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Overcoming Procrastination:

The Effect of Implementation Intentions1

Shane

Gregory

Owens

,

2

Christine

G. Bowman

,and

Charles

A. Dill

Hofstra University

A hypothesized solution for procrastination is the formation of an implementation

intention (Van Eerde, 2000). University students (N= 152) were assessed using the

Aitken Procrastination Inventory (Aitken, 1982) and were asked to report to an

experiment. Half of the participants formed implementation intentions to attend. An

odds ratio indicated that participants who formed implementation intentions were

nearly 8 times more likely to keep their appointments than those who did not. Low

procrastinators reported more often for the experiment than did high procrastina-

tors (Low = 49.4%; High = 30.1%), c2

(1, N= 152) = 5.84, p < .016. The interactionbetween implementation intentions and procrastination was not significant,

c2(1, N= 152) = 0.28, p < .60.

Procrastination has been defined as the purposeful postponement or

delaying of the performance of a task or the making of a decision (Ferrari,

2001; Milgram, Mey-Tal, & Levison, 1998). It has also been described as a

self-regulatory style that delays the start or completion of a task (Ferrari &

Tice, 2000). Van Eerde (2000) defined procrastination as the avoidance ofthe implementation of an intention(p. 374).

Procrastinators may be a form of inclined abstainers (Sheeran, 2002), who

intend to act but do not follow through with their intentions. Schouwenburg

(2004) highlighted the difference between state procrastination, in which

avoidance behavior is task-specific; and trait procrastination, as the ten-

dency to postpone that which is necessary to reach some goal (p. 5). Pos-

sessing this trait increases the probability that one will delay action initiation

in a variety of instances. It is clear from all of these definitions that the

essential aspect of procrastination is not performing a behavior when anadequate opportunity presents itself.

The negative consequences of procrastination include internal conse-

quences, such as depression, anxiety, and guilt (Burka & Yuen, 1983; Flett,

1Shane Gregory Owens is associate director of Psychological Services at Farmingdale StateCollege and an assistant professor at St. Johns University. Christine Bowman is now a graduatestudent at Fordham University. This paper is based on the second authors honors thesisresearch, which was supervised by the third author.

2Correspondence concerning this article should be addressed to Shane Gregory Owens, 283Commack Road, Suite LL 2, Commack, NY 11725. E-mail: [email protected]

366

Journal of Applied Social Psychology, 2008, 38, 2, pp. 366384.

2008 Copyright the Authors

Journal compilation 2008 Blackwell Publishing, Inc.
mailto:[email protected]:[email protected]


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Blankstein, & Martin, 1995); and external consequences, such as lateness at

work (Van Eerde, 2000), fines for late library books, failed marriages (Burka

& Yuen, 1983), and poor performance on class exercises, final exams, and

lower overall course grades (Steel, Brothen, & Wambach, 2001). Procrasti-

nation is also associated with negative effects on physical health (Sirois,

Melia-Gordon, & Pychyl, 2003; Tice & Baumeister, 1997). On a larger scale,

procrastination has negative effects on political and economic decisions

(Holland, 2001; Kegley, 1989).

According to a meta-analysis by Van Eerde (2003), there is a positive,

direct relationship between procrastination and missing a deadline, and a

negative, direct relationship between procrastination and task preparation.

In addition, there are moderated, moderately negative relationships between

procrastination and course grade, and procrastination and overall grade

point average (Van Eerde, 2003). In terms of affective variables, Van Eerde

found a consistent positive relationship between procrastination and depres-

sion and between procrastination and anxiety.3

Procrastination is a prevalent problem, especially among young adults. It

is estimated that anywhere between 50% and 95% of college students engage

in procrastination (Day, Mensink, & OSullivan, 2000; Ellis & Knaus, 1977;

Knaus, 2000; Potts, 1987; Pychyl, Lee, Thibodeau, & Blunt, 2000; Solomon

& Rothblum, 1984). Harriott and Ferrari (1996) reported that approximately

15% to 20% of adults engage in chronic procrastination.

Though the amount of literature regarding procrastination has increased

over the past decade, most of this literature is devoted to finding the causes or

predictors of procrastination, and not to finding strategies to overcome it.

Those sources that do focus on preventing procrastination are mostly limited

to self-help literature. Some authors have outlined techniques for overcoming

procrastination, but have offered little empirical support for any of these

techniques. Examples include Ferrari (2001), who outlined 10 strategies,

including learning organizational skills, using reminder notes, and breaking

complex goals into smaller, more manageable pieces; and Van Eerde (2000)

who advocated the training of self-regulation skills, altering the context in

which tasks are supposed to be performed, and planning. Schouwenburg,

Lay, Pychyl, and Ferrari (2004) described techniques for assisting academic

3Some researchers have identified a type of procrastinator who is engaging in a behavior thatenhances his or her functioning. Chu and Choi (2005) differentiated between passive procrasti-

nators and active procrastinators. As opposed to the traditional negative procrastinator, anactive procrastinator makes decisions to delay action and is likely to achieve more satisfactoryoutcomes than is the passive procrastinator. In addition, Schouwenburg (2004) stated that somedilatory behavior may, in some situations, be wise. For the purposes of the current experiment,no distinction was made between these two types of procrastinators, as the experimenters wereinterested only in procrastination behavior that carries some measure of harm.

OVERCOMING PROCRASTINATION 367


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procrastinators to overcome their difficulties. Their work utilized research on

procrastination and application of therapeutic techniques (e.g., cognitive-

behavior therapy) to outline methods for those who counsel procrastinators

in academic settings.

The current study examines the relationship between intentions and

behavior, which has been established as an important issue in procrastina-

tion. In addition, it seeks to apply Gollwitzers (1999) concept of implemen-

tation intentions to the effort to overcome procrastination and its deleterious

effects.

Decision and Action

Gollwitzer (1999) delineated two phases involved in the decision to act

and the performance of actions. The first phase is motivational and involves

the selection of an action to bring about certain personal and social conse-

quences. The second phase is volitional and involves the performance of

selected actions. As it has been established that behavior involves the moti-

vational aspect of intending to behave in a certain way and the volitional

aspect of acting on those intentions, it is important to use an understanding

of both phases in any strategy to overcome procrastination.

While Ajzens (1985, 1991; Ajzen & Madden, 1986) theory of planned

behavior (TPB) provides insight into the motivational phase of behavioral

enactment, Gollwitzers (1999) theory of implementation intentions provides

a strategy to overcome difficulties during the volitional phase. Procrastina-

tion seems to be a phenomenon that involves both phases of behavioral

enactment, as behavioral enactment is the result of intentions to behave

(motivational) and actions toward goal achievement (volitional). A full

understanding of procrastination would, therefore, have to involve both

motivational and volitional factors.

Behavioral Intentions

Ajzens (1985, 1991; Ajzen & Madden, 1986) TPB states that the intention

to perform a behavior is an important proximal determinant for that behav-

ior. Sirois (2004) suggested that procrastinators do not engage in healthy

behaviors because they have weaker intentions to perform such behaviors.

Van Eerdes (2000) definition of procrastination indicates that it is a break-

down of the implementation of an intention to act, and Steel et al. (2001)

stated that procrastination reflects a discrepancy between intentions and

actions, and that procrastinators show the largest intentionaction gap.

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All of these explanations for procrastination highlight the importance of

behavioral intentions as a predictor of behavioral enactment. Reviews of

research on the TPB (Ajzen, 1985, 1991) indicate that, while the theory has

good predictive validity, it often accounts for only 20% to 30% of behavioral

enactment. Sheeran, Webb, and Gollwitzer (2005) highlighted the impor-

tance of strong goal intentions in completing goal-related tasks. Taken

together, this evidence suggests that, while good intentions may be important

in explaining why a person behaves, they may not be sufficient conditions for

behavioral enactment, and procrastinators may have problems in translating

even strong intentions into action.

While the TPB accounts for intention to perform a certain action, behav-

ioral intentions are not sufficient in and of themselves to explain behavioral

enactment. Implementation intentions have been shown to augment the

predictive validity of the TPB (e.g., Orbell, Hodgkins, & Sheeran, 1997).

Implementation Intentions

Gollwitzer (1999) developed implementation intentions in order to effec-

tively translate intention into action. Implementation intentions augment goal

intentions by adding specifications of when and where to perform a specified

behavior. For example, the goal intention I will write the first draft of my

article becomes I will write the first draft of my article in my office at 6:00

p.m. on Sunday when supplemented with an implementation intention.

Gollwitzer and Sheeran (in press) reviewed problems in goal pursuit

related to procrastination. In terms of starting a task, they mentioned prob-

lems with remembering to act, utilizing appropriate opportunities for action,

and overcoming reluctance to act. Gollwitzer (1999) indicated that forming

an implementation intention causes the memory for the context specified in it

to become highly activated and more readily accessible, making it the kind of

plan that is likely to overcome distraction, which, according to Van Eerde

(2000), is important in overcoming procrastination. In addition, the context

specified by the when and the where of the implementation intention is

assumed to elicit the behavior specified in the implementation intention

immediately, efficiently (i.e., not requiring much processing capacity), and

without conscious intent (Brandsttter, Lengfelder, & Gollwitzer, 2001,

p. 947). In short, by passing control of the behavior to environmental cues

contained in the implementation intention, the behaviors demand few cog-

nitive resources and the effects of distractions, competing and other difficult

goals, other habits, and procrastination are minimized (Brandsttter et al.,

2001; Gollwitzer & Brandsttter, 1997). In addition, Van Eerde (2000) men-

tioned implementation intentions as a way to overcome procrastination by



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visualizing an appropriate time and place for action ahead of time, and using

this context efficiently when it arrives.

There has been much research demonstrating the efficacy of implementa-

tion intentions in initiating behaviors. For example, Verplanken and Faes

(1999) found that implementation intentions helped participants to initiate a

healthy diet. Sheeran and Orbell (1999) found that implementation intentions

enhanced the taking of vitamins, helped patients return to regular physical

activity quickly following surgery (Orbell & Sheeran, 2000), and ensured that

women would perform breast self-examination (Orbell et al., 1997). In addi-

tion, Dill, Owens, and colleagues (Dill, Owens, Homburger, Travers, &

Lancaster, 2002; Owens, 2003; Owens, Dill, Levine, & Goldfarb, 2001, 2002)

found that forming implementation intentions helped college students to

complete academic-related tasks, such as sending weekly e-mails to instruc-

tors and keeping a weekly journal. Furthermore, Koole and vant Spijker

(2000) found that participants who formed implementation intentions wrote

a report of experiences during a specific day significantly faster than those

participants who did not form implementation intentions.4

Rationale and Hypotheses for the Current Study

Many researchers (Sirois, 2004; Steel et al., 2001; Van Eerde, 2000) have

indicated that behavioral intentions play a role in procrastination. While

some (e.g., Van Eerde, 2000) have indicated that implementation intentions

may have some utility in counteracting procrastination, there have been no

published studies to date examining their effectiveness in counteracting pro-

crastination. The current study is undertaken to examine the relationship

between behavioral intentions and behavioral enactment in procrastinators

and to examine the effects of forming implementation intentions on keeping

an appointment in college students who identify themselves as high or lowprocrastinators. We hypothesize the following:

Hypothesis 1. Individuals who form implementation intentions

will perform the behavior more frequently than those who do

not.

Hypothesis 2. Individuals who rate themselves as low procras-

tinators will perform the behavior more frequently than those

who rate themselves as high procrastinators.

4See Gollwitzer and Sheeran (in press) for a meta-analytic review in which they calculatedthe implementation intention effect across 94 studies with 8,461 participants (d= .65), andKoestner, Lekes, Powers, and Chicoine (2002) for a meta-analytic review in which they calcu-lated the implementation intention effect across 13 published studies (d+ = .54).

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Hypothesis 3. Individuals with high intentions will be more

likely to enact the behavior than those with low intentions.

Hypothesis 4. The best fitting model of behavioral enactment

will contain main effects for procrastination, behavioral inten-

tions, and implementation intentions.

Hypothesis 5. Individuals who have strong intentions and who

form implementation intentions will have the highest rate of

behavioral enactment.

Method

Participants

The participants were 152 university students (96 female, 56 male) who

volunteered to participate for extra course credit. The students were pri-

marily from the upper divisions (60.4% were juniors or seniors), most of them

were psychology majors (63.2%), and their mean grade point average (GPA)

was 3.07 (SD = 0.48). The students were treated in accordance with the

ethical standards of the APA.5

Procedure and Materials

The experiment was performed in two parts. In order to decrease the

effect of social desirability on the behavioral measure of attending a second

appointment, participants were told that the two parts were separate experi-

ments when, in fact, there was a single experiment.

During the first meeting, the experimenter administered the Aitken

Procrastination Inventory (API; Aitken, 1982) and three items measuringbehavioral intention. Following administration of the questionnaires, the

experimenter thanked the students for their participation and explained that

any questions could be directed to her through the psychology department.

Each participant was given extra credit for the class for which they were

participating.

The API (Aitken, 1982) includes 19 items rated on a 5-point Likert scale

ranging from 1 (False) to 5 (True). The ratings are summed after appropriate

reverse-scoring, resulting in a total score ranging from 19 to 95, where higher

scores indicate higher levels of procrastination. The API was developed to

5Missing data were not a problem for any of the measures in the present study except fordemographic data. There were 3 students who did not report class standing, 4 who did not reporttheir majors, and 6 who did not report their GPAs.



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differentiate chronic procrastinators from nonprocrastinators among college

undergraduates. Cronbachs alpha (a measure of internal consistency used to

estimate reliability) for both Aitkens sample and the present sample was .82.

A three-item questionnaire was developed to measure the behavioral

intention variable (see Appendix A). The items were developed according to

the guidelines specified by Ajzen (1985). This methodological issue is dealt

with further in the Discussion. The items were measured using a 7-point scale

ranging from 1 (strongly disagree/unlikely) to 7 (strongly agree/likely). For the

purpose of analysis, the items were summed to create an overall behavioral

intention score. Cronbachs alpha was found to be .71. API and behavioral

intention were significantly correlated (r = -.47, p < .001).In the second part of the experiment, the professor in each of the classes

distributed sheets of paper describing an opportunity to earn more extra

credit. The paper listed 10 times during which the participant could report to

a laboratory in the psychology department in order to take part in another

experiment. The nonimplementation intention participants received no

further instructions. Implementation intention participants were given the

following instructions along with the list of times:

You are more likely to keep your appointment if you commit

yourself to arriving to the assigned room at one of the timeslisted above. Select now the time at which you plan to come for

the second experiment, write it at the bottom of the second

page, and return that page to your instructor.

When participants reported for the second experiment, it was explained to

them that, in fact, there was no second experiment and that this meeting

related back to the questionnaires that they completed in class at the begin-

ning of the semester. Each participant was debriefed thoroughly and given

the opportunity to ask questions. Participants presence was noted for extracredit. Those participants who did not attend the second experiment did not

receive the extra credit.

Results

Procrastination and Implementation Intention

Univariate analyses. There was a statistically significant difference

between implementation intention (II) and no II in behavioral enactment,

thus supporting Hypothesis 1. A majority (47 of 76; 61.8%) of the II partici-

pants enacted the behavior, as opposed to 18.4% (14 of 76) of the no-II

participants, c2(1, N =152) = 29.82, f2 = .20, p < .001.

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There was also a statistically significant effect for procrastination, thus

supporting Hypothesis 2. Participants were split (Mdn = 50) into lowprocrastinators (LP) and high procrastinators (HP).6 LPs evidenced more

behavioral enactment (39 out of 79; 49.4%) than did HPs (22 out of 73;

30.1%). This difference in behavioral rates was statistically significant, c2(1,N= 152) = 5.84, f2 = .04, p < .016.

Logistic regression analyses. Behavioral enactment was the outcome, and

II and procrastination were the explanatory variables.7 At Step 1, both

explanatory variables were simultaneously entered into the equation.8 At

Step 2, the attribute by treatment interaction (Procrastination II) wasentered. The interaction was nonsignificant, c2(1, N= 152) = 0.28, p < .598,and was deleted from the model. The model containing procrastination and

II differed from the intercept-only model, c2(2, N= 152) = 35.30, p < .001,and is presented in Table 1 (Model I). It can be seen in Table 1 that both

variables partial B weights were significant (a < .05) using the Wald andlikelihood ratio tests (see Cohen, Cohen, West, & Aiken, 2003).

The odds ratio of II controlling for procrastination (see Table 1) can be

interpreted as follows: The odds of behavioral enactment were about 7.73

times greater for II participants than for no-II participants.9 The Hosmer

Lemeshow statistic (Hosmer & Lemeshow, 2000) was used to assess how

adequately Model I fits the data. A good-fitting model will produce a

nonsignificant chi square. As can be seen in Table 1, Model I was found to

provide an acceptable fit to the data ( p < .345).

6Procrastination was kept as a continuous variable for the logistic regression analyses. It wasdichotomized here to simplify the results and to make them similar in metric (i.e., percentages)to the chi-square analysis for II effect on behavioral enactment that was just given. Procrasti-nation was normally distributed; and skewness and kurtosis were nonsignificant (M= 49.59,SD = 10.84). Scores ranged from 23 to 83.

7

Scaling for the variables was as follows: behavioral enactment, 0 = no enactment, 1 = enact-ment; implementation intention, 0 = no II, 1 = II. For procrastination, higher scores indicatemore procrastination.

8This experiment can be conceptualized as an ANCOVA, where II is the treatment manipu-lation (to which participants are randomly assigned) and procrastination is a continuous pre-dictor or covariate. The typical ANCOVA analysis assesses a main effect, eliminating the maineffect of the other variable. The analysis here follows the ANCOVA approach used for experi-mental data. This analysis approach was utilized to test the main effects for all models testedhere.

9The odds-ratio results are scale-dependent or are not standardized. For this reason, they arenot directly comparable. A 1-unit change for the dichotomous II variable covers the entire rangeof that variable; while for the continuous procrastination variable, a 1-unit change covers only

a small section of the variables range. Thus, dichotomous variables tend to have larger oddsratios than do continuous variables. Cohen et al. (2003) recommended some adjustments tobetter compare odds ratios across variables. For example, if one could state that a 2-unit changeon procrastination is considered meaningful, then the odds ratio would go from 0.96 to 6.88. Inthe present study, it is not clear what a meaningful change in the procrastination metric wouldbe. For this reason, its odds ratio is not interpreted.



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Table1

SummaryofLogisticRegressionMod

elsforUnderstandingBehavioralEnactment

Variable

B

SEW

alda

p

Likelihood

ratio(c2)b

p

Odds

ratio

Measuresoffit

Hosme

r

Lemesh

ow

RL2

Cox

Snell

R2

Nagelkerkes

R2

c2

p

ModelI

PROC

-0.04

0.02

3.97*

.046

4.21*

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