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Academic procrastination: The pattern and correlates of behavioural postponement
Personality and Individual Differences 40 (2006) 1519–1530 www.elsevier.com/locate/paid
Academic procrastination: The pattern and correlates of behavioural postponement Andrew J. Howell a
a,*
, David C. Watson a, Russell A. Powell a, Karen Buro
b
Department of Psychology, Grant MacEwan College, P.O. Box 1796, Edmonton, Alberta, Canada T5J 2P2 b Department of Mathematics and Statistics, Grant MacEwan College, P.O. Box 1796, Edmonton, Alberta, Canada T5J 2P2 Received 5 July 2005; accepted 16 November 2005 Available online 29 March 2006
Abstract Using a series of computer-based assignments, we examined whether students’ submission patterns revealed a hyperbolic pattern of temporal discounting, such that few assignments are submitted far ahead of the deadline and submission of assignments accelerates at an increasing rate as the deadline becomes imminent. We further examined whether variables related to self-regulation – namely, self-reported procrastination, implementation intentions, say-do correspondence, and perceived academic control – correlated with behavioural postponement. Results revealed strong behavioural evidence of temporal discounting, especially among those who identified themselves as procrastinators. Among the self-regulation measures, only say-do correspondence consistently correlated with procrastination. Ó 2006 Elsevier Ltd. All rights reserved. Keywords: Procrastination; Temporal discounting; Self-regulation; Academic performance
1. Introduction Procrastination has been defined as the tendency to delay initiation or completion of important tasks (Lay, 1986) or to delay tasks to the point of discomfort (Solomon & Rothblum, 1984). As *
Corresponding author. Tel.: +1 780 497 5329; fax: +1 780 497 5308. E-mail address: [email protected] (A.J. Howell).
0191-8869/$ - see front matter Ó 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.paid.2005.11.023
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such, a number of procrastination studies have assessed behavioural postponement by recording the date on which a term paper is handed in (Tice & Baumeister, 1997), the date on which questionnaires are returned to the experimenter (Lay, 1986), the timing of quiz completion (Moon & Illingworth, 2005; Solomon & Rothblum, 1984; Steel, Brothen, & Wambach, 2001), and the timing of laboratory task initiation and completion (Senecal, Lavoie, & Koestner, 1997). Few studies have described the dynamic, unfolding nature of procrastination across time (Moon & Illingworth, 2005). One exception is the research of Schouwenburg and colleagues (Dewitte & Schouwenburg, 2002; Schouwenburg & Groenewoud, 2001), who characterized procrastination as involving temporal discounting, or the tendency to discount the value of delayed rewards. For example, studying for an exam may be postponed while the rewards of such behaviour (such as attainment of a good mark and/or avoidance of a poor mark) are distant, and studying may increase in occurrence when rewards become more immediate. To test this hypothesis, Schouwenburg and Groenewoud (2001) measured students’ estimates of the number of hours they would typically study during each week of a 12-week period leading up to an examination. The relationship between number of weeks prior to the final examination and number of hours that students reported they would study followed a hyperbolic pattern, in which studying rarely occurs far in advance of the examination, and then increases in an accelerating fashion as the examination approaches. Similar findings emerged for students’ judgements of the importance of the examination and their ability to resist social temptation. Moreover, Schouwenburg and Groenewoud (2001) showed that those scoring highest on a measure of self-reported procrastination showed the greatest degree of temporal discounting of study behaviour. Converging results emerged for students reporting the actual number of hours they studied at the end of each week across an 11-week term (Dewitte & Schouwenburg, 2002). A limitation of the above research is that postponement was not assessed with a behavioural measure. A major aim in the current research was to examine whether a hyperbolic pattern of postponement emerges for submission of a series of computer-based course assignments. Computerized environments permit submission of assignments at any time during the assignment completion interval, and can provide an objective indication of postponement across a large number of students (Steel et al., 2001). Because students cannot receive credit for their assignment (and for completing it on time) until it is submitted, the act of assignment submission is a critical aspect of attaining the reward associated with assignment completion. Moreover, while it is possible that students may procrastinate only on the act of submitting the assignment, correlations of submission times with self-reported procrastination can affirm validity of the behavioural measure. 1.1. The ‘‘nomological network’’ of procrastination The hyperbolic pattern of postponement can be conceptualized as a failure of self-regulation, such that procrastinators relative to non-procrastinators have a reduced ability to resist social temptations when the benefits of academic preparation are distant (Dewitte & Schouwenburg, 2002; Schouwenburg & Groenewoud, 2001). Given the characterization of procrastination as self-regulation failure (Ariely & Wertenbroch, 2002; Blunt & Pychyl, 2005; Dewitte & Lens, 2000a; Ferrari, 2001; Ferrari & Emmons, 1995; Milgram, Sroloff, & Rosenbaum, 1988; Senecal, Koestner, & Vallerand, 1995; Senecal et al., 1997; Tuckman, 1991; van Eerde, 2000; Wolters,
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2003), a second aim of the current research was to extend into the domain of self-regulation the ‘‘nomological network’’ of procrastination described by van Eerde (2003). Using meta-analysis, van Eerde (2003) summarized the association between procrastination and several domains of functioning, including ability, personality, self-image, motives, affective states, and performance. While several of the variables considered by van Eerde (2003) are relevant to self-regulation (e.g., self-efficacy, affect), self-regulation was not explicitly examined in the meta-analysis, and additional variables closely associated with self-regulatory processes remain unexplored. We therefore sought to examine the relationship between procrastination and implementation intentions, saydo correspondence, and perceived control. An implementation intention (or plan) involves determining the when, where and how of one’s behaviour in relation to accomplishing a personal goal (Gollwitzer, Fujita, & Oettingen, 2004). For example, students intending to work on an assignment may benefit by establishing ahead of time specifically when they will do it, where they will do it, and how they will do it. The use of implementation intentions has been shown to enhance academic functioning (e.g., Oettingen, Honig, & Gollwitzer, 2000). Relatedly, some research (e.g., Dewitte & Lens, 2000b; Dewitte & Schouwenburg, 2002; Steel et al., 2001) has shown that procrastination is related to gaps between intentions and actions (i.e., which might be bridged via implementation intentions). However, no study has examined direct relationships between implementation intentions, procrastination, and behavioural postponement. In addition to planning the explicit steps toward meeting an important goal in the form of implementation intentions, it is important to then follow our intentions or plans. Say-do correspondence refers to the extent to which individuals do what they say they will do or carry out promises they have made (Risley & Hart, 1968). Say-do correspondence has been theorized to be a critical process in the development of self-regulation (Risley, 1977), and training in say-do correspondence has been found to improve academic performance (Anderson & Merrett, 1997). No research has examined the extent to which say-do correspondence is related to procrastination, behavioural postponement, and implementation intentions. Finally, procrastination may be associated with self-regulatory beliefs concerning the extent to which one exerts influence over important outcomes in one’s life. Perceived academic control reflects students’ beliefs about whether factors within themselves or outside of themselves determine academic success (Perry, Hladkyj, Pekrun, & Pelletier, 2001). Believing that one has control over one’s academic success (e.g., believing that studying increases exam performance) should facilitate the exertion of actual control over events related to such success (e.g., the initiation of exam preparation). Mixed findings have emerged when perceived control has been related to procrastination. For example, Dewitte and Schouwenburg (2002), Ferrari, Parker, and Ware (1992) and Steel et al. (2001) found no relationship between locus-of-control and procrastination, whereas Carden, Bryant, and Moss (2004) reported that procrastinating students, relative to non-procrastinating students, had an external locus-of-control. No study to date has demonstrated a relationship between perceived academic control and behavioural postponement. 1.2. The current study We predicted that students would reveal a pattern of temporal discounting on a series of computer-based course assignments, such that few assignments would be submitted far ahead of the
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deadline and, as the deadline approaches, submission of assignments would accelerate at an increasing rate. We further predicted that this pattern would especially characterize those who describe themselves as procrastinators. We also predicted that implementation intentions, say-do correspondence, and perceived academic control would correlate positively with each other and negatively with both self-reported procrastination and behavioural postponement.
2. Method 2.1. Participants Participants were 95 introductory psychology students at a university-studies college, of which 70 were female and 24 were male; 1 participant did not specify his or her sex. Ages ranged from 17 to 45 years (M = 19.75, SD = 4.00). 2.2. Materials On the 12-item Procrastination Assessment Scale – Students (PASS; Solomon & Rothblum, 1984), students rated the extent to which they procrastinate in six academic areas and the extent to which procrastination in these areas is a problem for them. Ratings are made on five-point scales with endpoints labeled 1 (never procrastinate) and 5 (always procrastinate) for the prevalence items and 1 (not at all a problem) and 5 (always a problem) for the perceived problem items. Responses are summed across the 12 items to yield a total score, ranging from 12 to 60, with higher scores indicating greater procrastination. Ferrari (1989) reported good internal consistency and test–retest reliability for the PASS. Solomon and Rothblum (1984) demonstrated significant correlations between PASS scores and a behavioural measure of procrastination. The 16-item Procrastination Scale (Tuckman, 1991) measures the tendency to delay task initiation or completion, as well as tendencies toward indecisiveness and poor time management in the completion of tasks. Items are rated on 4-point scales with endpoints labeled 1 (that’s me for sure) and 4 (that’s not me for sure). In producing total scores, we reversed the rating scale, so that higher scores indicated greater procrastination. Total scores are created by summing across the 16 items and thus range from 16 to 64. Tuckman (1991) demonstrated the internal consistency of the Procrastination Scale and reported significant associations between scale scores and a behavioural measure of procrastination. The eight-item Perceived Academic Control measure (Perry et al., 2001) assesses perceived control over academic success. Ratings are made using 5-point scales with endpoints 1 (strongly disagree) and 5 (strongly agree). Items are summed to yield a total score, ranging from 8 to 40, with higher scores denoting higher perceived control. Perry et al. (2001) demonstrated the scale’s reliability and reported a significant positive association with course performance and a significant negative association with course anxiety. We devised three items to assess students’ self-reported assignment procrastination: Students rated ‘‘to what extent, in general, did you submit your WebCT assignments in this course earlier than you intended?’’ and two similar items in which ‘‘later’’ and ‘‘at the point in time’’ were substi-
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tuted for the word ‘‘earlier’’. We also devised three items to assess implementation intentions: Students rated ‘‘to what extent do your study plans include the times at which you intend to study each day?’’ and two similar items substituting ‘‘the topic that’’ and ‘‘where’’ for the phrase ‘‘the times at which’’. Finally, we devised three items to assess say-do correspondence: Students rated ‘‘to what extent do you keep your promises to yourself even if later on you don’t feel like doing what you had promised yourself to do?’’; ‘‘to what extent do you keep your promises to yourself even when other people are unaware of your promises and only you know about them?’’; and ‘‘to what extent do you use the act of making a promise to yourself to accomplish certain tasks?’’. All of the above items employed 7-point rating scales with endpoints labeled 1 (never) and 7 (always). For each of the three indices, we created overall scores by summing across the three items composing the measure, such that total scores on each measure had a range of 3– 21. For self-reported assignment procrastination, higher scores denote more procrastination (i.e., the first and third items listed above were reverse-scored before summing). For implementation intentions and say-do correspondence, higher scores denote more intentionality and greater say-do correspondence, respectively. 2.3. Procedure Late in the academic term (i.e., after their assignments had been completed), students of the first author were invited to participate in research documenting the submission of course assignments in relation to the deadline for those assignments. They were also invited to complete questionnaires related to their procrastination. They gave their consent for researchers to obtain their grade in introductory psychology as well as their submission times and dates from a series of seven chapter assignments due at midnight on seven Mondays during the 15-week academic term. The assignments together comprised 7.5% of students’ course grades. For each assignment, students completed a subset of learning objectives for the chapter and commented critically upon them. These assignments were completed outside of class time and were submitted into a WebCT discussion group consisting of approximately 10 students. WebCT is web-based course software that records the exact time and date of assignment submissions. For each student, we recorded the time prior to the midnight deadline that each assignment was submitted, with assignments submitted past the deadline recorded as ‘‘0’’.
3. Results 3.1. Pattern of assignment submission We summed frequency data for assignment submissions falling into 5-h intervals ranging from 0 h (i.e., the deadline) to 100 h (i.e., about 4 days prior to the deadline). Fig. 1 conveys the pattern of submission for the seven assignments. The data reveal that a large number of students made submissions close to the submission deadline. These findings were consistent across the seven assignments. The data also suggest an increase in submissions about a day prior to the deadline.
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A1 A2 A3 A4 A5 A6 A7
Frequency
50 40 30 20 10 0 0
10
20 30 40 50 60 70 80 90 100 Hours Prior to Submission Deadline
Fig. 1. Frequency polygon of the number of students submitting each of seven assignments (denoted A1 through A7) as a function of hours prior to the submission deadline.
Fig. 2 displays the assignment submission data averaged across the seven assignments. It also shows a curve depicting the best-fitting hyperbolic function, in the form given by Schouwenburg and Groenewoud (2001): y ¼ c=ð1 þ kxÞ þ constant in which ‘‘y’’ is the actual number of students submitting their assignments, ‘‘c’’ is the number of submissions when x = 0, ‘‘k’’ is the rate of acceleration of ‘‘c’’, and ‘‘x’’ is number of hours prior to the submission deadline. A nonlinear least-squares analysis yielded estimates of 24.96 for c, .09 for k, and 2.37 for the constant. The resulting function, depicted in Fig. 2, accounted for 89.1% of the variance. The value of k is significantly different from zero, t(18) = 3.30, p < .01. Thus, assignment submissions follow a hyperbolic pattern, with the number of submissions increasing in a positively accelerated fashion as the deadline becomes imminent. Fig. 3 displays the assignment submission data for participants who scored above and below the median (11.0) on the self-reported assignment procrastination measure. We reasoned that students who acknowledged that they had procrastinated on their assignments should evidence more procrastination on the behavioural measure than students who reported that they did not engage
25 Actual
Frequency
20
Predicted
15 10 5 0 0
10
20 30 40 50 60 70 80 90 100 Hours Prior to Submission Deadline
Fig. 2. Best-fitting curve for the number of students submitting assignments as a function of hours prior to the submission deadline, averaged across the seven assignments.
Frequency
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Low Self-Reported Procrastination High Self-Reported Procrastination
0
10 20 30 40 50 60 70 80 90 100 Hours Prior to Submission Deadline
Fig. 3. Frequency polygons of the number of students in the high and low self-reported procrastination categories submitting assignments as a function of hours prior to the submission deadline, averaged across the seven assignments.
in procrastination. Among those high in self-reported procrastination, the hyperbolic function accounted for 93.8% of variation in the frequency of assignment submissions, with the slope significantly different from zero, k = .14, t(18) = 4.70, p < .001. Among those low in self-reported procrastination, the hyperbolic function still accounted for 69.2% of the variation, even though the slope did not differ significantly from zero, k = .02. 3.2. Descriptive statistics and inter-correlations among measures Means, standard deviations, and alpha coefficients for all measures are reported in Table 1. Age was not a correlate of any of the variables listed in Table 1. No gender differences emerged on the variables listed in Table 1 with the exception of implementation intentions, on which women (M = 13.66, SD = 4.04) scored higher than men (M = 11.50, SD = 4.79), t(92) = 2.16, p < .05.
Table 1 Means, standard deviations, coefficients alpha, and inter-correlations for all measures Measure
M
SD
a
Intercorrelations 2
1. Procrastination Assessment Scale – Students 2. Procrastination scale 3. Self-reported assignment procrastination 4. Average assignment submission times (in hours) 5. Implementation intentions 6. Say-do correspondence 7. Perceived academic control 8. Grade * **
p < .05. p < .001.
34.30
5.71
.75
.62
40.75 11.94
8.00 3.93
.91 .74
–
21.74
21.12
.65
13.10 13.52 34.64 74.48
4.30 3.19 4.62 11.34
.67 .67 .74 n/a
3 **
.30
4 *
.51** –
.24
5 *
6 *
7
8
.09
.05
.13 .02
.14 .25*
.17
.29
.38** .45**
.25* .19
.57** .37**
–
.06
.04
.04
.19
–
.36** –
.03 .01 –
.14 .12 .28* –
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We calculated Pearson correlations among all measures. Scores on the three self-report measures of procrastination correlated significantly with each other. They also correlated with the behavioural measures of postponement, such that greater self-reported procrastination was associated with greater delay in submitting assignments. The three self-report measures of procrastination were inversely correlated with the measure of say-do correspondence, such that greater self-reported procrastination was associated with a reduced tendency to carry out verbal promises to one self. There was also a significant inverse correlation between Procrastination Scale scores and implementation intentions. Implementation intentions also correlated with say-do correspondence, such that a tendency to specify when, where and how one intended to study was associated with a tendency to keep verbal promises. A significant inverse correlation emerged between selfreported assignment procrastination and grades whereas Perceived Academic Control was positively associated with grades.
4. Discussion Our aims in this research were twofold: to investigate a behavioural measure of postponement and to further explore the ‘‘nomological network’’ (van Eerde, 2003) of procrastination. 4.1. Assignment submissions Our behavioural findings reveal a tendency to postpone submission of assignments until the hours immediately prior to the submission deadline. Moreover, the findings show an increasing rate of assignment submissions as the deadline becomes imminent. These results provide evidence of a hyperbolic pattern of postponement using an objective behavioural measure, thereby extending the findings of Schouwenburg and Groenewoud (2001) and Dewitte and Schouwenburg (2002). The consistency of the findings across a large proportion of students also concurs with Schouwenburg and Groenewoud’s (2001) suggestion that ‘‘. . . a certain amount of procrastination belongs to ‘normal’ behaviour’’ (p. 238). The tendency to postpone assignment submissions was most pronounced among students who reported that they procrastinated on their assignment submissions. Indeed, the behavioural measure of postponement consistently correlated with self-report measures of procrastination, suggesting that assignment submission tendencies reflected delays in the doing, and not just in the submission, of assignments. The behavioural measure did not correlate with grades. In the current context, course performance may have been generally insensitive to postponement tendencies, consistent with the fact that the self-report measures of procrastination also failed to correlate with performance. The lack of association between the behavioural measure of postponement and course performance may also reflect the relative specificity of the behavioural measure in contrast to the more generalized performance assessment. Indeed, others have failed to reveal an association between a broad performance criterion and a narrowly defined behavioural index of procrastination (Lay, 1986). Where previous studies have shown a relationship between behavioural procrastination and performance, either narrow definitions of both performance and procrastination were employed (i.e., the same task was used to generate both scores; Moon & Illingworth, 2005) or the two constructs were not measured independently (Steel et al., 2001).
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4.2. The nomological network of procrastination Our findings revealed some informative associations between procrastination and other individual difference measures. First, the three self-report procrastination measures correlated significantly with each other and with the behavioural measure of postponement. Second, the three measures of self-reported procrastination correlated significantly with say-do correspondence. This suggests that those who report a tendency to procrastinate also have a generalized tendency not to do what they said they will do. The consistent emergence of this correlation across the three self-report measures of procrastination is noteworthy for two reasons: First, say-do correspondence items were not concerned with academic tasks, but rather more generally with keeping, and benefiting from, promises made to oneself. Second, the generalized nature of this measure suggests overlap with personality dimensions, particularly the five-factor dimension of conscientiousness (i.e., that dimension emphasizing responsibility and dependability). It is noteworthy that conscientiousness is the personality dimension most strongly associated with procrastination (van Eerde, 2003) and the self-discipline facet of conscientiousness (emphasizing self-regulatory processes related to say-do correspondence) is the facet most closely associated with procrastination (Watson, 2001). Finally, our findings revealed that say-do correspondence did not correlate with the behavioural measure of postponement, perhaps because some students promised to themselves (quite reasonably) that they would complete the assignment within the final hours of the deadline and subsequently did so. This tendency toward planned postponement, or ‘‘pseudo-procrastination’’, would detract from an overall association between behavioural postponement and say-do correspondence. Say-do correspondence correlated significantly with implementation intentions, such that those who reported a general tendency to do as they say also reported a tendency to make use of implementation intentions to facilitate studying. Implementation intentions, however, were not consistently related to procrastination. Combining the findings concerning implementation intentions and say-do correspondence, it appears that procrastination may involve deficits in the latter more than in the former. Moreover, the relationship between say-do correspondence and implementation intentions may be complex. For example, students with high say-do correspondence might be more capable of utilizing implementation intentions, but might also be capable of fulfilling less specific study plans (‘‘I will study a lot today’’), such that implementation intentions are not always required. A more in-depth investigation of say-do correspondence and its relationship to implementation intentions and procrastination seems warranted. Finally, perceived academic control correlated with course grades (and did so at a magnitude virtually identical to that reported by Perry et al., 2001) and these two variables showed no consistent relationship with procrastination or the remaining self-regulation variables. The lack of association between perceived control and procrastination may reflect the fact that ‘‘control over one’s own academic success’’ can take many forms including, perhaps, the deliberate allocation of time for the completion and submission of assignments in the final hours leading-up to task deadline. It may also mean, however, that believing that one exerts significant influence over one’s own academic success is not a safeguard against procrastination. Despite perceiving themselves as responsible for their own academic successes, students high in perceived control may nonetheless experience difficulty resisting the temptation of more immediately reinforcing activities. Finally, the correlation between perceived academic control and grades may partially reflect a retrospective
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assessment of control based on performance; that is, ‘‘I did well (poorly) in the course, therefore, I must be (must not be) in control’’. 4.3. Limitations and future directions The representativeness of our student sample was limited. Participants were first-year undergraduates. It may be that the postponement pattern observed would not generalize to more experienced students. The meta-analysis of van Eerde (2003), for example, revealed a small but significant inverse correlation between age and procrastination. Most of the participants were female, such that the overall findings are more representative of women’s procrastination tendencies than men’s. Interestingly, the one gender difference that did emerge – women scored higher than men on implementation intentions – has been reported previously (Dewitte & Lens, 2000a). All students were enrolled in an introductory psychology course, again limiting the representativeness of the sample. Despite these limitations, our participants did resemble previous samples on measures of self-reported procrastination and perceived academic control. The current study is also characterized by measurement limitations. We created measures of say-do correspondence and implementation intentions which were internally consistent and face valid, and which correlated significantly with each other, but evidence of their construct validity is lacking. Students’ grades in their introductory psychology course were used as an index of their academic performance. This may not be highly reflective of their overall academic performance, and grade point averages are stronger correlates of procrastination than are grades in a single course (van Eerde, 2003). Finally, our behavioural measure of postponement was derived from students’ submission of assignments into an online discussion group composed of about 10 students per group. Students could see the time at which fellow members submitted their assignments and the content of the submissions made by their peers. On the one hand, the social nature of the assignment might reduce postponement tendencies by providing pressure to demonstrate one’s academic competency by submitting assignments ahead of the deadline. On the other hand, both informational and normative social influence processes may promote postponement: Informational social influence may encourage delay if students are motivated to glean further information about the appropriate manner in which to complete the assignment by examining the content of peers’ submitted assignments, while normative social influence may encourage postponement if students are motivated to submit assignments at a time similar to peers’ submissions. However, the fact that Schouwenburg and Groenewoud (2001) and Dewitte and Schouwenburg (2002) demonstrated postponement tendencies resembling our findings for more typical academic tasks suggests that social influences were not the only determinant of students’ tendencies to delay. van Eerde (2003) recently appealed for further research concerning social influences on procrastination, including the influence of groups on students’ procrastination. Our technology-based behavioural measure of procrastination lends itself to field experimentation, in which characteristics of the assignment, including its social nature, can be manipulated within an otherwise typical course. For example, the WebCT program allows submissions made to discussion groups to remain anonymous to fellow group members. Manipulating anonymity of submissions may influence rates of procrastination. Other manipulable characteristics of the assignment, such as the degree to which an assignment requires cooperation among students, may also affect postpone-
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Solomon, L. J., & Rothblum, E. D. (1984). Academic procrastination: frequency and cognitive-behavioural correlates. Journal of Counseling Psychology, 31, 504–510. Steel, P., Brothen, T., & Wambach, C. (2001). Procrastination and personality, performance, and mood. Personality and Individual Differences, 30, 95–106. Tice, D. M., & Baumeister, R. (1997). Longitudinal study of procrastination, performance, stress, and health: the costs and benefits of dawdling. Psychological Science, 8, 454–458. Tuckman, B. W. (1991). The development and concurrent validity of the Procrastination Scale. Educational and Psychological Measurement, 5, 473–480. van Eerde, W. (2000). Procrastination: self-regulation in initiating aversive goals. Applied Psychology: An International Review, 49, 372–389. van Eerde, W. (2003). A meta-analytically derived nomological network of procrastination. Personality and Individual Differences, 35, 1401–1418. Watson, D. C. (2001). Academic procrastination: a five-factor model facet level analysis. Personality and Individual Differences, 30, 149–158. Wolters, C. A. (2003). Understanding procrastination from a self-regulated learning perspective. Journal of Educational Psychology, 95, 179–187.
Academic procrastination: The pattern and correlates of behavioural postponement Andrew J. Howell a
a,*
, David C. Watson a, Russell A. Powell a, Karen Buro
b
Department of Psychology, Grant MacEwan College, P.O. Box 1796, Edmonton, Alberta, Canada T5J 2P2 b Department of Mathematics and Statistics, Grant MacEwan College, P.O. Box 1796, Edmonton, Alberta, Canada T5J 2P2 Received 5 July 2005; accepted 16 November 2005 Available online 29 March 2006
Abstract Using a series of computer-based assignments, we examined whether students’ submission patterns revealed a hyperbolic pattern of temporal discounting, such that few assignments are submitted far ahead of the deadline and submission of assignments accelerates at an increasing rate as the deadline becomes imminent. We further examined whether variables related to self-regulation – namely, self-reported procrastination, implementation intentions, say-do correspondence, and perceived academic control – correlated with behavioural postponement. Results revealed strong behavioural evidence of temporal discounting, especially among those who identified themselves as procrastinators. Among the self-regulation measures, only say-do correspondence consistently correlated with procrastination. Ó 2006 Elsevier Ltd. All rights reserved. Keywords: Procrastination; Temporal discounting; Self-regulation; Academic performance
1. Introduction Procrastination has been defined as the tendency to delay initiation or completion of important tasks (Lay, 1986) or to delay tasks to the point of discomfort (Solomon & Rothblum, 1984). As *
Corresponding author. Tel.: +1 780 497 5329; fax: +1 780 497 5308. E-mail address: [email protected] (A.J. Howell).
0191-8869/$ - see front matter Ó 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.paid.2005.11.023
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such, a number of procrastination studies have assessed behavioural postponement by recording the date on which a term paper is handed in (Tice & Baumeister, 1997), the date on which questionnaires are returned to the experimenter (Lay, 1986), the timing of quiz completion (Moon & Illingworth, 2005; Solomon & Rothblum, 1984; Steel, Brothen, & Wambach, 2001), and the timing of laboratory task initiation and completion (Senecal, Lavoie, & Koestner, 1997). Few studies have described the dynamic, unfolding nature of procrastination across time (Moon & Illingworth, 2005). One exception is the research of Schouwenburg and colleagues (Dewitte & Schouwenburg, 2002; Schouwenburg & Groenewoud, 2001), who characterized procrastination as involving temporal discounting, or the tendency to discount the value of delayed rewards. For example, studying for an exam may be postponed while the rewards of such behaviour (such as attainment of a good mark and/or avoidance of a poor mark) are distant, and studying may increase in occurrence when rewards become more immediate. To test this hypothesis, Schouwenburg and Groenewoud (2001) measured students’ estimates of the number of hours they would typically study during each week of a 12-week period leading up to an examination. The relationship between number of weeks prior to the final examination and number of hours that students reported they would study followed a hyperbolic pattern, in which studying rarely occurs far in advance of the examination, and then increases in an accelerating fashion as the examination approaches. Similar findings emerged for students’ judgements of the importance of the examination and their ability to resist social temptation. Moreover, Schouwenburg and Groenewoud (2001) showed that those scoring highest on a measure of self-reported procrastination showed the greatest degree of temporal discounting of study behaviour. Converging results emerged for students reporting the actual number of hours they studied at the end of each week across an 11-week term (Dewitte & Schouwenburg, 2002). A limitation of the above research is that postponement was not assessed with a behavioural measure. A major aim in the current research was to examine whether a hyperbolic pattern of postponement emerges for submission of a series of computer-based course assignments. Computerized environments permit submission of assignments at any time during the assignment completion interval, and can provide an objective indication of postponement across a large number of students (Steel et al., 2001). Because students cannot receive credit for their assignment (and for completing it on time) until it is submitted, the act of assignment submission is a critical aspect of attaining the reward associated with assignment completion. Moreover, while it is possible that students may procrastinate only on the act of submitting the assignment, correlations of submission times with self-reported procrastination can affirm validity of the behavioural measure. 1.1. The ‘‘nomological network’’ of procrastination The hyperbolic pattern of postponement can be conceptualized as a failure of self-regulation, such that procrastinators relative to non-procrastinators have a reduced ability to resist social temptations when the benefits of academic preparation are distant (Dewitte & Schouwenburg, 2002; Schouwenburg & Groenewoud, 2001). Given the characterization of procrastination as self-regulation failure (Ariely & Wertenbroch, 2002; Blunt & Pychyl, 2005; Dewitte & Lens, 2000a; Ferrari, 2001; Ferrari & Emmons, 1995; Milgram, Sroloff, & Rosenbaum, 1988; Senecal, Koestner, & Vallerand, 1995; Senecal et al., 1997; Tuckman, 1991; van Eerde, 2000; Wolters,
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2003), a second aim of the current research was to extend into the domain of self-regulation the ‘‘nomological network’’ of procrastination described by van Eerde (2003). Using meta-analysis, van Eerde (2003) summarized the association between procrastination and several domains of functioning, including ability, personality, self-image, motives, affective states, and performance. While several of the variables considered by van Eerde (2003) are relevant to self-regulation (e.g., self-efficacy, affect), self-regulation was not explicitly examined in the meta-analysis, and additional variables closely associated with self-regulatory processes remain unexplored. We therefore sought to examine the relationship between procrastination and implementation intentions, saydo correspondence, and perceived control. An implementation intention (or plan) involves determining the when, where and how of one’s behaviour in relation to accomplishing a personal goal (Gollwitzer, Fujita, & Oettingen, 2004). For example, students intending to work on an assignment may benefit by establishing ahead of time specifically when they will do it, where they will do it, and how they will do it. The use of implementation intentions has been shown to enhance academic functioning (e.g., Oettingen, Honig, & Gollwitzer, 2000). Relatedly, some research (e.g., Dewitte & Lens, 2000b; Dewitte & Schouwenburg, 2002; Steel et al., 2001) has shown that procrastination is related to gaps between intentions and actions (i.e., which might be bridged via implementation intentions). However, no study has examined direct relationships between implementation intentions, procrastination, and behavioural postponement. In addition to planning the explicit steps toward meeting an important goal in the form of implementation intentions, it is important to then follow our intentions or plans. Say-do correspondence refers to the extent to which individuals do what they say they will do or carry out promises they have made (Risley & Hart, 1968). Say-do correspondence has been theorized to be a critical process in the development of self-regulation (Risley, 1977), and training in say-do correspondence has been found to improve academic performance (Anderson & Merrett, 1997). No research has examined the extent to which say-do correspondence is related to procrastination, behavioural postponement, and implementation intentions. Finally, procrastination may be associated with self-regulatory beliefs concerning the extent to which one exerts influence over important outcomes in one’s life. Perceived academic control reflects students’ beliefs about whether factors within themselves or outside of themselves determine academic success (Perry, Hladkyj, Pekrun, & Pelletier, 2001). Believing that one has control over one’s academic success (e.g., believing that studying increases exam performance) should facilitate the exertion of actual control over events related to such success (e.g., the initiation of exam preparation). Mixed findings have emerged when perceived control has been related to procrastination. For example, Dewitte and Schouwenburg (2002), Ferrari, Parker, and Ware (1992) and Steel et al. (2001) found no relationship between locus-of-control and procrastination, whereas Carden, Bryant, and Moss (2004) reported that procrastinating students, relative to non-procrastinating students, had an external locus-of-control. No study to date has demonstrated a relationship between perceived academic control and behavioural postponement. 1.2. The current study We predicted that students would reveal a pattern of temporal discounting on a series of computer-based course assignments, such that few assignments would be submitted far ahead of the
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deadline and, as the deadline approaches, submission of assignments would accelerate at an increasing rate. We further predicted that this pattern would especially characterize those who describe themselves as procrastinators. We also predicted that implementation intentions, say-do correspondence, and perceived academic control would correlate positively with each other and negatively with both self-reported procrastination and behavioural postponement.
2. Method 2.1. Participants Participants were 95 introductory psychology students at a university-studies college, of which 70 were female and 24 were male; 1 participant did not specify his or her sex. Ages ranged from 17 to 45 years (M = 19.75, SD = 4.00). 2.2. Materials On the 12-item Procrastination Assessment Scale – Students (PASS; Solomon & Rothblum, 1984), students rated the extent to which they procrastinate in six academic areas and the extent to which procrastination in these areas is a problem for them. Ratings are made on five-point scales with endpoints labeled 1 (never procrastinate) and 5 (always procrastinate) for the prevalence items and 1 (not at all a problem) and 5 (always a problem) for the perceived problem items. Responses are summed across the 12 items to yield a total score, ranging from 12 to 60, with higher scores indicating greater procrastination. Ferrari (1989) reported good internal consistency and test–retest reliability for the PASS. Solomon and Rothblum (1984) demonstrated significant correlations between PASS scores and a behavioural measure of procrastination. The 16-item Procrastination Scale (Tuckman, 1991) measures the tendency to delay task initiation or completion, as well as tendencies toward indecisiveness and poor time management in the completion of tasks. Items are rated on 4-point scales with endpoints labeled 1 (that’s me for sure) and 4 (that’s not me for sure). In producing total scores, we reversed the rating scale, so that higher scores indicated greater procrastination. Total scores are created by summing across the 16 items and thus range from 16 to 64. Tuckman (1991) demonstrated the internal consistency of the Procrastination Scale and reported significant associations between scale scores and a behavioural measure of procrastination. The eight-item Perceived Academic Control measure (Perry et al., 2001) assesses perceived control over academic success. Ratings are made using 5-point scales with endpoints 1 (strongly disagree) and 5 (strongly agree). Items are summed to yield a total score, ranging from 8 to 40, with higher scores denoting higher perceived control. Perry et al. (2001) demonstrated the scale’s reliability and reported a significant positive association with course performance and a significant negative association with course anxiety. We devised three items to assess students’ self-reported assignment procrastination: Students rated ‘‘to what extent, in general, did you submit your WebCT assignments in this course earlier than you intended?’’ and two similar items in which ‘‘later’’ and ‘‘at the point in time’’ were substi-
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tuted for the word ‘‘earlier’’. We also devised three items to assess implementation intentions: Students rated ‘‘to what extent do your study plans include the times at which you intend to study each day?’’ and two similar items substituting ‘‘the topic that’’ and ‘‘where’’ for the phrase ‘‘the times at which’’. Finally, we devised three items to assess say-do correspondence: Students rated ‘‘to what extent do you keep your promises to yourself even if later on you don’t feel like doing what you had promised yourself to do?’’; ‘‘to what extent do you keep your promises to yourself even when other people are unaware of your promises and only you know about them?’’; and ‘‘to what extent do you use the act of making a promise to yourself to accomplish certain tasks?’’. All of the above items employed 7-point rating scales with endpoints labeled 1 (never) and 7 (always). For each of the three indices, we created overall scores by summing across the three items composing the measure, such that total scores on each measure had a range of 3– 21. For self-reported assignment procrastination, higher scores denote more procrastination (i.e., the first and third items listed above were reverse-scored before summing). For implementation intentions and say-do correspondence, higher scores denote more intentionality and greater say-do correspondence, respectively. 2.3. Procedure Late in the academic term (i.e., after their assignments had been completed), students of the first author were invited to participate in research documenting the submission of course assignments in relation to the deadline for those assignments. They were also invited to complete questionnaires related to their procrastination. They gave their consent for researchers to obtain their grade in introductory psychology as well as their submission times and dates from a series of seven chapter assignments due at midnight on seven Mondays during the 15-week academic term. The assignments together comprised 7.5% of students’ course grades. For each assignment, students completed a subset of learning objectives for the chapter and commented critically upon them. These assignments were completed outside of class time and were submitted into a WebCT discussion group consisting of approximately 10 students. WebCT is web-based course software that records the exact time and date of assignment submissions. For each student, we recorded the time prior to the midnight deadline that each assignment was submitted, with assignments submitted past the deadline recorded as ‘‘0’’.
3. Results 3.1. Pattern of assignment submission We summed frequency data for assignment submissions falling into 5-h intervals ranging from 0 h (i.e., the deadline) to 100 h (i.e., about 4 days prior to the deadline). Fig. 1 conveys the pattern of submission for the seven assignments. The data reveal that a large number of students made submissions close to the submission deadline. These findings were consistent across the seven assignments. The data also suggest an increase in submissions about a day prior to the deadline.
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A1 A2 A3 A4 A5 A6 A7
Frequency
50 40 30 20 10 0 0
10
20 30 40 50 60 70 80 90 100 Hours Prior to Submission Deadline
Fig. 1. Frequency polygon of the number of students submitting each of seven assignments (denoted A1 through A7) as a function of hours prior to the submission deadline.
Fig. 2 displays the assignment submission data averaged across the seven assignments. It also shows a curve depicting the best-fitting hyperbolic function, in the form given by Schouwenburg and Groenewoud (2001): y ¼ c=ð1 þ kxÞ þ constant in which ‘‘y’’ is the actual number of students submitting their assignments, ‘‘c’’ is the number of submissions when x = 0, ‘‘k’’ is the rate of acceleration of ‘‘c’’, and ‘‘x’’ is number of hours prior to the submission deadline. A nonlinear least-squares analysis yielded estimates of 24.96 for c, .09 for k, and 2.37 for the constant. The resulting function, depicted in Fig. 2, accounted for 89.1% of the variance. The value of k is significantly different from zero, t(18) = 3.30, p < .01. Thus, assignment submissions follow a hyperbolic pattern, with the number of submissions increasing in a positively accelerated fashion as the deadline becomes imminent. Fig. 3 displays the assignment submission data for participants who scored above and below the median (11.0) on the self-reported assignment procrastination measure. We reasoned that students who acknowledged that they had procrastinated on their assignments should evidence more procrastination on the behavioural measure than students who reported that they did not engage
25 Actual
Frequency
20
Predicted
15 10 5 0 0
10
20 30 40 50 60 70 80 90 100 Hours Prior to Submission Deadline
Fig. 2. Best-fitting curve for the number of students submitting assignments as a function of hours prior to the submission deadline, averaged across the seven assignments.
Frequency
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Low Self-Reported Procrastination High Self-Reported Procrastination
0
10 20 30 40 50 60 70 80 90 100 Hours Prior to Submission Deadline
Fig. 3. Frequency polygons of the number of students in the high and low self-reported procrastination categories submitting assignments as a function of hours prior to the submission deadline, averaged across the seven assignments.
in procrastination. Among those high in self-reported procrastination, the hyperbolic function accounted for 93.8% of variation in the frequency of assignment submissions, with the slope significantly different from zero, k = .14, t(18) = 4.70, p < .001. Among those low in self-reported procrastination, the hyperbolic function still accounted for 69.2% of the variation, even though the slope did not differ significantly from zero, k = .02. 3.2. Descriptive statistics and inter-correlations among measures Means, standard deviations, and alpha coefficients for all measures are reported in Table 1. Age was not a correlate of any of the variables listed in Table 1. No gender differences emerged on the variables listed in Table 1 with the exception of implementation intentions, on which women (M = 13.66, SD = 4.04) scored higher than men (M = 11.50, SD = 4.79), t(92) = 2.16, p < .05.
Table 1 Means, standard deviations, coefficients alpha, and inter-correlations for all measures Measure
M
SD
a
Intercorrelations 2
1. Procrastination Assessment Scale – Students 2. Procrastination scale 3. Self-reported assignment procrastination 4. Average assignment submission times (in hours) 5. Implementation intentions 6. Say-do correspondence 7. Perceived academic control 8. Grade * **
p < .05. p < .001.
34.30
5.71
.75
.62
40.75 11.94
8.00 3.93
.91 .74
–
21.74
21.12
.65
13.10 13.52 34.64 74.48
4.30 3.19 4.62 11.34
.67 .67 .74 n/a
3 **
.30
4 *
.51** –
.24
5 *
6 *
7
8
.09
.05
.13 .02
.14 .25*
.17
.29
.38** .45**
.25* .19
.57** .37**
–
.06
.04
.04
.19
–
.36** –
.03 .01 –
.14 .12 .28* –
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We calculated Pearson correlations among all measures. Scores on the three self-report measures of procrastination correlated significantly with each other. They also correlated with the behavioural measures of postponement, such that greater self-reported procrastination was associated with greater delay in submitting assignments. The three self-report measures of procrastination were inversely correlated with the measure of say-do correspondence, such that greater self-reported procrastination was associated with a reduced tendency to carry out verbal promises to one self. There was also a significant inverse correlation between Procrastination Scale scores and implementation intentions. Implementation intentions also correlated with say-do correspondence, such that a tendency to specify when, where and how one intended to study was associated with a tendency to keep verbal promises. A significant inverse correlation emerged between selfreported assignment procrastination and grades whereas Perceived Academic Control was positively associated with grades.
4. Discussion Our aims in this research were twofold: to investigate a behavioural measure of postponement and to further explore the ‘‘nomological network’’ (van Eerde, 2003) of procrastination. 4.1. Assignment submissions Our behavioural findings reveal a tendency to postpone submission of assignments until the hours immediately prior to the submission deadline. Moreover, the findings show an increasing rate of assignment submissions as the deadline becomes imminent. These results provide evidence of a hyperbolic pattern of postponement using an objective behavioural measure, thereby extending the findings of Schouwenburg and Groenewoud (2001) and Dewitte and Schouwenburg (2002). The consistency of the findings across a large proportion of students also concurs with Schouwenburg and Groenewoud’s (2001) suggestion that ‘‘. . . a certain amount of procrastination belongs to ‘normal’ behaviour’’ (p. 238). The tendency to postpone assignment submissions was most pronounced among students who reported that they procrastinated on their assignment submissions. Indeed, the behavioural measure of postponement consistently correlated with self-report measures of procrastination, suggesting that assignment submission tendencies reflected delays in the doing, and not just in the submission, of assignments. The behavioural measure did not correlate with grades. In the current context, course performance may have been generally insensitive to postponement tendencies, consistent with the fact that the self-report measures of procrastination also failed to correlate with performance. The lack of association between the behavioural measure of postponement and course performance may also reflect the relative specificity of the behavioural measure in contrast to the more generalized performance assessment. Indeed, others have failed to reveal an association between a broad performance criterion and a narrowly defined behavioural index of procrastination (Lay, 1986). Where previous studies have shown a relationship between behavioural procrastination and performance, either narrow definitions of both performance and procrastination were employed (i.e., the same task was used to generate both scores; Moon & Illingworth, 2005) or the two constructs were not measured independently (Steel et al., 2001).
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4.2. The nomological network of procrastination Our findings revealed some informative associations between procrastination and other individual difference measures. First, the three self-report procrastination measures correlated significantly with each other and with the behavioural measure of postponement. Second, the three measures of self-reported procrastination correlated significantly with say-do correspondence. This suggests that those who report a tendency to procrastinate also have a generalized tendency not to do what they said they will do. The consistent emergence of this correlation across the three self-report measures of procrastination is noteworthy for two reasons: First, say-do correspondence items were not concerned with academic tasks, but rather more generally with keeping, and benefiting from, promises made to oneself. Second, the generalized nature of this measure suggests overlap with personality dimensions, particularly the five-factor dimension of conscientiousness (i.e., that dimension emphasizing responsibility and dependability). It is noteworthy that conscientiousness is the personality dimension most strongly associated with procrastination (van Eerde, 2003) and the self-discipline facet of conscientiousness (emphasizing self-regulatory processes related to say-do correspondence) is the facet most closely associated with procrastination (Watson, 2001). Finally, our findings revealed that say-do correspondence did not correlate with the behavioural measure of postponement, perhaps because some students promised to themselves (quite reasonably) that they would complete the assignment within the final hours of the deadline and subsequently did so. This tendency toward planned postponement, or ‘‘pseudo-procrastination’’, would detract from an overall association between behavioural postponement and say-do correspondence. Say-do correspondence correlated significantly with implementation intentions, such that those who reported a general tendency to do as they say also reported a tendency to make use of implementation intentions to facilitate studying. Implementation intentions, however, were not consistently related to procrastination. Combining the findings concerning implementation intentions and say-do correspondence, it appears that procrastination may involve deficits in the latter more than in the former. Moreover, the relationship between say-do correspondence and implementation intentions may be complex. For example, students with high say-do correspondence might be more capable of utilizing implementation intentions, but might also be capable of fulfilling less specific study plans (‘‘I will study a lot today’’), such that implementation intentions are not always required. A more in-depth investigation of say-do correspondence and its relationship to implementation intentions and procrastination seems warranted. Finally, perceived academic control correlated with course grades (and did so at a magnitude virtually identical to that reported by Perry et al., 2001) and these two variables showed no consistent relationship with procrastination or the remaining self-regulation variables. The lack of association between perceived control and procrastination may reflect the fact that ‘‘control over one’s own academic success’’ can take many forms including, perhaps, the deliberate allocation of time for the completion and submission of assignments in the final hours leading-up to task deadline. It may also mean, however, that believing that one exerts significant influence over one’s own academic success is not a safeguard against procrastination. Despite perceiving themselves as responsible for their own academic successes, students high in perceived control may nonetheless experience difficulty resisting the temptation of more immediately reinforcing activities. Finally, the correlation between perceived academic control and grades may partially reflect a retrospective
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assessment of control based on performance; that is, ‘‘I did well (poorly) in the course, therefore, I must be (must not be) in control’’. 4.3. Limitations and future directions The representativeness of our student sample was limited. Participants were first-year undergraduates. It may be that the postponement pattern observed would not generalize to more experienced students. The meta-analysis of van Eerde (2003), for example, revealed a small but significant inverse correlation between age and procrastination. Most of the participants were female, such that the overall findings are more representative of women’s procrastination tendencies than men’s. Interestingly, the one gender difference that did emerge – women scored higher than men on implementation intentions – has been reported previously (Dewitte & Lens, 2000a). All students were enrolled in an introductory psychology course, again limiting the representativeness of the sample. Despite these limitations, our participants did resemble previous samples on measures of self-reported procrastination and perceived academic control. The current study is also characterized by measurement limitations. We created measures of say-do correspondence and implementation intentions which were internally consistent and face valid, and which correlated significantly with each other, but evidence of their construct validity is lacking. Students’ grades in their introductory psychology course were used as an index of their academic performance. This may not be highly reflective of their overall academic performance, and grade point averages are stronger correlates of procrastination than are grades in a single course (van Eerde, 2003). Finally, our behavioural measure of postponement was derived from students’ submission of assignments into an online discussion group composed of about 10 students per group. Students could see the time at which fellow members submitted their assignments and the content of the submissions made by their peers. On the one hand, the social nature of the assignment might reduce postponement tendencies by providing pressure to demonstrate one’s academic competency by submitting assignments ahead of the deadline. On the other hand, both informational and normative social influence processes may promote postponement: Informational social influence may encourage delay if students are motivated to glean further information about the appropriate manner in which to complete the assignment by examining the content of peers’ submitted assignments, while normative social influence may encourage postponement if students are motivated to submit assignments at a time similar to peers’ submissions. However, the fact that Schouwenburg and Groenewoud (2001) and Dewitte and Schouwenburg (2002) demonstrated postponement tendencies resembling our findings for more typical academic tasks suggests that social influences were not the only determinant of students’ tendencies to delay. van Eerde (2003) recently appealed for further research concerning social influences on procrastination, including the influence of groups on students’ procrastination. Our technology-based behavioural measure of procrastination lends itself to field experimentation, in which characteristics of the assignment, including its social nature, can be manipulated within an otherwise typical course. For example, the WebCT program allows submissions made to discussion groups to remain anonymous to fellow group members. Manipulating anonymity of submissions may influence rates of procrastination. Other manipulable characteristics of the assignment, such as the degree to which an assignment requires cooperation among students, may also affect postpone-
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