several weeks, can limit the overall viewpoints about procrastination and the effectiveness of conclusions. With the development of research, more and more longitudinal explorations are springing up to discuss long-term effects of procrastination through behavioral observation studies and so on. Besides, how to design the research and collect data evolves gradually. Self-reported was the dominant method to collect data in prior research, and measurements of procrastination usually depended on different scales. However, self-reported data are often distorted by personal processes and may not reflect the actual situation, even to overestimate the level of procrastination (Kim and Seo, 2015 ; Goroshit, 2018 ). Hence, innovative studies start to conduct field experimental designs to get observed information through randomized controlled trials. For the following research, how to combine self-reported data and observed data organically should be investigated and refined.

This bibliometric analysis to procrastination is expected to provide overall perspective for future research. However, certain limitations merit mentioning here. Owing to the limited number of pages allowed, it is difficult to clarify the related articles in detail, so discussion tends to be heuristic. Furthermore, the data for this research comes from the Web of Science database, and applying the same strategy to a different database might have yielded different results. In the future, we will conduct a systematic analysis using diverse databases to detect pivotal articles on procrastination research.

Data Availability Statement

Author contributions.

BY proposed the research question and conducted the research design. XZ analyzed the data and wrote primary manuscript. On the base of that work mentioned above, two authors discussed and adjusted the final manuscript together.

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.

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.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2022.809044/full#supplementary-material

• 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 Commun . 10 , 115–135. 10.1177/2050157921993896 [ CrossRef ] [ Google Scholar ]
• Akerlof G. A.. (1991). Procrastination and obedience . Am. Econ. Rev. 81 , 1–19. [ Google Scholar ]
• Ariely D., Wertenbroch K. (2002). Procrastination, deadlines, and performance: self-control by precommitment . Psychol. Sci . 13 , 219–224. 10.1111/1467-9280.00441 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Baker R., Evans B., Li Q., Cung B. (2019). Does inducing students to schedule lecture watching in online classes improve their academic performance? An experimental analysis of a time management intervention . Res. Higher Educ. 60 , 521–552. 10.1007/s11162-018-9521-3 [ CrossRef ] [ Google Scholar ]
• Balkis M.. (2013). Academic procrastination, academic life satisfaction and academic achievement: the mediation role of rational beliefs about studying . J. Cogn. Behav. Psychother. 13 , 57–74. [ Google Scholar ]
• Balkis M., Duru E. (2017). Gender differences in the relationship between academic procrastination, satisfaction with academic life and academic performance . Electr. J. Res. Educ. Psychol. 15 , 105–125. 10.25115/ejrep.41.16042 [ CrossRef ] [ Google Scholar ]
• Balkis M., Duru E. (2016). The analysis of relationships among person-environment fit, academic satisfaction, procrastination and academic achievement . Univ. J. Educ. 39 , 119–129. [ Google Scholar ]
• Bolden J., Fillauer J. P. (2020). “Tomorrow is the busiest day of the week”: executive functions mediate the relation between procrastination and attention problems . J. Am.College Health . 68 , 854–863. 10.1080/07448481.2019.1626399 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Broadbent J., Poon W. L. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: a systematic review . Inter Higher Educ. 27 , 1–13. 10.1016/j.iheduc.2015.04.007 [ CrossRef ] [ Google Scholar ]
• Chen C.. (2006). CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature . J. Am. Soc. Inf. Sci. Technol. 57 , 359–377. 10.1002/asi.20317 [ CrossRef ] [ Google Scholar ]
• Chen C.. (2012). Predictive effects of structural variation on citation counts . J. Am. Soc. Inf. Sci. Technol . 63 , 431–449. 10.1002/asi.21694 [ CrossRef ] [ Google Scholar ]
• Chen C.. (2013). Hindsight, insight, and foresight: a multi-level structural variation approach to the study of a scientific field . Technol. Analy. Strat. Manage . 25 , 619–640. 10.1080/09537325.2013.801949 [ CrossRef ] [ Google Scholar ]
• Chen C.. (2017). Science mapping: a systematic review of the literature . J. Data Inf. Sci. 2 , 1–40. 10.1515/jdis-2017-0006 [ CrossRef ] [ Google Scholar ]
• Chen C. M., Hu Z. G., Liu S. B., Tseng H. (2012). Emerging trends in regenerative medicine: a scientometric analysis in CiteSpace . Expert Opin. Biol. Therapy. 12 , 593–608. 10.1517/14712598.2012.674507 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chen C. M., Ibekwe-SanJuan F., Hou J. (2010). The structure and dynamics of co-citation clusters: a multiple-perspective co-citation analysis . J. Am. Soc. Inf. Sci. Technol . 61 , 1386–1409. 10.1002/asi.21309 [ CrossRef ] [ Google Scholar ]
• Chu A. H. C., Choi J. N. (2005). Rethinking procrastination: positive effects of “active” procrastination behavior on attitudes and performance . J. Soc. Psychol . 145 , 245–264. 10.3200/SOCP.145.3.245-264 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Corkin D. M., Yu S. L., Lindt S. F. (2011). Comparing active delay and procrastination from a self-regulated learning perspective . Learn. Indiv. Differ. 21 , 602–606. 10.1016/j.lindif.2011.07.005 [ CrossRef ] [ Google Scholar ]
• Diaz-Morales J. F., Ferrari J. R., Diaz K., Argumedo D. (2006). Factorial structure of three procrastination scales with a Spanish adult population . Eur. J. Psychol. Assess. 22 , 132–137. 10.1027/1015-5759.22.2.132 [ CrossRef ] [ Google Scholar ]
• Du J., Kerkhof P., van Koningsbruggen G. M. (2019). Predictors of social media self-control failure: immediate gratifications, habitual checking, ubiquity, and notifications . Cyber Psychol. Behav. Soc. Network. 22 , 477–485. 10.1089/cyber.2018.0730 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Duru E., Balkis M. (2017). Procrastination, self-esteem, academic performance, and well-being: a moderated mediation model . International J. Educ. Psychol . 6 , 97–119. 10.17583/ijep.2017.2584 [ CrossRef ] [ Google Scholar ]
• Elliot A. J., Harackiewicz J. M. (1996). Approach and avoidance achievement goals and intrinsic motivation: a mediational analysis . J. Personal. Soc. Psychol . 70 , 461–475. 10.1037/0022-3514.70.3.461 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fang Y., Yin J., Wu B. (2018). Climate change and tourism: a scientometric analysis using CiteSpace . J. Sustain. Tour . 26 , 108–126. 10.1080/09669582.2017.1329310 [ CrossRef ] [ Google Scholar ]
• Ferrari J. R.. (1991). Compulsive procrastination: some self-reported characteristics . Psychol. Reports. 68 , 455–458. 10.2466/pr0.1991.68.2.455 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ferrari J. R.. (1992). Psychometric validation of two Procrastination inventories for adults: arousal and avoidance measures . J. Psychopathol. Behav. Assess. 14 , 97–110. 10.1007/BF00965170 [ CrossRef ] [ Google Scholar ]
• Ferrari J. R., Diaz-Morales J. F., O'Callaghan J., Diaz K., Argumedo D. (2007). Frequent behavioral delay tendencies by adults - International prevalence rates of chronic procrastination . J. Cross-Cultural Psychol . 38 , 458–464. 10.1177/0022022107302314 [ CrossRef ] [ Google Scholar ]
• Ferrari J. R., Johnson J. L., McCown W. G. (1995). Procrastination and Task Avoidance: Theory, Research, and Treatment . US: Springer US. 10.1007/978-1-4899-0227-6 [ CrossRef ] [ Google Scholar ]
• Flett G. L., Stainton M., Hewitt P. L., Sherry S. B., Lay C. (2012). Procrastination automatic thoughts as a personality construct: an analysis of the procrastinatory cognitions inventory . J. Rational-Emot. Cogn. Behav. Therapy. 30 , 223–236. 10.1007/s10942-012-0150-z [ CrossRef ] [ Google Scholar ]
• Freeman L.. (1978). Centrality in social networks conceptual clarification . Soc. Netw. 1 , 215–239. 10.1016/0378-8733(78)90021-7 [ CrossRef ] [ Google Scholar ]
• Garfield E.. (1979). Is citation analysis a legitimate evaluation tool . Scientometrics . 1 , 359–375. 10.1007/BF02019306 [ CrossRef ] [ Google Scholar ]
• Garzon Umerenkova A., Gil-Flores J. (2017a). Psychometric properties of the Spanish version of the test procrastination assessment scale-students (PASS) . Rev. Iberoamericana De Diagnostico Y Evaluacion-E Avaliacao Psicol. 1 , 149–163. 10.21865/RIDEP43_149 [ CrossRef ] [ Google Scholar ]
• Garzon Umerenkova A., Gil-Flores J. (2017b). Academic procrastination in non-traditional college students . Electronic J. Res. Educ. Psychol . 15 , 510–531. 10.14204/ejrep.43.16134 [ CrossRef ] [ Google Scholar ]
• Germeijs V., Verschueren K. (2011). Indecisiveness and big five personality factors: relationship and specificity . Person. Indiv. Differ. 50 , 1023–1028. 10.1016/j.paid.2011.01.017 [ CrossRef ] [ Google Scholar ]
• Glick D. M., Orsillo S. M. (2015). An investigation of the efficacy of acceptance-based behavioral therapy for academic procrastination . J. Experim. Psychol. General. 144 , 400–409. 10.1037/xge0000050 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Goroshit M.. (2018). Academic procrastination and academic performance: an initial basis for intervention . J. Prevent Intervent. Commun. 46 , 131–142. 10.1080/10852352.2016.1198157 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Green M. C., Visser P. S., Tetlock P. E. (2000). Coping with accountability cross-pressures: low-effort evasive tactics and high-effort quests for complex compromises . Personal. Soc. Psychol. Bull. 26 , 1380–1391. 10.1177/0146167200263006 [ CrossRef ] [ Google Scholar ]
• Guilera G., Barrios M., Penelo E., Morin C., Steel P., Gomez-Benito J. (2018). Validation of the Spanish version of the irrational procrastination scale (IPS) . PLoS ONE. 13 , 1–11. 10.1371/journal.pone.0190806 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gustavson D. E., Miyake A., Hewitt J. K., Friedman N. P. (2014). Genetic relations among procrastination, impulsivity, and goal-management ability: implications for the evolutionary origin of procrastination . Psychol. Sci. 25 , 1178–1188. 10.1177/0956797614526260 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Harris N. N., Sutton R. I. (1983). Task procrastination in organizations: a framework for research . Human Relat. 36 , 987–995. 10.1177/001872678303601102 [ CrossRef ] [ Google Scholar ]
• He Q., Wu M., Wu W., Fu J. (2021). The effect of abusive supervision on employees' work procrastination behavior . Frontiers in Psychol . 12 , 596704. 10.3389/fpsyg.2021.596704 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hen M.. (2018). Causes for procrastination in a unique educational workplace . J. Prevent. Inter. Commun. 46 , 215–227. 10.1080/10852352.2018.1470144 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hen M., Goroshit M. (2018). General and life-domain procrastination in highly educated adults in Israel . Front. Psychol . 9 , 1173. 10.3389/fpsyg.2018.01173 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hong W., Liu R. D., Ding Y., Jiang S. Y., Yang X. T., Sheng X. T. (2021). Academic procrastination precedes problematic mobile phone use in Chinese adolescents: a longitudinal mediation model of distraction cognitions . Addic. Behav. 121 , 106993. 10.1016/j.addbeh.2021.106993 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hou J. H., Yang X. C., Chen C. M. (2020). Measuring researchers' potential scholarly impact with structural variations: four types of researchers in information science (1979-2018) . PLoS ONE. 15 , e0234347. 10.1371/journal.pone.0234347 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Howell A. J., Watson D. C. (2007). Procrastination: associations with achievement goal orientation and learning strategies . Personal. Indiv. Differ. 43 , 167–178. 10.1016/j.paid.2006.11.017 [ CrossRef ] [ Google Scholar ]
• Hubner K.. (2012). German crisis management and leadership-from ignorance to procrastination to action . Asia Eur. J. 9 , 159–177. 10.1007/s10308-012-0313-7 [ CrossRef ] [ Google Scholar ]
• Jiang Y., Ritchie B. W., Benckendorff P. (2019). Bibliometric visualization: an application in tourism crisis and disaster management research . Curr. Issues Tour . 22 , 1925–1957. 10.1080/13683500.2017.1408574 [ CrossRef ] [ Google Scholar ]
• Johnson J. L., Bloom A. M. (1995). An analysis of the contribution of the five factors of personality to variance in academic procrastination . Personal. Indiv. Differ. 18 , 127–133. 10.1016/0191-8869(94)00109-6 [ CrossRef ] [ Google Scholar ]
• Kim K. R., Seo E. H. (2015). The relationship between procrastination and academic performance: a meta-analysis . Personal. Indiv. Differ. 82 , 26–33. 10.1016/j.paid.2015.02.038 [ CrossRef ] [ Google Scholar ]
• Kim S., Fernandez S., Terrier L. (2017). Procrastination, personality traits, and academic performance: when active and passive procrastination tell a different story . Personal. Indiv. Differ. 108 , 154–157. 10.1016/j.paid.2016.12.021 [ CrossRef ] [ Google Scholar ]
• Klassen R. M., Krawchuk L. L., Rajani S. (2008). Academic procrastination of undergraduates: low self-efficacy to self-regulate predicts higher levels of procrastination . Contemp. Educ.Psychol . 33 , 915–931. 10.1016/j.cedpsych.2007.07.001 [ CrossRef ] [ Google Scholar ]
• Klein E.. (1971). A comprehensive etymological dictionary of the Hebrew language for readers of English . Tyndale House Publishers. [ Google Scholar ]
• Kleinberg J.. (2003). Bursty and hierarchical structure in streams . Data Mining Knowl. Disc. 7 , 373–397. 10.1023/A:1024940629314 [ CrossRef ] [ Google Scholar ]
• Klingsieck K. B.. (2013). Procrastination: when good things don't come to those who wait . Eur. Psychol . 18 , 24–34. 10.1027/1016-9040/a000138 [ CrossRef ] [ Google Scholar ]
• Lay C.. (1986). At last, my research article on procrastination . J. Res. Personal . 20 , 474–495. 10.1016/0092-6566(86)90127-3 [ CrossRef ] [ Google Scholar ]
• Lay C.. (1988). The relation of procrastination and optimism to judgments of time to complete an essay and anticipation of setbacks . J. Soc. Behav. Personal. 3 , 201–214. [ Google Scholar ]
• Legood A., Lee A., Schwarz G., Newman A. (2018). From self-defeating to other defeating: examining the effects of leader procrastination on follower work outcomes . J. Occup. Organiz. Psychol . 91 , 430–439. 10.1111/joop.12205 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Levin N., Lipshits-Braziler Y. (2021). Facets of adaptability in career decision-making . Int. J. Educ. Vocat. Guidance . 6 , 1–12. 10.1007/s10775-021-09489-w [ CrossRef ] [ Google Scholar ]
• Malouff J. M., Schutte N. S. (2019). The efficacy of interventions aimed at reducing procrastination: a meta-analysis of randomized controlled trials . J. Counsel. Develop. 97 , 117–127. 10.1002/jcad.12243 [ CrossRef ] [ Google Scholar ]
• Markscheffel B., Schroeter F. (2021). Comparison of two science mapping tools based on software technical evaluation and bibliometric case studies . Collnet J. Scientometr. Inf. Manage . 15 , 365–396. 10.1080/09737766.2021.1960220 [ CrossRef ] [ Google Scholar ]
• Metin U. B., Tanis T. W., Peeters M. C. W. (2016). Measuring procrastination at work and its associated workplace aspects . Personal. Indiv. Differ . 101 , 254–263. 10.1016/j.paid.2016.06.006 [ CrossRef ] [ Google Scholar ]
• Miraj S. A.. (2020). Coronavirus disease 2019: the public health challenge and our preparedness . Biosci. Biotechnol. Res. Commun. 13 , 361–364. 10.21786/bbrc/13.2/1 [ CrossRef ] [ Google Scholar ]
• Nauts S., Kamphorst B. A., Stut W., De Ridder D. T. D., Anderson J. H. (2019). The explanations people give for going to bed late: a qualitative study of the varieties of bedtime procrastination . Behav. Sleep Med. 17 , 753–762. 10.1080/15402002.2018.1491850 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nevill C. J.. (2009). Managing cumulative impacts: groundwater reform in the Murray-Darling basin, Australia . Water Res. Manage. 23 , 2605–2631. 10.1007/s11269-009-9399-0 [ CrossRef ] [ Google Scholar ]
• Olmeda-Gomez C., Roma-Mateo C., Ovalle-Perandones M. A. (2019). Overview of trends in global epigenetic research (2009-2017) . Scientometrics. 119 , 1545–1574. 10.1007/s11192-019-03095-y [ CrossRef ] [ Google Scholar ]
• Pan W. W., Jian L. R., Liu T. (2019). Grey system theory trends from 1991 to 2018: a bibliometric analysis and visualization . Scientometrics. 121 , 1407–1434. 10.1007/s11192-019-03256-z [ CrossRef ] [ Google Scholar ]
• Perdomo A. S., Feliciano-Garcia L. (2020). The influence of active procrastination: a profile on educational sciences students' academic achievement . Bordon-Rev. De Pedagogia. 72 , 157–170. 10.13042/Bordon.2020.73642 [ CrossRef ] [ Google Scholar ]
• Przepiorka A., Blachnio A., Cudo A. (2021). Procrastination and problematic new media use: the mediating role of future anxiety . Current Psychol . 5 , 1–10. 10.1007/s12144-021-01773-w [ CrossRef ] [ Google Scholar ]
• Pychyl T. A., Flett G. L. (2012). Procrastination and self-regulatory failure: an introduction to the special issue . J. Rational-Emotive Cogn. Behav. Therapy. 30 , 203–212. 10.1007/s10942-012-0149-5 [ CrossRef ] [ Google Scholar ]
• Rozental A., Forsell E., Svensson A., Andersson G., Carlbring P. (2017). Overcoming procrastination: one-year follow-up and predictors of change in a randomized controlled trial of Internet-based cognitive behavior therapy . Cogn. Behav.Therapy. 46 , 177–195. 10.1080/16506073.2016.1236287 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schouwenburg H. C., Lay C. H. (1995). Trait procrastination and the Big-five factors of personality . Personal. Indiv. Differ. 18 , 481–490. 10.1016/0191-8869(94)00176-S [ CrossRef ] [ Google Scholar ]
• Schraw G., Wadkins T., Olafson L. (2007). Doing the things we do: a grounded theory of academic procrastination . J. Educ.Psychol . 99 , 12–25. 10.1037/0022-0663.99.1.12 [ CrossRef ] [ Google Scholar ]
• Sheybani F., Gharraee B., Bakhshizadeh M., Tamanaeefar S. (2017). Decisional procrastination: prevalence among students and relationship with emotional intelligence and big five-factor model of personality . Int. J. Life Sci. Pharma Res. 7 , 26–32. [ Google Scholar ]
• Sirois F., Pychyl T. (2013). Procrastination and the priority of short-term mood regulation: consequences for future self . Soc. Personal. Psychol. Compass. 7 , 115–127. 10.1111/spc3.12011 [ CrossRef ] [ Google Scholar ]
• Sirois F. M.. (2004). Procrastination and intentions to perform health behaviors: the role of self-efficacy and the consideration of future consequences . Personal. Indiv. Differ. 37 , 115–128. 10.1016/j.paid.2003.08.005 [ CrossRef ] [ Google Scholar ]
• Sirois F. M.. (2021). Trait procrastination undermines outcome and efficacy expectancies for achieving health-related possible selves . Curr. Psychol . 40 , 3840–3847. 10.1007/s12144-019-00338-2 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sirois F. M., Molnar D. S., Hirsch J. K. (2017). A meta-analytic and conceptual update on the associations between procrastination and multidimensional perfectionism . Eur. J. Personal. 31 , 137–159. 10.1002/per.2098 [ CrossRef ] [ Google Scholar ]
• Steel P.. (2007). The nature of procrastination: a meta-analytic and theoretical review of quintessential self-regulatory failure . Psychol.Bull. 133 , 65–94. 10.1037/0033-2909.133.1.65 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Steel P.. (2010). Arousal, avoidant and decisional procrastinators: do they exist? Personal. Indiv. Differ. 48 , 926–934. 10.1016/j.paid.2010.02.025 [ CrossRef ] [ Google Scholar ]
• Steel P., Ferrari J. (2013). Sex, education and procrastination: an epidemiological study of procrastinators' characteristics from a global sample . Eur. J. Personal. 27 , 51–58. 10.1002/per.1851 [ CrossRef ] [ Google Scholar ]
• Steel P., Klingsieck K. B. (2016). Academic procrastination: psychological antecedents revisited . Austral. Psychol . 51 , 36–46. 10.1111/ap.12173 [ CrossRef ] [ Google Scholar ]
• Svartdal F.. (2017). Measuring procrastination: psychometric properties of the Norwegian versions of the irrational procrastination scale (IPS) and the pure procrastination scale (PPS) . Scand. J. Educ. Res . 61 , 18–30. 10.1080/00313831.2015.1066439 [ CrossRef ] [ Google Scholar ]
• Tao X., Hanif H., Ahmed H. H., Ebrahim N. A. (2021). Bibliometric analysis and visualization of academic procrastination . Front. Psychol . 12 , 722332. 10.3389/fpsyg.2021.722332 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tibbett T., Ferrari J. (2019). Return to the origin: what creates a procrastination identity? Curr. Issues Personal. Psychol . 7 , 1–7. 10.5114/cipp.2018.75648 [ CrossRef ] [ Google Scholar ]
• Tice D. M., Baumeister R. F. (1997). Longitudinal study of procrastination, performance, stress, and health: the costs and benefits of dawdling . Psychol. Sci. 8 , 454–458. 10.1111/j.1467-9280.1997.tb00460.x [ CrossRef ] [ Google Scholar ]
• van Eck N. J., Waltman L. (2010). Software survey: VOS viewer, a computer program for bibliometric mapping . Scientometrics. 84 , 523–538. 10.1007/s11192-009-0146-3 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• van Eerde W.. (2003). A meta-analytically derived nomological network of procrastination . Personal. Indiv. Differ. 35 , 1401–1418. 10.1016/S0191-8869(02)00358-6 [ CrossRef ] [ Google Scholar ]
• Vangsness L., Young M. E. (2020). Turtle, task ninja, or time waster? Who cares? traditional task-completion strategies are overrated . Psychol. Sci. 31 , 306–315. 10.1177/0956797619901267 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Visser L., Schoonenboom J., Korthagen F. A. J. (2017). A field experimental design of a strengths-based training to overcome academic procrastination: short- and long-term effect . Front. Psychol . 8 , 1949. 10.3389/fpsyg.2017.01949 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wessel J., Bradley G. L., Hood M. (2019). Comparing effects of active and passive procrastination: a field study of behavioral delay . Personal. Indiv. Differ. 139 , 152–157. 10.1016/j.paid.2018.11.020 [ CrossRef ] [ Google Scholar ]
• Widziewicz-Rzonca K., Tytla M. (2020). First systematic review on PM-bound water: exploring the existing knowledge domain using the CiteSpace software . Scientometrics. 124 , 1945–2008. 10.1007/s11192-020-03547-w [ CrossRef ] [ Google Scholar ]
• Wolters C. A.. (2003). Understanding procrastination from a self-regulated learning perspective . J. Educ.Psychol . 95 , 179–187. 10.1037/0022-0663.95.1.179 [ CrossRef ] [ Google Scholar ]
• Wypych M., Matuszewski J., Dragan W. Ł. (2018). Roles of impulsivity, motivation, and emotion regulation in procrastination – path analysis and comparison between students and non-students . Front. Psychol . 9 , 891. 10.3389/fpsyg.2018.00891 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Yi Y. T., Luo J. S., Wubbenhorst M. (2020). Research on political instability, uncertainty and risk during 1953-2019: a scientometric review . Scientometrics. 123 , 1051–1076. 10.1007/s11192-020-03416-6 [ CrossRef ] [ Google Scholar ]
• Zhang S. M., Verguts T., Zhang C. Y., Feng P., Chen Q., Feng T. Y. (2021). Outcome value and task aversiveness impact task procrastination through separate neural pathways . Cerebral Cortex . 31 , 3846–3855. 10.1093/cercor/bhab053 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zohar A. H., Shimone L. P., Hen M. (2019). Active and passive procrastination in terms of temperament and character . Peerj. 7 , e6988. 10.7717/peerj.6988 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

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• Published: 26 September 2022

A neuro-computational account of procrastination behavior

• Raphaël Le Bouc   ORCID: orcid.org/0000-0001-9982-3645 1 , 2 &
• Mathias Pessiglione   ORCID: orcid.org/0000-0002-6992-3677 1  

Nature Communications volume  13 , Article number:  5639 ( 2022 ) Cite this article

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• Computational neuroscience
• Human behaviour

A Publisher Correction to this article was published on 21 October 2022

This article has been updated

Humans procrastinate despite being aware of potential adverse consequences. Yet, the neuro-computational mechanisms underlying procrastination remain poorly understood. Here, we use fMRI during intertemporal choice to inform a computational model that predicts procrastination behavior in independent tests. Procrastination is assessed in the laboratory as the preference for performing an effortful task on the next day as opposed to immediately, and at home as the delay taken in returning completed administrative forms. These procrastination behaviors are respectively modeled as unitary and repeated decisions to postpone a task until the next time step, based on a net expected value that integrates reward and effort attributes, both discounted with delay. The key feature that is associated with procrastination behavior across individuals (both in-lab and at-home) is the extent to which the expected effort cost (signaled by the dorsomedial prefrontal cortex) is attenuated by the delay before task completion. Thus, procrastination might stem from a cognitive bias that would make doing a task later (compared to now) appear as much less effortful but not much less rewarding.

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Introduction

Almost all humans procrastinate to some extent, either on filling tax returns, paying bills, saving for retirement, or quitting addictive behaviors like smoking or gambling. People do so although knowing about potential adverse consequences, such as financial difficulty 1 , 2 or health damage 3 . Despite its high prevalence, affecting ~70% of students 4 and up to 20% of adults 5 , and its major economic or health consequences, the mechanisms leading to procrastination remain poorly understood.

Procrastination is considered a stable trait-like behavior 6 , with significant heritability demonstrated by twin studies 7 . However, the causal pathways through which genes could shape the brain architecture so as to produce procrastination behavior are not elucidated. Neuroimaging studies have not gone beyond correlations between procrastination scores on self-report questionnaires and brain anatomy, resting-state activity 8 , 9 , 10 , 11 , or task-related activity 12 , 13 , 14 . This questionnaire-based approach offers no mechanistic insight into the emergence of procrastination, which would require an operational definition at the cognitive level.

As suggested by its etymology (crastinus is a Latin word for tomorrow), the common meaning of procrastination is to postpone duties from one day to the next. This definition has been refined by psychologists as the unnecessary but voluntary delaying of task completion (either requested or intended) despite potential harmful outcomes 4 , 6 , 15 , 16 . For ancient philosophers such as Aristotle, procrastination is a prototypical case of akrasia, which designates a lack of self-control leading to act against one’s best judgment. This perspective is still present in the psychological literature on procrastination, which may be considered as resulting from self-regulation failure or ‘weakness of the will’ 6 , 17 , 18 .

In the framework of neoclassical economic theory, procrastination would be considered irrational, because it prevents maximizing utility in the long run, even when the right course of action is clearly identified. This seemingly irrational behavior has given rise to the development of alternative economic models that would preserve the principle of utility maximization.

An early economic account of procrastination 1 emphasized the importance of costs, meaning the effort and time associated with task completion, which should be traded against the remote benefits associated with the outcome of task completion. Procrastination would stem from present costs (if the task is done now) being perceived in a much more vivid manner than distant costs (if the task is done later) 1 . Thus, in this model, the present bias leading to procrastination is captured by a vividness parameter that amplifies the cost of immediate actions 1 . A variant of this model focuses on the opportunity cost of time, defined as the value of what is forgone when implementing a particular action 6 . In this variant, procrastination would result from the distant benefit of task completion being surpassed by the opportunity cost, i.e. the lost benefit of what could be enjoyed now. Thus, in both cases, a present bias would explain procrastination, either because completing the task now would be perceived as particularly aversive, or because enjoying now an alternative activity would seem particularly attractive.

To be complete, the model should explain not only why people procrastinate (why they postpone a task) but also why they stop procrastinating at some point (why they finally do it). Further variants of the procrastination model benefited from the development of economic decision theory in which expected utility is progressively discounted with the passage of time 19 , 20 , 21 , as opposed to a mere present bias. The core idea was to apply the same temporal discounting function to both costs and benefits. Functions that would diminish expected utility by some percentage every day would not account for procrastination, since they predict stable preferences over time. However, empirical work suggested that human choices are better accounted for by inconsistent temporal discounting models, using for instance hyperbolic functions 21 , in which the daily discount decreases over time 19 , 20 . These models allow for preference reversal, i.e. the inversion of the ranking between options when their outcomes get closer in time. Applied to the case of procrastination, preference reversal would mean that work may appear less valuable than leisure when deadlines are distant, but may nonetheless be favored when deadlines are closer 6 . This idea has been incorporated in recent psychological investigations 22 , following on the temporal motivation theory 6 , 23 , which borrows from economic decision models the notions of expected utility, temporal discounting and cost/benefit trade-off.

Thus, both economic and psychological accounts of procrastination build on assumptions about temporal discounting functions and share some limitations. First, these models only account for situations involving remote benefits, or at least a lag between task completion and reward delivery, although procrastination has also been observed with tasks that yield immediate benefits, such as redeeming gift vouchers 24 . Second, these models implicitly or explicitly consider that avoiding a cost is equivalent to receiving a benefit, the timing of benefits and the timing of costs being considered as just flip sides of the same phenomenon 25 . This makes procrastination a synonym of impulsivity—the preference for smaller sooner over larger later benefits—although these two traits have been shown to be dissociable 7 . Moreover, recent studies have shown that reward value and effort cost are processed by distinct neural circuits 26 , 27 , which may suggest separate processes for the temporal discounting of costs and benefits. Third, these models are static, in the sense that the decision about completion time is made once and for all, which is at odds with the psychological intuition that procrastination entails iterative decisions (to defer again and again).

Here, we suggest a framework that may overcome these limitations, by combining the seminal intuitions that procrastination relates to both effort perception and temporal discounting, resulting in the following assumptions: (1) procrastination stems from choice between options that integrate costs and benefits both estimated at different time points (now and later), (2) a unitary decision to procrastinate reflects a steeper temporal discounting for effort than for reward 28 , and (3) recurrent procrastination arises from iterative decisions repeated over time. From this set of assumptions, we derive the predictions that procrastinators would discount efforts with time more steeply than non-procrastinators, that these time preferences would be associated with neural activity reflecting temporal discounting of effort, and that dynamic models would outperform static models in predicting real-life procrastination. To test these predictions, we measure temporal discounting rates for reward and effort in intertemporal choice tasks and identify their neural signature using fMRI. We find that temporal discounting of effort cost, either inferred from choice behavior or from brain activity, could account for procrastination behavior, observed both in the lab, as a preference for postponing an effortful task until the next day, and at home, as a delay in returning completed administrative forms.

A total of 51 healthy adult volunteers performed a series of behavioral tasks: first, rating tasks (Fig.  1a ), to collect subjective effort costs and reward values that participants would assign to each task and outcome included in our set of items; second, intertemporal choice tasks (Fig.  1b ), to elicit computational and neural markers of temporal discounting for both the effort and reward domains; and third, tasks measuring the tendency to procrastinate, both in the lab (Fig.  1c ) and at home (Fig.  1d ), to assess the predictive validity of these markers. All tasks were performed in that order (Fig.  1e ), during a single visit to the lab, fMRI scanning being only applied to the intertemporal choice task. Participants were divided into three cohorts: one performing a pilot version (Exp. 1) of the intertemporal choice task ( n  = 8), one performing the main version (Exp. 2) of this task ( n  = 16) and one performing the main version (Exp. 2) within the MRI scanner ( n  = 27). Demographic details are provided separately for the three groups in Supplementary Table  1 .

Successive screens displayed in one trial are shown from left to right, with durations in ms. a Rating task. For each reward, effort and punishment (not shown), participants indicated on a keyboard the quantity that had the same subjective benefit (or subjective cost) than earning (or loosing) 1€ and 5€. b Intertemporal choice task. Participants first observed the two options shown successively and then indicated their preference by pressing one of two buttons with the left or right hand. The presentation order of sooner and later options was counterbalanced across trials. The task was divided into blocks of intertemporal choices between two rewards, two efforts, or two punishments (not shown). c ‘Now/tomorrow’ choice task. Participants were presented with an option combining reward and effort items. Then they indicated whether they preferred to exert the effort “Now”, and obtain the reward immediately, or “Tomorrow”, and obtain the reward the next day. The side of presentation of the “Now” and “Tomorrow” options was counterbalanced across trials. d ‘Form-filling’ home task. Participants were given 10 administrative forms, such as a passport renewal form. They had to fill in the forms and send a numeric copy via email within a time limit of 30 days, in order to receive their financial compensation for participating in the study. They were told that no compensation would be transferred after the deadline. e Experimental schedule. Tasks were performed in the alphabetic order. Only the intertemporal choice task was performed in the MRI scanner.

Time preferences

The purpose of the intertemporal choice task was to examine how participants discount costs and benefits with time. In order to infer temporal discount rate, we first needed to know the values that participants would assign to the various reward and effort items presented in the choice task. To this aim, we had participants rate the monetary value of hypothetical reward and effort items on an analog scale (Fig.  1a ). Reward items could be either food or goods, and effort items either motor or cognitive tasks. For a given reward item, participants rated how many elements they would claim for a given price (e.g., how many pieces of sushi are worth paying 5€). Reciprocal value ratings were obtained for effort items, by asking participants how much effort they would exert for a given payoff (e.g., how many sit-ups should be done for a fair payment of 5€). Punishment items (either bodily or abstract) were also included in the rating task, as a control for specificity in the second experiment (see below). The question was similar to that asked for effort ratings: participants rated how much punishment they would accept to endure for a given payoff (e.g., how many mild electric shocks should be endured for a fair payment of 5€). Thus, subjective ratings provided monetary values in euros, which represented the equivalent gain for one element of reward items (e.g., the price of one piece of sushi) and the equivalent loss for effort and punishment (e.g., the payoff for one sit-up or one electric shock).

In the intertemporal choice task, participants indicated their preference between a lower/sooner and a higher/later hypothetical reward, or between a lower/sooner and a higher/later hypothetical effort (Fig.  1b ). The two options of a choice were offering a same item already presented in the rating task but with different quantities (e.g., 5 pieces of sushi now vs. 10 pieces of sushi in a week). We changed the framing of delayed efforts between the pilot and test experiments. In a first pilot experiment ( n  = 8), delays indicated the precise dates at which efforts had to be exerted, or at which rewards were to be obtained. We examined the weight of the different choice factors using a logistic regression model (Fig.  2a ). Predictably, patient choice frequency in the reward domain was significantly impacted by the relative gain ( β  = 0.44, t (7) = 7.74, p  < 0.001) and negatively impacted by the relative delay ( β  = −0.28, t (7) = −7.23, p  < 0.001). However, in the effort domain, preferences revealed an unexpected pattern: patient choice rate did decrease with the relative cost ( β  = −0.35, t (7) = −3.78, p  = 0.007) but did not show any monotonic effect of delay ( β  = 0.01, t (7) = 0.29, p  = 0.78). The trend was even to expedite the task sooner, denoting a negative time preference. We assumed this reverse preference might have resulted from the framing of delay as the specific date at which effort should be exerted (which might have induced some dread phenomenon).

a Intertemporal choices. Plots show patient choice rate (preference for the delayed option), as a function of the difference between option values /costs (top row) or option delays (bottom row). Choices were made between two rewards (blue), two efforts (red) or two punishments (yellow). The two options of a choice differed only in delay and value / cost (quantity converted into euros based on subjective ratings). Note that value represents a gain for reward and cost a loss for effort and punishment. In Exp. 1 ( n  = 8 participants), rewards (dark blue) were to be received and efforts (dark red) were to be exerted at the specified date. In Exp. 2 ( n  = 43 participants), rewards (light blue) were to be received at the specified date, but efforts (light red) and punishments (yellow) could be exerted or endured at any time before the specified deadline. On each dot plot, the color mark indicates the mean, the horizonal line indicates the median, the thick whiskers indicate the range from 25th to 75th percentiles, and the thin whiskers indicate the range from 5th to 95th percentiles. b Temporal discounting of reward (left), effort (middle), and punishment (right). The plots display individual discount curves for reward (thin blue lines), effort (thin red lines), and punishment (thin orange lines), as well as the population means (bold lines) and medians (dashed lines). c Accuracy of model fitting. Plots show correlations between modeled and observed choice probability for delayed rewards, delayed efforts, and delayed punishments. Individual choices were divided into 8 bins of increasing modeled probability; each dot represents modeled and observed choice probability averaged within one bin. The within-subject (trial-by-trial) fit can be assessed with the distribution of balanced accuracy across participants. Balanced accuracy is the average of prediction accuracy calculated separately for the two types of choices (now or later). d Temporal discount rates for reward ( k R ), effort ( k E ) and punishment ( k P ). Significance values are based on two-tailed paired t tests ( n  = 43). e Proportion of patient choices for the different categories of rewards, efforts and punishments. Error bars are inter-subject standard error of the mean ( n  = 43). Source data are provided as a Source Data file.

We therefore conducted a second experiment ( n  = 43) using an intertemporal choice task proposing the same effort options, but with delays now meaning time limits for effort exertion. In Exp. 2, we also introduced punishments in the intertemporal choice task, to test whether temporal discounting of effort would generalize to any aversive dimension. The logistic fit of choice behavior in Exp. 2 showed an opposite pattern in the effort domain compared to the reward domain (Fig.  2a ). In the reward domain, patient choice frequency still increased with the relative gain ( β  = 0.34, t (42) = 14.67, p  < 0.001) and decreased with the relative delay between options ( β  = −0.17, t (42) = −7.96, p  < 0.001). However, in the effort domain, the change of framing resulted in the expected pattern, with a negative effect of relative cost ( β  = −0.32, t (42) = −11.40, p  < 0.001) and a positive effect of relative delay ( β  = 0.06, t (42) = 3.38, p  = 0.002), meaning that participants now preferred effort items that were more distant in the future. The same pattern was observed for punishments, with a negative impact of relative loss ( β  = −0.35, t (42) = −14.04, p  < 0.001) and a positive impact of relative delay ( β  = 0.09, t (42) = 5.72, p  < 0.001), denoting again a preference for delaying aversive events.

We then estimated temporal discount rates for reward, effort and punishment by fitting hyperbolic choice models onto choice behavior in Exp. 2 (Fig.  2b ). Hyperbolic choice model here means hyperbolic discounting function to generate option value, combined with softmax function to compare option values and generate choice probabilities:

With V A the value of option A calculated as the number of elements N Ai on offer multiplied by the gain equivalent R i for one unit of reward i (inferred from subjective rating), discounted by the delay D A multiplied a weight k R (temporal discount rate for reward). The discounted value of effort and punishment were computed in the same manner, except that gain equivalents were replaced by loss equivalents (similarly inferred from effort and punishment subjective ratings) and that different temporal discount rates ( k E and k P ) were used. In addition to temporal discount rate, the softmax included an additional parameter, the inverse temperature θ , to adjust for choice stochasticity. We compared this hyperbolic discounting to two classical models of intertemporal choices: a present-bias model (Eq.  3 ), and a quasi-hyperbolic discounting model ( βδ model) that includes both a present bias parameter β and a constant exponential discount factor δ (Eq.  4 ). Notice that there is a discontinuity in these functions such that, if D A  = 0, V A  =  N Ai x R i in both models.

We also tested whether time preferences would generalize across reward, effort and punishment intertemporal choices. Bayesian model comparison showed that hyperbolic discounting with category-specific discount rates provided the best trade-off between accuracy and complexity (Supplementary Fig.  1 , log group Bayes factor = 212, compared to the second-best model). The balanced accuracy (averaged over participants) was similar for reward, effort and punishment conditions (0.78, 0.79 and 0.77, respectively, see Fig.  2c ). Discount rates for reward and effort were weakly correlated (Pearson’s r (41) = 0.29, p  = 0.07) and were on average greater for effort than for reward (paired t test, t (42) = 2.48, p  = 0.017). Discount rates estimated for punishment were in between (Fig.  2d ), and significantly correlated with both reward ( r (41) = 0.46; p  = 0.002) and effort ( r (41) = 0.61; p  < 0.001). Participants, therefore, appeared more ‘impulsive’ with effort and more ‘patient’ with reward (i.e., they avoided immediate efforts more than they approached immediate rewards). We checked (Fig.  2e ) that this pattern was not dependent on subcategories of reward (food vs. goods, t (42) = −0.22, p  = 0.41), effort (physical vs. mental, t (42) = −0.35, p  = 0.36), or punishment (bodily vs. abstract, t (42) = −1.26, p  = 0.11). Note that the discount rates are comparable here because reward, effort and punishment values were expressed in the same unit (as equivalent gains and losses in euros). The results, therefore, suggest that time preferences for reward and effort can differ both within and between participants.

Neural correlates of time preferences

We next examined whether these distinct time preferences in the reward and effort domains would involve distinct neural circuits. A standard signature of the choice process in fMRI data is the decision variable, here the difference in value or cost between the two options, at the time of choice. To identify the neural signatures of discounted reward value and discounted effort or punishment cost, we regressed fMRI trial-by-trial time series against the chosen (most appetitive) minus unchosen (least appetitive) reward value, and against the chosen (least aversive) minus unchosen (most aversive) effort or punishment cost. All reward values and effort costs were generated from individual subjective ratings with the best-fitting hyperbolic discounting model (Fig.  3a ). We found partially dissociable brain systems, with activity in the ventromedial prefrontal cortex (vmPFC) positively related to discounted reward, activity in the anterior insula (AI) positively related to discounted effort, and activity in the dorsomedial prefrontal cortex (dmPFC) showing an effect in the two domains (negative relation to discounted reward and positive relation to discounted effort). The pattern of activity obtained for punishment was very similar to that of effort (with discounted punishment being positively reflected in the AI and dmPFC).

a Statistical parametric map (SPM) of discounted reward (chosen minus unchosen option discounted value), discounted effort and discounted punishment (chosen minus unchosen option discounted cost) during intertemporal choice, at the time of deliberation. Positive and negative effects are shown with yellow-red and green-blue color codes. b Regression coefficients (betas) of discounted reward (blue), discounted effort (red), and discounted punishment (orange) extracted from anatomically-defined regions of interest (see methods). Error bars are inter-subject standard error of the mean; significance values are based on two-tailed one-sample t tests ( n  = 27). Source data are provided as a Source Data file c Conjunction between the negative contrast for discounted reward and positive contrast for discounted effort, at the time of deliberation.

To illustrate how these regions represent values and costs, we extracted their average parameter estimates (Fig.  3b ) in corresponding ROIs taken from published probabilistic atlases (see methods). Activity in the vmPFC correlated positively with discounted reward ( t (26) = 3.20, p  = 0.004) but was not significantly affected by discounted effort ( t (26) = −1.04, p  = 0.31) or discounted punishment ( t (26) = 1.19, p  = 0.24). By contrast, activity in the AI correlated positively with discounted effort ( t (26) = 2.69, p  = 0.012) and discounted punishment ( t (26) = 3.67, p  = 0.001) but tended to be negatively associated with discounted reward ( t (26) = −1.88, p  = 0.07). Finally, activity in the dmPFC was negatively correlated with discounted reward ( t (26) = −2.96, p  = 0.007) and positively associated with discounted effort ( t (26) = 2.87, p  = 0.008) and discounted punishment ( t (26) = 5.56, p  < 0.001). We checked, in two control analyses (Supplementary Fig.  3 and 4 ), that this pattern of activity was robust across the different subcategories of reward (food vs. good), effort (physical vs. mental), and punishment (bodily vs. abstract), and when defining ROIs on the basis of a different probabilistic atlas (Supplementary Fig.  4 ). In a conjunction analysis (Fig.  3c ), we found that the dmPFC was the only region whose activity was significantly related to both discounted reward (negatively) and discounted effort (positively). This is in line with our previous suggestion that the dmPFC might integrate the costs and benefits signaled by regions more sensitive to one or the other 27 . Note that in many studies (including ours), this fMRI activation cluster has been labeled as dACC (for dorsal Anterior Cingulate Cortex), although strictly speaking it is not located within the cingulate gyrus (but in a more dorsal region of the medial wall, overlapping with the paracingulate gyrus). Our results are consistent with dmPFC/dACC and AI activity being greater when the chosen alternative (least aversive cost) was preferred (to the most aversive cost) by a smaller margin.

Procrastination behavior

The tendency to procrastinate was first assessed in the lab with a task involving choices between performing an effortful task now for an immediate reward, or postponing both task completion and the associated reward until the next day (Fig.  1c ). Participants were told that in any case, they would come to the lab twice on two consecutive days, so in practice, they were offered the choice of performing the task proposed in a given trial either during the first current visit or during the second visit on the next day. They were only informed at the end of the first visit that rewards and efforts were fictive and that the second visit would not be implemented. Although this is also a sort of intertemporal choice task, we call it the ‘now/tomorrow’ choice task. The key difference with standard intertemporal choice tasks is that there are two attributes (both reward and effort) to integrate with delay, and not just one (reward or effort). Another difference is that the option proposed at the two delays (today or tomorrow) was the same: it combined a given amount of a reward item and a given amount of an effort item. As expected (Fig.  4a ), logistic regression showed that procrastination (preference for ‘tomorrow’) decreased with reward value ( β  = −0.09, t (42) = −5.61, p  < 0.001) and increased with effort cost ( β  = 0.20, t (42) = 10.0, p  < 0.001), hence globally decreased with the net value ( β  = −0.20, t (42) = −10.23, p  < 0.001), i.e. the difference between the discounted benefit and cost associated to the task.

a Procrastination behavior in the lab ‘now/tomorrow’ choice task. Plots show choice rates for the ‘tomorrow’ option as a function of reward value (left), effort cost (middle), or the task net value (right). On each dot plot, the color mark indicates the mean, the horizonal line indicates the median, the thick whiskers indicate the range from 25th to 75th percentiles, and the thin whiskers indicate the range from 5th to 95th percentiles. ( n  = 43). Source data are provided as a Source Data file b Procrastination behavior in the at-home ‘form-filling’ task. Histograms show the distribution of the delay before participants completed and returned administrative forms. c Regression estimates of the temporal discount rates for reward ( k R ), effort ( k E ) and punishment ( k P ) obtained from fitting a linear model (also including age and gender) to procrastination level in the ‘now/tomorrow’ choice task. Error bars represent SD; significance values are based on two-tailed one-sample t tests ( n  = 43). d Regression estimates of reward, effort and punishment discount rates obtained from fitting a linear model (also including age and gender) to procrastination level in the ‘form-filling’ home task. Error bars represent SD; significance values are based on two-tailed one-sample t tests ( n  = 37). Note that in both cases (in lab or at home), discount rates are inferred from intertemporal choices, which were observed independently from the procrastination behavior that they contribute to explain. e Accuracy of model fitting. The plot shows the correlation between modeled and observed choice frequency for the ‘tomorrow’ option. Individual choices were divided into 8 bins of increasing modeled frequency; each dot represents modeled and observed choice frequency averaged within one bin for one participant. The within-subject (trial-by-trial) fit can be assessed with the distribution of individual balanced accuracy (inset). Balanced accuracy is the average of prediction accuracy calculated separately for the two types of choices (now or later).

We also assessed procrastination as the delay with which participants completed at home and sent us the 10 administrative forms that were mandatory for receiving their financial compensation (Fig.  1d ). The deadline was set to 30 days after the experiment in the lab. Almost all participants procrastinated to some extent (Fig.  4b ). Some of them ( n  = 6) never returned the forms and were therefore excluded from the analyses of delay distribution presented hereafter. The delay until completion of administrative forms at home was correlated across participants to the procrastination tendency observed in the lab ‘now/tomorrow’ choice task ( r (41) = 0.25, P  = 0.05, one-tailed), and to the procrastination score ( r (41) = 0.39, P  = 0.004, one-tailed) on a self-report questionnaire (Lay procrastination scale). However, direct correlation between procrastination in the ‘now/tomorrow’ task and procrastination score on the psychometric scale was not significant ( r (41) = 0.15, P  = 0.16, one-tailed). This may suggest that the psychometric score better reflects recurrent procrastination at home than one-shot decision to procrastinate as implemented in the lab choice task.

Predicting procrastination behavior from time preferences

To test whether procrastination could be explained by the differential temporal discounting of reward and effort, we regressed across participants the level of procrastination observed in the ‘now/tomorrow’ choice task, defined as the selection frequency of ‘tomorrow’ options (Fig.  4c ), against temporal discount rates inferred from intertemporal choice tasks. Effort discount rates were significantly associated with procrastination level ( β  = 0.40, t (37) = 2.50, p  = 0.017, two-tailed t test, model R 2  = 0.24), whereas the other factors included in the regression model (reward and punishment discount rates plus age and gender) had no significant effect. This pattern was similar when using a different criterion to define procrastinators, i.e. when fitting the regression model to procrastination level defined as the delay taken to send completed administrative forms (Fig.  4d ). Again, effort discount rates were significantly associated with procrastination level ( β  = 0.46, t (31) = 2.69, p  = 0.011, two-tailed t test, model R 2  = 0.22), while none of the other factors (reward and punishment discount rates plus age and gender) was significant. Thus, the hallmark of procrastination severity, whether measured in the lab or at home, was a steep temporal discounting of effort. This distinctive feature was specific to effort, since other aversive events such as punishments were not more steeply discounted in more severe procrastinators.

We then examined whether differential time preferences for reward and effort could account for individual procrastination behavior in the ‘now/tomorrow’ choice task. The temporal discount rates, as well as subjective ratings of reward and effort items, were rigidly incorporated in the choice model (Eq.  5 ) explaining the decision to perform the task ‘Now’ or ‘Tomorrow’, without any adjustment to the data (Fig.  4e ). This choice model simply compared in a softmax function the net values (i.e., the difference between hyperbolically discounted benefit and cost associated to the proposed task) of the ‘now’ and ‘tomorrow’ options:

With delay D being 0 or 1 depending on the considered option being ‘Now’ or ‘Tomorrow’, k R and k E the temporal discount rates for reward and effort (inferred from intertemporal choices), N R and N E the quantities of reward i and effort j on offer, whose unitary gain and loss equivalents were R i and E j (inferred from individual subjective ratings). The only parameter that was fitted to the now/tomorrow choices was the inverse temperature θ of the softmax function, which could not adjust the mean of individual preferences (i.e., procrastination level), but just their stochasticity.

Prediction of trial-by-trial choices was significantly above chance (mean balanced accuracy = 0.57, t (42) = 3.88, p  < 0.001, two-tailed t test). We checked that considering different time preferences for reward and effort provided a better fit than using a single common discount rate (Supplementary Fig.  2 , log group Bayes factor = 115). This suggests that the steeper discounting of effort relative to reward does help explain the decision to postpone a task until tomorrow. Indeed, it makes postponing a task beneficial because an effort scheduled for tomorrow would appear much less costly, while the reward delayed until tomorrow would not seem much devalued. In other words, the net value of the ‘tomorrow’ option would loom larger than that of the ‘now’ option.

Predicting procrastination behavior from neural correlates of time preferences

To estimate time preferences from their neural correlates, we focused on the dmPFC, which integrated all factors manipulated in the intertemporal choice task. The general linear model (GLM) used to explain choice-related neural activity now included two distinct regressors representing trial-by-trial variations in relative delay and undiscounted decision variable (meaning the difference in reward value or effort cost between the two options). In line with previous results, dmPFC activity was significantly modulated by both factors, as shown by a conjunction analysis (Fig.  5a ). We then extracted the delay regression estimates (betas), separately for the reward and effort sessions, within an anatomically-defined dmPFC ROI (see methods). To correct for any subject-specific noise in fMRI data that could corrupt regression estimates, we normalized these betas by the overall activation level (beta weight of the categorical regressor modeling choice onset) in the same ROI. The normalized betas thus represented neural estimates of time preferences, estimated independently from those based on choice behavior (temporal discount rates of the hyperbolic discount model). Yet neural and behavioral measures of time preferences were correlated across participants (Pearson’s r (79) = 0.31, p  = 0.002), combining reward, effort, and punishment estimates (Fig.  5b ).

a Statistical parametric maps (SPM) of delay (chosen minus unchosen delay), – (undiscounted) reward value and + (undiscounted) effort cost, and the conjunction between the two regressors, at the time of deliberation. b Correlation between behavioral and neural estimates of the weight on delay (temporal discount rates in choice model versus delay regression estimates in dmPFC activity). Source data are provided as a Source Data file. c Regression estimates of beta weights on dmPFC activity for delay and (undiscounted) value or cost of reward, effort, and punishment, obtained from fitting a linear model to procrastination level observed either in the lab choice task (preference for ‘tomorrow’ options) ( n  = 27) or at home (delay in returning administrative forms) ( n  = 23). Note that neural activity was recorded during intertemporal choices, independently from the procrastination behavior assessed in the lab or at home. Error bars represent SD; significance values are based on two-tailed one-sample t tests.

We then tested the link between neural time preferences and procrastination behaviors. We found that procrastination behaviors were associated with neural measures of temporal discount rates in the effort domain only (Fig.  5c ). Neural measures of temporal discounting for efforts were significantly associated both with procrastination in the ‘now/tomorrow’ lab choice task ( β  = 0.45, t (18) = 2.21, p  = 0.040, two-tailed t test, model R 2  = 0.59) and with the delay to return completed administrative forms ( β  = 1.29, t (14) = 2.50, p  = 0.026, two-tailed t test, model R 2  = 0.22). In contrast, there was no significant effect on the neural measures of temporal discount rates for reward or punishment, and no effect of the sensitivity to undiscounted value or cost. We also found no significant effect for gender, and age had a significant effect only on procrastination at home ( β  = −0.55, t (14) = −2.20, p  = 0.044, two-tailed t test), but not on procrastination in the lab. Thus, at the neural level, the hallmark of procrastination was a greater temporal discounting of effort expressed in dmPFC activity at the time of choice.

From static to dynamic computational model of recurrent procrastination

Finally, we developed computational models that articulate unitary decisions to procrastinate, as probed in the ‘now/tomorrow’ choice task, and daily-life recurrent procrastination, as probed in the ‘form-filling’ task. We compared two models that both incorporate the choice model, but fundamentally differed in how procrastination arises. The static model implemented a form of pre-commitment, in the sense that the date of task completion was determined a priori, when returning home, as the delay with the highest net value over all possible delays before the deadline (Fig.  6a ). The predicted date of task completion (i.e., the duration of procrastination) is, therefore, the one that maximizes the net value function:

With d* task the optimal day for task completion, V d the value of performing the task on day d, R i the financial compensation contingent on completing the administrative forms and E i the subjective cost of filling in those forms. Note that this model is not necessarily deterministic: the probability of completing the task after a given delay d could be calculated through a softmax function comparing net values over all delays.

a Static model. Effort cost and reward value are both discounted with time, and reward value vanishes after the deadline. The predicted delay for task completion (i.e., procrastination duration) is the one that maximizes the net value function (green dot). Simulations of procrastination duration under the static model shows that it mostly occurs with low temporal discount rates, even lower for reward than for effort b Dynamic model. Every day, the net value of completing the task now is compared to all other remaining available dates (white dots) through a softmax function that provides choice probabilities. As time goes by and the deadline gets closer, the comparison includes fewer dates, which increases the probability of performing the task. This probability obtained for each day is added to the probability of having completed the task on every past day, to generate a cumulative probability. The predicted delay of task completion is the expected value under this cumulative distribution. Simulations of procrastination duration under the dynamic model shows that it occurs with a wide range of temporal discount rates, provided that they are lower for reward than for effort. c Daily and cumulative probability of task completion across time, under the dynamic model. Each curve represents one participant. d Observed procrastination duration plotted against temporal discount rates for reward ( k R ) and effort ( k E ). Each dot is a participant. Red bars represent the means, and whiskers the 25th and 75th percentiles, in three bins of equal size. e Inter-participant one-tailed Pearson’s correlation between observed and modeled procrastination duration, under the dynamic model, based on behavioral data or neural data. Each dot is a participant. The number of dots corresponds to the number of participants who did send back the completed forms ( n  = 37 for behavioral data and n  = 23 for fMRI data). Source data are provided as a Source Data file.

In the dynamic model, the delay of task completion resulted from iterative decisions, repeated each day, to postpone the task or not (Fig.  6b ). Thus, the probability of having completed the task before day d i is the probability of having not postponed the task on every day before d i , which is given by one minus the product of probabilities to postpone the task (i.e., not doing it now) on every day until d i :

With τ the time at which the task is eventually performed, d i the considered day for task completion, D the deadline (30 days), V t the value of task completion at a given time t ( t  = 0 means now), calculated with the same function as in Eq.  6 , and θ an inverse temperature parameter. Note that the probability to perform the task P d increases with time because fewer dates before the deadline are available for comparison in the softmax function (Fig.  6c ). When deadline is reached ( d  =  D ), this probability becomes one, predicting that the last procrastinators should complete the task then. The cumulative probability of having completed the task P is further increasing across days as more probabilistic terms are introduced in the product (see Fig.  6c ). Under the dynamic model, the predicted task delay d* task is given by the expected time at which the task is performed:

Simulations showed that under both the static and dynamic models, participants would only postpone the task if temporal discount rates are higher for effort than for reward (Fig.  6a, b ), which was the case in our participants. This relates to the net value function that is used in both models. If the temporal discount rate is higher for reward than for effort, the optimal day of task completion is always now, whether the net value is positive or negative (Supplementary Fig.  5 ). However, only the dynamic model predicts a monotonic relationship between procrastination duration and decreasing reward or increasing effort discount rates. To assess which of these models was more consistent with behavioral data, we first examined how procrastination duration varied as a function of temporal discount rates across participants (Fig.  6d ). We found that the tendency to discount effort more steeply was associated with longer procrastination (Pearson’s r (35) = 0.35, p  = 0.02), while the tendency to discount reward more steeply showed an opposite but non-significant effect (Pearson’s r (35): −0.07, p  = 0.34). This pattern is qualitatively consistent with the predictions of the dynamic model and suggests that, although in principle postponing tasks could stem from both decreasing reward and increasing effort temporal discount rate, the latter was driving the procrastination behavior observed in our participants.

We then quantitatively compared the predictions of these models using Bayesian model selection. Note that, again, we did not fit temporal discount rates but rigidly incorporated those inferred from data collected in the intertemporal choice task, together with the subjective cost of administrative form filling expressed by participants in the rating task. Results of Bayesian model selection designated the dynamic model as more plausible than the static model (model log-evidence = −148.7 vs. −169.1, log Bayes Factor = 20.4). Moreover, the dynamic model significantly explained procrastination duration across participants (Fig.  6e ; one-tailed Pearson’s r (35) = 0.29, p  = 0.040). Together, these results support the idea that the delay in task completion resulted from iterative decisions to postpone the task, whose probability depended on differential time preferences for effort and reward. Finally, informing the dynamic model with neural time preferences provided equivalent accounts of task completion delays across participants (Fig.  6e ; one-tailed Pearson’s r (21) = 0.38, p  = 0.035), which confirms that recurrent procrastination at home may stem from how the brain discounts effort versus reward with time.

The main finding here is that procrastination is related to how effort is discounted with time, relative to reward. Indeed, discount factors estimated during intertemporal decisions between effortful options, whether inferred from behavior or from brain activity, were significantly associated with independent decisions probed in the lab to postpone a rewarded task until the next day. These discount factors were also significantly associated with the delay taken at home to fill in and send back administrative forms, which in turn was significantly correlated with procrastination scores measured using a standard questionnaire (Lay procrastination scale). Because our study may be underpowered to capture the full variability of naturally occurring individual differences in procrastination behavior, these findings require further confirmation in future studies with larger samples. They nevertheless provide initial support to a computational model assuming that (1) unitary decisions to postpone a task until the next day are based on a net value that integrates reward and effort attributes both discounted with time, and that (2) the date of task completion within the allotted time period results from iterative decisions to postpone the task or not. This computational model might therefore account for the recurrent procrastination behavior that is frequently observed in real-life situations.

Regarding unitary decisions to do a task now or tomorrow, we used hyperbolic discounting with time and linear integration of reward and effort. Hyperbolic discounting was used for consistency with intertemporal choices, which were better explained by hyperbolic models than with models including a present bias. This comparison between time discounting functions could not be performed in the now/tomorrow choice task which only compared two delays (0 and 1). Linear integration (without scaling factor) was made possible by using subjective ratings of reward and effort items that provided equivalent gains and losses in euros. In first approximation, the same delays were used for the effort and reward components, since the tasks proposed could be completed in a few seconds or minutes. Thus, the time between task completion and outcome delivery was negligible compared to the delay due to procrastination (one day). Yet, in real-life, task completion can take time and the outcome can be delayed further, which could aggravate procrastination behavior 22 . Our model can be easily generalized to those cases, by using different delays for reward and effort discounting. Thus, the differential time discounting of reward and effort may be considered as a general explanation: it accounts for procrastination behavior even when the aversive task completion is immediately followed by the rewarding outcome (e.g., when passing a phone call to cheer up a relative in pain), and a fortiori when the task is long to complete or the outcome delivered much later (e.g., when preparing an exam to obtain a diploma).

When accounting for now/tomorrow choices within participants, the model suggests that time discounting is not the only factor: procrastination is more likely to occur with less rewarded and more effortful tasks. This is in line with studies that emphasized the failure to regulate emotional responses to task aversiveness as a key determinant of procrastination behavior 6 , 17 , 18 , 22 . However, across participants, these factors were neutralized in the current study by using subjective ratings to adjust the pairing of effort cost and reward value, which may be viewed as emotional responses to task aversiveness and outcome attractivity. Inter-subject variability in procrastination (preference for tomorrow) was mainly captured by the weight on delay in the estimation of effort cost, which may represent a general trait explaining procrastination above and beyond the particular appraisal of specific tasks and outcomes. This weight on delay was obtained independently from fitting choices between effortful options in the intertemporal task, or by estimating the neural response to delay during those choices.

The fact that reward, effort and punishment are differentially discounted with time is supported by our model comparison based on intertemporal choice data. This is not a novel idea: early accounts of intertemporal decisions already suggested that discount rates may vary across attributes of actions and outcomes 29 , 30 . A specific temporal discount factor for effort was even hypothesized in both the economic 1 , 28 , 31 and motor-control literature 32 . However, although temporal discounting of reward has been studied extensively, very few studies have assessed temporal discounting of effort in humans so far 33 , 34 , and virtually none in animals. Among aversive outcomes, monetary losses have received more focus. Previous investigations have shown that losses are discounted with functions qualitatively similar to gains but with lower discount rates 29 , 35 , 36 , 37 , 38 , 39 , a gain-loss asymmetry called the sign effect 29 . This does not appear to be a general feature of aversive outcomes, since we observed that effort, unlike loss, is more steeply discounted than rewards.

Interestingly, although people generally want to postpone losses for as long as possible, other aversive outcomes, such as receiving a mild electric shock, are sometimes expedited rather than delayed. This negative time preference has been accounted for by dread, i.e. the desire to avoid the experience of anticipating unpleasant future outcomes 29 , 40 , 41 . At the neural level, the dread for electric shocks has been related to increased neural activity in the posterior elements of the cortical pain matrix, which has been interpreted as reflecting the attention devoted to the expected physical response 41 . A dread component might also play a role in the effort domain, explaining why tasks such as cleaning cages are rather expedited than delayed when choosing between specific dates 29 . In our pilot study (Exp. 1), which framed delays as the obligation to complete effortful tasks on specific dates, we observed a preference for intermediate delays, consistent with negative discounting of anticipation value combined with positive discounting of consumption value 29 . However, in Exp. 2, where delays were framed as deadlines for completing the effortful task or enduring the punishment, participants showed a preference for longer delays. More than the aversive nature of effort or punishment, it might therefore be their uncontrollable occurrence at precise dates that critically determines the weight of the dread component.

The differential discounting of reward and effort might relate to the recruitment of different brain networks. Neuroimaging studies have implicated both common and separate networks in the temporal discounting of gains and losses 42 , 43 , 44 , but to our best knowledge, no study has ever investigated the neural bases of effort temporal discounting. Our fMRI results are compatible with the view that appetitive and aversive events are signaled by opponent systems (vmPFC and AI, respectively) and integrated in a common region (dmPFC) 26 , 45 , 46 , 47 , 48 , 49 , 50 , 51 . This region was labeled dmPFC here because it was dorsal to the cingulate gyrus, although activation clusters positioned on similar locations are often labeled dACC in the literature on effort, conflict and cognitive control. Despite looking for brain responses to the sequential presentation of the two options, we only found significant neural correlates of option values at the time of choice, possibly because participants waited for this moment to consider the options. In all three clusters of interest, the neural representation of option values was framed by the choice, meaning that brain activity correlated (positively or negatively) with the difference between chosen and unchosen option values, as reported in many previous studies 52 , 53 , 54 . We note that the double dissociation between vmPFC and AI was only partial, as the vmPFC also tended to deactivate with effort (but less reliably than the activation with reward), while the AI also tended to deactivate with reward (but less reliably than the activation with effort). In the dmPFC, the pattern of activity was qualitatively similar to that observed in the AI, but correlations were significant with all option attributes, so the neural response to delay could be extracted from this region for reward, effort and punishment. This extraction of delay regression estimates was independent from the temporal discount factors estimated from choice behavior, but the two markers (neural and behavioral) were correlated across participants. Although the dmPFC was similarly sensitive to effort and punishment on average, procrastination was specifically related to its sensitivity to the delay of effortful tasks, not the delay of punishment outcomes.

This result suggests that procrastination behavior is related to how the brain discounts effort with time, but not necessarily to the specific computation operated by the dmPFC, which could just receive information about option attributes from other brain regions. Also, the direct contribution of the dmPFC to postponing a task could not be assessed here, because we have not scanned participants during the ‘now/tomorrow’ choice task. Still, one may speculate that during this kind of choice, the dmPFC would signal the decision variable, i.e. the net value of doing the task now relative to the net value of doing the task tomorrow (with both effort cost and reward value discounted by one day). Depending on this signal, the brain might engage or not in task completion. Engaging in task completion might itself recruit cognitive control brain systems, with regions such as the lateral prefrontal cortex (lPFC).

Such an interaction between the dmPFC (or dACC) signaling the need for control and the lPFC implementing the required control has already been postulated in published models 55 , 56 , 57 . It could represent a common proximal pathway between procrastination (difficulty in exerting an immediate effort) and impulsivity (difficulty in resisting an immediate pleasure), even if the distal causes are distinct. The existence of both shared and distinct mechanisms may help explain why, although they are distinguishable phenotypic traits 7 , procrastination and impulsivity are correlated across individuals 6 , 11 , 58 , and share a significant genetic variability in twin studies 7 , 59 . Self-regulation failure (or cognitive control deficiency) has indeed been proposed as a common core component of short-sighted behaviors 60 , 61 . Consistent with this idea, impulsivity has been associated with small lPFC volume 62 , reduced lPFC activity 63 and lPFC inactivation 64 , while procrastination has been associated with both lPFC volume 9 , lPFC resting-state activity 10 , and control-related lPFC activity 12 . Thus, the tendency to procrastinate might be due to both the dmPFC signaling values in favor of postponing the task and/or the lPFC failing to implement the control necessary for task completion.

Beyond unitary decisions to postpone the task, the results of our model comparison show that recurrent procrastination at home is better explained by iterative decisions (dynamic model) than by a direct readout of the net value function (static model). Without extra-assumptions, the dynamic scheme naturally accounts for the deadline effect (earlier deadline shortening procrastination duration) that has been reported in several studies 24 , 65 , 66 . Indeed, at every step, the number of possible time slots remaining to perform the task later is reduced, as the deadline approaches, until the probability of doing the task immediately reaches one on the last allotted slot. Note however that, if no deadline is set, meaning that the number of available time slots is high or even countless, the probability will not vary very much across days, such that the model is likely to make the same decision again and again. If it starts with a low probability of completing the task on the first day, there is a good chance that the task would never be completed, as is also commonly observed in real life.

Compared to the static model, the dynamic model adds a crucial factor in the occurrence of procrastination: the rate at which decisions to complete or postpone the task are considered. As there was no way for us to control the choice rate while participants were back home, we postulated a same constant rate (one decision per day) for everyone. Yet, even if it was the case that some participants thought about filling in the administrative forms every day, it is likely that many of them just forgot about these forms for some time. Integrating the choice rate in the model might therefore help better account for interindividual variability in procrastination behavior. Indeed, the model predicts that individuals who consider completing the task less frequently should procrastinate longer. This feature of the model may account for why prospective memory and external reminders have a significant impact on procrastination: 67 , 68 , 69 , 70 they may increase the rate at which the option of completing the task now is envisaged.

To conclude, our results are consistent with a neuro-computational mechanism accounting for why people repeatedly postpone a task, even when they consider that the benefits surpass the costs (when just comparing reward and effort, ignoring time). Yet these results remain silent about underlying causes, i.e., why effort cost is more discounted with time than reward value in the first place. The explanation might involve attentional processes, if for instance, people focus more on the benefit when the potential task is distant in the future and more on the cost related to its practical implementation when it gets closer in time 71 . At a different time scale, the explanation might involve evolutionary justifications, such as natural selection of the capacity to preserve energetic resources, until it becomes certain that the task needs to be done now. In modern life, procrastination might be adaptive for other reasons, one being that rushing before deadlines might speed up task completion and therefore save time in a busy agenda. In any case, our dynamic model of recurrent procrastination would imply a lack of self-awareness: participants would ignore that by making the same decision again and again, they are likely to miss the optimal date of task completion as defined by their own net value function. Thus, such a cognitive bias might result in many people never completing tasks that would yet improve their well-being.

Participants

In total, 51 healthy adults participated in the study (30 females, median age = 23 ± 2.5 y). This includes three different cohorts (Supplementary Table  1 ) of participants who participated in a pilot Experiment 1 ( n  = 8), in Experiment 2 with behavioral testing only ( n  = 16), and in Experiment 2 with fMRI ( n  = 27). All participants gave informed consent prior to participating and all data were recorded anonymously. Participants were screened for exclusion criteria: left-handedness, age below 18 or above 40, any history of neurologic or psychiatric illness, regular use of drugs or medication, and contraindications to MRI scanning. Participants were informed that they would receive a fixed amount for their participation (25€ for behavioral studies, 75€ for MRI sessions). The study was approved by the Ethics Committee of the Pitié-Salpêtrière Hospital (Paris, France).

Before performing the tasks, participants were given written instructions, which were also repeated orally step by step. Tasks presentation and behavioral recordings were programmed with MATLAB using the psychophysics Toolbox ( www.psychtoolbox.org ).

Rating tasks

Participants were instructed to report the subjective value of a set of rewards, the subjective cost of a set of efforts, and the subjective cost of a set of punishments, should they (hypothetically) experience these items. For each item, they were asked to indicate the quantity of the item that had the same subjective value (or subjective cost) than earning (or loosing) 1€ and 5€, successively. Items and their units (e.g. number, grams, meters, etc.) were written in the center of the screen, and participants had to indicate the equivalent quantity with a keyboard. Responses were self-paced. The task was made up of one block of reward items equally divided into food items (e.g., pieces of sushi) and goods (e.g., flowers), one block of effort items equally divided into cognitive (e.g., memorizing n digits) and motor efforts (e.g., doing n sit-ups), and one block of punishment items equally divided into bodily (e.g., enduring n mild electric shocks) and abstract losses (e.g., losing my smartphone for n hours). The order of block presentation was counterbalanced across participants. Each block contained 50 items in Exp. 1 and 60 items in Exp. 2.

Intertemporal choice task

This task was designed to assess how participants discounted reward, effort, and punishment with time. The punishment condition was used as a control to assess whether effort was discounted by a specific rate or by the same rate as other aversive outcomes. The task was made up of blocks of reward items, blocks of effort items, and blocks of punishment items, whose presentation order was counterbalanced across participants. In each block, participants were presented with a series of hypothetical choices between a sooner/lower quantity of an item and a later/greater quantity of the same item. Delays were randomly drawn from a set of ten delays (0, 1, 2, 3, 5, 7, 10, 14, 21, 30 days). The quantities for each option were adjusted on the basis of item-specific ratings, delays and a priori discount rates to evenly sample the value space. More specifically, the same distribution of option values was used for reward, effort, and punishment, and pseudo-randomly ordered across trials. This intertemporal choice task was used in two separate experiments. While delays indicated the date of delivery for reward items in both experiments, the framing was changed for effort and punishment items in Exp. 2. In Exp. 1 (8 participants), delays indicated the precise date at which efforts had to be exerted, or at which punishments had to be endured, which induced negative time preference (the desire to expedite efforts or suffer punishments sooner). In Exp. 2 (43 participants), however, delays indicated a time limit within which participants could freely decide when to exert the effort, or endure the punishment, which reverted the preferences toward deferring both efforts and punishments. Options were presented on the left and right sides of the screen. The side of the sooner option was counterbalanced across trials. Responses were self-paced, and the selected item was highlighted in red for 500 ms (plus a 0–2000 ms intertrial interval jitter in MRI sessions). The task consisted of 50 choices per block in Exp. 1, and 60 choices per block in Exp. 2, such that no item was repeated within a block. Reward, effort, and punishment blocks were repeated six times in Exp. 1 (behavioral sessions only, 8 participants), four times in behavioral sessions of Exp. 2 (16 participants), and twice in fMRI session of Exp. 2 (27 participants).

‘Now/Tomorrow’ choice task

This task was designed to assess how participants procrastinated in one-shot decisions that involved reward, effort, and delay. On each trial (Fig.  1c ), participants of Exp. 2 were proposed offers comprising both a reward and an effort item. They were asked to decide whether to produce the effort “Now” (and get the reward immediately) or “Tomorrow” (and get the reward on the next day). The two responses were randomly displayed on the left and right sides of the bottom part of the screen (position counterbalanced across trials). Reward and effort items were selected randomly from the same sets used in the rating tasks, with no repetition within a block. Reward and effort quantities were adjusted on the basis of participants’ item-specific ratings, such that reward values and efforts costs were drawn from the same uniform distribution and were pseudo-randomly ordered across trials. The task was self-paced and contained 3 blocks of 50 choices. To elicit genuine procrastination choices, participants had been asked, prior to participating in the study, to agree with coming twice to the lab for two experimental sessions on two consecutive days. They were also told that one trial would be randomly selected, and that they would receive the reward on the chosen day (today or tomorrow) and would have to perform the effort on the same day. They were informed only at the end of the experimental session that rewards and efforts were fictive and that a second visit was not required. During debriefing, none of the participants declared having doubted that the second visit would take place and that the rewards and efforts would be actually implemented.

Form-filling home task

In order to get an independent and naturalistic measure of recurrent procrastination, participants of exp. 2 ( n  = 43) were informed at the end of the experimental session that they would be given ten printed administrative forms (e.g., passport renewal form; Cerfa documents 10840, 12100, 12485, 12670, 12669, 14445, 14881, 50040, 50239, 50731) at the end of the experimental session, and that they would only receive their financial compensation for participating in the study after filling in the documents and sending a numeric copy by email within a time limit of 30 days. They were told that money transfer would occur as soon as the task was completed, and that no compensation would be transferred after the deadline. In reality, all participants were eventually paid, even those who did not completed the task within the allotted time. However, participants who never sent the forms back ( n  = 6) were not included in the analyses regarding this task, since there was no delay to predict in their case.

Behavioral analyses

Subjective values (equivalent gains) and subjective costs (equivalent losses) were estimated per unit for each item by averaging ratings made for 1€ questions and ratings made for 5€ questions (divided by 5). Using a linear scaling was a first-order approximation of the true utility function, which we considered reasonable given that reward values and effort costs were within the range of a few euros. To check whether this approximation was indeed reasonable, we fitted linear and power utility functions on individual ratings and use the fitted functions to estimate the values of options presented in the intertemporal choice task. The correlation between values estimated with linear and power functions was 0.98 for reward, 0.97 for effort, and 0.87 for punishment (Pearson’s correlation coefficients). We, therefore, kept the linear approximation to avoid introducing additional flexibility (with power parameters) in the choice models. In all choice tasks, we considered choices as the dependent variables, which were regressed against logistic models including experimental factors: difference in value (or cost) and difference in delay for the intertemporal choice tasks; reward value or effort cost on offer in the now/tomorrow choice task. Procrastination measures in the now/tomorrow choice task and in the form-filling home task were also regressed across participants against behavioral or neural measures of time preferences, as well as age and gender. Two-tailed t tests were used to assess the significance of regression coefficients across participants when they could in principle go both ways; one-tailed tests were used when the direction could only be one way (i.e., when testing correlations across participants between alternative measures of the same construct, such as procrastination level). For all t tests, the assumption of normality was tested using a Kolmogorov–Smirnov test. Comparisons of parameter means were performed using two-sample t tests assuming either equal or unequal variance. The assumption of homoscedasticity was tested using a two-sample F test for equal variance.

Computational models

In intertemporal choice tasks, options were discounted with delay D through classical hyperbolic discounting models, in which three discount rates k R , k E and k P characterized the steepness of the temporal discounting of reward, effort and punishment, respectively:

With V A the value of option A calculated as the number of elements N A on offer multiplied by the subjective value ( R i , inferred from subjective rating), discounted by the delay D A multiplied a temporal discount rate k.

We compared this hyperbolic discounting to two classical models of intertemporal choices: a present-bias model (Eq.  3 ), and a quasi-hyperbolic discounting model ( βδ model) that includes both a present bias parameter β and a constant exponential discount factor δ (Eq.  4 ). Notice that there is a discontinuity in these functions such that, if D A  = 0, V A  =  N Ai  ×  R i in both models. Further, both β and δ are constrained so that 0 ≥  β ≥ 1 and 0 ≥ δ ≥ 1.

Decisions were modeled with a softmax function that converted the value difference between the two options A and B into a choice probability, depending on a temperature parameter θ that captured choice stochasticity.

In the now/tomorrow choice task, the net value of options was modeled as the difference between the reward and the effort on offer, hyperbolically discounted with delay by rates k R and k E .

With delay D being 0 or 1 depending if the considered option was ‘Now’ or ‘Tomorrow’, k R and k E the temporal discount rates for reward and effort (inferred from intertemporal choices), N R and N E the quantities of reward i and effort j on offer, whose unitary gain and loss equivalents were R i and E j (inferred from subjective ratings). Again, a softmax function incorporating an inverse temperature parameter θ was used to map net value differences onto choice probabilities. The inverse temperature was individually adjusted, as a free parameter of no interest.

In the form-filling home task, models were meant to predict how many days participants would procrastinate in real-life when required to perform an effortful task before a deadline. We developed two models that both embedded the net value function with differential temporal discounting for effort and reward, but fundamentally differed in how procrastination was generated.

In the static model , procrastination results from a maximization, at the initial stage, of the expected net value over the course of the allotted time period. This model predicts that the task is completed on day d* task , which corresponds to the delay that maximizes the expected net value.

With d* task the optimal day for task completion, V d the value of performing the task on day d , R i the financial compensation contingent on completing the administrative forms and E i the subjective cost of filling in those forms.

In the dynamic model, by contrast, procrastination arises from binary decisions, repeated over time, about whether to postpone the task or not. On each day d , the probability of task completion is given by a softmax function that compares the net value V 0 of performing the task now to the net values V t of performing the task at any other moment before the deadline D . Net values were normalized per participant to account for interindividual differences in value range. Let τ be the time at which the task is eventually performed. The probability \({{{{{\rm{P}}}}}}\left(\tau \le {d}_{i}\right)\) of having completed the task before day d i is the probability of having not postponed the task on every day before d i , which is given by one minus the product of probabilities to postpone the task (i.e., not doing it now) on every day until d i :

where τ t is the delay that would be chosen at time t , under the assumption that the task was postponed until time t . Note that, by construction, \({{{{{\rm{P}}}}}}\left({\tau }_{D}=D\right)=1\) , i.e., the task cannot be postponed beyond the deadline. Under the dynamic model, the predicted task delay d* task is given by the expected time at which the task is performed:

where we have assumed that the decision about whether the task is postponed or not is repeated on each consecutive day.

To illustrate how the predicted delay of task completion was determined for each model, we simulated the expected net value of an option combining a subjective gain of 100 a.u. and a subjective cost of 85 a.u with a temporal discounting rate of 0.05 for reward and 0.2 for effort. We also performed 10 6 simulations, varying all free parameters with a deadline set to 30 days, and estimated the average procrastination duration in the space of discount rate parameters, marginalizing over reward values and effort costs. The simulations spanned the following ranges for the different parameters and variables: Ke  = 0–0.5, Kr  = 0–0.5, R  = 40–60 a.u., E  = 20–40 a.u., θ  = 0.5. We then informed these models with participant’s data (expected gratification, form-filling cost ratings, and temporal discounting rates for reward and effort) to model the observed procrastination duration.

Bayesian model estimation and selection

The different models were inverted using a variational Bayes approach under the Laplace approximation 72 , 73 , implemented in the VBA toolbox (available at https://mbb-team.github.io/VBA-toolbox ). This algorithm not only inverts nonlinear models with an efficient and robust parameter estimation, but also estimates the model evidence, which represents a trade-off between accuracy (goodness of fit) and complexity (degrees of freedom). The following non informative priors were used for parameters estimation: μ  = 0, σ  = 1 for Kr and Ke ; μ  = 1, σ  = 1 for θ . The model log-evidence was then used as a criterion to select which model best accounted for temporal discounting and recurrent procrastination.

Neuroimaging acquisition

Multiband T2*-weighted echoplanar images (EPIs) were acquired with blood oxygen level-dependent (BOLD) contrast on a 3.0 T MRI scanner (Siemens Trio) in 27 participants. The sample size was chosen to be larger than the sample size used in a previous study from our group that identified option values signals during intertemporal choice 74 . A tilted plane acquisition sequence was used to optimize functional sensitivity in the orbitofrontal cortex. To cover the whole brain (except the cerebellum), we used the following parameters: 1022 ms repetition time (TR), 25 ms echo time (TE), 45 slices, 2.5 mm slice thickness, 0.5 mm interslice gap, 2.5 mm × 2.5 mm in-plane resolution, 80 × 80 matrix, 60° flip angle, x3 acceleration factor. T1-weighted structural images were also acquired, coregistered with the mean EPI, segmented and normalized to a standard T1 template, and averaged across all participants to allow group-level anatomical localization. EPIs were analyzed in an event-related manner, within a GLM, using SPM12 ( www.fil.ion.ucl.ac.uk/spm ). The first five volumes of each session were discarded to allow for T1 equilibration effects. Preprocessing consisted of spatial realignment, normalization using the same transformation as structural images, and spatial smoothing using a Gaussian kernel with a full-width at half-maximum (FWHM) of 8 mm.

Neuroimaging analysis

We used a first GLM to generate SPMs of discounted reward and effort, as follows. All trials of the intertemporal choice tasks were modeled as single events with Dirac delta-functions at the time of deliberation onset. The difference in discounted value between chosen and unchosen rewards, or the difference in discounted cost between chosen and unchosen efforts or punishments, was incorporated as parametric modulation. These decision variables are typically signaled during the comparison of options by neural activity in key brain regions involved in value or cost estimation 52 , 53 , 54 . All regressors of interest were convolved with a canonical hemodynamic response function. To correct for motion artifact, subject-specific realignment parameters were modeled as covariates of no interest. Linear contrasts of regression coefficients (betas) were computed at the individual participant level and then taken to a group-level random effect analysis (using one-sample t test). All reported significant activations contained voxels surviving a threshold of p  < 0.05 after familywise error correction for multiple comparisons at the cluster level (c-FWE), unless otherwise mentioned.

To specify how procrastination was related to reward and effort temporal discounting, we extracted betas from a second GLM that incorporated one event per trial, at the time of deliberation onset. The difference in delay and in undiscounted value or cost between chosen and unchosen options were incorporated as parametric modulation. We extracted betas from vmPFC, AI and dmPFC regions of interest (ROIs) defined from published atlases: the vmPFC ROI corresponded to the 14 m area of the Mackey and Petrides probabilistic atlas 75 ; the anterior insula ROI consisted of the anterior short gyrus and the anterior inferior cortex from the Hammersmith atlas 76 ; and the dmPFC ROI corresponded to the paracingulate regions of the Harvard-Oxford brain atlas distributed with FSL ( https://www.fmrib.ox.ac.uk/fsl ) that do not extend anterior to the genu of the corpus callosum. Procrastination measures in the lab and at home were then regressed against a linear model that included the betas obtained for delay, undiscounted value and cost, as well as age and gender.

Reporting summary

Further information on research design is available in the  Nature Research Reporting Summary linked to this article.

Data availability

The raw behavioral data that support the findings of this study and brain maps are available for download ( https://github.com/rlebouc/procrastination ). Raw fMRI data can be obtained from the corresponding author upon reasonable request.  Source data are provided with this paper.

Code availability

All computer codes for analysis will be made available by the corresponding author upon reasonable request.

Change history

21 october 2022.

A Correction to this paper has been published: https://doi.org/10.1038/s41467-022-34142-7

Akerlof, G. A. Procrastination and obedience. Am. Econ. Rev. 81 , 1–19 (1991).

Google Scholar  

O’Donoghue, T. & Rabin, M. in Behavioral Dimensions of Retirement Economics (ed. Henry Aaron) (Brookings Institution Press, 1999).

Sirois, F. M. “I’ll look after my health, later”: a replication and extension of the procrastination-health model with community-dwelling adults. Pers. Indiv. Differ. 43 , 15–26 (2007).

Article   Google Scholar  

Ellis, A. & Knaus, W. J. Overcoming procrastination: or how to think and act rationally in spite of life’s inevitable hassles. (Institute for Rational Living, 1977).

Harriott, J. & Ferrari, J. R. Prevalence of procrastination among samples of adults. Psychol. Rep. 78 , 611–616 (1996).

Steel, P. The nature of procrastination: a meta-analytic and theoretical review of quintessential self-regulatory failure. Psychol. Bull. 133 , 65–94 (2007).

Article   PubMed   Google Scholar  

Gustavson, D., Miyake, A., Hewitt, J. & Friedman, N. Genetic relations among procrastination, impulsivity, and goal-management ability. Behav. Genet. 44 , 661–661 (2014).

Hu, Y., Liu, P. W., Guo, Y. Q. & Feng, T. Y. The neural substrates of procrastination: a voxel-based morphometry study. Brain Cogn. 121 , 11–16 (2018).

Liu, P. W. & Feng, T. Y. The overlapping brain region accounting for the relationship between procrastination and impulsivity: a voxel-based morphometry study. Neuroscience 360 , 9–17 (2017).

Article   CAS   PubMed   Google Scholar  

Zhang, W. W., Wang, X. P. & Feng, T. Y. Identifying the neural substrates of procrastination: a resting-state fMRI study. Sci. Rep. 6 , 33203 (2016).

Wu, H. et al. The procrastinators want it now: behavioral and event-related potential evidence of the procrastination of intertemporal choices. Brain Cogn. 107 , 16–23 (2016).

Wypych, M. et al. Attenuated brain activity during error processing and punishment anticipation in procrastination - a monetary Go/No-go fMRI study. Sci. Rep. 9 , 11492 (2019).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Zhang, S., Becker, B., Chen, Q. & Feng, T. Insufficient task‐outcome association promotes task procrastination through a decrease of hippocampal–striatal interaction. Hum. Brain Mapp. 40 , 597–607 (2019).

Zhang, S. et al. Outcome value and task aversiveness impact task procrastination through separate neural pathways. Cereb. Cortex 31 , 3846–3855 (2021).

Ferrari, J. R. & Tice, D. M. Procrastination as a self-handicap for men and women: a task-avoidance strategy in a laboratory setting. J. Res. Pers. 34 , 73–83 (2000).

Silver, M. & Sabini, J. Procrastinating. J. Theor. Soc. Behav. 11 , 207–221 (1981).

Chen, Z., Liu, P., Zhang, C. & Feng, T. Brain morphological dynamics of procrastination: the crucial role of the self-control, emotional, and episodic prospection network. Cereb. Cortex 30 , 2834–2853 (2020).

Sirois, F. & Pychyl, T. Procrastination and the priority of short‐term mood regulation: consequences for future self. Soc. Pers. Psychol. Compass 7 , 115–127 (2013).

Herrnstein, R. J. & Prelec, D. A theory of addiction. (1992).

Ainslie, G. Picoeconomics: The strategic interaction of successive motivational states within the person . (Cambridge University Press, 1992).

Mazur, J. E. An adjusting procedure for studying delayed reinforcement. In: Commons M.L., Mazur J.E., Nevin J.A. (eds) 55–73 (Psychology Press, 1987).

Zhang, S. & Feng, T. Modeling procrastination: asymmetric decisions to act between the present and the future. J. Exp. Psychol. 149 , 311 (2020).

Steel, P. & König, C. J. Integrating theories of motivation. Acad. Manag. Rev. 31 , 889–913 (2006).

Shu, S. B. & Gneezy, A. Procrastination of enjoyable experiences. J. Mark. Res. 47 , 933–944 (2010).

Ainslie, G. Procrastination, the basic impulse. The thief of time: Philosophical essays on procrastination , 11–27 (2010).

Skvortsova, V., Palminteri, S. & Pessiglione, M. Learning to minimize efforts versus maximizing rewards: computational principles and neural correlates. J. Neurosci. 34 , 15621–15630 (2014).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Pessiglione, M., Vinckier, F., Bouret, S., Daunizeau, J. & Le Bouc, R. Why not try harder? Computational approach to motivation deficits in neuro-psychiatric diseases. Brain 141 , 629–650 (2018).

Soman, D. The illusion of delayed incentives: evaluating future effort–money transactions. J. Mark. Res. 35 , 427–437 (1998).

Loewenstein, G. Anticipation and the valuation of delayed consumption. Economic J. 97 , 666–684 (1987).

Loewenstein, G. & Prelec, D. Anomalies in intertemporal choice: evidence and an interpretation. Q.J. Econ. 107 , 573–597 (1992).

Fischer, C. Read this paper even later: Procrastination with time-inconsistent preferences. (1999).

Shadmehr, R., Huang, H. J. & Ahmed, A. A. A representation of effort in decision-making and motor control. Curr. Biol. 26 , 1929–1934 (2016).

Augenblick, N., Niederle, M. & Sprenger, C. Working over time: dynamic inconsistency in real effort tasks. Q J. Econ. 130 , 1067–1115 (2015).

Haushofer, J., John, A. & Orkin, K. What motivates health behavior: preferences, beliefs, or constraints? evidence from psychological interventions in Kenya. (2018).

Yi, R., Gatchalian, K. M. & Bickel, W. K. Discounting of past outcomes. Exp. Clin. Psychopharmacol. 14 , 311 (2006).

Murphy, J. G., Vuchinich, R. E. & Simpson, C. A. Delayed reward and cost discounting. Psychol. Rec. 51 , 5 (2001).

Baker, F., Johnson, M. W. & Bickel, W. K. Delay discounting in current and never-before cigarette smokers: similarities and differences across commodity, sign, and magnitude. J. Abnorm. Psychol. 112 , 382 (2003).

Thaler, R. Some empirical-evidence on dynamic inconsistency. Econ. Lett. 8 , 201–207 (1981).

MacKeigan, L. D., Larson, L. N., Draugalis, J. R., Bootman, J. L. & Burns, L. R. Time preference for health gains versus health losses. Pharmacoeconomics 3 , 374–386 (1993).

Story, G. W. et al. Dread and the disvalue of future pain. PLoS Comput. Biol. 9 , e1003335 (2013).

Article   PubMed   PubMed Central   Google Scholar  

Berns, G. S. et al. Neurobiological substrates of dread. Science 312 , 754–758 (2006).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Xu, L., Liang, Z.-Y., Wang, K., Li, S. & Jiang, T. Neural mechanism of intertemporal choice: from discounting future gains to future losses. Brain Res. 1261 , 65–74 (2009).

Bickel, W. K., Pitcock, J. A., Yi, R. & Angtuaco, E. J. Congruence of BOLD response across intertemporal choice conditions: fictive and real money gains and losses. J. Neurosci. 29 , 8839–8846 (2009).

Zhang, Y. Y. et al. Separate neural networks for gains and losses in intertemporal choice. Neurosci. Bull. 34 , 725–735 (2018).

Hare, T. A., Camerer, C. F. & Rangel, A. Self-control in decision-making involves modulation of the vmPFC valuation system. Science 324 , 646–648 (2009).

Article   ADS   CAS   PubMed   Google Scholar  

Rangel, A. & Hare, T. Neural computations associated with goal-directed choice. Curr. Opin. Neurobiol. 20 , 262–270 (2010).

Croxson, P. L., Walton, M. E., O’Reilly, J. X., Behrens, T. E. & Rushworth, M. F. Effort-based cost-benefit valuation and the human brain. J. Neurosci. 29 , 4531–4541 (2009).

Kurniawan, I. T., Guitart-Masip, M., Dayan, P. & Dolan, R. J. Effort and valuation in the brain: the effects of anticipation and execution. J. Neurosci. 33 , 6160–6169 (2013).

Prevost, C., Pessiglione, M., Metereau, E., Clery-Melin, M. L. & Dreher, J. C. Separate valuation subsystems for delay and effort decision costs. J. Neurosci. 30 , 14080–14090 (2010).

Rudebeck, P. H., Walton, M. E., Smyth, A. N., Bannerman, D. M. & Rushworth, M. F. S. Separate neural pathways process different decision costs. Nat. Neurosci. 9 , 1161–1168 (2006).

Arulpragasam, A. R., Cooper, J. A., Nuutinen, M. R. & Treadway, M. T. Corticoinsular circuits encode subjective value expectation and violation for effortful goal-directed behavior. Proc. Natl Acad. Sci. USA 115 , E5233–E5242 (2018).

Klein-Flugge, M. C., Kennerley, S. W., Friston, K. & Bestmann, S. Neural signatures of value comparison in human cingulate cortex during decisions requiring an effort-reward trade-off. J. Neurosci. 36 , 10002–10015 (2016).

Boorman, E. D., Rushworth, M. F. & Behrens, T. E. Ventromedial prefrontal and anterior cingulate cortex adopt choice and default reference frames during sequential multi-alternative choice. J. Neurosci. 33 , 2242–2253 (2013).

Hunt, L. T. et al. Mechanisms underlying cortical activity during value-guided choice. Nat. Neurosci. 15 , 470–476 (2012).

Shenhav, A., Botvinick, M. M. & Cohen, J. D. The expected value of control: an integrative theory of anterior cingulate cortex function. Neuron 79 , 217–240 (2013).

Smith, E. H. et al. Widespread temporal coding of cognitive control in the human prefrontal cortex. Nat. Neurosci. 22 , 1883–1891 (2019).

Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S. & Cohen, J. D. Conflict monitoring and cognitive control. Psychol. Rev. 108 , 624–652 (2001).

Ferrari, J. R. Procrastination and impulsiveness: two sides of a coin? (1993).

Loehlin, J. C. & Martin, N. G. The genetic correlation between procrastination and impulsivity. Twin Res. Hum. Genet. 17 , 512–515 (2014).

Ferrari, J. R. & Emmons, R. A. Methods of procrastination and their relation to self-control and self-reinforcement - an exploratory-study. J. Soc. Behav. Pers. 10 , 135–142 (1995).

O’Donoghue, T. & Rabin, M. Doing it now or later. Am. Econ. Rev. 89 , 103–124 (1999).

Bjork, J. M., Momenan, R. & Hommer, D. W. Delay discounting correlates with proportional lateral frontal cortex volumes. Biol. Psychiatry 65 , 710–713 (2009).

Blain, B., Hollard, G. & Pessiglione, M. Neural mechanisms underlying the impact of daylong cognitive work on economic decisions. Proc. Natl Acad. Sci. USA 113 , 6967–6972 (2016).

Figner, B. et al. Lateral prefrontal cortex and self-control in intertemporal choice. Nat. Neurosci. 13 , 538 (2010).

Janakiraman, N. & Ordóñez, L. Effect of effort and deadlines on consumer product returns. J. Consum. Psychol. 22 , 260–271 (2012).

Tversky, A. & Shafir, E. Choice under conflict: the dynamics of deferred decision. Psychol. Sci. 3 , 358–361 (1992).

Taubinsky, D. From intentions to actions: a model and experimental evidence of inattentive choice. Harvard University Working Paper . (2014).

Altgassen, M., Scheres, A. & Edel, M. A. Prospective memory (partially) mediates the link between ADHD symptoms and procrastination. Atten. Defic. Hyperact. Disord. 11 , 59–71 (2019).

Holman, J. & Zaidi, F. The economics of prospective memory. Available at SSRN 1662183 (2010).

Ericson, K. M. On the interaction of memory and procrastination: implications for reminders, deadlines, and empirical estimation. J. Eur. Econ. Assoc. 15 , 692–719 (2017).

Trope, Y. & Liberman, N. Temporal construal. Psychol. Rev. 110 , 403 (2003).

Daunizeau, J., Friston, K. J. & Kiebel, S. J. Variational Bayesian identification and prediction of stochastic nonlinear dynamic causal models. Phys. D. 238 , 2089–2118 (2009).

Article   MathSciNet   CAS   MATH   Google Scholar  

Friston, K. J., Mattout, J., Trujillo-Barreto, N., Ashburner, J. & Penny, W. Variational free energy and the Laplace approximation. Neuroimage 34 , 220–234 (2007).

Lebreton, M., Jorge, S., Michel, V., Thirion, B. & Pessiglione, M. An automatic valuation system in the human brain: evidence from functional neuroimaging. Neuron 64 , 431–439 (2009).

Mackey, S. & Petrides, M. Architecture and morphology of the human ventromedial prefrontal cortex. Eur. J. Neurosci. 40 , 2777–2796 (2014).

Faillenot, I., Heckemann, R. A., Frot, M. & Hammers, A. Macroanatomy and 3D probabilistic atlas of the human insula. Neuroimage 150 , 88–98 (2017).

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This work was supported by a research grant of the French Medical Research Foundation (Fondation pour la Recherche Médicale) to R.L.B. and by an ERC starting grant to M.P. We thank Jean Daunizeau for helpful advice on computational modeling, and the Cenir (neuroimaging center of Paris Brain Institute) staff for help with the fMRI experiment.

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

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REVIEW article

How study environments foster academic procrastination: overview and recommendations.

• 1 Department of Psychology, UiT The Arctic University of Norway, Tromsø, Norway
• 2 Evaluation of Studies and Teaching and Higher Education Research, University of Cologne, Cologne, Germany
• 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.

Table 1. 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.

Figure 1. 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.

Table 2. 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.

• ^ 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 ).
• ^ Svartdal et al. (2020) . Unpublished data.

Achieve Inc. (2005). Rising to the Challenge: Are High School Graduates Prepared for College and Work?. Washington, DC: Achieve Inc.

Google Scholar

Ackerman, D. S., and Gross, B. L. (2005). My instructor made me do it: task characteristics of procrastination. J. Market. Educ. 27, 5–13. doi: 10.1177/0273475304273842

CrossRef Full Text | Google Scholar

Ames, C. (1992). “Achievement goals and the classroom motivational climate,” in Student Perceptions in the Classroom , eds D. H. Schunk and J. L. Meece(Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.), 327–348.

Ariely, D., and Wertenbroch, K. (2002). Procrastination, deadlines, and performance: self-control by precommitment. Psychol. Sci. 13, 219–224. doi: 10.1111/1467-9280.00441

PubMed Abstract | CrossRef Full Text | Google Scholar

Bandura, A. (1985). Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ: Prentice-Hall.

Bandura, A. (1997). Self-efficacy: The Exercise of Control. New York, NY: Macmillan.

Bandura, A., and Schunk, D. H. (1981). Cultivating competence, self-efficacy, and intrinsic interest through proximal self-motivation. J. Pers. Soc. Psychol. 41, 586–598. doi: 10.1037/0022-3514.41.3.586

Baumeister, R. F., and Heatherton, T. F. (1996). Self-regulation failure: an overview. Psychol. Inq. 7, 1–15. doi: 10.1207/s15327965pli0701_1

Baumeister, R. F., and Tierney, J. (2011). Willpower: Rediscovering The Greatest Human Strength. New York, NY: Penguin.

Blasiman, R. N., Dunlosky, J., and Rawson, K. A. (2017). The what, how much, and when of study strategies: comparing intended versus actual study behaviour. Memory 25, 784–792. doi: 10.1080/09658211.2016.1221974

Blunt, A. K., and Pychyl, T. A. (2000). Task aversiveness and procrastination: a multi-dimensional approach to task aversiveness across stages of personal projects. Pers. Indiv. Differ. 28, 153–167. doi: 10.1016/S0191-8869(99)00091-4

Breuer, J., and Bente, G. (2010). Why so serious? On the relation of serious games and learning. J. Comp. Game Cult. 4, 7–24.

Chen, B., Shi, Z., and Wang, Y. (2016). Do peers matter? Resistance to peer influence as a mediator between self-esteem and procrastination among undergraduates. Front. Psychol. 7:1529. doi: 10.3389/fpsyg.2016.01529

Codina, N., Valenzuela, R., Pestana, J. V., and Gonzalez-Conde, J. (2018). Relations between student procrastination and teaching styles: autonomy-supportive and controlling. Front. Psychol. 9:809. doi: 10.3389/fpsyg.2018.00809

Deci, E. L., and Ryan, R. M. (2002). “Overview of self-determination theory: an organismic dialectical perspective,” in Handbook of Self-Determination Research , eds E. L. Deci and R. M. Ryan (Rochester, NY: University of Rochester Press), 3–33.

Duckworth, A. L., Grant, H., Loew, B., Oettingen, G., and Gollwitzer, P. M. (2011). Self-regulation strategies improve self-discipline in adolescents: benefits of mental contrasting and implementation intentions. Educ. Psychol. 31, 17–26. doi: 10.1080/01443410.2010.506003

Duckworth, A. L., and Seligman, M. E. (2005). Self-discipline outdoes IQ in predicting academic performance of adolescents. Psychol. Sci. 16, 939–944. doi: 10.1111/j.1467-9280.2005.01641.x

Dunlosky, J., and Rawson, K. A. (2015). Practice tests, spaced practice, and successive relearning: tips for classroom use and for guiding students’ learning. Scholarsh. Teach. Learn. Psychol. 1:72. doi: 10.1037/stl0000024

Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., and Willingham, D. T. (2013). Improving students’ learning with effective learning techniques: promising directions from cognitive and educational psychology. Psychol. Sci. Publ. Interest 14, 4–58. doi: 10.1177/1529100612453266

Durkin, K., and Main, A. (2002). Discipline-based study skills support for first-year undergraduate students. Active Learn. High. Educ. 3, 24–39. doi: 10.1177/1469787402003001003

Dute, D. J., Bemelmans, W. J. E., and Breda, J. (2016). Using mobile apps to promote a healthy lifestyle among adolescents and students: a review of the theoretical basis and lessons learned. JMIR mHealth uHealth 4:e39. doi: 10.2196/mhealth.3559

Eberly, M. B., Newton, S. E., and Wiggins, R. A. (2001). The syllabus as a tool for student-centered learning. J. Gen. Educ. 50, 56–74. doi: 10.1353/jge.2001.0003

Ellis, A., and Knaus, W. (1977). Overcoming Procrastination. New York, NY: Signet.

Flett, G. L., Blankstein, K. R., and Martin, T. R. (1995). “Procrastination, negative self-evaluation, and stress in depression and anxiety,” in Procrastination and Task Avoidance: Theory, Research, and Treatment , eds J. R. Ferrari, J. L. Johnson, and W. G. McCown (New York, NY: Plenum Press), 41–46.

Gettinger, M., and Seibert, J. K. (2002). Contributions of study skills to academic competence. Schl. Psychol. Rev. 31, 350–365. doi: 10.1080/02796015.2002.12086160

Gierk, B., Kohlmann, S., Kroenke, K., Spangenberg, L., Zenger, M., Brähler, E., et al. (2014). The somatic symptom scale-8 (SSS-8): a brief measure of somatic symptom burden. JAMA Inter. Medic. 174, 399–407. doi: 10.1001/jamainternmed.2013.12179

Goffe, W. L., and Kauper, D. (2014). A survey of principles instructors: why lecture prevails. J. Econ. Educ. 45, 360–375. doi: 10.1080/00220485.2014.946547

Gollwitzer, P. M., and Sheeran, P. (2006). Implementation intentions and goal achievement: a meta-analysis of effects and processes. Adv. Exp. Soc. Psychol. 38, 69–119. doi: 10.1016/S0065-2601(06)38002-1

Grau, J., and Minguillon, J. (2013). When procrastination leads to dropping out: analysing students at risk. ELearn Center Res. Pap. Ser. 6, 63–74.

Gröpel, P., and Steel, P. (2008). A mega-trial investigation of goal setting, interest enhancement, and energy on procrastination. Pers. Indiv. Differ. 45, 406–411. doi: 10.1016/j.paid.2008.05.015

Grunschel, C., Patrzek, J., and Fries, S. (2013). Exploring reasons and consequences of academic procrastination: an interview study. Eur. J. Psychol. Educ. 28, 841–861. doi: 10.1007/s10212-012-0143-4

Hagger, M. S., Wood, C., Stiff, C., and Chatzisarantis, N. L. D. (2010). Ego depletion and the strength model of self-control: a meta-analysis. Psychol. Bull. 136, 495–525. doi: 10.1037/a0019486

Harris, N. N., and Sutton, R. I. (1983). Task procrastination in organizations: a framework for research. Hum. Relat. 36, 987–995. doi: 10.1177/001872678303601102

Hattie, J., Biggs, J., and Purdie, N. (1996). Effects of learning skills interventions on student learning: a meta-analysis. Rev. Educ. Res. 66, 99–136. doi: 10.3102/00346543066002099

Haycock, L. A., McCarthy, P., and Skay, C. L. (1998). Procrastination in college students: the role of self-efficacy and anxiety. J. Counsel. Dev. 76, 317–324. doi: 10.1002/j.1556-6676.1998.tb02548.x

Hinsch, C., and Sheldon, K. M. (2013). The impact of frequent social internet consumption: increased procrastination and lower life satisfaction. J. Consum. Behav. 12, 496–505. doi: 10.1002/cb.1453

Howell, A. J., and Watson, D. C. (2007). Procrastination: associations with achievement goal orientation and learning strategies. Pers. Indiv. Differ. 43, 167–178. doi: 10.1016/j.paid.2006.11.017

Jacobson, N. S., Martell, C. R., and Dimidjian, S. (2001). Behavioral activation treatment for depression: returning to contextual roots. Clin. Psychol. Sci. Pract. 8, 255–270. doi: 10.1093/clipsy.8.3.255

Janssen, T., and Carton, J. S. (1999). The effects of locus of control and task difficulty on procrastination. J. Genet. Psychol. 160, 436–442. doi: 10.1080/00221329909595557

Johnson, D. W., and Johnson, R. T. (2002). Social interdependence theory and university instruction – theory into practice. Swiss J. Psychol. 61, 119–129. doi: 10.1024//1421-0185.61.3.119

Johnson, D. W., and Johnson, R. T. (2009). An educational psychology success story: social interdependence theory and cooperative learning. Educ. Res. 38, 365–379. doi: 10.3102/0013189X09339057

Kahneman, D., and Lovallo, D. (1993). Timid choices and bold forecasts: a cognitive perspective on risk taking. Manag. Sci. 39, 17–31. doi: 10.1287/mnsc.39.1.17

Kahneman, D., and Tversky, A. (1979). Intuitive prediction: biases and corrective procedures. TIMS Stud. Manag. Sci. 12, 313–327.

Karau, S. J., and Williams, K. D. (1993). Social loafing: a meta-analytic review and theoretical integration. J. Pers. Soc. Psychol. 65, 681–706. doi: 10.1037/0022-3514.65.4.681

Kim, K. R., and Seo, E. H. (2015). The relationship between procrastination and academic performance: a meta-analysis. Pers. Indiv. Differ. 82, 26–33. doi: 10.1016/j.paid.2015.02.038

Klassen, R. M., Krawchuk, L. L., and Rajani, S. (2008). Academic procrastination of undergraduates: low self-efficacy to self-regulate predicts higher levels of procrastination. Contemp. Educ. Psychol. 33, 915–931. doi: 10.1016/j.cedpsych.2007.07.001

Klingsieck, K. B. (2013). Procrastination: when good things don’t come to those who wait. Eur. Psychol. 18, 24–34. doi: 10.1027/1016-9040/a000138

Klingsieck, K. B., Grund, A., Schmid, S., and Fries, S. (2013). Why students procrastinate: a qualitative approach. J. Coll. Stud. Dev. 54, 397–412. doi: 10.1353/csd.2013.0060

Klomegah, R. Y. (2007). Predictors of academic performance of university students: an application of the goal efficacy model. Coll. Stud. J. 41, 407–415.

Knapstad, M., Heradstveit, O., and Sivertsen, B. (2018). Studentenes Helse- og Trivselsundersøkelse 2018. [Students’ Health and Wellbeing Study 2018]. Oslo: SiO.

Kreber, C., Castleden, H., Erfani, N., and Wright, T. (2005). Self-regulated learning about university teaching: an exploratory study. Teach. High. Educ. 10, 75–97. doi: 10.1080/1356251052000305543

Kurtovic, A., Vrdoljak, G., and Idzanovic, A. (2019). Predicting procrastination: the role of academic achievement, self-efficacy and perfectionism. Int. J. Educ. Psychol. 8, 1–26. doi: 10.17583/ijep.2019.2993

Lay, C. H. (1986). At last, my research article on procrastination. J. Res. Pers. 20, 474–495. doi: 10.1016/0092-6566(86)90127-3

Lay, C. H. (1992). Trait procrastination and the perception of person-task characteristics. J. Soc. Behav. Person. 7, 483–494.

Lay, C. H., and Schouwenburg, H. C. (1993). Trait procrastination, time management, and academic behavior. J. Soc. Behav. Pers. 8, 647–662.

Lepp, A., Barkley, J. E., and Karpinski, A. C. (2015). The relationship between cell phone use and academic performance in a sample of US college students. Sage Open 5, doi: 10.1177/2158244015573169

Li, L., Gao, H., and Xu, Y. (2020). The mediating and buffering effect of academic self-efficacy on the relationship between smartphone addiction and academic procrastination. Comput. Educ. 159:104001. doi: 10.1016/j.compedu.2020.104001

Lund, H. G., Reider, B. D., Whiting, A. B., and Prichard, J. R. (2010). Sleep patterns and predictors of disturbed sleep in a large population of college students. J. Adolesc. Health 46, 124–132. doi: 10.1016/j.jadohealth.2009.06.016

Milgram, N., Marshevsky, S., and Sadeh, C. (1995). Correlates of academic procrastination: discomfort, task aversiveness, and task capability. J. Psychol. 129, 145–155. doi: 10.1080/00223980.1995.9914954

Milgram, N. A., Sroloff, B., and Rosenbaum, M. (1988). The procrastination of everyday life. J. Res. Pers. 22, 197–212. doi: 10.1016/0092-6566(88)90015-3

Morehead, K., Rhodes, M. G., and DeLozier, S. (2016). Instructor and student knowledge of study strategies. Memory 24, 257–271. doi: 10.1080/09658211.2014.1001992

Nordby, K., Klingsieck, K., and Svartdal, F. (2017). Do procrastination-friendly environments make students delay unnecessarily? Soc. Psychol. Educ. 20, 491–512. doi: 10.1007/s11218-017-9386-x

Panek, E. (2014). Left to their own devices: college students’ “guilty pleasure” media use and time management. Commun. Res. 41, 561–577. doi: 10.1177/0093650213499657

Patrzek, J., Grunschel, C., and Fries, S. (2012). Academic procrastination: the perspective of university counsellors. Int. J. Adv. Counsel. 34, 185–201. doi: 10.1007/s10447-012-9150-z

Pychyl, T. A., Morin, R. W., and Salmon, B. R. (2000). Procrastination and the planning fallacy: an examination of the study habits of university students. J. Soc. Behav. Pers. 15, 135–150.

Quan-Haase, A., and Young, A. L. (2010). Uses and gratifications of social media: a comparison of facebook and instant messaging. Bull. Sci. Technol. Soc. 30, 350–361. doi: 10.1177/0270467610380009

Reinecke, L., and Hofmann, W. (2016). Slacking off or winding down? An experience sampling study on the drivers and consequences of media use for recovery versus procrastination. Hum. Commun. Res. 42, 441–461. doi: 10.1111/hcre.12082

Reinecke, L., Meier, A., Aufenanger, S., Beutel, M. E., Dreier, M., Quiring, O., et al. (2018a). Permanently online and permanently procrastinating? The mediating role of Internet use for the effects of trait procrastination on psychological health and well-being. New Media Soc. 20, 862–880. doi: 10.1177/1461444816675437

Reinecke, L., Meier, A., Beutel, M. E., Schemer, C., Stark, B., Wölfling, K., et al. (2018b). The relationship between trait procrastination, Internet use, and psychological functioning: results from a community sample of German adolescents. Front. Psychol. 9:913. doi: 10.3389/fpsyg.2018.00913

Robbins, S. B., Lauver, K., Le, H., Davis, D., Langley, R., and Carlstrom, A. (2004). Do psychosocial and study skill factors predict college outcomes? A meta-analysis. Psychol. Bull. 130, 261–288. doi: 10.1037/0033-2909.130.2.261

Rogers, P. M. (2008). The Development of Writers and Writing Abilities: A Longitudinal Study Across and Beyond the College-Span. Doctoral dissertation. Oakland, CA: University of California.

Rowe, A. D., Fitness, J., and Wood, L. N. (2015). University student and lecturer perceptions of positive emotions in learning. Int. J. Qual. Stud. Educ. 28, 1–20. doi: 10.1080/09518398.2013.847506

Schouwenburg, H. C. (1995). “Academic procrastination,” in Procrastination and Task Avoidance: Theory, Research, and Treatment , eds J. R. Ferrari, J. L. Johnson, and W. G. McCown (New York, NY: Plenum Press), 71–96. doi: 10.1007/978-1-4899-0227-6_4

Schouwenburg, H. C. (2004). “Procrastination in academic settings: general introduction,” in Counseling the Procrastinator in Academic Settings , eds H. C. Schouwenburg, C. H. Lay, T. A. Pychyl, and J. R. Ferrari (Washington, DC: American Psychological Association), 3–17. doi: 10.1037/10808-001

Schouwenburg, H. C., and Groenewoud, J. T. (2001). Study motivation under social temptation; effects of trait procrastination. Pers. Indiv. Differ. 30, 229–240. doi: 10.1016/S0191-8869(00)00034-9

Schraw, G., Wadkins, T., and Olafson, L. (2007). Doing the things we do: a grounded theory of academic procrastination. J. Educ. Psychol. 99, 12–25. doi: 10.1037/0022-0663.99.1.12

Sirois, F. M. (2007). “I’ll look after my health, later”: a replication and extension of the procrastination–health model with community-dwelling adults. Pers. Indiv. Differ. 43, 15–26. doi: 10.1016/j.paid.2006.11.003

Sirois, F. M. (2014). Procrastination and stress: exploring the role of self-compassion. Self Ident. 13, 128–145. doi: 10.1080/15298868.2013.763404

Sirois, F. M., and Pychyl, T. (2013). Procrastination and the priority of short-term mood regulation: consequences for future self. Soc. Pers. Psychol. Comp. 7, 115–127. doi: 10.1111/spc3.12011

Solomon, L. J., and Rothblum, E. D. (1984). Academic procrastination: frequency and cognitive-behavioral correlates. J. Counsel. Psychol. 31, 503–509. doi: 10.1037/0022-0167.31.4.503

Springer, L., Stanne, M. E., and Donovan, S. S. (1999). Effects of small-group learning on undergraduates in science, mathematics, engineering, and technology: a meta-analysis. Rev. Educ. Res. 69, 21–51. doi: 10.3102/00346543069001021

Steel, P. (2007). The nature of procrastination: a meta-analytic and theoretical review of quintessential self-regulatory failure. Psychol. Bull. 133, 65–94. doi: 10.1037/0033-2909.133.1.65

Steel, P. (2010). Arousal, avoidant, and decisional procrastinators: do they exist? Pers. Indiv. Differ. 48, 926–934. doi: 10.1016/j.paid.2010.02.025

Steel, P., and König, C. J. (2006). Integrating theories of motivation. Acad. Manag. Rev. 3, 889–913. doi: 10.5465/amr.2006.22527462

Steel, P., Svartdal, F., Thundiyil, T., and Brothen, T. (2018). Examining procrastination across multiple goal stages: a longitudinal study of temporal motivation theory. Front. Psychol. 9:327. doi: 10.3389/fpsyg.2018.00327

Stöber, J., and Joormann, J. (2001). Worry, procrastination, and perfectionism: differentiating amount of worry, pathological worry, anxiety, and depression. Cogn. Ther. Res. 25, 49–60. doi: 10.1023/A:1026474715384

Svartdal, F., Klingsieck, K. B., Steel, P., and Gamst-Klaussen, T. (2020). Measuring implemental delay in procrastination: separating onset and sustained goal striving. Pers. Indiv. Differ. 156:109762. doi: 10.1016/j.paid.2019.109762

Tice, D. M., and Baumeister, R. F. (1997). Longitudinal study of procrastination, performance, stress, and health: the costs and benefits of dawdling. Psychol. Sci. 8, 454–458. doi: 10.1111/j.1467-9280.1997.tb00460.x

Tice, D. M., Bratslavsky, E., and Baumeister, R. F. (2001). Emotional distress regulation takes precedence over impulse control: if you feel bad, do it! J. Pers. Soc. Psychol. 80, 53–67. doi: 10.1037/0022-3514.80.1.53

Tressel, T., Lajoie, S. P., and Duffy, M. C. (2019). A guide for study terminology: reviewing a fragmented domain. Can. Psychol. 60, 115–127. doi: 10.1037/cap0000138

Tuckman, B. W. (1991). The development and concurrent validity of the procrastination scale. Educ. Psychol. Meas. 51, 473–480. doi: 10.1177/0013164491512022

Tuckman, B. W., and Schouwenburg, H. C. (2004). “Behavioral interventions for reducing procrastination among university students,” in Counseling the Procrastinator in Academic Setting , eds H. C. Shouwenburg, C. H. Lay, T. A. Pychl, and J. R. Ferrari (Washington, DC: American Psychological Association), 91–103. doi: 10.1037/10808-007

Valenzuela, R., Codina, N., Castillo, I., and Pestana, J. V. (2020). Young university students’ academic self-regulation profiles and their associated procrastination: autonomous functioning requires self-regulated operations. Front. Psychol. 11:354. doi: 10.3389/fpsyg.2020.00354

van Eerde, W. (2000). Procrastination: self-regulation in initiating aversive goals. Appl. Psychol. 49, 372–389. doi: 10.1111/1464-0597.00021

van Eerde, W. (2003). A meta-analytically derived nomological network of procrastination. Pers. Indiv. Differ. 35, 1401–1418. doi: 10.1016/S0191-8869(02)00358-6

van Eerde, W., and Klingsieck, K. B. (2018). Overcoming procrastination? A meta-analysis of intervention studies. Educ. Res. Rev. 25, 73–85. doi: 10.1016/j.edurev.2018.09.002

van Grinsven, L., and Tillema, H. (2006). Learning opportunities to support student self-regulation: comparing different instructional formats. Educ. Res. 48, 77–91. doi: 10.1080/00131880500498495

Veale, D. (2008). Behavioural activation for depression. Adv. Psychiatr. Treat. 14, 29–36. doi: 10.1192/apt.bp.107.004051

Visser, L., Korthagen, F. A., and Schoonenboom, J. (2018). Differences in learning characteristics between students with high, average, and low levels of academic procrastination: students’ views on factors influencing their learning. Front. Psychol. 9:808. doi: 10.3389/fpsyg.2018.00808

Wäschle, K., Allgaier, A., Lachner, A., Fink, S., and Nückles, M. (2014). Procrastination and self-efficacy: tracing vicious and virtuous circles in self-regulated learning. Learn. Instruct. 29, 103–114. doi: 10.1016/j.learninstruc.2013.09.005

Weber, B., and Hertel, G. (2007). Motivation gains of inferior group members: a meta-analytical review. J. Pers. Soc. Psychol. 93, 973–993. doi: 10.1037/0022-3514.93.6.973

Weinstein, C. E., Husman, J., and Dierking, D. R. (2000). “Self-regulation interventions with a focus on learning strategies,” in Handbook of Self-Regulation , eds M. Boekaerts, P. R. Pintrich, and M. Zeidner (San Diego, CA: Academic Press), 727–747. doi: 10.1016/b978-012109890-2/50051-2

Wibrowski, C. R., Matthews, W. K., and Kitsantas, A. (2017). The role of a skills learning support program on first-generation college students’ self-regulation, motivation, and academic achievement: a longitudinal study. J. Coll. Stud. Retent. Res. Theory Pract. 19, 317–332. doi: 10.1177/1521025116629152

Wieman, C., and Gilbert, S. (2015). Taking a scientific approach to science education, Part II-Changing teaching. Microbe 10, 203–207. doi: 10.1128/microbe.10.203.1

Wingate, U. (2007). A framework for transition: supporting ‘learning to learn’ in higher education. High. Educ. Q. 61, 391–405. doi: 10.1111/j.1468-2273.2007.00361.x

Wolters, C. A. (2003). Understanding procrastination from a self-regulated learning perspective. J. Educ. Psychol. 95, 179–187. doi: 10.1037/0022-0663.95.1.179

Zarick, L. M., and Stonebraker, R. (2009). I’ll do it tomorrow: the logic of procrastination. Coll. Teach. 57, 211–215. doi: 10.1080/87567550903218687

Zhang, Y., Dong, S., Fang, W., Chai, X., Mei, J., and Fan, X. (2018). Self-efficacy for self-regulation and fear of failure as mediators between self-esteem and academic procrastination among undergraduates in health professions. Adv. Health Sci. Educ. 23, 817–830. doi: 10.1007/s10459-018-9832-3

Zhu, M., Bagchi, R., and Hock, S. J. (2019). The mere deadline effect: why more time might sabotage goal pursuit. J. Consum. Res. 45, 1068–1084. doi: 10.1093/jcr/ucy030

Zimmerman, B. J. (2000). “Attaining self-regulation: a social cognitive perspective,” in Handbook of Self-Regulation , eds M. Boekaerts, P. R. Pintrich, and M. Zeidne (Cambridge, MA:Academic Press), 13–39. doi: 10.1016/B978-012109890-2/50031-7

Zimmerman, B., and Schunk, D. (2004). “Self-regulating intellectual processes and outcomes: a social cognitive perspective,” in Motivation, Emotion and Cognition, Integrative Perspectives on Intellectual Functioning and Development , eds D. Dai and R. Sternberg (Abingdon: Routledge), 323–349.

Zimmerman, B. J., and Cleary, T. J. (2006). “Adolescents’ development of personal agency,” in The Role of Self-Efficacy Belief and Self-Regulatory Skill , eds F. Pajares and T. Urdan (Greenwich, CT: Information Age Publishing).

Keywords : academic procrastination, study environments, social factors, self-regulation, impulsivity, task aversiveness

Citation: Svartdal F, Dahl TI, Gamst-Klaussen T, Koppenborg M and Klingsieck KB (2020) How Study Environments Foster Academic Procrastination: Overview and Recommendations. Front. Psychol. 11:540910. doi: 10.3389/fpsyg.2020.540910

Received: 06 March 2020; Accepted: 12 October 2020; Published: 02 November 2020.

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Copyright © 2020 Svartdal, Dahl, Gamst-Klaussen, Koppenborg 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: Frode Svartdal, [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.

What Research Has Been Conducted on Procrastination? Evidence From a Systematical Bibliometric Analysis

Affiliation.

• 1 School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China.
• PMID: 35185729
• PMCID: PMC8847795
• DOI: 10.3389/fpsyg.2022.809044

Procrastination is generally perceived as a common behavioral tendency, and there are a growing number of literatures to discuss this complex phenomenon. To elucidate the overall perspective and keep abreast of emerging trends in procrastination research, this article presents a bibliometric analysis that investigates the panorama of overviews and intellectual structures of related research on procrastination. Using the Web of Science Database, we collected 1,635 articles published between 1990 and 2020 with a topic search on "procrastination" and created diverse research maps using CiteSpace and VOS viewer. Bibliometric analysis in our research consists of category distribution, keyword co-occurrence networks, main cluster analysis, betweenness centrality analysis, burst detection analysis, and structure variation analysis. We find that most research has focused on students' samples and has discussed the definition, classification, antecedents, consequences and interventions to procrastination, whereas procrastination in diverse contexts and groups remains to be investigated. Regarding the antecedents and consequences, research has mainly been about the relationship between procrastination and personality differences, such as the five-factor model, temperament, character, emotional intelligence, and impulsivity, but functions of external factors such as task characteristics and environmental conditions to procrastination have drawn scant attention. To identify the nature and characteristics of this behavior, randomized controlled trials are usually adopted in designing empirical research. However, the predominant use of self-reported data collection and for a certain point in time rather than longitudinal designs has limited the validation of some conclusions. Notably, there have been novel findings through burst detection analysis and structure variation analysis. Certain research themes have gained extraordinary attention in a short time period, have evolved progressively during the time span from 1990 to 2020, and involve the antecedents of procrastination in a temporal context, theoretical perspectives, research methods, and typical images of procrastinators. And emerging research themes that have been investigated include bedtime procrastination, failure of social media self-control, and clinical interventions. To our knowledge, this is almost the first time to conduct systematically bibliometric analysis on the topic of procrastination and findings can provide an in-depth view of the patterns and trends in procrastination research.

Keywords: CiteSpace; bibliometric analysis; co-citation analysis; intellectual structure; procrastination.

Copyright © 2022 Yan and Zhang.

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• Systematic Review

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Exploring 40 years on affective correlates to procrastination: a literature review of situational and dispositional types

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• Volume 41 , pages 1097–1111, ( 2022 )

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• Reza Feyzi Behnagh   ORCID: orcid.org/0000-0002-4109-3501 1 &
• Joseph R. Ferrari 2  

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The relationship between different emotions with situational (e.g., academic) and dispositional (chronic) procrastination was examined extensively in the literature since the early days of procrastination research. A review of empirical studies over the past 40 years might shed light on the role of emotions in procrastination in different contexts with different populations. The current paper reviewed 83 studies (from 1977 to 2021) exploring the relationship between 9 different emotions and situational and dispositional procrastination. The emotions examined, listed in the order of the extent of focus of scholarly research are: anxiety, fear, shame, guilt, regret, boredom, frustration, anger, and revenge. Findings highlight the important role of emotions as motives, antecedents, correlates, or consequences of situational and dispositional procrastination. Based on the findings, a lack of a comprehensive theory summarizing dispositional and situational procrastination is pointed out and avenues for future research are outlined and recommended.

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Aitken, N. D. (1982). College student performance, satisfaction, and retention: Specification and estimation of a structural model. The Journal of Higher Education, 53 (1), 32–50. https://doi.org/10.1080/00221546.1982.11780423 .

Article   Google Scholar  

Ariani, D. W., & Susilo, Y. S. (2018). Why do it later? Goal orientation, self-efficacy, test anxiety, on procrastination. ECPS Journal, 17 , 45–73. https://doi.org/10.7358/ecps-2018-017-wahy .

Ashwin, G., Polizzi, C. P., & Mattson, R. E. (2019). Mindfulness, procrastination, and anxiety: Assessing their interrelationships. Psychology of consciousness: Theory, research, and practice . . https://doi.org/10.1037/cns0000209 .

Book   Google Scholar  

Azure, J. A. (2011). Correlates of course anxiety and academic procrastination in higher education. Global Journal of Educational Research, 10 (1), 55–65.

Google Scholar  

Balkis, M. (2013). Academic procrastination, academic life satisfaction, and academic achievement: The mediating role of rational beliefs about studying. Journal of Cognitive and Behavioral Psychotherapies, 13 (1), 57–74.

Balkis, M., & Duru, E. (2019). Procrastination and rational/irrational beliefs: A moderated mediation model. Journal of Rational-Emotive & Cognitive-Behavior Therapy, 37 (3), 299–315. https://doi.org/10.1007/s10942-019-00314-6 .

Berking, M., & Whitley, B. (2014). Affect regulation training (ART) Springer https://doi.org/10.1007/978-1-4939-1022-9 .

Berking, M., Wupperman, P., Reichardt, A., Pejic, T., Dippel, A., & Znoj, H. (2008). Emotion regulation skills as a treatment target in psychotherapy. Behavior Research and Therapy, 46 (11), 1230–1237. https://doi.org/10.1016/j.brat.2008.08.005 .

Beswick, G., Rothblum, E. D., & Mann, L. (1988). Psychological antecedents of student procrastination. Australian Psychologist, 23 (2), 207–217. https://doi.org/10.1080/00050068808255605 .

Blunt, A., & Pychyl, T. A. (1998). Volitional action and inaction in the lives of undergraduate students: State orientation, procrastination, and proneness to boredom. Personality and Individual Differences, 24 (5), 837–846. https://doi.org/10.1016/S0191-8869(98)00018-X .

Blunt, A., & Pychyl, T. A. (2005). Task aversiveness and procrastination: A multi-dimensional approach to task aversiveness across stages of personal projects. Personality and Individual Differences, 28 , 153–167. https://doi.org/10.1016/S0191-8869(99)00091-4 .

Börsch-Supan, A. H., Bucher-Koenen, T., Hurd, M. D., & Rohwedder, S. (2018). Saving regret (No. w25238). National Bureau of Economic Research. https://doi.org/10.3386/w25238 .

Burka, J. B., & Yuen, L. M. (1983). Procrastination: Why you do it and what to do about it. Addison-Wesley.

Burka, J. B., & Yuen, L. M. (2008). Procrastination: Why you do it, what to do about it now. Da Capo Press.

Cohen, J. R., & Ferrari, J. R. (2010). Take some time to think this over: The relation between rumination, indecision, and creativity. Creativity Research Journal, 22 (1), 68–73. https://doi.org/10.1080/10400410903579601 .

Constantin, K., English, M. M., & Mazmanian, D. (2018). Anxiety, depression, and procrastination among students: Rumination plays a larger mediating role than worry. Journal of Rational Emotional Cognitive-Behavioral Therapy, 36 , 15–27. https://doi.org/10.1007/s10942-017-0271-5 .

Custer, N. (2018). Test anxiety and academic procrastination among prelicensure nursing students. Nursing Education Perspectives, 39 (3), 162–163.

Day, V., Mesnick, D., & O’Sullivan, M. (2000). Patterns of academic procrastination. Journal of College Reading and Learning, 30 , 120–134. https://doi.org/10.1080/10790195.2000.10850090 .

Diaz-Morales, J. F., Ferrari, J. R., Argumedo, D., & Diaz, K. (2006). Procrastination and demographic characteristics in Spanish adults: Further evidence. The Journal of Social Psychology, 146 (5), 629–633. https://doi.org/10.3200/SOCP.146.5.629-633 .

Article   PubMed   Google Scholar  

Dunn, K. (2014). Why wait? The influence of academic self-regulation, intrinsic motivation, and statistics anxiety on procrastination in online statistics. Innovation Higher Education, 39 , 33–44. https://doi.org/10.1007/s10755-013-9256-1 .

Eckert, M., Ebert, D. D., Lehr, D., Sieland, B., & Berking, M. (2016). Overcoming procrastination: Enhancing emotion regulation skills reduce procrastination. Learning and Individual Differences, 52 , 10–18. https://doi.org/10.1016/j.lindif.2016.10.001 .

Ellis, A., & Knaus, W. J. (1977). Overcoming procrastination . Institute for Rational Living.

Endler, N. S., Edwards, J. M., Vitelli, R., & Parker, J. D. (1989). Assessment of state and trait anxiety: Endler multidimensional Anxiety Scales. Anxiety Research, 2 (1), 1–14. https://doi.org/10.1080/08917778908249322 .

Farran, B. (2004). Predictors of academic procrastination in college students. (Publication No. 3125010) [Doctoral Dissertation, Fordham University]. ProQuest Dissertations Publishing.

Fee, R. L., & Tangney, J. P. (2000). Procrastination: A means of avoiding shame or guilt? Journal of Social Behavior and Personality, 15 (5), 167–184.

Ferrari, J. R. (1989). Reliability of academic and dispositional measures of procrastination. Psychological Reports, 64 , 1057–1058. https://doi.org/10.2466/pr0.1989.64.3c.1057 .

Ferrari, J. R. (1992). Psychometric validation of two procrastination inventories for adults: Arousal and avoidance measures. Journal of Psychopathology and Behavioral Assessment, 14 (2), 97–110. https://doi.org/10.1007/BF00965170 .

Ferrari, J. R. (2000). Procrastination and attention: Factor analysis of attention deficit, boredomness, intelligence, self-esteem, and task delay frequencies. Journal of Social Behavior and Personality, 15 (5), 185–196.

Ferrari, J. R. (2010). Still procrastinating? The no-regrets guide to getting it done. Wiley.

Ferrari, J. R. (2011). Chronic procrastination: Life beyond ineffective time management. In A. P. Varga (Ed.), Time management (pp. 83–91). Nova Science Publishers. https://doi.org/10.1007/978-0-230-34371-9_22 .

Ferrari, J. R., & Beck, B. L. (1998). Affective responses before and after fraudulent excuses by academic procrastination. Education, 118 (4), 529–537.

Ferrari, J. R., & Emmons, R. A. (1994). Procrastination as revenge: Do people report using delays as a strategy for vengeance? Personality and Individual Differences, 17 (4), 539–544. https://doi.org/10.1016/0191-8869(94)90090-6 .

Ferrari, J. R., & Olivette, M. J. (1994). Parental authority and the development of female dysfunctional procrastination. Journal of Research in Personality, 28 , 87–100. https://doi.org/10.1006/jrpe.1994.1008 .

Ferrari, J. R., & Scher, S. J. (2000). Toward an understanding of academic and nonacademic tasks procrastinated by students: The use of daily logs. Psychology in the Schools, 34 , 359–366. https://doi.org/10.1002/1520-6807(200007)37:4%3c367::AID-PITS7%3e3.0.CO;2-Y .

Ferrari, J. R., & Tibbett, T. (2017). Procrastination. In V. Zeigler-Hill & T. K. Shackelford (Eds.), Encyclopedia of personality and individual differences (pp. 1–8). Springer International Publishing. https://doi.org/10.1007/978-3-319-28099-8_2272-1 .

Ferrari, J. R., Parker, J. T., & Ware, C. B. (1992). Academic procrastination: Personality correlates with Myers-Briggs types, self-efficacy, and academic locus of control. Journal of Social Behavior and Personality, 7 (3), 495–502.

Ferrari, J. R., Johnson, J. L., & McCown, W. G. (1995). Procrastination and task avoidance: Theory, research, and treatment. Plenum Press . https://doi.org/10.1007/978-1-4899-0227-6 .

Ferrari, J. R., Keane, S. M., Wolfe, R. N., & Beck, B. L. (1998). The antecedents and consequences of academic excuse making: Examining individual differences in procrastination. Research in Higher Education, 39 (2), 199–215. https://doi.org/10.1023/A:1018768715586 .

Ferrari, J. R., O’Callaghan, J., & Newbegin, I. (2005). Prevalence of procrastination in the United States, United Kingdom, and Australia: Arousal and avoidance delays among adults. North American Journal of Psychology, 7 (1), 1–6.

Ferrari, J. R., Diaz-Morales, J. F., O’Callaghan, J., Diaz, K., & Argumedo, D. (2007). Frequent behavioral delay tendencies by adults: International prevalence rates of chronic procrastination. Journal of Cross-Cultural Psychology, 38 (4), 458–464. https://doi.org/10.1177/0022022107302314 .

Ferrari, J. R., Özer, B. U., & Demir, A. (2009). Chronic procrastination among Turkish adults: Exploring decisional, avoidant, and arousal styles. The Journal of Social Psychology, 149 (3), 302–307. https://doi.org/10.3200/SOCP.149.3.402-408 .

Ferrari, J. R., Barnes, K. L., & Steel, P. (2019). Life regrets by avoidant and arousal procrastinators. Journal of Individual Differences, 30 (3), 163–168. https://doi.org/10.1027/1614-0001.30.3.163 .

Flett, G. L., Blankstein, K. R., Hewitt, P. L., & Koledin, S. (1992). Components of perfectionism and procrastination in college students. Social Behavior and Personality, 20 (2), 85–94.

Flett, G. L., Blankstein, K. R., & Martin, T. R. (1995). Procrastination, negative self-evaluation, and stress in depression and anxiety. In J. Ferrari, J. L. Johnson, & W. McCown (Eds.), Procrastination and task avoidance: Theory, research, and treatment (pp. 137–167). Plenum Publications. https://doi.org/10.1007/978-1-4899-0227-6_7 .

Flett, A. L., Haghbin, M., & Pychyl, T. A. (2016). Procrastination and depression from a cognitive perspective: An exploration of the associations among procrastinatory automatic thoughts, rumination, and mindfulness. Journal of Rational Emotional Cognitive Behavior Therapy, 34 , 169–286. https://doi.org/10.1007/s10942-016-0235-1 .

Gadosey, C. K., Schnettler, T., Scheunemann, A., Fries, S., & Grunschel, C. (2021). The intraindividual co-occurrence of anxiety and hope in procrastination episodes during exam preparations: An experience sampling study. Learning & Individual Differences, 88 , 1–11. https://doi.org/10.1016/j.lindif.2021.102013 .

Glick, D. M., Millstein, D. J., & Orsillo, S. M. (2014). A preliminary investigation of the role of psychological inflexibility in academic procrastination. Journal of Contextual Behavioral Science, 3 , 81–88. https://doi.org/10.1016/j.jcbs.2014.04.002 .

Goroshit, M., Hen, M., & Ferrari, J. R. (2020). Life-domain regret regarding procrastination (LDR-P): Scale validation in the United States and Israel. Current Psychology, 39 (3), 900–912. https://doi.org/10.1007/s12144-018-9801-2 .

Gross, J. J. (1998). The emerging field of emotion regulation: An integrative review. Review of General Psychology, 2 (3), 271–299. https://doi.org/10.1037/1089-2680.2.3.271 .

Grunschel, C., Patrzek, J., & Fries, S. (2013). Exploring reasons and consequences of academic procrastination: An interview study. European Journal of Psychology Education, 28 , 841–861. https://doi.org/10.1007/s10212-012-0143-4 .

Haghbin, M., McCaffrey, A., & Pychyl, T. A. (2012). The complexity of the relation between fear of failure and procrastination. Journal of Rational-Emotive and Cognitive-Behavior Therapy, 30 , 249–263. https://doi.org/10.1007/s10942-012-0153-9 .

Harriett, J., & Ferrari, J. (1996). Prevalence of procrastination among samples of adults. Psychological Reports, 78 , 611–616. https://doi.org/10.2466/pr0.1996.78.2.611 .

Harrington, N. (2005). It’s too difficult! Frustration intolerance beliefs and procrastination. Personality and Individual Differences, 39 , 873–883. https://doi.org/10.1016/j.paid.2004.12.018 .

Haycock, L. A., McCarthy, P., & Skay, C. L. (1998). Procrastination in college students: The role of self-efficacy and anxiety. Journal of Counseling & Development, 76 , 317–324. https://doi.org/10.1002/j.1556-6676.1998.tb02548.x .

Hen, M., & Goroshit, M. (2018). General and life-domain procrastination in highly educated adults in Israel. Frontiers in Psychology, 9 , 1–8. https://doi.org/10.3389/fpsyg.2018.01173 .

Kim, K. R., & Seo, E. H. (2015). The relationship between procrastination and academic performance: A meta-analysis. Personality and Individual Differences, 82 , 26–33. https://doi.org/10.1016/j.paid.2015.02.038 .

Klingsieck, K. B. (2013). Procrastination in different life domains: Is procrastination domain specific? Current Psychology, 32 , 175–185. https://doi.org/10.1007/s12144-013-9171-8 .

Knaus, W. J. (1979). Do it now: How to stop procrastinating . Prentice-Hall.

Kroese, F. M., de Ridder, D. T. D., Evers, C., & Adriaanse, M. A. (2014). Bedtime procrastination: Introducing a new area of procrastination. Frontiers in Psychology, 5 , 611–617. https://doi.org/10.3389/fpsyg.2014.00611 .

Article   PubMed   PubMed Central   Google Scholar  

Kroese, F. M., Nauts, S., Kamphorst, B. A., Anderson, J. H., & de Ridder D. T. D. (2016). Bedtime procrastination: A behavioral perspective on sleep insufficiency. In F. M. Sirois & T. A. Pychyl, (Eds.), Procrastination, health, and well-being (pp. 93–119). Academic Press. https://doi.org/10.1016/B978-0-12-802862-9.00005-0 .

Kuhl, J. (1994). A theory of action and state orientations. In J. Kuhl & J. Bechmann (Eds.), Volition and personality: Action vs state orientation (pp. 9–46). Hegrefe and Huber.

Kuhnle, C., Hofer, M., & Kilian, B. (2011). The relationship of self-control, procrastination, motivational interference, and regret with school grades and life balance. Diskurs Kindheits-Und Jungendforschung, 1 , 31–44.

Lavoie, J. A. A., & Pychyl, T. A. (2001). Cyberslacking and the procrastination superhighway: A web-based survey of online procrastination, attitudes, and emotion. Social Science Computer Review, 19 (4), 431–444. https://doi.org/10.1177/089443930101900403 .

Lay, C. H. (1986). At last, my research on procrastination. Journal of Research in Personality, 20 , 474–495. https://doi.org/10.1016/0092-6566(86)90127-3 .

Lay, C. H., & Silverman, S. (1996). Trait procrastination, anxiety, and dilatory behavior. Personality and Individual Differences, 21 (1), 61–67. https://doi.org/10.1016/0191-8869(96)00038-4 .

Lay, C. H., Edwards, J. M., Parker, J. D. A., & Endler, N. S. (1989). An assessment of appraisal, anxiety, coping, and procrastination during an examination period. European Journal of Personality, 3 , 195–208. https://doi.org/10.1002/per.2410030305 .

Lewis, H. B. (1971). Shame and guilt in neurosis. Psychoanalytic Review, 58 (3), 419–438.

PubMed   Google Scholar  

Yang, Z., Asbury, K., & Griffiths, M. D. (2019). An exploration of problematic smartphone use among Chinese university students: Associations with academic anxiety, academic procrastination, self-regulation, and subjective wellbeing. International Journal of Mental Health Addiction, 17 , 596–614. https://doi.org/10.1007/s11469-018-9961-1 .

Mann, L. (1982). Decision-making questionnaire . Flinders University of South Australia (Unpublished manuscript).

Mann, L. (2016). Procrastination revisited: A commentary. Australian Psychologist, 51 , 47–51. https://doi.org/10.1111/ap.12208 .

Mariani, M. G., & Ferrari, J. R. (2012). Adult inventory of procrastination Scale (AIP): A comparison of models with an Italian sample. TPM, 19 (1), 3–14.

Martinčeková, L., & Enright, R. D. (2020). The effects of self-forgiveness and shame-proneness on procrastination: Exploring the mediating role of affect. Current Psychology, 39 , 428–437. https://doi.org/10.1007/s12144-018-9926-3 .

McCown, W., & Johnson, J. (1989). Procrastination, a principal components analysis. Personality & Individual Differences, 10 , 197–202. https://doi.org/10.1016/0191-8869(89)90204-3 .

Milgram, N. A., & Tourbiana, Y. (1999). Academic anxiety, academic procrastination, and parental involvement in students and their parents. British Journal of Educational Psychology, 69 , 345–361. https://doi.org/10.1348/000709999157761 .

Milgram, N. A., Dangour, W., & Raviv, A. (1992). Situational and personal determinants of academic procrastination. The Journal of General Psychology, 119 (2), 123–133. https://doi.org/10.1080/00221309.1992.9921166 .

Milgram, N. A., Marshevsky, S., & Sadeh, C. (1995). Correlates of academic procrastination: Discomfort, task aversiveness, and task capability. The Journal of Psychology, 129 (2), 145–155. https://doi.org/10.1080/00223980.1995.9914954 .

Muller, S. M., Wegmann, E., Stolze, D., & Brand, M. (2020). Maximizing social outcomes? Social zapping and fear of missing out mediate the effects of maximization and procrastination on problematic social network use. Computers in Human Behavior, 107 , 1–10. https://doi.org/10.1016/j.chb.2020.106296 .

Onwuegbuzie, A. J. (2004). Academic procrastination and statistics anxiety. Assessment and Evaluation in Higher Education, 29 (1), 3–19. https://doi.org/10.1080/0260293042000160384 .

Özer, B. U., Demir, A., & Ferrari, J. R. (2009). Exploring academic procrastination among Turkish students: Possible gender differences in prevalence and reasons. The Journal of Social Psychology, 149 (2), 241–257. https://doi.org/10.3200/SOCP.149.2.241-257 .

Pacht, A. (1984). Reflections on perfection. American Psychologist, 39 (4), 386–390. https://doi.org/10.1037/0003-066X.39.4.386 .

Panek, E. (2014). Left to their own devices: College students’ “guilty pleasure” media use and time management. Communication Research, 41 (4), 561–577. https://doi.org/10.1177/0093650213499657 .

Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students' self-regulated learning and achievement: A program of quantitative and qualitative research. Educational Psychologist, 37 , 91–106.

Pekrun, R., Frenzel, A. C., Goetz, T., & Perry, R. (2007). The control-value theory of achievement emotions: An integrative approach to emotions in education. In P. A. Schutz & R. Pekrun (Eds.), Emotion in education (pp. 13–36). Academic Press. https://doi.org/10.1016/B978-012372545-5/50003-4 .

Przepiorka, A., Blanchnio, A., & Cudo, A. (2021). Procrastination and problematic new media use: The mediating role of future anxiety. Current Psychology . https://doi.org/10.1007/s12144-021-01773-wx .

Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in Human Behavior, 29 , 1841–1848. https://doi.org/10.1016/j.chb.2013.02.014 .

Pychyl, T. A. (2013). Solving the procrastination puzzle: A concise guide to strategies for change. Penguin.

Pychyl, T. A., & Sirois, F. M. (2016). Procrastination, emotion regulation, and well-being. In F. M. Sirois & T. A. Pychyl (Eds.), Procrastination, health, and well-being (pp. 163–188). Academic Press. https://doi.org/10.1016/B978-0-12-802862-9.00008-6 .

Pychyl, T. A., Lee, J., Thibodeau, R., & Blunt, A. (2000). Five days of emotion: An experience sampling study of undergraduate student procrastination. Journal of Social Behavior and Personality, 15 , 239–254.

Pyman, D. (2020). Academic procrastination, self-regulation, anxiety, and personality (ASAP): ‘Some’ day is not a day of the week [Master’s Thesis, Trent University]. Trent University Library & Archives. http://digitalcollections.trentu.ca/objects/etd-792 .

Rahardjo, W., & Juneman, S. Y. (2013). Computer anxiety, academic stress, and academic procrastination on college students. Journal of Education and Learning, 7 (3), 147–152. https://doi.org/10.11591/edulearn.v7i3.179 .

Rahimi, S. (2019). Understanding academic procrastination: A longitudinal analysis of procrastination and emotions in undergraduate and graduate students. (Publication no. 28254487) [Doctoral Dissertation, McGill University]. ProQuest Dissertations Publishing.

Reinecke, L., Hartmann, T., & Eden, A. (2014). The guilty couch potato: The role of ego depletion in reducing recovery through media use. Journal of Communication, 64 , 569–589. https://doi.org/10.1111/jcom.12107 .

Rothblum, E. D., Solomon, L. J., & Murakami, J. (1986). Affective, cognitive, and behavioral differences between high and low procrastinators. Journal of Counseling Psychology, 33 (4), 387–394. https://doi.org/10.1037/0022-0167.33.4.387 .

Saklofske, D. H. (2018). Editorial. Personality and Individual Differences, 122 , A1–A2.

Saddler, C. D., & Buley, J. (1999). Predictors of academic procrastination in college students. Psychological Reports, 84 , 686–688. https://doi.org/10.2466/pr0.1999.84.2.686 .

Sarason, I. G. (1972). Test anxiety and cognitive modeling. Journal of Personality and Social Psychology, 28 (1), 58–61. https://doi.org/10.1037/h0035476 .

Scher, S. J., & Ferrari, J. R. (2000). The recall of completed and non-completed tasks through daily logs to measure procrastination. Journal of Social Behavior & Personality, 15 , 255–265.

Scher, S. J., & Osterman, N. M. (2001). Procrastination, conscientiousness, anxiety, and goals: Exploring the measurement and correlates of procrastination among school-aged children. Psychology in the Schools, 39 (4), 385–398. https://doi.org/10.1002/pits.10045 .

Schouwenburg, H. C. (1992). Procrastinators and fear of failure: An exploration of reasons for procrastination. European Journal of Personality, 6 , 225–236. https://doi.org/10.1002/per.2410060305 .

Schouwenburg, H. C. (1995). Academic procrastination: Theoretical notions, measurement, and research. In J. R. Ferrari, J. L. Johnson, & W. G. McCown (1995). Procrastination and task avoidance: Theory, research, and treatment, (pp. 71–95). Plenum Press. https://doi.org/10.1007/978-1-4899-0227-6_4 .

Senecal, C., Koestner, R., & Vallerand, R. J. (1995). Self-regulation and academic procrastination. The Journal of Social Psychology, 135 (5), 607–619. https://doi.org/10.1080/00224545.1995.9712234 .

Sirois, F. M., & Giguère, B. (2018). Giving in when feeling less good: Procrastination, action control, and social temptations. British Journal of Social Psychology, 57 , 404–427. https://doi.org/10.1111/bjso.12243 .

Sirois, F. M., & Kitner, R. (2015). Less adaptive or more maladaptive? A meta-analytic investigation of procrastination and coping. European Journal of Personality, 29 (4), 433–444. https://doi.org/10.1002/per.1985 .

Sirois, F. M., & Pychyl, T. A. (2013). Procrastination and the priority of short-term mood regulation: Consequences for future self. Social and Personality Psychology Compass, 7 (2), 115–127. https://doi.org/10.1111/spc3.12011 .

Sirois, F. M., Nauts, S., & Molnar, D. S. (2019). Self-compassion and bedtime procrastination: An emotion regulation perspective. Mindfulness, 10 , 434–445. https://doi.org/10.1007/s12671-018-0983-3 .

Sirois, F. M., & Pychyl, T. A. (2016). Procrastination, health, and well-being Academic Press https://doi.org/10.1007/978-3-319-18582-8 .

Solomon, L., & Rothblum, E. (1984). Academic procrastination: Frequency and cognitive-behavioral correlates. Journal of Counselling Psychology, 31 (4), 503–509. https://doi.org/10.1037/0022-0167.31.4.503 .

Spada, M. M., Hiou, K., & Nikcevic, A. V. (2006). Metacognitions, emotions, and procrastination. Journal of Cognitive Psychotherapy: An International Quarterly, 20 (3), 319–326. https://doi.org/10.1891/jcop.20.3.319 .

Stöber, J., & Joorman, J. (2001). Worry, procrastination, and perfectionism: Differentiating amount of worry, pathological worry, anxiety, and depression. Cognitive Therapy and Research, 25 (1), 49–60. https://doi.org/10.1023/A:1026474715384 .

Subotnik, R., Steiner, C., & Chakraborty, B. (1999). Procrastination revisited: The constructive use of delayed response. Creativity Research Journal, 12 (2), 151–160. https://doi.org/10.1207/s15326934crj1202_7 .

Sudler, E. L. (2014). Academic procrastination as mediated by executive functioning, perfectionism, and frustration intolerance in college students. (Publication No. 3575249) [Doctoral Dissertation. St. John's University]. ProQuest Dissertations Publishing.

Tangney, J. P. (1993). Shame and guilt. In C. G. Costello (Ed.), Symptoms of depression (pp. 161–180). Wiley.

Tangney, J. P., Wagner, P., Fletcher, C., & Gramzow, R. (1992). Shamed into anger? The relation of shame and guilt to anger and self-reported aggression. Journal of Personality and Social Psychology, 62 (4), 669–675. https://doi.org/10.1037/0022-3514.62.4.669 .

Tibbett, T. P., & Ferrari, J. R. (2018). The U.S. as a procrastiNATION: Assessing indecision on life satisfaction and life regret. North American Journal of Psychology, 20 (1), 111–116.

Tice, D. M., & Baumeister, R. F. (1997). Longitudinal study of procrastination, performance, stress, and health: The costs and benefits of dawdling. Psychological Science, 8 (6), 454–458. https://doi.org/10.1111/j.1467-9280.1997.tb00460.x .

Tice, D. M., & Bratslavsky, E. (2000). Giving in to feel good: The place of emotion regulation in the context of general self-control. Psychological Inquiry, 11 (3), 149–159. https://doi.org/10.1207/S15327965PLI1103_03 .

Tuckman, B. (1991). The development and concurrent validity of the procrastination scale. Educational and Psychological Measurement, 51 , 473–480. https://doi.org/10.1177/0013164491512022 .

vanEerde, W. (2013). A meta-analytically derived nomological network of procrastination. Personality and Individual Differences, 35 , 1401–1418. https://doi.org/10.1016/S0191-8869(02)00358-6 .

Vodanovich, S. J., & Rupp, D. E. (1999). Are procrastinators prone to boredom? Social Behavior and Personality, 27 (1), 11–16. https://doi.org/10.2224/sbp.1999.27.1.11 .

Walsh, J., & Ugumba-Agwunobi, G. (2002). Individual differences in statistics anxiety: The roles of perfectionism, procrastination, and trait anxiety. Personality and Individual Differences, 33 , 239–251. https://doi.org/10.1016/S0191-8869(01)00148-9 .

Wan, H. C., Downey, L. A., & Stough, C. (2014). Understanding non-work presenteeism: Relationships between emotional intelligence, boredom, procrastination, and job stress. Personality and Individual Differences, 65 , 86–90. https://doi.org/10.1016/j.paid.2014.01.018 .

Wang, J., Wang, P., Yang, X., Zhang, G., Wang, X., Zhao, F., & Lei, L. (2019). Fear of missing out and procrastination as mediators between sensation seeking and adolescent smartphone addiction. International Journal of Mental Health and Addiction, 17 (4), 1049–1062. https://doi.org/10.1007/s11469-019-00106-0 .

Wartberg, L., Thomasius, R., & Paschke, K. (2021). The relevance of emotion regulation, procrastination, and perceived stress for problematic social media use in a representative sample of children and adolescents. Computers in Human Behavior, 121 , 1–7. https://doi.org/10.1016/j.chb.2021.106788 .

Yerdelen, S., McCaffrey, A., & Klasse, R. M. (2016). Longitudinal examination of procrastination and anxiety, and their relation to self-efficacy for self-regulated learning: Latent growth curve modeling. Educational Sciences: Theory & Practice, 16 (1), 5–22. https://doi.org/10.12738/estp.2016.1.0108 .

Xu, P., Gonzalez-Vallejo, C., & Xiong, Z. H. (2016). State anxiety reduces procrastinating behavior. Motivation and Emotion, 40 , 625–637. https://doi.org/10.1007/s11031-016-9554-x .

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

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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

Shareinia, H., Ghiyasvandian, S., Rooddehghan, Z., & Esteghamati, A. (2022). Types of health-related procrastination in patients with type-2 diabetes: A qualitative content analysis. Journal of Diabetes & Metabolic Disorders , 21 (2), 1509-1517. https://doi.org/10.1007/s40200-022-01092-2

Shaw, A., & Choi, J. (2022). Big Five personality traits predicting active procrastination at work: When self- and supervisor-ratings tell different stories. Journal of Research in Personality ,  99 , Article 104261. https://doi.org/10.1016/j.jrp.2022.104261

Steel, P., Taras, D., Ponak, A., & Kammeyer-Mueller, J. (2022). Self-regulation of slippery deadlines: The role of procrastination in work performance. Frontiers in Psychology , 12 , Article 783789. https://doi.org/10.3389/fpsyg.2021.783789

Suárez, A., Ruiz, Z., & Garcés, Y. (2022). Profiles of undergraduates’ networks addiction: Difference in academic procrastination and performance.  Computers & Education , 181 , Article 104459. https://doi.org/10.1016/j.compedu.2022.104459

Sun, T., & Kim, J. E. (2022). The effect of procrastination heterogeneity on team performance. International Journal of Industrial Ergonomics , 87 , Article 103231. https://doi.org/10.1016/j.ergon.2021.103231

Svartdal, F., & Løkke, J. A. (2022). The ABC of academic procrastination: Functional analysis of a detrimental habit. Frontiers in Psychology , 13 , Article 1019261. https://doi.org/10.3389/fpsyg.2022.1019261

Uzun, B., LeBlanc, S., Guclu, I. O., Ferrari, J. R., & Aydemir, A. (2022). Mediation effect of family environment on academic procrastination and life satisfaction: Assessing emerging adults. Current Psychology , 41 (2), 1124-1130. https://doi.org/10.1007/s12144-021-02652-0

Vangsness, L., Voss, N. M., Maddox, N., Devereaux, V., & Martin, E. (2022). Self-report measures of procrastination exhibit inconsistent concurrent validity, predictive validity, and psychometric properties. Frontiers in Psychology , 13 , Article 784471. https://doi.org/10.3389/fpsyg.2022.784471

Wieland, L. M., Hoppe, J. D., Wolgast, A., & Ebner-Priemer, U. W. (2022). Task ambiguity and academic procrastination: An experience sampling approach. Learning and Instruction , 81 , Article 101595. https://doi.org/10.1016/j.learninstruc.2022.101595

Xu, J. (2022). More than minutes: A person-centered approach to homework time, homework time management, and homework procrastination. Contemporary Educational Psychology , 70 , Article 102087. https://doi.org/10.1016/j.cedpsych.2022.102087

Zhou, M., Lam, K. K. L., & Zhang, Y. (2022). Metacognition and academic procrastination: A meta-analytical examination. Journal of Rational-Emotive & Cognitive-Behavior Therapy , 40 (2), 334-368. https://doi.org/10.1007/s10942-021-00415-1

Alblwi, A., McAlaney, J., Al Thani, D. A. S., Phalp, K., & Ali, R. (2021). Procrastination on social media: Predictors of types, triggers and acceptance of countermeasures. Social Network Analysis and Mining , 11 (1), 1-18. https://doi.org/10.1007/s13278-021-00727-1

Chen, Z., Liu, P., Zhang, C., Yu, Z., & Feng, T. (2021). Neural markers of procrastination in white matter microstructures and networks. Psychophysiology , 58 (5), e13782. https://doi.org/10.1111/psyp.13782

Cui, G., Yin, Y., Li, S., Chen, L., Liu, X., Tang, K., & Li, Y. (2021). Longitudinal relationships among problematic mobile phone use, bedtime procrastination, sleep quality and depressive symptoms in Chinese college students: A cross-lagged panel analysis. BMC Psychiatry , 21 , Article 449. https://doi.org/10.1186/s12888-021-03451-4

Exelmans, L., & Van den Bulck, J. (2021). “Glued to the tube”: The interplay between self-control, evening television viewing, and bedtime procrastination. Communication Research , 48 (4), 594-616. https://doi.org/10.1177/0093650216686877

Gadosey, C. K., Schnettler, T., Scheunemann, A., Fries, S., & Grunschel, C. (2021). The intraindividual co-occurrence of anxiety and hope in procrastination episodes during exam preparations: An experience sampling study. Learning and Individual Differences , 88 , Article 102013. https://doi.org/10.1016/j.lindif.2021.102013

Geng, Y., Gu, J., Wang, J., & Zhang, R. (2021). Smartphone addiction and depression, anxiety: The role of bedtime procrastination and self-control. Journal of Affective Disorders , 293 , 415-421. https://doi.org/10.1016/j.jad.2021.06.062

Goroshit, M., & Hen, M. (2021). Academic procrastination and academic performance: Do learning disabilities matter?. Current Psychology , 40 , 2490-2498. https://doi.org/10.1007/s12144-019-00183-3

Harima, A., Gießelmann, J., Göttsch, V., & Schlichting, L. (2021). Entrepreneurship? Let us do it later: Procrastination in the intention–behavior gap of student entrepreneurship. International Journal of Entrepreneurial Behavior & Research , 27 (5), 1189-1213. https://doi.org/10.1108/IJEBR-09-2020-0665

Hen, M., Goroshit, M., & Viengarten, S. (2021). How decisional and general procrastination relate to procrastination at work: An investigation of office and non-office workers. Personality and Individual Differences , 172 , Article 110581. https://doi.org/10.1016/j.paid.2020.110581

Hong, W., Liua, R. D., Ding, Y., Jiang, S., Yang, X., & Sheng, X. (2021). Academic procrastination precedes problematic mobile phone use in Chinese adolescents: A longitudinal mediation model of distraction cognitions. Addictive Behaviors , 121 , Article 106993. https://doi.org/10.1016/j.addbeh.2021.106993

Kelly, S. M., & Walton, H. R. (2021). “I’ll work out tomorrow”: The procrastination in exercise scale. Journal of Health Psychology , 26 (13), 2613-2625. https://doi.org/10.1177/1359105320916541

Liu, H., Ji, Y., & Dust, S. B. (2021). “Fully recharged” evenings? The effect of evening cyber leisure on next-day vitality and performance through sleep quantity and quality, bedtime procrastination, and psychological detachment, and the moderating role of mindfulness. Journal of Applied Psychology , 106 (7), 990-1006. https://doi.org/10.1037/apl0000818

Oguchi, M., Takahashi, T., Nitta, Y., & Kumano, H. (2021). The Moderating Effect of Attention-Deficit Hyperactivity Disorder Symptoms on the Relationship Between Procrastination and Internalizing Symptoms in the General Adult Population. Frontiers in Psychology , 12 , Article 708579. https://doi.org/10.3389/fpsyg.2021.708579

Sarid, M., Peled, Y., & Vaknin-Nusbaum, V. (2021). The relationship between second language college students’ perceptions of online feedback on draft-writing and academic procrastination. Reading and Writing , 34 (5), 1247-1271. https://doi.org/10.1007/s11145-020-10111-8

Shaffer, C. A., & Kazerouni, A. M. (2021). The impact of programming project milestones on procrastination, project outcomes, and course outcomes: A quasi-experimental study in a third-year data structures course. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (pp. 907-913). https://doi.org/10.1145/3408877.3432356

Steinert, C., Heim, N., & Leichsenring, F. (2021). Procrastination, Perfectionism, and Other Work-Related Mental Problems: Prevalence, Types, Assessment, and Treatment—A Scoping Review. Frontiers in Psychiatry , 12 , Article 736776. https://doi.org/10.3389/fpsyt.2021.736776

Wartberg, L., Thomasius, R., & Paschke, K. (2021). The relevance of emotion regulation, procrastination, and perceived stress for problematic social media use in a representative sample of children and adolescents. Computers in Human Behavior , 121 , 106788. https://doi.org/10.1016/j.chb.2021.106788

Wieland, L. M., Ebner-Priemer, U. W., Limberger, M. F., & Nett, U. E. (2021). Predicting delay in goal-directed action: An experience sampling approach uncovering within-person determinants involved in the onset of academic procrastination behavior. Frontiers in Psychology , 12 , Article 695927. https://doi.org/10.3389/fpsyg.2021.695927

Xu, T., Sirois, F. M., Zhang, L., Yu, Z., & Feng, T. (2021). Neural basis responsible for self-control association with procrastination: Right MFC and bilateral OFC functional connectivity with left dlPFC. Journal of Research in Personality , 91 , Article 104064. https://doi.org/10.1016/j.jrp.2021.104064

Yang, X., Liu, R. D., Ding, Y., Hong, W., & Jiang, S. (2021). The relations between academic procrastination and self-esteem in adolescents: A longitudinal study. Current Psychology . Advance online publication. https://doi.org/10.1007/s12144-021-02075-x

You, Z., Li, X., Ye, N., & Zhang, L. (2021). Understanding the effect of rumination on sleep quality: A mediation model of negative affect and bedtime procrastination. Current Psychology . Advance online publication. https://doi.org/10.1007/s12144-020-01337-4

Zuber, S., Ballhausen, N., Haas, M., Cauvin, S., Da, C., Coelho, S., … & Kliegel, M. (2021). I could do it now, but I’d rather (forget to) do it later: Examining links between procrastination and prospective memory failures. Psychological Research , 85 , 1602-1612. https://doi.org/10.1007/s00426-020-01357-6

Aznar-Díaz, I., Romero-Rodríguez, J. M., García-González, A., & Ramírez-Montoya, M. S. (2020). Mexican and Spanish university students’ Internet addiction and academic procrastination: Correlation and potential factors. PloS one , 15 (5), Article e0233655. https://doi.org/10.1371/journal.pone.0233655

Bisin, A., & Hyndman, K. (2020). Present-bias, procrastination and deadlines in a field experiment. Games and Economic Behavior , 119 , 339-357. https://doi.org/10.1016/j.geb.2019.11.010

Bolden, J., & Fillauer, J. P. (2020). “Tomorrow is the busiest day of the week”: Executive functions mediate the relation between procrastination and attention problems. Journal of American College Health , 68 (8), 854-863. https://doi.org/10.1080/07448481.2019.1626399

Chauhan, R. S., MacDougall, A. E., Buckley, M. R., Howe, D. C., Crisostomo, M. E., & Zeni, T. (2020). Better late than early? Reviewing procrastination in organizations. Management Research Review , 43 (10), 1289-1308. https://doi.org/10.1108/MRR-09-2019-0413

Chung, S. J., An, H., & Suh, S. (2020). What do people do before going to bed? A study of bedtime procrastination using time use surveys. Sleep , 43 (4), zsz267. https://doi.org/10.1093/sleep/zsz267

Doty, D. H., Wooldridge, B. R., Astakhova, M., Fagan, M. H., Marinina, M. G., Caldas, M. P., & Tunçalp, D. (2020). Passion as an excuse to procrastinate: A cross-cultural examination of the relationships between Obsessive Internet passion and procrastination. Computers in Human Behavior , 102 , 103-111. https://doi.org/10.1016/j.chb.2019.08.014

Ferrari, J. R., & Tibbett, T. P. (2020). Procrastination. In V. Zeigler-Hill & T. K. Shackelford (Eds.), Encyclopedia of personality and individual differences (1st ed., pp. 4046–4053). Springer. https://doi.org/10.1007/978-3-319-24612-3_2272

Frakes, M. D., & Wasserman, M. F. (2020). Procrastination at the patent office?. Journal of Public Economics , 183 , 104140. https://doi.org/10.1016/j.jpubeco.2020.104140

Franz, I. (2020). Unintentional procrastination, self control, and academic achievements. Education Economics , 28 (5), 508-525. https://doi.org/10.1080/09645292.2020.1801596

Goroshit, M., Hen, M., & Ferrari, J. R. (2020). Life-domain regret regarding procrastination (LDR-P): Scale validation in the United States and Israel. Current Psychology , 39 (3), 900-912. https://doi.org/10.1007/s12144-018-9801-2

Guo, J., Meng, D., Ma, X., Zhu, L., Yang, L., & Mu, L. (2020). The impact of bedtime procrastination on depression symptoms in Chinese medical students. Sleep & Breathing , 24 , 1247-1255. https://doi.org/10.1007/s11325-020-02079-0

Hen, M., & Goroshit, M. (2020). The effects of decisional and academic procrastination on students’ feelings toward academic procrastination. Current Psychology , 39 (2), 556-563. https://doi.org/10.1007/s12144-017-9777-3

Hensley, L. C., & Munn, K. J. (2020). The power of writing about procrastination: Journaling as a tool for change. Journal of Further and Higher Education , 44 (10), 1450-1465. https://doi.org/10.1080/0309877X.2019.1702154

Kadzikowska-Wrzosek, R. (2020). Insufficient sleep among adolescents: The role of bedtime procrastination, chronotype and autonomous vs. controlled motivational regulations. Current Psychology , 39 (3), 1031-1040. https://doi.org/10.1007/s12144-018-9825-7

Kaftan, O. J., & Freund, A. M. (2020). How to work out and avoid procrastination: The role of goal focus. Journal of Applied Social Psychology , 50 (3), 145-159. https://doi.org/10.1111/jasp.12646

Kınık, Ö., & Odacı, H. (2020). Effects of dysfunctional attitudes and depression on academic procrastination: Does self-esteem have a mediating role?. British Journal of Guidance & Counselling , 48 (5), 638-649. https://doi.org/10.1080/03069885.2020.1780564

Magalhães, P., Cruz, V., Teixeira, S., Fuentes, S., & Rosário, P. (2020). An exploratory study on sleep procrastination: Bedtime vs. while-in-bed procrastination. International Journal of Environmental Research and Public Health , 17 (16), Article 5892. https://doi.org/10.3390/ijerph17165892

Martinčeková, L., & Enright, R. D. (2020). The effects of self-forgiveness and shame-proneness on procrastination: Exploring the mediating role of affect. Current Psychology , 39 (2), 428-437. https://doi.org/10.1007/s12144-018-9926-3

Pollack, S., & Herres, J. (2020). Prior day negative affect influences current day procrastination: A lagged daily diary analysis. Anxiety, Stress, & Coping , 33 (2), 165-175. https://doi.org/10.1080/10615806.2020.1722573

Roster, C. A., & Ferrari, J. R. (2020). Does work stress lead to office clutter, and how? Mediating influences of emotional exhaustion and indecision. Environment and Behavior , 52 (9), 923-944. https://doi.org/10.1177/0013916518823041

Roster, C. A., & Ferrari, J. R. (2020). Time is on my side—Or is it? Assessing how perceived control of time and procrastination influence emotional exhaustion on the job. Behavioral Sciences , 10 (6), 98. https://doi.org/10.3390/bs10060098

Rubin, R. (2020). Matters of the mind—Bedtime procrastination, relaxation-induced anxiety, lonely Tweeters. JAMA , 323 (1), 15-16. https://doi.org/10.1001/jama.2019.20014

Schutte, N. S., & de Bolger, A. D. P. (2020). Greater mindfulness is linked to less procrastination. International Journal of Applied Positive Psychology , 5 (1), 1-12. https://doi.org/10.1007/s41042-019-00025-4

Shin, J., & Grant, A. M. (2020). When putting work off pays off: The curvilinear relationship between procrastination and creativity. Academy of Management Journal , 64 (3), 772-798. https://doi.org/10.5465/amj.2018.1471

Svartdal, F., Dahl, T. I., Gamst-Klaussen, T., Koppenborg, M., & Klingsieck, K. B. (2020). How study environments foster academic procrastination: Overview and recommendations. Frontiers in Psychology , 11 , Article 540910. https://doi.org/10.3389/fpsyg.2020.540910

Svartdal, F., Klingsieck, K. B., Steel, P., & Gamst-Klaussen, T. (2020). Measuring implemental delay in procrastination: Separating onset and sustained goal striving. Personality and Individual Differences , 156 , Article 109762. https://doi.org/10.1016/j.paid.2019.109762

Valshtein, T. J., Oettingen, G., & Gollwitzer, P. M. (2020). Using mental contrasting with implementation intentions to reduce bedtime procrastination: Two randomised trials. Psychology & Health , 35 (3), 275-301. https://doi.org/10.1080/08870446.2019.1652753

Zanjani, S., Yunlu, D. G., & Beigh, J. N. S. (2020). Creative procrastinators: Mapping a complex terrain. Personality and Individual Differences , 154 , Article 109640. https://doi.org/10.1016/j.paid.2019.109640

Zhang, S., & Feng, T. (2020). Modeling procrastination: Asymmetric decisions to act between the present and the future. Journal of Experimental Psychology: General , 149 (2), 311. https://doi.org/10.1037/xge0000643

Zhang, M. X., & Wu, A. M. (2020). Effects of smartphone addiction on sleep quality among Chinese university students: The mediating role of self-regulation and bedtime procrastination. Addictive Behaviors , 111 , Article 106552. https://doi.org/10.1016/j.addbeh.2020.106552

Zhu, L., Meng, D., Ma, X., Guo, J., & Mu, L. (2020). Sleep timing and hygiene practices of high bedtime procrastinators: A direct observational study. Family Practice , 37 (6), 779-784. https://doi.org/10.1093/fampra/cmaa079

Altgassen, A. M., Scheres, A. P. J., & Edel, M. A. (2019). Prospective memory (partially) mediates the link between ADHD symptoms and procrastination. ADHD Attention Deficit and Hyperactivity Disorders , 11 , 59-71. https://doi.org/10.1007/s12402-018-0273-x

Balkis, M., & Duru, E. (2019). Procrastination and rational/irrational beliefs: A moderated mediation model.  Journal of Rational-Emotive & Cognitive-Behavior Therapy , 37 (3), 299-315. https://doi.org/10.1007/s10942-019-00314-6

Bernecker, K., & Job, V. (2019). Too exhausted to go to bed: Implicit theories about willpower and stress predict bedtime procrastination. British Journal of Psychology , 111 (1), 126-147. https://doi.org/10.1111/bjop.12382

Cheung, R. Y., & Ng, M. C. (2019). Being in the moment later? Testing the inverse relation between mindfulness and procrastination. Personality and Individual Differences , 141 , 123-126. https://doi.org/10.1016/j.paid.2018.12.015

Exelmans, L., Meier, A., Reinecke, L., & Van Den Bulck, J. (2019). Just one more episode: Predictors of procrastination with television and implications for sleep quality. Mass Communication and Society , 22 (5), 654-685. https://doi.org/10.1080/15205436.2019.1606246

Gamst-Klaussen, T., Steel, P., & Svartdal, F. (2019). Procrastination and personal finances: Exploring the roles of planning and financial self-efficacy. Frontiers in Psychology , 10 , Article 775. https://doi.org/10.3389/fpsyg.2019.00775

Gautam, A., Polizzi, C. P., & Mattson, R. E. (2019). Mindfulness, procrastination, and anxiety: Assessing their interrelationships. Psychology of Consciousness: Theory, Research, and Practice . Advance online publication. https://doi.org/10.1037/cns0000209

Hall, N. C., Lee, S. Y., & Rahimi, S. (2019). Self-efficacy, procrastination, and burnout in post-secondary faculty: An international longitudinal analysis. PLOS ONE ,  14 (12), Article e0226716. https://doi.org/10.1371/journal.pone.0226716

Hernández, C., Ottenberger, D. R., Moessner, M., Crosby, R. D., & Ditzen, B. (2019). Depressed and swiping my problems for later: The moderation effect between procrastination and depressive symptomatology on internet addiction. Computers in Human Behavior , 97 , 1-9. https://doi.org/10.1016/j.chb.2019.02.027

Herzog-Krzywoszanska, R., & Krzywoszanski, L. (2019). Bedtime procrastination, self-reported sleep outcomes and demographic factors in an online survey on a Polish sample. Frontiers in Neuroscience , 13 , Article 963. https://doi.org/10.3389/fnins.2019.00963

Klein, E. M., Beutel, M. E., Müller, K. W., Wölfling, K., Brähler, E., & Zenger, M. (2019). Assessing procrastination: Dimensionality and measurement invariance of the General Procrastination Scale–Screening (GPS-S) in a representative sample. European Journal of Psychological Assessment , 35 (5), 633-640. https://doi.org/10.1027/1015-5759/a000441

Ko, C. Y. A., & Chang, Y. (2019). Investigating the relationships among resilience, social anxiety, and procrastination in a sample of college students. Psychological Reports , 122 (1), 231-245. https://doi.org/10.1177/0033294118755111

Krispenz, A., Gort, C., Schültke, L., & Dickhäuser, O. (2019). How to reduce test anxiety and academic procrastination through inquiry of cognitive appraisals: Investigating the role of academic self-efficacy. Frontiers in Psychology , 10 , Article 1917. https://doi.org/10.3389/fpsyg.2019.01917

Laybourn, S., Frenzel, A. C., & Fenzl, T. (2019). Teacher procrastination, emotions, and stress: A qualitative study. Frontiers in Psychology , 10 , Article 2325. https://doi.org/10.3389/fpsyg.2019.02325

Liu, P., & Feng, T. (2019). The effect of future time perspective on procrastination: The role of parahippocampal gyrus and ventromedial prefrontal cortex. Brain Imaging and Behavior , 13 (3), 615-622. https://doi.org/10.1007/s11682-018-9874-4

Malouff, J. M., & Schutte, N. S. (2019). The efficacy of interventions aimed at reducing procrastination: A meta‐analysis of randomized controlled trials. Journal of Counseling & Development , 97 (2), 117-127. https://doi.org/10.1002/jcad.12243

Nauts, S., Kamphorst, B. A., Stut, W., De Ridder, D. T., & Anderson, J. H. (2019). The explanations people give for going to bed late: A qualitative study of the varieties of bedtime procrastination. Behavioral Sleep Medicine , 17 (6), 753-762. https://doi.org/10.1080/15402002.2018.1491850

Nordby, K., Løkken, R. A., & Pfuhl, G. (2019). Playing a video game is more than mere procrastination. BMC Psychology , 7 , Article 33. https://doi.org/10.1186/s40359-019-0309-9

Pinxten, M., De Laet, T., Van Soom, C., Peeters, C., & Langie, G. (2019). Purposeful delay and academic achievement. A critical review of the Active Procrastination Scale. Learning and Individual Differences , 73 , 42-51. https://doi.org/10.1016/j.lindif.2019.04.010

Schlüter, C., Arning, L., Fraenz, C., Friedrich, P., Pinnow, M., Güntürkün, O., … & Genc, E. (2019). Genetic variation in dopamine availability modulates the self-reported level of action control in a sex-dependent manner. Social Cognitive and Affective Neuroscience , 14 (7), 759-768. https://doi.org/10.1093/scan/nsz049

Sirois, F. M., Nauts, S., & Molnar, D. S. (2019). Self-compassion and bedtime procrastination: An emotion regulation perspective. Mindfulness , 10 (3), 434-445. https://doi.org/10.1007/s12671-018-0983-3

Sirois, F. M., Yang, S., & van Eerde, W. (2019). Development and validation of the General Procrastination Scale (GPS-9): A short and reliable measure of trait procrastination. Personality and Individual Differences , 146 , 26-33. https://doi.org/10.1016/j.paid.2019.03.039

Wessel, J., Bradley, G. L., & Hood, M. (2019). Comparing effects of active and passive procrastination: A field study of behavioral delay. Personality and Individual Differences , 139 , 152-157. https://doi.org/10.1016/j.paid.2018.11.020

Xu, Y. (2019). Proud and productive procrastination? What do we talk about when we talk about #procrastination on Twitter. In International Conference on Digital Transformation and Global Society (pp. 661-671). Springer. https://doi.org/10.1007/978-3-030-37858-5_56

Zhang, S., Becker, B., Chen, Q., & Feng, T. (2019). Insufficient task‐outcome association promotes task procrastination through a decrease of hippocampal–striatal interaction. Human Brain Mapping , 40 (2), 597-607. https://doi.org/10.1002/hbm.24397

Zhang, S., Liu, P., & Feng, T. (2019). To do it now or later: The cognitive mechanisms and neural substrates underlying procrastination. Wiley Interdisciplinary Reviews: Cognitive Science , 10 (4), e1492. https://doi.org/10.1002/wcs.1492

Abramowski, A. (2018). Is procrastination all that “bad”? A qualitative study of academic procrastination and self-worth in postgraduate university students. Journal of Prevention & Intervention in the Community , 46 (2), 158-170. https://doi.org/10.1080/10852352.2016.1198168

Ashworth, B., & McCown, W. (2018). Trait procrastination, hoarding, and continuous performance attention scores. Current Psychology , 37 (2), 454-459. https://doi.org/10.1007/s12144-017-9696-3

Barboza, G. (2018). I will pay tomorrow, or maybe the day after. Credit card repayment, present biased and procrastination. Economic Notes: Review of Banking, Finance and Monetary Economics , 47 (2-3), 455-494. https://doi.org/10.1111/ecno.12106

Duckworth, A. L., Milkman, K. L., & Laibson, D. (2018). Beyond willpower: Strategies for reducing failures of self-control. Psychological Science in the Public Interest , 19 (3), 102-129. https://doi.org/10.1177/1529100618821893

Kamphorst, B. A., Nauts, S., De Ridder, D. T., & Anderson, J. H. (2018). Too depleted to turn in: The relevance of end-of-the-day resource depletion for reducing bedtime procrastination. Frontiers in Psychology , 9, Article 252. https://doi.org/10.3389/fpsyg.2018.00252

Chowdhury, S. F., & Pychyl, T. A. (2018). A critique of the construct validity of active procrastination. Personality and Individual Differences , 120 , 7-12. https://doi.org/10.1016/j.paid.2017.08.016

Constantin, K., English, M. M., & Mazmanian, D. (2018). Anxiety, depression, and procrastination among students: Rumination plays a larger mediating role than worry. Journal of Rational-Emotive & Cognitive-Behavior Therapy , 36 (1), 15-27. https://doi.org/10.1007/s10942-017-0271-5

Ferrari, J. R., & Roster, C. A. (2018). Delaying disposing: Examining the relationship between procrastination and clutter across generations. Current Psychology , 37 (2), 426-431. https://doi.org/10.1007/s12144-017-9679-4

Ferrari, J. R., Roster, C. A., Crum, K. P., & Pardo, M. A. (2018). Procrastinators and clutter: An ecological view of living with excessive “stuff”. Current Psychology , 37 (2), 441-444. https://doi.org/10.1007/s12144-017-9682-9

Göncü Köse, A., & Metin, U. B. (2018). Linking leadership style and workplace procrastination: The role of organizational citizenship behavior and turnover intention. Journal of Prevention & Intervention in the Community , 46 (3), 245-262. https://doi.org/10.1080/10852352.2018.1470369

Goroshit, M., & Hen, M. (2018). Decisional, general and online procrastination: Understanding the moderating role of negative affect in the case of computer professionals. Journal of Prevention & Intervention in the Community , 46 (3), 279-294. https://doi.org/10.1080/10852352.2018.1470421

Grunschel, C., Patrzek, J., Klingsieck, K. B., & Fries, S. (2018). “I’ll stop procrastinating now!” Fostering specific processes of self-regulated learning to reduce academic procrastination. Journal of Prevention & Intervention in the Community , 46 (2), 143-157. https://doi.org/10.1080/10852352.2016.1198166

Hen, M. (2018). Academic procrastination and feelings toward procrastination in LD and non-LD students: Preliminary insights for future intervention. Journal of Prevention & Intervention in the Community , 46 (2), 199-212. https://doi.org/10.1080/10852352.2016.1198173

Hen, M. (2018). Causes for procrastination in a unique educational workplace.  Journal of Prevention & Intervention in the Community ,  46 (3), 215-227. https://doi.org/10.1080/10852352.2018.1470144

Hen, M., & Goroshit, M. (2018). General and life-domain procrastination in highly educated adults in Israel. Frontiers in Psychology , 9 , Article 1173. https://doi.org/10.3389/fpsyg.2018.01173

Hoppe, J. D., Prokop, P., & Rau, R. (2018). Empower, not impose!—Preventing academic procrastination. Journal of Prevention & Intervention in the Community , 46 (2), 184-198. https://doi.org/10.1080/10852352.2016.1198172

Hu, Y., Liu, P., Guo, Y., & Feng, T. (2018). The neural substrates of procrastination: A voxel-based morphometry study. Brain and Cognition , 121 , 11-16. https://doi.org/10.1016/j.bandc.2018.01.001

Kadzikowska-Wrzosek, R. (2018). Self-regulation and bedtime procrastination: The role of self-regulation skills and chronotype. Personality and Individual Differences , 128 , 10-15. https://doi.org/10.1016/j.paid.2018.02.015

Kroese, F. M., Adriaanse, M. A., Evers, C., Anderson, J., & de Ridder, D. (2018). Commentary: Why don’t you go to bed on time? A daily diary study on the relationships between chronotype, self-control resources and the phenomenon of bedtime procrastination. Frontiers in Psychology , 9 , Article 915. https://doi.org/10.3389/fpsyg.2018.00915

Kühnel, J., Sonnentag, S., Bledow, R., & Melchers, K. G. (2018). The relevance of sleep and circadian misalignment for procrastination among shift workers. Journal of Occupational and Organizational Psychology , 91 (1), 110-133. https://doi.org/10.1111/joop.12191

Kühnel, J., Syrek, C. J., & Dreher, A. (2018). Why don’t you go to bed on time? A daily diary study on the relationships between chronotype, self-control resources and the phenomenon of bedtime procrastination. Frontiers in Psychology , 9 , Article 77. https://doi.org/10.3389/fpsyg.2018.00077

Legood, A., Lee, A., Schwarz, G., & Newman, A. (2018). From self‐defeating to other defeating: Examining the effects of leader procrastination on follower work outcomes. Journal of Occupational and Organizational Psychology , 91 (2), 430-439. https://doi.org/10.1111/joop.12205

Metin, U. B., Peeters, M. C., & Taris, T. W. (2018). Correlates of procrastination and performance at work: The role of having “good fit”. Journal of Prevention & Intervention in the Community , 46 (3), 228-244. https://doi.org/10.1080/10852352.2018.1470187

Mouratidis, A., Michou, A., Aelterman, N., Haerens, L., & Vansteenkiste, M. (2018). Begin-of-school-year perceived autonomy-support and structure as predictors of end-of-school-year study efforts and procrastination: the mediating role of autonomous and controlled motivation. Educational Psychology , 38 (4), 435-450. https://doi.org/10.1080/01443410.2017.1402863

Prem, R., Scheel, T. E., Weigelt, O., Hoffmann, K., & Korunka, C. (2018). Procrastination in daily working life: A diary study on within-person processes that link work characteristics to workplace procrastination. Frontiers in Psychology , 9 , Article 1087. https://doi.org/10.3389/fpsyg.2018.01087

Reinecke, L., Meier, A., Aufenanger, S., Beutel, M. E., Dreier, M., Quiring, O., … & Müller, K. W. (2018). Permanently online and permanently procrastinating? The mediating role of Internet use for the effects of trait procrastination on psychological health and well-being. New Media & Society , 20 (3), 862-880. https://doi.org/10.1177/1461444816675437

Rozental, A., Forsström, D., Lindner, P., Nilsson, S., Mårtensson, L., Rizzo, A., … & Carlbring, P. (2018). Treating procrastination using cognitive behavior therapy: A pragmatic randomized controlled trial comparing treatment delivered via the Internet or in groups. Behavior Therapy , 49 (2), 180-197. https://doi.org/10.1016/j.beth.2017.08.002

Schnauber-Stockmann, A., Meier, A., & Reinecke, L. (2018). Procrastination out of habit? The role of impulsive versus reflective media selection in procrastinatory media use. Media Psychology , 21 (4), 640-668. https://doi.org/10.1080/15213269.2018.1476156

Sirois, F. M., & Giguère, B. (2018). Giving in when feeling less good: Procrastination, action control, and social temptations.  British Journal of Social Psychology ,  57 (2), 404-427. https://doi.org/10.1111/bjso.12243

Steel, P., Svartdal, F., Thundiyil, T., & Brothen, T. (2018). Examining procrastination across multiple goal stages: A longitudinal study of temporal motivation theory. Frontiers in Psychology , 9 , Article 327. https://doi.org/10.3389/fpsyg.2018.00327

van den Berg, J., & Roosen, S. (2018). Two faces of employee inactivity: Procrastination and recovery. Journal of Prevention & Intervention in the Community , 46 (3), 295-307. https://doi.org/10.1080/10852352.2018.1470423

van Eerde, W., & Klingsieck, K. B. (2018). Overcoming procrastination? A meta-analysis of intervention studies. Educational Research Review , 25 , 73-85. https://doi.org/10.1016/j.edurev.2018.09.002

van Eerde, W., & Venus, M. (2018). A daily diary study on sleep quality and procrastination at work: The moderating role of trait self-control. Frontiers in Psychology , 9 , Article 2029. https://doi.org/10.3389/fpsyg.2018.02029

Van Hooft, E. A., & Van Mierlo, H. (2018). When teams fail to self-regulate: Predictors and outcomes of team procrastination among debating teams. Frontiers in Psychology , 9 , Article 464. https://doi.org/10.3389/fpsyg.2018.00464

Zacks, S., & Hen, M. (2018). Academic interventions for academic procrastination: A review of the literature. Journal of Prevention & Intervention in the Community , 46 (2), 117-130. https://doi.org/10.1080/10852352.2016.1198154

Zhang, Y., Dong, S., Fang, W., Chai, X., Mei, J., & Fan, X. (2018). Self-efficacy for self-regulation and fear of failure as mediators between self-esteem and academic procrastination among undergraduates in health professions. Advances in Health Sciences Education , 23 (4), 817-830. https://doi.org/10.1007/s10459-018-9832-3

Ziegler, N., & Opdenakker, M. C. (2018). The development of academic procrastination in first-year secondary education students: The link with metacognitive self-regulation, self-efficacy, and effort regulation. Learning and Individual Differences , 64 , 71-82. https://doi.org/10.1016/j.lindif.2018.04.009

Blouin‐Hudon, E. M. C., & Pychyl, T. A. (2017). A mental imagery intervention to increase future self‐continuity and reduce procrastination. Applied Psychology , 66 (2), 326-352. https://doi.org/10.1111/apps.12088

Chen, B. B., & Qu, W. (2017). Life history strategies and procrastination: The role of environmental unpredictability. Personality and Individual Differences , 117 , 23-29. https://doi.org/10.1016/j.paid.2017.05.036

Closson, L. M., & Boutilier, R. R. (2017). Perfectionism, academic engagement, and procrastination among undergraduates: The moderating role of honors student status. Learning and Individual Differences , 57 , 157-162. https://doi.org/10.1016/j.lindif.2017.04.010

Fernie, B. A., Bharucha, Z., Nikčević, A. V., Marino, C., & Spada, M. M. (2017). A metacognitive model of procrastination. Journal of Affective Disorders , 210 , 196-203. https://doi.org/10.1016/j.jad.2016.12.042

Gustavson, D. E., du Pont, A., Hatoum, A. S., Rhee, S. H., Kremen, W. S., Hewitt, J. K., & Friedman, N. P. (2017). Genetic and environmental associations between procrastination and internalizing/externalizing psychopathology. Clinical Psychological Science , 5 (5), 798-815. https://doi.org/10.1177/2167702617706084

He, S. (2017). A multivariate investigation into academic procrastination of university students. Open Journal of Social Sciences , 5 (10), 12. https://doi.org/10.4236/jss.2017.510002

Kim, S., Fernandez, S., & Terrier, L. (2017). Procrastination, personality traits, and academic performance: When active and passive procrastination tell a different story. Personality and Individual Differences , 108 , 154-157. https://doi.org/10.1016/j.paid.2016.12.021

Liu, P., & Feng, T. (2017). The overlapping brain region accounting for the relationship between procrastination and impulsivity: A voxel-based morphometry study. Neuroscience , 360 , 9-17. https://doi.org/10.1016/j.neuroscience.2017.07.042

Liu, W., Pan, Y., Luo, X., Wang, L., & Pang, W. (2017). Active procrastination and creative ideation: The mediating role of creative self-efficacy. Personality and Individual Differences , 119 , 227-229. https://doi.org/10.1016/j.paid.2017.07.033

Müller, S., Fieseler, C., Meckel, M., & Suphan, A. (2018). Time well wasted? Online procrastination during times of unemployment. Social Science Computer Review , 36 (3), 263-276. https://doi.org/10.1177/0894439317715716

Nordby, K., Klingsieck, K. B., & Svartdal, F. (2017). Do procrastination-friendly environments make students delay unnecessarily?. Social Psychology of Education , 20 (3), 491-512. https://doi.org/10.1007/s11218-017-9386-x

Rozental, A., Forsell, E., Svensson, A., Andersson, G., & Carlbring, P. (2017). Overcoming procrastination: One-year follow-up and predictors of change in a randomized controlled trial of Internet-based cognitive behavior therapy. Cognitive Behaviour Therapy , 46 (3), 177-195. https://doi.org/10.1080/16506073.2016.1236287

Sirois, F. M., Molnar, D. S., & Hirsch, J. K. (2017). A meta–analytic and conceptual update on the associations between procrastination and multidimensional perfectionism. European Journal of Personality , 31 (2), 137-159. https://doi.org/10.1002/per.2098

Smith, M. M., Sherry, S. B., Saklofske, D. H., & Mushqaush, A. R. (2017). Clarifying the perfectionism-procrastination relationship using a 7-day, 14-occasion daily diary study. P ersonality and Individual Differences , 112 , 117-123. https://doi.org/10.1016/j.paid.2017.02.059

Svartdal, F., & Steel, P. (2017). Irrational delay revisited: examining five procrastination scales in a global sample. Frontiers in Psychology , 8 , Article 1927. https://doi.org/10.3389/fpsyg.2017.01927

Westgate, E. C., Wormington, S. V., Oleson, K. C., & Lindgren, K. P. (2017). Productive procrastination: academic procrastination style predicts academic and alcohol outcomes. Journal of Applied Social Psychology , 47 (3), 124-135. https://doi.org/10.1111/jasp.12417

Wolters, C. A., Won, S., & Hussain, M. (2017). Examining the relations of time management and procrastination within a model of self-regulated learning. Metacognition and Learning , 12 (3), 381-399. https://doi.org/10.1007/s11409-017-9174-1

Yeh, Y. C., Wang, P. W., Huang, M. F., Lin, P. C., Chen, C. S., & Ko, C. H. (2017). The procrastination of Internet gaming disorder in young adults: The clinical severity. Psychiatry Resear ch, 254 , 258-262. https://doi.org/10.1016/j.psychres.2017.04.055

Zhang, C., Ni, Y., & Feng, T. (2017). The effect of regulatory mode on procrastination: Bi-stable parahippocampus connectivity with dorsal anterior cingulate and anterior prefrontal cortex. Behavioural Brain Research , 329 , 51-57. https://doi.org/10.1016/j.bbr.2017.04.019

Beutel, M. E., Klein, E. M., Aufenanger, S., Brähler, E., Dreier, M., Müller, K. W., … & Wölfling, K. (2016). Procrastination, distress and life satisfaction across the age range – A German representative community study. PLOS One , 11 (2), Article e0148054. https://doi.org/10.1371/journal.pone.0148054

Boysan, M., & Kiral, E. (2017). Associations between procrastination, personality, perfectionism, self-esteem and locus of control. British Journal of Guidance & Counselling , 45 (3), 284-296. https://doi.org/10.1080/03069885.2016.1213374

Chen, B. B., & Chang, L. (2016). Procrastination as a fast life history strategy. Evolutionary Psychology , 14 (January-March), no page/article number. https://doi.org/10.1177/1474704916630314

Chen, B. B., Shi, Z., & Wang, Y. (2016). Do peers matter? Resistance to peer influence as a mediator between self-esteem and procrastination among undergraduates. Frontiers in Psychology , 7 , Article 1529. https://doi.org/10.3389/fpsyg.2016.01529

Eckert, M., Ebert, D. D., Lehr, D., Sieland, B., & Berking, M. (2016). Overcome procrastination: Enhancing emotion regulation skills reduce procrastination. Learning and Individual Differences , 52 , 10-18. https://doi.org/10.1016/j.lindif.2016.10.001

Flett, A. L., Haghbin, M., & Pychyl, T. A. (2016). Procrastination and depression from a cognitive perspective: An exploration of the associations among procrastinatory automatic thoughts, rumination, and mindfulness. Journal of Rational-Emotive & Cognitive-Behavior Therapy , 34 (3), 169-186. https://doi.org/10.1007/s10942-016-0235-1

Giguère, B., Sirois, F. M., & Vaswani, M. (2016). Delaying things and feeling bad about it? A norm-based approach to procrastination. In F. M. Sirois & T. A. Pychyl (Eds.), Procrastination, health, and well-being (pp. 189-212). Academic Press. https://doi.org/10.1016/B978-0-12-802862-9.00009-8

Grunschel, C., Schwinger, M., Steinmayr, R., & Fries, S. (2016). Effects of using motivational regulation strategies on students’ academic procrastination, academic performance, and well-being. Learning and Individual Differences , 49 , 162-170. https://doi.org/10.1016/j.lindif.2016.06.008

Hairston, I. S., & Shpitalni, R. (2016). Procrastination is linked with insomnia symptoms: The moderating role of morningness-eveningness. Personality and Individual Differences , 101 , 50-56. https://doi.org/10.1016/j.paid.2016.05.031

Hensley, L. C. (2016). The draws and drawbacks of college students’ active procrastination. Journal of College Student Development , 57 (4), 465-471. https://doi.org/10.1353/csd.2016.0045

Johnson Jr, P. E., Perrin, C. J., Salo, A., Deschaine, E., & Johnson, B. (2016). Use of an explicit rule decreases procrastination in university students. Journal of Applied Behavior Analysis , 49 (2), 346-358. https://doi.org/10.1002/jaba.287

Kroese, F. M., Nauts, S., Kamphorst, B. A., Anderson, J. H., & de Ridder, D. T. (2016). Bedtime procrastination: A behavioral perspective on sleep insufficiency. In F. M. Sirois & T. A. Pychyl (Eds.), Procrastination, health, and well-being  (pp. 93-119). Academic Press. https://doi.org/10.1016/B978-0-12-802862-9.00005-0

Kroese, F. M., Evers, C., Adriaanse, M. A., & de Ridder, D. T. (2016). Bedtime procrastination: A self-regulation perspective on sleep insufficiency in the general population. Journal of Health Psychology , 21 (5), 853-862. https://doi.org/10.1177/1359105314540014

Kühnel, J., Bledow, R., & Feuerhahn, N. (2016). When do you procrastinate? Sleep quality and social sleep lag jointly predict self‐regulatory failure at work. Journal of Organizational Behavior , 37 (7), 983-1002. https://doi.org/10.1002/job.2084

Mann, L. (2016). Procrastination revisited: A commentary. Australian Psychologist , 51 (1), 47-51. https://doi.org/10.1111/ap.12208

Meier, A., Reinecke, L., & Meltzer, C. E. (2016). “Facebocrastination”? Predictors of using Facebook for procrastination and its effects on students’ well-being. Computers in Human Behavior , 64 , 65-76. https://doi.org/10.1016/j.chb.2016.06.011

Metin, U. B., Taris, T. W., & Peeters, M. C. (2016). Measuring procrastination at work and its associated workplace aspects. Personality and Individual Differences , 101 , 254-263. https://doi.org/10.1016/j.paid.2016.06.006

Pychyl, T. A., & Sirois, F. M. (2016). Procrastination, emotion regulation, and well-being. In F. M. Sirois & T. A. Pychyl (Eds.), Procrastination, health, and well-being (pp. 163-188). Academic Press. https://doi.org/10.1016/B978-0-12-802862-9.00008-6

Rahimi, S., Hall, N. C., & Pychyl, T. A. (2016). Attributions of responsibility and blame for procrastination behavior. Frontiers in Psychology , 7 , Article 1179. https://doi.org/10.3389/fpsyg.2016.01179

Rebetez, M. M. L., Barsics, C., Rochat, L., D’Argembeau, A., & Van der Linden, M. (2016). Procrastination, consideration of future consequences, and episodic future thinking. Consciousness and Cognition , 42 , 286-292. https://doi.org/10.1016/j.concog.2016.04.003

Reinecke, L., & Hofmann, W. (2016). Slacking off or winding down? An experience sampling study on the drivers and consequences of media use for recovery versus procrastination. Human Communication Research , 42 (3), 441-461. https://doi.org/10.1111/hcre.12082

Sirois, F. M. (2016). Introduction: Conceptualizing the relations of procrastination to health and well-being. In F. M. Sirois & T. A. Pychyl (Eds.), Procrastination, health, and well-being (pp. 3-20). Academic Press. https://doi.org/10.1016/B978-0-12-802862-9.00001-3

Sirois, F. M. (2016). Procrastination, stress, and chronic health conditions: A temporal perspective. In F. M. Sirois & T. A. Pychyl (Eds.), Procrastination, health, and well-being  (pp. 67-92). Academic Press. https://doi.org/10.1016/B978-0-12-802862-9.00004-9

Sirois, F. M., & Pychyl, T. A. (2016). Future of research on procrastination, health, and well-being: Key themes and recommendations. In F. M. Sirois & T. A. Pychyl (Eds.), Procrastination, health, and well-being (pp. 255-271). Academic Press. https://doi.org/10.1016/B978-0-12-802862-9.00012-8

Sirois, F. M., & Pychyl, T. A. (2016). Procrastination. In Encyclopedia of mental health (2nd ed., Vol. 3, pp. 330-338). Academic Press. https://doi.org/10.1016/B978-0-12-397045-9.00166-X

Steel, P., & Klingsieck, K. B. (2016). Academic procrastination: Psychological antecedents revisited. Australian Psychologist , 51 (1), 36-46. https://doi.org/10.1111/ap.12173

van Eerde, W. (2016). Procrastination and well-being at work. In F. M. Sirois & T. A. Pychyl (Eds.), Procrastination, health, and well-being  (pp. 233-253). Academic Press. https://doi.org/10.1016/B978-0-12-802862-9.00011-6

Wu, H., Gui, D., Lin, W., Gu, R., Zhu, X., & Liu, X. (2016). The procrastinators want it now: behavioral and event-related potential evidence of the procrastination of intertemporal choices. Brain and Cognition , 107 , 16-23. https://doi.org/10.1016/j.bandc.2016.06.005

Yockey, R. D. (2016). Validation of the short form of the Academic Procrastination Scale.  Psychological Reports ,  118 (1), 171- 179. https://doi.org/10.1177/0033294115626825

Balkis, M., & Duru, E. (2016). Procrastination, self-regulation failure, academic life satisfaction, and affective well-being: Underregulation or misregulation form. European Journal of Psychology of Education , 31, 439-459. https://doi.org/10.1007/s10212-015-0266-5

Gevers, J., Mohammed, S., & Baytalskaya, N. (2015). The conceptualisation and measurement of pacing styles. Applied Psychology , 64 (3), 499-540. https://doi.org/10.1111/apps.12016

Glick, D. M., & Orsillo, S. M. (2015). An investigation of the efficacy of acceptance-based behavioral therapy for academic procrastination. Journal of Experimental Psychology: General, 144 (2), 400. https://doi.org/10.1037/xge0000050

Gustavson, D. E., Miyake, A., Hewitt, J. K., & Friedman, N. P. (2015). Understanding the cognitive and genetic underpinnings of procrastination: Evidence for shared genetic influences with goal management and executive function abilities. Journal of Experimental Psychology: General , 144 (6), 1063. https://doi.org/10.1037/xge0000110

Haghbin, M. (2015). Conceptualization and operationalization of delay: Development and validation of the multifaceted measure of academic procrastination and the delay questionnaire . (Doctoral dissertation). Carleton University, Ottawa, Canada. https://doi.org/10.22215/etd/2015-11051

Kim, K. R., & Seo, E. H. (2015). The relationship between procrastination and academic performance: A meta-analysis. Personality and Individual Differences , 82 , 26-33. https://doi.org/10.1016/j.paid.2015.02.038

Myrick, J. G. (2015). Emotion regulation, procrastination, and watching cat videos online: Who watches Internet cats, why, and to what effect?. Computers in Human Behavior , 52 , 168-176. https://doi.org/10.1016/j.chb.2015.06.001

Patrzek, J., Sattler, S., van Veen, F., Grunschel, C., & Fries, S. (2015). Investigating the effect of academic procrastination on the frequency and variety of academic misconduct: A panel study. Studies in Higher Education , 40 (6), 1014-1029. https://doi.org/10.1080/03075079.2013.854765

Rebetez, M. M. L., Rochat, L., & Van der Linden, M. (2015). Cognitive, emotional, and motivational factors related to procrastination: A cluster analytic approach. Personality and Individual Differences , 76 , 1-6. https://doi.org/10.1016/j.paid.2014.11.044

Reuben, E., Sapienza, P., & Zingales, L. (2015). Procrastination and impatience. Journal of Behavioral and Experimental Economics , 58 , 63-76. https://doi.org/10.1016/j.socec.2015.07.005

Rozental, A., Forsell, E., Svensson, A., Andersson, G., & Carlbring, P. (2015). Internet-based cognitive—behavior therapy for procrastination: A randomized controlled trial. Journal of Consulting and Clinical Psychology , 83 (4), 808. https://doi.org/10.1037/ccp0000023

Rozental, A., Forsell, E., Svensson, A., Forsström, D., Andersson, G., & Carlbring, P. (2015). Differentiating procrastinators from each other: A cluster analysis. Cognitive Behaviour Therapy , 44 (6), 480-490. https://doi.org/10.1080/16506073.2015.1059353

Sirois, F. M. (2015). Is procrastination a vulnerability factor for hypertension and cardiovascular disease? Testing an extension of the procrastination–health model. Journal of Behavioral Medicine , 38 (3), 578-589. https://doi.org/10.1007/s10865-015-9629-2

Sirois, F. M., van Eerde, W., & Argiropoulou, M. I. (2015). Is procrastination related to sleep quality? Testing an application of the procrastination–health model. Cogent Psychology , 2 (1), 1074776. https://doi.org/10.1080/23311908.2015.1074776

Sirois, F. M., & Kitner, R. (2015). Less adaptive or more maladaptive? A meta–analytic investigation of procrastination and coping. European Journal of Personality , 29 (4), 433-444. https://doi.org/10.1002/per.1985

Steel, P., & Klingsieck, K. B. (2015). Procrastination. In International encyclopedia of the social & behavioral sciences (2nd ed., Vol. 19, pp. 73–78). Elsevier. https://doi.org/10.1016/B978-0-08-097086-8.25087-3

Tibbett, T. P., & Ferrari, J. R. (2015). The portrait of the procrastinator: Risk factors and results of an indecisive personality. Personality and Individual Differences , 82 , 175-184. https://doi.org/10.1016/j.paid.2015.03.014

van Eerde, W. (2015). Time management and procrastination. In M. D. Mumford & M. Frese (Eds.), The psychology of planning in organizations: Research and Applications (pp. 328-349). Routledge. https://doi.org/10.4324/9780203105894-20

Breems, N., & Basden, A. (2014). Understanding of computers and procrastination: A philosophical approach. Computers in Human Behavior , 31 , 211-223. https://doi.org/10.1016/j.chb.2013.10.024

Burnam, A., Komarraju, M., Hamel, R., & Nadler, D. R. (2014). Do adaptive perfectionism and self-determined motivation reduce academic procrastination?. Learning and Individual Differences , 36 , 165-172. https://doi.org/10.1016/j.lindif.2014.10.009

Dunn, K. (2014). Why wait? The influence of academic self-regulation, intrinsic motivation, and statistics anxiety on procrastination in online statistics. Innovative Higher Education , 39 (1), 33-44. https://doi.org/10.1007/s10755-013-9256-1

Hensley, L. C. (2014). Reconsidering active procrastination: Relations to motivation and achievement in college anatomy. Learning and Individual Differences , 36 , 157-164. https://doi.org/10.1016/j.lindif.2014.10.012

Mohsin, F. Z., & Ayub, N. (2014). The relationship between procrastination, delay of gratification, and job satisfaction among high school teachers. Japanese Psychological Research , 56 (3), 224-234. https://doi.org/10.1111/jpr.12046

Gustavson, D. E., Miyake, A., Hewitt, J. K., & Friedman, N. P. (2014). Genetic relations among procrastination, impulsivity, and goal-management ability: Implications for the evolutionary origin of procrastination. Psychological Science , 25 (6), 1178-1188. https://doi.org/10.1177/0956797614526260

Häfner, A., Oberst, V., & Stock, A. (2014). Avoiding procrastination through time management: An experimental intervention study. Educational Studies , 40 (3), 352-360. https://doi.org/10.1080/03055698.2014.899487

Hen, M., & Goroshit, M. (2014). Academic procrastination, emotional intelligence, academic self-efficacy, and GPA: A comparison between students with and without learning disabilities. Journal of Learning Disabilities , 47 (2), 116-124. https://doi.org/10.1177/0022219412439325

Katz, I., Eilot, K., & Nevo, N. (2014). “I’ll do it later”: Type of motivation, self-efficacy and homework procrastination. Motivation and Emotion , 38 (1), 111-119. https://doi.org/10.1007/s11031-013-9366-1

Krause, K., & Freund, A. M. (2014). Delay or procrastination–A comparison of self-report and behavioral measures of procrastination and their impact on affective well-being. Personality and Individual Differences , 63 , 75-80. https://doi.org/10.1016/j.paid.2014.01.050

Krause, K., & Freund, A. M. (2014). How to beat procrastination: The role of goal focus. European Psychologist , 19 (2), 132. https://doi.org/10.1027/1016-9040/a000153

Kroese, F. M., de Ridder, D. T., Evers, C., & Adriaanse, M. A. (2014). Bedtime procrastination: Introducing a new area of procrastination. Frontiers in Psychology , 5 , Article 611. https://doi.org/10.3389/fpsyg.2014.00611

Loehlin, J. C., & Martin, N. G. (2014). The genetic correlation between procrastination and impulsivity. Twin Research and Human Genetics , 17 (6), 512-515. https://doi.org/10.1017/thg.2014.60

Niermann, H. C., & Scheres, A. (2014). The relation between procrastination and symptoms of attention‐deficit hyperactivity disorder (ADHD) in undergraduate students. International Journal of Methods in Psychiatric Research , 23 (4), 411-421. https://doi.org/10.1002/mpr.1440

Rozental, A., & Carlbring, P. (2014). Understanding and treating procrastination: A review of a common self-regulatory failure. Psychology , 5 (13), 1488-1502. https://doi.org/10.4236/psych.2014.513160

Sirois, F. M. (2014). Absorbed in the moment? An investigation of procrastination, absorption and cognitive failures. Personality and Individual Differences , 71 , 30-34. https://doi.org/10.1016/j.paid.2014.07.016

Sirois, F. M. (2014). Out of sight, out of time? A meta–analytic investigation of procrastination and time perspective. European Journal of personality , 28 (5), 511-520. https://doi.org/10.1002/per.1947

Sirois, F. M. (2014). Procrastination and stress: Exploring the role of self-compassion. Self and Identity , 13 (2), 128-145. https://doi.org/10.1080/15298868.2013.763404

Uzun Ozer, B., O’Callaghan, J., Bokszczanin, A., Ederer, E., & Essau, C. (2014). Dynamic interplay of depression, perfectionism and self-regulation on procrastination. British Journal of Guidance & Counselling , 42 (3), 309-319. https://doi.org/10.1080/03069885.2014.896454

Wan, H. C., Downey, L. A., & Stough, C. (2014). Understanding non-work presenteeism: Relationships between emotional intelligence, boredom, procrastination and job stress. Personality and Individual Differences , 65 , 86-90. https://doi.org/10.1016/j.paid.2014.01.018

Wäschle, K., Allgaier, A., Lachner, A., Fink, S., & Nückles, M. (2014). Procrastination and self-efficacy: Tracing vicious and virtuous circles in self-regulated learning. Learning and Instruction , 29 , 103-114. https://doi.org/10.1016/j.learninstruc.2013.09.005

Wu, Y., Ramachandran, K., & Krishnan, V. (2014). Managing cost salience and procrastination in projects: Compensation and team composition. Production and Operations Management , 23 (8), 1299-1311. https://doi.org/10.1111/poms.12095

Acorn, A., & Buttuls, J. (2013). The not now habit: Procrastination, legal ethics and legal education. Legal Ethics , 16 (1), 73-96. https://doi.org/10.5235/1460728x.1.1.73

Grunschel, C., Patrzek, J., & Fries, S. (2013). Exploring different types of academic delayers: A latent profile analysis. Learning and Individual Differences , 23 , 225-233. https://doi.org/10.1016/j.lindif.2012.09.014

Grunschel, C., Patrzek, J., & Fries, S. (2013). Exploring reasons and consequences of academic procrastination: An interview study. European Journal of Psychology of Education , 28 (3), 841-861. https://doi.org/10.1007/s10212-012-0143-4

Hinsch, C., & Sheldon, K. M. (2013). The impact of frequent social Internet consumption: Increased procrastination and lower life satisfaction. Journal of Consumer Behaviour , 12 (6), 496-505. https://doi.org/10.1002/cb.1453

Kim, E., & Seo, E. H. (2013). The relationship of flow and self-regulated learning to active procrastination. Social Behavior and Personality: An International Journal , 41 (7), 1099-1113. https://doi.org/10.2224/sbp.2013.41.7.1099

Klingsieck, K. B. (2013). Procrastination in different life-domains: Is procrastination domain specific?. Current Psychology , 32 (2), 175-185. https://doi.org/10.1007/s12144-013-9171-8

Klingsieck, K. B. (2013). Procrastination: When good things don’t come to those who wait. European Psychologist , 18 , 24-34. https://doi.org/10.1027/1016-9040/a000138

Nguyen, B., Steel, P., & Ferrari, J. R. (2013). Procrastination’s impact in the workplace and the workplace’s impact on procrastination. International Journal of Selection and Assessment , 21 (4), 388-399. https://doi.org/10.1111/ijsa.12048

Ozer, B. U., Demir, A., & Ferrari, J. R. (2013). Reducing academic procrastination through a group treatment program: A pilot study. Journal of Rational-Emotive & Cognitive-Behavior Therapy , 31 (3), 127-135. https://doi.org/10.1007/s10942-013-0165-0

Özer, B. U., Saçkes, M., & Tuckman, B. W. (2013). Psychometric properties of the Tuckman Procrastination Scale in a Turkish sample. Psychological Reports , 113 (3), 874-884. https://doi.org/10.2466/03.20.PR0.113x28z7

Rozental, A., & Carlbring, P. (2013). Internet-based cognitive behavior therapy for procrastination: Study protocol for a randomized controlled trial. JMIR Research Protocols , 2 (2), e46. https://doi.org/10.2196/resprot.2801

Sirois, F., & Pychyl, T. (2013). Procrastination and the priority of short‐term mood regulation: Consequences for future self. Social and Personality Psychology Compass , 7 (2), 115-127. https://doi.org/10.1111/spc3.12011

Steel, P., & Ferrari, J. (2013). Sex, education and procrastination: An epidemiological study of procrastinators’ characteristics from a global sample. European Journal of Personality , 27 (1), 51-58.  https://doi.org/10.1002/per.1851

Verešová, M. (2013). Procrastination, stress and coping among primary school teachers. Procedia-Social and Behavioral Sciences , 106 , 2131-2138. https://doi.org/10.1016/j.sbspro.2013.12.243

Cao, L. (2012). Examining ‘active’ procrastination from a self-regulated learning perspective. Educational Psychology , 32 (4), 515-545. https://doi.org/10.1080/01443410.2012.663722

Gupta, R., Hershey, D. A., & Gaur, J. (2012). Time perspective and procrastination in the workplace: An empirical investigation. Current Psychology , 31 (2), 195-211. https://doi.org/10.1007/s12144-012-9136-3

Haghbin, M., McCaffrey, A., & Pychyl, T. A. (2012). The complexity of the relation between fear of failure and procrastination. Journal of Rational-Emotive & Cognitive-Behavior Therapy , 30 (4), 249-263. https://doi.org/10.1007/s10942-012-0153-9

Mushquash, A. R., & Sherry, S. B. (2012). Understanding the socially prescribed perfectionist’s cycle of self-defeat: A 7-day, 14-occasion daily diary study. Journal of Research in Personality , 46 (6), 700-709. https://doi.org/10.1016/j.jrp.2012.08.006

Patrzek, J., Grunschel, C., & Fries, S. (2012). Academic procrastination: The perspective of university counsellors. International Journal for the Advancement of Counselling , 34 (3), 185-201. https://doi.org/10.1007/s10447-012-9150-z

Pychyl, T. A., & Flett, G. L. (2012). Procrastination and self-regulatory failure: An introduction to the special issue. Journal of Rational-Emotive & Cognitive-Behavior Therapy , 30 (4), 203-212. https://doi.org/10.1007/s10942-012-0149-5

Sirois, F. M., & Tosti, N. (2012). Lost in the moment? An investigation of procrastination, mindfulness, and well-being. Journal of Rational-Emotive & Cognitive-Behavior Therapy , 30 (4), 237-248. https://doi.org/10.1007/s10942-012-0151-y

Burger, N., Charness, G., & Lynham, J. (2011). Field and online experiments on self-control. Journal of Economic Behavior & Organization , 77 (3), 393-404. https://doi.org/10.1016/j.jebo.2010.11.010

Cadena, X., Schoar, A., Cristea, A., & Delgado-Medrano, H. M. (2011). Fighting procrastination in the workplace: An experiment. NBER Working Paper w16944. National Bureau of Economic Research. https://doi.org/10.3386/w16944

Corkin, D. M., Shirley, L. Y., & Lindt, S. F. (2011). Comparing active delay and procrastination from a self-regulated learning perspective. Learning and Individual Differences , 21 (5), 602-606. https://doi.org/10.1016/j.lindif.2011.07.005

Freeman, E. K., Cox-Fuenzalida, L. E., & Stoltenberg, I. (2011). Extraversion and arousal procrastination: Waiting for the kicks. Current Psychology , 30 (4), 375-382. https://doi.org/10.1007/s12144-011-9123-0

Herweg, F., & Müller, D. (2011). Performance of procrastinators: On the value of deadlines. Theory and Decision , 70 (3), 329-366. https://doi.org/10.1007/s11238-010-9195-6

Howell, A. J., & Buro, K. (2011). Relations among mindfulness, achievement-related self-regulation, and achievement emotions. Journal of Happiness Studies , 12 (6), 1007-1022. https://doi.org/10.1007/s10902-010-9241-7

Jadidi, F., Mohammadkhani, S., & Tajrishi, K. Z. (2011). Perfectionism and academic procrastination. Procedia-Social and Behavioral Sciences , 30 , 534-537. https://doi.org/10.1016/j.sbspro.2011.10.104

Strunk, K. K., & Steele, M. R. (2011). Relative contributions of self-efficacy, self-regulation, and self-handicapping in predicting student procrastination. Psychological Reports , 109 (3), 983-989. https://doi.org/10.2466/07.09.20.PR0.109.6.983-989

Çapan, B. E. (2010). Relationship among perfectionism, academic procrastination and life satisfaction of university students. Procedia – Social and Behavioral Sciences , 5 , 1665-1671. https://doi.org/10.1016/j.sbspro.2010.07.342

Hussain, I., & Sultan, S. (2010). Analysis of procrastination among university students. Procedia-Social and Behavioral Sciences , 5 , 1897-1904. https://doi.org/10.1016/j.sbspro.2010.07.385

Stead, R., Shanahan, M. J., & Neufeld, R. W. (2010). “I’ll go to therapy, eventually”: Procrastination, stress and mental health. Personality and Individual Differences , 49 (3), 175-180. https://doi.org/10.1016/j.paid.2010.03.028

Steel, P. (2010). Arousal, avoidant and decisional procrastinators: Do they exist?. Personality and Individual Differences , 48 (8), 926-934. https://doi.org/10.1016/j.paid.2010.02.025

Wohl, M. J., Pychyl, T. A., & Bennett, S. H. (2010). I forgive myself, now I can study: How self-forgiveness for procrastinating can reduce future procrastination.  Personality and Individual Differences , 48 (7), 803-808. https://doi.org/10.1016/j.paid.2010.01.029

Choi, J. N., & Moran, S. V. (2009). Why not procrastinate? Development and validation of a new active procrastination scale.  The Journal of Social Psychology ,  149 (2), 195-212. https://doi.org/10.3200/SOCP.149.2.195-212

Ferrari, J. R., Barnes, K. L., & Steel, P. (2009). Life regrets by avoidant and arousal procrastinators: Why put off today what you will regret tomorrow?. Journal of Individual Differences , 30 (3), 163-168. https://doi.org/10.1027/1614-0001.30.3.163

Ferrari, J. R., Özer, B. U., & Demir, A. (2009). Chronic procrastination among Turkish adults: Exploring decisional, avoidant, and arousal styles. The Journal of Social Psychology , 149 (3), 402-408. https://doi.org/10.3200/SOCP.149.3.402-408

Hofmann, W., Friese, M., & Strack, F. (2009). Impulse and self-control from a dual-systems perspective. Perspectives on Psychological Science , 4 (2), 162-176. https://doi.org/10.1111/j.1745-6924.2009.01116.x

Klassen, R. M., Ang, R. P., Chong, W. H., Krawchuk, L. L., Huan, V. S., Wong, I. Y., & Yeo, L. S. (2009). A cross‐cultural study of adolescent procrastination. Journal of Research on Adolescence , 19 (4), 799-811. https://doi.org/10.1111/j.1532-7795.2009.00620.x

Klassen, R. M., & Kuzucu, E. (2009). Academic procrastination and motivation of adolescents in Turkey. Educational Psychology , 29 (1), 69-81. https://doi.org/10.1080/01443410802478622

Özer, B. U., Demir, A., & Ferrari, J. R. (2009). Exploring academic procrastination among Turkish students: Possible gender differences in prevalence and reasons. The Journal of Social Psychology , 149 (2), 241-257. https://doi.org/10.3200/SOCP.149.2.241-257

Rosário, P., Costa, M., Núñez, J. C., González-Pienda, J., Solano, P., & Valle, A. (2009). Academic procrastination: Associations with personal, school, and family variables. The Spanish Journal of Psychology , 12 (1), 118-127. https://doi.org/10.1017/S1138741600001530

Simpson, W. K., & Pychyl, T. A. (2009). In search of the arousal procrastinator: Investigating the relation between procrastination, arousal-based personality traits and beliefs about procrastination motivations. Personality and Individual Differences , 47 (8), 906-911. https://doi.org/10.1016/j.paid.2009.07.013

Beeftink, F., van Eerde, W., & Rutte, C. G. (2008). The effect of interruptions and breaks on insight and impasses: Do you need a break right now?. Creativity Research Journal , 20 (4), 358-364. https://doi.org/10.1080/10400410802391314

Díaz-Morales, J. F., Ferrari, J. R., & Cohen, J. R. (2008). Indecision and avoidant procrastination: The role of morningness—eveningness and time perspective in chronic delay lifestyles. The Journal of General Psychology , 135 (3), 228-240. https://doi.org/10.3200/genp.135.3.228-240

Eggens, L., Van der Werf, M. P. C., & Bosker, R. J. (2008). The influence of personal networks and social support on study attainment of students in university education. Higher Education , 55 (5), 553-573. https://doi.org/10.1007/s10734-007-9074-4

Gröpel, P., & Steel, P. (2008). A mega-trial investigation of goal setting, interest enhancement, and energy on procrastination. Personality and Individual Differences , 45 (5), 406-411. https://doi.org/10.1016/j.paid.2008.05.015

Klassen, R. M., Krawchuk, L. L., Lynch, S. L., & Rajani, S. (2008). Procrastination and motivation of undergraduates with learning disabilities: A Mixed‐Methods inquiry. Learning Disabilities Research & Practice , 23 (3), 137-147. https://doi.org/10.1111/j.1540-5826.2008.00271.x

Klassen, R. M., Krawchuk, L. L., & Rajani, S. (2008). Academic procrastination of undergraduates: Low self-efficacy to self-regulate predicts higher levels of procrastination. Contemporary Educational Psychology , 33 (4), 915-931. https://doi.org/10.1016/j.cedpsych.2007.07.001

Langberg, J. M., Epstein, J. N., & Graham, A. J. (2008). Organizational-skills interventions in the treatment of ADHD. Expert Review of Neurotherapeutics , 8 (10), 1549-1561. https://doi.org/10.1586/14737175.8.10.1549

McCrea, S. M., Liberman, N., Trope, Y., & Sherman, S. J. (2008). Construal level and procrastination. Psychological Science , 19 (12), 1308-1314. https://doi.org/10.1111/j.1467-9280.2008.02240.x

O’Donoghue, T., & Rabin, M. (2008). Procrastination on long-term projects. Journal of Economic Behavior & Organization , 66 (2), 161-175. https://doi.org/10.1016/j.jebo.2006.05.005

Pittman, T. S., Tykocinski, O. E., Sandman‐Keinan, R., & Matthews, P. A. (2008). When bonuses backfire: An inaction inertia analysis of procrastination induced by a missed opportunity. Journal of Behavioral Decision Making , 21 (2), 139-150. https://doi.org/10.1002/bdm.576

Seo, E. H. (2008). Self-efficacy as a mediator in the relationship between self-oriented perfectionism and academic procrastination. Social Behavior and Personality: An International Journal , 36 (6), 753-764. https://doi.org/10.2224/sbp.2008.36.6.753

Thatcher, A., Wretschko, G., & Fridjhon, P. (2008). Online flow experiences, problematic Internet use and Internet procrastination. Computers in Human Behavior , 24 (5), 2236-2254. https://doi.org/10.1016/j.chb.2007.10.008

Ackerman, D. S., & Gross, B. L. (2007). I can start that JME manuscript next week, can’t I? The task characteristics behind why faculty procrastinate. Journal of Marketing Education , 29 (2), 97-110. https://doi.org/10.1177/0273475307302012

Alexander, E. S., & Onwuegbuzie, A. J. (2007). Academic procrastination and the role of hope as a coping strategy. Personality and Individual Differences , 42 (7), 1301-1310. https://doi.org/10.1016/j.paid.2006.10.008

Bui, N. H. (2007). Effect of evaluation threat on procrastination behavior.  The Journal of Social Psychology ,  147 (3), 197-209. https://doi.org/10.3200/SOCP.147.3.197-209

Ferrari, J. R., & Díaz-Morales, J. F. (2007). Procrastination: Different time orientations reflect different motives. Journal of Research in Personality , 41 (3), 707-714. https://doi.org/10.1016/j.jrp.2006.06.006

Ferrari, J. R., Díaz-Morales, J. F., O’Callaghan, J., Díaz, K., & Argumedo, D. (2007). Frequent behavioral delay tendencies by adults: International prevalence rates of chronic procrastination. Journal of Cross-Cultural Psychology , 38 (4), 458-464. https://doi.org/10.1177/0022022107302314

Ferrari, J. R., & Pychyl, T. A. (2007). Regulating speed, accuracy and judgments by indecisives: Effects of frequent choices on self-regulation depletion. Personality and Individual Differences , 42 (4), 777-787. https://doi.org/10.1016/j.paid.2006.09.001

Grant, A. M., & Franklin, J. (2007). The transtheoretical model and study skills. Behaviour Change , 24 (2), 99-113. https://doi.org/10.1375/bech.24.2.99

Schraw, G., Wadkins, T., & Olafson, L. (2007). Doing the things we do: A grounded theory of academic procrastination. Journal of Educational Psychology , 99 (1), 12. https://doi.org/10.1037/0022-0663.99.1.12

Shanahan, M. J., & Pychyl, T. A. (2007). An ego identity perspective on volitional action: Identity status, agency, and procrastination. Personality and Individual Differences , 43 (4), 901-911. https://doi.org/10.1016/j.paid.2007.02.013

Sirois, F. M. (2007). “I’ll look after my health, later”: A replication and extension of the procrastination–health model with community-dwelling adults. Personality and Individual Differences , 43 (1), 15-26. https://doi.org/10.1016/j.paid.2006.11.003

Steel, P. (2007). The nature of procrastination: A meta-analytic and theoretical review of quintessential self-regulatory failure. Psychological Bulletin , 133 (1), 65. https://doi.org/10.1037/0033-2909.133.1.65

Howell, A. J., & Watson, D. C. (2007). Procrastination: Associations with achievement goal orientation and learning strategies. Personality and Individual Differences , 43 (1), 167-178.  https://doi.org/10.1016/j.paid.2006.11.017

Howell, A. J., Watson, D. C., Powell, R. A., & Buro, K. (2006). Academic procrastination: The pattern and correlates of behavioural postponement. Personality and Individual Differences , 40 (8), 1519-1530. https://doi.org/10.1016/j.paid.2005.11.023

Steel, P., & König, C. J. (2006). Integrating theories of motivation. Academy of Management Review , 31 (4), 889-913. https://doi.org/10.5465/amr.2006.22527462

Ackerman, D. S., & Gross, B. L. (2005). My instructor made me do it: Task characteristics of procrastination. Journal of Marketing Education , 27 (1), 5-13. https://doi.org/10.1177/0273475304273842

Chun Chu, A. H., & Choi, J. N. (2005). Rethinking procrastination: Positive effects of “active” procrastination behavior on attitudes and performance. The Journal of Social Psychology , 145 (3), 245-264. https://doi.org/10.3200/SOCP.145.3.245-264

Lee, E. (2005). The relationship of motivation and flow experience to academic procrastination in university students. The Journal of Genetic Psychology , 166 (1), 5-15. https://doi.org/10.3200/GNTP.166.1.5-15

Tuckman, B. W. (2005). Relations of academic procrastination, rationalizations, and performance in a web course with deadlines. Psychological Reports , 96 (3), 1015-1021. https://doi.org/10.2466/pr0.96.3c.1015-1021

Carden, R., Bryant, C., & Moss, R. (2004). Locus of control, test anxiety, academic procrastination, and achievement among college students. Psychological Reports , 95 (2), 581-582. https://doi.org/10.2466/pr0.95.2.581-582

Ferrari, J. R. (2004). Trait procrastination in academic settings: An overview of students who engage in task delays. In H. C. Schouwenburg, C. H. Lay, T. A. Pychyl, & J. R. Ferrari (Eds.), Counseling the procrastinator in academic settings (pp. 19-27). American Psychological Association. https://doi.org/10.1037/10808-002

Ferrari, J. R., & Patel, T. (2004). Social comparisons by procrastinators: Rating peers with similar or dissimilar delay tendencies. Personality and Individual Differences , 37 (7), 1493-1501. https://doi.org/10.1016/j.paid.2004.02.006

Flett, G. L., Hewitt, P. L., Davis, R. A., & Sherry, S. B. (2004). Description and counseling of the perfectionistic procrastinator. In H. C. Schouwenburg, C. H. Lay, T. A. Pychyl, & J. R. Ferrari (Eds.), Counseling the procrastinator in academic settings (pp. 181-194). American Psychological Association. https://doi.org/10.1037/10808-013

Onwuegbuzie, A. J. (2004). Academic procrastination and statistics anxiety. Assessment & Evaluation in Higher Education , 29 (1), 3-19. https://doi.org/10.1080/0260293042000160384

Schouwenburg, H. C. (2004). Procrastination in academic settings: General introduction. In H. C. Schouwenburg, C. H. Lay, T. A. Pychyl, & J. R. Ferrari (Eds.), Counseling the procrastinator in academic settings (pp. 3-17). American Psychological Association. https://doi.org/10.1037/10808-001

Sirois, F. M. (2004). Procrastination and intentions to perform health behaviors: The role of self-efficacy and the consideration of future consequences. Personality and Individual Differences , 37 (1), 115-128. https://doi.org/10.1016/j.paid.2003.08.005

van Eerde, W. (2004). Procrastination in academic settings and the Big Five model of personality: A meta-analysis. In H. C. Schouwenburg, C. H. Lay, T. A. Pychyl, & J. R. Ferrari (Eds.), Counseling the procrastinator in academic settings (pp. 29-40). American Psychological Association. https://doi.org/10.1037/10808-003

Wolters, C. A. (2004). Advancing achievement goal theory: Using goal structures and goal orientations to predict students’ motivation, cognition, and achievement. Journal of Educational Psychology , 96 (2), 236-250. https://doi.org/10.1037/0022-0663.96.2.236

Buehler, R., & Griffin, D. (2003). Planning, personality, and prediction: The role of future focus in optimistic time predictions. Organizational Behavior and Human Decision Processes , 92 (1-2), 80-90. https://doi.org/10.1016/S0749-5978(03)00089-X

Elvers, G. C., Polzella, D. J., & Graetz, K. (2003). Procrastination in online courses: Performance and attitudinal differences. Teaching of Psychology , 30 (2), 159-162. https://doi.org/10.1207/S15328023TOP3002_13

Fritzsche, B. A., Young, B. R., & Hickson, K. C. (2003). Individual differences in academic procrastination tendency and writing success. Personality and Individual Differences , 35 (7), 1549-1557. https://doi.org/10.1016/S0191-8869(02)00369-0

Sirois, F. M., Melia-Gordon, M. L., & Pychyl, T. A. (2003). “I’ll look after my health, later”: An investigation of procrastination and health. Personality and Individual Differences , 35 (5), 1167-1184.  https://doi.org/10.1016/S0191-8869(02)00326-4

van Eerde, W. (2003). A meta-analytically derived nomological network of procrastination. Personality and Individual Differences , 35 (6), 1401-1418. https://doi.org/10.1016/S0191-8869(02)00358-6

van Eerde, W. (2003). Procrastination at work and time management training. The Journal of Psychology , 137 (5), 421-434. https://doi.org/10.1080/00223980309600625

Wolters, C. A. (2003). Understanding procrastination from a self-regulated learning perspective. Journal of Educational Psychology , 95 (1), 179. https://doi.org/10.1037/0022-0663.95.1.179

Ariely, D., & Wertenbroch, K. (2002). Procrastination, deadlines, and performance: Self-control by precommitment. Psychological Science , 13 (3), 219-224. https://doi.org/10.1111/1467-9280.00441

Dewitte, S., & Schouwenburg, H. C. (2002). Procrastination, temptations, and incentives: The struggle between the present and the future in procrastinators and the punctual. European Journal of Personality , 16 (6), 469-489. https://doi.org/10.1002/per.461

Frederick, S., Loewenstein, G., & O’donoghue, T. (2002). Time discounting and time preference: A critical review. Journal of Economic Literature , 40 (2), 351-401. https://doi.org/10.1257/jel.40.2.351

Hammer, C. A., & Ferrari, J. R. (2002). Differential incidence of procrastination between blue and white-collar workers. Current Psychology , 21 (4), 333-338. https://doi.org/10.1007/s12144-002-1022-y

Scher, S. J., & Osterman, N. M. (2002). Procrastination, conscientiousness, anxiety, and goals: Exploring the measurement and correlates of procrastination among school‐aged children. Psychology in the Schools , 39 (4), 385-398. https://doi.org/10.1002/pits.10045

Ferrari, J. R. (2001). Procrastination as self‐regulation failure of performance: effects of cognitive load, self‐awareness, and time limits on ‘working best under pressure’. European Journal of Personality , 15 (5), 391-406. https://doi.org/10.1002/per.413

Lavoie, J. A., & Pychyl, T. A. (2001). Cyberslacking and the procrastination superhighway: A web-based survey of online procrastination, attitudes, and emotion. Social Science Computer Review , 19 (4), 431-444. https://doi.org/10.1177/089443930101900403

O’Donoghue, T., & Rabin, M. (2001). Choice and procrastination. The Quarterly Journal of Economics , 116 (1), 121-160.  https://doi.org/10.1162/003355301556365

Onwuegbuzie, A. J., & Collins, K. M. (2001). Writing apprehension and academic procrastination among graduate students. Perceptual and Motor Skills , 92 (2), 560-562. https://doi.org/10.2466/pms.2001.92.2.560

Schouwenburg, H. C., & Groenewoud, J. (2001). Study motivation under social temptation; Effects of trait procrastination. Personality and Individual Differences , 30 (2), 229-240.  https://doi.org/10.1016/S0191-8869(00)00034-9

Steel, P., Brothen, T., & Wambach, C. (2001). Procrastination and personality, performance, and mood. Personality and Individual Differences , 30 (1), 95-106. https://doi.org/10.1016/S0191-8869(00)00013-1

Stöber, J., & Joormann, J. (2001). Worry, procrastination, and perfectionism: Differentiating amount of worry, pathological worry, anxiety, and depression. Cognitive Therapy and Research , 25 (1), 49-60. https://doi.org/10.1023/A:1026474715384

Tice, D. M., Bratslavsky, E., & Baumeister, R. F. (2001). Emotional distress regulation takes precedence over impulse control: If you feel bad, do it!. Journal of Personality and Social Psychology , 80 (1), 53. https://doi.org/10.1037/0022-3514.80.1.53

Watson, D. C. (2001). Procrastination and the five-factor model: A facet level analysis. Personality and Individual Differences , 30 (1), 149-158. https://doi.org/10.1016/S0191-8869(00)00019-2

Blunt, A. K., & Pychyl, T. A. (2000). Task aversiveness and procrastination: A multi-dimensional approach to task aversiveness across stages of personal projects. Personality and Individual Differences , 28 (1), 153-167. https://doi.org/10.1016/S0191-8869(99)00091-4

Day, V., Mensink, D., & O’Sullivan, M. (2000). Patterns of academic procrastination. Journal of College Reading and Learning , 30 (2), 120-134. https://doi.org/10.1080/10790195.2000.10850090

Dewitte, S., & Lens, W. (2000). Procrastinators lack a broad action perspective. European Journal of Personality , 14 (2), 121-140. https://doi.org/10.1002/(sici)1099-0984(200003/04)14:2%3C121::aid-per368%3E3.3.co;2-r

Ferrari, J. R., & Tice, D. M. (2000). Procrastination as a self-handicap for men and women: A task-avoidance strategy in a laboratory setting. Journal of Research in Personality , 34 (1), 73-83.  https://doi.org/10.1006/jrpe.1999.2261

Ferrari, J. R., & Scher, S. J. (2000). Toward an understanding of academic and nonacademic tasks procrastinated by students: The use of daily logs. Psychology in the Schools , 37 (4), 359-366. https://doi.org/10.1002/1520-6807(200007)37:4%3C367::AID-PITS7%3E3.0.CO;2-Y

Milgram, N., & Tenne, R. (2000). Personality correlates of decisional and task avoidant procrastination. European Journal of Personality , 14 (2), 141-156. https://doi.org/10.1002/(SICI)1099-0984(200003/04)14:2%3C141::AID-PER369%3E3.0.CO;2-V

O’Donoghue, T., & Rabin, M. (2000). The economics of immediate gratification. Journal of Behavioral Decision Making , 13 (2), 233-250. https://doi.org/10.1002/(sici)1099-0771(200004/06)13:2%3C233::aid-bdm325%3E3.0.co;2-u

Tice, D. M., & Bratslavsky, E. (2000). Giving in to feel good: The place of emotion regulation in the context of general self-control. Psychological Inquiry , 11 (3), 149-159. https://doi.org/10.1207/s15327965pli1103_03

van Eerde, W. (2000). Procrastination: Self‐regulation in initiating aversive goals. Applied Psychology , 49 (3), 372-389. https://doi.org/10.1111/1464-0597.00021

Janssen, T., & Carton, J. S. (1999). The effects of locus of control and task difficulty on procrastination. The Journal of Genetic Psychology , 160 (4), 436-442. https://doi.org/10.1080/00221329909595557

Milgram, N., & Toubiana, Y. (1999). Academic anxiety, academic procrastination, and parental involvement in students and their parents. British Journal of Educational Psychology , 69 (3), 345-361. https://doi.org/10.1348/000709999157761

Subotnik, R., Steiner, C., & Chakraborty, B. (1999). Procrastination revisited: The constructive use of delayed response. Creativity Research Journal , 12 (2), 151-160). https://doi.org/10.1207/s15326934crj1202_7

Blunt, A., & Pychyl, T. A. (1998). Volitional action and inaction in the lives of undergraduate students: State orientation, procrastination and proneness to boredom. Personality and Individual Differences , 24 (6), 837-846. https://doi.org/10.1016/S0191-8869(98)00018-X

Ferrari, J. R., Harriott, J. S., & Zimmerman, M. (1998). The social support networks of procrastinators: Friends or family in times of trouble?. Personality and Individual Differences , 26 (2), 321-331. https://doi.org/10.1016/S0191-8869(98)00141-X

Haycock, L. A., McCarthy, P., & Skay, C. L. (1998). Procrastination in college students: The role of self‐efficacy and anxiety. Journal of Counseling & Development , 76 (3), 317-324. https://doi.org/10.1002/j.1556-6676.1998.tb02548.x

Milgram, N. N., Mey-Tal, G., & Levison, Y. (1998). Procrastination, generalized or specific, in college students and their parents. Personality and Individual Differences , 25 (2), 297-316. https://doi.org/10.1016/S0191-8869(98)00044-0

Tuckman, B. W. (1998). Using tests as an incentive to motivate procrastinators to study. The Journal of Experimental Education , 66 (2), 141-147. https://doi.org/10.1080/00220979809601400

Ferrari, J. R., Harriott, J. S., Evans, L., Lecik‐Michna, D. M., & Wenger, J. M. (1997). Exploring the time preferences by procrastinators: Night or day, which is the one?. European Journal of Personality , 11 (3), 187-196. https://doi.org/10.1002/(SICI)1099-0984(199709)11:3%3C187::AID-PER287%3E3.0.CO;2-6

Paden, N., & Stell, R. (1997). Reducing procrastination through assignment and course design. Marketing Education Review , 7 (2), 17-25. https://doi.org/10.1080/10528008.1997.11488587

Tice, D. M., & Baumeister, R. F. (1997). Longitudinal study of procrastination, performance, stress, and health: The costs and benefits of dawdling. Psychological Science, 8 (6), 454-458. https://doi.org/10.1111/j.1467-9280.1997.tb00460.x

Harriott, J., & Ferrari, J. R. (1996). Prevalence of procrastination among samples of adults. Psychological Reports , 78 (2), 611-616. https://doi.org/10.2466/pr0.1996.78.2.611

Lay, C., & Silverman, S. (1996). Trait procrastination, anxiety, and dilatory behavior. Personality and Individual Differences , 21 (1), 61-67. https://doi.org/10.1016/0191-8869(96)00038-4

Milgram, N. N., & Naaman, N. (1996). Typology in procrastination. Personality and Individual Differences , 20 (6), 679-683. https://doi.org/10.1016/0191-8869(96)00018-9

Flett, G. L., Blankstein, K. R., & Martin, T. R. (1995). Procrastination, negative self-evaluation, and stress in depression and anxiety: A review and preliminary model. In J. R. Ferrari, J. L. Johnson, & W. G. McCown (Eds.), Procrastination and task avoidance: Theory, research, and treatment  (pp. 137-167). Springer. https://doi.org/10.1007/978-1-4899-0227-6_7

Flett, G. L., Hewitt, P. L., & Martin, T. R. (1995). Dimensions of perfectionism and procrastination. In J. R. Ferrari, J. L. Johnson, & W. G. McCown (Eds.), Procrastination and task avoidance: Theory, research, and treatment  (pp. 137-167). Springer. https://doi.org/10.1007/978-1-4899-0227-6_6

Milgram, N., Marshevsky, S., & Sadeh, C. (1995). Correlates of academic procrastination: Discomfort, task aversiveness, and task capability. The Journal of Psychology , 129 (2), 145-155. https://doi.org/10.1080/00223980.1995.9914954

Schouwenburg, H. C. (1995). Academic procrastination. In J. R. Ferrari, J. L. Johnson, & W. G. McCown (Eds.), Procrastination and task avoidance: Theory, research, and treatment  (pp. 71-96). Springer. https://doi.org/10.1007/978-1-4899-0227-6_4

Senecal, C., Koestner, R., & Vallerand, R. J. (1995). Self-regulation and academic procrastination. The Journal of Social Psychology , 135 (5), 607-619. https://doi.org/10.1080/00224545.1995.9712234

Tykocinski, O. E., Pittman, T. S., & Tuttle, E. E. (1995). Inaction inertia: Foregoing future benefits as a result of an initial failure to act. Journal of Personality and Social Psychology , 68 (5), 793. https://doi.org/10.1080/10463283.2013.841481

Ferrari, J. R. (1994). Dysfunctional procrastination and its relationship with self-esteem, interpersonal dependency, and self-defeating behaviors. Personality and Individual Differences , 17 (5), 673-679. https://doi.org/10.1016/0191-8869(94)90140-6

Ferrari, J. R., & Emmons, R. A. (1994). Procrastination as revenge: Do people report using delays as a strategy for vengeance?. Personality and Individual Differences , 17 (4), 539-544. https://doi.org/10.1016/0191-8869(94)90090-6

Milgram, N. A., Batori, G., & Mowrer, D. (1993). Correlates of academic procrastination. Journal of School Psychology , 31 (4), 487-500. https://doi.org/10.1016/0022-4405(93)90033-F

Saddler, C. D., & Sacks, L. A. (1993). Multidimensional perfectionism and academic procrastination: Relationships with depression in university students. Psychological Reports , 73 , 863-871. https://doi.org/10.1177/00332941930733pt123

Ferrari, J. R. (1992). Procrastination in the workplace: Attributions for failure among individuals with similar behavioral tendencies. Personality and Individual Differences , 13 (3), 315-319. https://doi.org/10.1016/0191-8869(92)90108-2

Ferrari, J. R. (1992). Psychometric validation of two procrastination inventories for adults: Arousal and avoidance measures. Journal of Psychopathology and Behavioral Assessment , 14 (2), 97-110. https://doi.org/10.1007/bf00965170

Flett, G. L., Blankstein, K. R., Hewitt, P. L., & Koledin, S. (1992). Components of perfectionism and procrastination in college students. Social Behavior and Personality: An International Journal , 20 (2), 85-94. https://doi.org/10.2224/sbp.1992.20.2.85

Schouwenburg, H. C. (1992). Procrastinators and fear of failure: An exploration of reasons for procrastination. European Journal of Personality , 6 (3), 225-236. https://doi.org/10.1002/per.2410060305

Ferrari, J. R. (1991). Compulsive procrastination: Some self-reported characteristics. Psychological Reports , 68 (2), 455-458. https://doi.org/10.2466/pr0.1991.68.2.455

Ferrari, J. R. (1991). Self-handicapping by procrastinators: Protecting self-esteem, social-esteem, or both?. Journal of Research in Personality , 25 (3), 245-261. https://doi.org/10.1016/0092-6566(91)90018-L

Tuckman, B. W. (1991). The development and concurrent validity of the procrastination scale.  Educational and Psychological Measurement ,  51 (2), 473-480. https://doi.org/10.1177/0013164491512022

Lay, C. H., Edwards, J. M., Parker, J. D., & Endler, N. S. (1989). An assessment of appraisal, anxiety, coping, and procrastination during an examination period. European Journal of Personality , 3 (3), 195-208. https://doi.org/10.1002/per.2410030305

Beswick, G., Rothblum, E. D., & Mann, L. (1988). Psychological antecedents of student procrastination. Australian Psychologist , 23 (2), 207-217. https://doi.org/10.1080/00050068808255605

Milgram, N. A., Sroloff, B., & Rosenbaum, M. (1988). The procrastination of everyday life. Journal of Research in Personality , 22 (2), 197-212. https://doi.org/10.1016/0092-6566(88)90015-3

Lay, C. H. (1986). At last, my research article on procrastination. Journal of Research in Personality , 20(4) , 474-495. https://doi.org/10.1016/0092-6566(86)90127-3

Folkman, S., & Lazarus, R. S. (1985). If it changes it must be a process: Study of emotion and coping during three stages of a college examination. Journal of Personality and Social Psychology , 48 (1), 150. https://doi.org/10.1037/0022-3514.48.1.150

Solomon, L. J., & Rothblum, E. D. (1984). Academic procrastination: Frequency and cognitive-behavioral correlates. Journal of Counseling Psychology , 31 (4), 503. https://doi.org/10.1037/0022-0167.31.4.503