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

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Original research article, on the behavioral side of procrastination: exploring behavioral delay in real-life settings.

• 1 Department of Psychology, UiT The Arctic University of Norway, Tromsø, Norway
• 2 Faculty of Health and Welfare, Østfold University College, Fredrikstad, Norway
• 3 Bjørknes University College, Oslo, Norway

This paper examines how procrastinators behave differently from non-procrastinators in implementing intended behavior. By focusing on time-related attributes of behavior, we demonstrate in five studies (aggregated N = 965) that onset delay seems to be a preferred option for procrastinators in common daily situations. Thus, when an action possibility is available for intended behavior, procrastinators tend to delay behavior onset, both in actual behavior and in onset preferences, often instigating chains of events with negative consequences. We discuss possible mechanisms responsible for such delays and explore how such mechanisms generate and sustain dilatory behavior. We conclude that a better understanding of why behavioral delays occur in early phases of action implementation is of importance in understanding and preventing procrastination.

Introduction

Procrastination involves unnecessary and unwanted delay, be it decisional, implemental, or lack of timeliness ( Lay, 1986 ; McCown et al., 1989 ; Mann et al., 1997 ; Steel, 2010 ). Furthermore, Steel (2007) emphasized that a core characteristic of procrastination is the realization by the actor that one will be worse off because of the delay. Hence, procrastination can be seen as irrational behavior—delaying some intended course of action, realizing that it is disadvantageous ( Klingsieck, 2013 ). Behavioral delay in procrastination is observed in at least two ways. First, during action implementation, the person may divert to an alternative and more tempting course of action ( Tice et al., 2001 ), indirectly delaying the original plan. Second, in a longer time perspective, the negative consequences of such diversions become visible, as for example when people postpone seeing their doctors until treatment is no longer an option ( Worthley et al., 2006 ), or postpone the initiation of personal retirement plans ( Byrne et al., 2006 ). In a longitudinal study, Tice and Baumeister (1997) demonstrated both forms of delay in a student sample. Students procrastinating early in the semester created a stress-free and pleasant situation for themselves, only to experience that these short-time benefits had long-term costs at the end of the semester.

Although the core problem of procrastination is behavioral delay, studies such as those discussed are in the minority in the procrastination literature. Most studies of procrastination have instead focused on self-reported delay as measured by procrastination scales and inventories ( Steel, 2007 ; Rozental and Carlbring, 2014 ; Svartdal and Steel, 2017 ). An obvious motivation for this preference is that dilatory behavior is inherently subjective, making it reasonable to classify a given course of action as dilatory or not depending on the person’s intention, which is conveniently assessedby self-report. Another reason for favoring measurement scales in procrastination research is that dilatory behavior is often difficult to operationalize, as it is characterized by not occurring (given a plan). Again, resorting to self-reported deviations from plans is a convenient solution (e.g., Krause and Freund, 2014 ).

However, reliance on self-reported delay has moved procrastination research away from the core characteristic of procrastination, behavioral delay. As will be discussed, the number of studies focusing on behavior in procrastination research is scarce. Furthermore, reliance solely on self-reported procrastination may bias results. Notably, self-reported procrastination lacks a calibration mechanism that may help differentiate between trivial but harshly judged procrastination and more serious forms (e.g., Gröpel and Steel, 2008 ; Svartdal and Steel, 2017 ), which again has implications for prevalence estimates (e.g., Rozental and Carlbring, 2014 ). Third, as existing procrastination scales often address domain- and culture-specific behavior themes (e.g., Christmas shopping; cf. Lay, 1986 ), conclusions may be vulnerable to personal, cultural, and contextual variability ( Svartdal et al., 2016 ). Hence, bringing back behavior into the procrastination equation may be worthwhile for a number of reasons.

In the present paper, we attempt to do so by focusing on behavioral delay when action possibility presents itself. Thus, rather than addressing the common measure of behavioral delay, lateness/timeliness in completing intended behavior ( McCown et al., 1989 ; Tice and Baumeister, 1997 ), we address the implementation phase of intended action when the person can choose swift vs. delayed action. Such a focus on promptness (e.g., Schouwenburg, 1995 ) allows for a focus on time-related behavioral dimensions with less emphasis on what people are procrastinating, but stronger emphasis on how people behave when procrastinating. Looking forward, we argue that such a shift better captures important properties of behavioral onset delay seen in procrastination.

We start with a short discussion of behavioral delay and of possible models that explain the ontogeny of behavioral delay. Then we briefly examine existing literature on dilatory behavior in procrastination, demonstrating that there are surprisingly few studies examining the relation between self-reported procrastination and corresponding behavioral delay, and in particular onset delay. Finally, we report five studies that illuminate how onset delay manifests itself in procrastination.

Models for Understanding Behavioral Delay

Analyzing procrastination from an evolutionary life history perspective, Chen and Chang (2016) argued that the procrastinator lives by a fast life strategy with a psychological time orientation on the present. Such a fast life strategy has been functional in unpredictable environments during evolution, fostering impulsivity, high risk-taking, overlooking consequences, and discounting the future. However, as contemporary life emphasizes planning, personal control, and accountability, a fast life strategy has become maladaptive. Accordingly, research has amply documented that procrastination is closely linked to impulsivity ( van Eerde, 2003 ; Steel, 2007 ; Gustavson et al., 2014 ), with a preference for instantly gratifying options rather than more beneficial longer-term goals. Such a preference is associated with negative consequences that make habitual procrastination maladaptive. Thus, procrastination is associated with a number of adverse states and problems, including increased stress, lower task performance, reduced well-being, regret and suffering, and risk of mental and physical illness ( Tice and Baumeister, 1997 ; Steel, 2007 ; Klingsieck, 2013 ; Steel and Ferrari, 2013 ; Sirois, 2014 ).

As impulsivity suggests a preference for immediate outcomes ( Sharma et al., 2014 ; Steel and Weinhardt, 2017 ), but procrastination is characterized by delay, identification of mechanisms that can mediate the relation between impulsivity and behavioral delay is crucial. A common answer to this dilemma is that impulsive diversions to more attractive alternatives occur during implementation of plans, indirectly creating delays in realizing them (e.g., Schouwenburg, 1995 ; Tice et al., 2001 ; Steel, 2007 ). This may be seen as temporal discounting in that salient and immediately available rewards dominate over distant rewards (e.g., Steel and Weinhardt, 2017 ). Immediate rewards may be situational, but not necessarily. For example, taking a break from working with aversive, stressful, or difficult tasks, gaining swift mood repair, stress relief, and satisfaction (e.g., Tice and Bratslavsky, 2000 ; Tice et al., 2001 ; Sirois, 2007 ) may be highly rewarding, resulting in a direct form of task delay in that the primary motivation is to stop ongoing behavior rather than switching to something more attractive in the situation.

Temporal discounting of negative stimuli is also a possible mechanism causing behavioral delay. Response cost may serve as an example. As the cost of immediate action is more salient than the perceived cost of future action, behavioral delay may occur because the cost of performing it now seems higher than performing it later (e.g., Akerlof, 1991 ). A related and probably more important mechanism is avoidance of aversive tasks, preventing the occurrence of negative feelings, stress, and other forms of aversive states, resulting in avoidant styles of functioning (e.g., Díaz-Morales et al., 2008 ) and subsequent delays in task execution.

Note that the mechanisms discussed here imply delayed task execution; for all but the first mechanism, task delay should be reflected in reduced behavioral vigor directly. The mechanisms discussed are summarized in Table 1 . We are not aware of prior research examining the effects of these mechanisms on behavioral onset delay specifically, but predictions are quite straightforward. First, the overall effect of these mechanisms should be delay in the execution of planned behavior, increasing the possibility of passivity, hesitation, and lingering. Second, it is likely that repeated occurrences involving one or several of these mechanisms may have established learned habits and reactions that themselves can cause delay. In both cases, when facing action possibility, it is likely that the procrastinator will respond with hesitation and lack of promptness. This response should be readily observable, differentiating procrastinators from non-procrastinators in situations where prompt action is possible and often advantageous. For the procrastinator, this response has most probably also generalized, so that delay is the default response in a variety of situations and modalities. Thus, when facing an action possibility, a “delay” rule should not only reveal itself in behavior but also in behavioral onset preferences. Although some situations should be especially prone to triggering delay (e.g., aversive or boring situations) and some not (e.g., situations with positive valence) ( Steel, 2007 ), it is likely that the “delay” rule will be a default and automatic response in ordinary, everyday situations, potentially instigating a chain of events with negative long-term consequences for the procrastinator.

TABLE 1. Common contexts for behavioral delay.

Research on Implemental Delay and Its Relation to Self-Reported Procrastination

As the focus of this paper is on delayed onset of intended behavior, we examined the procrastination literature for research concerning this issue specifically. We also traced research that address the relation between dilatory behavior and self-reported procrastination more generally. As seen in Table 2 , we have identified only seven studies that address the self-reported procrastination–behavior relation explicitly, and only three address onset delay specifically ( Senécal et al., 1997 ; Steel et al., 2001 ; Moon and Illingworth, 2005 ). Senécal et al. (1997) presented participants with a series of tasks differing in dimensions related to motivation (e.g., boring/difficult vs. interesting/easy), and measured (a) time to start the boring/difficult task, and (b) time to complete all tasks. For participants (students) expecting an evaluation of performance, onset delay of the boring/difficult task was markedly higher for high vs. low procrastinators, as was the total time to complete all tasks. However, the difference in implemental delay seemed to be due to the fact that high procrastinators not expecting to be evaluated demonstrated much shorter delays, even much shorter compared to low procrastinators ( Senécal et al., 1997 , Figure 1), making an inference from this study somewhat difficult. Steel et al. (2001) examined the intention-action gap and found that procrastinators differed from non-procrastinators only at the beginning and at the end of the course, and in the predicted directions: Procrastinators did less work than intended in the start of the semester, and more toward course completion. Finally, Moon and Illingworth (2005) obtained repeated behavioral measures of student procrastination (time from opening for tests to be taken to actual test time in repeated test windows throughout the semester) and found moderate correlations between a dispositional procrastination score and test onset delay. However, the pattern of procrastination throughout the semester did not differ between high and low procrastinators. Thus, although the students might differ in their initial levels of procrastination, all followed the same procrastination pattern over the course of the semester ( Moon and Illingworth, 2005 , p. 306).

TABLE 2. Studies assessing the self-reported procrastination–behavior relation.

Table 2 also includes studies that have assessed the self-reported procrastination–behavior relation by including a measure of self-reported behavior rather than observed behavior. Further, some studies that experimentally demonstrate procrastination have often done so without connecting behavioral procrastination to dispositional procrastination as measured by scales. Two examples are listed in the table. Note also that some studies not included in the table address behavioral measures of procrastination indirectly by comparing self-reported planned behavior to self-reported actual behavior, thereby obtaining a difference score used as a proxy for behavioral procrastination (e.g., Krause and Freund, 2014 ). Other studies have linked dispositional procrastination score to a product of behavior (e.g., grades; Steel et al., 2018 ).

The studies listed in Table 2 assume a relatively close relation between dispositional procrastination and dilatory behavior documented by self-report or by actual behavioral delay (e.g., lateness in submitting reports). Overall, this expectation has gained support, but some qualifications should be noted. First, as noted, the number of studies having focused on the dispositional procrastination–behavioral onset delay relation is very low, suggesting that this is a research area in need of increased focus. As argued, we believe that focus on onset delay is of particular interest. Although timeliness and lateness correlate highly with implemental delay ( Svartdal and Steel, 2017 ), they are possibly more susceptible to cultural and contextual differences ( Svartdal et al., 2016 ). This highlights the need for an analysis of behavioral procrastination that is less influenced by cultural and contextual differences. Focus on the timeliness of a finished task also often overlooks the fact that many forms of behavior are temporally distributed, one part being preparatory and often necessary for subsequent parts (e.g., Sheeran and Webb, 2016 ). Increased focus on delay early in such sequences may be more informative of the early phases of procrastination than focusing on the end product of the completed (or not completed) chain. A related issue concerns the way behavior has been conceptualized in procrastination studies. By addressing domain-specific behaviors described in a high-level language (e.g., Christmas shopping), time-related, low-level attributes of behavior may be easily overlooked. Low-level attributes of behavior ( Vallacher and Wegner, 1987 ) address how actions are performed (e.g., in terms of tempo, speed, and onset delay) rather than which actions are performed. Because low-level attributes relate to the way actions are performed, and such attributes are rather difficult to describe and modulate verbally ( Svartdal, 1995 ), they may provide information about procrastination that is not captured in current self-reported procrastination scales.

Finally, as seen in Table 2 , several of the studies included self-reported dilatory behavior rather than objective measures of behavioral delay. Lane et al. (2013) examined a construct closely related to procrastination, impulsivity, and found that psychometric instruments focusing on this construct tended to correlate reliably with each other, whereas behavioral measures obtained through laboratory tests focusing on the same construct demonstrated lower within-tests correlations. Also, the psychometric scales correlated poorly with behavioral tests, even though they targeted the same construct. Such findings indicate that psychometric instruments may give an exaggerated impression of orderliness that does not match behavioral proxies well. For procrastination, this is particularly important, as behavioral delay is a defining characteristic of procrastination.

The Present Studies

In the present studies, we examine various forms of behavioral delay related to the mechanisms discussed, focusing on implemental delay. Specifically, we address three facets of how behavioral delay expresses itself. Study 1 focused on delayed (as opposed to immediate) onset of intended behavior; Studies 2a and 2b addressed preferences for later rather than earlier, and Studies 3a and 3b addressed delay in preparatory behavior. Rather than looking at the overall delay in action implementation, we focus on delay in intentional behavior when an action possibility presents itself, either observed or as expressed in preferences.

We examine such delays in simplified and ecologically valid situations, that is, situations in which the observation of naturally occurring behavioral delay is possible. Thus, when a person is confronted with a simple choice situation with freely available alternatives, a preference for delay or actual behavioral delay displayed in that situation may be potentially informative of an underlying disposition to procrastinate. The overall model for the present thinking assumes that dilatory behavior as described above is a direct or indirect consequence of a “later” dictum and that such delay subsequently may be a contributing factor to the negative consequences seen in procrastination. Preferences and behavior were correlated with scores on procrastination instruments. The overall hypothesis is that people scoring relatively high on instrument measuring disposition procrastination adheres to a “later” dictum, and hence demonstrates behaviors and preferences accordingly.

As a direct exploration of the “later” dictum in implementing intended behavior, Study 1 observed people entering a shopping mall by escalators. When using an escalator, one has a simple choice: Remain still and let the escalator bring you up, or add speed by walking. In the present context, we assume that people opting for the first alternative adhere to a “wait” or “delay” rule, whereas people who select to walk do not. Hence, we expected that people standing still would demonstrate higher scores on a test for procrastination compared to walkers.

Participants

Participants were 56 adults (mean age = 38.5 years), 28 females. All were visitors to a shopping mall in northern Norway.

Procedure and Material

Observers (student assistants) were located at the upper end of different escalators in a shopping mall, being able to observe whether a potential participant walked versus did not walk while being on the escalator. People walking most or all of the escalator were classified as Walking; people standing still although being free to move (e.g., not hindered by a person standing in front; not carrying a heavy package) were classified as Standing. When leaving the escalator, each participant was approached by the assistant and asked to answer the Irrational Procrastination Scale ( Steel, 2010 ). Participants filled in the questionnaire as well as age and gender information (paper and pencil). After completing these steps, the participant was thanked and given a short explanation of the study. Finally, the assistant coded the sheet as “S(tanding)” or “W(alking)” as well as gender and time of day (morning, noontime, evening). All information was given voluntarily and anonymously, and all participants gave informed consent to contribute after receiving brief information of the study.

The current studies were part of a larger project that received ethical approval from the Regional Ethical Board in Tromsø, Norway (REK nord 2014/2313). Participation was voluntary, anonymous, and confidential. Participants were read a consent form describing the nature and purpose of the study and then provided informed consent before responding. No payment was provided.

An ANOVA with S(tand) versus W(alk) and gender as predictors and IPS score as the dependent measure revealed that walkers demonstrated lower IPS scores compared to people standing still ( M = 2.45 vs. 2.88), F (1,50) = 4.926, p = 0.031, η p 2 = 0.090. Also, the ANOVA indicated a significant interaction effect, F (1,50) = 7.586, p = 0.008, reflecting that walking women had lower IPS scores compared to men and also that standing women had higher IPS scores. The main effect of gender was not significant.

We also performed a separate ANOVA to test the potential effect of time (morning, noon, afternoon) on escalator behavior ( Díaz-Morales et al., 2008 ). Here the ANOVA indicated a significant interaction effect of time, F (2,50) = 4.381, p = 0.018, reflecting that the stand versus walk effect primarily was visible in the afternoon, less so in the morning and at noon.

As predicted, people using an escalator to enter a shopping mall selected to walk or stand still in accord with their propensity to procrastinate. All participants shared the same overall intention to enter, yet differences appeared in the promptness of action implementation in accord with dispositional procrastination score. Although this difference may be influenced by a number of factors involved in goal striving ( Steel et al., 2018 ), we believe that the important result in this context is that some people—procrastinators—chose to stand still. This response is consistent with a preference for delay when swift action is possible. In the present sample, this tendency was stronger in females compared to men and stronger in the afternoon compared to morning and noontime. Nonetheless, these results indicate that a simple choice behavior—stand versus walk—is related to procrastination score. As discussed (see Table 1 ), this automatic inclination to delay is consistent with an overall prediction that procrastinators delay action implementation.

Study 2 explored the hypothesis that procrastination score as measured by a self-report instrument is related to time preferences in choice. If procrastinators adhere to a simple “later” dictum, behavior as well as choices involving time options (e.g., early vs. late) should be predictably related to procrastination score. This hypothesis was investigated in two different settings, training studio time preferences (i.e., preference for visiting the training studio early or late in the day) and seminar time preferences (i.e., preference for participating in seminars starting early vs. late in the day). The “later” dictum indicates that such preferences should be visible in that people visiting training studios late in the day should demonstrate higher procrastination scores (Study 2a) and that a similar difference should be observed in students participating in seminars later in the day (Study 2b).

Participants were 119 adults (mean age = 30.83 years), 59 females. All were visitors at three different training studios in a city in Northern Norway.

A student assistant approached visitors outside training centers early (9–12) or later in the day (12–15) and asked participants to answer the Irrational Procrastination Scale (IPS; Steel, 2010 ) as well as provide age and gender information (paper and pencil). Then, the assistant coded the sheet as “Early” or “Late.” All information was given voluntarily and anonymously, and all participants gave informed consent after receiving brief information about the study.

An overall ANOVA with IPS as dependent variable and time (early vs. late), gender, and three different training locations as predictors indicated a significant effect of time ( M Late = 2.79; M Early = 2.46), F (1,105) = 6.668, p = 0.011, η p 2 = 0.060. None of the other factors were significant, but one of the training locations demonstrated a lower overall IPS score in men compared to the two other locations, resulting in a significant location ∗ gender interaction, F (2,105) = 3.302, p = 0.040. This effect was not considered relevant for the present study.

As hypothesized, visitors at training centers early (09–12) versus late (12–15) demonstrated a significant difference in procrastination scores in the predicted direction. Although this difference is hypothesized to be a result of a dictum of “later” in people procrastinating more, this rule may have worked in direct ways as well as indirectly. Thus, on a given day, a procrastinating person may have selected training time later in the day although an earlier time slot was available (direct effect of a “later” rule). Alternatively, selection of later training time may be a consequence of time-related choices the day before, as in bedtime procrastination ( Kroese et al., 2014 ), necessitating later training times the day after. Third, training time may have been occasioned by previously determined self-chosen time slots, reflecting a choice at an earlier occasion. Finally, training time may have been determined outside the person’s control and thus not informative of the person’s time preferences at all. Despite the noise introduced by the final possibility, the present data indicate that the behavior of choosing training times are predictably related to procrastination score, indicating that one or more of the procrastination-informative mechanisms are in operation.

These results are consistent with findings that procrastinators demonstrate a preference for eveningness ( Díaz-Morales et al., 2008 ; Digdon and Howell, 2008 ; Hairston and Shpitalni, 2016 ). In the Díaz-Morales et al. (2008) study, participants completed the Early/Late Preference Scale ( Smith et al., 2002 ) as well as two procrastination scales, the Adult Inventory of Procrastination (AIP; McCown et al., 1989 ) and the Decisional Procrastination Scale (DPS; Mann, 1982, unpublished). Díaz-Morales et al. (2008) reported a low but significant correlation between the AIP and the morningness-eveningness scale, r = -0.28, and a lower and non-significant correlation between the DPS and the morningness-eveningness scale. The present study extends these results by demonstrating that such time preferences are reflected in actual behavior.

Although training at training centers is popular, only part of the population exercises, potentially restricting the external validity of the finding of Study 2a. Therefore, we conducted a second study with students enrolled in a large introductory course. As part of this course, seminar teaching was offered. At the beginning of the semester, students chose between available seminars, available at specific time slots throughout the day, with start times from 08.15 to 16.15. We expected that students selecting seminar times before versus after noon would do so at least in part as a reflection of the dictum “delay.” Hence procrastination scores should be higher in students selecting post-noon seminar times.

Participants were students ( N = 140, 110 females) at an introductory course in psychology, recruited as part of an examination of procrastination instruments for an intervention study on procrastination ( Nordby et al., 2016 ).

Procedure and Instruments

Questionnaires were distributed to registered students at seminars. All answered a procrastination scale, the Pure Procrastination Scale (PPS; Steel, 2010 ; Svartdal et al., 2016 ). This scale correlates highly, r = 0.87, with the IPS ( Steel, 2010 ; Svartdal, 2015 ). As part of the questionnaire students reported which seminar group they attended, allowing for a grouping of seminars as “Early” (pre-noon) and “Late” (post-noon). We also asked participants to answer an additional question regarding typical bedtime on a weekday (21–00, 00–02, 02–05).

First, the ANOVA with PPS as the dependent variable and Early/Late and gender as predictors indicated a significant main effect of Early/Late, M Early = 2.53 versus M Late = 2.97, F (1,135) = 8.888, p = 0.003, η p 2 = 0.062. The main effect of gender and the Early/Late ∗ gender interaction were not significant. Second, the mean PPS scores for participants indicating when they typically go to sleep were significantly different over typical bedtimes, M 21-00 = 2.56, M 00-02 = 2.98, M 02-05 = 3.41, F (1,133) = 6.515, p = 0.002, η p 2 = 0.089. In both cases, the main effect of gender was non-significant, as was the interaction effects.

These results demonstrate that preferred time for seminar attendance was predictably related to dispositional procrastination. As attendance time was determined at the start of the semester, procrastinators opting for seminars later in the day in accord with a “later” dictum. This result is again consistent with prior research on a preference for eveningness ( Díaz-Morales et al., 2008 ) and may at least indirectly reflect reported bedtime preferences, which were also predictably related to procrastination level (cf. Kroese et al., 2014 ). Importantly, the “later” preference demonstrated here had specific behavioral consequences throughout the semester, illustrating how a simple and spontaneous decision at one occasion generates long-time consequences. The findings reported here are also consistent with results reported by Solomon and Rothblum (1984) , who found that procrastinating students participating in experimental sessions tended to prefer sessions late in the semester. Similarly, Cassidy and Kangas (2014) showed that students signing up for a study on discounting behavior (measuring self-control/impulsivity) demonstrated a negative relation between self-control and time selected over the semester. Cassidy and Kangas (2014 , p. 3) noted that “signing up earlier in the semester can be conceptualized as self-controlled activity,” and that signing up for late timeslots may be seen as a form of procrastination. The present data indicated a similar relation, albeit in time preferences over the day rather than the semester. Note that these results, as well as the Solomon and Rothblum (1984) and the Cassidy and Kangas (2014) results, go beyond an explanation in terms of morningness–eveningness, suggesting a simpler explanation in terms of a “later” rule with a possible origin in the mechanisms discussed in Table 1 .

Studies 1 and 2 demonstrated that procrastinators seem to adhere to the rule “later” when confronted with simple choice situations, resulting in a preference for options later rather than earlier. As the preferences demonstrated in these studies do not represent any specific disadvantages, this strategy may seem inconsequential. Thus, even if procrastinating students prefer late seminars, the outcome of attending late seminars may be just as high as attending seminars earlier in the day. Hence, for the “later” dictum to represent a disadvantage for procrastinators, detrimental consequences must be demonstrated. Here, bedtime procrastination ( Kroese et al., 2014 , 2016 ) may be a good model. First, a defining characteristic of bedtime procrastination is that the person fails to go to bed at the intended time, “while no external circumstances prevent a person from doing so.” Second, going to bed later than planned has potential negative short- and long-time consequences that demonstrate the disadvantage of such a habit.

In the same vein, we propose that other simple choices reflecting a preference for “later” may put the procrastinating person in a more disadvantageous position compared to non-procrastinators. Studies 3a and 3b focus on a possibly important example, how the “later” preference represents a financial disadvantage to the procrastinator. Here we address one of the delay mechanisms already discussed, delay in preparatory behavior. As preparatory behavior (e.g., preparing a shopping list) is distant from the actual planned behavior (shopping), we hypothesize that such preparatory behavior is easily delayed or even skipped by procrastinators. Hence, Study 3a examined lunch habits among students and employees in Norway. In this country, bringing your lunch is a long-standing tradition ( ISIC, n.d. ). Financially, this practice makes sense, as buying lunch at the cantina is much more expensive compared to the cost of bringing your lunch. However, as bringing your lunch requires planning and preparation of food before one leaves home, it is reasonable to assume that procrastinators delay this step, leaving home without food. Not planning lunch before leaving home may put the procrastinator in a disadvantageous situation at lunchtime. Clearly, if this habit continues over time, it will be financially disadvantageous to procrastinators.

Participants were students and employees at a Norwegian University ( N = 123) at three different cantinas.

Student assistants approached visitors at the cantinas and asked them to fill in a shortened version of IPS (six items consistent with procrastination, a version found to be psychometrically equivalent to the full version; Svartdal and Steel, 2017 ). Upon completion of this task, the assistant coded whether the visitor had bought lunch in the cantina or brought his/her own. All participants agreed to participate, and all information was provided anonymously.

Initial analyses indicated that males reported higher procrastination levels compared to females, but the gender factor did not interact with the bring versus buy factor. An ANOVA with IPS score as the dependent variable and bring versus buy as the predictor indicated, as predicted, a significant difference between the groups, M Bring = 2.92 versus M Bought = 3.32, F (1,115) = 11.254, p = 0.001.

The present results demonstrated a difference in procrastination scores between cantina visitors bringing their food for lunch versus visitors buying their lunch, procrastinators ending up with the more expensive alternative. As the more expensive alternative was necessitated by not bringing a lunch package in the first place, a key to understanding the observed difference is related to an explanation of why respondents did not bring lunch from home. We propose a simple explanation in terms of impulsivity: Procrastinators are impulsive, and as planning and life organization correlate negatively with procrastination (e.g., Steel, 2007 ), it is understandable that procrastinators do not worry so much about events hours away. Thus, the present results may be seen as a consequence of procrastinators adhering to a “later” dictum, postponing the issue of lunch until lunchtime appears. In general, such a delay may be relatively inconsequential, but in Norway, such behavior will be quite costly, especially if it establishes itself as a habit.

To substantiate the findings of Study 3a, we administered a survey focusing on personal finance, including a question on lunch habits.

Participants were 527 adults (377 females), mean age = 30.96 years ( SD = 11.67). The majority of participants were employees ( n = 253) and students ( n = 194); the remaining 46 categorized themselves as “other.” All were recruited through social media (e.g., Facebook).

Materials and Procedure

Participants were invited to answer a web-based questionnaire 1 containing questions about habits related to personal finance, procrastination (IPS; Steel, 2010 ), as well as age and gender. The questions about finance included (a) yearly income (five categories), (b) expectation for own economic situation in 1 year (five categories, 1 = very bad – 5 = very good ), and (c) lunch habits (“usually bring my own lunch” and “usually buy in the cantina”). The questionnaire also contained other questions not included in the present study.

Results and Discussion

A majority of the sample ( n = 307) reported that they usually bring a lunch from home, the rest ( n = 178) indicated that they usually buy in the cantina. An ANOVA with bring versus buy and gender as predictors indicated a significant lower mean IPS score in the Bring group, M Bring = 2.71 ( SD = 0.06) compared to the Buy group, M Buy = 2.99 ( SD = 0.07), F (1,446) = 10.271, p = 0.001, η p 2 = 0.023. The effect of gender and gender ∗ bring/buy interaction were not significant. Thus, this result repeats the finding of Study 3a, albeit in self-report form.

Although buying food in the cantina is an expensive habit, it becomes increasingly detrimental for individuals with lower income levels. We therefore performed an ANOVA with bring versus buy and income level as predictors. The ANOVA indicated a significant main effect of bring ( M = 2.65) versus buy ( M = 3.00), F (1,442) = 20.901, p = 0.000, as well as a significant main effect of income levels, F (4,442) = 6.806, p = 0.000, reflecting an overall reduction in IPS scores with increasing income levels. The interaction effect was not significant, F (4,442) = 1.611, p = 0.170. The main effect of bring versus buy revealed itself primarily at lower income levels, indicating that people that would benefit the most from changing their expensive habit (procrastinators) suffer the most. A parallel analysis with financial situation expected in 1 year rendered very similar results.

These results demonstrate the importance of preparatory behavior in procrastination. This detrimental delay strategy may be encountered in many areas. For example, buying airline tickets long before traveling is often much cheaper than buying them a short time before traveling, putting the procrastinating airline passenger in a financially disadvantageous situation compared to non-procrastinating passengers. In the health domain, postponing vaccination may have negative effects ( Baker, 2011 ). Finally, in the student domain, preparatory behaviors may be of potentially high significance, as the value of a given insight may be dramatically increased if it is acquired early rather than later. For example, understanding the concept of correlation early in a statistics course versus understanding it days before an exam implies very different learning benefits, even though outcome results as measured in examinations may not differ very much.

General Discussion

The present paper hypothesized that procrastinators adhere to a simple behavioral rule—“later”—in common daily situations, resulting in predictable delays in behavioral onset, time preferences for “later” rather than “sooner,” and delayed preparatory behaviors with detrimental consequences later on. Such a “later” rule is connected to specific mechanisms assumed to mediate the relation between impulsiveness and behavioral delay. Examples of behavioral delays were given in observed or indirect form in five separate studies over a variety of situations. Common to these studies was that procrastination manifested itself behaviorally, predictably related to dispositional procrastination score. Overall, the present results, summarized in Table 3 , provide examples of behavioral delays related to dispositional procrastination, confirming the overall assumption that higher procrastination is associated with behavioral onset delay. These studies provided this evidence in situations rarely previously studied, thus extending the procrastination phenomenon to new domains. More importantly, the present studies focused on time-related, low-level attributes of behavior rather than domain-specific, high-level behaviors. As such low-level behavioral attributes are rather difficult to modulate by rules ( Svartdal, 1995 ), actual behavior may in such cases be more informative about procrastination than self-reported habits and behaviors. Also, as such low-level behaviors are difficult to describe by the actor, they may cause delays in ways that occur largely unnoticed and hence are difficult to report, for example in procrastination self-report measures. Increased focus on low-level aspects of behavior may, therefore, be important both in the understanding of procrastination and in self-reported procrastination.

TABLE 3. Results, present studies.

The present studies addressed delay in the implementation of planned behavior. Such delay, often named the intention-action delay, constitute a core attribute of procrastination ( Steel, 2010 ). Most studies that have examined behavioral delay have focused on lateness/timeliness in completing the intended behavior (e.g., Lay, 1986 ; Tice and Baumeister, 1997 ; Howell et al., 2006 ). In contrast, the present studies focused on action onset delay and preferences for onset delay. Most planned action requires sustained performance over time, completing it being dependent on a number of factors that may or may not be under control of the actor. By focusing on delay in intended behavior as expressed when action possibility presents itself, either as observed action or implemental intentions expressed in preferences, the present studies address on an important and under-investigated part of the procrastination problem. The fact that the behavioral delay examples included in the present studies all were freely chosen, minimally influenced by contextual or cultural factors, as well as non-reflective, indicate that procrastinators are inclined to delay when an action possibility presents itself.

Focus on the initial delay of planned action, rather than on timeliness/lateness, may be important in understanding the procrastination problem and the problems unnecessary delay brings on the procrastinator. Such delays may manifest themselves as lingering or hesitation once an action possibility occurs (Study 1), a preference for later rather than sooner (Studies 2a and 2b), and delay in behavior necessary for subsequent behavior (Studies 3a and 3b), as well as in other ways. Such implemental delay may contribute to negative consequences over time. For example, delaying the onset of planned behavior decreases the time-window for completion, which may negatively affect performance and increase stress ( Tice and Baumeister, 1997 ). Early onset of implemental action may also have beneficial psychological consequences, even if implementation is not completed. For example, having started the implementation of some planned project (e.g., reading a book, writing an essay, painting the house) turns an abstract intention into something concrete, thereby facilitating execution of planned action (e.g., McCrea et al., 2008 ). Even if the task is not finished, having started it may increase rather than decrease motivation to re-engage ( Reeve et al., 1986 ). Finally, getting an early start on some project may change motivation, with self-perceptions shifting from “not doing = not interested” to “doing = interested” ( Bem, 1972 ). In sum, instigating rapid implementation of intentions may prevent many of the negative behavior inclinations observed in procrastination. Techniques that help people in formulating and realizing their intentions (e.g., Gollwitzer and Sheeran, 2006 ; Sheeran and Webb, 2016 ), may, therefore, be of prime importance in reducing and preventing procrastination.

Future Studies

A common theme to the studies of this paper, as well as in the definition of procrastination and its measurement, is that the procrastinator delays when prompt action is possible and preferable. This contrasts with the conception of procrastination as an impulsive person ( van Eerde, 2003 ; Steel, 2007 ), preferring immediate rather than delayed outcomes. Why, then, do procrastinators seem to follow a “delay” rule when the opposite is possible and preferable? We have discussed a number of mechanisms that may mediate the impulsivity–delay relation (cf. Table 1 ), and the overall effect of these mechanisms seems to be a simple “delay” rule. Future research should explore this relation more thoroughly to determine additional mechanisms. The long-time effect of escaping and avoiding aversive events are of particular interest. Escaping or avoiding aversive tasks simply by stopping action (take a break) or avoiding the situation may relieve stress and induce a better mood in the short run ( Baumeister et al., 1994 ; Tice and Bratslavsky, 2000 ). However, the immediacy of rewarding consequences from such strategies points to a potentially very powerful mechanism in generating and sustaining procrastination because diversion or passivity is effectively reinforced. Hence, a long history of escaping or avoiding aversive situations by simply doing nothing (i.e., passivity) may give passivity secondary reinforcement properties in just the same way as effort associated with reward can acquire secondary reinforcement properties ( Eisenberger, 1992 ). Accordingly, delay, hesitation, and lingering may be activities that are reinforcing to the procrastinator, and hence may be hypothesized to represent a hedonically attractive outcome that is always available for the procrastinating person. Such a mechanism may help explain the relation between procrastination and the passivity seen in depressed individuals as well as in everyday procrastination. If true, passivity is a continuously available reward for the procrastinator, and increasingly so as the procrastination habit is getting more firmly established. Clearly, if delayed onset is a characteristic of procrastination, increased focus on various forms of such delay is of interest, both in terms of understanding procrastination and in prevention and intervention measures.

In the studies reported in this paper, dilatory behavior was predictably related to dispositional measures of procrastination. This is reassuring and demonstrates that dispositional measures are informative of behavioral inclinations, albeit in relatively crude form. However, as self-reported procrastination lacks a calibration mechanism that may differentiate between trivial but harshly judged procrastination and more serious forms (e.g., Gröpel and Steel, 2008 ; Rozental and Carlbring, 2014 ; Svartdal and Steel, 2017 ), more work is needed in developing measures, both behavioral and in self-report form, that may assist in such calibration efforts. Here, objective behavior-focused measures of procrastination may represent important supplements to self-report measures.

Author Contributions

FS collected the data, ran analyses, and wrote the draft. SG and FF participated in discussions and in editing the document.

The research presented in this paper was partially funded by UiT/NFR student stipends to Eirin Høegh and Anna Hessen. Andrea Chluba Kværne participated in data collection. The publication charges for this article were funded by a grant from the publication fund of UiT The Arctic University of Norway.

Conflict of Interest Statement

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.

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Keywords : procrastination, delay, dilatory behavior, behavioral measures, procrastination scale

Citation: Svartdal F, Granmo S and Færevaag FS (2018) On the Behavioral Side of Procrastination: Exploring Behavioral Delay in Real-Life Settings. Front. Psychol. 9:746. doi: 10.3389/fpsyg.2018.00746

Received: 04 January 2018; Accepted: 30 April 2018; Published: 16 May 2018.

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Copyright © 2018 Svartdal, Granmo and Færevaag. 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 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]

This article is part of the Research Topic

New Perspectives on Procrastination

Understanding procrastination: A case of a study skills course

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

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

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

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1 Introduction

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

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

1.1 Factors explaining academic procrastination

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

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

1.2 Time and effort management skills behind procrastination

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

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

1.3 Psychological flexibility and academic self-efficacy beliefs

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

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

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

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

1.4 Aim of the study

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

2 Methodology

2.1 participants.

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

2.2 Instruments

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

2.3 Statistical analysis

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

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

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

3.1 Regression analysis

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

3.2 Differences in score groups

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

4 Discussion

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

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

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

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

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

4.1 Limitations

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

4.2 Practical implications and conclusions

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

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

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

Data availability

The data is available on demand.

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

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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

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

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