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

Affiliation.

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

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

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

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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.

Distribution of publications on the…

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

Distribution of categories involved in…

Distribution of categories involved in procrastination research.

Keywords co-occurrence network for procrastination…

Keywords co-occurrence network for procrastination research.

Landscape view of co-citation network…

Landscape view of co-citation network of procrastination research.

Top 20 references with the…

Top 20 references with the strongest citation bursts.

Bibliometric analysis and science map…

Bibliometric analysis and science map of the literature on procrastination.

Brief conclusions on procrastination research.

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SYSTEMATIC REVIEW article

What research has been conducted on procrastination evidence from a systematical bibliometric analysis.

$\nBo Yan$

School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China

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.

Figure 1 . 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.

Figure 2 . 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 .

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.

Figure 4 . 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.

Table 1 . 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.

Figure 5 . 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.

Figure 6 . 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.

Figure 7 . 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

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

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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Keywords: procrastination, co-citation analysis, intellectual structure, CiteSpace, bibliometric analysis

Citation: Yan B and Zhang X (2022) What Research Has Been Conducted on Procrastination? Evidence From a Systematical Bibliometric Analysis. Front. Psychol. 13:809044. doi: 10.3389/fpsyg.2022.809044

Received: 04 November 2021; Accepted: 10 January 2022; Published: 02 February 2022.

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

*Correspondence: Xiaomin Zhang, zhangxiaomin2014@mail.sjtu.edu.cn

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Published: 25 June 2024

Temporal discounting predicts procrastination in the real world

Pei Yuan Zhang 1 &
Wei Ji Ma 1 , 2

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

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Human behaviour

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

A neuro-computational account of procrastination behavior

procrastination research paper introduction

Temporal discounting correlates with directed exploration but not with random exploration

Rationally inattentive intertemporal choice

Introduction.

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

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

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

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

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

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

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

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

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

Participants

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

Characterizing individual variability in procrastination

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

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

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

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

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

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

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

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

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

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

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

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

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

No evidence of a correlation between risk attitude and behavioral procrastination

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

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

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

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

Self-reports of procrastination behavior

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

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

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

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

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

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

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

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

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

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

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

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

Participant inclusion

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

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

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

Delay discounting task

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

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

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

Risky-choice task

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

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

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

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

Incentive compatibility

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

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

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

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

Data and code availability

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

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

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Pei Yuan Zhang & Wei Ji Ma

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P.Y.Z. designed research; P.Y.Z. performed research; P.Y.Z. and W.J.M. contributed new reagents/analytic tools; P.Y.Z. analyzed data; P.Y.Z. wrote the original draft. W.J.M. edited the draft.

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Zhang, P.Y., Ma, W.J. Temporal discounting predicts procrastination in the real world. Sci Rep 14 , 14642 (2024). https://doi.org/10.1038/s41598-024-65110-4

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Academic Procrastination and Goal Accomplishment: A Combined Experimental and Individual Differences Investigation

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This study examined the relationship between academic procrastination and goal accomplishment in two novel ways. First, we experimentally tested whether undergraduate students ( N = 177) could reduce their academic procrastination over a course of three weeks after performing goal-related exercises to set so-called SMART goals and/or to prepare those students with specific strategies to resist their temptations (forming implementation intentions). Second, we conducted systematic regression analyses to examine whether academic procrastination at baseline uniquely predicts later goal-related outcomes, controlling for various correlated variables, including personality traits (e.g., impulsivity), motivational factors (e.g., motivation for the generated goals), and situational factors (e.g., memory for the goals). Results indicated that neither the SMART-goal nor implementation-intention intervention significantly reduced academic procrastination in the three-week interval, even when relevant moderating variables were examined. Initial levels of academic procrastination, however, were predictive of the success of accomplishing the goals generated during the initial exercises, above and beyond a wide range of other candidate correlates. These results provided new correlational evidence for the association between academic procrastination and goal accomplishment, but suggest a need for further research to understand what interventions are effective at reducing academic procrastination.

1. Introduction

Academic procrastination—the voluntarily delay of action on academic tasks despite expecting to be worse off for that delay—is so pervasive that, according to some estimates, 50–80% of college students procrastinate moderately or severely ( Day, Mensink, & O'Sullivan, 2000 ; Gallagher, Golin, & Kelleher, 1992 ). Moreover, almost all students who procrastinate report the desire to reduce their procrastination ( Gallagher et al., 1992 ). Such prevalence of academic procrastination suggests a need for systematic research that documents the extent to which procrastination negatively contributes to the achievement of students’ academic goals and that explores potential ways to reduce procrastination.

A starting point for this study is some recent work that highlights goal-management abilities as an important factor for individual differences in procrastination. Recent theoretical accounts, for example, have suggested that various aspects of goal management, such as goal setting ( Steel & König, 2006 ) and goal focus ( Krause & Freund, 2014a ), may influence procrastination. Some of these theoretical claims have also received support from a growing set of empirical studies (e.g., Blunt & Pychyl, 2000 , 2005 ; Gröpel & Steel, 2008 ; Gustavson, Miyake, Hewitt, & Friedman, 2014 , 2015 ; Krause & Freund, 2016 ).

Our own research has focused on specifying the cognitive and genetic influences underlying the association between procrastination and goal-management abilities. In large-scale twin studies ( Gustavson et al., 2014 , 2015 ), we have found, at the level of latent variables, a substantial correlation between procrastination and goal-management failures in everyday life ( r = .67–.76). Further, this association was primarily due to shared genetic influences, which also explained substantial variation in impulsivity ( Gustavson et al., 2014 ) and executive functions ( Gustavson et al., 2015 ), a set of higher-level cognitive abilities that support goal-directed behaviors and regulate one's thought and action (Friedman & Miyake, 2017; Miyake & Friedman, 2012 ). Such prior evidence for a common goal-management factor accounting for individual differences in procrastination, impulsivity, and executive functions have led us to conclude that procrastination and goal-management abilities are deeply intertwined.

Although it has become clear that goal management is an important contributing factor to procrastination, it is not clear whether helping students set and manage their goals can lead them to actually reduce their academic procrastination. Furthermore, self-report measures of procrastination have been shown to be correlated with as academic achievement, such as course grades (e.g., Kim & Seo, 2015 ; Morris & Fritz, 2015 ), and with levels of success at fulfilling one's academic intensions, as measured with study time ( Steel, Brothen, & Wambach, 2001 ) or the amount of reading assignments completed ( Glick & Orsillo, 2015 ). However, little is known about whether academic procrastination is related to the achievement of academic goals generated by students themselves that more directly reflect their specific needs.

To make an initial step toward filling such gaps in the literature, we conducted a two-session laboratory study that combined experimental and individual differences approaches. In the first session, college students completed the initial baseline assessment of their academic procrastination and other related individual differences measures. They then completed two goal-related exercises that required them to create personal academic goals to be accomplished in the next few weeks and to identify anticipated temptations that might distract them from making progress on those goals. Specifically, students were assigned to one of four groups resulting from crossing two types of interventions (creating SMART goals and forming implementation intentions). They returned to the lab about three weeks later to provide postintervention measures of academic procrastination (how much they procrastinated since the initial session) and goal accomplishment (whether they accomplished those goals they had set).

1.1. Goal-Related Interventions for Procrastination

Due to its high prevalence, many popular-press books have been written about procrastination (e.g., Burka & Yuen, 1983 ; Ferrari, 2010 ; Pychyl, 2013 ; Steel, 2010 ). Because delaying action on long-term goals in favor of short-term temptations is a central component of procrastination ( Steel, 2007 ), these books highlight the importance of identifying specific goals to be accomplished, breaking these goals down into smaller subgoals, and following a time-defined schedule. Despite the sensibility of such advice, little research has directly tested the effectiveness of these goal-related strategies in reducing procrastination, academic or otherwise.

In fact, over two decades ago, Ferrari, Johnson, and McCown (1995) pointed out “an absence of double-blind attention-placebo trials [...] necessary to establish demonstrated efficacy of a treatment” on reducing procrastination (p. 187). After summarizing preliminary results from some intervention studies that targeted altering students’ misconceptions about academic procrastination (e.g., underestimation of task demands, overestimation of motivation and time left to complete task), Ferrari et al. (1995) stated that “our hope is that these clinically derived interventions can be eventually subjected to empirical testing” (p. 187).

Responding to this call, a small but growing number of studies published since have examined procrastination-related interventions (e.g., Rozental, Forsell, Svensson, Andersson, & Carlbring, 2015 ; Rozental, Forsstrom, Tangen, & Carlbring, 2015 ). However, intervention studies that have targeted academic procrastination are still limited in number (e.g., Ariely & Wertenbroch, 2002 ; Gieselmann, Pietrowsky, 2016 , Toker & Avci, 2015 ; Tuckman, 1998 ; Tuckman & Schouwenburg, 2004 ). Moreover, although some intervention studies on academic procrastination have focused on cognitive behavioral strategies, such as identifying and challenging irrational thoughts ( Ozer, Demir, & Ferrari, 2013 ; Toker & Avci, 2015 ; Wang et al., 2015 ), only a few have targeted goal-management processes ( Glick & Orsillo, 2015 ; Häfner, Oberst, & Stock, 2014 ).

In the Häfner et al. (2014) study, for example, 96 college students selected an important academic task to complete (e.g., writing a thesis) in the next 4 weeks and received 2 hours of either (a) time-management training that targeted some goal-related processes (e.g., developing a strategy for achieving the goal, identifying the next steps to take) or (b) control training that involved simply discussing their own time-management problems. All participants were then asked to record the time they spent for their respective academic goals every day, and the records from those subjects who kept their time diaries for all four weeks were analyzed ( n 's = 22 and 23 in the experimental and control groups, respectively). Results indicated that subjects in the control group indeed spent more time working toward their goals in Week 4 than those in the experimental group. Importantly, however, the times the two groups spent on their goals in Weeks 1–3 did not differ, thus providing little evidence that the experimental group successfully reduced their procrastination by spending more time on their goals early on. In light of the small final sample sizes due to high drop-out rates (~50%), this study provides limited evidence for the positive influence of time-management training on academic procrastination.

More recently, Glick and Orsillo (2015) compared the effectiveness of two different procrastination interventions delivered online via a 20-min video to 117 college students: (a) an acceptance-based intervention that targeted mindfulness and emotion regulation (e.g., anxiety) and (b) a time-management intervention that more directly targeted goal-management skills, such as setting a schedule and preparing for last-minute obstacles. Although there was some evidence that the time-management intervention led to greater goal accomplishment (operationalized as the amount of reading assignments completed) than the acceptance-based intervention, there were no group differences in actual academic procrastination (operationalized as the actual/ideal ratio) after the interventions. There was, however, some evidence for the moderating influence of self-reported academic values, suggesting that the acceptance-based intervention was most effective for those students with high academic values.

Taken together with other intervention studies that similarly offered some promising but limited evidence (e.g., Ariely & Wertenbroch, 2002 ; Ozer et al., 2013 ; Tuckman, 1998 ; Tuckman & Schouwenburg, 2004 ; Wang et al., 2015 ), these studies ( Glick & Orsillo, 2015 ; Häfner et al., 2014 ) suggest that, although it may not be easy to reduce academic procrastination, interventions that target goal-related processes may help students achieve specific academic goals.

In this study, we tested the effectiveness of two goal-related interventions in reducing academic procrastination: creating SMART goals and forming implementation intentions. Although not extensively examined in the context of procrastination, these goal-related activities are often touted as effective ways to reduce the so-called intention–behavior gap, a fundamental problem underlying procrastination. Because, as noted shortly, these two interventions target different aspects of goal-management processes, we crossed them to test whether their positive influences, if any, would be additive or interactive.

The first intervention—creating SMART goals—targets the goal-setting process and involves clarifying what students want to achieve by developing concrete personal goals that are Specific, Measurable, Achievable, Realistic, and Time-defined ( Bovend'Eerdt, Botell, & Wade, 2009 ; O'Neill, 2000 ; Resnick, 2009 ). 1 SMART goals are prominently featured in various self-help books and online sources, but little research has been conducted to test the effectiveness of creating SMART goals on reducing procrastination. Some component characteristics of SMART goals (i.e., specificity, measurability, and time-defined schedules), however, have been highlighted as important for goal accomplishment in popular-press books ( Burka & Yuen, 1983 ; Ferrari, 2010 ; Grant Halvorson, 2010 ; Pychyl, 2013 ) and in long-held theoretical accounts of goal setting ( Locke & Latham, 2002 , 2006 ). We thus reasoned that asking students to create SMART goals would provide a good starting point for exploring whether goal-setting interventions could help reduce their academic procrastination.

The second intervention—forming implementation intentions—targeted a different aspect of goal management that requires the effective maintenance and retrieval of long-term goals: resisting temptations. Previous research has established impulsivity as a substantial correlate of procrastination ( Ferrari, 1993 ; Steel, 2007 ), perhaps because impulsive individuals may be more likely to lose sight of their long-term goals by favoring short-term temptations ( Gustavson et al., 2014 ). Thus, we reasoned that, in addition to setting good goals, it may also be important to prepare individuals for likely distracting temptations by providing specific strategies to combat them. A good candidate for such an intervention is implementation intentions, which involve forming if/then rules that can be targeted at specific temptations ( Gollwitzer & Brandstatter, 1997 ; Gollwitzer & Sheeran, 2006 ). Moreover, forming implementation intentions have been shown to be effective in reducing the intention–behavior gap in the domains of health psychology (for recent meta-analyses, see Adriaanse, Vinkers, De Ridder, Hox & De Wit, 2011 ; Bélanger-Gravel, Godin, & Amireault, 2013 ).

Despite such promise, the existing evidence regarding potential benefits of implementation intentions for reducing procrastination is highly limited, especially when it comes to academic procrastination (e.g., Howell, Watson, Powell, & Buro, 2006 ; Van Hooft, Born, Taris, van der Flier, & Blonk, 2005 ). Moreover, the existing evidence for the relationship between implementation intentions and procrastination tends to be correlational in nature (perhaps with an exception of Owens, Bowman, & Dill, 2008 ), thus necessitating an experimental investigation that directly tests the effectiveness of implementation intentions in reducing academic procrastination.

1.2. Procrastination and Goal Accomplishment

The second aim of this study was to examine whether individual differences in academic procrastination uniquely predict the extent to which students successfully achieve their self-generated academic goals, above and beyond the influence of other relevant correlates. Much of research examining the association between academic procrastination and achievement has focused on global measures like course grades and has demonstrated that higher levels of self-reported procrastination are generally associated with lower grades (see Kim & Seo, 2015 , for a recent meta-analysis). Although students clearly want to receive as high course grades as possible, such global measures cannot serve as a direct measure of their goal accomplishment.

Other work has focused on more specific and more direct indices of students’ academic accomplishment, such as the amount of reading assignments completed ( Glick & Orsillo, 2015 ) and the gap between intended and actual study hours ( Steel et al., 2001 ). These studies have also produced some evidence for significant associations between academic procrastination and goal accomplishment, but, in these studies, the goals generated by the students were simple numerical values (e.g., intended study hours), rather than individually tailored descriptions of what they wanted to achieve (e.g., SMART goals). In fact, little research has examined how academic procrastination is related to the achievement of personal goals that students themselves generated in light of their own specific academic needs. Moreover, the existing evidence is limited as to whether this hypothesized association between academic procrastination and goal achievement is uniquely attributable to individual differences in procrastination per se, rather than other correlated factors (e.g., personality, motivation, and situational factors).

To address these issues, we asked subjects to report, in the second session, the extent to which they accomplished the goals they set in the first session and examined what specific individual differences variable(s) uniquely predicted self-reported goal achievement. We hypothesized that if procrastination is uniquely associated with the accomplishment of self-generated goals, this association should remain significant even after controlling for other potential correlates of procrastination. As a secondary question, we also examined what individual differences variable(s) would uniquely predict other outcome measures in this study, such as levels of success at resisting distracting temptations and postintervention levels of academic procrastination. To make the testing of our hypotheses rigorous, we included a wide range of candidate correlate variables, which we briefly summarize and justify below.

As for personality measures, we assessed trait levels of impulsivity, conscientiousness, and perfectionism because they are some of the most widely studied correlates of procrastination ( Steel, 2007 ). They may also be relevant to goal accomplishment because impulsive individuals are more prone to give into their distracting temptations and avoid work ( Gustavson et al., 2014 ; Pychyl, 2013 ), whereas conscientious individuals tend to be better organized and persevere until tasks are completed ( Costa & McCrae, 1992 ). A component of perfectionism, known as personal standards (having high standards for oneself), has also been associated with less procrastination ( Steel, 2007 ) and will likely be related to stronger goal accomplishment. In addition, we assessed subjects’ everyday procrastination outside academic domains.

As a novel addition, we included a measure of mindset on procrastination—a growth versus fixed mindset for procrastination—to assess the extent to which one believes that procrastination is a malleable (rather than immutable) trait. When studied in the context of positive traits such as intelligence, a growth mindset has been associated with various positive outcomes ( Dweck, 2006 ). For example, a recent meta-analysis ( Burnette, O'Boyle, VanEpps, Pollack, & Finkel, 2013 ) suggests that growth mindsets (e.g., of intelligence) predicts multiple self-regulatory behaviors, such as better goal setting, goal operating, and goal monitoring. We adapted the mindset questionnaire of intelligence for procrastination to examine whether one's belief about procrastination may be associated with levels of goal achievement and academic procrastination. This variable was potentially an important one to explore, because there was some suggestion that one's beliefs might moderate the effect of an intervention (e.g., Valentiner, Jencius, Jarek, Gier-Lonsway, & McGrath, 2013 ). For example, the benefit of intervention effects could be greater for those students who believe in the malleability of procrastination.

As for motivational factors, we included two trait-like aspects of motivation: (a) internal academic motivation—the drive to do well for oneself—which has known to be negatively correlated with procrastination and (b) external academic motivation—the drive to do well because to impress parents, teachers, or peers—which has known to be positively correlated with procrastination ( Senécal, Koestner, & Vallerand, 1995 ; Steel, 2007 ). We also included more specific aspects of motivation, including (a) motivation to achieve the specific academic goals generated during the intervention exercise and (b) confidence (or self-efficacy) for being able to achieve their self-generated goals.

Finally, we assessed subjects’ memory for their self-generated goals, specifically, the extent to which subjects accurately remembered the specific goals and implementation intentions they had formed three weeks ago. We judged that this variable could be important for resisting short-term temptations and/or accomplishing self-generated goals, because individuals who cannot retrieve and maintain their goals when needed may have great difficulty completing them.

Although our selection of the variables is not exhaustive, the wide range of variables included in this study should help us better differentiate those variables that uniquely predict procrastination and goal accomplishment from those that are not unique predictors once controlling for other predictors.

1.3. The Current Study

We conducted a two-session intervention study that also included various individual differences measures. Our intervention procedure was modeled after the Personal Project Analysis approach ( Little, 1983 ), in which subjects generate personal goals in an initial brainstorming session and then choose some of their most important goals ( Blunt & Pychyl, 2000 , 2005 ). We supplemented this approach by introducing two different goal-related interventions after these initial goal-setting brainstorming exercises.

The procedure for this study is summarized in Figure 1 . In the first session, subjects completed measures of baseline academic procrastination, personality, motivation, and other situational factors. They then completed the goal-setting exercises in which they brainstormed multiple academic goals (9 total), chose the most important academic goals (3 total), and elaborated on the importance of accomplishing these goals. Half of the subjects also honed their goals into SMART goals. Afterward, they brainstormed and identified key temptations that they would likely encounter in the next three weeks, and half of the subjects additionally formed implementation intentions for these temptations. Finally, subjects completed motivation and confidence ratings for their personal academic goals.

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Summary of the procedure of the two-session study.

In the second session, which occurred approximately three weeks later, subjects completed postintervention measures of academic procrastination. They were also assessed with their memory for the specific academic goals they had set earlier and reported whether they were able to accomplish those self-generated goals.

Our assessment of intervention-related changes in procrastination focused on 3 of 6 possible academic domains that each student chose as their most problematic areas (e.g., studying for exams, writing term papers), thereby maximizing the likelihood of observing reductions in procrastination due to the intervention. Subjects reported levels of their academic procrastination for the 3-week period prior to the intervention in the first session (baseline) and then for the 3-week period prior to the second session (postintervention). If the two interventions can help students reduce their academic procrastination, then the postintervention measures of academic procrastination should be significantly lower than their baseline counterparts, especially for the three academic domains targeted by the goal-setting exercises (Aim 1).

In addition, we collected a postintervention measure of goal accomplishment (how well they were able to achieve the goals they had set for themselves). If academic procrastination is a unique predictor of the actual accomplishment of self-generated goals, then the initial levels of academic procrastination should still be a significant predictor of goal accomplishment even after controlling for those candidate correlates included in the study (Aim 2). As a secondary question, we also explored what individual differences variables would significantly predict (a) the extent to which subjects were successful at resisting the specific temptations they had identified as potentially problematic and (b) postintervention levels of academic procrastination.

2.1. Subjects

The participants were 177 college students (110 women and 67 men) from an introductory psychology course who participated for course credit and completed both sessions. They were randomly assigned to the four between-subjects groups ( n = 45 in the SMART-goal/implementation-intention group, 46 in the SMART-goal/temptations-only group, 39 in the control-goal/implementation-intention group, and 47 in the control-goal/temptations-only group). Thirteen additional subjects participated in both sessions, but their data were excluded for the following reasons: failing to complete the key intervention exercises due to time constraints ( n = 2), having participated in a pilot study ( n = 1), or failing to generate at least two acceptable temptations ( n = 1) or implementation intentions ( n = 9), as judged by independent raters (see Section 2.3. regarding the coder ratings).

2.2. Design and Procedure

This experiment used a 2 (SMART vs. control) × 2 (implementation intentions vs. control) between-subjects design. Almost all questionnaires and intervention exercises were administered on Macintosh computers using Qualtrics software, although some measures required paper-and-pencil responses. 2 All procedures were approved by the Institutional Review Board of the University of Colorado Boulder.

2.2.1. Session 1: Individual differences measures

Virtually all questionnaire items asked subjects to indicate how each statement was true of them “in general” so that they would serve as trait-level individual differences. The important exception was the primary dependent measure of academic procrastination, which asked about the level of their academic procrastination during the “past three weeks.” This change was made to ensure that the postintervention responses for academic procrastination could reflect intervention effects, if any, and would also be directly comparable to their baseline counterparts.

As the primary measure of academic procrastination, we used a modified version of the Procrastination Assessment Scale for Students (PASS; Solomon & Rothblum, 1984 ). In addition to the time-frame change noted above, the measures also deviated from typical use in another way: Because subjects chose 3 out of the 6 PASS domains to focus on before the goal-setting exercise, the primary independent variable for baseline academic procrastination was the average score of responses for only those 3 target domains. For each of the 3 target domains, subjects responded to 2 items 3 relating to the degree of their procrastination on a 1–5 scale. Thus, each subject received a score for academic procrastination in their target domains based on 6 items. A second scale was created based on self-reported procrastination in the 3 domains that were not chosen for the intervention (nontarget domains), also based on 6 items (2 per domain).

Six other questionnaires were also administered in Session 1. Domain-general, nonacademic procrastination was measured (on a 1–5 scale) with the 15-item Adult Inventory of Procrastination ( McCown, Johnson, & Petzel, 1989 ). Impulsivity was measured with the 30-item Barratt Impulsivity Scale ( Patton, Stanford, & Barratt, 1995 ), and conscientiousness was measured with the 9-item conscientiousness subscale of a short version of the Neo-Five Factor Inventory ( Costa & McCrae, 1992 ), both on a 1–5 scale. Perfectionism was measured (on a 1–7 scale) with the standards subscale of the Almost Perfect Scale ( Slaney, Rice, Mobley, Trippi, & Ashby, 2001 ). Academic motivation was measured (on a 1–5 scale) with the 33-item Internal/External Motivation Scale ( Lepper, Corpus, & Iyengar, 2005 ).

Finally, mindset about procrastination was measured with a 4-item scale, adapted from previous assessments of fixed versus growth mindsets of intelligence ( Blackwell, Trzesniewski, & Dweck, 2007 ; Dweck, 1999 ). These items were: People can always change how much they procrastinate; You can learn new habits, but you can't really change whether or not you are a procrastinator (reverse scored); Someone's procrastination is a part of them that they can't change (reverse scored); and You can change your procrastination tendencies considerably. These procrastination mindset items were on a 1 (Strongly Disagree)–6 (Strongly Agree) scale.

2.2.2. Session 1: Goal-setting exercise

Subjects were randomly assigned to one of two conditions: SMART goals or control. Both the SMART and control exercises can be found in the supplemental material . First, subjects were given a list of six academic domains, corresponding to the six domains of the PASS, and asked to rank order the domains in order of how much they typically procrastinate on them. These responses were used to create the primary measures of academic procrastination in target and nontarget domains (ranked 1–3 and 4–6, respectively). Afterward, all subjects were instructed to brainstorm, for each target academic domain, three goals that they would like to accomplish before the second session (3 domains × 3 goals = 9 goals total) before selecting their top goal for each of their most problematic domains.

Next, for the SMART-goal condition only, subjects were given a worksheet with an explanation of each of the SMART criteria, as well as examples of how to hone a general goal into a SMART goal (see the supplement for this worksheet). Although the examples featured general health-related goals, subjects were instructed to make sure that all of their academic goals were related to their top three academic domains and could be accomplished before the next session of the study (approximately within the next 3 weeks).

Both groups then completed a few final questions for each of their goals, which encouraged subjects to elaborate on the importance of these academic goals (e.g., Which course(s) does this goal apply to?; Why is it important that you accomplish this goal?). Finally, the experimenter checked over their goals to ensure that they were related to an academic goal (control condition) or that they met the SMART criteria (SMART goal condition). If not, the experimenter instructed the subject to continue to work, giving suggestions only if the subject still struggled to complete the exercise. Pilot testing revealed that this checking process was important because creating SMART goals was not necessarily easy for all subjects.

2.2.3. Session 1: Temptation identification exercise

Subjects were also randomly assigned to one of two conditions: temptations only or temptations plus implementation intentions. The implementation intentions exercise can be found in the supplemental material . In this exercise, subjects were instructed to brainstorm six different temptations that typically distract them from accomplishing goals related to their top three academic domains (from the previous step). To be acceptable, subjects’ temptations had to be concrete activities that actively distract them from accomplishing goals (e.g., boredom, lack of interest, or anxiety was not acceptable). These instructions ensured that all subjects generated temptations that could be targeted, regardless of condition, with implementation intentions. From the six possible temptations they generated, subjects selected the three temptations that they thought would likely distract them the most.

Afterward, in the implementation intentions condition only, subjects were instructed to form specific implementation intentions for each of these top three temptations. They were given a brief explanation of implementation intentions and two illustrative examples related to health goals. The subjects in the temptations-only group skipped this step.

Finally, the experimenter completed a preliminary check of all responses to make sure that the temptations were acceptable. In the implementation-intention condition, the experimenter also checked whether each implementation intention specified some real action that the subject could take, rather than simply stating that he/she would not give in to his/her temptation. Again, the experimenter instructed the subject to continue to work if they did not deem the temptations and/or implementation intentions acceptable.

2.2.4. Session 1: Final phase

In the final phase of Session 1, subjects were instructed to reproduce from memory their top three goals and temptations (and implementation intentions) they generated earlier. They were told that rewriting these goals and temptations would make them more likely to remember them over the coming weeks. Because subsequent rater judgements indicated that subjects remembered their goals and temptations essentially perfectly at this point, these data will not be discussed further. Importantly, subjects then answered two questions that assessed, respectively, their motivation for each goal ( How motivated are you to achieve this goal? ) and confidence for being able to achieve each goal ( How confident are you that you will achieve it? ).

Finally, subjects were asked two manipulation-check questions: (a) How much do you think the exercises will help you reduce your academic procrastination? and (b) How much do you think the exercises will help you resist your temptations? These items were included to assess whether subjects in the intervention conditions (SMART goals and/or implementation intentions), as a whole, felt that those intervention exercises would be more helpful for achieving goals and resisting temptations than did those in the control conditions. All of these ratings completed in this final phase of Session 1 were based on a 1–7 scale.

2.2.5. Session 2

Subjects returned approximately 2.5 weeks after the first session ( M = 17.48 days; SD = 4.50 days; Range = 12–38 days). At the beginning of Session 2, subjects completed individual differences measures for academic procrastination for all 6 domains (3 target and 3 nontarget domains) using the same scale as the baseline (PASS) and the same instructions as to the timeframe of academic procrastination (i.e., regarding procrastination in the past three weeks, regardless of the actual time between sessions). Subjects were then asked to write down, from memory, their top three goals, temptations, and implementation intentions (if applicable) from Session 1 as best they could remember. This recall exercise was completed to obtain a measure of subjects’ memory for their goals, as determined by three raters.

Finally, subjects were shown their actual responses at Session 1 and answered the three questions about the effectiveness of the goal-generation exercises (all on a 1–5 scale). For each goal, subjects were asked: Was your goal accomplished? (our primary dependent measure of goal accomplishment). For each temptation, they were asked: On average, how much per week did this temptation arise? and When this temptation arose, what percent of the time did it distract you? The responses to the latter question formed the primary measure of successfully resisting temptations (the response was on a 1–5 scale, although the question mentioned percentage).

2.3. Coding Criteria and Procedures for Rater Judgments

Before conducting the primary analyses, we used raters’ judgments to assess relevant study-level variables of interest. All rater judgments were made by three raters, who were blind to condition with the following exception. During rater judgements of subjects’ memory for their goals, temptations, and implementation intentions, raters could readily tell whether the subject was in the implementation-intention condition or not (because subjects wrote all responses on a single worksheet and the section for recalling implementation intentions was either absent or present). Raters were always blind to the SMART condition.

Three raters independently evaluated whether each goal was accurately recalled at the start of Session 2, using the following 0–3 scale:

a score of 0 indicated that the subject wrote nothing;
a score of 1 indicated that the subject wrote something, but his/her response was not about the same PASS domain as the goal;
a score of 2 indicated that the subject correctly identified the PASS domain that his/her goal was written about, but did not remember any more significant details;
a score of 3 indicated that the subject remembered the correct domain and at least one significant detail of his/her academic goal (e.g., studying for a calculus exam).

The kappa interrater reliability estimate for each of the three memory ratings was high (> .95). Similar memory ratings were also provided for temptations and implementation intentions, but they are not discussed further because memory for temptations were near ceiling for most subjects ( M = 2.53 out of 3, SD = .50 averaged across all 3 temptations) and only half of the sample wrote implementation intentions.

Raters also judged the compliance of responses to the exercises (i.e., SMART goals, temptations, and implementation intentions). They coded whether each goal met each of the five SMART criteria (0 or 1 for each SMART criterion), whether each temptation was a real distraction (0 or 1 for each temptation), and whether each implementation intention was a real action that they could take toward resisting the temptations (0 or 1 for each implementation intention). Kappa reliability estimates for the individual SMART goal ratings were high (> .91). The kappa estimates for the ratings for temptations (.66–.89) and implementation intentions (.64–.74) were lower, likely due to the high rate of compliance, but were still acceptable.

All analyses were conducted with ANOVA or multiple regression procedures using SPSS or R, with an alpha threshold of .05.

3.1. Descriptive Statistics and Session 1 Individual Differences

Table 1 displays the academic domains chosen as target domains for the goal-setting exercise. Subjects chose writing term papers as their most problematic academic domain for procrastination (75% of subjects ranked it as one of their top three domains). Studying for exams (62%) and keeping up with weekly reading and homework assignments (55%) were also chosen by over half of the subjects as one of their top three domains. The other three domains, though chosen less frequently, were still ranked in the top three by at least 30% of the sample.

Academic Domains Ranked as Most Problematic in Session 1

Domains	Rank 1	Rank 2	Rank 3	Sum (%)
Writing Term Papers	64	36	32	132 (74.6)
Study for Exams	24	48	37	109 (61.6)
Reading/Homework	37	27	35	99 (55.9)
Administrative Tasks	16	20	18	54 (30.5)
Attendance Tasks	20	24	23	67 (37.9)
Activities in General	16	22	32	70 (39.5)

Note : Ranks 1, 2, and 3 indicate the academic domains chosen as problematic in terms of procrastination during Session 1 (and for which their academic goals were written about in the goal-setting exercise). N = 177 for Ranks 1, 2, and 3. N = 531 for the sum. % = the percent of subjects who ranked this domain in any of their top three (i.e., sum / 177).

Descriptive statistics for the measures of procrastination and other individual differences variables are displayed in Table 2 . Important to note, mean levels of academic procrastination in this sample were similar to those reported by other studies that used the PASS scale ( Corkin, Shirley, & Lindt, 2011 ; Glick & Orsillo, 2015 ; Howell et al., 2006 ).

Descriptive Statistics of Individual Differences Variables and Dependent Measures

	N	Mean	SD	Range	Skewness	Kurtosis	Reliability

Academic Procrastination
Target Domains	177	3.05	0.65	1.00–4.67	−0.40	0.71	0.79
Nontarget Domains	177	2.33	0.61	1.00–4.00	0.20	−0.32	0.79
Everyday Procrastination	177	2.39	0.56	1.20–4.00	0.18	−0.32	0.83
Impulsivity	177	2.14	0.35	1.30–3.13	0.25	0.19	0.83
Conscientiousness	177	3.75	0.60	2.00–5.00	−0.16	−0.70	0.78
Perfectionism	177	5.99	0.72	3.29–7.00	−1.12	1.64	0.87
Fixed vs. Growth Mindset	177	2.41	0.77	1.00–4.50	0.12	−0.31	0.84
Academic Motivation
Internal	177	3.55	0.50	2.00–4.82	−0.44	0.58	0.89
External	177	3.14	0.49	1.63–4.31	−0.29	0.27	0.83
Motivation for Goals	170	5.43	0.93	2.00–7.00	−0.82	1.29	0.64
Confidence for Goals	170	5.28	0.97	1.00–7.00	−0.60	1.42	0.68
Memory for Goals	173	2.24	0.67	0.00–3.00	−0.58	−0.41	N/A

Academic Procrastination
Target Domains	177	3.07	0.59	1.67–5.00	0.37	0.30	0.79
Nontarget Domains	177	2.38	0.64	1.00–4.00	0.05	−0.41	0.81
Goals Accomplished	176	3.29	0.82	1.00–5.00	−0.22	−0.18	0.35
Temptations Resisted	177	2.42	0.73	1.00–5.00	0.49	0.13	0.41

Note : Means indicate the average response for all questionnaire items. Academic procrastination in the target domains refer to the responses for the three academic domains that were chosen as part of the goal-setting intervention. The score for nontarget domains refer to the responses for the other three academic domains that were not chosen as most problematic (see Table 1 for list of domains). Reliability estimates were computed using Cronbach's alpha.

The zero-order correlations between these individual differences measures are shown in Table 3 . As expected, initial levels of procrastination assessed in Session 1 were correlated with the other individual differences measures (except for memory for goals), whether procrastination was assessed in the target or nontarget academic domains or in nonacademic domains.

Correlations Among Individual Differences at Session 1

	1	2	3	4	5	6	7	8	9	10	11
1. Academic Procrastination (Target Domains)	1
2. Academic Procrastination (Nontarget Domains)		1
3. Everyday Procrastination			1
4. Impulsivity				1
5. Conscientiousness					1
6. Perfectionism						1
7. Fixed vs. Growth Mindset							1
8. Internal Academic Motivation								1
9. External Academic Motivation	.15					−.12			1
10. Motivation for Goals							−.14		−.04	1
11. Confidence for Goals									.05		1
12. Memory for Goals	.03	−.11	.00	−.15	.09	.11	.10	.10	−.15	.00	−.01

Note: Significant correlations are displayed in bold ( p < .05).

3.1.1. Manipulation checks

First, we checked whether there were any group differences at the pretest between the four conditions in the study. The four groups showed neither baseline differences in academic procrastination (target domains), F (3, 173) = 1.18, p = .320, η p 2 = .02, nor in other individual differences variables obtained in Session 1, F (3, 173) < 1.71, p s > .167, suggesting that random assignment was successful.

Second, analyses of rater judgments of compliance revealed that subjects in the SMART conditions were creating goals that met more SMART criteria ( M = 4.25 out of 5 per goal, SD = .60) than did those in the control group ( M = 3.05, SD = .85), F (1, 202) = 204.71, p < .001, η p 2 = .50. This result suggests that the intervention successfully encouraged subjects in the SMART goal condition to set more specific, measurable, and time-defined goals than those who simply self-generated goals without any guidance. Of note, there was no variability in the rater judgments for the Achievable ( M = 3.0, SD = .00, out of 3 goals) and Realistic ( M = 2.99, SD = .08) criteria across subjects, suggesting that the goals generated in the two conditions differed primarily for the three other SMART criteria: Specific ( M = 1.77, SD = .96), Measureable ( M = 1.54, SD = 1.05), and Time-Defined ( M = 1.57, SD = 1.19). Rater judgments also indicated high compliance with the instructions for the temptations ( M = 2.94 out of 3, SD = .09) and implementation intentions ( M = 2.61, SD = .66).

Third, we conducted a 2 (SMART vs. control) × 2 (implementation intentions vs. temptations only) ANOVA to examine group differences in responses to the two final questions of Session 1, which asked subjects how much they expected that the intervention exercises they just completed would be associated with success at (a) reducing academic procrastination and (b) resisting the urge to give into their temptations in the coming weeks. Although no subjects were aware of the specific experimental manipulations, we hypothesized that those subjects who received the experimental manipulations should perceive those intervention exercises to be likely more effective in reducing their academic procrastination and/or in resisting specific temptations they identified. These expectations were confirmed.

For reducing procrastination, individuals in the SMART-goal condition expected to reduce their procrastination more than those in the control group, F (1, 166) = 4.64, p = .033, η p 2 = .03, as did individuals who wrote implementation intentions (compared to those who did not), F (1, 166) = 12.69, p < .001, η p 2 = .07. As for giving into temptations, there were no group differences between the SMART-goal and control conditions, F (1, 166) < 1, but, importantly, those in the implementation-intention condition expected to resist their temptations more successfully than those who simply identified their temptations, F (1, 166) = 5.14, p = .025, η p 2 = .03. There was no two-way interaction for either type of expectations, F s(1, 166) < 2.84, p s > .094. These results suggest that the subjects in the respective intervention conditions judged those intervention exercises to be likely helpful, at least at the time of the intervention.

3.2. Aim 1: Effects of the Intervention Exercises

First, we examined whether the two interventions administered in Session 1 reduced academic procrastination or increased the likelihood of goal achievement, assessed at Session 2.

3.2.1. Basic effects of the interventions

The main results are displayed in Table 4 , which shows mean levels of baseline and postintervention academic procrastination in target ( Table 4a ) and nontarget ( Table 4b ) domains. We conducted mixed ANOVAs with one within-subjects variable and two between-subjects variables—2 (baseline vs. postintervention) × 2 (SMART vs. control goals) × 2 (implementation intentions vs. temptations only)—separately for the target and nontarget academic domains.

Condition Means for Academic Procrastination at Session 1 and Session 2

	Session 1 ( )	Session 2 ( )


Temptations Only ( = 47)	3.00 (.10)	2.97 (.09)
Implementation Intention ( = 39)	2.99 (.12)	3.05 (.10)

Temptations Only ( = 46)	3.21 (.09)	3.17 (.09)
Implementation Intention ( = 45)	2.99 (.10)	3.07 (.08)
	3.05 (.10)	3.07 (.09)


Temptations Only ( = 47)	2.18 (.08)	2.15 (.09)
Implementation Intention ( = 39)	2.45 (.09)	2.42 (.12)

Temptations Only ( = 46)	2.40 (.09)	2.42 (.10)
Implementation Intention ( = 45)	2.33 (.10)	2.40 (.09)
	2.34 (.09)	2.35 (.10)

Note: Condition means for academic procrastination in (A) the three domains chosen as most problematic, and (B) the three domains chosen as least problematic. Means indicate the average response on a 1–5 scale.

As expected for the nontarget domains ( Table 4b ), there was no evidence that individuals in the SMART condition reduced their academic procrastination across sessions more than those in the control condition, F (1, 173) < 1. Nor was there any evidence that creating implementation intentions led to a greater reduction in procrastination, F (1, 173) = 1.16, p = .283, η p 2 = .01. There was also no evidence for the three-way interaction, F (1, 173) < 1, or main effect of session, F (1, 173) < 1. These results confirm that academic procrastination in nontarget domains did not change over the three-week interval.

More important, as shown in Table 4a , there was no change in academic procrastination for the target domains, either. The two-way interaction between session and SMART condition was not significant, F (1, 173) < 1, nor was the interaction between session and implementation-intention condition, F (1, 173) = 2.27, p = .134, η p 2 = .01, providing no evidence that subjects in either condition successfully reduced their procrastination more than those in the control groups. The three-way interaction (session × SMART condition × implementation-intention condition) was also not significant, F (1, 173) < 1. Such lack of significant effects of either intervention on the postintervention levels of academic procrastination presents a stark contrast to the manipulation-check results noted in Section 3.1.1., which suggested that subjects in the SMART and/or implementation-intention conditions expected that they would benefit more from those interventions than those in the control condition.

Finally, there was no main effect of session, F (1, 173) < 1, suggesting that there was no overall change in academic procrastination even when collapsing across the four conditions. This last finding rules out the possibility that our use of “active” control groups in this study masked potential benefits of going through these intervention exercises. More specifically, there was no evidence that the lack of significant intervention effects were observed because subjects in the control conditions benefitted from the exercises involving generating academic goals and identifying possible temptations and thereby successfully reduced their procrastination as much as the experimental groups did.

In addition to the postintervention levels of academic procrastination, we also examined whether the intervention exercises resulted in higher endorsement of the items related to the accomplishment of self-generated goals as well as levels of success in resisting the temptations they identified. Because there were no baseline measures of goal accomplishment, these analyses were conducted with between-subjects 2 (SMART condition) × 2 (implementation-intentions condition) ANOVAs. These results are summarized in Table 5 .

Condition Means Goal Accomplishment and Success at Resisting Temptations

	Goal Accomplishment ( )	Temptations Resisted ( )

Temptations Only ( = 47)	3.29 (.79)	2.38 (.75)
Implementation Intention ( = 39)	3.29 (.75)	2.42 (.66)

Temptations Only ( = 46)	3.18 (.96)	2.60 (.82)
Implementation Intention ( = 45)	3.39 (.75)	2.29 (.66)
	3.29 (.82)	2.42 (.73)

Note: Condition means for posttest measures of goal accomplishment and success at resisting temptations. Means indicate the average response on a 1–5 scale.

Like academic procrastination, analyses of each of these dependent measures revealed no main effects of either intervention on the self-reported measures of goal accomplishment, F s(1, 172) < 1, or success at resisting temptations, F s(1, 173) < 1.43, p s > .233, η p 2 s < .01. There was also no two-way interaction effect (SMART × implementation-intentions) for either goal accomplishment or resisting temptations, F s(1, 179) < 2.77, p s > .097, η p 2 s < .02.

3.2.2. Exploratory follow-up analyses

Given the lack of significant effects for the two interventions, we conducted a series of exploratory analyses to examine the possibility that (a) one or more of the baseline individual differences variables moderated the effect of the interventions or that (b) the interventions were effective only for certain academic domains (e.g., writing term papers).

3.2.2.1. Moderation analyses.

First, in regression analyses treating each potential moderator as a continuous variable, we tested whether any variable demonstrated significant interaction effects with one or both of the experimentally manipulated (intervention) variables. In these exploratory analyses, we tested two categories of what we judged to be most likely candidate moderators. The first category included individual differences variables from Session 1 that were significantly predictive of the key dependent measures in the multiple regression models described below (see Section 3.3 and Table 6 ): Session 1 academic procrastination (target domains), impulsivity, fixed versus growth mindset, and motivation for the goals generated in Session 1. The second category consisted of study-specific variables that we judged could be associated with the effectiveness of the interventions: rater judgements of the number of SMART criteria met per goal (i.e., the quality of the generated goals), rater judgements for subjects’ memory for their goals, and the number of days between sessions (which could be associated with the scope of the goals generated and/or the amount of time available to make progress toward their goals).

Zero-Order Correlations Between Various Individual Differences Measures (administered in Session 1) and the Main Dependent Measures of the Study (measured in Session 2)

	Goals Accomplished	Temptations Resisted	Academic Procrastination in Target Domains
Academic Procrastination
Target Domains
Nontarget Domains
Nonacademic
Procrastination
Impulsivity
Conscientiousness
Perfectionism		.08	−.10
Fixed vs. Growth Mindset
Academic Motivation
Internal
External	−.02	−.14	.13
Motivation for Goals
Confidence for Goals
Memory for Goals	.11	.00	−.04

Note: Significant correlations are displayed in bold ( p < .05). All variables listed on top were dependent measures taken in Session 2, and all variables on the left column were taken during Session 1 (except Memory for Goals, which was based on rater judgements for responses at the beginning of Session 2).

The results of these exploratory moderation analyses are summarized in the Appendix for all three dependent measures (Session 2 academic procrastination in top domains, goals accomplished, and temptations resisted). As is clear from Table A1 , the analyses revealed little evidence for any moderating effects of individual difference variables in this study.

Of all the analyses we conducted, only two effects emerged as significant. The first is the moderating effect of baseline procrastination (target domains) on implementation intentions for the measure of the success at resisting temptations, F (1, 170) = 4.80, p = .030, η p 2 = .03, indicating that implementation intentions were more effective for individuals with greater baseline procrastination. The second is the moderating effect of subjects’ memory for their goals on SMART intervention for academic procrastination, F (1, 165) = 4.11, p = .044, η p 2 = .03, indicating that better memory for goals were associated with less procrastination in the control group, F (1, 80) = 4.69, p = .033, η p 2 = .06, but not in SMART group, F (1, 83) = .60, p = .443, η p 2 = .01. Although these two moderating effects were significant, they were not predicted a priori, and at least the latter effect is not readily interpretable. Thus, we tend to think that they likely reflected Type I errors due to the large number of statistical tests performed (in fact, we would expect about two Type I errors in the 42 tests summarized in Table A1 ). Overall, then, we found little evidence that the lack of significant intervention effects was due to some moderating variables masking the main effects for the intervention manipulations.

3.2.2.2. Academic domains chosen in Session 1.

We also examined the possibility that the benefits of the two interventions may have been specific to certain academic domains. Because over 60% of the sample chose writing term papers and/or studying for exams as their target domains (see Table 1 ), we conducted two ANOVAs, one for each domain, to examine whether we see any evidence for the reduction in procrastination in these broadly problematic domains.

Even when focusing on these specific domains, however, there was no evidence for any intervention effects. For writing term papers (chosen by 75% of the sample, n = 132), there was no main effect of SMART condition, F (1, 128) = 2.34, p = .129, η p 2 = .02, no main effect of implementation-intention condition, F (1, 128) = .26, p = .613, η p 2 < .01, and no interaction between conditions, F (1, 128) = 1.00, p = .319, η p 2 < .01. Similarly, for studying for exams (chosen by 62% of the sample, n = 109), there was no main effect of SMART condition, F (1, 105) = .07, p = .794, η p 2 < .01, or implementation-intention condition, F (1, 105) = 1.22, p = .271, η p 2 = .01. There was no two-way interaction, either, F (1, 105) = .17, p = .683, η p 2 < .01. These results provide no evidence that the interventions reduced academic procrastination in only those specific, commonly chosen academic domains.

3.3. Aim 2: Procrastination and Goal Success

The second aim of this study was to test whether initial levels of academic procrastination uniquely predicted the actual achievement of the personal academic goals subjects generated themselves, above and beyond the effects of other possible correlates. We also examined which individual differences variables uniquely predicted the levels of success at resisting the specific temptations they had identified as well as the postintervention levels of academic procrastination.

The zero-order correlations between the three primary dependent measures and individual differences variables included in the study are reported in Table 6 . As shown in the table, higher initial levels of procrastination in the target domains were associated with less success in accomplishing goals and with more difficulty resisting temptations when they arose. As expected, however, many other measures were also significantly correlated with the three dependent measures. Thus, for each dependent measure, we conducted simultaneous regression analyses, including all of the independent variables listed in Table 6 . Because the two interventions had no effect on the dependent measures assessed here, we did not include in the models the experimental effects of the SMART-goal and implementation-intention interventions. The results from these regression analyses are summarized in Table 7 .

Multiple Regression Models for the Main Dependent Measures of the Study

	Goals Accomplished		Temptations Resisted		Academic Procrastination in Target Domains

Academic Procrastination
Target Domains			.03	.764
Nontarget Domains	.06	.543	−.07	.468	−.05	.483
Nonacademic Procrastination	−.11	.266	−.01	.889
Impulsivity	−.02	.878	−.10	.352
Conscientiousness	.05	.654	.13	.288	−.01	.949
Perfectionism	−.08	.409	−.11	.281	.06	.423
Fixed vs. Growth Mindset	−.11	.165	−.07	.412
Academic Motivation
Internal	.05	.581	.09	.384	.07	.396
External	.10	.267	.02	.871	−.01	.932
Motivation for Goals	.13	.143			−.04	.574
Confidence for Goals	.09	.342	−.09	.401	−.04	.655
Memory for Goals			−.02	.774	−.05	.452

Total R of model (Adjusted R )	21 (.15)		.17 (.11)		.48 (.44)

Note : Standardized beta coefficients ( β ) and significance values ( p ) for regression models of outcome measures at Session 2. Significant predictors are displayed in bold ( p < .05).

For the primary dependent measure of goal accomplishment, two variables emerged as significant predictors. 4 Most important, as hypothesized, higher initial levels of academic procrastination in target domains were uniquely associated with less goal accomplishment (standardized β = −.20). Subjects’ memory for their goals was additionally associated with more goal accomplishment ( β = .15). However, because this variable was not significant by itself (see Table 6 ) and became significant only when the effects of the other variables were controlled for, this result for the memory-for-goals variable should be interpreted with caution. In contrast, the significant effect for baseline academic procrastination in the target domains was hypothesized a priori and was observed even when other candidate measures were included in the analysis.

We also conducted analogous regression analyses for the secondary outcome variable of success in resisting temptations when they arose. As shown in Table 7 , the only significant predictor was motivation to accomplish their self-generated goals ( β = .29), suggesting that motivational factors, especially the motivation to achieve those specific goals (rather than general academic motivation), uniquely predicts the success at resisting temptations.

Finally, Table 7 also summarizes the results of the regression model predicting Session 2 academic procrastination in target domains. Not surprisingly, initial levels of procrastination in those same target domains were the best predictor of academic procrastination ( β = .44), but, above and beyond this expected effect, levels of postintervention academic procrastination were uniquely predicted by nonacademic procrastination ( β = .18), impulsivity ( β = .17), and mindset about procrastination ( β = .18). Specifically, individuals who reported the most academic procrastination in Session 2 were those who procrastinated the most in the previous three weeks (in not only academic but also nonacademic settings), were more impulsive, and were more likely to hold the belief that procrastination is malleable. This final result is perhaps the most surprising, because, contrary to the results from other domains (e.g., intelligence), individuals who tended to believe that procrastination is malleable procrastinated the most. The results of these regression analyses will be further discussed later (Section 4.2).

4. Discussion

The two aims of this study were (a) to provide an initial test of whether goal-related interventions (SMART goals and implementation intentions) could help students reduce their academic procrastination and (b) to examine whether baseline academic procrastination is uniquely predictive of success in achieving self-generated goals. The main results of the study were clear-cut for both aims. As for the first aim, there was no evidence that the SMART-goal or implementation-intentions intervention helped reduce academic procrastination or achieve their self-generated academic goals, at least as implemented in the current study. As for the second aim, we found that initial levels of academic procrastination were a unique and substantial predictor of the later achievement of self-generated goals, even after controlling for other personality, motivational, and situational correlates. In the rest of this article, we will discuss the implications of these findings for each aim.

4.1. Aim 1: Implications for Procrastination Interventions

The current study was one of the few empirical attempts at testing whether academic procrastination could be reduced by interventions that target specific goal-related processes such as goal setting (creating SMART goals) and resisting temptations (forming implementation intentions). Although both interventions are often mentioned as useful strategies in popular press, we were not able to obtain any evidence that either intervention resulted in significant reductions in academic procrastination, despite the fact that the intervention exercises led subjects to expect more beneficial effects for the interventions. Furthermore, we were unable to identify any evidence for key moderating variables or domain-specific intervention effects. Taken together, these results suggest that reducing academic procrastination may not be as simple as identifying the important goals to accomplish, turning them into SMART goals, and planning how to react when distracting temptations arise.

The lack of significant intervention effects we observed are generally in line with the existing intervention research on academic procrastination reviewed earlier (e.g., Glick & Orsillo, 2015 ; Häfner et al., 2014 ). For example, Glick and Orsillo (2015) found that neither a time-management intervention that emphasized goal-related strategies nor a mindfulness-based intervention that emphasized emotion regulation led to a significant reduction in academic procrastination. Moreover, although forming implementation intentions may be successful at reducing intention–behavior gaps in certain situations (e.g., Owens et al., 2008 ), existing studies have not yet provided causal evidence that forming implementation intentions actually reduces procrastination. Thus, our finding of no significant effects of the SMART-goal or implementation-intention interventions on academic procrastination might not be entirely inconsistent with the prior evidence.

Nevertheless, it is important to emphasize that this is only one study and that the lack of evidence for the hypothesized intervention effects should not be interpreted to mean that these goal-related interventions have no beneficial effects whatsoever. In fact, as we will discuss shortly, other ways of implementing and testing SMART-goal and implementation-intention interventions could prove to be more effective in reducing academic procrastination. More generally, additional intervention studies targeting goal-related processes are clearly needed to further test the idea that the often hypothesized relationship between procrastination and goal management is causal in nature. In this regard, we acknowledge here some limitations of the current study and discuss possible ways to overcome them in the future.

First, as with some previous studies (e.g., Glick & Orsillo, 2015 ; Häfner et al., 2014 ), the two goal-related interventions we used were relatively short (subjects had only 30–45 min to complete the two intervention exercises). Similarly, the time lag between the first and second sessions was fairly short (approximately 3 weeks), which likely limited the types of self-generated academic goals. Given that procrastination shows some traitlike characteristic (e.g., Schouwenburg & Lay, 1995 ; Steel, 2007 ), it is possible that successful interventions may require more intensive and longer treatment programs to produce measurable improvements in academic procrastination. Unfortunately, some existing studies (Rozental et al., 2015a, 2015b; Toker & Avci, 2015 ; Wang et al., 2015 ) that administered extended interventions (e.g., 8-10 weeks) trained participants on multiple cognitive and/or acceptance-based behavioral strategies (e.g., cognitive restructuring, addressing distortions and irrational thoughts, and accepting emotions as natural). Thus, it is currently unclear whether the training gains demonstrated in these studies were due to the length of training, the breadth of the skills trained in the intervention, or both.

Second, we did not provide to subjects any records or reminders of the specific goals, temptations, and implementation intentions that they generated during the intervention exercises. Because their memory for those goals were in part predictive of goal accomplishment in this study, it is possible that sending concrete reminders of the goals and implementation intentions (or at least letting subjects take home a written copy of their intervention exercise) might have made the interventions more successful. In light of recent research highlighting the importance of reminders for following through on one's intentions ( Rogers & Milkman, 2016 ), combining the current interventions with timely reminders might be effective.

Third, the main dependent measures we used were retrospective self-reports, rather than (or in addition to) behavioral measures of academic procrastination that could be objectively verified and free from self-report biases such as social desirability ( Gustavson et al., 2015 ). Although the existing evidence regarding the relative benefits of self-report versus objective measures of procrastination is limited (e.g., Krause & Freund, 2014b ; Moon & Illingworth, 2005 ), using more objective and precisely quantifiable behavioral measures may be more sensitive to the effects of interventions. For example, such behavioral measures of procrastination may include the difference between intended study hours and actual study hours ( DeWitte & Schouwenburg, 2002 ; Steel et al., 2001 ) and the time lag between when online tests/quizzes are posted and when students actually attempted them ( Moon & Illingworth, 2005 ). Given the increasing popularity of online pedagogical platforms that automatically record when each student completed a particular assignment, this latter type of behavioral measures could be particularly useful for future research on academic procrastination.

In summary, although it is not clear how these limitations acknowledged above might have contributed, either individually or jointly, to the lack of significant intervention effects on academic procrastination in the current study, addressing these limitations in future studies will provide more rigorous tests of the effectiveness of SMART-goals and implementation-intentions interventions in reducing academic procrastination. Thus, even though we were not successful in demonstrating significant intervention effects, the current study still provides as a useful basis for future intervention efforts to build on.

4.2. Aim 2: Implications for Procrastination and Goal Accomplishment

The current study also provided further evidence that academic procrastination is uniquely associated with goal accomplishment. Previous studies have linked levels of procrastination to different types of measures related to goal achievements, such as course grades ( Kim & Seo, 2015 ; Morris & Fritz, 2015 ), individual differences in self-reported frequencies of goal setting ( Gröpel & Steel, 2008 ), and actual studying behaviors ( Glick & Orsillo, 2015 ; Steel et al., 2001 ). The current study extends these earlier results by demonstrating that baseline levels of academic procrastination were significantly associated with the accomplishment of academic goals that subjects generated themselves in light of their specific academic needs and formulated in the form of explicit verbal descriptions.

Moreover, we demonstrated that this significant association between baseline academic procrastination and goal accomplishment was still present even after statistically controlling for other candidate correlates (e.g., personality traits, motivational factors, and/or situational factors). These candidate variables were wide-ranging and were correlated significantly (and in some cases even substantially) with the measures of both academic procrastination and goal accomplishments. Therefore, this result points to the unique and important (but not necessarily causal) role of students’ propensity for academic procrastination in their failures to achieve their personally relevant academic goals.

We acknowledge here that, though reasonably broad, our selection of the candidate correlates was not exhaustive. Thus, it is possible that the addition of some other candidate variables, such as fear of failure, task aversion, and emotion regulation (e.g., Krause & Freund, 2014a ; Steel & Klingsieck, 2016 ), would change the pattern of regression results. Despite this limitation, the current results provide a useful basis for deriving more specific, a priori predictions for future research examining the relationship between academic procrastination and goal achievement.

Although our primary focus for Aim 2 was to predict individual differences in the achievement of specific self-generated goals, the study design also allowed us to address two additional questions: (a) whether baseline levels of procrastination would also uniquely predict the success at resisting distracting temptations and (b) what individual differences variables uniquely predict postintervention levels of academic procrastination. With regard to resisting temptations, academic procrastination was correlated with less success at avoiding anticipated temptations, but did not emerge as a significant predictor in the full model. Instead, the only significant variable in that analysis was self-reported levels of motivation for those academic goals that subjects set for themselves. As for the predictors of postintervention levels of procrastination, we observed that, besides the baseline levels of academic procrastination, nonacademic procrastination and impulsivity were also significant predictors, suggesting that domain-general factors (general procrastination and trait-level tendency to give into desires and temptations) are also relevant to individual differences in academic procrastination.

Finally, a novel and somewhat surprising finding of our regression analyses was that, although it was not predicted a priori, a growth mindset for procrastination—the belief that an individual's procrastination is malleable—was associated with higher, not lower, levels of academic procrastination. Moreover, the moderation analyses revealed no evidence that the interventions benefitted only those individuals who held the growth mindset for procrastination. Given that previous research has repeatedly shown that believing in the malleability of positive traits (e.g., intelligence) is more beneficial ( Burnette et al., 2013 ; Dweck, 2006 ), this negative effect of the growth mindset on procrastination may initially seem counterintuitive. However, this result could be interpreted in the following way: The belief in the malleability of procrastination may lead to the unrealistic and counterproductive thought that one can stop procrastinating at any time, which, in turn, may ironically lead to more procrastination. Future research is needed to test this speculative interpretation of why the growth mindset was negatively associated with academic procrastination.

More generally, this mindset finding suggests that, when it comes to reducing procrastination, it may not be sufficient (or might even be harmful) to simply hold a belief that procrastination is malleable, without personally internalizing that belief. Rather, what may matter more might be an individual's willingness or readiness to actually change his/her procrastination. In this regard, it might be informative to examine the effects of such willingness- and readiness-to-change variables, together with the mindset variable included in this study.

4.3. Concluding Remarks

The current study adds to a small but growing body of research that have investigated whether (and how) intervention exercises may help individuals reduce their procrastination and achieve their personal goals. Although we obtained further correlational evidence for the relationship between academic procrastination and goal accomplishment, we were not able to obtain any evidence for the causal influence of the two goal-related interventions (SMART goals and implementation intentions) on reducing academic procrastination. Thus, identifying reliably effective ways to reduce academic procrastination and establishing a causal link between goal management and procrastination remains elusive. Nevertheless, the current study provides a useful basis for future intervention efforts targeting goal-related processes. More generally, future studies will benefit from combined individual differences and experimental approaches in better elucidating the nature of the association between procrastination and goal accomplishment.

This study tested two goal-related interventions: SMART goals and implementation intentions.

The study uniquely combined experimental and individual differences approaches.

Neither goal-related intervention significantly reduced academic procrastination.

Baseline academic procrastination, however, uniquely predicted achieving self-generated goals.

Believing in the malleability of procrastination was associated with greater procrastination.

Supplementary Material

Acknowledgments.

This research was supported by Grants MH016880 and AG050595 from the National Institutes of Health and Grant DRL1252385 from the National Science Foundation.

The authors would like to thank Marjorie McIntire, Robert Eastwood, JoEllen Fresia, Wesley Tran, Emily Coyle, Joy Walters, Samantha Macchiaverna, Elizabeth Suhler, and Jane Baker for their assistance with data collection and coding.

Summary of the Results of the Exploratory Moderation Analyses

	Procrastination in Target Domains (Session 2)		Goals Accomplished		Temptations Resisted


	df = (1, 171)		df = (1, 170)		df = (1, 171)
SMART Condition × Procrastination	0.42	0.518	1.30	0.256	0.01	0.939
Implementation Intention Condition × Procrastination	0.61	0.438	0.01	0.933
	df = (1, 170)		df = (1, 170)		df = (1, 171)
SMART Condition × Impulsivity	0.97	0.327	0.04	0.845	0.13	0.719
Implementation Intention Condition × Impulsivity	< .01	0.996	0.28	0.600	2.15	0.145
	df = (1, 170)		df = (1, 170)		df = (1, 171)
SMART Condition × Beliefs	0.85	0.358	0.15	0.694	0.77	0.381
Implementation Intention Condition × Beliefs	0.10	0.750	2.26	0.135	0.35	0.557
	df = (1, 163)		df = (1, 163)		df = (1, 164)
SMART Condition × Motivation	0.29	0.592	0.93	0.337	0.56	0.454
Implementation Intention Condition × Motivation	0.24	0.626	0.16	0.214	3.49	0.064

	df = (1, 166)		df = (1, 166)		df = (1, 167)
SMART Condition × Memory			0.01	0.922	0.03	0.871
Implementation Intention Condition × Memory	3.76	0.054	0.67	0.413	0.09	0.770
	df = (1, 167)		df = (1, 167)		df = (1, 168)
SMART Condition × Criteria Met	0.66	0.419	0.05	0.821	1.18	0.280
Implementation Intention Condition × Criteria Met	0.46	0.500	0.73	0.393	1.72	0.191
	df = (1, 170)		df = (1, 170)		df = (1, 171)
SMART Condition × Days	0.02	0.902	0.83	0.365	0.11	0.737
Implementation Intention Condition × Days	0.92	0.340	1.26	0.264	0.03	0.865

Note: Moderation analyses were performed, treating each potential moderator as a continuous variable interacting with the SMART (vs control) or implementation intentions (vs. temptation only) condition. The main effects of the intervention conditions are not shown in this table, but none reached statistical significance ( p < .05). In all analyses involving procrastination in target domains at Session 2 (the first column), we controlled for baseline levels of procrastination (also in target domains), but did not allow that variable to interact with conditions (except in the first row where these effects were directly tested). In all analyses, we did not include, in the models, the two-way interaction between both intervention conditions or the three-way interaction between both conditions and the moderator, because there was no evidence for condition interactions in the primary analyses. Degrees of freedom vary in part because the n s were different for different measures (see Table 2 ) and also because baseline procrastination was controlled for only when postintervention procrastination was the dependent measure (the first column).

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1 Some sources use different labels for the SMART abbreviation (e.g., A = Actionable, R = Relevant). In this study, the instructions for the goal-setting exercise emphasized creating Achievable and Realistic goals because we wanted to ensure that subjects would generate goals that could be achieved in the allotted three-week time window.

2 For full disclosure, we additionally administered the following individual differences measures: (a) working memory capacity (the reading span and letter-rotation span tasks), assessed in Session 1; (b) two other subscales of perfectionism (the Discrepancy and Order subscales of the Almost Perfect Scale), also assessed in Session 1; (c) measures of nonacademic procrastination and impulsivity, assessed in Session 2; and (d) subjects’ perceptions of the effectiveness of each of their implementation intentions, also assessed in Session 2. These measures will not be reported here for the following reasons: (a) Working memory measures were not correlated with any key outcome measures; (b) only the Personal Standards subscale is typically discussed in procrastination research and seems most directly relevant to the second aim of the study; (c) there was no reason to expect any changes in the trait-level everyday procrastination and impulsivity after the intervention; and, finally, (d) ratings for the perceived effectiveness of implementation intentions were completed by only those subjects assigned to the implementation-intention conditions and did not allow any meaningful statistical comparisons (subjects, however, reported that they found forming implementation intentions generally useful, M = 3.75 on a 1–5 scale, SD = 1.28).

3 The two questions included in our analyses were: To what degree do you procrastinate on this task? and To what degree is procrastination a problem for you? A third PASS question (To what extent do you want to decrease your tendency to procrastinate?) was excluded because it was about subjects’ desire to reduce procrastination, rather than their actual levels of procrastination. The results remained the same, however, even if the third item was also included in the analyses.

4 The results of the regression models involving goal accomplishment and resisting temptations remained the same even when postintervention levels of academic procrastination are substituted for baseline levels of procrastination. This result is not surprising, given that the baseline and postintervention levels of procrastination correlated substantially ( r = .60).

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McGraw Center for Teaching and Learning

Understanding and overcoming procrastination.

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

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

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

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

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

Awareness: The First Step

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

Time Management Techniques: One Piece of the Puzzle

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

Motivation: Finding Productive Reasons for Engaging in Tasks

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

Staying Motivated: Be Active to be Engaged

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

Summary of Tips for Overcoming Procrastination

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

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

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

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

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

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

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

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

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

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

Chronic Procrastination Among Iranians: Prevalence Estimation, Latent Profile and Network Analyses

Original Paper
Published: 26 June 2024

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Mehdi Akbari 1 ,
Mohammad Seydavi 1 ,
Kianoush Zahrakar 2 ,
Joseph R. Ferrari 3 &
Mark D. Griffiths 4

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Procrastination is the deliberate, unjustified postponing of an intended course of action despite its costs or unfavorable effects. The present study used a self-report online survey and collected data from a large convenience sample of the general adult population ( N = 2,076; females = 55.73%; M age = 35.1 years [SD ± 12.7]) with diverse demographics. Following the ring-curve distribution, the results indicated a 15.4% prevalence rate of procrastination among the Iranian community, which was significantly higher among women and divorced individuals and lower among nomadic individuals and those with higher academic degrees. A latent profile analysis demonstrated two distinct profiles, one for procrastinators (high scores on chronic procrastination, psychological distress, neuroticism, and extraversion; and low scores on general self-efficacy, self-esteem, satisfaction with life, openness, agreeableness, and conscientiousness) and one for non-procrastinators (demonstrating a reverse pattern compared to procrastinators). Moreover, additional network analysis suggested that the examined networks were invariant across procrastination status and gender. The results indicate that procrastination differs by demographic characteristics and is associated with a unique psychological profile. However, none of the aforementioned key study variables were considered a potential vulnerability for procrastinators due to the finding that all variables were peripheral and none were central in the examined networks. Therefore, relying on the differences in mean scores on psychometric scales does not appear to be an optimal way of determining the most important variables in a therapeutic context when treating procrastination.

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Mehdi Akbari: Conceptualization, Supervision, Methodology, Validation, Data Curation, Writing - Review & Editing; Mohammad Seydavi: Methodology, Formal analysis, Writing - Original Draft, Review & Editing; Kianoush Zahrakar: Investigation, Data Curation, and Writing - Original Draft; Joseph R. Ferrari: Supervision, Validation, Writing - Review & Editing; Mark D. Griffiths: Validation, Writing - Review & Editing.

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Akbari, M., Seydavi, M., Zahrakar, K. et al. Chronic Procrastination Among Iranians: Prevalence Estimation, Latent Profile and Network Analyses. Psychiatr Q (2024). https://doi.org/10.1007/s11126-024-10076-9

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What Research Has Been Conducted on Procrastination? Evidence From a
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 ...
(PDF) PROCRASTINATION
Introduction "Procrastination ... (a term-paper outline, a term paper, and a research questionnaire) were recorded and correlated with measures of indecision, irrational beliefs, and self-esteem ...
Procrastination and Stress: A Conceptual Review of Why Context Matters
Procrastination is moderately and positively associated with perceived stress in samples of adolescents , university students [6,9,12,16,22], community-dwelling adults [5,8,23], and individuals with hypertension and cardiovascular disease . This research is primarily cross-sectional, making it difficult to confirm the direction of influence.
Understanding procrastination: A case of a study skills course
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.
Understanding procrastination: A motivational approach
Introduction. Procrastination is an enigmatic phenomenon. Why do individuals act against their good intentions (i.e., not doing what they intended to do)? In the present paper we argue that much of the mysterious character of procrastination vanishes when motivational rather than volitional construals such as self-regulatory failure are considered.
Understanding The Factors Influencing Academic Procrastination: A
Introduction: Academic procrastination, the act of delaying or postponing academic tasks, is a widespread and persistent challenge faced by students in educational settings worldwide.
Frontiers
Introduction. In academia, procrastination is a well-known, almost commonplace phenomenon. Students often delay tasks and activities inherent to learning and studying, despite knowing that they will be worse off because of the delay (cf. Steel, 2007; Steel and Klingsieck, 2016).For some students, academic procrastination can be specific to a situation (i.e., state procrastination), for others ...
Interventions to reduce academic procrastination: A systematic review
Motivation is present in several studies, such as A23, where the treatment content was based on research on procrastination and interventions, including goal-setting techniques, motivation, and behavioral activation. Study A03 focuses on arousing students' motivation by introducing technologies to help reduce procrastination.
A neuro-computational account of procrastination behavior
Procrastination would stem from present costs (if the task is done now) being perceived in a much more vivid manner than distant costs (if the task is done later) 1. Thus, in this model, the ...
Understanding academic procrastination: A Longitudinal analysis of
The research presented in this paper examined the relationships between academic procrastination and learning-specific emotions, and how these variables predict one another over time among undergraduate (n = 354) and graduate students (n = 816).Beyond findings showing expected valences of relations between procrastination and positive emotions (enjoyment, hope, and pride) and negative emotions ...
On the Behavioral Side of Procrastination: Exploring Behavioral Delay
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 ...
Frontiers
How Study Environments Foster Academic Procrastination: Overview and Recommendations. Frode Svartdal 1* Tove I. Dahl 1 Thor Gamst-Klaussen 1 Markus Koppenborg 2 Katrin B. Klingsieck 3. 1 Department of Psychology, UiT The Arctic University of Norway, Tromsø, Norway. 2 Evaluation of Studies and Teaching and Higher Education Research, University ...
What Research Has Been Conducted on Procrastination? Evidence From a
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 ...
Frontiers
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 ...
Study Habits and Procrastination: The Role of Academic Self-Efficacy
The potential role of study self-efficacy was not examined in the Kim and Seo paper, but other research (e.g., Balkis, Citation 2011) has demonstrated study self-efficacy as a moderator variable. The results of the present paper indicate that study self-efficacy should receive increased attention as a moderator or mediator variable in studies ...
PDF Academic procrastination and the performance of graduate-level ...
Keywords: Cooperative learning, academic procrastination, graduate student, research methodology course I. Introduction. The goal of the present inquiry was to contribute to a program of research assessing the role of group dynamics on academic performance of graduate students by examining the potential
(PDF) Procrastination
Academic procrastination (AP) is "the voluntarily delay of an intended course of study-related action despite expecting to be worse off for the delay" (Steel & Klingsieck, 2016, p. 37). AP is ...
Academic Procrastination in Children and Adolescents: A Scoping Review
1. Introduction. Procrastination is a very common and pervasive behavior in different areas of human activity. It involves the intentional delay of actions and behaviors that have a time limit within which they should be completed [1,2].There is a tendency to assume that everyone procrastinates or delays some necessary activity (e.g., medical appointments, exercise, paying fines, going to bed ...
Procrastination in Academic Settings: General Introduction.
Abstract. This introductory chapter introduces general guidelines for therapeutic intervention derived from procrastination research. It thus sets the stage for the presentation of intervention methods in the chapters to follow. This chapter begins with a definition of procrastination that draws a clear distinction between dilatory behavior and ...
Temporal discounting predicts procrastination in the real world
Procrastination in the real world. (A) Examples of time courses of work progress, with blue triangles marking the Mean Unit Completion Day (MUCD).Top: a low procrastinator who started on the first ...
Academic Procrastination and Goal Accomplishment: A Combined
1. Introduction. Academic procrastination—the voluntarily delay of action on academic tasks despite expecting to be worse off for that delay—is so pervasive that, according to some estimates, 50-80% of college students procrastinate moderately or severely (Day, Mensink, & O'Sullivan, 2000; Gallagher, Golin, & Kelleher, 1992).Moreover, almost all students who procrastinate report the ...
(PDF) Academic procrastination and academic performance: An initial
Academic procrastination is a prevalent phenomenon with a. range of negative outcomes. Many studies focused on causes. and correlates of academic procrastination; however, the study. of ...
Understanding and Overcoming Procrastination
Classroom Resources for Addressing Procrastination, by Dominic J. Voge Source: Research and Teaching in Developmental Education excerpted from Vol. 23, No. 2 (Spring 2007), pp. 88-96 Why do so many people procrastinate and how do you overcome it? For most people procrastination, irrespective of what they say, is NOT about being lazy. In fact, wh...
Chronic Procrastination Among Iranians: Prevalence Estimation, Latent
Procrastination is the deliberate, unjustified postponing of an intended course of action despite its costs or unfavorable effects. The present study used a self-report online survey and collected data from a large convenience sample of the general adult population (N = 2,076; females = 55.73%; Mage = 35.1 years [SD ± 12.7]) with diverse demographics. Following the ring-curve distribution ...
(PDF) Academic Procrastination
So, in conclusion, procrastination reduces th e. quality of academic work while increasing st ress (Schraw et al., 2007). The majority of these research projects are ab out the individual factors ...

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

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SYSTEMATIC REVIEW article

Introduction

Data and Methodology

Panoramic Overview of Procrastination Research

Keyword Co-occurrence Network: Core Contents

Main Research Cluster: Core Theme and Hot Topics

Intellectual Structure of Procrastination Research

Betweenness Centrality Analysis

Definition and Classification of Procrastination

Procrastination Behavior in a Temporal Context

Causes of and Interventions for Procrastination

Burst Detection Analysis

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

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

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

Structure Variation Analysis

Further Investigations Into Academic Procrastination

Relationships Between Procrastination and Diverse Personality Traits

Procrastination in Different Life-Domains and Settings

Interventions to Procrastination

Discussion and Conclusion

Conclusions and Limitations

Data Availability Statement

Author Contributions

Conflict of Interest

Publisher's Note

Supplementary Material

Temporal discounting predicts procrastination in the real world

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A neuro-computational account of procrastination behavior

Temporal discounting correlates with directed exploration but not with random exploration

Rationally inattentive intertemporal choice

Participants

Characterizing individual variability in procrastination

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

No evidence of a correlation between risk attitude and behavioral procrastination

Self-reports of procrastination behavior

Participant inclusion

Delay discounting task

Risky-choice task

Incentive compatibility

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

Data and code availability

Acknowledgements

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Academic Procrastination and Goal Accomplishment: A Combined Experimental and Individual Differences Investigation

1. Introduction

1.1. Goal-Related Interventions for Procrastination

1.2. Procrastination and Goal Accomplishment

1.3. The Current Study

2.1. Subjects

2.2. Design and Procedure

2.2.1. Session 1: Individual differences measures

2.2.2. Session 1: Goal-setting exercise

2.2.3. Session 1: Temptation identification exercise

2.2.4. Session 1: Final phase

2.2.5. Session 2

2.3. Coding Criteria and Procedures for Rater Judgments

3.1. Descriptive Statistics and Session 1 Individual Differences

3.1.1. Manipulation checks

3.2. Aim 1: Effects of the Intervention Exercises

3.2.1. Basic effects of the interventions

3.2.2. Exploratory follow-up analyses

3.2.2.1. Moderation analyses.

3.2.2.2. Academic domains chosen in Session 1.