Journal of Research in Personality 34, 127–137 (2000)

doi:10.1006/jrpe.1999.2247, available online at http://www.idealibrary.com on

BRIEF REPORT

Examining Behavioral Processes in Indecision:Decisional Procrastination and Decision-Making Style

Joseph R. Ferrari

DePaul University

and

John F. Dovidio

Colgate University

The present research examined the behavioral processes by which individual dif-ferences in decisional procrastination are reflected in decision-making style. It washypothesized, on the basis of previous research suggesting that people higher indecisional procrastination have a higher threshold for certainty before making adecision, that participants higher in decisional procrastination would not only takelonger to complete the task, but also would seek more information about an alterna-tive eventually chosen before making a decision. Participants, who had previouslycompleted a decisional procrastination scale, were instructed within a behavioralprocess paradigm to search information about alternative choices on a decision-making task involving the selection of college courses. The number of dimensions(four vs six) and number of alternatives (two vs five) were systematically varied.The results provided convergent evidence with descriptive studies suggesting thatrather than being unsystematic and easily distracted in their information searches,people higher in decisional procrastination are systematic and strategic but searchfor more information specifically about chosen alternatives. Theoretical and practi-cal implications are considered. 2000 Academic Press

Decisional procrastination (see Ferrari, Johnson, & McCown, 1995;Mann, 1982) is a maladaptive pattern of postponing a decision when facedwith conflicts and choices. Although there is considerable evidence that peo-ple higher in decisional procrastination take longer to make decisions (Bes-

Address correspondence and reprint requests to Joseph R. Ferrari, Department of Psychology,DePaul University, 2219 N. Kenmore, Chicago, IL 60614. E-mail: jferrari@wppost.depaul.edu.

1270092-6566/00 $35.00

Copyright 2000 by Academic PressAll rights of reproduction in any form reserved.

128 BRIEF REPORT

wick, Rothblum, & Mann, 1988; Effert & Ferrari, 1989; Ferrari & Emmons,1994; Frost & Shows, 1993), there is little research examining how peoplehigh and low in decisional procrastination differ in the way they make deci-sions. The present research examined how an individual difference variableassociated with procrastination predicts not only how long it takes to reacha decision but also the styles that people use to make decisions.

The indirect evidence that does exist about how people higher in decisionalprocrastination make decisions is somewhat contradictory in its implications.Some research shows that people higher in decisional procrastination aremore easily distracted (Harriott, Ferrari, & Dovidio, 1996), suggesting thatthey may be less systematic in their decision-making styles. Alternatively,other research implies that people who score high in decisional procrastina-tion may actually be as systematic as those low in decisional procrastination,but they may continue to search out additional information about the chosenalternative after people low in decisional procrastination are prepared tomake a decision. Indecisiveness is associated with a lower level of confi-dence (Greblo & Mirels, 1997) and a higher threshold for certainty beforemaking a decision (Frost & Shows, 1993), which in turn may relate to at-tempts to minimize the risk of making a mistake (Salizman, 1979). Consis-tent with this reasoning, individuals who score higher in decisional procrasti-nation also report greater hoarding behaviors and higher levels ofperfectionism (Beswick et al., 1988; Effert & Ferrari, 1989; Ferrari, 1992;Ferrari & Emmons, 1994; Frost & Shows, 1993). Perhaps because of theirgreater cautiousness, people higher in decisional procrastination may bemore behaviorally rigid and thus be more likely to hold onto previous perfor-mance styles, even when they are no longer optimal. Ferrari and Dovidio(1998) found that higher scorers in decisional procrastination were less ableto shift with ‘‘functional efficiency’’ across situations and tasks.

Based empirically on the preponderance of correlational evidence and the-oretically on the findings that decisional procrastination is associated withmotivations to minimize the risk of making a mistake (Frost & Shows, 1993)and avoid attributions of personal failure (Beswick et al., 1988), we hypothe-sized that people higher in decisional procrastination search out more infor-mation about a chosen alternative than do people lower in decisional procras-tination before they make a decision. Furthermore, this tendency may bemore pronounced with more complicated decisions. These more difficult de-cisions present a higher subjective likelihood of making a mistake and offailing and thus may exacerbate the effects of decisional procrastination.

The problem-solving situation presented to the participants of the presentstudy, which involved determining preferences for a hypothetical collegecourse from their major, represents the ‘‘behavioral process approach’’ (Ja-coby, Jaccard, Kuss, Troutman, & Mazursky, 1987) used in consumer re-search and decision-making research. Participants were instructed to search

BRIEF REPORT 129

information about each of several alternatives and then to make a decisionabout the one alternative they most desired. The complexity of the decision-making task was manipulated by varying the number (four or six) of attributedimensions (e.g., time of day, quality of instructor) of potential courses andthe number (two or five) of alternative college courses that could be chosen.

The main dependent measures for this task are not the actual choice of aspecific alternative but rather the search process that leads to this selection.One measure of the search style is the depth of the search, operationallydefined as the proportion of available information searched (i.e., the numberof cards removed and read during the task relative to the total number ofcards available). Of particular interest for the present study is the distinctionbetween the amount of available information searched concerning the alter-native chosen and the amount of information searched about other, nonse-lected alternatives. A second primary type of measure relates to the searchsequence, which represents the consistency of the strategy. It refers to thenumber and type of transitions made from one cell to another throughoutthe entire search and provides insight into rules people employ when makingdecisions (Payne, Bettman, & Johnson, 1993).

In general, we predicted that participants relatively high in decisional pro-crastination would take longer to complete the task, particularly when morealternative solutions were available. We further predicted, based on researchsuggesting that procrastination may be rooted in motivations to minimizerisk of making a mistake (e.g., Frost & Shows, 1993), that people higher indecisional procrastination would search out more information about a chosenalternative before making a decision than would people lower in decisionalprocrastination. Support for this prediction would be reflected specificallyin people higher in decisional procrastination searching for more informationabout the chosen alternative and making more intradimensional shifts. Peo-ple higher or lower in decisional procrastination were not expected to differin the amount of information search about alternatives not chosen or in thenumber of interdimensional shifts.

METHOD

Participants

Participants were 130 undergraduate students (105 women and 25 men between 18 and 21years old) who received extra course credit for being their involvement in the study.

Procedure

All participants had been previously tested in large groups at least 2 weeks earlier on Mann’s(1982) 5-item (1–5 response format) Decisional Procrastination Scale. This scale has beenshown to have good test–retest reliability (.69 after 1 month, Beswick et al., 1988; see alsoFerrari et al., 1995) and a substantial number of validity studies supporting its use (see Ferrari

130 BRIEF REPORT

et al., 1995). For this sample, scores ranged from 5 to 23, with a median of 12.0, a mean of11.86, and standard deviation of 3.86. The Cronbach α was .70.

Upon arrival at the laboratory for the decision-making task, each participant, who was testedindividually, was informed by one of two female experimenters that the study they were aboutto begin involved how people reach ‘‘real-world’’ decisions, such as choosing college courses.On a 36 3 48-in. information board, each row represented a different target course and eachcolumn represented different course attributes or dimensions (e.g., amount of work).

Participants were randomly assigned to conditions in which there were either two or fiverows (alternative course selections) and four or six columns (course attributes to consider).The five potential courses were labeled alphabetically from ‘‘A’’ to ‘‘E.’’ The six potentialcourse attributes were the time of day that the course was offered, instructor quality, careerrelevance, transferability, amount of work, and peer recommendation. The sequence oflabels and the information about attributes were randomized across participants. Index cards(3 3 5 in), placed face-down in each cell, indicated the attributes that characterized the targetalong one of three levels. For example, the information for time of day included 8:30 A.M.,11:45 A.M., or 4:45 P.M.; instructor quality was poor, good, or excellent; and career relevancewas poor, good, or excellent.

Each participant was given an explanation of the information board and instruction on howto use it. Participants were told that information describing each college course was printedon the back of the index cards. These cards had to be turned over to reveal the informationabout that attribute for a particular course. Participants were allowed to turn over as few oras many cards as they needed to make a decision. The participants were informed that thestudy would be concluded when they made a decision based on the information provided.After completing the task, participants were verbally debriefed, given credit for their participa-tion, and dismissed.

Half of the participants were exposed to an information board with only two rows, eachrepresenting a college course alternative (course ‘‘A’’ or course ‘‘B’’), whereas the other halfof the participants were presented with all five rows as alternatives (courses ‘‘A’’ to ‘‘E’’).Furthermore, half of the participants were presented with four course attribute dimensions,whereas the other half of the participants were presented with all six attributes. Therefore, themanipulations in this study produced a 2 (Number of Alternatives: two or five) 3 2 (Numberof Dimensions: four or six) between-groups design. The total amount of information betweenconditions varied as 8, 12, 20, or 30 pieces of data (or cards). The experimenter, unaware ofthe participant’s decisional procrastination score, recorded the time (in seconds) for the partic-ipant to make a decision, the sequence of searching, the total number of cards turned over,the number of cards turned over for the alternative that was chosen, and the number of cardsturned over for alternatives that were not selected.

The primary dependent measures in this study included the amount of time taken to reacha decision, the percentage of total information searched, as well as the separate percentageof cards searched for the alternative chosen and for alternatives that were not chosen. Thesemeasures permitted an overall assessment of the depth of search plus a comparison of searchdepth between preferred and nonpreferred alternatives by people varying in level of decisionalprocrastination. In addition, the frequency of interdimensional and intradimensional transitionsmade during the search were examined. A measure of the proportion of intradimensional shiftswas also computed as the number of intradimensional shifts relative to the total number oftransitions (intradimensional shifts 1 interdimensional shifts). These measures permit an anal-ysis of how participants search the available information as they make their decision.

RESULTS

The primary analyses involved were multiple regressions in which Alter-natives (two vs five) and Dimensions (four vs six) were treated as dichoto-

BRIEF REPORT 131

mous independent variables (dummy coded) and Decisional Procrastinationwas treated as a continuous independent variable. Hierarchical multiple re-gressions first examined the main effects for the three predictor variables,then the two-way interactions, and finally the three-way interaction (Cohen &Cohen, 1975). The dependent measures were time to make a decision andaspects of the search processes.1 Preliminary analyses revealed no differ-ences between men and women on decisional procrastination (consistentwith previous research; see Ferrari et al., 1995) or on any of the dependentvariables, and the regressions demonstrated no systematic main effects orinteractions for participant sex. Consequently, this variable was not includedin subsequent analyses. Table 1 presents the correlation matrix for the pre-dictor variables and the dependent measures (including their means and stan-dard deviations).

Decision-Making Time

As expected, people who scored higher in decisional procrastination tooklonger overall to reach a decision (β 5 .20, t 5 2.71, p , .008). However,this effect was moderated by the number of alternatives; a marginally sig-nificant Decisional Procrastination 3 Alternative interaction was obtained(β 5 .48, t 5 1.94, p , .054). The effect associated with decisional procrasti-nation tended to be stronger when there were five alternatives (β 5 .29,t 5 2.41, p , .019) than when there were two (β 5 .20, t 5 1.64,p , .110). The full regression equation accounted for 36% of the variancein decision times. Thus, consistent with previous research, people higher indecisional procrastination took longer to make decisions, particularly whenchoosing from more alternatives.

Amount of Information Searched

There were three measures of the amount of information searched: thepercentage searched of all the available information, the percentage of infor-mation searched relating to the alternative chosen, and the percentage relatedto alternatives not chosen. Although participants higher in decisional procras-tination did not search a significantly higher percentage of all available infor-mation (β 5 .12, t 5 1.45, p , .150) or about nonchosen alternatives (β 5.10, t 5 1.16, p , .267), consistent with the hypothesis that people higherin decisional procrastination are more focused in their information search,they did search a significantly higher percentage of information about the

1 The results reported focus on the effects (main effects and interactions) involving deci-sional procrastination. More detailed information about the analyses is available from theauthors. For example, consistent with the intended manipulation of task complexity, partici-pants took longer to make decisions when there were five relative to two alternatives (p ,.001) and when there were six rather than four dimensions involved (p , .006).

132 BRIEF REPORT

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BRIEF REPORT 133

choice they ultimately made (β 5 .19, t 5 2.21, p , .030). The full regres-sion model accounted for 8% of the variance.

Inter- and Intradimensional Shifts

Three other measures of participants’ search strategies were examined:(1) the number of intradimensional shifts, (2) the number of intradimensionalshifts, and (3) the proportion of intradimensional shifts relative to the totalnumber of shifts (intradimensional and interdimensional). It was hypothe-sized that participants higher in decisional procrastination would make moreintradimensional than interdimensional choices, reflecting a narrower searchstrategy.

As predicted, participants higher in decisional procrastination made moreintradimensional shifts (β 5 .19, t 5 2.43, p , .017). A marginally signifi-cant Decisional Procrastination 3 Alternatives interaction was also obtained(β 5 .49, t 5 1.87, p , .064), indicating that the effect for Decisional Pro-crastination was somewhat stronger in the five-alternative condition (β 5.26, t 5 2.13, p , .037) than in the two-alternative condition (β 5 .22, t 51.82, p , .075). The regression model accounted for 27% of the variance.Decisional procrastination was not related to the number of interdimensionalshifts (β 5 2.05, t 5 0.70, p , .487). With respect to the relative proportionsof search selections, as expected, participants who scored higher in deci-sional procrastination tended to make a higher relative proportion of intradi-mensional shifts (β 5 .17, t 5 1.95, p , .054). The full regression equationaccounted for 7% of the variance.

Does Search Style Mediate the Relationship between DecisionalProcrastination and Time?

A primary objective of the present study was to understand whether peoplediffering in levels of decisional procrastination engaged in different decision-making styles. The results suggest that they do. A secondary question iswhether these differences in decision styles and strategies directly mediatethe relationship between decisional procrastination and decision-makingtime. To explore this possibility, the multiple regression mediation approachproposed by Baron and Kenny (1986) was used. Evidence for mediationwould be reflected in three findings: (1) level of decisional procrastinationpredicts decision-making time; (2) level of decisional procrastination pre-dicts search style measures; and (3) the relation of level of decisional procras-tination to decision-making time is substantially attenuated after controllingfor the effect of the search style measures, while the effects of the searchstyle mediators are significant.

Reflecting the simple correlation presented in Table 1, in the first regres-sion decisional procrastination significantly predicted decision-making time(β 5 .20, t 5 2.33, p , .022). The second set of regressions, in which

134 BRIEF REPORT

decisional procrastination was included as the independent variable and theneach of the measures of search strategy (available information searched,choice information searched, nonchoice information searched, intradimen-sional shifts, interdimensional shifts, and relative number of shifts) were con-sidered as dependent measures, revealed that two measures potentially quali-fied as mediators: choice information searched (β 5 .18, t 5 2.09, p , .039)and intradimensional shifts (β 5 .20, t 5 2.28, p , .024). As indicated inTable 1, none of the other correlations were statistically significant. The lastequation considered decisional procrastination and the two measures ofsearch style that qualified as mediators (percentage of information searchedabout the chosen alternative and the number of intradimensional shifts) si-multaneously as predictors of decision-making time. This equation revealedevidence of mediation. When the two potential mediating variables wereconsidered, decisional procrastination no longer significantly predicted deci-sion-making time (β 5 .11, t 5 1.29, p , .20). However, when tested simul-taneously in this equation, the number of intradimensional shifts (β 5 .34,t 5 4.14, p , .001) significantly predicted decision-making time and thepercentage of information searched about the chosen alternative was margin-ally significant (β 5 .15, t 5 1.82, p , .072) Thus, the relationship betweendecisional procrastination and decision time was mediated, at least in part,by search strategy.

DISCUSSION

Consistent with previous research on the fundamental behavioral manifes-tation of decisional procrastination (see Ferrari et al., 1995), in the presentstudy participants higher in decisional procrastination took longer to com-plete the task, particularly when more alternatives were available to consider.The relationship between decisional procrastination scores and time to com-plete the task was somewhat modest (for example, r 5 .20 overall), however.One factor that might have limited this relationship is the fact that there wasat least a 2-week gap between assessment of decisional procrastination andthe experimental procedure. If decisional procrastination scores are unstableacross time, the relationship would be attenuated. Previous research has dem-onstrated satisfactory, but not especially high, test–retest reliability for thescale (.69 after 1 month, Beswick et al., 1988). Nevertheless, the relationshipwe obtained in a relatively constrained laboratory context was somewhatlower but still comparable to the one found by Burnett, Mann, and Beswick(1989) between decisional procrastination and planning for course selectionsby college students under more naturalistic circumstances (r 5 .32).

The main focus of the present study, however, was on how people differingin decisional procrastination make their decisions. Our results provide con-vergent evidence with a number of descriptive studies (Beswick et al., 1988;Effert & Ferrari, 1989; Ferrari & Emmons, 1994), suggesting that rather than

BRIEF REPORT 135

being associated with unsystematic and easily distracted search strategies,people relatively high in decisional procrastination do engage in systematicand strategic searches—but in a way different than those low in decisionalprocrastination. Those higher in decisional procrastination, who were hy-pothesized to require more certainty before they make a decision, searcheda larger percentage of information about the alternative eventually chosenand made more intradimensional shifts. These effects occurred uniquely forbehaviors associated with the chosen alternative, not for informationsearched about for nonchosen alternatives or for interdimensional transitions.Searching for more information about the chosen alternative and makingmore of intradimensional shifts, in turn, mediated the relationship betweendecisional procrastination and decision-making time. These results supportedthe predictions.

In addition to providing evidence for the process we hypothesized, it isalso possible that people high and low in decisional procrastination differmore fundamentally in the types of decision strategies they use. That is, peo-ple higher in decisional procrastination may be using more narrow and rigidstrategies. Searching for more information about the chosen alternative andfocusing primarily in an intradimensional direction may reflect this narrowstrategy. However, even though generally plausible, this explanation doesnot provide the most parsimonious account for the data obtained in the pres-ent study. In particular, it is not clear why a more narrow search, by itself,would lead to longer decision times. Moreover, if people higher in decisionalprocrastination were using more narrow searches, then it is likely that theywould be less affected in their decision times when more alternatives werepresented. The statistical interaction between decisional procrastination andthe number of available alternatives that was demonstrated in the presentstudy shows the opposite pattern, however. Nevertheless, given the plausibil-ity of this interpretation with some partial supporting evidence in the presentstudy, future research might productively focus on the potentially differentstrategies used by people varying in decisional procrastination as well asexamine more directly some of the mechanisms hypothesized in the presentresearch by including subjective measures of uncertainty, exploring the ef-fects of systematic variations in the attractiveness of alternatives (see Petty &Cacioppo, 1986), or using alternative paradigms for testing specifically howinformation access can reduce uncertainty (see, in particular, Jacoby, Jac-card, Currim, Ansari, & Troutman, 1994). These studies could also use‘‘think aloud’’ procedures that compare the ongoing deliberations of partici-pants.

Understanding how different decision-making styles potentially underliedecisional procrastination also has practical value in terms of guiding inter-ventions to address problems of decisional procrastination. Procrastination,according to the cognitive-behavioral explanation first popularized by Ellis

136 BRIEF REPORT

and Knaus (1977), may be based on irrational fears and self-criticism aboutone’s ability to complete a task successfully. This reasoning is compatiblewith the conclusion of Frost and Shows (1993) that decisional procrastinationmay reflect a higher threshold for certainty before making a decision to mini-mize the risk of making a mistake, as well as with our own findings. There-fore, despite the potential immediate negative consequences of decisionalprocrastination, it may have positive long-term functions for the individual.That is, although procrastination may often be self-defeating, this behaviormay also be in one’s self-interest in maintaining a positive self-image andsense of personal control (Ferrari, 1994). If, as the present research suggests,decisional procrastination is associated with greater caution and the desirefor more information about an alternative to be chosen, then the implicationsfor addressing problems of decisional procrastination are significantly differ-ent than if the problem were caused by distractibility or absentmindedness.Thus, the present research demonstrates that people varying in levels of deci-sional procrastination systematically differ not only in how long they taketo make a decision but also in how they make their decisions.

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